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Shir Meir Lador, Intuit | WiDS 2023


 

(gentle upbeat music) >> Hey, friends of theCUBE. It's Lisa Martin live at Stanford University covering the Eighth Annual Women In Data Science. But you've been a Cube fan for a long time. So you know that we've been here since the beginning of WiDS, which is 2015. We always loved to come and cover this event. We learned great things about data science, about women leaders, underrepresented minorities. And this year we have a special component. We've got two grad students from Stanford's Master's program and Data Journalism joining. One of my them is here with me, Hannah Freitag, my co-host. Great to have you. And we are pleased to welcome from Intuit for the first time, Shir Meir Lador Group Manager at Data Science. Shir, it's great to have you. Thank you for joining us. >> Thank you for having me. >> And I was just secrets girl talking with my boss of theCUBE who informed me that you're in great company. Intuit's Chief Technology Officer, Marianna Tessel is an alumni of theCUBE. She was on at our Supercloud event in January. So welcome back into it. >> Thank you very much. We're happy to be with you. >> Tell us a little bit about what you're doing. You're a data science group manager as I mentioned, but also you've had you've done some cool things I want to share with the audience. You're the co-founder of the PyData Tel Aviv Meetups the co-host of the unsupervised podcast about data science in Israel. You give talks, about machine learning, about data science. Tell us a little bit about your background. Were you always interested in STEM studies from the time you were small? >> So I was always interested in mathematics when I was small, I went to this special program for youth going to university. So I did my test in mathematics earlier and studied in university some courses. And that's when I understood I want to do something in that field. And then when I got to go to university, I went to electrical engineering when I found out about algorithms and how interested it is to be able to find solutions to problems, to difficult problems with math. And this is how I found my way into machine learning. >> Very cool. There's so much, we love talking about machine learning and AI on theCUBE. There's so much potential. Of course, we have to have data. One of the things that I love about WiDS and Hannah and I and our co-host Tracy, have been talking about this all day is the impact of data in everyone's life. If you break it down, I was at Mobile World Congress last week, all about connectivity telecom, and of course we have these expectation that we're going to be connected 24/7 from wherever we are in the world and we can do whatever we want. I can do an Uber transaction, I can watch Netflix, I can do a bank transaction. It all is powered by data. And data science is, some of the great applications of it is what it's being applied to. Things like climate change or police violence or health inequities. Talk about some of the data science projects that you're working on at Intuit. I'm an intuit user myself, but talk to me about some of those things. Give the audience really a feel for what you're doing. >> So if you are a Intuit product user, you probably use TurboTax. >> I do >> In the past. So for those who are not familiar, TurboTax help customers submit their taxes. Basically my group is in charge of getting all the information automatically from your documents, the documents that you upload to TurboTax. We extract that information to accelerate your tax submission to make it less work for our customers. So- >> Thank you. >> Yeah, and this is why I'm so proud to be working at this team because our focus is really to help our customers to simplify all the you know, financial heavy lifting with taxes and also with small businesses. We also do a lot of work in extracting information from small business documents like bill, receipts, different bank statements. Yeah, so this is really exciting for me, the opportunity to work to apply data science and machine learning to solution that actually help people. Yeah >> Yeah, in the past years there have been more and more digital products emerging that needs some sort of data security. And how did your team, or has your team developed in the past years with more and more products or companies offering digital services? >> Yeah, so can you clarify the question again? Sorry. >> Yeah, have you seen that you have more customers? Like has your team expanded in the past years with more digital companies starting that need kind of data security? >> Well, definitely. I think, you know, since I joined Intuit, I joined like five and a half years ago back when I was in Tel Aviv. I recently moved to the Bay Area. So when I joined, there were like a dozens of data scientists and machine learning engineers on Intuit. And now there are a few hundreds. So we've definitely grown with the year and there are so many new places we can apply machine learning to help our customers. So this is amazing, so much we can do with machine learning to get more money in the pocket of our customers and make them do less work. >> I like both of those. More money in my pocket and less work. That's awesome. >> Exactly. >> So keep going Intuit. But one of the things that is so cool is just the the abstraction of the complexity that Intuit's doing. I upload documents or it scans my receipts. I was just in Barcelona last week all these receipts and conversion euros to dollars and it takes that complexity away from the end user who doesn't know all that's going on in the background, but you're making people's lives simpler. Unfortunately, we all have to pay taxes, most of us should. And of course we're in tax season right now. And so it's really cool what you're doing with ML and data science to make fundamental processes to people's lives easier and just a little bit less complicated. >> Definitely. And I think that's what's also really amazing about Intuit it, is how it combines human in the loop as well as AI. Because in some of the tax situation it's very complicated maybe to do it yourself. And then there's an option to work with an expert online that goes on a video with you and helps you do your taxes. And the expert's work is also accelerated by AI because we build tools for those experts to do the work more efficiently. >> And that's what it's all about is you know, using data to be more efficient, to be faster, to be smarter, but also to make complicated processes in our daily lives, in our business lives just a little bit easier. One of the things I've been geeking out about recently is ChatGPT. I was using it yesterday. I was telling everyone I was asking it what's hot in data science and I didn't know would it know what hot is and it did, it gave me trends. But one of the things that I was so, and Hannah knows I've been telling this all day, I was so excited to learn over the weekend that the the CTO of OpenAI is a female. I didn't know that. And I thought why are we not putting her on a pedestal? Because people are likening ChatGPT to like the launch of the iPhone. I mean revolutionary. And here we have what I think is exciting for all of us females, whether you're in tech or not, is another role model. Because really ultimately what WiDS is great at doing is showcasing women in technical roles. Because I always say you can't be what you can't see. We need to be able to see more role models, female role role models, underrepresented minorities of course men, because a lot of my sponsors and mentors are men, but we need more women that we can look up to and see ah, she's doing this, why can't I? Talk to me about how you stay the course in data science. What excites you about the potential, the opportunities based on what you've already accomplished what inspires you to continue and be one of those females that we say oh my God, I could be like Shir. >> I think that what inspires me the most is the endless opportunities that we have. I think we haven't even started tapping into everything that we can do with generative AI, for example. There's so much that can be done to further help you know, people make more money and do less work because there's still so much work that we do that we don't need to. You know, this is with Intuit, but also there are so many other use cases like I heard today you know, with the talk about the police. So that was really exciting how you can apply machine learning and data to actually help people, to help people that been through wrongful things. So I was really moved by that. And I'm also really excited about all the medical applications that we can have with data. >> Yeah, yeah. It's true that data science is so diverse in terms of what fields it can cover but it's equally important to have diverse teams and have like equity and inclusion in your teams. Where is Intuit at promoting women, non-binary minorities in your teams to progress data science? >> Yeah, so I have so much to say on this. >> Good. >> But in my work in Tel Aviv, I had the opportunity to start with Intuit women in data science branch in Tel Aviv. So that's why I'm super excited to be here today for that because basically this is the original conference, but as you know, there are branches all over the world and I got the opportunity to lead the Tel Aviv branch with Israel since 2018. And we've been through already this year it's going to be it's next week, it's going to be the sixth conference. And every year our number of submission to make talk in the conference doubled itself. >> Nice. >> We started with 20 submission, then 50, then 100. This year we have over 200 submissions of females to give talk at the conference. >> Ah, that's fantastic. >> And beyond the fact that there's so much traction, I also feel the great impact it has on the community in Israel because one of the reason we started WiDS was that when I was going to conferences I was seeing so little women on stage in all the technical conferences. You know, kind of the reason why I guess you know, Margaret and team started the WiDS conference. So I saw the same thing in Israel and I was always frustrated. I was organizing PyData Meetups as you mentioned and I was always having such a hard time to get female speakers to talk. I was trying to role model, but that's not enough, you know. We need more. So once we started WiDS and people saw you know, so many examples on the stage and also you know females got opportunity to talk in a place for that. Then it also started spreading and you can see more and more female speakers across other conferences, which are not women in data science. So I think just the fact that Intuits started this conference back in Israel and also in Bangalore and also the support Intuit does for WiDS in Stanford here, it shows how much WiDS values are aligned with our values. Yeah, and I think that to chauffeur that I think we have over 35% females in the data science and machine learning engineering roles, which is pretty amazing I think compared to the industry. >> Way above average. Yeah, absolutely. I was just, we've been talking about some of the AnitaB.org stats from 2022 showing that 'cause usually if we look at the industry to you point, over the last, I don't know, probably five, 10 years we're seeing the number of female technologists around like a quarter, 25% or so. 2022 data from AnitaB.org showed that that number is now 27.6%. So it's very slowly- >> It's very slowly increasing. >> Going in the right direction. >> Too slow. >> And that representation of women technologists increase at every level, except intern, which I thought was really interesting. And I wonder is there a covid relation there? >> I don't know. >> What do we need to do to start opening up the the top of the pipeline, the funnel to go downstream to find kids like you when you were younger and always interested in engineering and things like that. But the good news is that the hiring we've seen improvements, but it sounds like Intuit is way ahead of the curve there with 35% women in data science or technical roles. And what's always nice and refreshing that we've talked, Hannah about this too is seeing companies actually put action into initiatives. It's one thing for a company to say we're going to have you know, 50% females in our organization by 2030. It's a whole other ball game to actually create a strategy, execute on it, and share progress. So kudos to Intuit for what it's doing because that is more companies need to adopt that same sort of philosophy. And that's really cultural. >> Yeah. >> At an organization and culture can be hard to change, but it sounds like you guys kind of have it dialed in. >> I think we definitely do. That's why I really like working and Intuit. And I think that a lot of it is with the role modeling, diversity and inclusion, and by having women leaders. When you see a woman in leadership position, as a woman it makes you want to come work at this place. And as an evidence, when I build the team I started in Israel at Intuit, I have over 50% women in my team. >> Nice. >> Yeah, because when you have a woman in the interviewers panel, it's much easier, it's more inclusive. That's why we always try to have at least you know, one woman and also other minorities represented in our interviews panel. Yeah, and I think that in general it's very important as a leader to kind of know your own biases and trying to have defined standard and rubrics in how you evaluate people to avoid for those biases. So all of that inclusiveness and leadership really helps to get more diversity in your teams. >> It's critical. That thought diversity is so critical, especially if we talk about AI and we're almost out of time, I just wanted to bring up, you brought up a great point about the diversity and equity. With respect to data science and AI, we know in AI there's biases in data. We need to have more inclusivity, more representation to help start shifting that so the biases start to be dialed down and I think a conference like WiDS and it sounds like someone like you and what you've already done so far in the work that you're doing having so many females raise their hands to want to do talks at events is a good situation. It's a good scenario and hopefully it will continue to move the needle on the percentage of females in technical roles. So we thank you Shir for your time sharing with us your story, what you're doing, how Intuit and WiDS are working together. It sounds like there's great alignment there and I think we're at the tip of the iceberg with what we can do with data science and inclusion and equity. So we appreciate all of your insights and your time. >> Thank you very much. >> All right. >> I enjoyed very, very much >> Good. We hope, we aim to please. Thank you for our guests and for Hannah Freitag. This is Lisa Martin coming to you live from Stanford University. This is our coverage of the eighth Annual Women in Data Science Conference. Stick around, next guest will be here in just a minute.

Published Date : Mar 8 2023

SUMMARY :

Shir, it's great to have you. And I was just secrets girl talking We're happy to be with you. from the time you were small? and how interested it is to be able and of course we have these expectation So if you are a Intuit product user, the documents that you upload to TurboTax. the opportunity to work Yeah, in the past years Yeah, so can you I recently moved to the Bay Area. I like both of those. and data science to make and helps you do your taxes. Talk to me about how you stay done to further help you know, to have diverse teams I had the opportunity to start of females to give talk at the conference. Yeah, and I think that to chauffeur that the industry to you point, And I wonder is there the funnel to go downstream but it sounds like you guys I build the team I started to have at least you know, so the biases start to be dialed down This is Lisa Martin coming to you live

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Ben Hirschberg, Armo Ltd | CloudNativeSecurityCon 23


 

(upbeat music) >> Hello everyone, welcome back to theCUBE's coverage of Cloud Native SecurityCon North America 2023. Obviously, CUBE's coverage with our CUBE Center Report. We're not there on the ground, but we have folks and our CUBE Alumni there. We have entrepreneurs there. Of course, we want to be there in person, but we're remote. We've got Ben Hirschberg, CTO and Co-Founder of Armo, a cloud native security startup, well positioned in this industry. He's there in Seattle. Ben, thank you for coming on and sharing what's going on with theCUBE. >> Yeah, it's great to be here, John. >> So we had written on you guys up on SiliconANGLE. Congratulations on your momentum and traction. But let's first get into what's going on there on the ground? What are some of the key trends? What's the most important story being told there? What is the vibe? What's the most important story right now? >> So I think, I would like to start here with the I think the most important thing was that I think the event is very successful. Usually, the Cloud Native Security Day usually was part of KubeCon in the previous years and now it became its own conference of its own and really kudos to all the organizers who brought this up in, actually in a short time. And it wasn't really clear how many people will turn up, but at the end, we see a really nice turn up and really great talks and keynotes around here. I think that one of the biggest trends, which haven't started like in this conference, but already we're talking for a while is supply chain. Supply chain is security. I think it's, right now, the biggest trend in the talks, in the keynotes. And I think that we start to see companies, big companies, who are adopting themselves into this direction. There is a clear industry need. There is a clear problem and I think that the cloud native security teams are coming up with tooling around it. I think for right now we see more tools than adoption, but the adoption is always following the tooling. And I think it already proves itself. So we have just a very interesting talk this morning about the OpenSSL vulnerability, which was I think around Halloween, which came out and everyone thought that it's going to be a critical issue for the whole cloud native and internet infrastructure and at the end it turned out to be a lesser problem, but the reason why I think it was understood that to be a lesser problem real soon was that because people started to use (indistinct) store software composition information in the environment so security teams could look into, look up in their systems okay, what, where they're using OpenSSL, which version they are using. It became really soon real clear that this version is not adopted by a wide array of software out there so the tech surface is relatively small and I think it already proved itself that the direction if everyone is talking about. >> Yeah, we agree, we're very bullish on this move from the Cloud Native Foundation CNCF that do the security conference. Amazon Web Services has re:Invent. That's their big show, but they also have re:Inforce, the security show, so clearly they work together. I like the decoupling, very cohesive. But you guys have Kubescape of Kubernetes security. Talk about the conversations that are there and that you're hearing around why there's different event what's different around KubeCon and CloudNativeCon than this Cloud Native SecurityCon. It's not called KubeSucSecCon, it's called Cloud Native SecurityCon. What's the difference? Are people confused? Is it clear? What's the difference between the two shows? What are you hearing? >> So I think that, you know, there is a good question. Okay, where is Cloud Native Computing Foundation came from? Obviously everyone knows that it was somewhat coupled with the adoption of Kubernetes. It was a clear understanding in the industry that there are different efforts where the industry needs to come together without looking be very vendor-specific and try to sort out a lot of issues in order to enable adoption and bring great value and I think that the main difference here between KubeCon and the Cloud Native Security Conference is really the focus, and not just on Kubernetes, but the whole ecosystem behind that. The way we are delivering software, the way we are monitoring software, and all where Kubernetes is only just, you know, maybe the biggest clog in the system, but, you know, just one of the others and it gives great overview of what you have in the whole ecosystem. >> Yeah, I think it's a good call. I would add that what I'm hearing too is that security is so critical to the business model of every company. It's so mainstream. The hackers have a great business model. They make money, their costs are lower than the revenue. So the business of hacking in breaches, ransomware all over the place is so successful that they're playing offense, everyone's playing defense, so it's about time we can get focus to really be faster and more nimble and agile on solving some of these security challenges in open source. So I think that to me is a great focus and so I give total props to the CNC. I call it the event operating system. You got the security group over here decoupled from the main kernel, but they work together. Good call and so this brings back up to some of the things that are going on so I have to ask you, as your startup as a CTO, you guys have the Kubescape platform, how do you guys fit into the landscape and what's different from your tools for Kubernetes environments versus what's out there? >> So I think that our journey is really interesting in the solution space because I think that our mode really tries to understand where security can meet the actual adoption because as you just said, somehow we have to sort out together how security is going to be automated and integrated in its best way. So Kubescape project started as a Kubernetes security posture tool. Just, you know, when people are really early in their adoption of Kubernetes systems, they want to understand whether the installation is is secure, whether the basic configurations are look okay, and giving them instant feedback on that, both in live systems and in the CICD, this is where Kubescape came from. We started as an open source project because we are big believers of open source, of the power of open source security, and I can, you know I think maybe this is my first interview when I can say that Kubescape was accepted to be a CNCF Sandbox project so Armo was actually donating the project to the CNCF, I think, which is a huge milestone and a great way to further the adoption of Kubernetes security and from now on we want to see where the users in Armo and Kubescape project want to see where the users are going, their Kubernetes security journey and help them to automatize, help them to to implement security more fast in the way the developers are using it working. >> Okay, if you don't mind, I want to just get clarification. What's the difference between the Armo platform and Kubescape because you have Kubescape Sandbox project and Armo platform. Could you talk about the differences and interaction? >> Sure, Kubescape is an open source project and Armo platform is actually a managed platform which runs Kubescape in the cloud for you because Kubescape is part, it has several parts. One part is, which is running inside the Kubernetes cluster in the CICD processes of the user, and there is another part which we call the backend where the results are stored and can be analyzed further. So Armo platform gives you managed way to run the backend, but I can tell you that backend is also, will be available within a month or two also for everyone to install on their premises as well, because again, we are an open source company and we are, we want to enable users, so the difference is that Armo platform is a managed platform behind Kubescape. >> How does Kubescape differ from closed proprietary sourced solutions? >> So I can tell you that there are closed proprietary solutions which are very good security solutions, but I think that the main difference, if I had to pick beyond the very specific technicalities is the worldview. The way we see that our user is not the CISO. Our user is not necessarily the security team. From our perspective, the user is the DevOps and the developers who are working on the Kubernetes cluster day to day and we want to enable them to improve their security. So actually our approach is more developer-friendly, if I would need to define it very shortly. >> What does this risk calculation score you guys have in Kubscape? That's come up and we cover that in our story. Can you explain to the folks how that fits in? Is it Kubescape is the platform and what's the benefit, what's the purpose? >> So the risk calculation is actually a score we are giving to clusters in order for the users to understand where they are standing in the general population, how they are faring against a perfect hardened cluster. It is based on the number of different tests we are making. And I don't want to go into, you know, the very specifics of the mathematical functions, but in general it takes into account how many functions are failing, security tests are failing inside your cluster. How many nodes you are having, how many workloads are having, and creating this number which enables you to understand where you are standing in the global, in the world. >> What's the customer value that you guys pitching? What's the pitch for the Armo platform? When you go and talk to a customer, are they like, "We need you." Do they come to you? Is it word of mouth? You guys have a strategy? What's the pitch? What's so appealing to the customers? Why are they enthusiastic about you guys? >> So John, I can tell you, maybe it's not so easy to to say the words, but I nearly 20 years in the industry and though I've been always around cyber and the defense industry and I can tell you that I never had this journey where before where I could say that the the customers are coming to us and not we are pitching to customers. Simply because people want to, this is very easy tool, very very easy to use, very understandable and it very helps the engineers to improve security posture. And they're coming to us and they're saying, "Well, awesome, okay, how we can like use it. Do you have a graphical interface?" And we are pointing them to the Armor platform and they are falling in love and coming to us even more and we can tell you that we have a big number of active users behind the platform itself. >> You know, one of the things that comes up every time at KubeCon and Cloud NativeCon when we're there, and we'll be in Amsterdam, so folks watching, you know, we'll see onsite, developer productivity is like the number one thing everyone talks about and security is so important. It's become by default a blocker or anchor or a drag on productivity. This is big, the things that you're mentioning, easy to use, engineering supporting it, developer adoption, you know we've always said on theCUBE, developers will be the de facto standards bodies by their choices 'cause developers make all the decisions. So if I can go faster and I can have security kind of programmed in, I'm not shifting left, it's just I'm just having security kind of in there. That's the dream state. Is that what you guys are trying to do here? Because that's the nirvana, everyone wants to do that. >> Yeah, I think your definition is like perfect because really we had like this, for a very long time we had this world where we decoupled security teams from developers and even for sometimes from engineering at all and I think for multiple reasons, we are more seeing a big convergence. Security teams are becoming part of the engineering and the engineering becoming part of the security and as you're saying, okay, the day-to-day world of developers are becoming very tangled up in the good way with security, so the think about it that today, one of my developers at Armo is creating a pull request. He's already, code is already scanned by security scanners for to test for different security problems. It's already, you know, before he already gets feedback on his first time where he's sharing his code and if there is an issue, he already can solve it and this is just solving issues much faster, much cheaper, and also you asked me about, you know, the wipe in the conference and we know no one can deny the current economic wipe we have and this also relates to security teams and security teams has to be much more efficient. And one of the things that everyone is talking, okay, we need more automation, we need more, better tooling and I think we are really fitting into this. >> Yeah, and I talked to venture capitalists yesterday and today, an angel investor. Best time for startup is right now and again, open source is driving a lot of value. Ben, it's been great to have you on and sharing with us what's going on on the ground there as well as talking about some of the traction you have. Just final question, how old's the company? How much funding do you have? Where you guys located? Put a plug in for the company. You guys looking to hire? Tell us about the company. Were you guys located? How much capital do you have? >> So, okay, the company's here for three years. We've passed a round last March with Tiger and Hyperwise capitals. We are located, most of the company's located today in Israel in Tel Aviv, but we have like great team also in Ukraine and also great guys are in Europe and right now also Craig Box joined us as an open source VP and he's like right now located in New Zealand, so we are a really global team, which I think it's really helps us to strengthen ourselves. >> Yeah, and I think this is the entrepreneurial equation for the future. It's really great to see that global. We heard that in Priyanka Sharma's keynote. It's a global culture, global community. >> Right. >> And so really, really props you guys. Congratulations on Armo and thanks for coming on theCUBE and sharing insights and expertise and also what's happening on the ground. Appreciate it, Ben, thanks for coming on. >> Thank you, John. >> Okay, cheers. Okay, this is CUB coverage here of the Cloud Native SecurityCon in North America 2023. I'm John Furrier for Lisa Martin, Dave Vellante. We're back with more of wrap up of the event after this short break. (gentle upbeat music)

Published Date : Feb 3 2023

SUMMARY :

and sharing what's going on with theCUBE. What is the vibe? and at the end it turned that do the security conference. the way we are monitoring software, I call it the event operating system. the project to the CNCF, What's the difference between in the CICD processes of the user, is the worldview. Is it Kubescape is the platform It is based on the number of What's the pitch for the Armo platform? and the defense industry This is big, the things and the engineering becoming the traction you have. So, okay, the company's Yeah, and I think this is and also what's happening on the ground. of the Cloud Native SecurityCon

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Ayal Yogev, Anjuna Security | AWS re:Invent 2022


 

(gentle music) >> Good morning, fellow cloud nerds, and welcome back to day four of AWS re:Invent. We are here in fabulous Las Vegas, Nevada. I'm joined by my cohost Paul Gillin. I'm Savannah Peterson. We're on theCUBE. Paul, how you doing? You doing well? >> We're staggering to the conclusion. >> (laughing) It's almost the end then. >> And I say that only talking about my feet. This event is still going strong. The great keynote this morning by Werner Vogels about system architecture and really teaching 70,000 people how to design systems. AWS really taking advantage of this event to educate its customer base and- >> So much education here. >> Yeah, and that was a fantastic sort of cap to the keynotes we've seen this week. >> Yeah, I'm impressed Paul, our first AWS re:Invent. I think we're doing pretty good all things considered. >> Well, we're still alive. >> And our next guest actually looks like he's been sleeping this week, which is remarkable. Please welcome Ayal to the show. Ayal, how you doing today? >> I'm good, I'm good. Thank you for having me. >> It's our pleasure. You're with Anjuna. >> Yes. >> Just in case the audience isn't familiar, what's Anjuna? >> Anjuna is an enterprise security company. We focus in the space of confidential computing. And essentially we enable people to run anything they want in any environment with complete security and privacy. >> Which is a top priority for pretty much every single person here. >> Ayal: That is true. >> Now, confidential computing, I keep hearing that term. >> Yeah, let's go there. >> Is it, I mean, is there a trademark associated with it? Is there a certification? Is the concept or is it actually a set of principles and frameworks? >> Savannah: Give us the scoop. >> Yeah, so confidential computing is essentially a set of technologies that were added to the hardware itself, to the CPU, and now to GPUs by the hardware vendors. So Intel, AMD, Arm, Nvidia AWS with their own hardware solution for this. And essentially what it allows you to do is to run workloads on top of the CPU and the GPU in a way that even if somebody gets full access to the infrastructure, you know, root access, physical access, they're not going to have any access to the data and the code running on top of it. And as you can imagine in cloud environments, this is extremely, extremely (indistinct). >> And this done through encryption? >> It involves encryption. If you go one step deeper, it involves protecting the data while it's running, data and memory, when the application is processing it. Which is always been the missing piece in terms of where you protect data. >> So I got excited when I looked at the show notes because you are serving some of the most notoriously security strict customers in the market. Can you tell us about the Israeli Ministry of Defense? >> Sure. So essentially what we do with the Israel Ministry of Defense and other customers, especially on the on the government side, one of the challenges government has is that they have to, if they want security and privacy in the cloud, they have to use something like a gov cloud. And sometimes that makes sense, but sometimes either the gov cloud is not ready because of legal battles or just it takes time to set it up. In some countries, it's just not going to make financial sense for the clouds to create a gov cloud. So what we do is we enable them to run in the commercial cloud with the security and privacy of a gov cloud. >> Was that, I can imagine, so you took them to the public cloud, correct? >> Ayal: Yes. >> Was that a challenging process? When I think of national security, I can imagine a business transformation like that would be a little nerve-wracking. >> Oh, definitely. It was a long process and they went like, "This is probably one of the best security experts on the planet." And they went extremely deep in making sure that this aligns with what they would be able to do to actually move sensitive data to the commercial cloud. Which, obviously, that the requirements are higher than anything I've ever seen from anybody else. And the fact that they were willing to publicly talk about this and be a public reference for us shows the level of confidence that they have in the underlying technology, in the security and privacy that this allows them to achieve. >> We still hear reservations, particularly from heavily regulated industries, about moving into the cloud. Concerns about security, data ownership, shared responsibility. >> Ayal: Yes. >> Are those real, are those valid? Or is the technology foundation now strong enough that they should not be worried about those things? >> Yeah, this is an excellent question, because the the shared responsibility model, is exactly sort of the core of what this is about. The shared responsibility model essentially means the cloud's, sort of by definition, the cloud is somebody else managing the infrastructure for you, right? And if somebody's managing the infrastructure for you they have full access to what you do on top of that infrastructure. That's almost the definition. And that's always been sort of one of the core security problems that was never solved. Confidential computing solves this. It means that you can use the cloud without the clouds having any access to what you do on top of their infrastructure. And that means that if the clouds get hacked, your data is safe. If an employee of the cloud decides to get access to your data, they can't. They just don't have any access. Or if the government comes to the cloud with a subpoena, the clouds can't give them access to your data, which is obviously very important for European customers and other customers outside of the US. So this is essentially what confidential computing does and it allows to break that shared responsibility model, where you as the customer get full control of your data back. >> Now, do you need the hardware foundation to do that? Or are you solving this problem in software? >> No. So we do need a hardware foundation for this which is now available in every cloud. And it's part of every server CPU that Intel ship, that AMD ship. This is part of almost every data center in AWS. But what we bring to the table at Anjuna, is every time there was a fundamental shift in computer architecture, you needed a software stack on top of it to essentially make it usable. And I think the best last example was VMware, right? But virtualization was extremely powerful technology that nobody was using until VMware built a software stack to make it super simple to virtualize anything. And to some extent that was the birth of the public cloud. We would never have a public cloud without virtualization. We're seeing the same level of shift now with confidential computing on the hardware side. And all the large players are behind this. They're all part of the confidential computing consortium that pushes this. But the challenge customers are running into, is for them to go use this they have to go refactor and rebuild every application. >> Why? >> And nobody's going to go do that. And that's exactly what we help them with. >> Yeah. >> In terms of why, as part of confidential computing, what it essentially means is that the operating system is outside the cross cycle. You, you don't want to cross the operating system because you don't want somebody with root access to have any access to your data. And what this means is every application obviously communicates with the operating system pretty often, right? To send something to the network or some, you know, save something to the file system, which means you have to re-architect your application and break it into two: a confidential piece and a piece that's communicating with the operating system and build some channel for the two sides to communicate. Nobody's going to go do that for every application. We allow you to essentially do something like Anjuna run application and it just runs in a confidential computing environment. No changes. >> Let's talk a little bit more about that. So when we're thinking about, I think we've talked a little bit about it, but I think there's a myth of control when we're talking about on-prem. Everybody thinks that things are more secure. >> Right. >> It's not the case. Tell us how enterprise security changes once when a customer has adopted Anjuna. >> Yeah, so I think you're absolutely right. I think the clouds can put a lot more effort and expertise into bringing security than the data center. But you definitely have this sort of more sense of security in your data center because you own the full stack, right? It's your people, it's your servers, it's your networks in the cloud >> Savannah: It's in your house, so to speak. Yeah. >> Exactly. And the cloud is the third party managing all that for you. And people get very concerned about that, and to some extent for a good reason. Because if a breach happens regardless of whose fault it is, the customer's going to be the one sort of left holding the bag and dealing with the aftermath of the breach. So they're right to be concerned. In terms of what we do, once you run things in confidential computing, you sort of solve the core problem of security. One of the core problems of security has always been when somebody gets access to the infrastructure especially root access to the infrastructure, it's game over. They have access to everything. And a lot of how security's been built is almost like these bandaid solutions to try to solve. Like perimeter security is how do I make sure nobody gets access to the infrastructure if they don't need to, right? All these detection solutions is once they're in the infrastructure, how do I detect that they've done something they shouldn't have? A lot of the vulnerability management is how do I make sure everything is patched? Because if somebody gets access how do I make sure they don't get root access? And then they really get access to everything. And conversation computing solves all of that. It solves the root cause, the root problem. So even if somebody gets root access, even if somebody has full access to the infrastructure, they don't have access to anything, which allows you to one, essentially move anything you want to the public cloud regardless, of the sensitivity of it, but also get rid of a lot of these other sort of bandaid solutions that you use today to try to stop people from getting that access because it doesn't matter anymore. >> Okay. So cyber security is a one and a half trillion dollar industry, growing at over 10% a year. Are you saying that if organizations were to adopt confidential computing universally that industry would not be necessary? >> No, I think a lot of it will have to change with confidential computing. Exactly, like the computer industry changed with virtualization. If you had asked when VMware just got started if the data centers are going to like, "Oh, this is going to happen," I don't think anybody could have foreseen this. But this is exactly what virtualization did. Confidential computing will change the the security industry in a massive way, but it doesn't solve every security problem. What it essentially does is it moves the perimeter from the machine itself, which used to be sort of the smallest atom, to be around the workload. And what happens in the machine doesn't matter anymore. You still need to make sure that your workload is protected. So companies that make sure that you write secure code are still going to be needed. Plus you're going to need security for things like denial of service. Because if somebody runs, you know, gets access to their infrastructure, they can stop you from running but your data is going to be protected. You're not going to need any of these data protection solutions around the box anymore. >> Let's hang out there for a second. Where do you see, I mean what an exciting time to be you, quite frankly, and congratulations on all of your success so far. Where are we going in the next two to five years? >> Yeah, I think with confidential computing the first thing that this is going to enable is essentially moving everything to the public cloud. I think the number one concern with the cloud kind of like you mentioned, is security and privacy. >> Savannah: Right. >> And this essentially eliminates that need. And that's why the clouds are so excited about this. That's why AWS talks about it. And I think Steve Schmidt, the of CISO of Amazon, used to be the CISO of AWS, talks about confidential computing as the future of data security and privacy. And there's a reason why he does that. We've seen other clouds talk about this and push this. That's why the clouds are so excited about this. But even more so again, I think over time this will allow you to essentially remove a lot of the security tools that exist there, kind of reimagine security in a better way. >> Savannah: Clean it up a little bit. Yeah. >> Exactly. And over time, I think it's going to change the world of compute even more because one of the things this allows you to do is the closer you get to the edge, the more security and privacy problems you have. >> Savannah: Right. And so many variables. >> Exactly. And it's basically out there in the wild, and people can get physical access. >> Quite literally a lot of the time, yeah. >> Exactly. And what confidential computing does, it provides that complete security and privacy regardless of even if somebody has physical access, which will allow you to move workloads much closer to the edge or to the edge itself instead of sending everything back to your backend to process things. >> We have interviewed a number of security companies here during this event, and I have to say, confidential computing has never come up. They don't talk about it. Why is that? Is there an awareness problem? >> Savannah: Are they threatened? >> Yeah, so I think the biggest, and to some extent, this is exactly like I kept bringing up VMware. Like VMware's, you can think of Salesforce, when they talked about SaaS, they sort of embedded the concept of SaaS. No other company on the planet was talking about SaaS. They created a new category and now almost everything is SaaS. VMware with virtualization, right? Nobody was using it, and now, almost everything is virtualized. Confidential computing is a new way of doing things. It's basically a kind have to shift the way of how you think about security and how you think about privacy. And this is exactly what we're seeing. I don't expect other security companies to talk about this. And to some extent, one of the things I've realized that we're almost more of an infrastructure company than a security company, because we bake security to be part of the infrastructure. But we're seeing more and more the clouds talk about this. The CPU vendors talk about this. We talk to customers more and more. Like almost every large bank I talk to now has a confidential computing strategy for 2023. This is now becoming part of the mainstream. And yeah, security companies will have to adopt or die if they don't fit into that new world that it is going to create >> This is the new world order, baby, get on the train or get left behind. >> Ayal: Exactly. >> I love it. This is a really fascinating conversation and honestly what you're doing makes so much sense. Yeah, you don't need me to validate your business model, but I will, just for the sake of that. >> Thank you. >> We have a new challenge here at re:Invent on theCUBE where we are looking for your 30 second Instagram reel hot take, thought leadership. What's the biggest theme, key takeaway from the show or experience this year for you? >> Yeah, so for me, obviously focusing on confidential computing. I think this is just going to be similar to how no network was encrypted 10 years ago and today every network is encrypted with TLS and HTTPS. And how five years ago no disc was encrypted, and today every disc is encrypted with disc encryption. The one missing piece is memory. Memory is where data is exposed now. I think within a few years all memory is going to be encrypted and it's just going to change two industries: the security industry as well as the computer industry. >> Paul: Does that include cache memory? >> What's that? >> Does that include cache memory? >> That is encrypting the RAM essentially. So everything, this is the one last place where data is not encrypted, and that's exactly what confidential computing brings to the table. >> Are there any performance concerns with encrypting memory? >> That's a phenomenal question. One of the really nice things about confidential computing is that the heavy lifting is done by the hardware vendors themselves as part of the hardware and not part of the critical path in the CPU. It's very similar to the TLS acceleration cards, if you remember those, which allows us to be extremely, extremely performant. And that's why I think this is going to be for everything. Because every time we had a security solution that had no performance impact and was super simple to use it just became the default, because why wouldn't you use it for everything? >> Ayal, this has been absolutely fascinating. We could talk to you all day. Unfortunately, we're out of time. But really thank you so much for coming on the show. Now, we feel more confident in terms of our confidential computing knowledge and definitely learned a lot. Thank all of you for tuning in to our fantastic four day live stream at AWS re:Invent here in Sin City with Paul Gillin. I'm Savannah Peterson. You're watching theCUBE, the leader in high tech coverage. (gentle music)

Published Date : Dec 1 2022

SUMMARY :

Paul, how you doing? And I say that only to the keynotes we've seen this week. I think we're doing pretty Ayal, how you doing today? Thank you for having me. You're with Anjuna. We focus in the space of Which is a top priority I keep hearing that term. and the code running on top of it. Which is always been the missing piece I looked at the show notes for the clouds to create a gov cloud. like that would be a And the fact that they were willing about moving into the cloud. they have full access to what you do And all the large players are behind this. And nobody's going to go do that. that the operating system I think we've talked It's not the case. than the data center. house, so to speak. the customer's going to be the to adopt confidential if the data centers are going to like, to be you, quite frankly, this is going to enable as the future of data Savannah: Clean it the closer you get to the edge, And so many variables. And it's basically lot of the time, yeah. or to the edge itself during this event, and I have to say, And to some extent, one of This is the new world order, baby, Yeah, you don't need me to What's the biggest theme, I think this is just going to be similar That is encrypting the RAM essentially. is that the heavy lifting We could talk to you all day.

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David Schmidt, Dell Technologies and Scott Clark, Intel | SuperComputing 22


 

(techno music intro) >> Welcome back to theCube's coverage of SuperComputing Conference 2022. We are here at day three covering the amazing events that are occurring here. I'm Dave Nicholson, with my co-host Paul Gillin. How's it goin', Paul? >> Fine, Dave. Winding down here, but still plenty of action. >> Interesting stuff. We got a full day of coverage, and we're having really, really interesting conversations. We sort of wrapped things up at Supercomputing 22 here in Dallas. I've got two very special guests with me, Scott from Intel and David from Dell, to talk about yeah supercomputing, but guess what? We've got some really cool stuff coming up after this whole thing wraps. So not all of the holiday gifts have been unwrapped yet, kids. Welcome gentlemen. >> Thanks so much for having us. >> Thanks for having us. >> So, let's start with you, David. First of all, explain the relationship in general between Dell and Intel. >> Sure, so obviously Intel's been an outstanding partner. We built some great solutions over the years. I think the market reflects that. Our customers tell us that. The feedback's strong. The products you see out here this week at Supercompute, you know, put that on display for everybody to see. And then as we think about AI in machine learning, there's so many different directions we need to go to help our customers deliver AI outcomes. Right, so we recognize that AI has kind of spread outside of just the confines of everything we've seen here this week. And now we've got really accessible AI use cases that we can explain to friends and family. We can talk about going into retail environments and how AI is being used to track inventory, to monitor traffic, et cetera. But really what that means to us as a bunch of hardware folks is we have to deliver the right platforms and the right designs for a variety of environments, both inside and outside the data center. And so if you look at our portfolio, we have some great products here this week, but we also have other platforms, like the XR4000, our shortest rack server ever that's designed to go into Edge environments, but is also built for those Edge AI use cases that supports GPUs. It supports AI on the CPU as well. And so there's a lot of really compelling platforms that we're starting to talk about, have already been talking about, and it's going to really enable our customers to deliver AI in a variety of ways. >> You mentioned AI on the CPU. Maybe this is a question for Scott. What does that mean, AI on the CPU? >> Well, as David was talking about, we're just seeing this explosion of different use cases. And some of those on the Edge, some of them in the Cloud, some of them on Prem. But within those individual deployments, there's often different ways that you can do AI, whether that's training or inference. And what we're seeing is a lot of times the memory locality matters quite a bit. You don't want to have to pay necessarily a cost going across the PCI express bus, especially with some of our newer products like the CPU Max series, where you can have a huge about of high bandwidth memory just sitting right on the CPU. Things that traditionally would have been accelerator only, can now live on a CPU, and that includes both on the inference side. We're seeing some really great things with images, where you might have a giant medical image that you need to be able to do extremely high resolution inference on or even text, where you might have a huge corpus of extremely sparse text that you need to be able to randomly sample very efficiently. >> So how are these needs influencing the evolution of Intel CPU architectures? >> So, we're talking to our customers. We're talking to our partners. This presents both an opportunity, but also a challenge with all of these different places that you can put these great products, as well as applications. And so we're very thoughtfully trying to go to the market, see where their needs are, and then meet those needs. This industry obviously has a lot of great players in it, and it's no longer the case that if you build it, they will come. So what we're doing is we're finding where are those choke points, how can we have that biggest difference? Sometimes there's generational leaps, and I know David can speak to this, can be huge from one system to the next just because everything's accelerated on the software side, the hardware side, and the platforms themselves. >> That's right, and we're really excited about that leap. If you take what Scott just described, we've been writing white papers, our team with Scott's team, we've been talking about those types of use cases using doing large image analysis and leveraging system memory, leveraging the CPU to do that, we've been talking about that for several generations now. Right, going back to Cascade Lake, going back to what we would call 14th generation power Edge. And so now as we prepare and continue to unveil, kind of we're in launch season, right, you and I were talking about how we're in launch season. As we continue to unveil and launch more products, the performance improvements are just going to be outstanding and we'll continue that evolution that Scott described. >> Yeah, I'd like to applaud Dell just for a moment for its restraint. Because I know you could've come in and taken all of the space in the convention center to show everything that you do. >> Would have loved to. >> In the HPC space. Now, worst kept secrets on earth at this point. Vying for number one place is the fact that there is a new Mission Impossible movie coming. And there's also new stuff coming from Intel. I know, I think allegedly we're getting close. What can you share with us on that front? And I appreciate it if you can't share a ton of specifics, but where are we going? David just alluded to it. >> Yeah, as David talked about, we've been working on some of these things for many years. And it's just, this momentum is continuing to build, both in respect to some of our hardware investments. We've unveiled some things both here, both on the CPU side and the accelerator side, but also on the software side. OneAPI is gathering more and more traction and the ecosystem is continuing to blossom. Some of our AI and HPC workloads, and the combination thereof, are becoming more and more viable, as well as displacing traditional approaches to some of these problems. And it's this type of thing where it's not linear. It all builds on itself. And we've seen some of these investments that we've made for a better half of a decade starting to bear fruit, but that's, it's not just a one time thing. It's just going to continue to roll out, and we're going to be seeing more and more of this. >> So I want to follow up on something that you mentioned. I don't know if you've ever heard that the Charlie Brown saying that sometimes the most discouraging thing can be to have immense potential. Because between Dell and Intel, you offer so many different versions of things from a fit for function perspective. As a practical matter, how do you work with customers, and maybe this is a question for you, David. How do you work with customers to figure out what the right fit is? >> I'll give you a great example. Just this week, customer conversations, and we can put it in terms of kilowatts to rack, right. How many kilowatts are you delivering at a rack level inside your data center? I've had an answer anywhere from five all the way up to 90. There's some that have been a bit higher that probably don't want to talk about those cases, kind of customers we're meeting with very privately. But the range is really, really large, right, and there's a variety of environments. Customers might be ready for liquid today. They may not be ready for it. They may want to maximize air cooling. Those are the conversations, and then of course it all maps back to the workloads they wish to enable. AI is an extremely overloaded term. We don't have enough time to talk about all the different things that tuck under that umbrella, but the workloads and the outcomes they wish to enable, we have the right solutions. And then we take it a step further by considering where they are today, where they need to go. And I just love that five to 90 example of not every customer has an identical cookie cutter environment, so we've got to have the right platforms, the right solutions, for the right workloads, for the right environments. >> So, I like to dive in on this power issue, to give people who are watching an idea. Because we say five kilowatts, 90 kilowatts, people are like, oh wow, hmm, what does that mean? 90 kilowatts is more than 100 horse power if you want to translate it over. It's a massive amount of power, so if you think of EV terms. You know, five kilowatts is about a hairdryer's around a kilowatt, 1,000 watts, right. But the point is, 90 kilowatts in a rack, that's insane. That's absolutely insane. The heat that that generates has got to be insane, and so it's important. >> Several houses in the size of a closet. >> Exactly, exactly. Yeah, in a rack I explain to people, you know, it's like a refrigerator. But, so in the arena of thermals, I mean is that something during the development of next gen architectures, is that something that's been taken into consideration? Or is it just a race to die size? >> Well, you definitely have to take thermals into account, as well as just the power of consumption themselves. I mean, people are looking at their total cost of ownership. They're looking at sustainability. And at the end of the day, they need to solve a problem. There's many paths up that mountain, and it's about choosing that right path. We've talked about this before, having extremely thoughtful partners, we're just not going to common-torily try every single solution. We're going to try to find the ones that fit that right mold for that customer. And we're seeing more and more people, excuse me, care about this, more and more people wanting to say, how do I do this in the most sustainable way? How do I do this in the most reliable way, given maybe different fluctuations in their power consumption or their power pricing? We're developing more software tools and obviously partnering with great partners to make sure we do this in the most thoughtful way possible. >> Intel put a lot of, made a big investment by buying Habana Labs for its acceleration technology. They're based in Israel. You're based on the west coast. How are you coordinating with them? How will the Habana technology work its way into more mainstream Intel products? And how would Dell integrate those into your servers? >> Good question. I guess I can kick this off. So Habana is part of the Intel family now. They've been integrated in. It's been a great journey with them, as some of their products have launched on AWS, and they've had some very good wins on MLPerf and things like that. I think it's about finding the right tool for the job, right. Not every problem is a nail, so you need more than just a hammer. And so we have the Xeon series, which is incredibly flexible, can do so many different things. It's what we've come to know and love. On the other end of the spectrum, we obviously have some of these more deep learning focused accelerators. And if that's your problem, then you can solve that problem in incredibly efficient ways. The accelerators themselves are somewhere in the middle, so you get that kind of Goldilocks zone of flexibility and power. And depending on your use case, depending on what you know your workloads are going to be day in and day out, one of these solutions might work better for you. A combination might work better for you. Hybrid compute starts to become really interesting. Maybe you have something that you need 24/7, but then you only need a burst to certain things. There's a lot of different options out there. >> The portfolio approach. >> Exactly. >> And then what I love about the work that Scott's team is doing, customers have told us this week in our meetings, they do not want to spend developer's time porting code from one stack to the next. They want that flexibility of choice. Everyone does. We want it in our lives, in our every day lives. They need that flexibility of choice, but they also, there's an opportunity cost when their developers have to choose to port some code over from one stack to another or spend time improving algorithms and doing things that actually generate, you know, meaningful outcomes for their business or their research. And so if they are, you know, desperately searching I would say for that solution and for help in that area, and that's what we're working to enable soon. >> And this is what I love about oneAPI, our software stack, it's open first, heterogeneous first. You can take SYCL code, it can run on competitor's hardware. It can run on Intel hardware. It's one of these things that you have to believe long term, the future is open. Wall gardens, the walls eventually crumble. And we're just trying to continue to invest in that ecosystem to make sure that the in-developer at the end of the day really gets what they need to do, which is solving their business problem, not tinkering with our drivers. >> Yeah, I actually saw an interesting announcement that I hadn't been tracking. I hadn't been tracking this area. Chiplets, and the idea of an open standard where competitors of Intel from a silicone perspective can have their chips integrated via a universal standard. And basically you had the top three silicone vendors saying, yeah, absolutely, let's work together. Cats and dogs. >> Exactly, but at the end of the day, it's whatever menagerie solves the problem. >> Right, right, exactly. And of course Dell can solve it from any angle. >> Yeah, we need strong partners to build the platforms to actually do it. At the end of the day, silicone without software is just sand. Sand with silicone is poorly written prose. But without an actual platform to put it on, it's nothing, it's a box that sits in the corner. >> David, you mentioned that 90% of power age servers now support GPUs. So how is this high-performing, the growth of high performance computing, the demand, influencing the evolution of your server architecture? >> Great question, a couple of ways. You know, I would say 90% of our platforms support GPUs. 100% of our platforms support AI use cases. And it goes back to the CPU compute stack. As we look at how we deliver different form factors for customers, we go back to that range, I said that power range this week of how do we enable the right air coolant solutions? How do we deliver the right liquid cooling solutions, so that wherever the customer is in their environment, and whatever footprint they have, we're ready to meet it? That's something you'll see as we go into kind of the second half of launch season and continue rolling out products. You're going to see some very compelling solutions, not just in air cooling, but liquid cooling as well. >> You want to be more specific? >> We can't unveil everything at Supercompute. We have a lot of great stuff coming up here in the next few months, so. >> It's kind of like being at a great restaurant when they offer you dessert, and you're like yeah, dessert would be great, but I just can't take anymore. >> It's a multi course meal. >> At this point. Well, as we wrap, I've got one more question for each of you. Same question for each of you. When you think about high performance computing, super computing, all of the things that you're doing in your partnership, driving artificial intelligence, at that tip of the spear, what kind of insights are you looking forward to us being able to gain from this technology? In other words, what cool thing, what do you think is cool out there from an AI perspective? What problem do you think we can solve in the near future? What problems would you like to solve? What gets you out of bed in the morning? Cause it's not the little, it's not the bits and the bobs and the speeds and the feats, it's what we're going to do with them, so what do you think, David? >> I'll give you an example. And I think, I saw some of my colleagues talk about this earlier in the week, but for me what we could do in the past two years to unable our customers in a quarantine pandemic environment, we were delivering platforms and solutions to help them do their jobs, help them carry on in their lives. And that's just one example, and if I were to map that forward, it's about enabling that human progress. And it's, you know, you ask a 20 year version of me 20 years ago, you know, if you could imagine some of these things, I don't know what kind of answer you would get. And so mapping forward next decade, next two decades, I can go back to that example of hey, we did great things in the past couple of years to enable our customers. Just imagine what we're going to be able to do going forward to enable that human progress. You know, there's great use cases, there's great image analysis. We talked about some. The images that Scott was referring to had to do with taking CAT scan images and being able to scan them for tumors and other things in the healthcare industry. That is stuff that feels good when you get out of bed in the morning, to know that you're enabling that type of progress. >> Scott, quick thoughts? >> Yeah, and I'll echo that. It's not one specific use case, but it's really this wave front of all of these use cases, from the very micro of developing the next drug to finding the next battery technology, all the way up to the macro of trying to have an impact on climate change or even the origins of the universe itself. All of these fields are seeing these massive gains, both from the software, the hardware, the platforms that we're bringing to bear to these problems. And at the end of the day, humanity is going to be fundamentally transformed by the computation that we're launching and working on today. >> Fantastic, fantastic. Thank you, gentlemen. You heard it hear first, Intel and Dell just committed to solving the secrets of the universe by New Years Eve 2023. >> Well, next Supercompute, let's give us a little time. >> The next Supercompute Convention. >> Yeah, next year. >> Yeah, SC 2023, we'll come back and see what problems have been solved. You heard it hear first on theCube, folks. By SC 23, Dell and Intel are going to reveal the secrets of the universe. From here, at SC 22, I'd like to thank you for joining our conversation. I'm Dave Nicholson, with my co-host Paul Gillin. Stay tuned to theCube's coverage of Supercomputing Conference 22. We'll be back after a short break. (techno music)

Published Date : Nov 17 2022

SUMMARY :

covering the amazing events Winding down here, but So not all of the holiday gifts First of all, explain the and the right designs for What does that mean, AI on the CPU? that you need to be able to and it's no longer the case leveraging the CPU to do that, all of the space in the convention center And I appreciate it if you and the ecosystem is something that you mentioned. And I just love that five to 90 example But the point is, 90 kilowatts to people, you know, And at the end of the day, You're based on the west coast. So Habana is part of the Intel family now. and for help in that area, in that ecosystem to make Chiplets, and the idea of an open standard Exactly, but at the end of the day, And of course Dell can that sits in the corner. the growth of high performance And it goes back to the CPU compute stack. in the next few months, so. when they offer you dessert, and the speeds and the feats, in the morning, to know And at the end of the day, of the universe by New Years Eve 2023. Well, next Supercompute, From here, at SC 22, I'd like to thank you

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Shaked Askayo & Amit Eyal Govrin, Kubiya | KubeCon+CloudNativeCon NA 2022


 

>> Good afternoon everyone, and welcome back to theCUBE where we're coming to you live from Detroit, Michigan at KubeCon and Cloud Native Con. We're going to keep theCUBE puns coming this afternoon because we have the pleasure of being joined by not one but two guests from Kubiya. John Furrier, my wonderful co-host. You're familiar with these guys. You just chatted with them last week. >> We broke the story of their launch and featured them on theCUBE in our studio conversation. This is a great segment. Real innovative company with lofty goals, and they're really good ones. Looking forward to it. >> If that's not a tease to keep watching I don't know what is. (John laughs) Without further ado, on that note, allow me to introduce Amit and Shaked who are here to tell us all about Kubiya. And I'm going to blow the pitch for you a little bit just because this gets me excited. (all laugh) They're essentially the Siri of DevOps, but that means you can, you can create using voice or chat or any medium. Am I right? Is this? Yeah? >> You're hired. >> Excellent. (all laugh) >> Okay. >> We'll take it. >> Who knows what I'll tell the chat to do or what I'll, what I will control with my voice, but I love where you're. >> Absolutely. I'll just give the high level. Conversational AI for the world of DevOps. Kind of redefining how self-service DevOps is supposed to be essentially accessed, right? As opposed to just having siloed information. You know, having different platforms that require an operator or somebody who's using it to know exactly how they're accessing what they're doing and so forth. Essentially, the ability to express your intent in natural language, English, or any language I use. >> It's quite literally the language barrier sometimes. >> Precisely. >> Both from the spoken as well as code language. And it sounds like you're eliminating that as an obstacle. >> We're essentially saying, turn simple, complex cast into simple conversations. That's, that's really what we're saying here. >> So let's get into the launch. You just launched a fresh startup. >> Yeah, yeah, yeah. >> Yeah. >> So you guys are going to take on the world. Lofty goals if that. I had the briefing. Where's the origination story come from? What, how did you guys get here? Was it a problem that you saw, you were experiencing, an itch you were scratching? What was the motivation and what's the origination story? >> Shaked: So. >> Amit: Okay, go first please. >> Essentially everything started with my experience as being an operator. I used to be a DevOps engineer for a few years for a large (indistinct) company. On later stages I even managed an SRE team. So all of these access requires Q and A staff is something that I experience nonstop on Slack or Teams, all of these communication channels. And usually I find out that everything happens from the chat. So essentially back then I created a chat bot. I connect this chat bot to the different organizational tools, and instead of the developers approaching to me or the team using the on call channel or directly they will just approach the bot. But essentially the bot was very naive, and they still needed to know what they, they want to do inside the bot. But it's still managed to solve 70% of the complexity and the toil on us as a team so we could focus on innovation. So Kubiya's a more advanced version of it. Basically with Kubiya you can define what we call workflows, and we convert all of these complexity of access request into simple conversations that the end users, which could be developers, but not only, are having with a DevOps team. So that's essentially how it works, and we're very excited about it. >> So you were up all night answering the same question over and over again. (all laugh) And you said, Hey, screw it. I'm going to just create a bot, bot myself up. (Shaked laughs) But it gets at something important. I mean, I'm not just joking. It probably happened, right? That was probably the case. You were up all night telling. >> Yeah, I mean it was usually stuff that we didn't need to maintain. It was training requests and questions that just keep on repeating themselves. And actually we were in Israel, but we sell three different time zones of developers. So all of these developers, as soon as the day finishes in Israel, the day in the US started. So they will approach us from the US. So we didn't really sleep. (all laugh) As with these requests non-stop. >> It's that 24 hour. >> Yeah, yeah. 24 hours for a single team. >> So the world clock global (indistinct) catches you a little sometimes. Yeah. >> Yeah, exactly. >> So you basically take all the things that you know that are common and then make a chat bot answering as if you're you. But this brings up the whole question of chat bot utilization. There's been a lot of debate in the AI circles that chat bots really haven't made it. They're not, they haven't been good enough. So 'cause NLP and other trivial, >> Amit: Sure. or things that haven't really clicked. What's different now? How do you guys see your approach cracking the code to go that kind of reasoning level? Bots can reason? Now we're in business. >> Yeah. Most of the chat bots are general purpose, right? We're coming with the domain expertise. We know the pain from the inside. We know how the operators want to define such conversations that users might have with the virtual assistant. So we combined all of the technical tools that are needed in order to get it going. So we have a a DSL, domain specific language, where the operators can define these easy conversations and combine all of the different organizational tools which can be done using DSDK. Besides this fact, we have a no code, for less technical people to create such workflows even with no code interface. And we have a CLI, which you could use to leverage the power of the virtual assist even right from your terminal. So that's how I see the domain expertise coming in that we have different communication channels for everyone that needs to be inside the loop. >> That's awesome. >> And I, and I can add to that. So that's one element, which is the domain expertise. The other one is really our huge differentiator, the ability to let the end users influence the system itself. So essentially. >> John: Like how? Give me an example. >> Sure. We call it teach me feature, but essentially if you have any type of a request and the system hasn't created an automation or hasn't, doesn't recognize it, you can go ahead and bind that into your intent and next time, and you can define the scope for yourself only, for the team, or even for the entire organization that actually has to have permission to access the request and control and so on. >> Savannah: That's something. Yeah, I love that as a knowledge base. I mean a custom tool kit. >> Absolutely. >> And I like that you just said for the individual. So let's say I have some crazy workflows that I don't need anybody else to know about. >> 100 percent. >> I can customize my experience. >> Mm hmm. >> Do you see your, this is really interesting, and I'm, it's surprising to me we haven't seen a lot of players in this space before because what you're doing makes a lot of sense to me, especially as someone who is less technical. >> Yeah. >> Do you view yourselves as a gateway tool for more folks to be involved in more complex technology? >> So, so I'll take that. It's not that we haven't seen advanced virtual assistants. They've existed in different worlds. >> Savannah: Right. >> Up until now they've existed more in CRM tools. >> Savannah: Right. >> Call centers, right? >> Shaked: Yeah. >> You go on to Ralph Lauren, Calvin Klein, you go and chat with. Now imagine you can bring that into a world of dev tools that has high domain expertise, high technical amplitude, and now you can go and combine the domain expertise with the accessibility of conversational AI. That's, that's a unique feature here. >> What's the biggest thing that's surprised you with the launch so far? The reaction to the name, Kubiya, which is Cube in Hebrew. >> Amit: Yes. >> Apparently. >> Savannah: Which I love. >> Which by the way, you know, we have a TM and R on our Cube. (all laugh) So we can talk, you know, license rights. >> Let's drop the trademark rules today, John, here. We're here to share information. Confuse the audience. Sorry about that, by the way. (all laugh) >> We're an open source, inclusive culture. We'll let it slide this time. >> The KubeCon, theCUBE, and Kubiya. (John laughs) In the Hebrew we have this saying, third time we all have ice cream. So. (all laugh) >> I think there's some ice cream over there actually. >> There is. >> Yeah, yeah. There you go. >> All kidding aside, all fun. What's, what's been the reaction? Got some press coverage. We had the launch. You guys launched with theCUBE in here, big reception. What's been the common feedback? >> And really, I think I expected this, but I didn't expect this much. Really, the fact that people really believe in our thesis, really expect great things from us, right? We've starting to working with. >> Savannah: Now the pressure's on. >> Rolling out dozens of POCs, but even that requires obviously a lot of attention to the detail, which we're rolling out. This is effectively what we're seeing. People love the fact that you have a unique and fresh way to approaching the self-service which really has been stalled for a while. And we've recognized that. I think our thesis is where we. >> Okay, so as a startup you have lofty goals, you have investors now. >> Amit: Yeah. >> Congratulations. >> Amit: Thank you. >> They're going to want to keep the traction going, but as a north star, what's your, what are you going to, what are you going to take? What territory are you going to take? Is it new territory? Are you eating someone's lunch? Who are you going to be competing with? What's the target? What's the, what's the? >> Sure, sure. >> I'm sure you guys have it. Who are you takin' over? >> I think the gateway, the entry point to every organization is a bottleneck. You solve the hard problem first. That's where you can go into other directions, and you can imagine where other operational workflows and pains that we can help solve once we have essentially the DevOps. >> John: So you see this as greenfield, new opportunity? >> I believe so. >> Is there any incumbent you see out there? An old stodgy? >> Today we're on internal developer platform service catalog type of, you know, use cases. >> John: Yeah. >> But that's kind of where we can grow from there and have the ecosystem essentially embrace us. >> John: How about the technology platform? >> Amit: Yeah. >> What's the vision for the innovation? >> Essentially want to be able to integrate with all of the different cloud providers, cloud solutions, SaaS platforms, and take the atlas approach that they were using right to the chats from everywhere to anywhere. So essentially we want in the end that users will be able to do anything that they need inside all of these complicated platforms, which some of them are totally complicated, with plain English. >> So what's the biggest challenge for you then on that front leading the technology side of the team? >> So I would say that the conversational AI part is truly complicated because it requires to extract many types of intentions from different types of users and also integrate with so many tools and solutions. >> Savannah: Yeah. So it requires a lot of thinking, a lot of architecture, but we are doing it just fine. >> Awesome. What do you guys think about KubeCon this week? What's, what's the top story that you see emerging out of this? Just generally as an industry observer, what's the most important? >> Savannah: Maybe it's them. Announcement halo. >> What's the cover story that you see? (all laugh) I mean you guys are in the innovation intent-based infrastructure. I get that. >> So obviously everyone's looking to diversify their engineering, diversify their platforms to make sure they're as decoupled from the main CSPs as possible. So being able to build their own, and we're really helping enable a lot of that in there. We're really helping improve upon that open source together with managed platforms can really play a very nice game together. So. >> Awesome. So are you guys hiring, recruiting? Tell us about the team DNA. Now you're in Tel Aviv. You're in the bay. >> Shaked: Check our openings on LinkedIn. (all laugh) >> We have a dozen job postings on our website. Obviously engineering and sales then go to market. >> So when theCUBE comes to Tel Aviv, and we have a location there. >> Yeah. >> Will you be, share some space? >> Savannah: Is this our Tel Aviv office happening right now? I love this. >> Amit: We will be hosting you. >> John: theCube with a C and Kube with a K over there. >> Yeah. >> All one happy family. >> Would love that. >> Get some ice cream. >> Would love that. >> All right, so last question for you all. You just had a very big exciting announcement. It's a bit of a coming out party for you. What do you hope to be able to say in a year that you can't currently say right now? When you join us on theCUBE next time? >> No, no, it's absolutely. I think our thesis that you can turn conversations into operations. It's, it sounds obvious to you when you think about it, but it's not trivial when you look into the workflows, into the operations. The fact that we can actually go a year from today and say we got hundreds of customers, happy customers who've proven the thesis or sharing knowledge between themselves, that would be euphoric for us. >> All right. >> You really are about helping people. >> Absolutely. >> It doesn't seem like it's a lip service from both of you. >> No. (all laugh) >> Is there going to be levels of bot, like level one bot level two, level three, and then finally the SRE gets on the phone? Is that like some point? >> Is there going to be bot singularity? Is that, is that what we're exploring right now? (overlapping chatter) >> Some kind of escalation bot. >> Enlightenment with bots. >> We actually planning a feature we want to call a handoff where a human in the loop is required, which often is needed. Machine cannot do it alone. We'll just. >> Yeah, I think it makes total sense for geos, ops at the same. >> Shaked: Yeah. >> But not exactly the same. Really good, good solution. I love the direction. Congratulations on the launch. >> Shaked: Thank you so much. >> Amit: Thank you very much. >> Yeah, that's very exciting. You can obviously look, check out that news on Silicon Angle since we had the pleasure of breaking it. >> Absolutely. >> If people would like to say hi, stalk you on the internet, where's the best place for them to do that? >> Be on our Twitter and LinkedIn handles of course. So we have kubiya.ai. We also have a free trial until the end of the year, and we also have free forever tier, that people can sign up, play, and come say hi. I mean, we'd love to chat. >> I love it. Well, Amit, Shaked, thank you so much for being with us. >> Shaked: Thank you so much. >> John, thanks for sitting to my left for the entire day. I sincerely appreciate it. >> Just glad I can help out. >> And thank you all for tuning in to this wonderful edition of theCUBE Live from Detroit at KubeCon. Who knows what my voice will be controlling next, but either way, I hope you are there to find out. >> Amit: Love it.

Published Date : Oct 26 2022

SUMMARY :

where we're coming to you We broke the story of their launch but that means you can, (all laugh) or what I'll, what I will Conversational AI for the world of DevOps. It's quite literally the Both from the spoken what we're saying here. So let's get into the launch. Was it a problem that you and instead of the So you were up all night as soon as the day finishes in Israel, Yeah, yeah. So the world clock global (indistinct) that you know that are common cracking the code to go that And we have a CLI, which you the ability to let the end users John: Like how? and the system hasn't Yeah, I love that as a knowledge base. And I like that you just and I'm, it's surprising to me It's not that we haven't seen existed more in CRM tools. and now you can go and What's the biggest Which by the way, you know, about that, by the way. We'll let it slide this time. In the Hebrew we have this saying, I think there's some ice There you go. We had the launch. Really, the fact that people that you have a unique you have lofty goals, I'm sure you guys have it. and you can imagine where of, you know, use cases. and have the ecosystem and take the atlas approach the conversational AI part So it requires a lot of thinking, that you see emerging out of this? Savannah: Maybe it's What's the cover story that you see? So being able to build their own, So are you (all laugh) then go to market. and we have a location there. I love this. and Kube with a K over there. that you can't currently say right now? that you can turn lip service from both of you. feature we want to call a handoff ops at the same. I love the direction. the pleasure of breaking it. So we have kubiya.ai. Well, Amit, Shaked, thank you to my left for the entire day. And thank you all for tuning

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Amit Eyal Govrin, Kubiya.ai | Cube Conversation


 

(upbeat music) >> Hello everyone, welcome to this special Cube conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE in theCUBE Studios. We've got a special video here. We love when we have startups that are launching. It's an exclusive video of a hot startup that's launching. Got great reviews so far. You know, word on the street is, they got something different and unique. We're going to' dig into it. Amit Govrin who's the CEO and co-founder of Kubiya, which stands for Cube in Hebrew, and they're headquartered in Bay Area and in Tel Aviv. Amit, congratulations on the startup launch and thanks for coming in and talk to us in theCUBE >> Thank you, John, very nice to be here. >> So, first of all, a little, 'cause we love the Cube, 'cause theCUBE's kind of an open brand. We've never seen the Cube in Hebrew, so is that true? Kubiya is? >> Kubiya literally means cube. You know, clearly there's some additional meanings that we can discuss. Obviously we're also launching a KubCon, so there's a dual meaning to this event. >> KubCon, not to be confused with CubeCon. Which is an event we might have someday and compete. No, I'm only kidding, good stuff. I want to get into the startup because I'm intrigued by your story. One, you know, conversational AI's been around, been a category. We've seen chat bots be all the rage and you know, I kind of don't mind chat bots on some sites. I can interact with some, you know, form based knowledge graph, whatever, knowledge database and get basic stuff self served. So I can see that, but it never really scaled or took off. And now with Cloud Native kind of going to the next level, we're starting to see a lot more open source and a lot more automation, in what I call AI as code or you know, AI as a service, machine learning, developer focused action. I think you guys might have an answer there. So if you don't mind, could you take a minute to explain what you guys are doing, what's different about Kubiya, what's happening? >> Certainly. So thank you for that. Kubiya is what we would consider the first, or one of the first, advanced virtual assitants with a domain specific expertise in DevOps. So, we respect all of the DevOps concepts, GitOps, workflow automation, of those categories you've mentioned, but also the added value of the conversational AI. That's really one of the few elements that we can really bring to the table to extract what we call intent based operations. And we can get into what that means in a little bit. I'll save that maybe for the next question. >> So the market you're going after is kind of, it's, I love to hear starters when they, they don't have a Gartner Magic quadrant, they can fit nicely, it means they're onto something. What is the market you're going after? Because you're seeing a lot of developers driving a lot of the key successes in DevOps. DevOps has evolved to the point where, and DevSecOps, where developers are driving the change. And so having something that's developer focused is key. Are you guys targeting the developers, IT buyers, cloud architects? Who are you looking to serve with this new opportunity? >> So essentially self-service in the world of DevOps, the end user typically would be a developer, but not only, and obviously the operators, those are the folks that we're actually looking to help augment a lot of their efforts, a lot of the toil that they're experiencing in a day to day. So there's subcategories within that. We can talk about the different internal developer tools, or platforms, shared services platforms, service catalogs are tangential categories that this kind of comes on. But on top of that, we're adding the element of conversational AI. Which, as I mentioned, that's really the "got you". >> I think you're starting to see a lot of autonomous stuff going on, autonomous pen testing. There's a company out there doing I've seen autonomous AI. Automation is a big theme of it. And I got to ask, are you guys on the business side purely in the cloud? Are you born in the cloud, is it a cloud service? What's the product choice there? It's a service, right? >> Software is a service. We have the classic, Multi-Tenancy SAAS, but we also have a hybrid SAAS solution, which allows our customers to run workflows using remote runners, essentially hosted at their own location. >> So primary cloud, but you're agnostic on where they could consume, how they want to' consume the product. >> Technology agnostic. >> Okay, so that's cool. So let's get into the problem you're solving. So take me through, this will drive a lot of value here, when you guys did the company, what problems did you hone in on and what are you guys seeing as the core problem that you solve? >> So we, this is a unique, I don't know how unique, but this is a interesting proposition because I come from the business side, so call it the top down. I've been in enterprise sales, I've been in a CRO, VP sales hat. My co-founder comes from the bottom up, right? He ran DevOps teams and SRE teams in his previous company. That's actually what he did. So, we met each other halfway, essentially with me seeing a lot of these problems of self-service not being so self-service after all, platforms hitting walls with adoption. And he actually created his own self-service platform, within his last company, to address his own personal pains. So we essentially kind of met with both perspectives. >> So you're absolutely hardcore on self-service. >> We're enabling self-service. >> And that basically is what everybody wants. I mean, the developers want self-service. I mean, that's kind of like, you know, that's the nirvana. So take us through what you guys are offering, give us an example of use cases and who's buying your product, why, and take us through that whole piece. >> Do you mind if I take a step back and say why we believe self-service has somewhat failed or not gotten off. >> Yeah, absolutely. >> So look, this is essentially how we're looking at it. All the analysts and the industry insiders are talking about self-service platforms as being what's going to' remove the dependency of the operator in the loop the entire time, right? Because the operator, that scarce resource, it's hard to hire, hard to train, hard to retain those folks, Developers are obviously dependent on them for productivity. So the operators in this case could be a DevOps, could be a SecOps, it could be a platform engineer. It comes in different flavors. But the common denominator, somebody needs an access request, provisioning a new environment, you name it, right? They go to somebody, that person is operator. The operator typically has a few things on their plate. It's not just attending and babysitting platforms, but it's also innovating, spinning up, and scaling services. So they see this typically as kind of, we don't really want to be here, we're going to' go and do this because we're on call. We have to take it on a chin, if you may, for this. >> It's their child, they got to' do it. >> Right, but it's KTLOs, right, keep the lights on, this is maintenance of a platform. It's not what they're born and bred to do, which is innovate. That's essentially what we're seeing, we're seeing that a lot of these platforms, once they finally hit the point of maturity, they're rolled out to the team. People come to serve themselves in platform, and low and behold, it's not as self-service as it may seem. >> We've seen that certainly with Kubernetes adoption being, I won't say slow, it's been fast, but it's been good. But I think this is kind of the promise of what SRE was supposed to be. You know, do it once and then babysit in the sense of it's working and automated. Nothing's broken yet. Don't call me unless you need something, I see that. So the question, you're trying to make it easier then, you're trying to free up the talent. >> Talent to operate and have essentially a human, like in the loop, essentially augment that person and give the end users all of the answers they require, as if they're talking to a person. >> I mean it's basically, you're taking the virtual assistant concept, or chat bot, to a level of expertise where there's intelligence, jargon, experience into the workflows that's known. Not just talking to chat bot, get a support number to rebook a hotel room. >> We're converting operational workflows into conversations. >> Give me an example, take me through an example. >> Sure, let's take a simple example. I mean, not everyone provisions EC2's with two days (indistinct). But let's say you want to go and provision new EC2 instances, okay? If you wanted to do it, you could go and talk to the assistant and say, "I want to spin up a new server". If it was a human in the loop, they would ask you the following questions: what type of environment? what are we attributing this to? what type of instance? security groups, machine images, you name it. So, these are the questions that typically somebody needs to be armed with before they can go and provision themselves, serve themselves. Now the problem is users don't always have these questions. So imagine the following scenario. Somebody comes in, they're in Jira ticket queue, they finally, their turn is up and the next question they don't have the answer to. So now they have to go and tap on a friend, or they have to go essentially and get that answer. By the time they get back, they lost their turn in queue. And then that happens again. So, they lose a context, they lose essentially the momentum. And a simple access request, or a simple provision request, can easily become a couple days of ping pong back and forth. This won't happen with the virtual assistant. >> You know, I think, you know, and you mentioned chat bots, but also RPA is out there, you've seen a lot of that growth. One of the hard things, and you brought this up, I want to get your reaction to, is contextualizing the workflow. It might not be apparent, but the answer might be there, it disrupts the entire experience at that point. RPA and chat bots don't have that contextualization. Is that what you guys do differently? Is that the unique flavor here? Is that difference between current chat bots and RPA? >> The way we see it, I alluded to the intent based operations. Let me give a tangible experience. Even not from our own world, this will be easy. It's a bidirectional feedback loop 'cause that's actually what feeds the context and the intent. We all know Waze, right, in the world of navigation. They didn't bring navigation systems to the world. What they did is they took the concept of navigation systems that are typically satellite guided and said it's not just enough to drive down the 280, which typically have no traffic, right, and to come across traffic and say, oh, why didn't my satellite pick that up? So they said, have the end users, the end nodes, feed that direction back, that feedback, right. There has to be a bidirectional feedback loop that the end nodes help educate the system, make the system be better, more customized. And that's essentially what we're allowing the end users. So the maintenance of the system isn't entirely in the hands of the operators, right? 'Cause that's the part that they dread. And the maintenance of the system is democratized across all the users that they can teach the system, give input to the system, hone in the system in order to make it more of the DNA of the organization. >> You and I were talking before you came on this camera interview, you said playfully that the Siri for DevOps, which kind of implies, hey infrastructure, do something for me. You know, we all know Siri, so we get that. So that kind of illustrates kind of where the direction is. Explain why you say that, what does that mean? Is that like a NorthStar vision that you guys are approaching? You want to' have a state where everything's automated in it's conversational deployments, that kind of thing. And take us through why that Siri for DevOps is. >> I think it helps anchor people to what a virtual assistant is. Because when you hear virtual assistant, that can mean any one of various connotations. So the Siri is actually a conversational assistant, but it's not necessarily a virtual assistant. So what we're saying is we're anchoring people to that thought and saying, we're actually allowing it to be operational, turning complex operations into simple conversations. >> I mean basically they take the automate with voice Google search or a query, what's the score of the game? And, it also, and talking to the guy who invented Siri, I actually interviewed on theCUBE, it's a learning system. It actually learns as it gets more usage, it learns. How do you guys see that evolving in DevOps? There's a lot of jargon in DevOps, a lot of configurations, a lot of different use cases, a lot of new technologies. What's the secret sauce behind what you guys do? Is it the conversational AI, is it the machine learning, is it the data, is it the model? Take us through the secret sauce. >> In fact, it's all the above. And I don't think we're bringing any one element to the table that hasn't been explored before, hasn't been done. It's a recipe, right? You give two people the same ingredients, they can have complete different results in terms of what they come out with. We, because of our domain expertise in DevOps, because of our familiarity with developer workflows with operators, we know how to give a very well suited recipe. Five course meal, hopefully with Michelin stars as part of that. So a few things, maybe a few of the secret sauce element, conversational AI, the ability to essentially go and extract the intent of the user, so that if we're missing context, the system is smart enough to go and to get that feedback and to essentially feed itself into that model. >> Someone might say, hey, you know, conversational AI, that was yesterday's trend, it never happened. It was kind of weak, chat bots were lame. What's different now and with you guys, and the market, that makes a redo or a second shot at this, a second bite at the apple, as they say. What do you guys see? 'Cause you know, I would argue that it's, you know, it's still early, real early. >> Certainly. >> How do you guys view that? How would you handle that objection? >> It's a fair question. I wasn't around the first time around to tell you what didn't work. I'm not afraid to share that the feedback that we're getting is phenomenal. People understand that we're actually customizing the workflows, the intent based operations to really help hone in on the dark spots. We call it last mile, you know, bottlenecks. And that's really where we're helping. We're helping in a way tribalize internal knowledge that typically hasn't been documented because it's painful enough to where people care about it but not painful enough to where you're going to' go and sit down an entire day and document it. And that's essentially what the virtual assistant can do. It can go and get into those crevices and help document, and operationalize all of those toils. And into workflows. >> Yeah, I mean some will call it grunt work, or low level work. And I think the automation is interesting. I think we're seeing this in a lot of these high scale situations where the talented hard to hire person is hired to do, say, things that were hard to do, but now harder things are coming around the corner. So, you know, serverless is great and all this is good, but it doesn't make the complexity go away. As these inflection points continue to drive more scale, the complexity kind of grows, but at the same time so is the ability to abstract away the complexity. So you're starting to see the smart, hired guns move to higher, bigger problems. And the automation seems to take the low level kind of like capabilities or the toil, or the grunt work, or the low level tasks that, you know, you don't want a high salaried person doing. Or I mean it's not so much that they don't want to' do it, they'll take one for the team, as you said, or take it on the chin, but there's other things to work on. >> I want to add one more thing, 'cause this goes into essentially what you just said. Think about it's not the virtual system, what it gives you is not just the intent and that's one element of it, is the ability to carry your operations with you to the place where you're not breaking your workflows, you're actually comfortable operating. So the virtual assistant lives inside of a command line interface, it lives inside of chat like Slack, and Teams, and Mattermost, and so forth. It also lives within a low-code editor. So we're not forcing anyone to use uncomfortable language or operations if they're not comfortable with. It's almost like Siri, it travels in your mobile phone, it's on your laptop, it's with you everywhere. >> It makes total sense. And the reason why I like this, and I want to' get your reaction on this because we've done a lot of interviews with DevOps, we've met at every CubeCon since it started, and Kubernetes kind of highlights the value of the containers at the orchestration level. But what's really going on is the DevOps developers, and the CICD pipeline, with infrastructure's code, they're basically have a infrastructure configuration at their disposal all the time. And all the ops challenges have been around that, the repetitive mundane tasks that most people do. There's like six or seven main use cases in DevOps. So the guardrails just need to be set. So it sounds like you guys are going down the road of saying, hey here's the use cases you can bounce around these use cases all day long. And just keep doing your jobs cause they're bolting on infrastructure to every application. >> There's one more element to this that we haven't really touched on. It's not just workflows and use cases, but it's also knowledge, right? Tribal knowledge, like you asked me for an example. You can type or talk to the assistant and ask, "How much am I spending on AWS, on US East 1, on so and so customer environment last week?", and it will know how to give you that information. >> Can I ask, should I buy a reserve instances or not? Can I ask that question? 'Cause there's always good trade offs between buying the reserve instances. I mean that's kind of the thing that. >> This is where our ecosystem actually comes in handy because we're not necessarily going to' go down every single domain and try to be the experts in here. We can tap into the partnerships, API, we have full extensibility in API and the software development kit that goes into. >> It's interesting, opinionated and declarative are buzzwords in developer language. So you started to get into this editorial thing. So I can bring up an example. Hey cube, implement the best service mesh. What answer does it give you? 'Cause there's different choices. >> Well this is actually where the operator, there's clearly guard rails. Like you can go and say, I want to' spin up a machine, and it will give you all of the machines on AWS. Doesn't mean you have to get the X one, that's good for a SAP environment. You could go and have guardrails in place where only the ones that are relevant to your team, ones that have resources and budgetary, you know, guidelines can be. So, the operator still has all the control. >> It was kind of tongue in cheek around the editorialized, but actually the answer seems to be as you're saying, whatever the customer decided their service mesh is. So I think this is where it gets into as an assistant to architecting and operating, that seems to be the real value. >> Now code snippets is a different story because that goes on to the web, that goes onto stock overflow, and that's actually one of the things. So inside the CLI, you could actually go and ask for code snippets and we could actually go and populate that, it's a smart CLI. So that's actually one of the things that are an added value of that. >> I was saying to a friend and we were talking about open source and how when I grew up, there was no open source. If you're a developer now, I mean there's so much code, it's not so much coding anymore as it is connecting and integrating. >> Certainly. >> And writing glue layers, if you will. I mean there's still code, but it's not, you don't have to build it from scratch. There's so much code out there. This low-code notion of a smart system is interesting 'cause it's very matrix like. It can build its own code. >> Yes, but I'm also a little wary with low-code and no code. I think part of the problem is we're so constantly focused on categories and categorizing ourselves, and different categories take on a life of their own. So low-code no code is not necessarily, even though we have the low-code editor, we're not necessarily considering ourselves low-code. >> Serverless, no code, low-code. I was so thrown on a term the other day, architecture-less. As a joke, no we don't need architecture. >> There's a use case around that by the way, yeah, we do. Show me my AWS architecture and it will build the architect diagram for you. >> Again, serverless architect, this is all part of infrastructure's code. At the end of the day, the developer has infrastructure with code. Again, how they deploy it is the neuron. That's what we've been striving for. >> But infrastructure is code. You can destroy, you know, terraform, you can go and create one. It's not necessarily going to' operate it for you. That's kind of where this comes in on top of that. So it's really complimentary to infrastructure. >> So final question, before we get into the origination story, data and security are two hot areas we're seeing fill the IT gap, that has moved into the developer role. IT is essentially provisioned by developers now, but the OP side shifted to large scale SRE like environments, security and data are critical. What's your opinion on those two things? >> I agree. Do you want me to give you the normal data as gravity? >> So you agree that IT is now, is kind of moved into the developer realm, but the new IT is data ops and security ops basically. >> A hundred percent, and the lines are so blurred. Like who's what in today's world. I mean, I can tell you, I have customers who call themselves five different roles in the same day. So it's, you know, at the end of the day I call 'em operators 'cause I don't want to offend anybody because that's just the way it is. >> Architectural-less, we're going to' come back to that. Well, I know we're going to' see you at CubeCon. >> Yes. >> We should catch up there and talk more. I'm looking forward to seeing how you guys get the feedback from the marketplace. It should be interesting to hear, the curious question I have for you is, what was the origination story? Why did you guys come together, was it a shared problem? Was it a big market opportunity? Was it an itch you guys were scratching? Did you feel like you needed to come together and start this company? What was the real vision behind the origination? Take a take a minute to explain the story. >> No, absolutely. So I've been living in Palo Alto for the last couple years. Previous, also a founder. So, you know, from my perspective, I always saw myself getting back in the game. Spent a few years in AWS essentially managing partnerships for tier one DevOps partners, you know, all of the known players. Some in public, some of them not. And really the itch was there, right. I saw what everyone's doing. I started seeing consistency in the pains that I was hearing back, in terms of what hasn't been solved. So I already had an opinion where I wanted to go. And when I was visiting actually Israel with the family, I was introduced by a mutual friend to Shaked, Shaked Askayo, my co-founder and CTO. Amazing guy, unbelievable technologists, probably one the most, you know, impressive folks I've had a chance to work with. And he actually solved a very similar problem, you know, in his own way in a previous company, BlueVine, a FinTech company where he was head of SRE, having to, essentially, oversee 200 developers in a very small team. The ratio was incongruent to what the SRE guideline would tell. >> That's more than 10 x rate developer. >> Oh, absolutely. Sure enough. And just imagine it's four different time zones. He finishes day shift and you already had the US team coming, asking for a question. He said, this is kind of a, >> Got to' clone himself, basically. >> Well, yes. He essentially said to me, I had no day, I had no life, but I had Corona, I had COVID, which meant I could work from home. And I essentially programed myself in the form of a bot. Essentially, when people came to him, he said, "Don't talk to me, talk to the bot". Now that was a different generation. >> Just a trivial example, but the idea was to automate the same queries all the time. There's an answer for that, go here. And that's the benefit of it. >> Yes, so he was able to see how easy it was to solve, I mean, how effective it was solving 70% of the toil in his organization. Scaling his team, froze the headcount and the developer team kept on going. So that meant that he was doing some right. >> When you have a problem, and you need to solve it, the creativity comes out of the woodwork, you know, invention is the mother of necessity. So final question for you, what's next? Got the launch, what are you guys hope to do over the next six months to a year, hiring? Put a plug in for the company. What are you guys looking to do? Take a minute to share the future vision and get a plug in. >> A hundred percent. So, Kubiya, as you can imagine, announcing ourselves at CubeCon, so in a couple weeks. Opening the gates towards the public beta and NGA in the next couple months. Essentially working with dozens of customers, Aston Martin, and business earn in. We have quite a few, our website's full of quotes. You can go ahead. But effectively we're looking to go and to bring the next operator, generation of operators, who value their time, who value the, essentially, the value of tribal knowledge that travels between organizations that could be essentially shared. >> How many customers do you guys have in your pre-launch? >> It's above a dozen. Without saying, because we're actually looking to onboard 10 more next week. So that's just an understatement. It changes from day to day. >> What's the number one thing people are saying about you? >> You got that right. I know it's, I'm trying to be a little bit more, you know. >> It's okay, you can be cocky, startups are good. But I mean they're obviously, they're using the product and you're getting good feedback. Saving time, are they saying this is a dream product? Got it right, what are some of the things? >> I think anybody who doesn't feel the pain won't know, but the folks who are in the trenches, or feeling the pain, or experiencing this toil, who know what this means, they said, "You're doing this different, you're doing this right. You architected it right. You know exactly what the developer workflows," you know, where all the areas, you know, where all the skeletons are hidden within that. And you're attending to that. So we're happy about that. >> Everybody wants to clone themselves, again, the tribal knowledge. I think this is a great example of where we see the world going. Make things autonomous, operationally automated for the use cases you know are lock solid. Why wouldn't you just deploy? >> Exactly, and we have a very generous free tier. People can, you know, there's a plugin, you can sign up for free until the end of the year. We have a generous free tier. Yeah, free forever tier, as well. So we're looking for people to try us out and to give us feedback. >> I think the self-service, I think the point is, we've talked about it on the Cube at our events, everyone says the same thing. Every developer wants self-service, period. Full stop, done. >> What they don't say is they need somebody to help them babysit to make sure they're doing it right. >> The old dashboard, green, yellow, red. >> I know it's an analogy that's not related, but have you been to Whole Foods? Have you gone through their self-service line? That's the beauty of it, right? Having someone in a loop helping you out throughout the time. You don't get confused, if something's not working, someone's helping you out, that's what people want. They want a human in the loop, or a human like in the loop. We're giving that next best thing. >> It's really the ratio, it's scale. It's a scaling. It's force multiplier, for sure. Amit, thanks for coming on, congratulations. >> Thank you so much. >> See you at KubeCon. Thanks for coming in, sharing the story. >> KubiyaCon. >> CubeCon. Cube in Hebrew, Kubiya. Founder, co-founder and CEO here, sharing the story in the launch. Conversational AI for DevOps, the theory of DevOps, really kind of changing the game, bringing efficiency, solving a lot of the pain points of large scale infrastructure. This is theCUBE, CUBE conversation, I'm John Furrier, thanks for watching. (upbeat electronic music)

Published Date : Oct 18 2022

SUMMARY :

on the startup launch We've never seen the Cube so there's a dual meaning to this event. I can interact with some, you know, but also the added value of the conversational AI. a lot of the key successes in DevOps. a lot of the toil that they're What's the product choice there? We have the classic, Multi-Tenancy SAAS, So primary cloud, So let's get into the call it the top down. So you're absolutely I mean, the developers want self-service. Do you mind if I take a step back So the operators in this keep the lights on, this is of the promise of what SRE all of the answers they require, experience into the We're converting operational take me through an example. So imagine the following scenario. Is that the unique flavor here? that the end nodes help the Siri for DevOps, So the Siri is actually a is it the data, is it the model? the system is smart enough to a second bite at the apple, as they say. on the dark spots. And the automation seems to it, is the ability to carry So the guardrails just need to be set. the assistant and ask, I mean that's kind of the thing that. and the software development implement the best service mesh. of the machines on AWS. but actually the answer So inside the CLI, you could actually go I was saying to a And writing glue layers, if you will. So low-code no code is not necessarily, I was so thrown on a term the around that by the way, At the end of the day, You can destroy, you know, terraform, that has moved into the developer role. the normal data as gravity? is kind of moved into the developer realm, in the same day. to' see you at CubeCon. the curious question I have for you is, And really the itch was there, right. the US team coming, asking for a question. myself in the form of a bot. And that's the benefit of it. and the developer team kept on going. of the woodwork, you know, and NGA in the next couple months. It changes from day to day. bit more, you know. It's okay, you can be but the folks who are in the for the use cases you know are lock solid. and to give us feedback. everyone says the same thing. need somebody to help them That's the beauty of it, right? It's really the ratio, it's scale. Thanks for coming in, sharing the story. sharing the story in the launch.

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Adam Meyers, CrowdStrike | CrowdStrike Fal.Con 2022


 

>> We're back at the ARIA Las Vegas. We're covering CrowdStrike's Fal.Con 22. First one since 2019. Dave Vellante and Dave Nicholson on theCUBE. Adam Meyers is here, he is the Senior Vice President of Intelligence at CrowdStrike. Adam, thanks for coming to theCUBE. >> Thanks for having me. >> Interesting times, isn't it? You're very welcome. Senior Vice President of Intelligence, tell us what your role is. >> So I run all of our intelligence offerings. All of our analysts, we have a couple hundred analysts that work at CrowdStrike tracking threat actors. There's 185 threat actors that we track today. We're constantly adding more of them and it requires us to really have that visibility and understand how they operate so that we can inform our other products: our XDR, our Cloud Workload Protections and really integrate all of this around the threat actor. >> So it's that threat hunting capability that CrowdStrike has. That's what you're sort of... >> Well, so think of it this way. When we launched the company 11 years ago yesterday, what we wanted to do was to tell customers, to tell people that, well, you don't have a malware problem, you have an adversary problem. There are humans that are out there conducting these attacks, and if you know who they are what they're up to, how they operate then you're better positioned to defend against them. And so that's really at the core, what CrowdStrike started with and all of our products are powered by intelligence. All of our services are our OverWatch and our Falcon complete, all powered by intelligence because we want to know who the threat actors are and what they're doing so we can stop them. >> So for instance like you can stop known malware. A lot of companies can stop known malware, but you also can stop unknown malware. And I infer that the intelligence is part of that equation, is that right? >> Absolutely. That that's the outcome. That's the output of the intelligence but I could also tell you who these threat actors are, where they're operating out of, show you pictures of some of them, that's the threat intel. We are tracking down to the individual persona in many cases, these various threats whether they be Chinese nation state, Russian threat actors, Iran, North Korea, we track as I said, quite a few of these threats. And over time, we develop a really robust deep knowledge about who they are and how they operate. >> Okay. And we're going to get into some of that, the big four and cyber. But before we do, I want to ask you about the eCrime index stats, the ECX you guys call it a little side joke for all your nerds out there. Maybe you could explain that Adam >> Assembly humor. >> Yeah right, right. So, but, what is that index? You guys, how often do you publish it? What are you learning from that? >> Yeah, so it was modeled off of the Dow Jones industrial average. So if you look at the Dow Jones it's a composite index that was started in the late 1800s. And they took a couple of different companies that were the industrial component of the economy back then, right. Textiles and railroads and coal and steel and things like that. And they use that to approximate the overall health of the economy. So if you take these different stocks together, swizzle 'em together, and figure out some sort of number you could say, look, it's up. The economy's doing good. It's down, not doing so good. So after World War II, everybody was exuberant and positive about the end of the war. The DGI goes up, the oil crisis in the seventies goes down, COVID hits goes up, sorry, goes down. And then everybody realizes that they can use Amazon still and they can still get the things they need goes back up with the eCrime index. We took that approach to say what is the health of the underground economy? When you read about any of these ransomware attacks or data extortion attacks there are criminal groups that are working together in order to get things spammed out or to buy credentials and things like that. And so what the eCrime index does is it takes 24 different observables, right? The price of a ransom, the number of ransom attacks, the fluctuation in cryptocurrency, how much stolen material is being sold for on the underground. And we're constantly computing this number to understand is the eCrime ecosystem healthy? Is it thriving or is it under pressure? And that lets us understand what's going on in the world and kind of contextualize it. Give an example, Microsoft on patch Tuesday releases 56 vulnerabilities. 11 of them are critical. Well guess what? After hack Tuesday. So after patch Tuesday is hack Wednesday. And so all of those 11 vulnerabilities are exploitable. And now you have threat actors that have a whole new array of weapons that they can deploy and bring to bear against their victims after that patch Tuesday. So that's hack Wednesday. Conversely we'll get something like the colonial pipeline. Colonial pipeline attack May of 21, I think it was, comes out and all of the various underground forums where these ransomware operators are doing their business. They freak out because they don't want law enforcement. President Biden is talking about them and he's putting pressure on them. They don't want this ransomware component of what they're doing to bring law enforcement, bring heat on them. So they deplatform them. They kick 'em off. And when they do that, the ransomware stops being as much of a factor at that point in time. And the eCrime index goes down. So we can look at holidays, and right around Thanksgiving, which is coming up pretty soon, it's going to go up because there's so much online commerce with cyber Monday and such, right? You're going to see this increase in online activity; eCrime actors want to take advantage of that. When Christmas comes, they take vacation too; they're going to spend time with their families, so it goes back down and it stays down till around the end of the Russian Orthodox Christmas, which you can probably extrapolate why that is. And then it goes back up. So as it's fluctuating, it gives us the ability to really just start tracking what that economy looks like. >> Realtime indicator of that crypto. >> I mean, you talked about, talked about hack Wednesday, and before that you mentioned, you know, the big four, and I think you said 185 threat actors that you're tracking, is 180, is number 185 on that list? Somebody living in their basement in their mom's basement or are the resources necessary to get on that list? Such that it's like, no, no, no, no. this is very, very organized, large groups of people. Hollywood would have you believe that it's guy with a laptop, hack Wednesday, (Dave Nicholson mimics keyboard clacking noises) and everything done. >> Right. >> Are there individuals who are doing things like that or are these typically very well organized? >> That's a great question. And I think it's an important one to ask and it's both it tends to be more, the bigger groups. There are some one-off ones where it's one or two people. Sometimes they get big. Sometimes they get small. One of the big challenges. Have you heard of ransomware as a service? >> Of course. Oh my God. Any knucklehead can be a ransomwarist. >> Exactly. So we don't track those knuckleheads as much unless they get onto our radar somehow, they're conducting a lot of operations against our customers or something like that. But what we do track is that ransomware as a service platform because the affiliates, the people that are using it they come, they go and, you know, it could be they're only there for a period of time. Sometimes they move between different ransomware services, right? They'll use the one that's most useful for them that that week or that month, they're getting the best rate because it's rev sharing. They get a percentage that platform gets percentage of the ransom. So, you know, they negotiate a better deal. They might move to a different ransomware platform. So that's really hard to track. And it's also, you know, I think more important for us to understand the platform and the technology that is being used than the individual that's doing it. >> Yeah. Makes sense. Alright, let's talk about the big four. China, Iran, North Korea, and Russia. Tell us about, you know, how you monitor these folks. Are there different signatures for each? Can you actually tell, you know based on the hack who's behind it? >> So yeah, it starts off, you know motivation is a huge factor. China conducts espionage, they do it for diplomatic purposes. They do it for military and political purposes. And they do it for economic espionage. All of these things map to known policies that they put out, the Five Year Plan, the Made in China 2025, the Belt and Road Initiative, it's all part of their efforts to become a regional and ultimately a global hegemon. >> They're not stealing nickels and dimes. >> No they're stealing intellectual property. They're stealing trade secrets. They're stealing negotiation points. When there's, you know a high speed rail or something like that. And they use a set of tools and they have a set of behaviors and they have a set of infrastructure and a set of targets that as we look at all of these things together we can derive who they are by motivation and the longer we observe them, the more data we get, the more we can get that attribution. I could tell you that there's X number of Chinese threat groups that we track under Panda, right? And they're associated with the Ministry of State Security. There's a whole other set. That's too associated with the People's Liberation Army Strategic Support Force. So, I mean, these are big operations. They're intelligence agencies that are operating out of China. Iran has a different set of targets. They have a different set of motives. They go after North American and Israeli businesses right now that's kind of their main operation. And they're doing something called hack and lock and leak. With a lock and leak, what they're doing is they're deploying ransomware. They don't care about getting a ransom payment. They're just doing it to disrupt the target. And then they're leaking information that they steal during that operation that brings embarrassment. It brings compliance, regulatory, legal impact for that particular entity. So it's disruptive >> The chaos creators that's.. >> Well, you know I think they're trying to create a they're trying to really impact the legitimacy of some of these targets and the trust that their customers and their partners and people have in them. And that is psychological warfare in a certain way. And it, you know is really part of their broader initiative. Look at some of the other things that they've done they've hacked into like the missile defense system in Israel, and they've turned on the sirens, right? Those are all things that they're doing for a specific purpose, and that's not China, right? Like as you start to look at this stuff, you can start to really understand what they're up to. Russia very much been busy targeting NATO and NATO countries and Ukraine. Obviously the conflict that started in February has been a huge focus for these threat actors. And then as we look at North Korea, totally different. They're doing, there was a major crypto attack today. They're going after these crypto platforms, they're going after DeFi platforms. They're going after all of this stuff that most people don't even understand and they're stealing the crypto currency and they're using it for revenue generation. These nuclear weapons don't pay for themselves, their research and development don't pay for themselves. And so they're using that cyber operation to either steal money or steal intelligence. >> They need the cash. Yeah. >> Yeah. And they also do economic targeting because Kim Jong Un had said back in 2016 that they need to improve the lives of North Koreans. They have this national economic development strategy. And that means that they need, you know, I think only 30% of North Korea has access to reliable power. So having access to clean energy sources and renewable energy sources, that's important to keep the people happy and stop them from rising up against the regime. So that's the type of economic espionage that they're conducting. >> Well, those are the big four. If there were big five or six, I would presume US and some Western European countries would be on there. Do you track, I mean, where United States obviously has you know, people that are capable of this we're out doing our thing, and- >> So I think- >> That defense or offense, where do we sit in this matrix? >> Well, I think the big five would probably include eCrime. We also track India, Pakistan. We track actors out of Columbia, out of Turkey, out of Syria. So there's a whole, you know this problem is getting worse over time. It's proliferating. And I think COVID was also, you know a driver there because so many of these countries couldn't move human assets around because everything was getting locked down. As machine learning and artificial intelligence and all of this makes its way into the cameras at border and transfer points, it's hard to get a human asset through there. And so cyber is a very attractive, cheap and deniable form of espionage and gives them operational capabilities, not, you know and to your question about US and other kind of five I friendly type countries we have not seen them targeting our customers. So we focus on the threats that target our customers. >> Right. >> And so, you know, if we were to find them at a customer environment sure. But you know, when you look at some of the public reporting that's out there, the malware that's associated with them is focused on, you know, real bad people, and it's, it's physically like crypted to their hard drive. So unless you have sensor on, you know, an Iranian or some other laptop that might be target or something like that. >> Well, like Stuxnet did. >> Yeah. >> Right so. >> You won't see it. Right. See, so yeah. >> Well Symantec saw it but way back when right? Back in the day. >> Well, I mean, if you want to go down that route I think it actually came from a company in the region that was doing the IR and they were working with Symantec. >> Oh, okay. So, okay. So it was a local >> Yeah. I think Crisis, I think was the company that first identified it. And then they worked with Symantec. >> It Was, they found it, I guess, a logic controller. I forget what it was. >> It was a long time ago, so I might not have that completely right. >> But it was a seminal moment in the industry. >> Oh. And it was a seminal moment for Iran because you know, that I think caused them to get into cyber operations. Right. When they realized that something like that could happen that bolstered, you know there was a lot of underground hacking forums in Iran. And, you know, after Stuxnet, we started seeing that those hackers were dropping their hacker names and they were starting businesses. They were starting to try to go after government contracts. And they were starting to build training offensive programs, things like that because, you know they realized that this is an opportunity there. >> Yeah. We were talking earlier about this with Shawn and, you know, in the nuclear war, you know the Cold War days, you had the mutually assured destruction. It's not as black and white in the cyber world. Right. Cause as, as Robert Gates told me, you know a few years ago, we have a lot more to lose. So we have to be somewhat, as the United States, careful as to how much of an offensive posture we take. >> Well here's a secret. So I have a background on political science. So mutually assured destruction, I think is a deterrent strategy where you have two kind of two, two entities that like they will destroy each other if they so they're disinclined to go down that route. >> Right. >> With cyber I really don't like that mutually assured destruction >> That doesn't fit right. >> I think it's deterrents by denial. Right? So raising the cost, if they were to conduct a cyber operation, raising that cost that they don't want to do it, they don't want to incur the impact of that. Right. And think about this in terms of a lot of people are asking about would China invade Taiwan. And so as you look at the cost that that would have on the Chinese military, the POA, the POA Navy et cetera, you know, that's that deterrents by denial, trying to, trying to make the costs so high that they don't want to do it. And I think that's a better fit for cyber to try to figure out how can we raise the cost to the adversary if they operate against our customers against our enterprises and that they'll go someplace else and do something else. >> Well, that's a retaliatory strike, isn't it? I mean, is that what you're saying? >> No, definitely not. >> It's more of reducing their return on investment essentially. >> Yeah. >> And incenting them- disincening them to do X and sending them off somewhere else. >> Right. And threat actors, whether they be criminals or nation states, you know, Bruce Lee had this great quote that was "be like water", right? Like take the path of least resistance, like water will. Threat actors do that too. So, I mean, unless you're super high value target that they absolutely have to get into by any means necessary, then if you become too hard of a target, they're going to move on to somebody that's a little easier. >> Makes sense. Awesome. Really appreciate your, I could, we'd love to have you back. >> Anytime. >> Go deeper. Adam Myers. We're here at Fal.Con 22, Dave Vellante, Dave Nicholson. We'll be right back right after this short break. (bouncy music plays)

Published Date : Sep 21 2022

SUMMARY :

he is the Senior Vice Senior Vice President of Intelligence, so that we can inform our other products: So it's that threat hunting capability And so that's really at the core, And I infer that the intelligence that's the threat intel. the ECX you guys call it What are you learning from that? and positive about the end of the war. and before that you mentioned, you know, One of the big challenges. And it's also, you know, Tell us about, you know, So yeah, it starts off, you know and the longer we observe And it, you know is really part They need the cash. And that means that they need, you know, people that are capable of this And I think COVID was also, you know And so, you know, See, so yeah. Back in the day. in the region that was doing the IR So it was a local And then they worked with Symantec. It Was, they found it, I so I might not have that completely right. moment in the industry. like that because, you know in the nuclear war, you know strategy where you have two kind of two, So raising the cost, if they were to It's more of reducing their return and sending them off somewhere else. that they absolutely have to get into to have you back. after this short break.

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Denise Persson, Snowflake | Snowflake Summit 2022


 

>>Hello from the show floor in Las Vegas. This is the snowflake summit 22 at Caesar's forum. We've been live the last day and a half. Lisa Martin here with Dave ante covering a lot of ground. We're so excited to have the chief marketing officer at snowflake. Join us next, Denise Pearson. And welcome back to the cube. >>Thank you so much. So great to be here with you. So great to have you here at SEL meets as well. Thank >>You. That's unreal. Isn't it? Yeah. I mean, everybody's so excited to be face to face and you know, Lisa and I have been doing a few of these shows, but we, we hear the same thing over and over. It's like, oh, so good to be back, right? Yeah. >>Well, even in the keynote yesterday, when we got in, we saw a standing room only there were overflows. People are ready to hear from snowflake in person. And as we were, you were just talking with Frank, I think the 2019 show had less than 2000 people. And now here we are at close to 10,000, this step leap factor in terms of the audience and also the momentum of the company, the capabilities, lot of growth in that timeframe. Yeah, >>No. Yeah. Two, three years ago we were about 1800 people out to Hilton and San Francisco. We had about 40 partners attending this week were close to 10,000 at this year, almost 10,000 people online as well. And over over 200 partners here on the show floor, >>Right? 250 plus sessions, breakouts, keynotes, technical certifications, developer zone, a lot going on here. The buzz has been enormous from yesterday morning. It still is today. Talk about the theme of the event, the world of data collaboration. We've been talking a lot about data collaboration. Yeah. But from Snowflake's perspective, as Dave you've pointed out, that really seems like quite a differentiation of where snowflake is versus the guys in the root view mirror. Yeah. Number >>One of the very unique capabilities with snowflake is that the ability to share data with each other within your ecosystem. So you can both collaborate now on, on with your data, but also collaborate on building in a new business opportunities set together. So I think it's really a message that we, we, we fully fully own. It' really unique differentiator as well. So >>You used to talk about, you still talk about data sharing, but just kind of evolve the messaging to collaboration, explain why and, and how is that a wider scope and more appealing to the ecosystem in your >>Customers? I mean, data sharing is a terminology used for, for years and years, sounds from any data sharing it's about using, you know, FTP or, you know, APIs, those things. And we of course do it in a very, very different way where, where you do it without, you know, APIs so that you can share data with anyone in your ecosystem, without the data actually ever leaving, ever leaving your, your instance. So it's in a very different way. And also the fact that you can, again, you know, build applications together with other companies, you know, in your ecosystem. And it's a, it's a true collaboration around, you know, data in a way we've never seen before >>The other subtle change was data marketplace to marketplace. Why that change explain kind of what's behind that. >>Yeah. One of our big announcements here this week is around building native, you know, data apps and all snowflakes. Now you can both, you know, build the apps and you can distribute them and monetizing them in our marketplace. So in the past, you know, we only really had data sets within our marketplace within the data marketplace at that time. So you could now, you know, we can publish your data, you could monetize your data, but again, now moving forward, you will also be able to again, build apps and distribute them in the marketplace and also monetize them. And for Mon many startups, right? The, the big challenge is just a monetization piece as well. You build your product. You also need to find a way to, to both distribute and, and monetize it, an invoice for that product. And we solve all that for, for our customers. Now, >>A lot of customer growth, I saw Frank's slide yesterday over 5,900. I think you have 500 plus in the Forbes global 2000, a tremendous amount of growth in customers with a million plus ARR. Yes. >>Where >>Are the customers and the ecosystem in terms of that, that what you just described in the, going from the data marketplace to the marketplace are customers and, and the ecosystem influential in saying, Hey, snowflake, we need to go in this direction. >>Yeah. And also one key thing also with larger companies, they have their own marketplaces built, you know, snowflake as well. So you don't have to publish your, your, your data or app on our marketplace. The many of our larger companies, they're building those own marketplaces around themselves, you know, to distribute their data, you know, to their partners. So there are many ways you can, again, distribute and monetize their data. >>What are the marketing challenges? You, you started out kind of better data where simpler data warehouse, cloud data, warehouse, zero to snowflake was kind of the, the messaging and then the rise of the data cloud. And now it's all about applications. You're obviously building on top of that, but how, how have you, how do you think about that sort of messaging architecture going, you know, where you've come from and going forward? >>Yeah. Obviously the capabilities of the data cloud is kind of building and building it every day. And it's also a positioning that we can, you know, grow with as well. The big difference, you know, for us over the past two years is really that we are more and more really talking to the, to the business side, you know, of our, our customers that that's really where the demand is coming from. And we're truly, you know, with the data cloud, we're truly, you know, build bringing the business side and the it side together to solve these, you know, problems. And also, also together with all our partners as well. >>And I was just gonna ask you what, what's the partners role in the data cloud narrative? How do they help accomplish that? >>I would say, I mean, the data cloud is all about the partners it's, and also this event here, this event is not about, you know, snowflake it's about really our partners, you know, and our customers, you know, coming together, the data cloud is really it's. The foundation is of course, you know, the core capabilities, our platform, but then it's also all, all the data that is in there that other companies can access from our customers, but then all the applications and capabilities that are built, you know, by our partners and also our partners like the, you know, or the SI partners that are here, they are the ones, you know, doing the work, you know, with our customers, they are the ones that are, you know, migrating the data to, to snowflake and the data cloud and helping these companies build this new, you know, business model. So snowflake is a very, very partner first company. And the only thing I really care about this week here is that all this, you know, 200 partners here that they're gonna be tremendous successful if they're successful. That means that, you know, all our customers are successful as well. >>So how is your digital strategy evolving and how do you include the partners in that? >>Yeah, I mean, we learned so much over the past, you know, three, three years in regards to that. So, I mean, we all had to just accelerate our, the, the digital growth, you know, of our marketing capabilities and how to do that in a, with our, our partners. So with many of them, you know, we started developing this joint account based digital, you know, marketing program. Some, we just all had to adapt and innovate really fast, and we're gonna continue, of course, a lot of those motions as well. But at the same time, there's nothing like being out and meeting, you know, customers, you know, face to face. And what's also so important is the alignment we have with our local sales organization and our partners as well. So all these marketing programs that we develop in the fields, those are us again, opportunities cannot build those relationships as well. >>Can you talk about the sales marketing alignment at snowflake? I think it seems to be pretty strong, but we've talked a lot in the last day and a half about the retail data cloud healthcare life sciences, media finance. Talk to us about the marketing sales element, how marketing is facilitating, maybe from a campaign perspective, some of those big sales plays in the S yeah, >>Maybe both unique here. Our C Chris Dham, I think has been here early on the show. I mean, we work together for over six years now and we truly work as one, one team. We, we don't really even see the lines between sort of sales and marketings. We truly share exactly, you know, the same objectives every day. We share the same focus on, on putting our customers and our partners, you know, first, every day, his priorities, you know, are my priorities, you know, vice versa. And I think the biggest challenges we see often in some companies between sales and marketing, is that they're just shifting or it's different, you know, priorities. It's so important just to align the priorities and for us to making sure that our teams are all around the world now, or as aligned, you know, as Chris and I are as well, >>Couple other, yeah. Milestones or events come up, you're doing like, you're doing a worldwide tour and you got the dev conference in November, start with the worldwide tour. What's that all about? >>So we get little break near now, here for, for a couple of weeks. And then we're taking all the best of content here for, from, from summits and also all in our partners on a worldwide tour. We're starting in, in Asia, in August, and we're gonna target over 20 cities around the world. So, and again, I think this year, the challenge was many of our European customers and our customers in Asia. They couldn't make it. So we have smaller numbers, you know, coming from those regions. So it's more, more important than ever that we just come out to them, you know, instead, and bring this content in to them. >>Is that all face to face or at Lisa is all face to face. Is there a digital component as well? Yeah, >>It's actually gonna be all face to face and there will be some, some digital components as well. We're ending the tour in San Francisco. And that's also where we go doing all our winter announcements, you know, as well. And also our build our developer conference. That will be all virtual. The big, the global one will be all virtual at the same time, you know, from San Francisco. >>Okay. Am I confusing that with the November developer conference or >>The, that is, that is the conference, but it, that one will be virtual this year. Okay. >>So dev the build is all virtual. >>Yeah. Build be all virtual. And it's just, so we have that opportunity to reach as many people as we possibly can. >>And then is the, is this, is the intent to eventually bring them in to one place? >>Absolutely. I mean, I think the dev conference, the plan is to really take that around the world, you know, as well. We're seeing markets like Israel, for instance, there's a massive developer community that is, that is looking at snowflake right now. Markets like Indonesia, big developer segment as well. So I think it's not about, you know, having people come to us, it's about we, you know, coming out to them. So markets like Israel and Indonesia. And >>Will you also in future summits include a, a development component. You probably have something here. I just haven't seen it yet, but, but like the conference within the conference, or is it more, Hey, we want to cater to the t-shirt crowd, you know, separately, what do you, yeah, >>I think we cater to them separately. And I said again, that we it's really about taking our content, you know, out, out to them. And when we're talking about the developer audience, we're talking about hundreds and thousands of people and they can't physically, you know, come here. So our plan is really to come out and meet them where they are. How >>Did you make the decision to do this summit fourth annual in person? I'm sure the attendance figures are probably blowing your mind, but that's a, that's a big decision and that's a challenging decision to make. How did they go about doing that? >>I think, I think if there was one thing we've learned during the past three years, it's really about that. Adaptability is the new superpower, you know, of bus business. So of course we've had to adapt, you know, you know, every month. And of course, even two months ago, we were not sure, you know, how, how many people that will be able to come here today, but we're incredibly happy. Were they, were they, were they with the number of people that, you know, came here and yeah, we're already storing planning for next year. >>I mean, it definitely must have exceeded your expectations. Is that fair? Or >>We set expectations high. Yeah. Okay. But again, it's that unknown that we all had to deal with, you know, every day. And I think we're gonna continue to have to, to live with that. >>Yeah. Well, this is, yeah, this is one of the largest shows we've done. Yeah. SIM it's a reinvent, obviously different. That was last year, but this year, this is the biggest event I think we've been to, and we've been to some big brand events, so yeah. Yeah. Punching above the weight as usual. >>Yeah. And again, I wanna just give a big shout out to our whole, you know, partner ecosystem, you know, here, because again, this is very much of an ecosystem, you know, partner you in a conference and it's really all our 200 plus partners here making this conference, what it is. I mean, today >>It's remarkable to pace at which you've been able to grow the ecosystem, but why do you think that is? What's the secret there? >>I think we fully understand that we don't solve all the problems ourselves, you know, for, for our customers. It's really an ecosystem of, of products and services that solve those problems and customers. They are looking for vendors that partner well with others. They're looking for vendors that integrate well, you know, with each other. So we always have an outside in view on things and that's something we challenge ourselves every morning. We wake up, how do we put ourselves in the customer's shoes in terms of, of, of their needs and their problems and how to solve those? We don't solve them alone. We, we solve them with these 200 plus in apart. Make >>It sound so simple. >>Speaking of challenges, you have something called the startup challenge. That's in its second annual >>Yes. Tomorrow we're kicking off the, the final of the second annual startup challenge. We have three finalists here, three very different, you know, companies. And we had a couple hundred applications this year and we have everything from a company that makes AI and ML more accessible to a company, focus on, you know, retail, you know, analytics. It's gonna be very exciting tomorrow, big price for the winner. The winner is going to win a million dollar of investment from, from snowflake ventures. So >>Very exciting. It's a nice incentive. It is a nice incentive, >>Very nice incentive. And also all the exposure you will get as well. We will put a lot of our marketing support, you know, behind this companies as well. >>Excellent. >>And now the data driver awards program, we've had a couple of data drivers on the program in the last day >>And a half. Yes. We announced to know those winners as well, you know, early in the week. So a lot of recognition for both our customers, but also we're gonna see, you know, the next interesting companies here to watch tomorrow during the startup challenge, you >>Get a little bit of something for everybody here, right? I mean the, the, the, the partner awards, right? These other little side opportunities for ecosystem to get recognition, sometimes funding it's >>Yeah. Everyone wants to be recognized, you know, for the great work they're doing. So, yeah. Yeah. >>So what's next for marketing, obviously, a break and then you start the, the road show. >>So of course yesterday we made an number of very, very large in announcements. Many of those, you know, we've been working on for years here at snowflake, like Unior, you know, for instance has been probably three years, you know, in the making. So our goal now is to take all those announcements to every customer around the world, both through, you know, local events really starting this week, and then also the world tour this fall. And it's gonna be a big, big focus on the developer segments. Obviously what our most exciting announcements is, the native apps, you know, capabilities. And that finally, you know, we can bring the work, you know, to the data and not again, taking the data to the work. And as you know, our mission has really been around breaking down the data silos. Cause those have been the biggest, you know, challenges companies have faced. That's really, what's been standing in the way for customers to become you a data Rav, and now bringing the work to the data from a developer's standpoint is gonna break down even further, those silos. So, >>Yeah. And it's good physics. >>Yeah. It good physics. Yeah. >>Yeah. Tremendous opportunity. Congratulations on a great successful event. It's not even done yet, but obviously we've seen so much success. Great news coming out. We'll be excited to be hearing some of the outcomes of the road show and the developer conference coming up in the fall. We appreciate your insights, your time and for having the cube here at the summit. >>Thank you for being here. Thank you. Thanks for having >>Me, our pleasure for Denise Pearson and Dave Valante I'm Lisa Martin. You're watching the cubes coverage of snowflake summit 22 live from Las Vegas, Dave and I will be back after a short break.

Published Date : Jun 15 2022

SUMMARY :

This is the snowflake summit 22 at Caesar's forum. So great to have you here at SEL meets as well. I mean, everybody's so excited to be face to face and you know, Lisa and I have been doing you were just talking with Frank, I think the 2019 show had less than 2000 people. here on the show floor, Talk about the theme of the event, the world of data collaboration. So you can both collaborate And also the fact that you can, again, you know, build applications together with Why that change explain kind the past, you know, we only really had data sets within our marketplace within the I think you have 500 plus in the Forbes global 2000, Are the customers and the ecosystem in terms of that, that what you just described in the, around themselves, you know, to distribute their data, you know, to their partners. You, you started out kind of better data where simpler data warehouse, And it's also a positioning that we can, you know, grow with as well. you know, doing the work, you know, with our customers, they are the ones that are, you know, migrating the data to, So with many of them, you know, we started developing this joint account based Can you talk about the sales marketing alignment at snowflake? our partners, you know, first, every day, his priorities, you know, the dev conference in November, start with the worldwide tour. So we have smaller numbers, you know, coming from those regions. Is that all face to face or at Lisa is all face to face. you know, as well. The, that is, that is the conference, but it, that one will be virtual this year. And it's just, so we have that opportunity to reach as many people So I think it's not about, you know, having people come to us, or is it more, Hey, we want to cater to the t-shirt crowd, you know, separately, you know, out, out to them. Did you make the decision to do this summit fourth annual in person? Adaptability is the new superpower, you know, of bus business. I mean, it definitely must have exceeded your expectations. it's that unknown that we all had to deal with, you know, Punching above the weight as usual. you know, here, because again, this is very much of an ecosystem, you know, partner you in a conference and you know, for, for our customers. Speaking of challenges, you have something called the startup challenge. focus on, you know, retail, you know, analytics. It's a nice incentive. And also all the exposure you will get as well. gonna see, you know, the next interesting companies here to watch tomorrow Yeah. And that finally, you know, we can bring the work, Yeah. some of the outcomes of the road show and the developer conference coming up in the fall. Thank you for being here. Me, our pleasure for Denise Pearson and Dave Valante I'm Lisa Martin.

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Daniel Fried, Veeam | VeeamON 2022


 

(digital music) >> Welcome back to VeeamON 2022. We're in the home stretch, actually, Dave Nicholson and Dave Vellante here. Daniel Fried is the general manager and senior vice president for EMEA and Worldwide Channel. Daniel, welcome to theCUBE. You got a big job. >> No, I don't have a big job. I have a job that I love. (chuckles) >> Yeah, a job you love. But seriously Veeam, all channel. I mean it has been. >> Yeah, I mean, it's something which just, just a few seconds on, on that piece here, the channel piece, it's something that I love because the ecosystem of partners, an ecosystem of partners, is something which is spending its time moving and developing and changing. You've got a lot of partners changing their roles, their missions, the type of services, type of product that they offer. They all adapt to what the market needs and all the markets around the world are very different because of all these different cultures, languages, and everything. So it's very interesting. In the middle of all that, you know, these tens of thousands of partners and you try to create and try to understand how you can organize, how you can make them happy. So this is fantastic. >> So you're a native of the continent in Europe, obviously. We heard Anton, today, who couldn't be here or chose not to be here, cause he's supporting family and friends in Ukraine. What's the climate like now? Can you share with us what's it like Europe? Just the overall climate and obviously the business climate. >> So the overall climate, the way I see it or I feel it, and obviously there may be some different opinions, that I will always appreciate as also very good opinions. My view is that it seems in Europe that there are a distinction between what people do for businesses, Their thinking for the business, which may be impacted by the situations that we know in Europe between, because of obviously the issues between Ukraine, because of Russia, let's put it this way. And then there is the personal view, which is okay. That happens from time to time, but life continues and we just continue pushing things and enjoying life, and getting the families together and so on and so forth. So, this is in most of the countries in Europe. Obviously, there are a number of countries, which are a little bit more sensitive, a little bit more impacted. All the ones who are next to Russia, or Belarus, so on and so forth. From an emotional standpoint, which is totally understandable. But overall, I'm pretty impressed by how the economy, how people, how the businesses are, you know, continue to thrive in Europe. >> Has Brexit had any...? What impact, if any, has it had? >> So for us Veeam, the impact is... So first there is an impact which is on the currencies. So all the European currencies are no, have slowed down and, and the US dollar is becoming much stronger. >> Despite its debt. >> Right. >> Shouldn't be, but yeah. >> But that doesn't impact on the business. I just... >> Yeah. Right. >> So everything which is economical, macroeconomical is impacted. We have the inflation also, which has an impact, which also has increased because of the oil, because of the gas of everything that they have been stuck, to be stuck. But people get used to it. As Veeam from a business standpoint, one of the big things is we stopped sales, selling into Russia and into Belarus and we are giving our technology, our product, our solutions for free to Ukraine. And that was a piece of the business that we were doing, within EMEA, which was non-neglectable. So it's, I would say a business hole, now that we need to try to fill with accelerating the business service in the other countries of Europe. >> I mean, okay. So thank you for that but we really didn't see it in last quarter's numbers that you guys shared with I mean, IBM. Similarly IBM said, it's noticeable, but it's not really a big impact on our business, but given the cultural ties that you had to Russia and the affinity, I mean you knew how to do business in Russia. It's quite remarkable that you're able to sort of power through that. How about privacy in, around data, in Europe, particularly versus the US? it seems like Europe is setting the trend on things like privacy, certainly on things like acquisitions, we saw the arm acquisition fail. >> Yeah. So there is a big difference. Effectively, there is a big difference between, I would say North America and the rest of the world. And I would say that EMEA, and within EMEA would say the EU is leading very much on what we call server sovereign cloud. So data privacy, which in other words, data is to as much as possible is to remain within either the EU or better within each of the countries, which means that there is again... It's I would say for in EMEA it's good, I would say for the business, for the partners, because then they have to develop around the cloud a number of functions to ensure that because of this data privacy, because of this GDPR or rules and things, all the data remains and resides in a given geographical environment. So it's, which is good because it creates a number of opportunities for the partners. It makes obviously the life of customers and their self a bit more difficult. But again, I think it's good. It's good. It's part of all the way we structure and we organize. And I think that it's going to expand because data is becoming so key, a key limit, a key asset of companies that we absolutely need to take care of it. And it is where Veeam plays a big role in that because we help paying companies managing their data and secure the data in sort of way. >> Yeah. Ransomware has been a big topic of conversation this week. Do you sense that the perception of that as a threat is universal? Are there, are there differences between North America and the EU and other parts of the world? Universal? >> Yeah, it is universal. We see that everywhere. And I think this is a good point, a good question too, is that it's very interesting because we need to get acquainted to the fact that we are going to ever. And so we are going to be attacked. No way out, no. There... Anybody the morning, is waking up, is going on emails and click clicking on an email. Too late. Was a run somewhere. What can you do against that? You know, all humans make mistakes. You can't so it'll happen, but where, where it's absolutely very important and where Veeam plays a big role and where our partners are going to play an even bigger role with our technology is that they can educate the customers to understand that, to have run somewhere is not an issue. What has, what happened is not a problem. What they have to do is to organize so that if they have run somewhere, their letter is safe. And this is where our place a big place. A couple hours back, I was, I was doing a kind of bar with something else. It's totally crazy, but that's okay. I'm going to say it. It's about the COVID. What, no, what do we do? Do we have, do we have something against COVID? No. People were going to get COVID, certainly many people still doing it, but what is important is to be capable of not being too sick. So it is the prevention, which is important. It's the same thing here. So there is this mindset we have psychologically with the partners and they have, they have to provide that services to their customers on how to organize their data using the technology of Veeam in order to be safe, if anything happens. >> So another related question, if I may. When Snowden blew the whistle on the NSA and divulged that the NSA was listening to all the phone calls, there was seemed to be at the time, as I recall, a backlash sentiment in Europe, particularly toward big tech and cloud providers and skepticism toward the cloud. Has the pandemic and the reliance on cloud and the rise of ransomware changed that sentiment? Had the sentiment changed before then? Obviously plenty of Cloud going on in Europe. But can you describe that dynamic? >> Yeah, no, I think that's... Yeah. I think that people were too... You know, as usual. It absolutely reminds me when I was at VMware, when we went from the physical boxes to the virtual machines. I remember the IT people in the company said, "No, I want to be capable of touching." Something here. When you talk about cloud, you talk about something which is virtual, but virtual outside, even outside somewhere. So there is a resistance, psychological resistance to where is my data? How do I control my data? And that is, I think that is very human. Then you need to, you know, it takes time. And again, depending on the cultures, you need to get acquainted to it. So that's what happened be before the pandemic, but then the pandemic took place. And then there was a big problem. There was nobody anymore in the data centers because they couldn't work there and then people were starting to, to work remotely. So the IT needed to be organized to compensate for all these different changes. And cloud was one of them where the data could be stored, where the data could reside, where things could happen. And that's how actually it has accelerated at least in a number of countries where people are a bit leg out to accept the adoption of cloud, cloud-based data. >> So is there a difference in terms of the level of domination by a small group of hyperscale clouds versus smaller service providers? You know, in theory, you have EU behaving in a unified way in sort of the same way that the United States behaves in sort of a federated way. Do you have that same level of domination or is there more, is there more market share available for smaller players in cloud? Any regional differences? >> Yeah. There are big differences. There are big differences again, because of this sovereignty, which is absolutely approved very much in Europe. I'm tell you, I'm going... I'm giving you an example that it was in, I think in October last year, somewhere. The French, the French administration said, "We don't want anymore. Any administration investing in Microsoft 365, because the data is in Azure. The data is out in the cloud." That's what they said. So now these last days, this last week that has changed because Microsoft, you know, introduced a number of technologies, data centers in France, and so on and so forth. So things are going to get better. But the sovereignty, the fact that the data, the privacy of data, everything has to remain in the countries is doing something like the technology of the hyperscalers is used locally wrapped by local companies like systematic writers, local systematic writers, to ensure that the sovereign is set and that the privacy of the data is for real and according to GDPR. So again, it's a value add. It makes things more complex. It doesn't mean that the Google, the Google cloud, the Azure, or the AWS are not going to exist in Europe, but there are going to be a number of layers between them and the customers in order to make sure that everything is totally brought up and that it complies with the EU regulations. >> Help us understand the numbers, Daniel. So the number of customers is mind-boggling it's over 400,000 now, is that right? >> Yeah. Correct. >> Yes. Comparable to VMware, which is again, pretty astounding and the partner ecosystem. Can you help us understand the scope of that? Part one. part two is how do you service and provide that partnership love to all those companies? >> The partners. So yeah, we have about 35,000 around the world, 35,000 partners, but again, it's 10 times less than Microsoft, by the way. So, and this is very interesting. I often have the questions, how do we manage? So first of all, we do tiering, like anybody does. >> Sure. >> We have an organization for that. And we have a two chair sales motion. That means that we use the distributors to take care of the mass, the volume of the smaller, smaller partners. We help the distributors, we help. So it's a leverage system. And we take care obviously more directly, of the large partners or the more complex partners or the ones of interest. But we don't want to forget any of those because even the small one is very important to us because he has these customers maybe in the middle of nowhere, but he's got a few of them. And again, to have a few of these customers, when you adapt, you know, it makes.. At the end, it makes a big business. You know, one plus one plus 1 million times makes, you know, makes huge things. And plus we are in the recurring business now, now that we've introduced three, four years ago, our subscription licenses, which means that it's only incremental. So it's just like the know the telephony, know the telephony business, where the number, the cell phone plans, you know, it's always grabbing as many as possible consumers in this case. So it was the same thing or I have the same, the same kind of, I do a parallel with the French, the French bakery, the French Boulangerie where I say they do their business with the baguette. And then from time to time, they sell the patisserie or they sell the cake, cookie or something, but the same of small things makes a big things. So it is important to have all these small partners everywhere that, that have their small customers or big customers, and that can serve them. So that's that's way. We segment by geography and what we do now is, it is something which is new. We segment by competencies. So it's what I call the soft segmentation. Because if not, we will have a lot of these partners competing to each other, just to sell Veeam. Veeam being number one in many countries, that is what is taking place. And we want them to be happy. We want, we don't want them to fight against each other. So what we do is we do soft segmentation and soft segmentation is this partner is competent in this field with that kind of use case doing this or this or this or this. It's just like you, when you go to the restaurant, you want the restaurant next to your place. So you click for the geography and then you want to, to go for Indian food. So you click restaurant Indian food, and then you want something. So we want to give that possibility to the customers to say, "Yeah, I think I know what I want." And then you can just click and get the partners or the list of partners, which are the most suited for, for his needs. So it's what I call the soft segmentation. The other thing which is important is the network. It's very interesting because when we look at a lot of companies, it's not the network. You've got VARs, you've got cloud and service providers. You've got SARs, you've got all the things. But if you take each of those individually, they don't have the competencies to answer all the request of the customer. So the networking is partnering with partner. That means to have the, the connection so that the partner A who has his customer, but these customer's are requests that this partner cannot fulfill because it's not its competency. That it's going to find the partners or the other partners that can feel this competency and work together. And then it's between them to have the model that they want so that together they can please the customer with their requests. >> Do you ever want to have VeeamON... I mean, I'm happy it's in the US and I like going to Europe, but you, have you ever want to have VeeamON in Europe? >> Yeah, we have VeeamON. We have many VeeamONs in Europe. >> Yeah. The mini ones. Okay. >> VeeamON tours. >> Globally. So where do you have them? >> Europe in APJ, that's what we do. Yes. >> Where do you do it in a APJ? In Japan, obviously in... >> Yeah. I don't know all the locations, tens and tens of them. >> A lot of them. Okay. >> The small ones. What we do, replicate what is done here on one day and then it goes. >> And you'll do that in UK. France, Germany. >> Yeah. Yeah. >> Local. >> And also small countries in Saudi, in South Africa, in Israel, in Bulgaria, in all these countries. Because, you know, we can be virtual. That's nice. >> Oh, right. >> But I love to be having a breakfast or a lunch or drink next to a partner or a customer because you learn so much more. The informal information is so important to understand how the business and how the market develops and what the needs are of customers and so on and so forth. >> How was the European attendance this year? It must have been down. It's hard to get into US. It's actually easier to go back to Europe. >> Virtually I, don't have the numbers, but I- >> No. Virtual. I'm sure it was huge. Yeah. But physical. >> Physical here, we've got about 300, 300 Europeans. >> Yeah. Okay. Out of, do we know? What are the numbers here? Do we know? Have we heard numbers? >> I know 45 was supposed to be around 45K combined. >> That's hybrid. >> So, yeah. >> It's hard to get into the US. We're still figuring that out. So I'm not surprised, but now you... >> But it's complimentary. Yeah. >> Do you go to 'em all? >> No >> You can't. >> No. That's not possible. I cannot. I actually, I would love... >> But some, yes. >> I would love to be capable of duplicate myself, but- >> You go to the one. >> I'm unique. >> You go to the one in France, obviously. Yeah? >> Yeah. Usually in France. Well... >> Depends if you're home. >> Yeah. You know, that is interesting is, the way we organize, the way we organize in Europe is I really want the local leaders to be the ones managing the countries. I'm there to support. I'm not there to be, you know? Yeah. The big boss is coming, he showing. No. It is not that. Again, if they request me to come, if they want me to pass a message to certain type of customer partners, I'll do that. But I don't want to run the show. It's not the way I manage that. >> Yeah. I get that. You want to respect that as if you show up in France and that's your home country, it's like rat man showing up here. It's like taking over the stage. You'll be like, you know, it's our turn. >> But it's just like, you know, I give you another example. So obviously we have... It's even the headquarters, the EMEA headquarters is in France. Right? But it is the French office. And I don't go there. I try not to be there because it is the place for the French people taking care of the French market. And for the French manager, if I go there, everybody's going to come and ask me questions and ask me to make decisions and things. No, they have to run their business. >> So where do you spend, where and how do you spend your time? >> In airports and in planes. (indistinct) What are you asking? >> Of course. >> Do you have another question? >> Actually, if we have time really quickly on just on that subject of sovereignty, we are here in Nevada just across the border, California. People in California have no problem at all, replicating things here for disaster recovery, because it's in the US. Now, is there sort of a cultural sense that tearing down those borders from a sovereignty perspective within Europe would fundamentally change the business climate and maybe tilt things in favor of the AWS and GCPs of the world instead of local regional business? The joke that I heard recently from someone, I thought it was funny. I don't know if it would offend either Germans or French, but it was that it was that AWS was confused and they were planning on putting a data center in Strasbourg, because they thought it was in Germany and it was- >> A joke. >> But the point is, the point is it's like, it's a gum bear. >> Is it true? >> No. But it was a dumb American joke. This was told by a French person basically saying... >> But this person was certainly not from- >> Yes. Right. >> Tell you, because I would've been a very bad way. >> But the point is this idea that you have these mega hyper clouds coming in and saying, "Okay, boom, we're putting one here and you're going to use us regardless of the country you're in." How does that, you know... Is there a push within the EU to tear those barriers down? Or are those sovereignty walls enjoyed by the majority because of the way that it changes the business climate? Any thoughts from that perspective? >> Oh yeah. Yeah. To me, it's very simple. It is a hybrid thing. That means that these big hyperscalers are there, not going to be used but what they do is they're going to partition themselves and work with these local people. So that their big thing appears as being independent, smaller data centers. That's the only thing, you know. You build a house and then you put walls between the different, between the different rooms. That's the only thing that happens. So it's not at all, no. At all to Azures or Google cloud. No, it's not that. It just means that there is a structure and organization that has to be put in place in order that the data resides in given geographical locations using their infrastructures, their technologies. That make, does it make sense? >> Yeah. Except that it puts them in the position of having to have a physical presence in each place, which is advantageous in one way and maybe less efficient in another. >> Yeah. But there are some big markets. >> Yeah. And they eventually got to get there. Right. I mean... >> Yeah. >> They started it. One patient in the world where they restarted was in ANZ. And that's what they did. You know, what, 5, 6, 7 years ago. They put their data centers over there because they wanted to gain the Australian market and the New Zealand market. >> So build it and they will come. Daniel, thanks so much for coming to the theCUBE. Very interesting conversation. >> Pleasure. >> Appreciate it. >> Thank you very much. >> All right, we're wrapping up. Day two at VeeamON 2022. Keep it right there. Dave and I will be back right after this break. (vibrant music)

Published Date : May 18 2022

SUMMARY :

We're in the home stretch, actually, I have a job that I love. Yeah, a job you love. and all the markets around obviously the business climate. because of obviously the What impact, if any, has it had? and the US dollar is on the business. because of the gas of everything and the affinity, and secure the data in sort of way. and the EU and other parts of the world? So it is the prevention, and divulged that the NSA was listening So the IT needed to be organized in sort of the same way that and that the privacy So the number of the partner ecosystem. I often have the questions, So it's just like the know the telephony, I mean, I'm happy it's in the Yeah, we have VeeamON. Okay. So where do you have them? Europe in APJ, that's what we do. Where do you do it in a APJ? tens and tens of them. A lot of them. and then it goes. And you'll do that in UK. Because, you know, we can be virtual. how the business and It's hard to get into US. I'm sure it was huge. Physical here, we've got about 300, What are the numbers here? to be around 45K combined. It's hard to get into the US. But it's complimentary. I actually, I would love... You go to the one in the local leaders to be the It's like taking over the stage. But it is the French office. In airports and in planes. and GCPs of the world But the point is, No. But it was a dumb American joke. Tell you, because I that it changes the business climate? in order that the data resides of having to have a physical presence eventually got to get there. and the New Zealand market. for coming to the theCUBE. Dave and I will be back

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Dave Russell, Veeam | VeeamON 2022


 

>>The cube is back at Vemo 2022. I was happy to be live. Dave ante, Dave Nicholson and Dave Russell three Daves. Dave is the vice president of enterprise strategy at Veeam. Great to see you again, my friend. Thanks for coming >>On. Uh, it's always a pleasure. And Dave, I can remember your name. I can't remember >>Your name as well. <laugh> so wow. How many years has it been now? I mean, add on COVID is four years now. >>Yeah, well, three, three solid three. Yeah, Fallon blue. Uh, last year, Miami little secret. We're gonna go there again next year. >>Okay, so you joined Veeam >>Three. Oh, me four. Yeah, >>Yeah, yeah. Four is four, right? Okay. Wow. >>Um, time flies, man. >>Interesting. What your background, former analyst analyze your time at Veeam and the market and the changes in the customer base. What, what have you seen? What are the big takeaways? Learnings? >>Yeah. You know, what's amazing to me is we've done a lot more research now, ourselves, right? So things that we intuitively thought, things that we experienced by talking to customers, and of course our partners, we can now actually prove. So what I love is that we take the exact same product and we go down market up market. We go across geographies, we go different verticals and we can sell that same exact product to all constituencies because the differences between them are not that great. If it was the three Dave company or the 3m company, what you're looking for is reliable recovery, ease of use those things just transcend. And I think there used to be a time when we thought enterprise means something very different than mid-market than does SMB. And certainly your go to market plans are that way, but not the product plans. >>So the ransomware study, we had Jay buff on earlier, we were talking about it and we just barely scratched the surface. But how were you able to get people to converse with you in such detail? Was it, are you using phone surveys? Are you, are, are you doing web surveys? Are you doing a combination? Deep >>Dives? Yeah. So it was web based and it was anonymous on both ends, meaning no one knew VE was asking the questions. And also we made the promise that none of your data is ever gonna get out, not even to say a large petroleum company, right. Everything is completely anonymized. And we were able to screen people out very effectively, a lot of screener questions to make sure we're dealing with the right person. And then we do some data integrity checking on the back end. But it's amazing if you give people an opportunity, they're actually very willing to tell you about their experience as long as there's no sort of ramification about putting the company or themselves at risk. >>So when I was at IDC, we did a lot of surveys, tons of surveys. I'm sure you did a lot of surveys at Gartner. And we would look at vendor surveys like, eh, well, this kind of the questions are rigged or it's really self-serving. I don't sense that in your surveys, you you've, you've always, you've still got that independent analyst gene. Is that, I mean, it's gotta be, is it by design? Is it just happen that ransomware is a topic that just sort of lends itself to that. Maybe you could talk about your philosophy there. >>Yeah. Well, two part answer really, because it's definitely by design. We, we really want the information. I mean, we're using this to fuel or inform our understanding of the market, what we should build next, what we should message next. So we really want the right data. So we gotta ask the right questions. So Jason, our colleague, Julie, myself, we work really hard on trying to make sure we're not leading the witness down a certain path. We're not trying to prove our own thesis. We're trying to understand what the market really is thinking. And when it comes to ransomware, we wanna know what we don't know, meaning we found a few surprises along the way. A lot of it was confirmational, but that's okay too. As long as you can back that up, cuz then it's not just Avenger's opinion. Of course, a vendor that says that they can help you do something has data that says, they think you uni have a problem with this, but now we can actually point to it and have a more interesting kind of partnership conversation about if you are like 1000 other enterprises globally, this may be what you're seeing. >>And there are no wrong answers there. Meaning even if they say that is absolutely not what we're seeing. Great. Let's have that conversation that's specific to you. But if you're not sure where to start, we've got a whole pool of data to help guide that conversation. >>Yeah. Shout out to Julie Webb does a great job. She's a real pro and yes. And, and really makes sure that, like you say, you want the real, real answers. So what were some of the things that you were excited about or to learn about? Um, in the survey again, we, we touched just barely touched on it in 15 minutes with Jason, but what, what's your take? Well, >>Two that I'd love to point out. I mean, unfortunately Jason probably mentioned this one, you know, only 19% answered when we said, did you pay the ransom? And only 19% said, no, I didn't pay the ransom. And I was a hundred percent successful in my recovery. You know, we're in Vegas, one out of five odds. That's not good. Right? That's a go out of business spot. That's not the kind of 80 20 you want to hear. That's not exactly exactly. Now more concerning to me is 5% said no ransom was asked for. And you know, my phrase on that is that's, that's an arson event. It's not an extortion event. Right. I just came to do harm. That's really troubling. Now there's a huge percentage there that said we paid the ransom about 24% said we paid the ransom and we still couldn't restore the data. So if you add up that 24 in that five, that 29%, that was really scary to me. >>Yeah. So you had the 19%. Okay. That's scary enough. But then you had the wrecking ball, right? Ah, we're just gonna, it's like the mayhem commercial. Yes. Yeah. See ya. Right. Okay. So <laugh>, that's, that's wild. So we've heard a lot about, um, ransomware. The thing that interests me is, and we've had a big dose of ransomware as analysts in these last, you know, 12, 18 months and more. But, but, but it's really escalated. Yeah. Seems like, and by the way, you're sharing this data, which is amazing. Right. So I actually want to dig in and steal some of the, the data. I think that's cool. Right? Definitely. You gave us a URL this morning. Um, so, but you, your philosophy is to share the data. So everybody sees it, your customers, your prospects, your competitors, but your philosophy is to why, why are you sharing that data? Why don't you just keep it to yourself and do it quietly with customers? >>Yeah. You know, I think this is such a significant event. No one vendor's gonna solve it all. Realistically, we may be tied for number one in market share statistically speaking, but we have 12.5%. Right. So we're not gonna be able to do greater good if we're keeping that to ourselves. And it's really a notion of this awareness level, just having the conversation and having that more open, even if it's not us, I think is gonna be beneficial. It speaks to the value of backup and why backup is still relevant this day and age. >>I dunno if you're comfortable answering this, but I'll ask anyway, when you were a Gartner analyst, did you get asked about ransomware a lot? >>No. >>Very rarely or never. >>Almost never. Yeah. And that was four years ago. Literally. Like it >>Was a thing back then, right? I mean it wasn't of course prominent, but it was, it was, I guess it wasn't that >>20 16, 20 17, you know, it's, it's interesting because at a couple of levels you have the, um, the willingness of participants to share their stories, which is a classic example of people coming together to fight a common fo. Yeah, yeah. Right. In the best of times, that's what happens. And now you're sharing that information out. One of the reasons why some would argue we've gotten to this place is because day zero exploits have been stockpiled and they haven't been shared. So you go to, you know, you go, you go through the lineage that gets you to not pet cat as an example. Yes. And where did it come from? Hey, it was something that we knew about. Uh, but we didn't share it. Right. We waited until it happened because maybe we thought we could use it in, in some way. It's, it's an, it's an interesting philosophical question. I, I don't know. I don't know. I don't know where, if that's, uh, the third, it's the one, the third rail you don't want to touch, but basically we're, we are, I guess we're just left to sort through whatever, whatever we have to sort through in that regard. But it is interesting left to industry's own devices. It's sharing an openness. >>Yeah. You know, it's, I almost think it's like open source code. Right? I mean, the promise there is together, we can all do something better. And I think that's true with this ransomware research and the rest of the research we do too. We we've freely put it out there. I mean, you can download the link, no problem. Right. And go see the report. We're fine with that. You know, we think it actually is very beneficial. I remember a long time ago, it was actually Sam Adams that said, uh, you know, Hey, there's a lot of craft brewers out there now, you know, is, are you as a craft brewery now? Successful? Are you worried about that? No. We want every craft brewery to be successful because it creates a better awareness. Well, an availability market, it's still Boston reference. >>What did another Boston reference? Yes. Thank you, >>Boston. And what <laugh>. >>Yeah. So, you know, I, I, I feel like we've seen these milestone, you know, watershed events in, in security. I mean, stucks net sort of yeah. Informed us what's possible with nation states, even though it's highly likely that us and Israel were, were behind that, uh, the, the solar winds hack people are still worried about. Yes. Okay. What's next. Even, even something now. And so everybody's now on high alert even, I don't know how close you guys followed it, but the, the, uh, the Okta, uh, uh, breach, which was a fairly benign incident. And technically it was, was very, very limited and very narrow in scope. But CISOs that I talked to were like, we are really paranoid that there's another shoe to drop. What do we do? So the, the awareness is way, way off the charts. It begs the question. What's next. Can you, can you envision, can you stay ahead? It's so hard to stay ahead of the bad guys, but, but how are you thinking about that? What this isn't the end of it from your standpoint? >>No, it's not. And unfortunately it's because there's money to be made, right? And the barrier to entry is relatively low. It's like hiring a Hitman. You know, you don't actually have to even carry out the bad act yourself and get your own hands dirty. And so it's not gonna end, but it it's really security is everyone's responsibility. Veeam is not really a full time security company, but we play a role in that whole ecosystem. And even if you're not in the data center as an employee of a company, you have a role to play in security. You know, don't click that link, lock the door behind you, that type of thing. So how do you stay ahead of it? I think you just continually keep putting a focus on it. It's like performance. You're never gonna be done. There's always something to tune and to work on, but that can be overwhelming. So the positive I try to tell someone is to your point, Dave, look, a lot of these vulnerabilities were known for quite some time. If you were just current on your patch levels, this could have been prevented, right? You could have closed that window. So the thing that I often say is if you can't do everything and probably none of us can do something and then repeat, do it again, try to get a little bit better every period of time. Whether that's every day, every quarter, what case may be, do what you can. >>Yeah. So ransomware obviously very lucrative. So your job is to increase the denominator. So the ROI is lower, right? And that's a, that's a constant game, right? >>Absolutely. It is a crime of opportunity. It's indiscriminate. And oftentimes non-targeted now there are state sponsored events to your point, but largely it's like the fishermen casting the net out into the ocean. No idea with certainty, what's gonna come back. So I'm just gonna keep trying and trying and trying our goal is to basically you wanna be the house on the neighborhood that looks the least inviting. >>We've talked about this. I mean, any, anyone can be a, a, a ransomware as to go in the dark web, ransomware's a service. Oh, I gotta, I can put a stick into a server and a way I go and I get some Bitcoin right. For it. So, so that's, so, so organizations really have to take this seriously. I think they are. Um, well you tell me, I mean, in your discussions with, with, with customers, >>It's changed. Yeah. You know, I would say 18 months ago, there was a subset of customers out there saying vendors, crying Wolf, you know, you're trying to scare us into making a purchase decision or move off of something that we're working with. Now. I think that's almost inverted. Now what we see is people are saying, look, my boss or my boss's boss's boss, and the security team are knocking on my door asking, what are we gonna do? What's our response? You know, how prepared are we? What kind of things do we have in place? What does our backup practice do to support ransomware? The good news though, going back to the awareness side is I feel like we're evangelizing this a little less as an industry. Meaning the security team is well aware of the role that proper backup and availability can play. That was not true. A handful of years ago. >>Well, that's the other thing too, is that your study showed the closer the practitioner was to the problem. Yes. The more problems there were, that's an awareness thing. Yes. That's not a, that's not, oh, just those guys had visibility. I wanna ask you cuz you've You understand from an application view, right. There's only so much Veeam can do. Um, and then the customer has to have processes in place that go beyond just the, the backup and recovery technology. So, so from an application perspective, what are you advising customers where you leave off and they really have to take over this notion of shared responsibility is really extending beyond cloud security. >>Yeah. Uh, the model that I like is interestingly enough, what we see with Caston in the Kubernetes space. Mm-hmm <affirmative> is there, we're selling into two different constituencies, potentially. It's the infrastructure team that they're worried about disaster recovery. They're worried about backup, but it's the app dev DevOps team. Hey, we're worried about creating the application. So we're spending a lot of focus with the casting group to say, great, go after that shift, left crowd, talk to them about a data availability, disaster recovery, by the way you get data movement or migration for free with that. So migration, maybe what you're first interested in on day one. But by doing that, by having this kind of capability, you're actually protecting yourself from day two issues as well. >>Yeah. So Let's see. Um, what haven't we hit on in this study? There was so much data in there. Uh, is that URL, is that some, a private thing that you guys shared >>Or is it no. Absolutely. >>Can, can you share the >>URL? Yeah, absolutely. It's V E E so V two E period am so V with the period between the E and the a forward slash RW 22. So ransomware 22 is the research project. >>So go there, you download the zip file, you get all the graphics. Um, I I'm gonna dig into it, uh, maybe as early as this, this Friday or this weekend, like to sort of expose that, uh it's you guys obviously want this, I think you're right. It's it's it's awareness needs to go up to solve this problem. You know, I don't know if it's ever solvable, but the only approach is to collaborate. Right. So I, I dunno if you're gonna collaborate with your head-to-head competitors, but you're certainly happy to share the data I've seen Dave, some competitors have pivoted from data protection or even data management to security. Yes. I see. I wonder if I could run a premise by, I see that as an adjacency to your business, but not sort of throwing you into the security bucket. What are your thoughts on that? >>Yeah. You know, certainly respect everything other competitors are doing, you know, and some are getting very, you know, making some good noise and getting picked up on that. However, we're unapologetically a backup company. Mm-hmm, <affirmative>, we're a backup company. First. We're worried about security. We're worried about, you know, data reuse and supporting shift, left types of things, but we're not gonna apologize for being in the backup availability business, not, not at all. However, there's a role that we can play. Having said that that we're a role. We're a component. If you're in the secondary storage market, like backup or archiving. And you're trying to imply that you're going to help prevent or even head off issues on the primary storage side. That might be a little bit of a stretch. Now, hopefully that can happen that we can go get better as an industry on that. >>But fundamentally we're about ensuring that you're recoverable with reliability and speed when you need it. Whether we're no matter what the issue is, because we like to say ransomware is a disaster. Unfortunately there's other kind of disasters that happen as well. Power failures still happen. Natural issues still occur, et cetera. So all these things have to be accounted for. You know, one of our survey, um, data points basically said all the things that take down a server that you didn't plan on. It's basically humans at the top human error, someone accidentally deleted something and then malicious humans, someone actually came after you, but there's a dozen other things that happened too. So you've gotta prepare for all of that. So I guess what I would end up with saying is you remember back in the centralized data centers, especially the mainframe days, people would say, we're worried about the smoking hole or the smoking crater event. Yeah. Yeah. The probability of a plane crashing into your data bunker was relatively low. That was when it got all the discussion though, what was happening every single day is somebody accidentally deleted a file. And so you need to account on both ends of the spectrum. So we don't wanna over rotate. And we also, we don't want to signal to 450,000 beam customers around the world that we're abandoning you that were not about backup. That's still our core >>Effort. No, it's pretty straightforward. You're just telling people to back up in a way that gives them a certain amount of mitigation yes. Or protection in the event that something happens. And no, I don't remember anything about mainframe. He does though though, much older than me >>EF SMS. So I even know what it stands for. Count key data don't even get me started. So, and, and it wasn't thank you for that answer. I didn't mean to sort of a set up question, but it was more of a strategy question and I wish wish I could put on your analyst hat because I, I feel, I'll just say it. I feel as though it's a move to try to get a tailwind. Maybe it's a valuation play. I don't know. But I, I, it resonated with me three years ago when everybody was talking data management and nobody knew what that meant. Data management. I'm like Oracle. >>Right. >>And now it's starting to become a little bit more clear. Um, but Danny Allen stuff and said, it's all about the backup. I think that was one of his keynote messages. So that really resonated with me cuz he said, yeah, it starts with backup and recovery. And that's what, what matters most to these customers. So really was a strategy question. Now maybe it does have valuation impact. Maybe there's a big market there that can be consolidated. You know, uh, we, this morning in the analyst session, we heard about your new CEO's objectives of, you know, grabbing more market share. So, and that's, that's an adjacency. So it's gonna be interesting to see how that plays out far too many security vendors. As, as we know, the backup and recovery space is getting more crowded and that is maybe causing people to sort of shift. I don't know, whatever right. Or left, I guess, shift. Right. I'm not sure, but um, it's gonna be really interesting to watch because this has now become a really hot space after, you know, it's been some really interesting momentum in certain pockets, but now it's everywhere it's coming ubiquitous. So I'll give you the last word Dave on, uh, day one, VEON 20, 22. >>Yeah. Well boy, so many things I could say to kind of land the plane on, but we're just glad to be back in person. It's been three years since we've had a live event in those three years, we've gone from 300,000 customers to 450,000 customers. The release cadence, even in the pandemic has been the greatest in the company's history in 2020, 2021, there's only about three dozen software only companies that have hit a billion dollars and we're one of them. And that, you know, that mission is why hasn't changed and that's why we wanna stay consistent. One of the things Danny always likes to say is, you know, we keep telling the same story because we're not wanting to deviate off of that story and there's more work to be done. And to honors point, you know, Hey, if you have ambitious goals, you're gonna have to look at spreading your wings out a little bit wider, but we're never gonna abandon being a backup. Well, >>It's, it's clear to me, Dave on was not brought in to keep you steady at a billion. I think he's got a site set on five and then who knows what's next? Dave Russell, thanks so much for coming back in the cube. Great to >>See always a pleasure. Thank you. >>All right. That's a wrap for Dave one. Dave ante and Dave Nicholson will be backed tomorrow with a full day of coverage. Check out Silicon angle.com for all the news, uh, youtube.com/silicon angle. You can get these videos. They're all, you know, flying up Wiki bond.com for some of the research in this space. We'll see you tomorrow.

Published Date : May 18 2022

SUMMARY :

Great to see you again, my friend. And Dave, I can remember your name. I mean, We're gonna go there again next year. Yeah, Four is four, right? What, what have you seen? And I think there used to be a time when we thought enterprise means something very different than mid-market So the ransomware study, we had Jay buff on earlier, we were talking about it and we just barely scratched a lot of screener questions to make sure we're dealing with the right person. Maybe you could talk about your philosophy there. kind of partnership conversation about if you are like 1000 other enterprises globally, Let's have that conversation that's specific to you. So what were some of the things that you were excited about or to learn about? That's not the kind of 80 20 you want to hear. ransomware as analysts in these last, you know, 12, 18 months So we're not gonna be able to do greater good if Like it I don't know where, if that's, uh, the third, it's the one, the third rail you don't want to touch, I mean, you can download the link, What did another Boston reference? And what <laugh>. And so everybody's now on high alert even, I don't know how close you guys followed it, but the, the, So the thing that I often say is if you can't do everything and probably none of us can do So the ROI is lower, right? And oftentimes non-targeted now there are state sponsored events to your point, but largely it's I mean, any, anyone can be a, a, a ransomware as to go in the dark customers out there saying vendors, crying Wolf, you know, you're trying to scare us into making a purchase decision or I wanna ask you cuz you've You availability, disaster recovery, by the way you get data movement or migration for free a private thing that you guys shared So ransomware 22 is the research project. like to sort of expose that, uh it's you guys obviously want this, I think you're right. and some are getting very, you know, making some good noise and getting picked up on that. So I guess what I would end up with saying is you remember back Or protection in the event that I didn't mean to sort of a set up question, but it was more of a strategy question and I wish wish So I'll give you the last word Dave One of the things Danny always likes to say is, you know, we keep telling the same story because we're It's, it's clear to me, Dave on was not brought in to keep you steady at a billion. See always a pleasure. They're all, you know,

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Michael Dell, Dell Technologies | Dell Technologies World 2022


 

>>The cube presents, Dell technologies world brought to you by Dell. >>Hello. Welcome to the cube here at Dell tech world. I'm John furry host of the cube with Dave Alon here with Michael Dell, the CEO of Dell technologies cube alumni comes on every year. We have the cube here. It's been two years. Michael, welcome to the cube. Get to see you. >>Hey, John, Dave, great to be with you guys. Thanks for being here. Wonderful to be back here in Vegas with >>You. Well, great to be in person two years ago, we had the cue with the pandemic a lot's happened. We were talking end to end solutions here at Dell tech world in person two years ago, pandemic hits. Thank God you had all that supply for the, for the people having the remote remote end to work now back in person. What's it look like now with, with Dell tech end to end, the edge is important. What's the story, >>You know, edge is, is the physical world. And if you, if you step back from clouds and, you know, multi-cloud, you sort of think about what is the purpose of a cloud or a data center? Well, it's to take data out of the physical world and move it to this place, to somehow enhance it or do something with it and create business value and hopefully create better outcomes. Well, it turns out that, you know, increasingly a lot of that data is gonna stay in the physical world and all of those nodes are gonna be connected. They're gonna be intelligent and we're seeing it in manufacturing and retail and healthcare, transportation, logistics. We're seeing this rapidly intelligent edge being formed. And then of course, with the new networks, the 5g we're seeing, you know, all, all this develop. And so here on the show floor, we're showing a lot of those solutions, but our customers are, are highly engaged. And certainly we think that's a, a big, a big growth factor for the next decade. >>And it's been ING to watch the transformation of the it world and cloudification and the as service, uh, consumption model, which you guys are putting out there has been very successful, but cloud operations is more prominent now on premises and edge and cloud. So the combination of cloud on-premise and edge hardware matters more now than ever before Silicon advances, um, abstraction layers from modern cloud native applications are what people are focused on. What's the story that you cite to the CIOs saying, we're here to help you with that new architecture cloud multi-cloud on premise and edge. What's the main story for you guys with the customers? >>Well, you know, customers want to go faster, right? And they want to accelerate their transformation. And so they wanna shift more resources over to developers, to applications, to access their data, to create competitive advantage. And so we talk a lot about the value line and what are those things below the value line, where we can provide that as a service on a consumption based model and accelerate their transformation, kind of, you know, do for them what we've done inside our own business. And, you know, it's absolutely resonating. We're seeing great growth there. People continue to, to need the solutions, but as we can automate the management and deployment of infrastructure and make it super easy, it gives them a lot of cycles back. >>You know, Michael, my, the favorite part, my favorite part of your book was you were in, I think you were in his, in his home court, in his dining room at Carl Icahn's house. And you said, well, why don't you just buy the company? And then you'll do what you're doing. I I'll buy it back for cheaper. Now, thankfully, you didn't have to do that. Cuz you had an environment of low interest rates and you obviously took it into the other direction, added tremendous value, 101 billion in revenue last year, 17% revenue growth, which was out astounding. When you think about that, um, now we're entering a new chapter with VMware untethered of course you're the chairman of both companies. So how should we think about the new Dell what's next? >>Well, so look, we, we have some unbelievable core businesses, right? We have our client system business and we've all learned during these last two years, how incredibly important it is to enable and empower your workforce with the right tools in the remote and high hybrid work. And we're showing off all kinds of new innovations here. That's a huge business force continues to grow, continues to be super important. Then we have our ISG, the cloud data center, the network of the future, the edge, you know, the, the sort of epicenter of where we're embracing, consumption based business models. That's absolutely huge. Then we have these new, new businesses that we're building with telco with edge, put it all together. It's a 1.3 trillion Tam that we operate in, as you said, more than a hundred billion dollars last year. So there's plenty of room for us to continue to grow and, and expand. And you know, as we make this shift to outcomes, it's obviously more valuable for customers and that, you know, increases our opportunity, increases the, the value we can create for all our stakeholders. >>And number one, number one, share in PCs, by the way, congratulations, again, hit that milestone. All of our gamer, uh, fans in our discord want to know what's the hottest chips coming. What's the fastest machines. What, how's the monitors coming? They want faster, cheaper. What's the coolest, uh, monitors out there right now and, and machines. >>Well, uh, you know, what what's, what's amazing is the, the pace of innovation continues to improve. So whether it's in the GPU, the CPU, the, the resolution, I I'm pretty partial to our 41, uh, display 11 million pixels of fun. And look, I mean, we, we it's, it's, it's clear that people are more productive when they have large screens and all the performance is enabling photo realistic, uh, you know, uh, gaming and photo realistic, everything. And these are immersive experiences. And, you know, again, uh, what companies have figured out to bring it back to, to, to a little bit of business here, John, is that when you, uh, give people the right tools, they're more productive, they're more engaged and look, people are smart. They know what tools are available. And, you know, uh, the thing that actually is most representative of how a person thinks about the tools they have at their organization is actually the thing that's right in front of 'em. And so, you know, this ability for us to provide a pool set of solutions for organizations to keep their workforce productive, to run their applications and infrastructure securely anywhere they want. That's, that's a winning proposition. >>Michael trust was a big theme of your keynote yesterday. And when you acquired EMC and got VMware, it really changed the dynamic with regard to your ability to, into new parts of organizations. You became a much more strategic supplier. I, I would argue. And now with VMware as a separate company, do you feel like you have built up over the, you know, five or whatever years that muscle memory you kinda earn that trust. So how do you see the customer relationship with that regard to that integration that they, they loved the eco. So system competitors might not have loved it so much, but the customers really did love. In fact, the, the U S a, a gentleman yesterday kind of mentioned that, how do you see it? >>You know, customers, uh, are not as interested in the balance sheet and what you know, where different holdings are, what they, they want things to work together, right? And they want partnerships in ecosystems. And certainly, you know, with VMware, even before the combination, we had a powerful partnership. It obviously solidified in a super special way. And now we have this first and best relationship and I've remained the chairman of VMware and super excited about their future. But our ecosystem is incredibly broad. And you see that here in this show floor, and again, making things work together better and more effectively building these engineered solutions that allow people to very quickly deploy the kind of capabilities they want, whether it's, you know, snowflake now working with the on premise and the edge data and more of these, you know, multi-cloud, uh, eco of systems that are being built. It's not gonna be just one company >>You called the edge a couple years ago. You're really prominent in your, in your speeches. And your keynotes data also is a big theme. You mentioned data now, data engineering seems to be the hottest track of, of, of students graduating with data engineering skills, not data science, data engineering, large scale data as code concepts. So what's your vision now with data, how's that fitting into the solutions and the role of data, obviously data protection with cybersecurity data as code is becoming really part of that next big thing. >>Yeah. I mean, if, if you look at anything that is interesting in the world today, uh, at the center of it is data, right? Whether it's the blockchain or the defi or the AI drug discovery, or the autonomous vehicles or whatever you wanna do, there's data in, in, in the middle of that. And of course with that data, well, you've gotta manage it. You, you need compute engines, right? You need to be able to protect it, secure it. And, you know, that's kind of what we do, and we're not going to create all those solutions, but we are gonna be an enabling layer to allow that data to be accessed no matter, you know, where, where it is. And, and, and of course, you know, leading in storage continues to be a super important part of our business. Number one, larger than number two than number three, number four, combined, and, and most of number five as well, and, and growing share. And, and you saw today, the software defined innovations, allowing that, you know, data layer to exist across the edge, the colos, the OnPrem, and the public clouds >>Throughout a stat yesterday. I can't remember if it was a keynote of the analyst round table, but it was 9 million cell towers. And if I heard, right, you kinda look at those as potential data centers talk about that's >>Right. It it's actually 7 million, but, but probably will be 9 million and not, not too long, I don't have the update, but so yeah, the public clouds all together is about 600 data centers. They're about 7 million cellular base stations in the world. Every single one of those is becoming a, you know, multi access, edge compute node. And what are they putting in there? They're putting many data centers of compute and GPS and storage. And, you know, 5g is not about, uh, connecting people that was 4g and before 5g is about connecting things. And there are way more things than there are people, right? And, uh, you know, this, this, this edge is, is rapidly developing. You'll also have private 5g and you'll have, you know, again, embedded intelligence I believe is gonna be in everything this next decade is going to be about that intelligent, connected future, taking that data, turning it into useful outsides in insights and outcomes. And, you know, lots of new businesses will be existing. Businesses will be transformed and also disrupted. >>Yeah. I mean, I think that's so right on and not to pat ourselves on the back day, but we called that edge distributed computing a couple years ago on the cube. And that's, what's turning into the home with COVID you saw that become a workplace, basically compute center, these compute nodes, tying it together as we, what everyone's talking about right now. So as customers say, okay, I want to keep my operations steady, steady, and secure. How do I glue it together? How do I bring these compute node together? That seems to be the top question on, on top of people's minds. And they want it to be cloud native, which means they want it to run cloud-like and they want to connect these compute node together. That's a big discussion point. What's your view on, >>Well, you know, if you, if you sort of have a, a cloud here, a cloud there cloud everywhere, and you, you know, have lots of different Kubernetes frameworks, uh, and you've got, you know, everything is, is spread out, it's a disaster, right? And, and, and it's, it's a, it's a, it's a real challenge to manage all that. So what people are trying to do is create ruthless standardization. It's like, how do you drive cost out and get speed? It's ruthless standardization create consistent environments where you can operate the across all the different domains that, that you want. And so, uh, you know, this is what we're bringing together in, in, in the capabilities that we're delivering. >>And that chaos is great opportunity for you. Um, how are you feeling about VMware these days, new team, uh, give us the update there. >>Yeah. The team is doing well. You know, I think the tons message is resonating. You know, people want Kubernetes and, and, and container based apps, for sure. That's the main, you know, growth in, in, in, in, in new, in new workloads. Uh, but they also want it to work with what they have. Yeah. And they don't want it to be locked into one particular infrastructure. So software finding everything, making it run in all the public clouds, you know, we've had a great success with VxRail, you know, that, that absolutely continues. We have, uh, 200,000 plus nodes, 15,000 customers and growing, we have edge satellite nodes and we continue to work together in SD wan in software defined networking in VMware cloud foundation, uh, you know, expressed, uh, in, in, in all locations. >>You know, one of the things that we've been seeing with the trend towards, um, future of work, which is a big theme, here is a lot of managed services are popping up where the complexity is so ha high that customers want to manage services. Uh, and also the workforce of it's kind of changing. You got a younger generation coming in, how do you see that future of the workforce? The next level? It's not gonna be like, yesterday's it, it's gonna be distributed computing dashboard based. And then you've got these managed services, you know, need to have the training and expertise maybe to run something at scale. How do, how do you see that connecting? Cuz that seems to be another big trend people are talking about, Hey, it's complex someone manage it for me. And I want ease of views. I want the easy button in it. >>Yeah. Well we we've all been at this a while. So we can remember, you know, the beginnings of converged infrastructure and then hyperconverged, which wasn't that long go. And now we have consumption based business models. These are all along the trajectory of the easy button that you're talking about and customers really thinking about the value line, where are the things that really differentiate and add value for their business. And it's not below the value line in those infrastructure areas are creating that easy button with appliances, with consumption based models and allowing them to deploy the scarce resources. They have to the things that really drive their unique differe. And you know, if you look at our managed services flex on demand, all the sort of ancestors and predecessors of apex, those have been great businesses for us. And now with apex, we're kind of industrializing this and, and making it, you know, at scale for all >>Customers, you know, the three of us, we go back, we, we, our first interactions with you separately, we're in the nine. And then we reconnected in the 2012. I think it was Tarkin Mayer had a little breakout session with CIOs. You brought us to early on a Dell tech world in Austin. And of course it was, >>It was just Dell world. Then Dell >>Four, we had Dell tech, you and then EMC world in 2010 was our first cube. And now that's all come together here in Las Vegas. So, you know, it's been great. Uh, the three of us come together and so really appreciate that. Yeah. >>Awesome. Absolutely awesome. >>Well, you know, really appreciate you guys being here, the wonderful work you do in thank you in, you know, bringing out the, the, the stories and, and showing off and helping us show off the innovations that, you know, our team has been working on. You know, during the past year >>It's been great in conversations and, and on a personal note, it's been great to have, uh, chat with all the top people and your company. Appreciate it. Um, someone told me to ask you this question, I want to ask you, you, we've all seen waves of innovation cycles up and down. We're kind of on one. Now you're seeing an inflection point, this next gen, uh, computing and, and web three cultural shit F with workforces and distributed computing decentralization. You mentioned that DFI earlier, how do you see this wave coming? Cause we've seen cycles come and go.com. Bubble kind of looks the same as the web three NFTs and stuff. Now it seems to be Look different, but how do you see this next wave? Cuz looking back on all the other ones that you you have lived through and you rode >>Well. So, you know, the, the way I see it is is, uh, to some extent, these are like foundational layers that have to be built for the next phase to occur. And if you look at the sort of new companies that are being founded today, and we see a lot of those, you, you, you, you see'em, we invest in a bunch of 'em, you know, they're, they're not going and, and kind of redoing the old foundational layers, they're going deeply into vertical businesses and, and disrupting and adding value on top of those. And I think that's, that's really the, the point of, of technology, right? It's enabling human progress us in, in all fields, it's making us healthier. It's making us safer. It's making us more successful in everything that, that we as humans do. And so all these layers of technology are enabling further progress and I think it's absolutely gonna continue. It's all been super exciting. Yeah. You know, so far for the first several decades, but as I, as I believe it, it's, it's just a pre-game show. >>And it's clear your strategy is, is, is really building that foundation of a layer, hardening it, but making it flexible enough, anybody read your book, you're a technology, visionary. A lot of people put you in a, you know, finance bucket, but you can, you can see that you can connect the dots. And that's what you're doing with your foundation of layers. You that's where you're making the bets, isn't it? Uh, you don't can't predict the future. You've said that many times, but you can sort of see where it's going and be prepared for >>It. Well, you, you, you know, you think about any company in, in the industry or any public sector organization, right? Uh, they're, they're, they're wanting to evolve more quickly and transform more quick, more quickly. Right. And we can give them an infrastructure or set of tools, a set of capabilities to help them go faster. >>Yeah. And the other one thing in the eighties, when you started Dell and we were in college, there was no open source really then if look at the growth of open source, talk about those layers, open source, better Silicon GPS, faster, cheap >>More now and now we even have, uh, open source instruction sets for processors. So I mean the whole world's changing. It's exciting. You have people around the world working together. I mean, when you see our development teams, uh, whether they're in Israel or Ireland or Bangalore or Singapore, Hopton Austin, Silicon valley, you know, Taiwan, they're, they're all, they're all collaborating together and, you know, driving, driving innovation and, and, and our business is not that dissimilar from our customers >>Like great to have you in the queue. Great. To have a physical event. People are excited. I'm talking to people, Hey, haven't been back in Vegas in two years. Thanks for having this event. Great to see you. Thanks for coming on the cube. >>Absolutely. Thank you guys. >>Michael Dell here in the cube CEO of Dell technologies. I'm John far, Dave Volante. We'll be right back, more live coverage here at Dell tech world.

Published Date : May 3 2022

SUMMARY :

I'm John furry host of the cube with Dave Alon here with Michael Hey, John, Dave, great to be with you guys. Thank God you had all that supply for the, for the people having the remote remote end to work now Well, it turns out that, you know, What's the story that you cite to the CIOs saying, we're here to help you with that new architecture cloud Well, you know, customers want to go faster, right? And you said, well, why don't you just buy the company? And you know, as we make this shift to outcomes, And number one, number one, share in PCs, by the way, congratulations, again, hit that milestone. all the performance is enabling photo realistic, uh, you know, uh, And now with VMware as a separate company, do you feel like you have built up the kind of capabilities they want, whether it's, you know, snowflake now working with the on premise and how's that fitting into the solutions and the role of data, obviously data protection with cybersecurity And, and, and of course, you know, And if I heard, right, you kinda look at those as potential data centers talk about of those is becoming a, you know, multi access, And that's, what's turning into the home with COVID you saw that And so, uh, you know, this is what we're bringing together Um, how are you feeling about VMware these days, everything, making it run in all the public clouds, you know, How do, how do you see that connecting? So we can remember, you know, the beginnings of converged infrastructure Customers, you know, the three of us, we go back, we, we, our first interactions with you separately, It was just Dell world. So, you know, it's been great. Well, you know, really appreciate you guys being here, the wonderful work you do in thank you in, Cuz looking back on all the other ones that you you have And if you look at the sort of new companies that are being founded today, you know, finance bucket, but you can, you can see that you can connect the dots. And we can give them an source really then if look at the growth of open source, talk about those layers, open source, you know, driving, driving innovation and, and, and our business is not that dissimilar from our Like great to have you in the queue. Thank you guys. Michael Dell here in the cube CEO of Dell technologies.

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Tanuja Randery, AWS | AWS re:Invent 2021


 

>>Hey, welcome back everyone to the cubes coverage of eaters reinvent 2021. So our third day wall-to-wall coverage. I'm my coach, Dave Alonzo. He we're getting all the action two sets in person. It's also a virtual hybrid events with a lot of great content online, bringing you all the fresh voices, all the knowledge, all the news and all the action and got great guests here today. As your renderer, managing director of AWS is Europe, middle east, and Africa also known as EMIA. Welcome to the cube. Welcome, >>Welcome. Thanks for coming on. Lovely to be here. >>So Europe is really hot. Middle east Africa. Great growth. The VC culture in Europe specifically has been booming this year. A lot of great action. We've done many cube gigs out there talking to folks, uh, entrepreneurship, cloud, native growth, and then for us it's global. It's awesome. So first question got to ask you is, is you're new to AWS? What brought you here? >>Yeah, no, John, thank you so much. I've been here about three and a half months now, actually. Um, so what brought me here? Um, I have been in and around the tech world since I was a baby. Um, my father was an entrepreneur. I sold fax machines and microfilm equipment in my early days. And then my career has spanned technology in some form or the other. I was at EMC when we bought VMware. Uh, I was a Colt when we did a FinTech startup joined Schneider in my background, which is industrial tech. So I guess I'm a bit of a tech nerd, although I'm not an engineer, that's for sure. The other thing is I've spent a huge part of my career advising clients. And so while I was at McKinsey on business transformation and cloud keeps coming up, especially post pandemic, huge, huge, huge enabler, right of transformation. So when I got the call from AWS, I thought here's my opportunity to finally take what companies are wrestling with, bring together a pioneer in cloud with our enterprise and start-up and SMB clients connect those dots between business and technology and make things happen. So it real magic. So that's what brought me here. And I guess the only other thing to say is I'd heard a lot of other culture, customer mash, obsession, and leadership principles. >>That's why I'm here. It's been a great success. I got to ask you too, now that your new ostium McKinsey, even seeing the front lines, all the transformation, the pandemic has really forced everybody globally to move faster. Uh, things like connect were popular in EMEA. How, how is that going out? There's at the same kind of global pressure on the digital transformation with cloud? What are you seeing out there? >>I've been traveling since I joined, uh, around 10 of the countries already. So Ben planes, trains, automobiles, and what you definitely see is massive acceleration. And I think it's around reinvention of the business. So people are adopting cloud because it's obviously there's cost reasons. There's MNA reasons. There's really increasingly more about innovating. How do I innovate my business? How do I reinvent my business? So you see that constantly. Um, and whether you're a enterprise company or you're a startup, they're all adopting cloud in different, different ways. Um, I mean, I want to tell a core to stack because it's really interesting. And Adam mentioned this in his keynote five to 15% only of workloads have moved to the cloud. So there's a tremendous runway ahead of us. Um, and the three big things on people's minds helped me become a tech company. So it doesn't matter who you are, you're retail, whether you're life sciences or healthcare. You've probably heard about the Roche, uh, work that we're doing with Roche around accelerating R and D with data, or if you're a shoes Addie desk, how do you accelerate again, your personalized experiences? So it doesn't matter who you have helped me become a tech company, give me skills, digital skills, and then help me become a more sustainable company. Those are the three big things I'm thinking of. >>So a couple of things to unpack there. So think about it. Transformation. We still have a long way to go to your point, whatever 10, 15%, depending on which numbers you look at. We've been talking a lot in the cube about the next decade around business transformation, deeper business integration, and the four smarts to digital. And the woke us up to that, accelerated that as you say, so as you travel around to customers in AMEA, what are you hearing with regard to that? I mean, many customers maybe didn't have time to plan. Now they can sit back and take what they've learned. What are you hearing? >>Yeah. And it's, it's a little bit different in different places, right? So, I mean, if you start, if you look at, uh, you know, our businesses, for example, in France, if you look at our businesses in Iberia or Italy, a lot of them are now starting they're on the, at least on the enterprise front, they are now starting to adopt cloud. So they stepping back and thinking about their overall strategy, right? And then the way that they're doing it is actually they're using data as the first trigger point. And I think that makes it easier to migrate because if you, if you look at large enterprises and if you think of the big processes that they've got and all the mainframes and everything that they need to do, if you S if you look at it as one big block, it's too difficult. But when you think about data, you can actually start to aggregate all of your data into one area and then start to analyze and unpack that. >>So I think what I'm seeing for sure is in those countries, data is the first trigger. If you go out to Israel, well that you've got all, it's really start up nation as you know, right. And then we've got more of the digital natives and they want to, you know, absorb all of the innovation that we're throwing at them. And you've heard a lot here at reinvent on some of the things, whether it's digital twins or robotics, or frankly, even using 5g private network, we've just announcement. They are adopting innovation and really taking that in. So it really does differ, but I think the one big message I would leave you with is bringing industry solutions to business is critical. So rather than just talking it and technology, we've got to be able to bring some of what we've done. So for example, the Goldman Sachs financial cloud, bring that to the rest of financial services companies and the media, or if you take the work we're doing on industrials and IOT. So it's really about connecting what industry use cases with. >>What's interesting about the Goldman Dave and I were commenting. I think we coined the term, the story we wrote on Thursday last week, and then PIP was Sunday superclouds because you look at the rise of snowflake and Databricks and Goldman Sachs. You're going to start to see people building on AWS and building these super clouds because they are taking unique platform features of AWS and then sacrificing it for their needs, and then offering that as a service. So there's kind of a whole nother tier developing in the natural evolution of clouds. So the partners are on fire right now because the creativity, the market opportunities are there to be captured. So you're seeing this opportunity recognition, opportunity, capture vibe going on. And it's interesting. I'd love to get your thoughts on how you see that, because certainly the VCs are here in force. I did when I saw all the top Silicon valley VCs here, um, and some European VCs are all here. They're all seeing this. >>So pick up on two things you mentioned that I think absolutely spot on. We're absolutely seeing with our partners, this integration on our platform is so important. So we talk about the power of three, which is you bring a JSI partner, you bring an ISV partner, you bring AWS, you create that power of three and you take it to our customers. And it doesn't matter which industry we are. Our partner ecosystem is so rich. The Adam mentioned, we have a hundred thousand partners around the world, and then you integrate that with marketplace. Um, and the AWS marketplace just opens the world. We have about 325,000 active customers on marketplace. So sassiphy cation integration with our platform, bringing in the GSI and the NSIs. I think that's the real power to, to, to coming back to your point on transformation on the second one, the unicorns, you know, it's interesting. >>So UK France, um, Israel, Mia, I spent a lot of time, uh, recently in Dubai and you can see it happening there. Uh, Africa, Nigeria, South Africa, I mean all across those countries, you're saying huge amount of VC funding going in towards developers, towards startups to at scale-ups more and more of a, um, our startup clients, by the way, uh, are actually going IPO. You know, initially it used to be a lot of M and a and strategic acquisitions, but they have actually bigger aspirations and they're going IPO and we've seen them through from when they were seed or pre-seed all the way to now that they are unicorns. Right? So that there's just a tremendous amount happening in EMEA. Um, and we're fueling that, you know, you know, I mean, born in the cloud is easy, right? In terms of what AWS brings to the table. >>Well, I've been sacred for years. I always talked to Andy Jassy about this. Cause he's a big sports nut. When you bring like these stadiums to certain cities that rejuvenates and Amazon regions are bringing local rejuvenation around the digital economies. And what you see with the startup culture is the ecosystems around it. And Silicon valley thrives because you have all the service providers, you have all the fear of failure goes away. There's support systems. You start to see now with AWS as ecosystem, that same ecosystem support the robustness of it. So, you know, it's classic, rising tide floats all boats kind of vibe. So, I mean, we don't really have our narrative get down on this, but we're seeing this ecosystem kind of play going on. Yeah. >>And actually it's a real virtuous circle, or we call flywheel right within AWS because a startup wants to connect to an enterprise. An enterprise wants to connect to a startup, right? A lot of our ISV partners, by the way, were startups. Now they've graduated and they're like very large. So what we are, I see our role. And by the way, this is one of the other reasons I came here is I see our role to be able to be real facilitators of these ecosystems. Right. And, you know, we've got something that we kicked off in EMEA, which I'm really proud of called our EMEA startup loft accelerator. And we launched that a web summit. And the idea is to bring startups into our space virtually and physically and help them build and help them make those connections. So I think really, I really do think, and I enterprise clients are asking us all the time, right? Who do I need to involve if I'm thinking IOT, who do I need to involve if I want to do something with data. And that's what we do. Super connectors, >>John, you mentioned the, the Goldman deal. And I think it was Adam in his keynote was talking about our customers are asking us to teach them how to essentially build a Supercloud. I mean, our words. But so with your McKinsey background, I would imagine there's real opportunities there, especially as you, I hear you talk about IMIA going around to see customers. There must be a lot of, sort of non-digital businesses that are now transforming to digital. A lot of capital needs there, but maybe you could talk about sort of how you see that playing out over the next several years in your role and AWS's role in affecting that transfer. >>Yeah, no, absolutely. I mean, you're right actually. And I, you know, maybe I will, from my past experience pick up on something, you know, I was in the world of industry, uh, with Schneider as an example. And, you know, we did business through the channel. Um, and a lot of our channel was not digitized. You know, you had point of sale, electrical distributors, wholesalers, et cetera. I think all of those businesses during the pandemic realized that they had to go digital and online. Right. And so they started from having one fax machine in a store. Real literally I'm not kidding nothing else to actually having to go online and be able to do click and collect and various other things. And we were able with AWS, you can spin up in minutes, right. That sort of service, right. I love the fact that you have a credit card you can get onto our cloud. >>Right. That's the whole thing. And it's about instances. John Adam talked about instances, which I think is great. How do businesses transform? And again, I think it's about unpacking the problem, right? So what we do a lot is we sit down with our customers and we actually map a migration journey with them, right? We look across their core infrastructure. We look at their SAP systems. For example, we look at what's happening in the various businesses, their e-commerce systems, that customer life cycle value management systems. I think you've got to go business by business by business use case by use case, by use case, and then help our technology enable that use case to actually digitize. And whether it's front office or back office. I think the advantages are pretty clear. It's more, I think the difficulty is not technology anymore. The difficulty is mindset, leadership, commitment, the operating model, the organizational model and skills. And so what we have to do is AWS is bringing not only our technology, but our culture of innovation and our digital innovation teams to help our clients on that journey >>Technology. Well, we really appreciate you taking the time coming on the cube. We have a couple more minutes. I do want to get into what's your agenda. Now that you're got you're in charge, got the landscape and the 20 mile stare in front of you. Cloud's booming. You got some personal passion projects. Tell us what your plans are. >>So, um, three or four things, right? Three or four, really big takeaways for me is one. I, I came here to help make sure our customers could leverage the power of the cloud. So I will not feel like my job's been done if I haven't been able to do that. So, you know, that five to 15% we talked about, we've got to go 50, 60, 70%. That that's, that's the goal, right? And why not a hundred percent at some point, right? So I think over the next few years, that's the acceleration we need to help bring in AMEA Americas already started to get there as you know, much more, and we need to drive that into me. And then eventually our APJ colleagues are going to do the same. So that's one thing. The other is we talked about partners. I really want to accelerate and expand our partner ecosystem. >>Um, we have actually a huge growth by the way, in the number of partners signing up the number of certifications they're taking, I really, really want to double down on our partners and actually do what they ask us for, which is join. Co-sell joined marketing globalization. So that's two, I think the third big thing is when you mentioned industry industry industry, we've got to bring real use cases and solutions to our customers and not only talk technology got to connect those two dots. And we have lots of examples to bring by the way. Um, and then for hire and develop the best, you know, we've got a new LP as you know, to strive to be at its best employer. I want to do that in a Mia. I want to make sure we can actually do that. We attract, we retain and we grow and we develop that. >>And the diversity has been a huge theme of this event. It's front and center in virtually every company. >>I am. I'm usually passionate about diversity. I'm proud actually that when I was back at Schneider, I launched something called the power women network. We're a network of a hundred senior women and we meet every month. I've also got a podcast out there. So if anyone's listening, it's called power. Women's speak. It is, I've done 16 over the pandemic with CEOs of women podcast, our women speak >>Or women speak oh, >>And Spotify and >>Everything else. >>And, um, you know, what I love about what we're doing is AWS on diversity and you heard Adam onstage, uh, talk to this. We've got our restock program where we really help under employed and unemployed to get a 12 week intensive course and get trained up on thought skills. And the other thing is, get it helping young girls, 12 to 15, get into stem. So lots of different things on the whole, but we need to do a lot more of course, on diversity. And I look forward to helping our clients through that as well. >>Well, we had, we had the training VP on yesterday. It's all free trainings free. >>We've got such a digital skills issue that I love that we've said 29 million people around the world, free cloud training. >>Literally the th the, the gap there between earnings with cloud certification, you can be making six figures like with cloud training. So, I mean, it's really easy. It's free. It's like, it's such a great thing. >>Have you seen the YouTube video on Charlotte Wilkins? Donald's fast food. She changed her mind. She wanted to take Korea. She now has a tech career as a result of being part of restock. Awesome. >>Oh, really appreciate. You got a lot of energy and love, love the podcast. I'm subscribing. I'm going to listen. We love doing the podcast as well. So thanks for coming on the >>Queue. Thank you so much for having me >>Good luck on anemia and your plans. Thank you. Okay. Cube. You're watching the cube, the leader in global tech coverage. We go to the events and extract the signal from the noise. I'm John furrier with Dave, a lot to here at re-invent physical event in person hybrid event as well. Thanks for watching.

Published Date : Dec 2 2021

SUMMARY :

It's also a virtual hybrid events with a lot of great content online, bringing you all the fresh voices, Lovely to be here. So first question got to ask you is, is you're new to AWS? And I guess the only other thing to say is I'd heard a lot of other culture, I got to ask you too, now that your new ostium McKinsey, even seeing the front So Ben planes, trains, automobiles, and what you definitely see is massive And the woke us up to that, accelerated that as you say, so as you travel around to customers in AMEA, and all the mainframes and everything that they need to do, if you S if you look at it as one big block, it's too difficult. So for example, the Goldman Sachs financial cloud, bring that to the rest of because the creativity, the market opportunities are there to be captured. second one, the unicorns, you know, it's interesting. and we're fueling that, you know, you know, I mean, born in the cloud is easy, right? all the service providers, you have all the fear of failure goes away. And the idea is to bring A lot of capital needs there, but maybe you could talk about sort of how you see that playing I love the fact that you have a credit card you can get onto our cloud. So what we do a lot is we sit down with our customers and we actually map Well, we really appreciate you taking the time coming on the cube. in AMEA Americas already started to get there as you know, much more, and we need to drive that into So that's two, I think the third big thing is when you mentioned industry industry And the diversity has been a huge theme of this event. back at Schneider, I launched something called the power women network. And I look forward to helping our clients through that as well. Well, we had, we had the training VP on yesterday. around the world, free cloud training. Literally the th the, the gap there between earnings with cloud certification, Have you seen the YouTube video on Charlotte Wilkins? So thanks for coming on the Thank you so much for having me We go to the events and extract the signal from the noise.

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Eric Herzog, Infinidat | CUBEconversations


 

(upbeat music) >> Despite its 70 to $80 billion total available market, computer storage is like a small town, everybody knows everybody else. We say in the storage world, there are a hundred people, and 99 seats. Infinidat is a company that was founded in 2011 by storage legend, Moshe Yanai. The company is known for building products with rock solid availability, simplicity, and a passion for white glove service, and client satisfaction. Company went through a leadership change recently, in early this year, appointed industry vet, Phil Bullinger, as CEO. It's making more moves, bringing on longtime storage sales exec, Richard Bradbury, to run EMEA, and APJ Go-To-Market. And just recently appointed marketing maven, Eric Hertzog to be CMO. Hertzog has worked at numerous companies, ranging from startups that were acquired, two stints at IBM, and is SVP of product marketing and management at Storage Powerhouse, EMC, among others. Hertzog has been named CMO of the year as an OnCon Icon, and top 100 influencer in big data, AI, and also hybrid cloud, along with yours truly, if I may say so. Joining me today, is the newly minted CMO of Infinidat, Mr.Eric Hertzog. Good to see you, Eric, thanks for coming on. >> Dave, thank you very much. You know, we love being on theCUBE, and I am of course sporting my Infinidat logo wear already, even though I've only been on the job for two weeks. >> Dude, no Hawaiian shirt, okay. That's a pretty buttoned up company. >> Well, next time, I'll have a Hawaiian shirt, don't worry. >> Okay, so give us the backstory, how did this all come about? you know Phil, my 99 seat joke, but, how did it come about? Tell us that story. >> So, I have known Phil since the late 90s, when he was a VP at LSA of Engineering, and he had... I was working at a company called Milax, which was acquired by IBM. And we were doing a product for HP, and he was providing the subsystem, and we were providing the fiber to fiber, and fiber to SCSI array controllers back in the day. So I met him then, we kept in touch for years. And then when I was a senior VP at EMC, he started originally as VP of engineering for the EMC Isilon team. And then he became the general manager. So, while I didn't work for him, I worked with him, A, at LSA, and then again at EMC. So I just happened to congratulate him about some award he won, and he said "Hey Herzog, "we should talk, I have a CMO opening". So literally happened over LinkedIn discussion, where I reached out to him, and congratulate him, he said "Hey, I need a CMO, let's talk". So, the whole thing took about three weeks in all honesty. And that included interviewing with other members of his exec staff. >> That's awesome, that's right, he was running the Isilon division for awhile at the EMC. >> Right. >> You guys were there, and of course, you talk about Milax, LSA, there was a period of time where, you guys were making subsystems for everybody. So, you sort of saw the whole landscape. So, you got some serious storage history and chops. So, I want to ask you what attracted you to Infinidat. I mean, obviously they're a leader in the magic quadrant. We know about InfiniBox, and the petabyte scale, and the low latency, what are the... When you look at the market, you obviously you see it, you talk to everybody. What were the trends that were driving your decision to join Infinidat? >> Well, a couple of things. First of all, as you know, and you guys have talked about it on theCUBE, most CIOs don't know anything about storage, other than they know a guy got to spend money on it. So the Infinidat message of optimizing applications, workloads, and use cases with 100% guaranteed availability, unmatched reliability, the set and forget ease of use, which obviously AIOps is driving that, and overall IT operations management was very attractive. And then on top of that, the reality is, when you do that consolidation, which Infinidat can do, because of the performance that it has, you can dramatically free up rack, stack, power, floor, and operational manpower by literally getting rid of, tons and tons of arrays. There's one customer that they have, you actually... I found out when I got here, they took out a hundred arrays from EMC Hitachi. And that company now has 20 InfiniBoxes, and InfiniBox SSAs running the exact same workloads that used to be, well over a hundred subsystems from the other players. So, that's got a performance angle, a CapEx and OPEX angle, and then even a clean energy angle because reducing Watson slots. So, lots of different advantages there. And then I think from just a pure marketing perspective, as someone has said, they're the best kept secret to the storage industry. And so you need to, if you will, amp up the message, get it out. They've expanded the portfolio with the InfiniBox SSA, the InfiniGuard product, which is really optimized, not only as the PBA for backup perspective, and it works with all the backup vendors, but also, has an incredible play on data and cyber resilience with their capability of local logical air gapping, remote logical air gapping, and creating a clean room, if you will, a vault, so that you can then recover their review for malware ransomware before you do a full recovery. So it's got the right solutions, just that most people didn't know who they were. So, between the relationship with Phil, and the real opportunity that this company could skyrocket. In fact, we have 35 job openings right now, right now. >> Wow, okay, so yeah, I think it was Duplessy called them the best kept secret, he's not the only one. And so that brings us to you, and your mission because it's true, it is the best kept secret. You're a leader in the Gartner magic quadrant, but I mean, if you're not a leader in a Gartner magic quadrant, you're kind of nobody in storage. And so, but you got chops and block storage. You talked about the consolidation story, and I've talked to many folks in Infinidat about that. Ken Steinhardt rest his soul, Dr. Rico, good business friend, about, you know... So, that play and how you handle the whole blast radius. And that's always a great discussion, and Infinidat has proven that it can operate at very very high performance, low latency, petabyte scale. So how do you get the word out? What's your mission? >> Well, so we're going to do a couple of things. We're going to be very, very tied to the channel as you know, EMC, Dell EMC, and these are articles that have been in CRN, and other channel publications is pulling back from the channel, letting go of channel managers, and there's been a lot of conflict. So, we're going to embrace the channel. We already do well over 90% of our business within general globally. So, we're doing that. In fact, I am meeting, personally, next week with five different CEOs of channel partners. Of which, only one of them is doing business with Infinidat now. So, we want to expand our channel, and leverage the channel, take advantage of these changes in the channel. We are going to be increasing our presence in the public relations area. The work we do with all the industry analysts, not just in North America, but in Europe as well, and Asia. We're going to amp up, of course, our social media effort, both of us, of course, having been named some of the best social media guys in the world the last couple of years. So, we're going to open that up. And then, obviously, increase our demand generation activities as well. So, we're going to make sure that we leverage what we do, and deliver that message to the world. Deliver it to the partner base, so the partners can take advantage, and make good margin and revenue, but delivering products that really meet the needs of the customers while saving them dramatically on CapEx and OPEX. So, the partner wins, and the end user wins. And that's the best scenario you can do when you're leveraging the channel to help you grow your business. >> So you're not only just the marketing guy, I mean, you know product, you ran product management at very senior levels. So, you could... You're like a walking spec sheet, John Farrier says you could just rattle it off. Already impressed that how much you know about Infinidat, but when you joined EMC, it was almost like, there was too many products, right? When you joined IBM, even though it had a big portfolio, it's like it didn't have enough relevant products. And you had to sort of deal with that. How do you feel about the product portfolio at Infinidat? >> Well, for us, it's right in the perfect niche. Enterprise class, AI based software defined storage technologies that happens run on a hybrid array, an all flash array, has a variant that's really tuned towards modern data protection, including data and cyber resilience. So, with those three elements of the portfolio, which by the way, all have a common architecture. So while there are three different solutions, all common architecture. So if you know how to use the InfiniBox, you can easily use an InfiniGuard. You got an InfiniGuard, you can easily use an InfiniBox SSA. So the capability of doing that, helps reduce operational manpower and hence, of course, OPEX. So the story is strong technically, the story has a strong business tie in. So part of the thing you have to do in marketing these days. Yeah, we both been around. So you could just talk about IOPS, and latency, and bandwidth. And if the people didn't... If the CIO didn't know what that meant, so what? But the world has changed on the expenditure of infrastructure. If you don't have seamless integration with hybrid cloud, virtual environments and containers, which Infinidat can do all that, then you're not relevant from a CIO perspective. And obviously with many workloads moving to the cloud, you've got to have this infrastructure that supports core edge and cloud, the virtualization layer, and of course, the container layer across a hybrid environment. And we can do that with all three of these solutions. Yet, with a common underlying software defined storage architecture. So it makes the technical story very powerful. Then you turn that into business benefit, CapEX, OPEX, the operational manpower, unmatched availability, which is obviously a big deal these days, unmatched performance, everybody wants their SAP workload or their Oracle or Mongo Cassandra to be, instantaneous from the app perspective. Excuse me. And we can do that. And that's the kind of thing that... My job is to translate that from that technical value into the business value, that can be appreciated by the CIO, by the CSO, by the VP of software development, who then says to VP of industry, that Infinidat stuff, we actually need that for our SAP workload, or wow, for our overall corporate cybersecurity strategy, the CSO says, the key element of the storage part of that overall corporate cybersecurity strategy are those Infinidat guys with their great cyber and data resilience. And that's the kind of thing that my job, and my team's job to work on to get the market to understand and appreciate that business value that the underlying technology delivers. >> So the other thing, the interesting thing about Infinidat. This was always a source of spirited discussions over the years with business friends from Infinidat was the company figured out a way, it was formed in 2011, and at the time the strategy perfectly reasonable to say, okay, let's build a better box. And the way they approached that from a cost standpoint was you were able to get the most out of spinning disk. Everybody else was moving to flash, of course, floyers work a big flash, all flash data center, etc, etc. But Infinidat with its memory cache and its architecture, and its algorithms was able to figure out how to magically get equivalent or better performance in an all flash array out of a system that had a lot of spinning disks, which is I think unique. I mean, I know it's unique, very rare anyway. And so that was kind of interesting, but at the time it made sense, to go after a big market with a better mouse trap. Now, if I were starting a company today, I might take a different approach, I might try to build, a storage cloud or something like that. Or if I had a huge install base that I was trying to protect, and maybe go into that. But so what's the strategy? You still got huge share gain potentials for on-prem is that the vector? You mentioned hybrid cloud, what's the cloud strategy? Maybe you could summarize your thoughts on that? >> Sure, so the cloud strategy, is first of all, seamless integration to hybrid cloud environments. For example, we support Outpost as an example. Second thing, you'd be surprised at the number of cloud providers that actually use us as their backend, either for their primary storage, or for their secondary storage. So, we've got some of the largest hyperscalers in the world. For example, one of the Telcos has 150 Infiniboxes, InfiniBox SSAS and InfiniGuards. 150 running one of the largest Telcos on the planet. And a huge percentage of that is their corporate cloud effort where they're going in and saying, don't use Amazon or Azure, why don't you use us the giant Telco? So we've got that angle. We've got a ton of mid-sized cloud providers all over the world that their backup is our servers, or their primary storage that they offer is built on top of Infiniboxes or InfiniBox SSA. So, the cloud strategy is one to arm the hyperscalers, both big, medium, and small with what they need to provide the right end user services with the right outside SLAs. And the second thing is to have that hybrid cloud integration capability. For example, when I talked about InfiniGuard, we can do air gapping locally to give almost instantaneous recovery, but at the same time, if there's an earthquake in California or a tornado in Kansas City, or a tsunami in Singapore, you've got to have that remote air gapping capability, which InfiniGuard can do. Which of course, is essentially that logical air gap remote is basically a cloud strategy. So, we can do all of that. That's why it has a cloud strategy play. And again we have a number of public references in the cloud, US signal and others, where they talk about why they use the InfiniBox, and our technologies to offer their storage cloud services based on our platform. >> Okay, so I got to ask you, so you've mentioned earthquakes, a lot of earthquakes in California, dangerous place to live, US headquarters is in Waltham, we're going to pry you out of the Golden State? >> Let's see, I was born at Stanford hospital where my parents met when they were going there. I've never lived anywhere, but here. And of course, remember when I was working for EMC, I flew out every week, and I sort of lived at that Milford Courtyard Marriott. So I'll be out a lot, but I will not be moving, I'm a Silicon Valley guy, just like that old book, the Silicon Valley Guy from the old days, that's me. >> Yeah, the hotels in Waltham are a little better, but... So, what's your priority? Last question. What's the priority first 100 days? Where's your focus? >> Number one priority is team assessment and integration of the team across the other teams. One of the things I noticed about Infinidat, which is a little unusual, is there sometimes are silos and having done seven other small companies and startups, in a startup or a small company, you usually don't see that silo-ness, So we have to break down those walls. And by the way, we've been incredibly successful, even with the silos, imagine if everybody realized that business is a team sport. And so, we're going to do that, and do heavy levels of integration. We've already started to do an incredible outreach program to the press and to partners. We won a couple awards recently, we're up for two more awards in Europe, the SDC Awards, and one of the channel publications is going to give us an award next week. So yeah, we're amping up that sort of thing that we can leverage and extend. Both in the short term, but also, of course, across a longer term strategy. So, those are the things we're going to do first, and yeah, we're going to be rolling into, of course, 2022. So we've got a lot of work we're doing, as I mentioned, I'm meeting, five partners, CEOs, and only one of them is doing business with us now. So we want to get those partners to kick off January with us presenting at their sales kickoff, going "We are going with Infinidat "as one of our strong storage providers". So, we're doing all that upfront work in the first 100 days, so we can kick off Q1 with a real bang. >> Love the channel story, and you're a good guy to do that. And you mentioned the silos, correct me if I'm wrong, but Infinidat does a lot of business in overseas. A lot of business in Europe, obviously the affinity to the engineering, a lot of the engineering work that's going on in Israel, but that's by its very nature, stovepipe. Most startups start in the US, big market NFL cities, and then sort of go overseas. It's almost like Infinidat sort of simultaneously grew it's overseas business, and it's US business. >> Well, and we've got customers everywhere. We've got them in South Africa, all over Europe, Middle East. We have six very large customers in India, and a number of large customers in Japan. So we have a sales team all over the world. As you mentioned, our white glove service includes not only our field systems engineers, but we have a professional services group. We've actually written custom software for several customers. In fact, I was on the forecast meeting earlier today, and one of the comments that was made for someone who's going to give us a PO. So, the sales guy was saying, part of the reason we're getting the PO is we did some professional services work last quarter, and the CIO called and said, I can't believe it. And what CIO calls up a storage company these days, but the CIO called him and said "I can't believe the work you did. We're going to buy some more stuff this quarter". So that white glove service, our technical account managers to go along with the field sales SEs and this professional service is pretty unusual in a small company to have that level of, as you mentioned yourself, white glove service, when the company is so small. And that's been a real hidden gem for this company, and will continue to be so. >> Well, Eric, congratulations on the appointment, the new role, excited to see what you do, and how you craft the story, the strategy. And we've been following Infinidat since, sort of day zero and I really wish you the best. >> Great, well, thank you very much. Always appreciate theCUBE. And trust me, Dave, next time I will have my famous Hawaiian shirt. >> Ah, I can't wait. All right, thanks to Eric, and thank you for watching everybody. This is Dave Vellante for theCUBE, and we'll see you next time. (bright upbeat music)

Published Date : Nov 4 2021

SUMMARY :

Hertzog has been named CMO of the year on the job for two weeks. That's a pretty buttoned up company. a Hawaiian shirt, don't worry. you know Phil, my 99 seat joke, So, the whole thing took about division for awhile at the EMC. and the low latency, what are the... the reality is, when you You're a leader in the And that's the best scenario you can do just the marketing guy, and of course, the container layer and at the time the strategy And the second thing the Silicon Valley Guy from Yeah, the hotels in Waltham and integration of the team a lot of the engineering work and one of the comments that was made the new role, excited to see what you do, Great, well, thank you very much. and thank you for watching everybody.

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Kacy Clarke & Elias Algna


 

>>you welcome to the cubes, continuing coverage of Splunk dot com. 21 I'm lisa martin of a couple guests here with me. Next talking about Splunk H P E N. Deloitte, please welcome Casey Clark, Managing Director and chief architect at Deloitte and Elias Alanya Master Technologists Office of the North American C T O at H P E. Guys welcome to the program. Great to have you. >>Thank you lisa. It's great to be here. >>Thanks lisa >>Here we still are in this virtual world the last 18 months, so many challenges, some opportunities, some silver linings but some of the big challenges that organizations are facing this rapid shift to remote work. The rapid acceleration In digital transformation ran somewhere up nearly 11 x in the first half of this year alone. Solar winds talk to me about some of the challenges that organizations are facing and how you're helping them deal with that Casey >>we'll start with you So most of our clients as we move to virtual um have accelerated their adoption of multiple cloud platforms. You know, moving into a W S into Azure into google. And one of the biggest challenges is in this distributed environment, they still have significant workloads on prem Part of the workloads are in office 3 65. Part of them are in salesforce part of them they're moving into AWS or big data workloads into google. How do you make this all manageable from both. A security point of view and accelerating threats. Uh make that much worse but also from an operational point of view, you know, how do I do application performance management when I have workloads in the cloud calling. Api is back on prem into the mainframe. How do I make an operationally when I have tons of containers and virtual machines operating out there? So the importance of Splunk and good log management observe ability along with all the security management and the security logs and being able to monitor for your environment in this complex distributed environment is absolutely critical and it's just going to get more complex as we get more distributed. >>How can companies given the complexity? How can companies with these complicated I. T. Landscapes get ahead of some of these issues? >>One of the things that we really focused on making sure that you're getting ahead of those and you know we work with organizations like Splunk and Deloitte is how do we how do we collect all of the data? Not just a little bit of it, you know Splunk, help and Deloitte are helping us look across all of those places. We want to make sure that we can can really ingest everything that's out there and then let the tools like Splunk then use all of that data. We found a lot of organizations really struggle with that and with the retention of that data it's been a challenge. So those are things that we really worked hard on figuring out with organizations out there um how to how to ingest retain and then modernize how they do those things at the same time. >>I was reading the Splunk state of Security report which they surveyed over 500 security leaders I think it was over nine um global economies and they said 78% of security and I. T. Leaders worry 78% that they're going to be hit by something like solar winds. Um That style of attack Splunk saying security is a data problem but also looking at all this talk about being on the defensive and preventing attacks the threat landscape escaping companies also have to plan for growth. They have to plan for agility. How do you both help them accomplished? Both at the same time Casey will start with you. >>Well fundamentally on the security front you start with security by design. You're designing the logging the monitoring the defenses into the systems as they are being designed up front as opposed to adding them when you get to Um you know you 80 or production environment. So security by design much like devops and Fc cops is pushing that attitude towards security back earlier in the process so that each of the systems as we're developing them um have the defenses that are needed and have the logging that are embedded in them and the standards for logging so that you don't just get a lot of different kinds of data you get the data you actually need coming into the system and then setting up the correlation of that data so you can identify those threats early through a i through predictive analytics, you get to identify things more quickly. You know, it's all about reducing cycle times and getting better information by designing it in from the beginning, >>standing in from the beginning that shifting left Elias. What are your thoughts about this, enabling that defense, designing an upfront and also enabling organizations to have the agility to grow and expand? >>Yes, sort of reminded of something our friends with the Blue oval used to say in manufacturing quality isn't inspected, it's built in right and and two cases point you have to build it in. We've we've definitely worked with delight to do that and we've set up systems so that they have true agility. We've done things like container ice block with kubernetes uh you know, work with object storage. A lot of the new modern technologies that maybe organizations aren't quite accustomed to yet are still getting on board with. And so we wrap those up in our HP Green Lake managed services so that we can provide those things to organizations that aren't maybe aren't ready for them yet. But the threat landscape is such that you have to be able to do those things if you're not orchestrating these thousands and thousands of containers with something like kubernetes, it's just it becomes such a manual labor intensive process. And so that that labor intensive, non automated process. That's the thing that we're trying to remove. >>Well that's an inhibitor to growth, right number one there, let's go ahead and dig into the HP. Deloitte Splunk solution case. I'm going to go back over to, you talk to me about kind of the catalyst for developing the solution and then we'll dig into it in terms of what it's delivering. >>So Deloitte has had long term partnerships with both H B E and Splunk and we're very excited about working together with them on this solution. Um the HP Green Light, which is hardware by subscription, the flexibility of that platform, you know, the cost effectiveness of the platform. Be able to run workloads like Splunk on it that are constantly changing. You have peaks and valleys depending on, you know, how much work you're doing, how many logs are coming in and so being able to expand that environment quickly through containerized architecture, Oz Funk, which is what we worked on, um you know, with the HP Green Light team uh and and also with spunk so that we can Federated the workloads and everything that's going on on prem with workloads that are in the cloud and doing it very flexibly with the HP on prim platform as well as, you know, Splunk on google and Azure and Splunk cloud um and then having one pane of glass that goes across all of it has been very exciting. You know, we were getting lots of interest in the demo of what we've done on the Green light platform and the partnership has been going great, uh >>that single pane of glass is so critical. We talked about cloud complexity a few minutes ago, customers are dealing with so many different applications there now in this hybrid multi cloud world, it's probably only going to proliferate, Let's talk to me about H P. S perspective and how you're going to help reduce the cloud complexity that customers in every industry are facing. >>Yeah, so within the HP Green Lake umbrella of portfolio, we have set up our uh admiral container platform, for example, are Green Lake management services. We bring all these things together in a way that that really can accelerate applications uh that can make the magic that Deloitte does work underneath. And so when, when our friends at Deloitte go and build something, someone has to, has to bring that to life, has to run it for for our customers. And so that's what Hb Green Lake does, then we do that in a way that fundamentally aligns to the business cycles that go on. And so, uh you know, we think of cloud as an operating model, not necessarily just a physical destination. And so we work on prem Coehlo public hybrid Green Lake spans across all of those and can bring together in a way that really helps customers. We've seen so many times, they have these silos and islands of data. Um you know, you've got uh data being generated in the cloud. Well, you need Splunk in the cloud, you've got the energy generated in uh, Amelia, Well you've got spunk into me and so so Deloitte's really done some great things to help us put that together and then we, we underpin that with the, with the green like uh management services with our software and our infrastructure to make it all >>work. Yeah, Elias, one of the areas that you just mentioned is is one of the hottest trends that we've noticed out there. A lot of clients, you know, with the competition for skilled resources out there on the engineering side and operations are looking at managed services as an option to building, you know, their own technology, you know, hiring their own team, running it themselves and the work that we do with both on the security side as well as operations to provide managed services for our clients in collaboration with companies like HP E and running of the Green Lake platform platforms as well as one cloud, those combined services together and delivered as a managed service uh to our clients is an exciting trend out there that um, is increasingly seen as very cost effective for our clients >>saving cost is key case. I want to get your perspective on what you think differentiates this, this solution, the technology alliance, what are the differentiators in this from Deloitte's lens. >>So bringing the expertise of a company like HP and the flexibility and expand ability of the Green lake platform and the container ization that they've done with Israel, you know, it's, it's bringing that cloud like automation and virtual and flexibility to on uh, the on prem and the hybrid cloud solution combined with Splunk who is rapidly expanding not only what they do in the security space where the constantly changing security landscape out there, but also in observe ability application, performance management, um, Ai ops, um, you know, fully automated and integrated response to operational events that are out there. So HP is doing what they do really well and adapting to this new world. Splunk is constantly changing their products to make it easier for us to go after those operational issues. And Deloitte is coming in with both the industry and the technical experience to bring it all together, you know, how do you log the right things, you know, how do you identify, you know, the real signal versus the noise out there? You know, when you're collecting massive amounts of log data, you know, how do you make it actionable? How can you automate those actions? So by bringing together all three of these berms together, uh we can bring a much better, much, much more effective solutions to our clients in much shorter time frames, >>Shorter time frames are key given that one of the things we've learned in the last 18 months, is that real time is really business critical for companies in every industry unless I want to get your perspective from a technology lens, talk to me about the differentiators here, what this solution is three way alliance brings to your customers. >>Yeah, sure thing. We've done a lot of work with Deloitte and with Intel also on performance optimization, which is, is key for any application and that gets to what I mentioned earlier of bringing more data in some of the work that we've done with until we've able been able to accelerate Are the ingest rate of Splunk by about 17 times, which is pretty incredible. Uh, and that allows us to do more or do more with less and that can help reduce the cost. Also done a lot of work on the, on the setup side. So there's a lot of complexities in running a big enterprise application like Splunk. Um, it does a lot of great things but with that comes some complications for sure. And so, uh, a lot of the work that we've done is to help really make this production ready at scale disaster tolerance and bring all of those things together. And that >>requires a fair amount of >>work on the back end to make sure that we can, we can do that at scale and, and to be a, you know, to run, you know, in a way that businesses of significant size can take advantage of these things without having to worry about what happens if I lose a data center or what happens if I lose a region. Um And and to do those things with absolute assurance >>That's critical case you have a question for you. How will this solution help facilitate one of the positives that we've seen during the last 18 months and that is the strengthening of the IT security relationship. What are your thoughts there? >>I think one of the important things here is that the standardization and automation of what we're what we're bringing together you know so that security can monitor all the different things that are being configured because I can go in and look at the automation that it's creating them. So we have a very dynamic environment now with the new cloud based and virtualized environment so going in and manually configuring anything anymore. It's just not possible. Not when you're managing tens of thousands of servers out there. So security working together very closely with operations and collaborating on that automation so that the managed services are are configured right from the beginning as we talked about security about design. Operations by design in the beginning it's that early collaboration and that shift left that is giving us the very close collaboration that results in good telemetry, good visibility you know good reaction times on the other end. >>That collaboration is something that we've also seen is really a key theme that's emerged I think from all of us in every industry in the last 18 months. And I want to punt the last question to you and that's where can customers go to learn more information? How do they get started with this solution? >>A great way to get started is to reach out to our partners like Deloitte, they can help you on that journey. Hp. Es there, of course. Hp dot com. We have a number of white papers, collateral presentations, reference architecture is you name it, it's out there. But really every organization is unique. Every every challenge that we come up with always requires a little bit of hard thinking and and so that's why we have the partnership >>to be able to work with customers and collaborate. I'll say to really identify what their challenges are, how they help them in this very dynamic. No doubt continuing to be dynamic market. Thank you both so much for joining me talking to me about what Deloitte Splunk NHP are doing, how you're helping customers address that cloud complexity from the security lens, the operations lens. We appreciate your time. >>Thanks lisa. Thank you lisa tonight >>For my guests. I'm Lisa Martin, you're watching the cubes coverage of splunk.com 21. Yeah. Mhm

Published Date : Oct 18 2021

SUMMARY :

Elias Alanya Master Technologists Office of the North American C T O at H P Thank you lisa. some opportunities, some silver linings but some of the big challenges that organizations are facing management and the security logs and being able to monitor for your environment How can companies given the complexity? One of the things that we really focused on making sure that you're getting ahead of those and How do you both help them accomplished? into the systems as they are being designed up front as opposed to adding them when you get standing in from the beginning that shifting left Elias. A lot of the new modern technologies that I'm going to go back over to, you talk to me about kind of the with the HP on prim platform as well as, you know, Splunk on google and going to help reduce the cloud complexity that customers in every industry are facing. And so, uh you know, we think of cloud as an operating model, Yeah, Elias, one of the areas that you just mentioned is is one of the hottest trends I want to get your perspective on what you think and expand ability of the Green lake platform and the container ization that they've done with Israel, is that real time is really business critical for companies in every industry unless I want to get your perspective of bringing more data in some of the work that we've done with until we've able been able and to be a, you know, to run, you know, in a way that businesses one of the positives that we've seen during the last 18 months and that is the strengthening of the IT security and automation of what we're what we're bringing together you know so that And I want to punt the last question to you and that's where can customers a number of white papers, collateral presentations, reference architecture is you name Thank you both so much for joining me talking to me about what Deloitte Splunk NHP are doing, Thank you lisa tonight I'm Lisa Martin, you're watching the cubes coverage of splunk.com 21.

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The New Data Equation: Leveraging Cloud-Scale Data to Innovate in AI, CyberSecurity, & Life Sciences


 

>> Hi, I'm Natalie Ehrlich and welcome to the AWS startup showcase presented by The Cube. We have an amazing lineup of great guests who will share their insights on the latest innovations and solutions and leveraging cloud scale data in AI, security and life sciences. And now we're joined by the co-founders and co-CEOs of The Cube, Dave Vellante and John Furrier. Thank you gentlemen for joining me. >> Hey Natalie. >> Hey Natalie. >> How are you doing. Hey John. >> Well, I'd love to get your insights here, let's kick it off and what are you looking forward to. >> Dave, I think one of the things that we've been doing on the cube for 11 years is looking at the signal in the marketplace. I wanted to focus on this because AI is cutting across all industries. So we're seeing that with cybersecurity and life sciences, it's the first time we've had a life sciences track in the showcase, which is amazing because it shows that growth of the cloud scale. So I'm super excited by that. And I think that's going to showcase some new business models and of course the keynotes Ali Ghodsi, who's the CEO Data bricks pushing a billion dollars in revenue, clear validation that startups can go from zero to a billion dollars in revenues. So that should be really interesting. And of course the top venture capitalists coming in to talk about what the enterprise dynamics are all about. And what about you, Dave? >> You know, I thought it was an interesting mix and choice of startups. When you think about, you know, AI security and healthcare, and I've been thinking about that. Healthcare is the perfect industry, it is ripe for disruption. If you think about healthcare, you know, we all complain how expensive it is not transparent. There's a lot of discussion about, you know, can everybody have equal access that certainly with COVID the staff is burned out. There's a real divergence and diversity of the quality of healthcare and you know, it all results in patients not being happy, and I mean, if you had to do an NPS score on the patients and healthcare will be pretty low, John, you know. So when I think about, you know, AI and security in the context of healthcare in cloud, I ask questions like when are machines going to be able to better meet or make better diagnoses than doctors? And that's starting. I mean, it's really in assistance putting into play today. But I think when you think about cheaper and more accurate image analysis, when you think about the overall patient experience and trust and personalized medicine, self-service, you know, remote medicine that we've seen during the COVID pandemic, disease tracking, language translation, I mean, there are so many things where the cloud and data, and then it can help. And then at the end of it, it's all about, okay, how do I authenticate? How do I deal with privacy and personal information and tamper resistance? And that's where the security play comes in. So it's a very interesting mix of startups. I think that I'm really looking forward to hearing from... >> You know Natalie one of the things we talked about, some of these companies, Dave, we've talked a lot of these companies and to me the business model innovations that are coming out of two factors, the pandemic is kind of coming to an end so that accelerated and really showed who had the right stuff in my opinion. So you were either on the wrong side or right side of history when it comes to the pandemic and as we look back, as we come out of it with clear growth in certain companies and certain companies that adopted let's say cloud. And the other one is cloud scale. So the focus of these startup showcases is really to focus on how startups can align with the enterprise buyers and create the new kind of refactoring business models to go from, you know, a re-pivot or refactoring to more value. And the other thing that's interesting is that the business model isn't just for the good guys. If you look at say ransomware, for instance, the business model of hackers is gone completely amazing too. They're kicking it but in terms of revenue, they have their own they're well-funded machines on how to extort cash from companies. So there's a lot of security issues around the business model as well. So to me, the business model innovation with cloud-scale tech, with the pandemic forcing function, you've seen a lot of new kinds of decision-making in enterprises. You seeing how enterprise buyers are changing their decision criteria, and frankly their existing suppliers. So if you're an old guard supplier, you're going to be potentially out because if you didn't deliver during the pandemic, this is the issue that everyone's talking about. And it's kind of not publicized in the press very much, but this is actually happening. >> Well thank you both very much for joining me to kick off our AWS startup showcase. Now we're going to go to our very special guest Ali Ghodsi and John Furrier will seat with him for a fireside chat and Dave and I will see you on the other side. >> Okay, Ali great to see you. Thanks for coming on our AWS startup showcase, our second edition, second batch, season two, whatever we want to call it it's our second version of this new series where we feature, you know, the hottest startups coming out of the AWS ecosystem. And you're one of them, I've been there, but you're not a startup anymore, you're here pushing serious success on the revenue side and company. Congratulations and great to see you. >> Likewise. Thank you so much, good to see you again. >> You know I remember the first time we chatted on The Cube, you weren't really doing much software revenue, you were really talking about the new revolution in data. And you were all in on cloud. And I will say that from day one, you were always adamant that it was cloud cloud scale before anyone was really talking about it. And at that time it was on premises with Hadoop and those kinds of things. You saw that early. I remember that conversation, boy, that bet paid out great. So congratulations. >> Thank you so much. >> So I've got to ask you to jump right in. Enterprises are making decisions differently now and you are an example of that company that has gone from literally zero software sales to pushing a billion dollars as it's being reported. Certainly the success of Data bricks has been written about, but what's not written about is the success of how you guys align with the changing criteria for the enterprise customer. Take us through that and these companies here are aligning the same thing and enterprises want to change. They want to be in the right side of history. What's the success formula? >> Yeah. I mean, basically what we always did was look a few years out, the how can we help these enterprises, future proof, what they're trying to achieve, right? They have, you know, 30 years of legacy software and, you know baggage, and they have compliance and regulations, how do we help them move to the future? So we try to identify those kinds of secular trends that we think are going to maybe you see them a little bit right now, cloud was one of them, but it gets more and more and more. So we identified those and there were sort of three or four of those that we kind of latched onto. And then every year the passes, we're a little bit more right. Cause it's a secular trend in the market. And then eventually, it becomes a force that you can't kind of fight anymore. >> Yeah. And I just want to put a plug for your clubhouse talks with Andreessen Horowitz. You're always on clubhouse talking about, you know, I won't say the killer instinct, but being a CEO in a time where there's so much change going on, you're constantly under pressure. It's a lonely job at the top, I know that, but you've made some good calls. What was some of the key moments that you can point to, where you were like, okay, the wave is coming in now, we'd better get on it. What were some of those key decisions? Cause a lot of these startups want to be in your position, and a lot of buyers want to take advantage of the technology that's coming. They got to figure it out. What was some of those key inflection points for you? >> So if you're just listening to what everybody's saying, you're going to miss those trends. So then you're just going with the stream. So, Juan you mentioned that cloud. Cloud was a thing at the time, we thought it's going to be the thing that takes over everything. Today it's actually multi-cloud. So multi-cloud is a thing, it's more and more people are thinking, wow, I'm paying a lot's to the cloud vendors, do I want to buy more from them or do I want to have some optionality? So that's one. Two, open. They're worried about lock-in, you know, lock-in has happened for many, many decades. So they want open architectures, open source, open standards. So that's the second one that we bet on. The third one, which you know, initially wasn't sort of super obvious was AI and machine learning. Now it's super obvious, everybody's talking about it. But when we started, it was kind of called artificial intelligence referred to robotics, and machine learning wasn't a term that people really knew about. Today, it's sort of, everybody's doing machine learning and AI. So betting on those future trends, those secular trends as we call them super critical. >> And one of the things that I want to get your thoughts on is this idea of re-platforming versus refactoring. You see a lot being talked about in some of these, what does that even mean? It's people trying to figure that out. Re-platforming I get the cloud scale. But as you look at the cloud benefits, what do you say to customers out there and enterprises that are trying to use the benefits of the cloud? Say data for instance, in the middle of how could they be thinking about refactoring? And how can they make a better selection on suppliers? I mean, how do you know it used to be RFP, you deliver these speeds and feeds and you get selected. Now I think there's a little bit different science and methodology behind it. What's your thoughts on this refactoring as a buyer? What do I got to do? >> Well, I mean let's start with you said RFP and so on. Times have changed. Back in the day, you had to kind of sign up for something and then much later you're going to get it. So then you have to go through this arduous process. In the cloud, would pay us to go model elasticity and so on. You can kind of try your way to it. You can try before you buy. And you can use more and more. You can gradually, you don't need to go in all in and you know, say we commit to 50,000,000 and six months later to find out that wow, this stuff has got shelf where it doesn't work. So that's one thing that has changed it's beneficial. But the second thing is, don't just mimic what you had on prem in the cloud. So that's what this refactoring is about. If you had, you know, Hadoop data lake, now you're just going to have an S3 data lake. If you had an on-prem data warehouse now you just going to have a cloud data warehouse. You're just repeating what you did on prem in the cloud, architected for the future. And you know, for us, the most important thing that we say is that this lake house paradigm is a cloud native way of organizing your data. That's different from how you would do things on premises. So think through what's the right way of doing it in the cloud. Don't just try to copy paste what you had on premises in the cloud. >> It's interesting one of the things that we're observing and I'd love to get your reaction to this. Dave a lot** and I have been reporting on it is, two personas in the enterprise are changing their organization. One is I call IT ops or there's an SRE role developing. And the data teams are being dismantled and being kind of sprinkled through into other teams is this notion of data, pipelining being part of workflows, not just the department. Are you seeing organizational shifts in how people are organizing their resources, their human resources to take advantage of say that the data problems that are need to being solved with machine learning and whatnot and cloud-scale? >> Yeah, absolutely. So you're right. SRE became a thing, lots of DevOps people. It was because when the cloud vendors launched their infrastructure as a service to stitch all these things together and get it all working you needed a lot of devOps people. But now things are maturing. So, you know, with vendors like Data bricks and other multi-cloud vendors, you can actually get much higher level services where you don't need to necessarily have lots of lots of DevOps people that are themselves trying to stitch together lots of services to make this work. So that's one trend. But secondly, you're seeing more data teams being sort of completely ubiquitous in these organizations. Before it used to be you have one data team and then we'll have data and AI and we'll be done. ' It's a one and done. But that's not how it works. That's not how Google, Facebook, Twitter did it, they had data throughout the organization. Every BU was empowered. It's sales, it's marketing, it's finance, it's engineering. So how do you embed all those data teams and make them actually run fast? And you know, there's this concept of a data mesh which is super important where you can actually decentralize and enable all these teams to focus on their domains and run super fast. And that's really enabled by this Lake house paradigm in the cloud that we're talking about. Where you're open, you're basing it on open standards. You have flexibility in the data types and how they're going to store their data. So you kind of provide a lot of that flexibility, but at the same time, you have sort of centralized governance for it. So absolutely things are changing in the market. >> Well, you're just the professor, the masterclass right here is amazing. Thanks for sharing that insight. You're always got to go out of date and that's why we have you on here. You're amazing, great resource for the community. Ransomware is a huge problem, it's now the government's focus. We're being attacked and we don't know where it's coming from. This business models around cyber that's expanding rapidly. There's real revenue behind it. There's a data problem. It's not just a security problem. So one of the themes in all of these startup showcases is data is ubiquitous in the value propositions. One of them is ransomware. What's your thoughts on ransomware? Is it a data problem? Does cloud help? Some are saying that cloud's got better security with ransomware, then say on premise. What's your vision of how you see this ransomware problem being addressed besides the government taking over? >> Yeah, that's a great question. Let me start by saying, you know, we're a data company, right? And if you say you're a data company, you might as well just said, we're a privacy company, right? It's like some people say, well, what do you think about privacy? Do you guys even do privacy? We're a data company. So yeah, we're a privacy company as well. Like you can't talk about data without talking about privacy. With every customer, with every enterprise. So that's obviously top of mind for us. I do think that in the cloud, security is much better because, you know, vendors like us, we're investing so much resources into security and making sure that we harden the infrastructure and, you know, by actually having all of this infrastructure, we can monitor it, detect if something is, you know, an attack is happening, and we can immediately sort of stop it. So that's different from when it's on prem, you have kind of like the separated duties where the software vendor, which would have been us, doesn't really see what's happening in the data center. So, you know, there's an IT team that didn't develop the software is responsible for the security. So I think things are much better now. I think we're much better set up, but of course, things like cryptocurrencies and so on are making it easier for people to sort of hide. There decentralized networks. So, you know, the attackers are getting more and more sophisticated as well. So that's definitely something that's super important. It's super top of mind. We're all investing heavily into security and privacy because, you know, that's going to be super critical going forward. >> Yeah, we got to move that red line, and figure that out and get more intelligence. Decentralized trends not going away it's going to be more of that, less of the centralized. But centralized does come into play with data. It's a mix, it's not mutually exclusive. And I'll get your thoughts on this. Architectural question with, you know, 5G and the edge coming. Amazon's got that outpost stringent, the wavelength, you're seeing mobile world Congress coming up in this month. The focus on processing data at the edge is a huge issue. And enterprises are now going to be commercial part of that. So architecture decisions are being made in enterprises right now. And this is a big issue. So you mentioned multi-cloud, so tools versus platforms. Now I'm an enterprise buyer and there's no more RFPs. I got all this new choices for startups and growing companies to choose from that are cloud native. I got all kinds of new challenges and opportunities. How do I build my architecture so I don't foreclose a future opportunity. >> Yeah, as I said, look, you're actually right. Cloud is becoming even more and more something that everybody's adopting, but at the same time, there is this thing that the edge is also more and more important. And the connectivity between those two and making sure that you can really do that efficiently. My ask from enterprises, and I think this is top of mind for all the enterprise architects is, choose open because that way you can avoid locking yourself in. So that's one thing that's really, really important. In the past, you know, all these vendors that locked you in, and then you try to move off of them, they were highly innovative back in the day. In the 80's and the 90's, there were the best companies. You gave them all your data and it was fantastic. But then because you were locked in, they didn't need to innovate anymore. And you know, they focused on margins instead. And then over time, the innovation stopped and now you were kind of locked in. So I think openness is really important. I think preserving optionality with multi-cloud because we see the different clouds have different strengths and weaknesses and it changes over time. All right. Early on AWS was the only game that either showed up with much better security, active directory, and so on. Now Google with AI capabilities, which one's going to win, which one's going to be better. Actually, probably all three are going to be around. So having that optionality that you can pick between the three and then artificial intelligence. I think that's going to be the key to the future. You know, you asked about security earlier. That's how people detect zero day attacks, right? You ask about the edge, same thing there, that's where the predictions are going to happen. So make sure that you invest in AI and artificial intelligence very early on because it's not something you can just bolt on later on and have a little data team somewhere that then now you have AI and it's one and done. >> All right. Great insight. I've got to ask you, the folks may or may not know, but you're a professor at Berkeley as well, done a lot of great work. That's where you kind of came out of when Data bricks was formed. And the Berkeley basically was it invented distributed computing back in the 80's. I remember I was breaking in when Unix was proprietary, when software wasn't open you actually had the deal that under the table to get code. Now it's all open. Isn't the internet now with distributed computing and how interconnects are happening. I mean, the internet didn't break during the pandemic, which proves the benefit of the internet. And that's a positive. But as you start seeing edge, it's essentially distributed computing. So I got to ask you from a computer science standpoint. What do you see as the key learnings or connect the dots for how this distributed model will work? I see hybrids clearly, hybrid cloud is clearly the operating model but if you take it to the next level of distributed computing, what are some of the key things that you look for in the next five years as this starts to be completely interoperable, obviously software is going to drive a lot of it. What's your vision on that? >> Yeah, I mean, you know, so Berkeley, you're right for the gigs, you know, there was a now project 20, 30 years ago that basically is how we do things. There was a project on how you search in the very early on with Inktomi that became how Google and everybody else to search today. So workday was super, super early, sometimes way too early. And that was actually the mistake. Was that they were so early that people said that that stuff doesn't work. And then 20 years later you were invented. So I think 2009, Berkeley published just above the clouds saying the cloud is the future. At that time, most industry leaders said, that's just, you know, that doesn't work. Today, recently they published a research paper called, Sky Computing. So sky computing is what you get above the clouds, right? So we have the cloud as the future, the next level after that is the sky. That's one on top of them. That's what multi-cloud is. So that's a lot of the research at Berkeley, you know, into distributed systems labs is about this. And we're excited about that. Then we're one of the sky computing vendors out there. So I think you're going to see much more innovation happening at the sky level than at the compute level where you needed all those DevOps and SRE people to like, you know, build everything manually themselves. I can just see the memes now coming Ali, sky net, star track. You've got space too, by the way, space is another frontier that is seeing a lot of action going on because now the surface area of data with satellites is huge. So again, I know you guys are doing a lot of business with folks in that vertical where you starting to see real time data acquisition coming from these satellites. What's your take on the whole space as the, not the final frontier, but certainly as a new congested and contested space for, for data? >> Well, I mean, as a data vendor, we see a lot of, you know, alternative data sources coming in and people aren't using machine learning< AI to eat out signal out of the, you know, massive amounts of imagery that's coming out of these satellites. So that's actually a pretty common in FinTech, which is a vertical for us. And also sort of in the public sector, lots of, lots of, lots of satellites, imagery data that's coming. And these are massive volumes. I mean, it's like huge data sets and it's a super, super exciting what they can do. Like, you know, extracting signal from the satellite imagery is, and you know, being able to handle that amount of data, it's a challenge for all the companies that we work with. So we're excited about that too. I mean, definitely that's a trend that's going to continue. >> All right. I'm super excited for you. And thanks for coming on The Cube here for our keynote. I got to ask you a final question. As you think about the future, I see your company has achieved great success in a very short time, and again, you guys done the work, I've been following your company as you know. We've been been breaking that Data bricks story for a long time. I've been excited by it, but now what's changed. You got to start thinking about the next 20 miles stair when you look at, you know, the sky computing, you're thinking about these new architectures. As the CEO, your job is to one, not run out of money which you don't have to worry about that anymore, so hiring. And then, you got to figure out that next 20 miles stair as a company. What's that going on in your mind? Take us through your mindset of what's next. And what do you see out in that landscape? >> Yeah, so what I mentioned around Sky company optionality around multi-cloud, you're going to see a lot of capabilities around that. Like how do you get multi-cloud disaster recovery? How do you leverage the best of all the clouds while at the same time not having to just pick one? So there's a lot of innovation there that, you know, we haven't announced yet, but you're going to see a lot of it over the next many years. Things that you can do when you have the optionality across the different parts. And the second thing that's really exciting for us is bringing AI to the masses. Democratizing data and AI. So how can you actually apply machine learning to machine learning? How can you automate machine learning? Today machine learning is still quite complicated and it's pretty advanced. It's not going to be that way 10 years from now. It's going to be very simple. Everybody's going to have it at their fingertips. So how do we apply machine learning to machine learning? It's called auto ML, automatic, you know, machine learning. So that's an area, and that's not something that can be done with, right? But the goal is to eventually be able to automate a way the whole machine learning engineer and the machine learning data scientist altogether. >> You know it's really fun and talking with you is that, you know, for years we've been talking about this inside the ropes, inside the industry, around the future. Now people starting to get some visibility, the pandemics forced that. You seeing the bad projects being exposed. It's like the tide pulled out and you see all the scabs and bad projects that were justified old guard technologies. If you get it right you're on a good wave. And this is clearly what we're seeing. And you guys example of that. So as enterprises realize this, that they're going to have to look double down on the right projects and probably trash the bad projects, new criteria, how should people be thinking about buying? Because again, we talked about the RFP before. I want to kind of circle back because this is something that people are trying to figure out. You seeing, you know, organic, you come in freemium models as cloud scale becomes the advantage in the lock-in frankly seems to be the value proposition. The more value you provide, the more lock-in you get. Which sounds like that's the way it should be versus proprietary, you know, protocols. The protocol is value. How should enterprises organize their teams? Is it end to end workflows? Is it, and how should they evaluate the criteria for these technologies that they want to buy? >> Yeah, that's a great question. So I, you know, it's very simple, try to future proof your decision-making. Make sure that whatever you're doing is not blocking your in. So whatever decision you're making, what if the world changes in five years, make sure that if you making a mistake now, that's not going to bite you in about five years later. So how do you do that? Well, open source is great. If you're leveraging open-source, you can try it out already. You don't even need to talk to any vendor. Your teams can already download it and try it out and get some value out of it. If you're in the cloud, this pay as you go models, you don't have to do a big RFP and commit big. You can try it, pay the vendor, pay as you go, $10, $15. It doesn't need to be a million dollar contract and slowly grow as you're providing value. And then make sure that you're not just locking yourself in to one cloud or, you know, one particular vendor. As much as possible preserve your optionality because then that's not a one-way door. If it turns out later you want to do something else, you can, you know, pick other things as well. You're not locked in. So that's what I would say. Keep that top of mind that you're not locking yourself into a particular decision that you made today, that you might regret in five years. >> I really appreciate you coming on and sharing your with our community and The Cube. And as always great to see you. I really enjoy your clubhouse talks, and I really appreciate how you give back to the community. And I want to thank you for coming on and taking the time with us today. >> Thanks John, always appreciate talking to you. >> Okay Ali Ghodsi, CEO of Data bricks, a success story that proves the validation of cloud scale, open and create value, values the new lock-in. So Natalie, back to you for continuing coverage. >> That was a terrific interview John, but I'd love to get Dave's insights first. What were your takeaways, Dave? >> Well, if we have more time I'll tell you how Data bricks got to where they are today, but I'll say this, the most important thing to me that Allie said was he conveyed a very clear understanding of what data companies are outright and are getting ready. Talked about four things. There's not one data team, there's many data teams. And he talked about data is decentralized, and data has to have context and that context lives in the business. He said, look, think about it. The way that the data companies would get it right, they get data in teams and sales and marketing and finance and engineering. They all have their own data and data teams. And he referred to that as a data mesh. That's a term that is your mock, the Gany coined and the warehouse of the data lake it's merely a node in that global message. It meshes discoverable, he talked about federated governance, and Data bricks, they're breaking the model of shoving everything into a single repository and trying to make that the so-called single version of the truth. Rather what they're doing, which is right on is putting data in the hands of the business owners. And that's how true data companies do. And the last thing you talked about with sky computing, which I loved, it's that future layer, we talked about multi-cloud a lot that abstracts the underlying complexity of the technical details of the cloud and creates additional value on top. I always say that the cloud players like Amazon have given the gift to the world of 100 billion dollars a year they spend in CapEx. Thank you. Now we're going to innovate on top of it. Yeah. And I think the refactoring... >> Hope by John. >> That was great insight and I totally agree. The refactoring piece too was key, he brought that home. But to me, I think Data bricks that Ali shared there and why he's been open and sharing a lot of his insights and the community. But what he's not saying, cause he's humble and polite is they cracked the code on the enterprise, Dave. And to Dave's points exactly reason why they did it, they saw an opportunity to make it easier, at that time had dupe was the rage, and they just made it easier. They was smart, they made good bets, they had a good formula and they cracked the code with the enterprise. They brought it in and they brought value. And see that's the key to the cloud as Dave pointed out. You get replatform with the cloud, then you refactor. And I think he pointed out the multi-cloud and that really kind of teases out the whole future and landscape, which is essentially distributed computing. And I think, you know, companies are starting to figure that out with hybrid and this on premises and now super edge I call it, with 5G coming. So it's just pretty incredible. >> Yeah. Data bricks, IPO is coming and people should know. I mean, what everybody, they created spark as you know John and everybody thought they were going to do is mimic red hat and sell subscriptions and support. They didn't, they developed a managed service and they embedded AI tools to simplify data science. So to your point, enterprises could buy instead of build, we know this. Enterprises will spend money to make things simpler. They don't have the resources, and so this was what they got right was really embedding that, making a building a managed service, not mimicking the kind of the red hat model, but actually creating a new value layer there. And that's big part of their success. >> If I could just add one thing Natalie to that Dave saying is really right on. And as an enterprise buyer, if we go the other side of the equation, it used to be that you had to be a known company, get PR, you fill out RFPs, you had to meet all the speeds. It's like going to the airport and get a swab test, and get a COVID test and all kinds of mechanisms to like block you and filter you. Most of the biggest success stories that have created the most value for enterprises have been the companies that nobody's understood. And Andy Jazz's famous quote of, you know, being misunderstood is actually a good thing. Data bricks was very misunderstood at the beginning and no one kind of knew who they were but they did it right. And so the enterprise buyers out there, don't be afraid to test the startups because you know the next Data bricks is out there. And I think that's where I see the psychology changing from the old IT buyers, Dave. It's like, okay, let's let's test this company. And there's plenty of ways to do that. He illuminated those premium, small pilots, you don't need to go on these big things. So I think that is going to be a shift in how companies going to evaluate startups. >> Yeah. Think about it this way. Why should the large banks and insurance companies and big manufacturers and pharma companies, governments, why should they burn resources managing containers and figuring out data science tools if they can just tap into solutions like Data bricks which is an AI platform in the cloud and let the experts manage all that stuff. Think about how much money in time that saves enterprises. >> Yeah, I mean, we've got 15 companies here we're showcasing this batch and this season if you call it. That episode we are going to call it? They're awesome. Right? And the next 15 will be the same. And these companies could be the next billion dollar revenue generator because the cloud enables that day. I think that's the exciting part. >> Well thank you both so much for these insights. Really appreciate it. AWS startup showcase highlights the innovation that helps startups succeed. And no one knows that better than our very next guest, Jeff Barr. Welcome to the show and I will send this interview now to Dave and John and see you just in the bit. >> Okay, hey Jeff, great to see you. Thanks for coming on again. >> Great to be back. >> So this is a regular community segment with Jeff Barr who's a legend in the industry. Everyone knows your name. Everyone knows that. Congratulations on your recent blog posts we have reading. Tons of news, I want to get your update because 5G has been all over the news, mobile world congress is right around the corner. I know Bill Vass was a keynote out there, virtual keynote. There's a lot of Amazon discussion around the edge with wavelength. Specifically, this is the outpost piece. And I know there is news I want to get to, but the top of mind is there's massive Amazon expansion and the cloud is going to the edge, it's here. What's up with wavelength. Take us through the, I call it the power edge, the super edge. >> Well, I'm really excited about this mostly because it gives a lot more choice and flexibility and options to our customers. This idea that with wavelength we announced quite some time ago, at least quite some time ago if we think in cloud years. We announced that we would be working with 5G providers all over the world to basically put AWS in the telecom providers data centers or telecom centers, so that as their customers build apps, that those apps would take advantage of the low latency, the high bandwidth, the reliability of 5G, be able to get to some compute and storage services that are incredibly close geographically and latency wise to the compute and storage that is just going to give customers this new power and say, well, what are the cool things we can build? >> Do you see any correlation between wavelength and some of the early Amazon services? Because to me, my gut feels like there's so much headroom there. I mean, I was just riffing on the notion of low latency packets. I mean, just think about the applications, gaming and VR, and metaverse kind of cool stuff like that where having the edge be that how much power there. It just feels like a new, it feels like a new AWS. I mean, what's your take? You've seen the evolutions and the growth of a lot of the key services. Like EC2 and SA3. >> So welcome to my life. And so to me, the way I always think about this is it's like when I go to a home improvement store and I wander through the aisles and I often wonder through with no particular thing that I actually need, but I just go there and say, wow, they've got this and they've got this, they've got this other interesting thing. And I just let my creativity run wild. And instead of trying to solve a problem, I'm saying, well, if I had these different parts, well, what could I actually build with them? And I really think that this breadth of different services and locations and options and communication technologies. I suspect a lot of our customers and customers to be and are in this the same mode where they're saying, I've got all this awesomeness at my fingertips, what might I be able to do with it? >> He reminds me when Fry's was around in Palo Alto, that store is no longer here but it used to be back in the day when it was good. It was you go in and just kind of spend hours and then next thing you know, you built a compute. Like what, I didn't come in here, whether it gets some cables. Now I got a motherboard. >> I clearly remember Fry's and before that there was the weird stuff warehouse was another really cool place to hang out if you remember that. >> Yeah I do. >> I wonder if I could jump in and you guys talking about the edge and Jeff I wanted to ask you about something that is, I think people are starting to really understand and appreciate what you did with the entrepreneur acquisition, what you do with nitro and graviton, and really driving costs down, driving performance up. I mean, there's like a compute Renaissance. And I wonder if you could talk about the importance of that at the edge, because it's got to be low power, it has to be low cost. You got to be doing processing at the edge. What's your take on how that's evolving? >> Certainly so you're totally right that we started working with and then ultimately acquired Annapurna labs in Israel a couple of years ago. I've worked directly with those folks and it's really awesome to see what they've been able to do. Just really saying, let's look at all of these different aspects of building the cloud that were once effectively kind of somewhat software intensive and say, where does it make sense to actually design build fabricate, deploy custom Silicon? So from putting up the system to doing all kinds of additional kinds of security checks, to running local IO devices, running the NBME as fast as possible to support the EBS. Each of those things has been a contributing factor to not just the power of the hardware itself, but what I'm seeing and have seen for the last probably two or three years at this point is the pace of innovation on instance types just continues to get faster and faster. And it's not just cranking out new instance types because we can, it's because our awesomely diverse base of customers keeps coming to us and saying, well, we're happy with what we have so far, but here's this really interesting new use case. And we needed a different ratio of memory to CPU, or we need more cores based on the amount of memory, or we needed a lot of IO bandwidth. And having that nitro as the base lets us really, I don't want to say plug and play, cause I haven't actually built this myself, but it seems like they can actually put the different elements together, very very quickly and then come up with new instance types that just our customers say, yeah, that's exactly what I asked for and be able to just do this entire range of from like micro and nano sized all the way up to incredibly large with incredible just to me like, when we talk about terabytes of memory that are just like actually just RAM memory. It's like, that's just an inconceivably large number by the standards of where I started out in my career. So it's all putting this power in customer hands. >> You used the term plug and play, but it does give you that nitro gives you that optionality. And then other thing that to me is really exciting is the way in which ISVs are writing to whatever's underneath. So you're making that, you know, transparent to the users so I can choose as a customer, the best price performance for my workload and that that's just going to grow that ISV portfolio. >> I think it's really important to be accurate and detailed and as thorough as possible as we launch each one of these new instance types with like what kind of processor is in there and what clock speed does it run at? What kind of, you know, how much memory do we have? What are the, just the ins and outs, and is it Intel or arm or AMD based? It's such an interesting to me contrast. I can still remember back in the very very early days of back, you know, going back almost 15 years at this point and effectively everybody said, well, not everybody. A few people looked and said, yeah, we kind of get the value here. Some people said, this just sounds like a bunch of generic hardware, just kind of generic hardware in Iraq. And even back then it was something that we were very careful with to design and optimize for use cases. But this idea that is generic is so, so, so incredibly inaccurate that I think people are now getting this. And it's okay. It's fine too, not just for the cloud, but for very specific kinds of workloads and use cases. >> And you guys have announced obviously the performance improvements on a lamb** does getting faster, you got the per billing, second billings on windows and SQL server on ECE too**. So I mean, obviously everyone kind of gets that, that's been your DNA, keep making it faster, cheaper, better, easier to use. But the other area I want to get your thoughts on because this is also more on the footprint side, is that the regions and local regions. So you've got more region news, take us through the update on the expansion on the footprint of AWS because you know, a startup can come in and these 15 companies that are here, they're global with AWS, right? So this is a major benefit for customers around the world. And you know, Ali from Data bricks mentioned privacy. Everyone's a privacy company now. So the huge issue, take us through the news on the region. >> Sure, so the two most recent regions that we announced are in the UAE and in Israel. And we generally like to pre-announce these anywhere from six months to two years at a time because we do know that the customers want to start making longer term plans to where they can start thinking about where they can do their computing, where they can store their data. I think at this point we now have seven regions under construction. And, again it's all about customer trice. Sometimes it's because they have very specific reasons where for based on local laws, based on national laws, that they must compute and restore within a particular geographic area. Other times I say, well, a lot of our customers are in this part of the world. Why don't we pick a region that is as close to that part of the world as possible. And one really important thing that I always like to remind our customers of in my audience is, anything that you choose to put in a region, stays in that region unless you very explicitly take an action that says I'd like to replicate it somewhere else. So if someone says, I want to store data in the US, or I want to store it in Frankfurt, or I want to store it in Sao Paulo, or I want to store it in Tokyo or Osaka. They get to make that very specific choice. We give them a lot of tools to help copy and replicate and do cross region operations of various sorts. But at the heart, the customer gets to choose those locations. And that in the early days I think there was this weird sense that you would, you'd put things in the cloud that would just mysteriously just kind of propagate all over the world. That's never been true, and we're very very clear on that. And I just always like to reinforce that point. >> That's great stuff, Jeff. Great to have you on again as a regular update here, just for the folks watching and don't know Jeff he'd been blogging and sharing. He'd been the one man media band for Amazon it's early days. Now he's got departments, he's got peoples on doing videos. It's an immediate franchise in and of itself, but without your rough days we wouldn't have gotten all the great news we subscribe to. We watch all the blog posts. It's essentially the flow coming out of AWS which is just a tsunami of a new announcements. Always great to read, must read. Jeff, thanks for coming on, really appreciate it. That's great. >> Thank you John, great to catch up as always. >> Jeff Barr with AWS again, and follow his stuff. He's got a great audience and community. They talk back, they collaborate and they're highly engaged. So check out Jeff's blog and his social presence. All right, Natalie, back to you for more coverage. >> Terrific. Well, did you guys know that Jeff took a three week AWS road trip across 15 cities in America to meet with cloud computing enthusiasts? 5,500 miles he drove, really incredible I didn't realize that. Let's unpack that interview though. What stood out to you John? >> I think Jeff, Barr's an example of what I call direct to audience a business model. He's been doing it from the beginning and I've been following his career. I remember back in the day when Amazon was started, he was always building stuff. He's a builder, he's classic. And he's been there from the beginning. At the beginning he was just the blog and it became a huge audience. It's now morphed into, he was power blogging so hard. He has now support and he still does it now. It's basically the conduit for information coming out of Amazon. I think Jeff has single-handedly made Amazon so successful at the community developer level, and that's the startup action happened and that got them going. And I think he deserves a lot of the success for AWS. >> And Dave, how about you? What is your reaction? >> Well I think you know, and everybody knows about the cloud and back stop X** and agility, and you know, eliminating the undifferentiated, heavy lifting and all that stuff. And one of the things that's often overlooked which is why I'm excited to be part of this program is the innovation. And the innovation comes from startups, and startups start in the cloud. And so I think that that's part of the flywheel effect. You just don't see a lot of startups these days saying, okay, I'm going to do something that's outside of the cloud. There are some, but for the most part, you know, if you saw in software, you're starting in the cloud, it's so capital efficient. I think that's one thing, I've throughout my career. I've been obsessed with every part of the stack from whether it's, you know, close to the business process with the applications. And right now I'm really obsessed with the plumbing, which is why I was excited to talk about, you know, the Annapurna acquisition. Amazon bought and a part of the $350 million, it's reported, you know, maybe a little bit more, but that isn't an amazing acquisition. And the reason why that's so important is because Amazon is continuing to drive costs down, drive performance up. And in my opinion, leaving a lot of the traditional players in their dust, especially when it comes to the power and cooling. You have often overlooked things. And the other piece of the interview was that Amazon is actually getting ISVs to write to these new platforms so that you don't have to worry about there's the software run on this chip or that chip, or x86 or arm or whatever it is. It runs. And so I can choose the best price performance. And that's where people don't, they misunderstand, you always say it John, just said that people are misunderstood. I think they misunderstand, they confused, you know, the price of the cloud with the cost of the cloud. They ignore all the labor costs that are associated with that. And so, you know, there's a lot of discussion now about the cloud tax. I just think the pace is accelerating. The gap is not closing, it's widening. >> If you look at the one question I asked them about wavelength and I had a follow up there when I said, you know, we riff on it and you see, he lit up like he beam was beaming because he said something interesting. It's not that there's a problem to solve at this opportunity. And he conveyed it to like I said, walking through Fry's. But like, you go into a store and he's a builder. So he sees opportunity. And this comes back down to the Martine Casada paradox posts he wrote about do you optimize for CapEx or future revenue? And I think the tell sign is at the wavelength edge piece is going to be so creative and that's going to open up massive opportunities. I think that's the place to watch. That's the place I'm watching. And I think startups going to come out of the woodwork because that's where the action will be. And that's just Amazon at the edge, I mean, that's just cloud at the edge. I think that is going to be very effective. And his that's a little TeleSign, he kind of revealed a little bit there, a lot there with that comment. >> Well that's a to be continued conversation. >> Indeed, I would love to introduce our next guest. We actually have Soma on the line. He's the managing director at Madrona venture group. Thank you Soma very much for coming for our keynote program. >> Thank you Natalie and I'm great to be here and will have the opportunity to spend some time with you all. >> Well, you have a long to nerd history in the enterprise. How would you define the modern enterprise also known as cloud scale? >> Yeah, so I would say I have, first of all, like, you know, we've all heard this now for the last, you know, say 10 years or so. Like, software is eating the world. Okay. Put it another way, we think about like, hey, every enterprise is a software company first and foremost. Okay. And companies that truly internalize that, that truly think about that, and truly act that way are going to start up, continue running well and things that don't internalize that, and don't do that are going to be left behind sooner than later. Right. And the last few years you start off thing and not take it to the next level and talk about like, not every enterprise is not going through a digital transformation. Okay. So when you sort of think about the world from that lens. Okay. Modern enterprise has to think about like, and I am first and foremost, a technology company. I may be in the business of making a car art, you know, manufacturing paper, or like you know, manufacturing some healthcare products or what have you got out there. But technology and software is what is going to give me a unique, differentiated advantage that's going to let me do what I need to do for my customers in the best possible way [Indistinct]. So that sort of level of focus, level of execution, has to be there in a modern enterprise. The other thing is like not every modern enterprise needs to think about regular. I'm competing for talent, not anymore with my peers in my industry. I'm competing for technology talent and software talent with the top five technology companies in the world. Whether it is Amazon or Facebook or Microsoft or Google, or what have you cannot think, right? So you really have to have that mindset, and then everything flows from that. >> So I got to ask you on the enterprise side again, you've seen many ways of innovation. You've got, you know, been in the industry for many, many years. The old way was enterprises want the best proven product and the startups want that lucrative contract. Right? Yeah. And get that beach in. And it used to be, and we addressed this in our earlier keynote with Ali and how it's changing, the buyers are changing because the cloud has enabled this new kind of execution. I call it agile, call it what you want. Developers are driving modern applications, so enterprises are still, there's no, the playbooks evolving. Right? So we see that with the pandemic, people had needs, urgent needs, and they tried new stuff and it worked. The parachute opened as they say. So how do you look at this as you look at stars, you're investing in and you're coaching them. What's the playbook? What's the secret sauce of how to crack the enterprise code today. And if you're an enterprise buyer, what do I need to do? I want to be more agile. Is there a clear path? Is there's a TSA to let stuff go through faster? I mean, what is the modern playbook for buying and being a supplier? >> That's a fantastic question, John, because I think that sort of playbook is changing, even as we speak here currently. A couple of key things to understand first of all is like, you know, decision-making inside an enterprise is getting more and more de-centralized. Particularly decisions around what technology to use and what solutions to use to be able to do what people need to do. That decision making is no longer sort of, you know, all done like the CEO's office or the CTO's office kind of thing. Developers are more and more like you rightly said, like sort of the central of the workflow and the decision making process. So it'll be who both the enterprises, as well as the startups to really understand that. So what does it mean now from a startup perspective, from a startup perspective, it means like, right. In addition to thinking about like hey, not do I go create an enterprise sales post, do I sell to the enterprise like what I might have done in the past? Is that the best way of moving forward, or should I be thinking about a product led growth go to market initiative? You know, build a product that is easy to use, that made self serve really works, you know, get the developers to start using to see the value to fall in love with the product and then you think about like hey, how do I go translate that into a contract with enterprise. Right? And more and more what I call particularly, you know, startups and technology companies that are focused on the developer audience are thinking about like, you know, how do I have a bottom up go to market motion? And sometime I may sort of, you know, overlap that with the top down enterprise sales motion that we know that has been going on for many, many years or decades kind of thing. But really this product led growth bottom up a go to market motion is something that we are seeing on the rise. I would say they're going to have more than half the startup that we come across today, have that in some way shape or form. And so the enterprise also needs to understand this, the CIO or the CTO needs to know that like hey, I'm not decision-making is getting de-centralized. I need to empower my engineers and my engineering managers and my engineering leaders to be able to make the right decision and trust them. I'm going to give them some guard rails so that I don't find myself in a soup, you know, sometime down the road. But once I give them the guard rails, I'm going to enable people to make the decisions. People who are closer to the problem, to make the right decision. >> Well Soma, what are some of the ways that startups can accelerate their enterprise penetration? >> I think that's another good question. First of all, you need to think about like, Hey, what are enterprises wanting to rec? Okay. If you start off take like two steps back and think about what the enterprise is really think about it going. I'm a software company, but I'm really manufacturing paper. What do I do? Right? The core thing that most enterprises care about is like, hey, how do I better engage with my customers? How do I better serve my customers? And how do I do it in the most optimal way? At the end of the day that's what like most enterprises really care about. So startups need to understand, what are the problems that the enterprise is trying to solve? What kind of tools and platform technologies and infrastructure support, and, you know, everything else that they need to be able to do what they need to do and what only they can do in the most optimal way. Right? So to the extent you are providing either a tool or platform or some technology that is going to enable your enterprise to make progress on what they want to do, you're going to get more traction within the enterprise. In other words, stop thinking about technology, and start thinking about the customer problem that they want to solve. And the more you anchor your company, and more you anchor your conversation with the customer around that, the more the enterprise is going to get excited about wanting to work with you. >> So I got to ask you on the enterprise and developer equation because CSOs and CXOs, depending who you talk to have that same answer. Oh yeah. In the 90's and 2000's, we kind of didn't, we throttled down, we were using the legacy developer tools and cloud came and then we had to rebuild and we didn't really know what to do. So you seeing a shift, and this is kind of been going on for at least the past five to eight years, a lot more developers being hired yet. I mean, at FinTech is clearly a vertical, they always had developers and everyone had developers, but there's a fast ramp up of developers now and the role of open source has changed. Just looking at the participation. They're not just consuming open source, open source is part of the business model for mainstream enterprises. How is this, first of all, do you agree? And if so, how has this changed the course of an enterprise human resource selection? How they're organized? What's your vision on that? >> Yeah. So as I mentioned earlier, John, in my mind the first thing is, and this sort of, you know, like you said financial services has always been sort of hiring people [Indistinct]. And this is like five-year old story. So bear with me I'll tell you the firewall story and then come to I was trying to, the cloud CIO or the Goldman Sachs. Okay. And this is five years ago when people were still like, hey, is this cloud thing real and now is cloud going to take over the world? You know, am I really ready to put my data in the cloud? So there are a lot of questions and conversations can affect. The CIO of Goldman Sachs told me two things that I remember to this day. One is, hey, we've got a internal edict. That we made a decision that in the next five years, everything in Goldman Sachs is going to be on the public law. And I literally jumped out of the chair and I said like now are you going to get there? And then he laughed and said like now it really doesn't matter whether we get there or not. We want to set the tone, set the direction for the organization that hey, public cloud is here. Public cloud is there. And we need to like, you know, move as fast as we realistically can and think about all the financial regulations and security and privacy. And all these things that we care about deeply. But given all of that, the world is going towards public load and we better be on the leading edge as opposed to the lagging edge. And the second thing he said, like we're talking about like hey, how are you hiring, you know, engineers at Goldman Sachs Canada? And he said like in hey, I sort of, my team goes out to the top 20 schools in the US. And the people we really compete with are, and he was saying this, Hey, we don't compete with JP Morgan or Morgan Stanley, or pick any of your favorite financial institutions. We really think about like, hey, we want to get the best talent into Goldman Sachs out of these schools. And we really compete head to head with Google. We compete head to head with Microsoft. We compete head to head with Facebook. And we know that the caliber of people that we want to get is no different than what these companies want. If you want to continue being a successful, leading it, you know, financial services player. That sort of tells you what's going on. You also talked a little bit about like hey, open source is here to stay. What does that really mean kind of thing. In my mind like now, you can tell me that I can have from given my pedigree at Microsoft, I can tell you that we were the first embraces of open source in this world. So I'll say that right off the bat. But having said that we did in our turn around and said like, hey, this open source is real, this open source is going to be great. How can we embrace and how can we participate? And you fast forward to today, like in a Microsoft is probably as good as open source as probably any other large company I would say. Right? Including like the work that the company has done in terms of acquiring GitHub and letting it stay true to its original promise of open source and community can I think, right? I think Microsoft has come a long way kind of thing. But the thing that like in all these enterprises need to think about is you want your developers to have access to the latest and greatest tools. To the latest and greatest that the software can provide. And you really don't want your engineers to be reinventing the wheel all the time. So there is something available in the open source world. Go ahead, please set up, think about whether that makes sense for you to use it. And likewise, if you think that is something you can contribute to the open source work, go ahead and do that. So it's really a two way somebody Arctic relationship that enterprises need to have, and they need to enable their developers to want to have that symbiotic relationship. >> Soma, fantastic insights. Thank you so much for joining our keynote program. >> Thank you Natalie and thank you John. It was always fun to chat with you guys. Thank you. >> Thank you. >> John we would love to get your quick insight on that. >> Well I think first of all, he's a prolific investor the great from Madrona venture partners, which is well known in the tech circles. They're in Seattle, which is in the hub of I call cloud city. You've got Amazon and Microsoft there. He'd been at Microsoft and he knows the developer ecosystem. And reason why I like his perspective is that he understands the value of having developers as a core competency in Microsoft. That's their DNA. You look at Microsoft, their number one thing from day one besides software was developers. That was their army, the thousand centurions that one won everything for them. That has shifted. And he brought up open source, and .net and how they've embraced Linux, but something that tele before he became CEO, we interviewed him in the cube at an Xcel partners event at Stanford. He was open before he was CEO. He was talking about opening up. They opened up a lot of their open source infrastructure projects to the open compute foundation early. So they had already had that going and at that price, since that time, the stock price of Microsoft has skyrocketed because as Ali said, open always wins. And I think that is what you see here, and as an investor now he's picking in startups and investing in them. He's got to read the tea leaves. He's got to be in the right side of history. So he brings a great perspective because he sees the old way and he understands the new way. That is the key for success we've seen in the enterprise and with the startups. The people who get the future, and can create the value are going to win. >> Yeah, really excellent point. And just really quickly. What do you think were some of our greatest hits on this hour of programming? >> Well first of all I'm really impressed that Ali took the time to come join us because I know he's super busy. I think they're at a $28 billion valuation now they're pushing a billion dollars in revenue, gap revenue. And again, just a few short years ago, they had zero software revenue. So of these 15 companies we're showcasing today, you know, there's a next Data bricks in there. They're all going to be successful. They already are successful. And they're all on this rocket ship trajectory. Ali is smart, he's also got the advantage of being part of that Berkeley community which they're early on a lot of things now. Being early means you're wrong a lot, but you're also right, and you're right big. So Berkeley and Stanford obviously big areas here in the bay area as research. He is smart, He's got a great team and he's really open. So having him share his best practices, I thought that was a great highlight. Of course, Jeff Barr highlighting some of the insights that he brings and honestly having a perspective of a VC. And we're going to have Peter Wagner from wing VC who's a classic enterprise investors, super smart. So he'll add some insight. Of course, one of the community session, whenever our influencers coming on, it's our beat coming on at the end, as well as Katie Drucker. Another Madrona person is going to talk about growth hacking, growth strategies, but yeah, sights Raleigh coming on. >> Terrific, well thank you so much for those insights and thank you to everyone who is watching the first hour of our live coverage of the AWS startup showcase for myself, Natalie Ehrlich, John, for your and Dave Vellante we want to thank you very much for watching and do stay tuned for more amazing content, as well as a special live segment that John Furrier is going to be hosting. It takes place at 12:30 PM Pacific time, and it's called cracking the code, lessons learned on how enterprise buyers evaluate new startups. Don't go anywhere.

Published Date : Jun 24 2021

SUMMARY :

on the latest innovations and solutions How are you doing. are you looking forward to. and of course the keynotes Ali Ghodsi, of the quality of healthcare and you know, to go from, you know, a you on the other side. Congratulations and great to see you. Thank you so much, good to see you again. And you were all in on cloud. is the success of how you guys align it becomes a force that you moments that you can point to, So that's the second one that we bet on. And one of the things that Back in the day, you had to of say that the data problems And you know, there's this and that's why we have you on here. And if you say you're a data company, and growing companies to choose In the past, you know, So I got to ask you from a for the gigs, you know, to eat out signal out of the, you know, I got to ask you a final question. But the goal is to eventually be able the more lock-in you get. to one cloud or, you know, and taking the time with us today. appreciate talking to you. So Natalie, back to you but I'd love to get Dave's insights first. And the last thing you talked And see that's the key to the of the red hat model, to like block you and filter you. and let the experts manage all that stuff. And the next 15 will be the same. see you just in the bit. Okay, hey Jeff, great to see you. and the cloud is going and options to our customers. and some of the early Amazon services? And so to me, and then next thing you Fry's and before that and appreciate what you did And having that nitro as the base is the way in which ISVs of back, you know, going back is that the regions and local regions. And that in the early days Great to have you on again Thank you John, great to you for more coverage. What stood out to you John? and that's the startup action happened the most part, you know, And that's just Amazon at the edge, Well that's a to be We actually have Soma on the line. and I'm great to be here How would you define the modern enterprise And the last few years you start off thing So I got to ask you on and then you think about like hey, And the more you anchor your company, So I got to ask you on the enterprise and this sort of, you know, Thank you so much for It was always fun to chat with you guys. John we would love to get And I think that is what you see here, What do you think were it's our beat coming on at the end, and it's called cracking the code,

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Ariel Assaraf, Coralogix | AWS Startup Showcase: The Next Big Thing in AI, Security, & Life Sciences


 

(upbeat music) >> Hello and welcome today's session for the AWS Startup Showcase, the next big thing in AI, Security and Life Sciences featuring Coralogix for the AI track. I'm your host, John Furrier with theCUBE. We're here we're joined by Ariel Assaraf, CEO of Coralogix. Ariel, great to see you calling in from remotely, videoing in from Tel Aviv. Thanks for coming on theCUBE. >> Thank you very much, John. Great to be here. >> So you guys are features a hot next thing, start next big thing startup. And one of the things that you guys do we've been covering for many years is, you're into the log analytics, from a data perspective, you guys decouple the analytics from the storage. This is a unique thing. Tell us about it. What's the story? >> Yeah. So what we've seen in the market is that probably because of the great job that a lot of the earlier generation products have done, more and more companies see the value in log data, what used to be like a couple rows, that you add, whenever you have something very important to say, became a standard to document all communication between different components, infrastructure, network, monitoring, and the application layer, of course. And what happens is that data grows extremely fast, all data grows fast, but log data grows even faster. What we always say is that for sure data grows faster than revenue. So as fast as a company grows, its data is going to outpace that. And so we found ourselves thinking, how can we help companies be able to still get the full coverage they want without cherry picking data or deciding exactly what they want to monitor and what they're taking risk with. But still give them the real time analysis that they need to make sure that they get the full insight suite for the entire data, wherever it comes from. And that's why we decided to decouple the analytics layer from storage. So instead of ingesting the data, then indexing and storing it, and then analyzing the stored data, we analyze everything, and then we only store it matters. So we go from the insights backwards. That allowed us to reduce the amount of data, reduce the digital exhaust that it creates, and also provide better insights. So the idea is that as this world of data scales, the need for real time streaming analytics is going to increase. >> So what's interesting is we've seen this decoupling with storage and compute be a great success formula and cloud scale, for instance, that's a known best practice. You're taking a little bit different. I love how you're coming backwards from it, you're working backwards from the insights, almost doing some intelligence on the front end of the data, probably sees a lot of storage costs. But I want to get specifically back to this real time. How do you do that? And how did you come up with this? What's the vision? How did you guys come up with the idea? What was the magic light bulb that went off for Coralogix? >> Yes, the Coralogix story is very interesting. Actually, it was no light bulb, it was a road of pain for years and years, we started by just you know, doing the same, maybe faster, a couple more features. And it didn't work out too well. The first few years, the company were not very successful. And we've grown tremendously in the past three years, almost 100X, since we've launched this, and it came from a pain. So once we started scaling, we saw that the side effects of accessing the storage for analytics, the latency it creates, the the dependency on schema, the price that it poses on our customers became unbearable. And then we started thinking, so okay, how do we get the same level of insights, because there's this perception in the world of storage. And now it started to happen in analytics, also, that talks about tiers. So you want to get a great experience, you pay a lot, you want to get a less than great experience, you pay less, it's a lower tier. And we decided that we're looking for a way to give the same level of real time analytics and the same level of insights. Only without the issue of dependencies, decoupling all the storage schema issues and latency. And we built our real time pipeline, we call it Streama. Streama is a Coralogix real time analysis platform that analyzes everything in real time, also the stateful thing. So stateless analytics in real time is something that's been done in the past and it always worked well. The issue is, how do you give a stateful insight on data that you analyze in real time without storing and I'll explain how can you tell that a certain issue happened that did not happen in the past three months if you did not store the past three months? Or how can you tell that behavior is abnormal if you did not store what's normal, you did not store to state. So we created what we call the state store that holds the state of the system, the state of data, were a snapshot on that state for the entire history. And then instead of our state being the storage, so you know, you asked me, how is this compared to last week? Instead of me going to the storage and compare last week, I go to the state store, and you know, like a record bag, I just scroll fast, I find out one piece of state. And I say, okay, this is how it looked like last week, compared to this week, it changed in ABC. And once we started doing that we on boarded more and more services to that model. And our customers came in and say, hey, you're doing everything in real time. We don't need more than that. Yeah, like a very small portion of data, we actually need to store and frequently search, how about you guys fit into our use cases, and not just sell on quota? And we decided to basically allow our customers to choose what is the use case that they have, and route the data through different use cases. And then each log records, each log record stops at the relevant stops in our data pipeline based on the use case. So just like you wouldn't walk into the supermarket, you fill in a bag, you go out, they weigh it and they say, you know, it's two kilograms, you pay this amount, because different products have different costs and different meaning to you. That same way, exactly, We analyze the data in real time. So we know the importance of data, and we allow you to route it based on your use case and pay a different amount per use case. >> So this is really interesting. So essentially, you guys, essentially capture insights and store those, you call them states, and then not have to go through the data. So it's like you're eliminating the old problem of, you know, going back to the index and recovering the data to get the insights, did we have that? So anyway, it's a round trip query, if you will, you guys are start saving all that data mining cost and time. >> We call it node zero side effects, that round trip that you that you described is exactly it, no side effects to an analysis that is done in real time. I don't need to get the latency from the storage, a bit of latency from the database that holds the model, a bit of latency from the cache, everything stays in memory, everything stays in stream. >> And so basically, it's like the definition of insanity, doing the same thing over and over again and expecting a different result. Here, that's kind of what that is, the old model of insight is go query the database and get something back, you're actually doing the real time filtering on the front end, capturing the insights, if you will, storing those and replicating that as use case. Is that right? >> Exactly. But then, you know, there's still the issue of customer saying, yeah, but I need that data. Someday, I need to really frequently search, I don't know, you know, the unknown unknowns, or some of the day I need for compliance, and I need an immutable record that stays in my compliance bucket forever. So we allowed customers, we have this some that screen, we call the TCO optimizer, that allows them to define those use cases. And they can always access the data by creating their remote storage from Coralogix, or carrying the hot data that is stored with Coralogix. So it's all about use cases. And it's all about how you consume the data because it doesn't make sense for me to pay the same amount or give the same amount of attention to a record that is completely useless. It's just there for the record or for a compliance audit, that may or may not happen in the future. And, you know, do the same with the most critical exception in my application log that has immediate business impact. >> What's really good too, is you can actually set some policy up if you want a certain use cases, okay, store that data. So it's not to say you don't want to store it, but you might want to store it on certain use cases. So I can see that. So I got to ask the question. So how does this differ from the competition? How do you guys compete? Take us through a use case of a customer? How do you guys go to the customer and you just say, hey, we got so much scar tissue from this, we learned the hard way, take it from us? How does it go? Take us through an example. >> So an interesting example of actually a company that is not the your typical early adopter, let's call it this way. A very advanced in technology and smart company, but a huge one, one of the largest telecommunications company in India. And they were actually cherry picking about 100 gigs of data per day, and sending it to one of the legacy providers which has a great solution that does give value. But they weren't even thinking about sending their entire data set because of cost because of scale, because of, you know, just a clutter. Whenever you search, you have to sift through millions of records that many of them are not that important. And we help them actually ask analyze their data and work with them to understand these guys had over a terabyte of data that had incredible insights, it was like a goldmine of insights. But now you just needed to prioritize it by their use case, and they went from 100 gig with the other legacy solution to a terabyte, at almost the same cost, with more advanced insights within one week, which isn't in that scale of an organization is something that is is out of the ordinary, took them four months to implement the other product. But now, when you go from the insights backwards, you understand your data before you have to store it, you understand the data before you have to analyze it, or before you have to manually sift through it. So if you ask about the difference, it's all about the architecture. We analyze and only then index instead of indexing and then analyzing. It sounds simple. But of course, when you look at this stateful analytics, it's a lot more, a lot more complex. >> Take me through your growth story, because first of all, I'll get back to the secret sauce in the same way. I want to get back to how you guys got here. (indistinct) you had this problem? You kind of broke through, you hit the magic formula, talking about the growth? Where's the growth coming from? And what's the real impact? What's the situation relative to the company's growth? >> Yeah, so we had a first rough three years that I kind of mentioned, and then I was not the CEO at the beginning, I'm one of the co founders. I'm more of the technical guy, was the product manager. And I became CEO after the company was kind of on the verge of closing at the end of 2017. And the CTO left the CEO left, the VP of R&D became the CTO, I became the CEO, we were five people with $200,000 in the bank that you know, you know that that's not a long runway. And we kind of changed attitudes. So we kind of, so we first we launched this product, and then we understood that we need to go bottoms up, you can go to enterprises and try to sell something that is out of the ordinary, or that changes how they're used to working or just, you know, sell something, (indistinct) five people will do under $1,000 in the bank. So we started going from bottoms up, and the earlier adopters. And it's still until today, you know, the the more advanced companies, the more advanced teams. This is our Gartner friend Coralogix, the preferred solution for Advanced, DevOps and Platform Teams. So they started adopting Coralogix, and then it grew to the larger organization, and they were actually pushing, there are champions within their organizations. And ever since. So until the beginning of 2018, we raised about $2 million and had sales or marginal. Today, we have over 1500, pink accounts, and we raised almost $100 million more. >> Wow, what a great pivot. That was great example of kind of getting the right wave here, cloud wave. You said in terms of customers, you had the DevOps kind of (indistinct) initially. And now you said expanded out to a lot more traditional enterprise, you can take me through the customer profile. >> Yeah, so I'd say it's still the core would be cloud native and (indistinct) companies. These are typical ones, we have very tight integration with AWS, all the services, all the integrations required, we know how to read and write back to the different services and analysis platforms in AWS. Also for Asia and GCP, but mostly AWS. And then we do have quite a few big enterprise accounts, actually, five of the largest 50 companies in the world use Coralogix today. And it grew from those DevOps and platform evangelists into the level of IT, execs and even (indistinct). So today, we have our security product that already sells to some of the biggest companies in the world, it's a different profile. And the idea for us is that, you know, once you solve that issue of too much data, too expensive, not proactive enough, too couple with the storage, you can actually expand that from observability logging metrics, now into tracing and then into security and maybe even to other fields, where the cost and the productivity are an issue for many companies. >> So let me ask you this question, then Ariel, if you don't mind. So if a customer has a need for Coralogix, is it because the data fall? Or they just got data kind of sprawled all over the place? Or is it that storage costs are going up on S3 or what's some of the signaling that you would see, that would be like, telling you, okay, okay, what's the opportunity to come in and either clean house or fix the mess or whatnot, Take us through what you see. What do you see is the trend? >> Yeah. So like the tip customer (indistinct) Coralogix will be someone using one of the legacy solution and growing very fast. That's the easiest way for us to know. >> What grows fast? The storage, the storage is growing fast? >> The company is growing fast. >> Okay. And you remember, the data grows faster than revenue. And we know that. So if I see a company that grew from, you know, 50 people to 500, in three years, specifically, if it's cloud native or internet company, I know that their data grew not 10X, but 100X. So I know that that company that might started with a legacy solution at like, you know, $1,000 a month, and they're happy with it. And you know, for $1,000 a month, if you don't have a lot of data, those legacy solutions, you know, they'll do the trick. But now I know that they're going to get asked to pay 50, 60, $70,000 a month. And this is exactly where we kick in. Because now, when it doesn't fit the economic model, when it doesn't fit the unit economics, and he started damaging the margins of those companies. Because remember, those internet and cloud companies, it's not costs are not the classic costs that you'll see in an enterprise, they're actually damaging your unit economics and the valuation of the business, the bigger deal. So now, when I see that type of organization, we come in and say, hey, better coverage, more advanced analytics, easier integration within your organization, we support all the common open source syntaxes, and dashboards, you can plug it into your entire environment, and the costs are going to be a quarter of whatever you're paying today. So once they see that they see, you know, the Dev friendliness of the product, the ease of scale, the stability of the product, it makes a lot more sense for them to engage in a PLC, because at the end of the day, if you don't prove value, you know, you can come with 90% discount, it doesn't do anything, not to prove the value to them. So it's a great door opener. But from then on, you know, it's a PLC like any other. >> Cloud is all about the PLC or pilot, as they say. So take me through the product, today, and what's next for the product, take us through the vision of the product and the product strategy. >> Yeah, so today, the product allows you to send any log data, metric data or security information, analyze it a million ways, we have one of the most extensive alerting mechanism to market, automatic anomaly detection, data flustering. And all the real law, you know, the real time pipeline, things that help companies make their data smarter, and more readable, parsing, enriching, getting external sources to enrich the data, and so on, so forth. Where we're stepping in now is actually to make the final step of decoupling the analytics from storage, what we call the datalist data platform in which no data will sit or reside within the Coralogix cloud, everything will be analyzed in real time, stored in a storage of choice of our customers, then we'll allow our customers to remotely query that incredible performance. So that'll bring our customers away, to have the first ever true SaaS experience for observability. Think about no quota plans, no retention, you send whatever you want, you pay only for what you send, you retain it, how long you want to retain it, and you get all the real time insights much, much faster than any other product that keeps it on a hot storage. So that'll be our next step to really make sure that, you know, we're kind of not reselling cloud storage, because a lot of the times when you are dependent on storage, and you know, we're a cloud company, like I mentioned, you got to keep your unit economics. So what do you do? You sell storage to the customer, you add your markup, and then you you charge for it. And this is exactly where we don't want to be. We want to sell the intelligence and the insights and the real time analysis that we know how to do and let the customers enjoy the, you know, the wealth of opportunities and choices their cloud providers offer for storage. >> That's great vision in a way, the hyper scalars early days showed that decoupling compute from storage, which I mentioned earlier, was a huge category creation. Here, you're doing it for data. We call hyper data scale, or like, maybe there's got to be a name for this. What do you see, about five years from now? Take us through the trajectory of the next five years, because certainly observability is not going away. I mean, it's data management, monitoring, real time, asynchronous, synchronous, linear, all the stuffs happening, what's the what's the five year vision? >> Now add security and observability, which is something we started preaching for, because no one can say I have observability to my environment when people you know, come in and out and steal data. That's no observability. But the thing is that because data grows exponentially, because it grows faster than revenue what we believe is that in five years, there's not going to be a choice, everyone are going to have to analyze the data in real time. Extract the insights and then decide whether to store it on a you know long term archive or not, or not store it at all. You still want to get the full coverage and insights. But you know, when you think about observability, unlike many other things, the more data you have many times, the less observability you get. So you think of log data unlike statistics, if my system was only in recording everything was only generating 10 records a day, I have full, incredible observability I know everything that I've done. what happens is that you pay more, you get less observability, and more uncertainty. So I think that you know, with time, we'll start seeing more and more real time streaming analytics, and a lot less storage based and index based solutions. >> You know, Ariel, I've always been saying to Dave Vellante on theCUBE, many times that there needs to be insights as to be the norm, not the exception, where, and then ultimately, it would be a database of insights. I mean, at the end of the day, the insights become more plentiful. You have the ability to actually store those insights, and refresh them and challenge them and model update them, verify them, either sunset them or add to them or you know, saying that's like, when you start getting more data into your organization, AI and machine learning prove that pattern recognition works. So why not grab those insights? >> And use them as your baseline to know what's important, and not have to start by putting everything in a bucket. >> So we're going to have new categories like insight, first, software (indistinct) >> Go from insights backwards, that'll be my tagline, if I have to, but I'm a terrible marketing (indistinct). >> Yeah, well, I mean, everyone's like cloud, first data, data is data driven, insight driven, what you're basically doing is you're moving into the world of insights driven analytics, really, as a way to kind of bring that forward. So congratulations. Great story. I love the pivot love how you guys entrepreneurially put it all together and had the problem your own problem and brought it out and to the to the rest of the world. And certainly DevOps in the cloud scale wave is just getting bigger and bigger and taking over the enterprise. So great stuff. Real quick while you're here. Give a quick plug for the company. What you guys are up to, stats, vitals, hiring, what's new, give the commercial. >> Yeah, so like mentioned over 1500 being customers growing incredibly in the past 24 months, hiring, almost doubling the company in the next few months. offices in Israel, East Center, West US, and UK and Mumbai. Looking for talented engineers to join the journey and build the next generation of data lists data platforms. >> Ariel Assaraf, CEO of Coralogix. Great to have you on theCUBE and thank you for participating in the AI track for our next big thing in the Startup Showcase. Thanks for coming on. >> Thank you very much John, really enjoyed it. >> Okay, I'm John Furrier with theCUBE. Thank you for watching the AWS Startup Showcase presented by theCUBE. (calm music)

Published Date : Jun 24 2021

SUMMARY :

Ariel, great to see you Thank you very much, John. And one of the things that you guys do So instead of ingesting the data, And how did you come up with this? and we allow you to route and recovering the data database that holds the model, capturing the insights, if you will, that may or may not happen in the future. So it's not to say you that is not the your sauce in the same way. and the earlier adopters. And now you said expanded out to And the idea for us is that, the opportunity to come in So like the tip customer and the costs are going to be a quarter and the product strategy. and let the customers enjoy the, you know, of the next five years, the more data you have many times, You have the ability to and not have to start by Go from insights backwards, I love the pivot love how you guys and build the next generation and thank you for Thank you very much the AWS Startup Showcase

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Michele Goetz,, Forrester Research | Collibra Data Citizens'21


 

>> From around the globe, it's theCUBE, covering Data Citizens '21. Brought to you by Collibra. >> For the past decade organizations have been effecting very deliberate data strategies and investing quite heavily in people, processes and technology, specifically designed to gain insights from data, better serve customers, drive new revenue streams we've heard this before. The results quite frankly have been mixed. As much of the effort is focused on analytics and technology designed to create a single version of the truth, which in many cases continues to be elusive. Moreover, the world of data is changing. Data is increasingly distributed making collaboration and governance more challenging, especially where operational use cases are a priority. Hello, everyone. My name is Dave Vellante and you're watching theCUBE coverage of Data Citizens '21. And we're pleased to welcome Michele Goetz who's the vice president and principal analyst at Forrester Research. Hello, Michele. Welcome to theCUBE. >> Hi, Dave. Thanks for having me today. >> It's our pleasure. So I want to start, you serve have a wide range of roles including enterprise architects, CDOs, chief data officers that is, analyst, the analyst, et cetera, and many data-related functions. And my first question is what are they thinking about today? What's on their minds, these data experts? >> So there's actually two things happening. One is what is the demand that's placed on data for our new intelligent digital systems. So we're seeing a lot of investment and interest in things like edge computing. And then how does that intersect with artificial intelligence to really run your business intelligently and drive new value propositions to be both adaptive to the market as well as resilient to changes that are unforeseen. The second thing is then you create this massive complexity to managing the data, governing the data, orchestrating the data because it's not just a centralized data warehouse environment anymore. You have a highly diverse and distributed landscape that you both control internally, as well as taking advantage of third party information. So really what the struggle then becomes is how do you trust the data? How do you govern it, and secure, and protect that data? And then how do you ensure that it's hyper contextualized to the types of value propositions that our intelligence systems are going to serve? >> Well, I think you're hitting on the key issues here. I mean, you're right. The data and I sort of refer to this as well is sort of out there, it's distributed at the edge. But generally our data organizations are actually quite centralized and as well you talk about the need to trust the data obviously that's crucial. But are you seeing the organization change? I know you're talking about this to clients, your discussion about collaboration. How are you seeing that change? >> Yeah, so as you have to bring data into context of the insights that you're trying to get or the intelligence that's automating and scaling out the value streams and outcomes within your business, we're actually seeing a federated model emerge in organizations. So while there's still a centralized data management and data services organization led typical enterprise architects for data, a data engineering team that's managing warehouses as in data lakes. They're creating this great platform to access and orchestrate information, but we're also seeing data, and analytics, and governance teams come together under chief data officers or chief data and analytics officers. And this is really where the insights are being generated from either BI and analytics or from data science itself and having dedicated data engineers and stewards that are helping to access and prepare data for analytic efforts. And then lastly, this is the really interesting part is when you push data into the edge the goal is that you're actually driving an experience and an application. And so in that case we are seeing data engineering teams starting to be incorporated into the solutions teams that are aligned to lines of business or divisions themselves. And so really what's happening is if there is a solution consultant who is also overseeing value-based portfolio management when you need to instrument the data to these new use cases and keep up with the pace of the business it's this engineering team that is part of the DevOps work bench to execute on that. So really the balances we need the core, we need to get to the insights and build our models for AI. And then the next piece is how do you activate all that? And there's a team over there to help. So it's really spreading the wealth and expertise where it needs to go. >> Yeah, I love that. You took a couple of things that really resonated with me. You talked about context a couple of times and this notion of a federated model, because historically the sort of big data architecture, the team, they didn't have the context, the business context, and my inference is that's changing and I think that's critical. Your talk at Data Citizens is called how obsessive collaboration fuels scalable DataOps. You talk about the data, the DevOps team. What's the premise you put forth to the audience? >> So the point about obsessive collaboration is sort of taking the hubris out of your expertise on the data. Certainly there's a recognition by data professionals that the business understands and owns their data. They know the semantics, they know the context of it and just receiving the requirements on that was assumed to be okay. And then you could provide a data foundation, whether it's just a lake or whether you have a warehouse environment where you're pulling for your analytics. The reality is that as we move into more of AI machine learning type of model, one, more context is necessary. And you're kind of balancing between what are the things that you can ascribe to the data globally which is what data engineers can support. And then there's what is unique about the data and the context of the data that is related to the business value and outcome as well as the feature engineering that is being done on the machine learning models. So there has to be a really tight link and collaboration between the data engineers, the data scientists, and analysts, and the business stakeholders themselves. You see a lot of pods starting up that way to build the intelligence within the system. And then lastly, what do you do with that model? What do you do with that data? What do you do with that insight? You now have to shift your collaboration over to the work bench that is going to pull all these components together to create the experiences and the automation that you're looking for. And that requires a different collaboration model around software development. And still incorporating the business expertise from those stakeholders, so that you're satisfying, not only the quality of the code to run the solution, but the quality towards the outcome that meets the expectation and the time to value that your stakeholders have. So data teams aren't just sitting in the basement or in another part of the organization and digitally disconnected anymore. You're finding that they're having to work much more closely and side by side with their colleagues and stakeholders. >> I think it's clear that you understand this space really well. Hubris out context in, I mean, that's kind of what's been lacking. And I'm glad you said you used the word anymore because I think it's a recognition that that's kind of what it was. They were down in the basement or out in some kind of silo. And I think, and I want to ask you this. I come back to organization because I think a lot of organizations look the most cost effective way for us to serve the business is to have a single data team with hyper specialized roles. That'll be the cheapest way, the most efficient way that we can serve them. And meanwhile, the business, which as you pointed out has the context is frustrated. They can't get to data. So there's this notion of a federated governance model is actually quite interesting. Are you seeing actual common use cases where this is being operationalized? >> Absolutely, I think the first place that you were seeing it was within the operational technology use cases. There the use cases where a lot of the manufacturing industrial device. Any sort of IOT based use case really recognized that without applying data and intelligence to whatever process was going to be executed. It was really going to be challenging to know that you're creating the right foundation, meeting the SLA requirements, and then ultimately bringing the right quality and integrity to the data, let alone any sort of data protection and regulatory compliance that has to be necessary. So you already started seeing the solution teams coming together with the data engineers, the solution developers, the analysts, and data scientists, and the business stakeholders to drive that. But that is starting to come back down into more of the IT mindset as well. And so DataOps starts to emerge from that paradigm into more of the corporate types of use cases and sort of parrot that because there are customer experience use cases that have an IOT or edge component to though. We live on our smart phones, we live on our smart watches, we've got our laptops. All of us have been put into virtual collaboration. And so we really need to take into account not just the insight of analytics but how do you feed that forward. And so this is really where you're seeing sort of the evolution of DataOps as a competency not only to engineer the data and collaborate but ensure that there sort of an activation and alignment where the value is going to come out, and still being trusted and governed. >> I got kind of a weird question, but I'm going. I was talking to somebody in Israel the other day and they told me masks are off, the economy's booming. And he noted that Israel said, hey, we're going to pay up for the price of a vaccine. The cost per dose out, 28 bucks or whatever it was. And he pointed out that the EU haggled big time and they don't want to pay $19. And as a result they're not as far along. Israel understood that the real value was opening up the economy. And so there's an analogy here which I want to come back to my organization and it relates to the DataOps. Is if the real metric is, hey, I have an idea for a data product. How long does it take to go from idea to monetization? That seems to me to be a better KPI than how much storage I have, or how much geometry petabytes I'm managing. So my question is, and it relates to DataOps. Can that DataOps, should that DataOps individual maybe live, and then maybe even the data engineer live inside of the business and is that even feasible technically with this notion of federated governance? Are you seeing that and maybe talk a little bit more about this DataOps role. Is it. >> Yeah. >> Fungible. >> Yeah, it's definitely fungible. And in fact, when I talked about sort of those three units of there's your core enterprise data services, there's your BI and data, and then there's your line of business. All of those, the engineering and the ops is the DataOps which is living in all of those environments and being as close as possible to where the value proposition is being defined and designed. So absolutely being able to federate that. And I think the other piece on DataOps that is really important is recognizing how the practices around continuous integration and continuous deployment using agile methodologies is really reshaping. A lot of the waterfall approaches that were done before where data was lagging 12 to 18 months behind any sort of insights, but a lot of the platforms today assume that you're moving into a standard mature software development life cycle. And you can start seeing returns on investment within a quarter, really, so that you can iterate and then speed that up so that you're delivering new value every two weeks. But it does change the mindset this DataOps team aligned to solution development, aligned to a broader portfolio management of business capabilities and outcomes needs to understand how to appropriately scope the data products that they're delivering to incremental value-based milestones. So the business feels that they're getting improvements over time and not just waiting. So there's an MVP, you move forward on that and optimize, optimize, extend scale. So again, that CICD mindset is helping to not bottleneck and wait for the complete field of dreams to come from your data and your insights. >> Thank you for that, Michelle. I want to come back to this idea of collaboration because over the last decade we've seen attempts, I've seen software come out to try to help the various roles collaborate and some of it's been okay, but you have these hyper specialized roles. You've got data scientists, data engineers, quality engineers, analysts, et cetera. And they tend to be in their own little worlds. But at the end of the day we rely on them all to get answers. So how can these data scientists, all these stewards, how can they collaborate better? What are you seeing there? >> You need to get them onto the same process. That's really what it comes down to. If you're working from different points of view, that's one thing. But if you're working from different processes collaborating is really challenging. And I think the one thing that's really come out of this move to machine learning and AI is recognizing that you need processes that reinforce collaboration. So that's number one. So you see agile development in CICD not just for DataOps, not just for DevOps, but also encouraging and propelling these projects and iterations for the data science teams as well or even if there's machine learning engineers incorporated. And then certainly the business stakeholders are inserted within there as appropriate to accept what it is that is going to be developed. So processes is number one. And number two is what is the platform that's going to reinforce those processes and collaboration. And it's really about what's being shared. How do you share? So certainly what we're seeing within the platforms themselves is everybody contributing into some sort of a library where their components and products are being ascribed to and then that's able to help different teams grab those components and build out what those solutions are going to be. And in fact, what gets really cool about that is you don't always need hardcore data scientists anymore as you have this social platform for data product and analytic product development. This is where a lot of the auto ML begins because those who are less data science-oriented but can build an insight pipeline, can grab all the different components from the pipelines to the transformations, to capture mechanisms, to bolting into the model itself and allowing that to be delivered to the application. So really kind of balancing out between process and platforms that enable and encourage, and almost force you to collaborate and manage through sharing. >> Thank you for that. I want to ask you about the role data governance. You've mentioned trust and that's data quality, and you've got teams that are focused on and specialists focused on data quality. There's the data catalog. Here's my question. You mentioned edge a couple of times and I can see a lot of that. I mean, today, most AI is are a lot of value, I would say most is modeling. And in the future, you mentioned edge it's going to be a lot of influencing in real time. And people maybe not going to have the time or be involved in that decision. So what are you seeing in terms of data governance, federate. We talked about federated governance, this notion of a data catalog and maybe automating data quality without necessarily having it be so labor intensive. What are you seeing the trends there? >> Yeah, so I think our new environment, our new normal is that you have to be composable, interoperable, and portable. Portability is really the key here. So from a cataloging perspective and governance we would bring everything together into our catalogs and business glossaries. And it would be a reference point, it was like a massive Wiki. Well, that's wonderful, but why just how's it in a museum. You really want to activate that. And I think what's interesting about the technologies today for governance is that you can turn those rules, and business logic, and policies into services that are composable components and bring those into the solutions that you're defining. And in that way what happens is that creates portability. You can drive them wherever they need to go. But from the composability and the interoperability portion of that you can put those services in the right place at the right time for what you need for an outcome so that you start to become behaviorally driven on executing on governance rather than trying to write all of the governance down into transformations and controls to where the data lives. You can have quality and observability of that quality and performance right at the edge and context of behavior and use of that solution. You can run those services and in governance on gateways that are managing and routing information at those edge solutions and we synchronization between the edge and the cloud comes up. And if it's appropriate during synchronization of the data back into the data lake you can run those services there. So there's a lot more flexibility and elasticity for today's modern approaches to cataloging, and glossaries, and governance of data than we had before. And that goes back into what we talked about earlier of like, this is the new wave of DataOps. This is how you bring data products to fruition now. Everything is about activation. >> So how do you see the future of DataOps? I mean, I kind of been pushing you to a more decentralized model where the business has more control 'cause the business has the context. I mean, I feel as though, hey, we've done a great job of contextualizing our operational systems. The sales team they know when the data is crap within my CRM, but our data systems are context agnostic generally. And you obviously understand that problem well. But so how do you see the future of DataOps? >> So I think what's kind of interesting about that is we're going to go to governance on greed versus governance on right more so. What do I mean by that? That means that from a business perspective there's two sides of it. There's ensuring that where governance is run is as we talked about before executing at the appropriate place at the appropriate time. It's semantically domain-centric driven not logical and systems centric. So that's number one. Number two is also recognizing that business owners or business operations actually plays a role in this, because as you're working within your CRM systems, like a Salesforce, for example you're using an iPaaS MuleSoft to connect to other applications, connect to other data sources, connect to other analytics sources. And what's happening there is that the data is being modeled and personalized to whatever view insight our task has to happen within those processes. So even CRM environments where we think of as sort of traditional technologies that we're used to are getting a lift, both in terms of intelligence from the data but also your flexibility and how you execute governance and quality services within that environment. And that actually opens up the data foundations a lot more and avoids you from having to do a lot of moving, copying centralizing data and creating an over-weighted business application and an over, both in terms of the data foundation but also in terms of the types of business services, and status updates, and processes that happen in the application itself. You're drawing those tasks back down to where they should be and where performance can be managed rather than trying to over customize your application environment. And that gives you a lot more flexibility later too for any sort of upgrades or migrations that you want to make because all of the logic is contained back down in a service layer instead. >> Great perspectives, Michelle, you obviously know your stuff and it's been a pleasure having you on. My last question is when you look out there anything that really excites you or any specific research that you're working on that you want to share, that you're super pumped about? >> I think there's two things. One is it's truly incredible the amount of insight and growth that is coming through data profiling and observation. Really understanding and contextualizing data anomalies so that you understand is data helping or hurting the business value and tying it very specifically to processes and metrics, which is fantastic as well as models themselves like really understanding how data inputs and outputs are making a difference whether the model performs or not. And then I think the second thing is really the emergence of more active data, active insights. And as what we talked about before your ability to package up services for governance and quality in particular that allow you to scale your data out towards the edge or where it's needed. And doing so not just so that you can run analytics but that you're also driving overall processes and value. So the research around the operationalization and activation of data is really exciting. And looking at the networks and service mesh to bring those things together is kind of where I'm focusing right now because what's the point of having data in a database if it's not providing any value. >> Michele Goetz, Forrester Research, thanks so much for coming on theCUBE. Really awesome perspectives. You're in an exciting space, so appreciate your time. >> Absolutely, thank you. >> And thank you for watching Data Citizens '21 on theCUBE. My name is Dave Vellante. (upbeat music)

Published Date : Jun 17 2021

SUMMARY :

Brought to you by Collibra. of the truth, which in many Thanks for having me today. So I want to start, you serve that you both control internally, the need to trust the data the data to these new use cases What's the premise you and the time to value that And meanwhile, the business, But that is starting to come back down and it relates to the DataOps. and the ops is the DataOps And they tend to be in and allowing that to be And in the future, you mentioned edge of that you can put those services I mean, I kind of been pushing you And that gives you a lot more flexibility on that you want to share, that allow you to scale your so appreciate your time. And thank you for watching

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Janice Zdankus, HPE | HPE Discover 2021


 

>>from the cube studios in Palo alto in boston connecting with thought leaders all around the world. This is a cute conversation. >>Welcome to the cubes coverage of HP discoverer 2021. I'm lisa martin Janice Zenga's joins me next. The vice president of innovation for social impact in H. P. S. Office of the C T. O Janice. Welcome to the cube. Hi lisa. Great to be here. So let's talk about this. You lead H. P. S. Tech for good program. I always love talking about programs like this. Talk to me about that industry tech academia government partnering to solve key challenges that society is facing and crack that for us. Yeah. So so >>we we um are really proud to be able to look at big challenges in the world and look where our strengths, where our innovations are emerging technologies and our employee expertise could actually contribute to a problem. And so >>we began >>a program uh to actually pick some projects particularly in food systems, world hunger and Health Systems, where we thought some of our technologies could really be impactful. And so >>we have been working with a number of >>clients and partners to actually uh work on ai contributions, high performance compute contributions um and uh and a contribution around this notion of data spaces that we're talking about all of these emerged through um complex interactions around social, good engagement. >>So the concept you mentioned, data space is the concept of data spaces isn't new but do explain that. Give us an overview Janice for those folks that might not be familiar with what it is. >>So so the notion of data spaces is to connect data producers to data consumers. And so um in the past um you know connecting producers and consumers has really been limited about, you know, where is your data located? Um Do you have access to the right data? Um Is the data a good quality set of data? What's the providence of that data? What's the quality of it? And is it trustworthy? And so um >>our >>concept of data spaces is actually trying to address all of those um notions with with a new approach >>so collecting ensuring data isn't anything new. But of course what we talk about every day on this program is the volume of data in that context. What are some of the challenges that you're seeing with clients and how can you help them eliminate those challenges and be able to make data driven decisions? >>So um the first challenge is finding the data. And uh there is a big challenge. I mean there's new roles emerging called data hunters and a great amount of time being spent by data scientists just trying to find sources of data. And that's a big challenge. And then when you find this data, is it in the right format and how expensive is it to move the data so that you could have it in a place where it can actually be analyzed. So, um, so what we're working on recognizing that there is a vast amount of data at the edge, a vast amount of data that's probably never going to move from the edge and from those locations. Um but what we're trying to do is recognize that and actually work to bring the algorithms and the analytics to the data and to work with making sure that data is accessible >>and can be >>understood and >>and processed uh in a consistent way. And >>today there is um a lot >>of silos in in place around uh where data >>exists. And uh and so our approach here is to kind of address is from an open source community perspective to build uh and and provide >>a metadata layer, >>standard of all standards. Kind of a super metadata layer for a non technical way to represent that. And and then to use that to help um connect to uh analytics platforms, both citizen users. Um, you know, subject matter experts who may not be data scientists as well as the data scientists. So actually being able to connect a broader set of users into data analytics that are currently available and have the knowledge to be able to get information and insights out of that data. >>So democratizing that access to data. One of the things I'm curious about what you've seen is that's a cultural shift. You talked about some of the new rules. Data hunters and people get very sort of territorial about that. How I'm just curious what are some of the things that you've seen that where HP and data spaces have been able to help companies to be able to democratize that access and also kind of transform their culture >>Well. So um a few >>things. First of all, there has >>to be a strong motivation >>for someone to share data and in order for them to feel safe and sharing that data. Um you know there has to be security and trust established and most data producers want to control who gets to see their data and under what conditions there needs to be governance of data as well. So those are important aspects that have to be in place. Our approach is to kind of build in exchange for that so that um data consumers understand the conditions in which they can access and use the data um And and also potentially contribute back the new datasets that they're creating through their analytics back into a catalogue being a provisioning of data. This improving kind of the standardization and the simplicity Of how data gets exchanged today. In effect allows a greater democratization of access of data so so that you don't have to be a data scientist. I mean data scientist today can spend 7-8 months actually getting their data that they're going to use um into a format that they can they can actually process. And we think that that's inefficient. We think there's a lot that can be done. Um The other challenge around this is that oftentimes data is multi entity. Even inside of a company, you can find data, you >>know, in different departments and different >>businesses. Um >>But even when you think beyond a >>company, if you think about entities that are that are, you know globally >>distributed um >>and maybe multi, you know, multi entity, there are new challenges about how data can come together from those sources and still be of the right providence and be understood to be trustworthy. >>Well, one of the things that I think one of the many things I think we've learned during the last year is that the, the need and access for real time data has been a critical factor in helping businesses pivot and survive versus those that that might not. So what are you seeing in terms of like you said, data scientist spending so much time getting access to clean data, the opportunities to miss, you know, opportunities for new products and services and to and to meet customer demand in new ways to talk to me about how data spaces can facilitate that faster real time access. >>Right? So, so by having an exchange that can be implemented inside of an enterprise or across enterprises, we actually think it allows some of that kind of pre work to be done, allows that cataloging and provisions. So you can come to uh come to a place, it's a place where an exchange can occur and actually be able to, you know, um get more ready >>access to the data. You don't have to >>necessarily go through a cleansing process and through a deep investigation on providence and then, you know, oftentimes uh you learn as you process data about new data or the data sets change. Right? So, so can there be improvements around keeping those ml algorithms current and helping you that in a very efficient way without having to rerun and rewrite code and rerun your algorithms um every single time. So we think there's a lot of improvement that can be done there as well. >>So let's look at, did a great job of explaining data space is the opportunity, the challenges that we've seen the opportunities. But let us help the audience understand what makes what HPV is doing with data spaces different, unique. What are some of the differentiators? There? >>A few things. One is we're approaching this from an open source approach, so we expect to be able to contribute back to the open source communities and allow for a greater ecosystem to develop around these solutions and that will enable greater sharing and trustworthy sharing. The second thing is security, we intend to apply a great security layer into this that allows data to be trusted um and then the governance capabilities, so being able to use things like our data fabric to actually help support um the governance that producers and consumers want to have uh is also important. And then finally being able to work multi cloud across um, on prem and in the cloud >>is a great >>advantage. So you don't get vendor lock in, you'll be able to be able to kind of minimize your data egress because >>maybe you're not gonna be doing data egress out of the cloud >>and instead you'll be you'll be able to process your data right where it's at without having to pay for that movement. >>And I imagine that would facilitate that speed of real time that I mentioned a minute ago. >>That's right, That's right. >>So let's now look at HP data spaces compared to data marketplace. Give me the compare and contrast with respect to those two. So, data >>marketplaces are typically very siloed and very specific to a sector or an industry today, um, and they're they're typically built on their own platforms and to end, they're not always open by design. Um so uh we expect to be able to support multiple data data marketplaces through a plug in into the data spaces um platform that that we build and that will allow greater connectivity and greater access to many different marketplaces. Um and so the data spaces is not intended to be siloed by industry or narrowly kind of focused, >>so helping to remove those silos, which we also, another thing that we talk about, what are some, I'm just curious some of the feedback from the open source community about what you're doing here building on this open foundation. >>So it's um it's actually been very positive. So the very first thing we did was because of our work as I start at the top of the conversation in agriculture, which is a great, a great example of where there's immense amounts of data that is not well standardized, are structured in a way that can be used towards addressing things like world hunger and some of the food supply and food system challenges. We have we uh in working through this >>kind of distilled some >>of the problems that to being this lack of access to data. And so one of the reasons we explored was like why is there this lack of use of data and lack of access to data? And it came down to not being able to access the data where it's generated and not being able to actually share it broadly across entities. And so, um so what we did is we joined the Linux Foundation has a new open source community called Ag stack and we are a founding company uh as part of that new community and we have shared the concepts around data spaces and the metadata layer standardisation that we've envisioned uh into the community and that's just getting kicked off. But it's also a great first step for us um to kind of build an open source community around it. >>Excellent. Sounds like you said positive feedback. If we crack open the hood of data spaces, what are some of the technologies that we see underneath that are making it and its evolution possible? >>Right? So um multi cloud uh across uh data, you know, support um edge processing um data fabric um Israel's solution as well, so being able to kind of move data and then of course, kind of a key layer. This is this notion of a metadata layer, standard on top >>of metadata layer standards. >>And what is that going to allow in terms of connecting the data consumers with the data producers? >>It's going to make it easier, it's going to make it faster, it's going to minimize costs. Uh it's gonna allow for a quality exchange with more information for consumers to have that trust and most importantly the security. Um and it will also create kind of motivation, kind of give and take because exchange has to be equitable for producers and consumers to both be at the >>table. That's a great point about about the being equitable. So this whole initiative that we've been talking about is coming out of the Office of the CTO at HP where we talked about. So the focus is on Uh projects that are emerging not yet on the road map. So what can we expect, what can your audience expect in the next 12-18 months? >>So our approach in the Office of the CTO is to take emerging technologies and ideas and actually bring them into kind of what we would call advanced development stages. So we do proof of concepts, we do a lot of piloting, we worked with customers and clients directly to kind of tune and test commercialization possibilities uh and value of a solution that we're evolving and to kind of get it ready for market if it makes sense to do that. And so We have proof of concepts with the dozens of customers right now in this topic area and more that want to join in and get involved in having access to it as well. Um so I would say most of the work we do in the coming 12 months will be driven by what these proof of concepts with these clients actually uncovered for us. Um and so we know first and foremost we're working with, you know, a large financial services company, we're looking we're working on the agricultural front with a number of important customers that are testing kind of a multi entity data sharing aspects. Were working also with the health care industry client, which is looking at extreme sets of large data that are kind of unanticipated datasets, you would normally think that would be important for disease prediction. And so all of those different kind of use cases are helping us kind of think about um, you know, which features are most important and by when I can tell you the security, the trustworthiness, the data provenance, the data governance are essential elements that are going to have to be there. >>I think those are essential elements that in any industry, especially that security front. >>Yes, very much so. >>So. In terms of the event at hp, what are some of the things that the audience is going to be able to to learn and glean about? Data sources, data spaces? >>So we've had a kind of a great three days um first starting out with Antonio neary and and and F. I. S to talk about kind of the the insight, the age of insights and and how data is actually becoming the currency of the future if you will. And so that we started that way. And then on day two we had a panel of some of our clients talking about in their particular industry, what's happening with data. So you start to see the kind of um sharing out of uh requirements and how urgent these requirements are growing. Uh And then on day three we actually go into more technology. So you'll see there. We have a number of demos and sessions Uh one specifically around agriculture use case, another around health care use case as well. And then we go into a little bit more detail around the data spaces concept in the keynote for day three. >>So action packed three days Janice. Thank you so much for joining me. Talking to us about data space is what you guys are doing for social impact out of h p. S. Office of the C t. O. We appreciate your time. >>Thank you lisa >>for Janice. Thank yous. I'm lisa martin. You're watching the cubes coverage of HP discover 2021 mm.

Published Date : Jun 16 2021

SUMMARY :

from the cube studios in Palo alto in boston connecting with thought leaders all around the world. P. S. Office of the C T. O Janice. emerging technologies and our employee expertise could actually contribute to a problem. And so clients and partners to actually uh work on ai contributions, So the concept you mentioned, data space is the concept of data spaces isn't new but do explain So so the notion of data spaces is to connect data producers to data What are some of the challenges that And then when you find this data, is it in the right format and how expensive is it to move the data so that you could have And source community perspective to build uh and and provide And and then to use that to help um connect to uh analytics So democratizing that access to data. First of all, there has So those are important aspects that have to be in place. Um and maybe multi, you know, multi entity, there are new challenges about how data can to miss, you know, opportunities for new products and services and to and to meet customer demand So you can come to uh come to a place, access to the data. So we think there's a lot of improvement that can So let's look at, did a great job of explaining data space is the opportunity, so being able to use things like our data fabric to actually help support um the governance that So you don't get vendor lock in, you'll be able to be able to kind of minimize your data egress So let's now look at HP data spaces compared to data marketplace. Um and so the data spaces is not intended so helping to remove those silos, which we also, another thing that we talk about, So the very first thing we did of the problems that to being this lack of access to data. what are some of the technologies that we see underneath that are making it and its evolution possible? So um multi cloud uh across uh kind of give and take because exchange has to be equitable for producers and consumers to both be at the So the focus is on Uh projects that are emerging not yet on the road So our approach in the Office of the CTO is to take emerging technologies and ideas So. In terms of the event at hp, what are some of the things that the audience is going to be able to of the future if you will. is what you guys are doing for social impact out of h p. S. Office of the C t. O. I'm lisa martin.

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Michele Goetz, VP, Principal Analyst, Forrester Research EDIT


 

>> From around the globe, it's theCube covering Data Citizens '21, brought to you by Collibra. >> For the past decade, organizations have been effecting very deliberate data strategies investing quite heavily in people, processes, and technology specifically designed to gain insights from data, better serve customers, drive new revenue streams, we've heard this before. The results quite frankly have been mixed. As much of the effort is focused on analytics and technology designed to create a single version of the truth, which in many cases continues to be elusive. Moreover, the world of data is changing, data is increasingly distributed making collaboration in governance more challenging especially where operational use cases are a priority. Hello, everyone, my name is Dave Vellante and you're watching theCube's coverage of Data Citizens '21. And we're pleased to welcome Michele Goetz, who's the Vice President and Principal Analyst at Forrester Research. Hello, Michele, welcome to theCube. >> Hi, Dave thanks for having me today. >> It's our pleasure. So I want to start, you serve have a wide range of roles including enterprise architects, CDOs, chief data officers that is, the analyst et cetera, and many data related functions. And my first question is what are they thinking about today? What's on their minds? These data experts. >> So there's actually two things happening. One is what is the demand that's placed on data for our new intelligent digital systems. So we're seeing a lot of investment and interest in things like edge computing. And then how does that intersect with artificial intelligence to really run your business intelligently and drive new value propositions, to be both adaptive to the market as well as resilient to changes that are unforeseen. The second thing is then you create this massive complexity to managing the data, governing the data, orchestrating the data, because it's not just a centralized data warehouse environment anymore. You have a highly diverse and distributed landscape that you both control internally, as well as taking advantage of third party information. So really what the struggle then becomes is how do you trust the data? How do you govern it and secure or protect that data? And then how do you ensure that it's hyper-contextualized to the types of value propositions that our intelligence systems are going to serve? >> Well, I think you're hitting on the key issues here. I mean, you're right, the data and I sort of refer to this as well as sort of out there it's distributed as at the edge, but generally our data organizations are actually quite centralized. And as well, you talk about the need to trust the data, obviously that's crucial. But are you seeing the organization change? I know you're talking about this to clients, your discussion about collaboration. How are you seeing that change? >> Yeah, so as you have to bring data into context of the insights that you're trying to get or the intelligence that's automating and scaling out the value streams and outcomes within your business. We're actually seeing a federated model emerge in organizations. So while there's still a centralized data management and data services organization led typically by enterprise architects for data, a data engineering team that's managing warehouses and data lakes. They're creating this great platform to access and orchestrate information, but we're also seeing data and analytics and governance teams come together under chief data officers or chief data and analytics officers. And this is really where the insights are being generated from either BI and analytics or from data science itself and having dedicated data engineers and stewards that are helping to access and prepare data for analytic efforts. And then lastly, this is the really interesting part is when you push data into the edge, the goal is that you're actually driving an experience and an application. And so in that case, we are seeing data engineering teams starting to be incorporated into the solutions teams that are aligned to lines of business or divisions themselves. And so really what's happening is if there is a solution consultant who is also overseeing value-based portfolio management when you need to instrument the data to these new use cases and keep up with the pace of the business, it's this engineering team that is part of the DevOps work bench to execute on that. So really the balances we need the core, we need to get to the insights and build our models for AI. And then the next piece is how do you activate all that and there's a team over there to help? So it's really spreading the wealth and expertise where it needs to go. >> Yeah, I love that you to, a couple of things that really resonated with me. You talked about context a couple of times and this notion of a federated model, because historically the sort of big data architecture, the team, they didn't have the context, the business context, and you're the, my inference is that's changing. And I think that's critical. Your talk at Data Citizens is called how obsessive collaboration fuels scalable DataOps. You talk about the data, the DevOps team. What's the premise you put forth to the audience? >> So the point about obsessive collaboration is sort of taking the hubris out of your expertise on the data. Certainly, there's a recognition by data professionals that the business understands and owns their data. They know the semantics, they know the context of it and just receiving the requirements on that was assumed to be okay. And then you could provide a data foundation whether it's just a lake or whether you have a warehouse environment where you're pulling for your analytics. The reality is that as we move into more of AI machine learning type of model, one, more context is necessary and you're kind of balancing between what are the things that you can ascribe to the data globally which is what data engineers can support. And then there's what is unique about the data and the context of about the data that is related to the business value and outcome as well as the feature engineering that is being done on the machine learning models. So there has to be a really tight link and collaboration between the data engineers, the data scientists, and analysts, and the business stakeholders themselves. You see a lot of pods starting up that way to build the intelligence within the system. And then lastly, what do you do with that model? What do you do with that data? What do you do with that insight? You now have to shift your collaboration over to the work bench that is going to pull all these components together to create the experiences and the automation that you're looking for. And that requires a different collaboration model around software development and still incorporating the business expertise from those stakeholders so that you're satisfying, not only the quality of the code to run the solution, but the quality towards the outcome that meets the expectation and the time to value that your stakeholders have. So data teams aren't just sitting in the basement or in another part of the organization and digitally, disconnected anymore. You're finding that they're having to work much more closely and side by side with their colleagues and stakeholders. >> I think it's clear that you understand this space really well, hubris out, context in, I mean, that's kind of what's been lacking. And I'm glad you said, you used the word anymore because I think it's a recognition that that's kind of what it was. They were down in the basement or out in some kind of silo. And I think, and I want to ask you this, I'll come back to organization because I think a lot of organizations, look the most cost effective way for us to serve the businesses to have a single data team with hyper-specialized roles, that'll be the cheapest way, the most efficient way that we can serve them. And meanwhile, the business which as you pointed out has the context is frustrated. They can't get to data. So this notion of a federated governance model is actually quite interesting. Are you seeing actual common use cases where this is being operationalized? >> Absolutely, I think the first place that you were seeing it was within the operational technology use cases. The use cases where a lot of the manufacturing, industrial device, any sort of IoT-based use case really recognized that without applying data and intelligence to whatever process was going to be executed, it was really going to be challenging to know that you're creating the right foundation, meeting the SLA requirements, and then ultimately bringing the right quality and integrity to the data, let alone any sort of data protection and regulatory compliance that has to be necessary. So you already started seeing the solution teams coming together with the data engineers, the solution developers, the analysts, and data scientists, and the business stakeholders to drive that. But that is starting to come back down into more of the IT mindset as well. And so DataOps starts to emerge from that paradigm into more of the corporate types of use cases and sort of parrot that because there are customer experience use cases that have an IoT or edge component to them. We live on our smart phones, we live on our smart watches, we've got our laptops, all of us have been put into virtual collaboration. And so we really need to take into account not just the insight of analytics, but how do you feed that, you know, feed that forward. And so this is really where you're seeing sort of the evolution of DataOps as a competency not only to engineer the data and collaborate, but ensure that there sort of an activation and alignment where the value is going to come out and still being trusted and governed. >> I've got kind of a weird question, but I'm going to (indistinct). I was talking to somebody in Israel the other day and they told me masks are off, the economy's booming. And he noted that Israel said, "Hey, we're going to pay up for the price of a vaccine, the cost per dose around 28 bucks," or whatever it was. And he pointed out that the EU haggled big time and they go, "We're going to pay $19." And as a result, they're not, you know, as far along Israel understood that the real value was opening up the economy. And so there's an analogy here, which I want to come back to my organization and it relates to the DataOps. If the real metric is, "Hey, I have an idea for a data product." How long does it take to go from idea to monetization? That seems to me to be a better KPI than, you know, how much storage I have or how much petabytes I'm managing. So my question is, and it relates to DataOps, can that DataOps, should that DataOps individual maybe live and then maybe even the data engineer live inside of the business and is that even feasible technically with this notion of federated governance? Are you seeing that? And maybe talk a little bit more about this DataOps role. Is it-- >> Yeah. >> Fungible? >> Yeah, it's definitely fungible. And in fact, when I talked about sort of those three units of there's your core enterprise data services, there's your BI and data and then there's your line of business. All of those, the engineering and the ops is the DataOps which is living in all of those environments and being as close as possible to where the value proposition is being defined and designed. So absolutely being able to federate that. And I think the other piece on DataOps that is really important is recognizing how the practices around continuous integration and continuous deployment using agile methodologies is really reshaping a lot of the waterfall approaches that were done before where data was lagging 12 to 18 months behind any sort of insights, but a lot of the platforms today assume that you're moving into a standard mature software development life cycle. And you can start seeing returns on investment within a quarter really, so that you can iterate and then speed that up so that you're delivering new value every two weeks. But it does change the mindset, this DataOps team align to solution development, align to a broader portfolio management of business capabilities and outcomes needs to understand how to appropriately stop the data products that they're delivering to incremental value based milestones. So the business feels that they're getting improvements over time and not just waiting. So there's an MVP, you move forward on that and optimize, optimize, extend scale. So again, that CICD mindset is helping to not bottleneck and wait for the complete field of dreams to come from your data and your insights. >> Thank you for that, Michele. I want to come back to this idea of collaboration 'cause over the last decade, we've seen attempts. I've seen software come out to try to help the various roles, collaborate and some of it's been okay, but you have these hyper-specialized roles. You've got data scientists, data engineers, quality engineers, analysts, et cetera. And they tend to be in their own little worlds. But at the end of the day, we rely on them all to get answers. So how can these data scientists, all these stewards, how can they collaborate better? What are you seeing there? >> You need to get them onto the same process, that's really what it comes down to. If you're working from different points of view, that's one thing. But if you're working from different processes, collaborating is really challenging. And I think the one thing that's really come out of this move to machine learning and AI is recognizing that you need processes that reinforce collaboration. So that's number one. So you see agile development in CICD not just for DataOps, not just for DevOps, but also encouraging and propelling these projects and iterations before the data science teams as well or even if there's machine learning engineers incorporated. And then, certainly the business stakeholders are inserted within there as appropriate to accept what it is that is going to be developed. So process is number one. Number two is what is the platform that's going to reinforce those processes and collaboration. And it's really about what's being shared. How do you share? So certainly what we're seeing within the platforms themselves is everybody contributing into some sort of a library where their components and products are being ascribed to and then that's able to help different teams grab those components and build out what those solutions are going to be. And in fact, what gets really cool about that is you don't always need hardcore data scientists anymore as you have this social platform for data product and analytic product development. This is where a lot of the auto ML begins because those who are less data science oriented but can build an insight pipeline, can grab all the different components from the pipelines to the transformations, to capture mechanisms, to bolting into the model itself and allowing that to be delivered to the application. So really kind of balancing out between process and platforms that enable and encourage and almost force you to collaborate and manage through sharing. >> Thank you for that I want to ask you about the role of data governance. You've mentioned trust and that's data quality and you've got teams that are focused on and specialists focused on data quality. There's the data catalog and here's my question. You mentioned edge a couple of times and I can see a lot of that. I mean, today, most AI is a lot of the AI, I would say most is modeling. And in the future, you mentioned edge. It's going to be a lot of inferencing in real-time. And you know people maybe not going to have the time or be involved in that decision. So what are you seeing in terms of data governance, federate, we talked about federated governance, this notion of a data catalog and maybe automating data quality without necessarily having it be so labor-intensive. What are you seeing trends there? >> Yeah, so I think our new environment, our new normal is that you have to be composable, interoperable, and portable. Portability is really the key here. So from a cataloging perspective, in governance we would bring everything together into our catalogs and business glossaries. And it would be a reference point. It was like a massive Wiki. Well, that's wonderful, but why just how's it in a museum you really want to activate that. And I think what's interesting about the technologies today for governance is that you can turn those rules and business logic and policies into services that are composable components and bring those into the solutions that you're defining. And in that way, what happens is that creates portability. You can drive them wherever they need to go. But from the composability and the interoperability portion of that, you can put those services in the right place at the right time for what you need for an outcome so that you start to become behaviorally-driven on executing on governance, rather than trying to write all of the governance down into transformations and controls to where the data lives. You can have quality and observability of that quality and performance right at the edge in context of behavior and use of that solution. You can run those services and in governance on gateways that are managing and routing information at those edge solutions and where synchronization between the edge and the cloud comes up. And if it's appropriate during synchronization of the data back into the data lake, you can run those services there. So there's a lot more flexibility and elasticity for today's modern approaches to cataloging and glossaries and governance of data than we had before. And that goes back into what we talked about earlier of like this is the new wave of DataOps. This is how you bring data products to fruition now everything is about activation. >> So how do you see the future of DataOps? I mean, I kind of been pushing you to a more decentralized model where the business has more control 'cause the business has the context. I mean, I feel as though, hey, we've done a great job of contextualizing our operational systems. The sales team, they know when the data is crap within my CRM, but our data systems are context agnostic, which you know, generally and you obviously understand that problem well but so how do you see the future of DataOps? >> So I think what's kind of interesting about that is we're going to go to governance on greed versus governance on right, more so. What do I mean by that? That means that from a business perspective there's two sides of it. There's ensuring that where governance is run as we talked about before executing at the appropriate place at the appropriate time. It's semantically domain centric driven not logical and systems centric. So that's number one. Number two is also recognizing that business owners or business operations actually plays a role in this because as you're working within your CRM systems like a Salesforce, for example, you're using an I-PASS environment MuleSoft to connect to other applications, connect to other data sources, connect to other analytics sources, and what's happening there is that the data is being modeled and personalized to whatever view, insight, or task has to happen within those processes. So even CRM environments where we think of as sort of traditional technologies that we're used to are getting a lift to both in terms of intelligence from the data but also your flexibility and how you execute governance and quality services within that environment. And that actually opens up the data foundations a lot more and avoids you from having to do a lot of moving, copying, centralizing data, and creating an over-weighted business application and an over, you know, both in terms of the data foundation but also in terms of the types of business services and status updates and processes that happen in the application itself. You're drawing those tasks back down to where they should be and where performance can be managed rather than trying to over customize your application environment. And that gives you a lot more flexibility later too for any sort of upgrades or migrations that you want to make because all of the logic is contained back down in a service layer instead. >> Great perspectives, Michele, you obviously know your stuff and it's been a pleasure having you on. My last question is when you look out there anything that really excites you or any specific research that you're working on that you want to share that you're super-pumped about. >> I think there's two things. One is it's truly incredible the amount of insight and growth that is coming through data profiling and observation, really understanding and contextualizing data anomalies so that you understand is data helping or hurting the business value. And, you know tying it very specifically to processes and metrics which is fantastic as well as models themselves like really understanding how data inputs and outputs are making a difference whether the model performs or not. And then I think the second thing is really the emergence of more active data, active insights, as what we talked about before your ability to package up services for governance and quality in particular that allow you to scale your data out towards the edge or where it's needed and doing so, you know not just so that you can run analytics but that you're also driving overall processes and value. So the research around the operationalization and activation of data is really exciting. And looking at the networks and service mesh to bring those things is kind of where I'm focusing right now because what's the point of having data in a database if it's not providing any value. >> Michele Goetz, Forrester Research, thanks so much for coming on theCube really awesome perspectives. You're in an exciting space. So appreciate your time. >> Absolutely, thank you. >> And thank you for watching Data Citizens '21 on theCube. My name is Dave Vellante. (upbeat music)

Published Date : Jun 14 2021

SUMMARY :

brought to you by Collibra. of the truth, which in many So I want to start, you that you both control internally, and I sort of refer to this and stewards that are helping to access What's the premise you and the time to value that you understand and the business and it relates to the DataOps. and the ops is the DataOps And they tend to be in and allowing that to be And in the future, you mentioned edge. and controls to where the data lives. and you obviously understand And that gives you a lot and it's been a pleasure having you on. not just so that you can run analytics So appreciate your time. And thank you for watching

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Matt Maccaux


 

>>data by its very nature is distributed and siloed. But most data architectures today are highly centralized. Organizations are increasingly challenged to organize and manage data and turn that data into insights this idea of a single monolithic platform for data, it's giving way to new thinking. We're a decentralized approach with open cloud native principles and Federated governance will become an underpinning underpinning of digital transformations. Hi everybody, this is Day Volonte. Welcome back to HP discover 2021 the virtual version. You're watching the cubes continuous coverage of the event and we're here with Matt Mako is the field C T O for Israel software at H P E. And we're gonna talk about HP software strategy and esmeralda and specifically how to take a I analytics to scale and ensure the productivity of data teams. Matt, welcome to the cube. Good to see you. >>Good to see you again. Dave thanks for having me today. >>You're welcome. So talk a little bit about your role as CTO. Where do you spend your time? >>Yeah. So I spend about half of my time talking to customers and partners about where they are on their digital transformation journeys and where they struggle with this sort of last phase where we start talking about bringing those cloud principles and practices into the data world. How do I take those data warehouses, those data lakes, those distributed data systems into the enterprise and deploy them in a cloud like manner. And then the other half of my time is working with our product teams to feed that information back so that we can continually innovate to the next generation of our software platform. >>So when I remember I've been following HP and HP for a long, long time, the cube is documented. We go back to sort of when the company was breaking in two parts and at the time a lot of people were saying, oh HP is getting rid of the software business to get out of software. I said no, no, no hold on, they're really focusing and and the whole focus around hybrid cloud and and now as a service and so you're really retooling that business and sharpen your focus. So so tell us more about asthma, it's cool name. But what exactly is as moral software, >>I get this question all the time. So what is Israel? Israel is a software platform for modern data and analytics workloads using open source software components. And we came from some inorganic growth. We acquired a company called citing that brought us a zero trust approach to doing security with containers. We bought blue data who came to us with an orchestrator before kubernetes even existed in mainstream. They were orchestrating workloads using containers for some of these more difficult workloads, clustered applications, distributed applications like Hadoop. And then finally we acquired Map are which gave us this scale out, distributed file system and additional analytical capabilities. And so what we've done is we've taken those components and we've also gone out into the marketplace to see what open source projects exist, to allow us to bring those club principles and practices to these types of workloads so that we can take things like Hadoop and spark and Presto and deploy and orchestrate them using open source kubernetes, leveraging Gpu s while providing that zero trust approaches security. That's what Israel is all about. Is taking those cloud practices and principles but without locking you in again using those open source components where they exist and then committing and contributing back to the open source community where those projects don't exist. >>You know, it's interesting. Thank you for that history. And when I go back, I always been there since the early days of big data and Hadoop and so forth. The map are always had the best product. But but they can't get back then. It was like Kumbaya open source and they had this kind of proprietary system, but it worked and that's why it was the best product. And so at the same time they participated in open source projects because everybody that that's where the innovation is going. So you're making that really hard to use stuff easier to use with kubernetes orchestration. And then obviously I'm presuming with the open source chops, sort of leaning into the big trends that you're seeing in the marketplace. So my question is, what are those big trends that you're seeing when you speak to technology executives, which is a big part of what you do? >>Yeah. So the trends I think are a couple of fold and it's funny about Duke, I think the final nails in the coffin have been hammered in with the Hadoop space now. And so that that leading trend of of where organizations are going. We're seeing organizations wanting to go cloud first, but they really struggle with these data intensive workloads. Do I have to store my data in every cloud? Am I going to pay egress in every cloud? Well, what if my data scientists are most comfortable in AWS? But my data analysts are more comfortable in Azure. How do I provide that multi cloud experience for these data workloads? That's the number one question I get asked. And that's the probably the biggest struggle for these Chief Data Officers. Chief Digital Officer XYZ. How do I allow that innovation but maintaining control over my data compliance especially, we talk international standards like G. D. P. R. To restrict access to data, the ability to be forgotten in these multinational organizations. How do I sort of square all of those components and then how do I do that in a way that just doesn't lock me into another appliance or software vendors stack? I want to be able to work within the confines of the ecosystem. Use the tools that are out there but allow my organization to innovate in a very structured, compliant way. >>I mean I love this conversation. And just to me you hit on the key word which is organization. I want to I want to talk about what some of the barriers are. And again, you heard my wrap up front. I I really do think that we've created not only from a technology standpoint and yes, the tooling is important, but so is the organization. And as you said, you know, an analyst might want to work in one environment, a data scientist might want to work in another environment. The data may be very distributed. They maybe you might have situations where they're supporting the line of business. The line of business is trying to build new products. And if I have to go through this, hi this monolithic centralized organization, that's a barrier uh for me. And so we're seeing that change that kind of alluded to it upfront. But what do you see as the big, you know, barriers that are blocking this vision from becoming a reality? >>It very much is organization dave it's the technology is actually no longer the inhibitor here. We have enough technology, enough choices out there. That technology is no longer the issue. It's the organization's willingness to embrace some of those technologies and put just the right level of control around accessing that data because if you don't allow your data scientists and data analysts to innovate, they're going to do one of two things, they're either going to leave and then you have a huge problem keeping up with your competitors or they're gonna do it anyway, and they're gonna do it in a way that probably doesn't comply with the organizational standards. So the more progressive enterprises that I speak with have realized that they need to allow these various analytical users to choose the tools, they want to self provision those as they need to and get access to data in a secure and compliant way. And that means we need to bring the cloud to generally where the data is because it's a heck of a lot easier than trying to bring the data where the cloud is while conforming to those data principles. And that's, that's Hve strategy, you've heard it from our CEO for years now, everything needs to be delivered as a service. It's essential software that enables that capability, such as self service and secure data provisioning, etcetera. >>Again, I love this conversation because if you go back to the early days of the Duke, that was what was profound about. Do bring bring five megabytes of code, do a petabyte of data and it didn't happen. We shoved it all into a data lake and it became a data swamp. And so it's okay, you know, and that's okay. It's a one dato maybe maybe in data is is like data warehouses, data hubs data lake. So maybe this is now a four dot Oh, but we're getting there. Uh, so an open but open source one thing's for sure. It continues to gain momentum. It's where the innovation is. I wonder if you could comment on your thoughts on the role that open source software plays for large enterprises. Maybe some of the hurdles that are there, whether they're legal or licensing or or or just fears. How important is open source software today? >>I think the cloud native development, you know, following the 12 factor applications microservices based, pave the way over the last decade to make using open source technology tools and libraries mainstream, we have to tip our hats to red hat right for allowing organizations to embrace something. So core is an operating system within the enterprise. But what everyone realizes that its support, that's what has to come with that. So we can allow our data scientists to use open source libraries, packages and notebooks. But are we going to allow those to run in production? And so if the answer is no, then that if we can't get support, we're not going to allow that. So where HP es Merrill is taking the lead here is again embracing those open source capabilities, but if we deploy it, we're going to support it or we're going to work with the organization that has the committees to support it. You call HPD the same phone number you've been calling for years for tier 1 24 by seven support and we will support your kubernetes, your spark your presto your Hadoop ecosystem of components were that throat to choke and we'll provide all the way up to break fix support for some of these components and packages giving these large enterprises the confidence to move forward with open source but knowing that they have a trusted partner in which to do so >>and that's why we've seen such success with, say, for instance, managed services in the cloud or versus throwing out all the animals in the zoo and say, okay, figure it out yourself. But of course what we saw, which was kind of ironic was we, we saw people finally said, hey, we can do this in the cloud more easily. So that's where you're seeing a lot of data. A land. However, the definition of cloud or the notion of cloud is changing no longer. Is it just this remote set of services somewhere out there? In the cloud? Some data center somewhere. No, it's, it's moving on. Prem on prem is creating hybrid connections you're seeing, you know, co location facility is very proximate to the cloud. We're talking now about the edge, the near edge and the far edge deeply embedded, you know? And so that whole notion of cloud is, is changing. But I want to ask you, there's still a big push to cloud, everybody is a cloud first mantra. How do you see HP competing in this new landscape? >>I I think collaborating is probably a better word, although you could certainly argue if we're just leasing or renting hardware than it would be competition. But I think again, the workload is going to flow to where the data exists. So if the data is being generated at the edge and being pumped into the cloud, then cloud is prod, that's the production system. If the data is generated, the on system on premises systems, then that's where it's going to be executed, that's production. And so HBs approach is very much coexist, coexist model of if you need to do deaf tests in the cloud and bring it back on premises, fine or vice versa. The key here is not locking our customers and our prospective clients into any sort of proprietary stack, as we were talking about earlier, giving people the flexibility to move those workloads to where the data exists. That is going to allow us to continue to get share of wallet. Mindshare, continue to deploy those workloads and yes, there's going to be competition that comes along. Do you run this on a G C P or do you run it on a green lake on premises? Sure. We'll have those conversations. But again, if we're using open source software as the foundation for that, then actually where you run it is less relevant. >>So a lot of, there's a lot of choices out there when it comes to containers generally and kubernetes specifically, uh, you may have answered this, you get zero trust component, you've got the orchestrator, you've got the, the scale out, you know, peace. But I'm interested in hearing in your words why an enterprise would or should consider s morale instead of alternatives to kubernetes solutions? >>It's a fair question. And it comes up in almost every conversation. We already do kubernetes, so we have a kubernetes standard and that's largely true. And most of the enterprises I speak to their using one of the many on premises distributions of the cloud distributions and they're all fine. They're all fine for what they were built for. Israel was generally built for something a little different. Yes, everybody can run microservices based applications, devoPS based workloads, but where is Meryl is different is for those data intensive and clustered applications. Those sort of applications require a certain degree of network awareness, persistent storage etcetera, which requires either a significant amount of intelligence. Either you have to write in go lang or you have to write your own operators or Israel can be that easy button. We deploy those state full applications because we bring a persistent storage later that came from that bar we're really good at deploying those stable clustered applications and in fact we've open sourced that as a project cube director that came from Blue data and we're really good at securing these using spiffy inspire to ensure that there is that zero trust approach that came from side tail and we've wrapped all of that in kubernetes so now you can take the most difficult, gnarly, complex data intensive applications in your enterprise and deploy them using open source and if that means we have to coexist with an existing kubernetes distribution, that's fine. That's actually the most common scenario that I walk into is I start asking about what about these other applications you haven't done yet? The answer is usually we haven't gotten to him yet or we're thinking about it and that's when we talk about the capabilities of s role and I usually get the response, oh, a we didn't know you existed and be, well, let's talk about how exactly you do that. So again, it's more of a coexist model rather than a compete with model. Dave >>Well, that makes sense. I mean, I think again, a lot of people think, oh yeah, Kubernetes, no big deal, it's everywhere. But you're talking about a solution, I'm kind of taking a platform approach with capabilities, you've got to protect the data. A lot of times these microservices aren't some micro uh and things are happening really fast, You've got to be secure, you've got to be protected. And like you said, you've got a single phone number, you know, people say one throat to choke, Somebody said the other day said no, no single hand to shake, it's more of a partnership and I think that's a proposed for HPV met with your >>hair better. >>So you know, thinking about this whole, you know, we've gone through the pre big data days and the big data was all, you know, the hot buzz where people don't maybe necessarily use that term anymore, although the data is bigger and getting bigger, which is kind of ironic. Um where do you see this whole space going? We've talked about that sort of trends are breaking down the silos, decentralization. Maybe these hyper specialized roles that we've created maybe getting more embedded are lined with the line of business. How do you see it feels like the last, the next 10 years are going to be different than the last 10 years. How do you see it matt? >>I completely agree. I think we are entering this next era and I don't know if it's well defined, I don't know if I would go out on an edge to say exactly what the trend is going to be. But as you said earlier, data lakes really turned into data swamps. We ended up with lots of them in the enterprise and enterprises had to allow that to happen. They had to let each business unit or each group of users collect the data that they needed and I. T. Sort of had to deal with that down the road. And so I think the more progressive organizations are leading the way they are again taking those lessons from cloud and application developments, microservices and they're allowing a freedom of choice there, allowing data to move to where those applications are. And I think this decentralized approach is really going to be king. And you're gonna see traditional software packages, you're gonna see open source, you're going to see a mix of those. But what I think we'll probably be common throughout all of that is there's going to be this sense of automation, this sense that we can't just build an algorithm once released and then wish it luck that we've got to treat these these analytics and these these data systems as living things that there's life cycles that we have to support, which means we need to have devops for our data science. We need a ci cd for our data analytics. We need to provide engineering at scale like we do for software engineering. That's going to require automation and an organizational thinking process to allow that to actually occur. And so I think all of those things that sort of people process product, but it's all three of those things are going to have to come into play. But stealing those best ideas from cloud and application development, I think we're going to end up with probably something new over the next decade or so >>again, I'm loving this conversation so I'm gonna stick with it for a second. I it's hard to predict, but I'll some takeaways that I have matt from our conversation. I wonder if you could, you could comment. I think, you know, the future is more open source. You mentioned automation deV's are going to be key. I think governance as code, security designed in at the point of code creation is going to be critical. It's not no longer to be a bolt on and I don't think we're gonna throw away the data warehouse or the data hubs or the data lakes. I think they become a node. I like this idea and you know, jim octagon. But she has this idea of a global data mesh where these tools lakes, whatever their their node on the mesh, they're discoverable. They're shareable. They're they're governed uh in a way and that really I think the mistake a lot of people made early on in the big data movement, Oh we have data, we have to monetize our data as opposed to thinking about what products that I can I build that are based on data that then I can, you know, can lead to monetization. And I think and I think the other thing I would say is the business has gotten way too technical. All right. It's an alienated a lot of the business lines and I think we're seeing that change. Um and I think, you know, things like Edinburgh that simplify that are critical. So I'll give you the final thoughts based on my rent. >>I know you're ready to spot on. Dave. I think we we were in agreement about a lot of things. Governance is absolutely key. If you don't know where your data is, what it's used for and can apply policies to it, it doesn't matter what technology throw at it, you're going to end up in the same state that you're essentially in today with lots of swamps. Uh I did like that concept of of a note or a data mesh. It kind of goes back to the similar thing with a service smashed or a set of a P I is that you can use. I think we're going to have something similar with data that the trick is always how heavy is it? How easy is it to move about? And so I think there's always gonna be that latency issue. Maybe not within the data center, but across the land, latency is still going to be key, which means we need to have really good processes to be able to move data around. As you said, government determine who has access to what, when and under what conditions and then allow it to be free, allow people to bring their choice of tools, provision them how they need to while providing that audit compliance and control. And then again, as as you need to provision data across those notes for those use cases do so in a well measured and govern way. I think that's sort of where things are going. But we keep using that term governance. I think that's so key. And there's nothing better than using open source software because that provides traceability, the audit ability and this frankly openness that allows you to say, I don't like where this project is going. I want to go in a different direction and it gives those enterprises that control over these platforms that they've never had before. >>Matt. Thanks so much for the discussion. I really enjoyed it. Awesome perspectives. >>Well, thank you for having me. Dave are excellent conversation as always. Uh, thanks for having me again. >>All right. You're very welcome. And thank you for watching everybody. This is the cubes continuous coverage of HP discover 2021 of course, the virtual version next year. We're gonna be back live. My name is Dave a lot. Keep it right there. >>Yeah.

Published Date : Jun 2 2021

SUMMARY :

how to take a I analytics to scale and ensure the productivity of data Good to see you again. Where do you spend your time? innovate to the next generation of our software platform. We go back to sort of when the company was breaking in two parts and at the time gone out into the marketplace to see what open source projects exist, to allow us to bring those club that really hard to use stuff easier to use with kubernetes orchestration. the ability to be forgotten in these multinational organizations. And just to me you hit on the key word which is organization. they're either going to leave and then you have a huge problem keeping up with your competitors or they're gonna do it anyway, Again, I love this conversation because if you go back to the early days of the Duke, that was what was profound about. I think the cloud native development, you know, following the 12 factor How do you see HP competing in this new landscape? I I think collaborating is probably a better word, although you could certainly argue if we're just leasing or the scale out, you know, peace. And most of the enterprises I speak to their using And like you said, So you know, thinking about this whole, and I. T. Sort of had to deal with that down the road. I like this idea and you know, jim octagon. but across the land, latency is still going to be key, which means we need to have really good I really enjoyed it. Well, thank you for having me. And thank you for watching everybody.

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Ariel Assaraf, Coralogix | CUBE Conversation May 2021


 

(upbeat music) >> Well, hello everyone, John Walls here on theCUBE as we continue our CUBE conversations as part of the AWS Startup Showcase with Ariel Assaraf who is the CEO and co-founder of Coralogix based in Tel Aviv. And Ariel, thanks for joining us, especially under these trying circumstances I'm sure many people watching fully appreciate what's going on in Israel right now with the bombings that are happening on a perpetual basis. And I just hope you and family, friends and your coworkers are doing well and staying as safe as possible. >> Thank you very much, John. Yeah, this is a surreal period of time where we're in the office then occasionally going to the shelter for a couple minutes and then getting back to coding and planning. So yeah, thank you. >> Well, certainly take care and you're very much on our thoughts and in our hearts right now and we wish you all the well and safety. Let's talk about Coralogix though. This is obviously it's your baby and entering the wild world of data these days this exponential growth of data. You and I were talking about really the untapped potential of data a little bit early before the interview. So let's talk about maybe the genesis of Coralogix a little bit and why you came up with this concept and then the unique platform that you've now established to really help your clients make some sense of these vast reams of data that they have at their disposal. >> Yeah, I think that's a very interesting topic that a lot of companies are starting to now address each one with its own angle. We decided to go with the real-time streaming analytics approach. The problem starts with data growing exponentially like you mentioned, but it's not just growing exponentially it's growing faster than revenue. What happens is that, companies that are bound to the cost of data are getting to a point where their margins and our unit economics are being slaughtered by the amount of data that they need to analyze whether it's for BI or marketing, and certainly observability which is probably the largest data producer inside any organization. And what typically companies tend to do is to start cherry pick data. So they only collect relevant information or only collect areas or only collect specific servers or specific environments. And that causes that statistic you just mentioned from the MIT research, showing that 99.5 of the data remains untapped or unanalyzed. When we looked at it, we thought that, you want to monitor that data at a high level. You want to analyze it automatically or manually or visualize it with good performance. And so the approach that existed/exists in the market until today is to use storage tiers. But then you have to compromise the quality and the speed of analytics. And we chose instead of that, to unlike everyone that index and then analyze to ingest, analyze everything in real time, including the most stateful transformation and stateful analytics and only then store what matters that way giving broader coverage and allowing companies economically and also in terms of scale, to send everything get the full analytics layer that they need and basically improve both their businesses and their performance. >> Yeah, it sounds so sensible. It sounds so simple too. Right, we're just going to analyze data as it comes in real time, we'll make sense of it, we'll process that, we'll make it actionable and boom off we go. But obviously, as you know this is an extraordinarily complex series of operations occurring now especially in the microservices world, right? Because you have all these inputs and all these instances happening simultaneously in different environments. So untangle that for me a little bit in terms of microservices now, the complexity that that creates and your approach to that. >> Yeah. So two things that happen. One, there are more services in each company. Two, there are more versions uploaded to each service every day. So the world of CICB combined with the world of microservices creates a lot of uncertainty. On one hand that's great because you have less decoupling. You can go faster, you can be faster to market respond to the market faster. You can analyze data in specific units that allows you more flexibility and you can release a lot more. On the other hand, it gets much harder to triage, to figure out what specific microservice is causing a problem to monitor the communication between different microservices. And certainly to understand, what is the version that broke something? A lot of software problems come after upgrades or configuration changes. And these two factors together, they generate a lot of data that you need to start monitoring and analyze. Now, like you're saying, analyzing in real time that's been done in the past. That doesn't sound too complex but what happens is that the answer from real-time streaming was only applied to stateless things. Meaning, let me know when you see something. You see an event, send me an alert. You see a metric, send me an alert. What happens is that, it's still missing the longer term analytics. So it's some sort of an oxymoron to say, on one hand I'm doing real-time streaming on the other hand, I want to give you analytics that rely on a long-term state. Let me know if something happened more than it did last week. Let me know if something happened for the first time this month. Clustered a data based on a learning algorithm that learns the data continuously throughout the entire history of time. And this is Streama, the technology that we created that does the real-time streaming but also involves, components that store the state of the system at any given point in time. So while other solutions or other approaches use the storage as the state. So if I want to know what happened a week ago, I just go to the storage and see what's in there from last week. Now we hold the snapshot of state of everything relevant whether we automatically discovered or the customer defined it and make sure that our customers can go back in time and compare versions or compare matrix or compare graphs and see how specific versions affected specific microservices and how specific microservices affected their entire production systems. >> So where, or help me out here just in terms of cost efficiency, then now, if you kind of, you're not eliminating storage obviously, but you're kind of shifting responsibilities here or shifting process a little bit, right? And making it a little more accessible on a real-time basis. What kind of cost efficiencies do you get out of that, in terms of not having to go to storage for everything and dig everything out from a week or two weeks or a month ago? >> Yeah, that's a great question. So it affects multiple areas there. First of all, storage is one of the areas where you can least optimize because it is what it is besides compression that's been invented years ago and we're pretty much maxed out there. There's not a lot of waste to really save on storage. So what companies do they try to put it on lower tier storage, but then you lose performance. What we do is we bound ourselves only to CPU and CPU, when you do analytics, you can improve and optimize to the max and get to a point where you auto-scale analyze all data in real time, get better results and you can continuously improve your code and your microservices in a way that makes them more efficient. We're talking about roughly 70, 75% of savings when we compare that to the closest solution in our space. But it's more than that actually. We believe that at the end of the day the storage approach is not going to be a feasible because storage doesn't scale great, like any, you know, any CPU that you increase, you get better performance, you get faster performance, you get more power. But when you increase storage size when you store more data for a longer term you actually lose performance. It's actually slower, it's more cluttered. And so what happens is that companies that need long-term analytics one, they have to use the storage. They can do it in real time, but two, they also have to have that storage stored for a very long period of time. So it exponentially grows. And we believe that we'll come to an era because data grows exponentially and many of our users are engineers that understand exponential growth. It's going to get to a point where it's almost impossible to write all the data to the disk and then companies are going to need to compromise again. So we feel that the market is going to a place where you'd like to get the analytics taken out of the data and only relevant information for the analytics being stored because the matrix and the logs and the traces they are a means to an end. They're not the purpose for which we are actually generating and storing them. >> Right. And that's what your clients are all about too. Right? Get me the need, you know, get me the gold, the data, you know, that I actually need and help me separate the wheat from the chaff here. What about AWS? How did they come into play? Or what about your relationship with them and how has that developed and currently where does that sit? >> Yes. So we actually moved to AWS about two years ago and moved our entire production and built it on the AWS infrastructure. Our infrastructure is entirely on Kubernetes. We're using Terraform and we have our own CI/CD tool that we actually released as an open source. And we scaled on AWS massively and started seeing the opportunities with most of our customers being on AWS. So we partnered with AWS partnerships teams. We went through the competencies the well-architected, the accelerate program. And now the relationship is at a level where our sales teams are working closely together with the AWS account managers to spot opportunities where AWS customers need an additional layer of analytics or better cloud security or cost reduction. And we're working together to find them that solution. Now to make it easier and more seamless for AWS customers to use us, we are onboarded to the AWS marketplace. So we're under the unified agreement of AWS and we can be paid through the AWS bill. So now Coralogix can be seen as an AWS service that you're using you don't have to use another vendor and you can get additional insights and lowered costs and 24/7 support that we provide. So that's how we partner with AWS. And of course, a lot of joint marketing and content activity. So we're running a webinar together with AWS teams at a general, not about us. In general, how can we give back to the community? How do you scale? For instance, we ran a webinar on how do you scale Kafka? Which is certainly not our domain, but definitely an issue that we had to handle and had to scale and it's a pain point for many AWS customers. So we're trying to give back, we're a lot from AWS and we are partnering with them to solve problems together. >> So what's it done for you then at Coralogix? So you said it's been a two year relationship so it's matured obviously and you've worked out something very nice. You're leveraging each other's strengths, you know, in a very smart and tactical way. So what does it mean to you though Coralogix and ultimately, what do you think it means to your end user, your client base when you bring the kind of this combined power into their needs? >> Yeah. So for us working together with AWS means that they help us where a startup is lacking the most strength. So startups, they can be extremely fast they can develop cutting edge technologies they can bring new approaches and products to the market. But when you start working with the larger organizations the most hardest part of a POC because the engineering teams see the value immediately is the procurement is the legal parts is getting there opening the door and showing them the value proposition that you have and working together with AWS allows us to first of all, meet these customers, understand their needs and then being able to route through the AWS marketplace. And of course, to make it easier for them. We created like 20 different plugins to all AWS services so they can seamlessly connect all their data. Cause you remember one of the things that we wanted to get to is people not having to cherry pick logs. We're not having to cherry pick matrix. So now they can connect their entire environment and get full cloud observability and security within minutes and do it in an economic way. >> Wait, you're talking about all these capabilities and providing the client base and obviously this is a field that we're talking about data and what you're doing with it that's growing so rapidly. What does it mean to you like inside your office there in terms of, do you have enough space for people? I assume your growth trajectory is pretty impressive right now. >> Yes. This is, it's something that we are trying to learn now. This is a third office in three years and we're now outgrowing this one and going to the next one. So we grew from about 10 people, two years ago when we moved to AWS to over a hundred people now and continuing to hire in East, center, West US and in Israel and in London and in India. And the company is going to double itself within the next few months. So it's definitely, you know, a challenge now with COVID era also, but thank God, you know here in Israel, we're kind of past that. And it seems like the US is going to be past that in the next few months. So we're going to get back and start hiring and growing the teams. >> Well, it sounds impressive. And congratulations on that particular aspect of your business. I know it's always fun to bring on new people. It's all a very positive sign. So congratulations on that front. Thank you for the time today. And most importantly, again, we do wish you a great health and wellness and safety given that all that's going on right now and our hopes and prayers are that it ends as quickly as possible and you can return back to business as usual there. >> Thank you very much, John. I appreciate your time. >> Thank you sir. >> You bet. My pleasure. Once again, we're talking about Coralogix here, on theCUBE Conversation as part of the AWS Startup Showcase with Ariel Assaraf, who is the CEO and co-founder. I'm John Walls. Thanks for joining us here on theCUBE. (upbeat music)

Published Date : May 18 2021

SUMMARY :

And I just hope you and family, friends and then getting back and we wish you all the well and safety. that they need to analyze and boom off we go. and you can release a lot more. in terms of not having to go and get to a point where you auto-scale and how has that developed and built it on the AWS infrastructure. So what does it mean to you though and then being able to route What does it mean to you and going to the next one. and you can return back Thank you very much, John. as part of the AWS Startup Showcase

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Breaking Analysis: Chaos Creates Cash for Criminals & Cyber Companies


 

from the cube studios in palo alto in boston bringing you data-driven insights from the cube and etr this is breaking analysis with dave vellante the pandemic not only accelerated the shift to digital but also highlighted a rush of cyber criminal sophistication collaboration and chaotic responses by virtually every major company in the planet the solar winds hack exposed supply chain weaknesses and so-called island hopping techniques that are exceedingly difficult to detect moreover the will and aggressiveness of well-organized cyber criminals has elevated to the point where incident responses are now met with counterattacks designed to both punish and extract money from victims via ransomware and other criminal activities the only upshot is the cyber security market remains one of the most enduring and attractive investment sectors for those that can figure out where the market is headed and which firms are best positioned to capitalize hello everyone and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll provide our quarterly update of the security industry and share new survey data from etr and thecube community that will help you navigate through the maze of corporate cyber warfare we'll also share our thoughts on the game of 3d chest that octa ceo todd mckinnon is playing against the market now we all know this market is complicated fragmented and fast moving and this next chart says it all it's an interactive graphic from optiv a denver colorado based si that's focused on cyber security they've done some really excellent research and put together this awesome taxonomy and mapped vendor names therein and this helps users navigate the complex security landscape and there are over a dozen major sectors high-level sectors within the security taxonomy in nearly 60 sub-sectors from monitoring vulnerability assessment identity asset management firewalls automation cloud data center sim threat detection and intelligent endpoint network and so on and so on and so on but this is a terrific resource and can help you understand where players fit and help you connect the dots in the space now let's talk about what's going on in the market the dynamics in this crazy mess of a landscape are really confusing sometimes now since the beginning of cyber time we've talked about the increasing sophistication of the adversary and the back and forth escalation between good and evil and unfortunately this trend is unlikely to stop here's some data from carbon black's annual modern bank heist report this is the fourth and of course now vmware's brand highlights the carbon black study since the acquisition and it catalyzed the creation of vmware's cloud security division destructive malware attacks according to the recent study are up 118 percent from last year now one major takeaway from the report is that hackers aren't just conducting wire fraud they are 57 of the bank surveyed saw an increase in wire fraud but the cyber criminals are also targeting non-public information such as future trading strategies this allows the bad guys to front run large block trades and profit it's become very lucrative practice now the prevalence of so-called island hopping is up 38 from already elevated levels this is where a virus enters a company's supply chain via a partner and then often connects with other stealthy malware downstream these techniques are more common where the malware will actually self-form with other infected parts of the supply chain and create actions with different signatures designed to identify and exfiltrate valuable information it's a really complex problem of major concern is that 63 of banking respondents in the study reported that responses to incidents were then met with retaliation designed to intimidate or initiate ransomware attacks to extract a final pound of flesh from the victim notably the study found that 75 percent of csos reported to the cio which many feel is not the right regime the study called for a rethinking of the right cyber regime where the cso has increased responsibility in a direct reporting line to the ceo or perhaps the co with greater exposure to boards of directors so many thanks to vmware and tom kellerman specifically for sharing this information with us this past week great work by your team now some of the themes that we've been talking about for several quarters are shown in the lower half of the chart cloud of course is the big driver thanks to work from home and the pandemic to pandemic and the interesting corollary of course is we see a rapid rethinking of endpoint and identity access management and the concept of zero trust in a recent esg survey two-thirds of respondents said that their use of cloud computing necessitated a change in how they approach identity access management now as shown in the chart from optiv the market remains highly fragmented and m a is of course way up now based on our research it looks like transaction volume has increased more than 40 percent just in the last five months so let's dig into the m a the merger and acquisition trends for just a moment we took a five month snapshot and we were able to count about 80 deals that were completed in that time frame those transactions represented more than 20 billion dollars in value some of the larger ones are highlighted here the biggest of course being the toma bravo taking proof point private for a 12 plus billion dollar price tag the stock went from the low 130s and is trading in the low 170s based on 176 dollar per share offer so there's your arbitrage folks go for it perhaps the more interesting acquisition was auth 0 by octa for 6.5 billion which we're going to talk about more in a moment there's more private equity action we saw as insight bought armis and iot security play and cisco shelled out 730 million dollars for imi mobile which is more of an adjacency to cyber but it's going to go under cisco's security and applications business run by g2 patel but these are just the tip of the iceberg some of the themes that we see connecting the dots of these acquisitions are first sis like accenture atos and wipro are making moves in cyber to go local they're buying secops expertise as i say locally in places like france germany netherlands canada and australia that last mile that belly-to-belly intimate service israel israeli-based startups chalked up five acquired companies in the space over the last five months also financial services firms are getting into the act with goldman and mastercard making moves to own its own part of the stack themselves to combat things like fraud and identity theft and then finally numerous moves to expand markets octa with zero crowdstrike buying a log management company palo alto picking up devops expertise rapid seven shoring up its kubernetes chops tenable expanding beyond insights and going after identity interesting fortinet filling gaps in a multi-cloud offering sale point extending to governance risk and compliance grc zscaler picked up an israeli firm to fill gaps in access control and then vmware buying mesh 7 to secure modern app development and distribution services so tons and tons of activity here okay so let's look at some of the etr data to put the cyber market in context etr uses the concept of market share it's one of the key metrics which is a measure of pervasiveness in the data set so for each sector it calculates the number of respondents for that sector divided by the total to get a sense for how prominent the sector is within the cio and i.t buyer communities okay this chart shows the full etr sector taxonomy with security highlighted across three survey periods april last year january this year in april this year now you wouldn't expect big moves in market share over time so it's relatively stable by sector but the big takeaway comes from observing which sectors are most prominent so you see that red line that dotted line imposed at the sixty percent level you can see there are only six sectors above that line and cyber security is one of them okay so we know that security is important in a large market but this puts it in the context of the other sectors however we know from previous breaking analysis episodes that despite the importance of cyber and the urgency catalyzed by the pandemic budgets unfortunately are not unlimited and spending is bounded it's not an open checkbook for csos as shown in this chart this is a two-dimensional graphic showing market share in the horizontal axis or pervasiveness and net score in the vertical axis net score is etr's measurement of spending velocity and we've superimposed a red line at 40 percent because anything over 40 percent we consider extremely elevated we've filtered and limited the number of sectors to simplify the graphic and you can see in the sectors that we've highlighted only the big four four are above that forty percent line ai containers rpa and cloud they exceed that sort of forty percent magic water line information security you can see that is highlighted and it's respectable but it competes for budget with other important sectors so this of course creates challenges for organization because not only are they strapped for talent as we've reported they like everyone else in it face ongoing budget pressures research firm cybersecurity ventures estimates that in 2021 6 trillion dollars worldwide will be lost on cyber crime conversely research firm canalis pegs security spending somewhere around 60 billion dollars annually idc has it higher around 100 billion so either way we're talking about spending between one to one point six percent annually of how much the bad guys are taking out that's peanuts really when you consider the consequences so let's double click into the cyber landscape a bit and further look at some of the companies here's that same x y graphic with the company's etr captures from respondents in the cyber security sector that's what's shown on the chart here now the usefulness of the red lines is 20 percent on the horizontal indicates the largest presence in the survey and the magic 40 percent line that we talked about earlier shows those firms with the most elevated momentum only microsoft and palo alto exceed both high water marks of course splunk and cisco are prominent horizontally and there are numerous companies to the left of the 20 percent line and many above that 40 percent high water mark on the vertical axis now in the bottom left quadrant that includes many of the legacy names that have been around for a long time and there are dozens of companies that show spending momentum on their platforms i.e above single digits so that picture is like the first one we showed you very very crowded space but so let's filter it a bit and only include companies in the etr survey that had at least a hundred responses so an n of a hundred or greater so it's a little easy to read but still it's kind of crowded when you think about it okay so same graphic and we've superimposed the data that determined the plot position over in the bottom right there so it's net score and shared n including only companies with more than 100 n so what does this data tell us about the market well microsoft is dominant as always it seems in all dimensions but let's focus on that red line for a moment some of the names that we've highlighted over the past two years show very well here first i want to talk about palo alto networks pre-covet as you might recall we highlighted the valuation divergence between palo alto and fortinet and we said fortinet was executing better on its cloud strategy and palo alto was at the time struggling with the transition especially with its go to market and its sales force compensation and really refreshing its portfolio but we told you that we were bullish on palo alto networks at the time because of its track record and the fact that cios consistently told us that they saw palo alto as a thought leader in the space that they wanted to work with they said that palo alto was the gold standard the best especially larger company cisos so that gave us confidence that palo alto a very well-run company was going to get its act together and perform better and palo alto has just done just that as we expected they've done very well and they've been rapidly moving customers to the next generation of platforms and we're very impressed by the company's execution and the stock has generally reflected that now some other names that hit our radar and the etr data a couple of years ago continue to perform well crowdstrike z-scaler sales sail point and cloudflare a cloudflare just reported and beat earnings but was off the stock fell on headwinds for tech overall the big rotation but the company is doing very well and they're growing rapidly and they have momentum as you can see from the etr data and we put that double star around proof point to highlight that it was worthy of fetching 12 and a half billion dollars from private equity firm so nice exit there supporting the continued control consolidation trend that we've predicted in cyber security now let's turn our attention to octa and auth zero this is where it gets interesting and is a clever play for octa we think and we want to drill into it a bit octa is acquiring auth zero for big money why well we think todd mckinnon octa ceo wants to run the table on identity and then continue to expand his tam he has to do that to justify his lofty valuation so octa's ascendancy around identity and single sign sign-on is notable the fragmented pictures that we've shown you they scream out for simplification and trust and that's what octa brings but it competes with some major players most notably microsoft with active directory so look of course microsoft is going to dominate in its massive customer base but the rest of the market that's like jump ball it's wide open and we think mckinnon saw the opportunity to go dominate that sector now octa comes at this from an enterprise perspective bringing top-down trust to the equation and throwing a big blanket over all the discrete sas platforms and unifying employee access octa's timing was perfect it was founded in 2009 just as the massive sasification trend was happening around crm and hr and service management and cloud etc but the one thing that octa didn't have that auth 0 does is serious developer chops while octa was crushing it with its enterprise sales strategy auth 0 was laser focused on developers and building a bottoms up approach to identity by acquiring auth0 octa can dominate both sides of the barbell and then capture the fat middle so yes it's a pricey acquisition but in our view it's a great move by mckinnon now i don't know mckinnon personally but last week i spoke to arun shrestha who's the ceo of security specialist beyond id they're a platinum services partner of octa and there a zero trust expert he worked for octa for a number of years and shared with me a bit about mckinnon's style and think big approach arun said something that caught my attention he said firewalls used to be the perimeter now people are and while that's self-serving to octa and probably beyond id it's true people apps and data are the new perimeter and they're not in one location and that's the point now unfortunately i had lined up an interview with dia jolly who was the chief product officer at octa in a cube alum for this past week knowing that we were running this segment in this episode but she unfortunately fell ill the day of our interview and had to cancel but i want to follow up with her and understand how she's thinking about connecting the dots with auth 0 with devs and enterprises and really test our thesis there this is a really interesting chess match that's going on let's look a little deeper into that identity space this chart here shows some of the major identity players it has some of the leaders in the identity market and there's a breakdown of etr's net score now net score comprises five elements the lime green is we're adding the platform new the forest green is we're spending six percent or more relative to last year the gray is flat send plus or minus flat spend plus or minus five percent the pinkish is spending less and the bright red is where exiting the platform retiring now you subtract the red from the green and that gets you the result for net score which you can see superimposed on the right hand chart at the bottom that first column there the far column is shared in which informs and indicates the number of responses and is a proxy for presence in the market oh look at the top two players in terms of spending momentum now sales sale point is right there but auth 0 combined with octa's distribution channel will extend octa's lead significantly in our view and then there's microsoft now just a caveat this includes all of microsoft's security offerings not just identity but it's there for context and cyber arc as well includes its acquisition of adaptive but also other parts of cyberarks portfolio so you can see some of the other names that are there many of which you'll find in the gartner magic quadrant for identity and as we said we really like this move by octa it combines positive market forces with lead offerings from very well-run companies that have winning dna and passionate people now to further emphasize emphasize what what's happening here take a look at this this chart shows etr data for octa within sale point and cyber arc accounts out of the 230 cyber and sale point customers in the data set there are 81 octa accounts that's a 35 overlap and the good news for octa is that within that base of sale point in cyber arc accounts octa is shown by the net score line that green line has a very elevated spending and momentum and the kicker is if you read the fine print in the right hand column etr correctly points out that while sailpoint and cyberarc have long been partners with octa at the recent octane 21 event octa's big customer event the company announced that it was expanding into privileged access management pam and identity governance hello and welcome to coopetition in the 2020s now our current thinking is that this bodes very well for octa and cyberark and sailpoint well they're going to have to make some counter moves to fend off the onslaught that is coming now let's wrap up with what has become a tradition in our quarterly security updates looking at those two dimensions of net score and market share we're going to see which companies crack the top 10 for both measures within the etr data set we do this every quarter so here on the left we have the top 20 sorted by net score or spending momentum and on the right we sort by shared n so again top 20 which informs shared end and forms the market share metric or presence in the data set that red horizontal lines those two lines on each separate the top 10 from the remaining 10 within those top 20. in our method what we do is we assign four stars to those companies that crack the top ten for both metrics so again you see microsoft palo alto networks octa crowdstrike and fortinet fortinet by the way didn't make it last quarter they've kind of been in and out and on the bubble but you know this company is very strong and doing quite well only the other four did last quarter there was same four last quarter and we give two stars to those companies that make it in both categories within the top 20 but didn't make the top 10. so cisco splunk which has been steadily decelerating from a spending momentum standpoint and z-scaler which is just on the cusp you know we really like z-scaler and the company has great momentum but that's the methodology it is what it is now you can see we kept carbon black on the rightmost chart it's like kind of cut off it's number 21 only because they're just outside looking in on netscore you see them there they're just below on on netscore number 11. and vmware's presence in the market we think that carbon black is really worth paying attention to okay so we're going to close with some summary and final thoughts last quarter we did a deeper dive on the solar winds hack and we think the ramifications are significant it has set the stage for a new era of escalation and adversary sophistication now major change we see is a heightened awareness that when you find intruders you'd better think very carefully about your next moves when someone breaks into your house if the dog barks or if you come down with a baseball bat or other weapon you might think the intruder is going to flee but if the criminal badly wants what you have in your house and it's valuable enough you might find yourself in a bloody knife fight or worse what's happening is intruders come to your company via island hopping or inside or subterfuge or whatever method and they'll live off the land stealthily using your own tools against you so they can you can't find them so easily so instead of injecting new tools in that send off an alert they just use what you already have there that's what's called living off the land they'll steal sensitive data for example positive covid test results when that was really really sensitive obviously still is or other medical data and when you retaliate they will double extort you they'll encrypt your data and hold it for ransom and at the same time threaten to release the sensitive information to crushing your brand in the process so your response must be as stealthy as their intrusion as you marshal your resources and devise an attack plan you face serious headwinds not only is this a complicated situation there's your ongoing and acute talent shortage that you tell us about all the time many companies are mired in technical debt that's an additional challenge and then you've got to balance the running of the business while actually affecting a digital transformation that's very very difficult and it's risky because the more digital you become the more exposed you are so this idea of zero trust people used to call it a buzzword it's now a mandate along with automation because you just can't throw labor at the problem this is all good news for investors as cyber remains a market that's ripe for valuation increases and m a activity especially if you know where to look hopefully we've helped you squint through the maze a little bit okay that's it for now thanks to the community for your comments and insights remember i publish each week on wikibon.com and siliconangle.com these episodes they're all available as podcasts all you do is search breaking analysis podcast put in the headphones listen when you're in your car out for your walk or run and you can always connect on twitter at divalante or email me at david.valante at siliconangle.com i appreciate the comments on linkedin and in clubhouse please follow me so you're notified when we start a room and riff on these topics and others and don't forget to check out etr.plus for all the survey data this is dave vellante for the cube insights powered by etr be well and we'll see you next time [Music] you

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Breaking Analysis with Dave Vellante: Intel, Too Strategic to Fail


 

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR, this is Braking Analysis with Dave Vellante. >> Intel's big announcement this week underscores the threat that the United States faces from China. The US needs to lead in semiconductor design and manufacturing. And that lead is slipping because Intel has been fumbling the ball over the past several years, a mere two months into the job, new CEO Pat Gelsinger wasted no time in setting a new course for perhaps, the most strategically important American technology company. We believe that Gelsinger has only shown us part of his plan. This is the beginning of a long and highly complex journey. Despite Gelsinger's clear vision, his deep understanding of technology and execution ethos, in order to regain its number one position, Intel we believe we'll need to have help from partners, competitors and very importantly, the US government. Hello everyone and welcome to this week's Wikibon CUBE insights powered by ETR. In this breaking analysis we'll peel the onion Intel's announcement of this week and explain why we're perhaps not as sanguine as was Wall Street on Intel's prospects. And we'll lay out what we think needs to take place for Intel to once again, become top gun and for us to gain more confidence. By the way this is the first time we're broadcasting live with Braking Analysis. We're broadcasting on the CUBE handles on Twitch, Periscope and YouTube and going forward we'll do this regularly as a live program and we'll bring in the community perspective into the conversation through chat. Now you may recall that in January, we kind of dismissed analysis that said Intel didn't have to make any major strategic changes to its business when they brought on Pat Gelsinger. Rather we said the exact opposite. Our view at time was that the root of Intel's problems could be traced to the fact that it wasn't no longer the volume leader. Because mobile volumes dwarf those of x86. As such we said that Intel couldn't go up the learning curve for next gen technologies as fast as its competitors and it needed to shed its dogma of being highly vertically integrated. We said Intel needed to more heavily leverage outsourced foundries. But more specifically, we suggested that in order for Intel to regain its volume lead, it needed to, we said at the time, spin out its manufacturing, create a joint venture sure with a volume leader, leveraging Intel's US manufacturing presence. This, we still believe with some slight refreshes to our thinking based on what Gelsinger has announced. And we'll talk about that today. Now specifically there were three main pieces and a lot of details to Intel's announcement. Gelsinger made it clear that Intel is not giving up its IDM or integrated device manufacturing ethos. He called this IDM 2.0, which comprises Intel's internal manufacturing, leveraging external Foundries and creating a new business unit called Intel Foundry Services. It's okay. Gelsinger said, "We are not giving up on integrated manufacturing." However, we think this is somewhat nuanced. Clearly Intel can't, won't and shouldn't give up on IDM. However, we believe Intel is entering a new era where it's giving designers more choice. This was not explicitly stated. However we feel like Intel's internal manufacturing arm will have increased pressure to serve its designers in a more competitive manner. We've already seen this with Intel finally embracing EUV or extreme ultraviolet lithography. Gelsinger basically said that Intel didn't lean into EUV early on and that it created more complexity in its 10 nanometer process, which dominoed into seven nanometer and as you know the rest of the story and Intel's delays. But since mid last year, it's embraced the technology. Now as a point of reference, Samsung started applying EUV for its seven nanometer technology in 2018. And it began shipping in early 2020. So as you can see, it takes years to get this technology into volume production. The point is that Intel realizes it needs to be more competitive. And we suspect, it will give more freedom to designers to leverage outsource manufacturing. But Gelsinger clearly signaled that IDM is not going away. But the really big news is that Intel is setting up a new division with a separate PNL that's going to report directly to Pat. Essentially it's hanging out a shingle and saying, we're open for business to make your chips. Intel is building two new Fabs in Arizona and investing $20 billion as part of this initiative. Now well Intel has tried this before earlier last decade. Gelsinger says that this time we're serious and we're going to do it right. We'll come back to that. This organizational move while not a spin out or a joint venture, it's part of the recipe that we saw as necessary for Intel to be more competitive. Let's talk about why Intel is doing this. Look at lots has changed in the world of semiconductors. When you think about it back when Pat was at Intel in the '90s, Intel was the volume leader. It crushed the competition with x86. And the competition at the time was coming from risk chips. And when Apple changed the game with iPod and iPhone and iPad, the volume equation flipped to mobile. And that led to big changes in the industry. Specifically, the world started to separate design from manufacturing. We now see firms going from design to tape out in 12 months versus taking three years. A good example is Tesla and his deal with ARM and Samsung. And what's happened is Intel has gone from number one in Foundry in terms of clock speed, wafer density, volume, lowest cost, highest margin to falling behind. TSMC, Samsung and alternative processor competitors like NVIDIA. Volume is still the maker of kings in this business. That hasn't changed and it confers advantage in terms of cost, speed and efficiency. But ARM wafer volumes, we estimate are 10x those of x86. That's a big change since Pat left Intel more than a decade ago. There's also a major chip shortage today. But you know this time, it feels a little different than the typical semiconductor boom and bust cycles. Semiconductor consumption is entering a new era and new use cases emerging from automobiles to factories, to every imaginable device piece of equipment, infrastructure, silicon is everywhere. But the biggest threat of all is China. China wants to be self-sufficient in semiconductors by 2025. It's putting approximately $60 billion into new chip Fabs, and there's more to come. China wants to be the new economic leader of the world and semiconductors are critical to that goal. Now there are those poopoo the China threat. This recent article from Scott Foster lays out some really good information. But the one thing that caught our attention is a statement that China's semiconductor industry is nowhere near being a major competitor in the global market. Let alone an existential threat to the international order and the American way of life. I think Scotty is stuck in the engine room and can't see the forest of the trees, wake up. Sure. You can say China is way behind. Let's take an example. NAND. Today China is at about 64 3D layers whereas Micron they're at 172. By 2022 China's going to be at 128. Micron, it's going to be well over 200. So what's the big deal? We say talk to us in 2025 because we think China will be at parody. That's just one example. Now the type of thinking that says don't worry about China and semi's reminds me of the epic lecture series that Clay Christiansen gave as a visiting professor at Oxford University on the history of, and the economics of the steel industry. Now if you haven't watched this series, you should. Basically Christiansen took the audience through the dynamics of steel production. And he asked the question, "Who told the steel manufacturers that gross margin was the number one measure of profitability? Was it God?" he joked. His point was, when new entrance came into the market in the '70s, they were bottom feeders going after the low margin, low quality, easiest to make rebar sector. And the incumbents nearly pulled back and their mix shifted to higher margin products and their gross margins went up and life was good. Until they lost the next layer. And then the next, and then the next, until it was game over. Now, one of the things that got lost in Pat's big announcement on the 23rd of March was that Intel guided the street below consensus on revenue and earnings. But the stock went up the next day. Now when asked about gross margin in the Q&A segment of the announcement, yes, gross margin is a if not the key metric in semi's in terms of measuring profitability. When asked Intel CFO George Davis explained that with the uptick in PCs last year there was a product shift to the lower margin PC sector and that put pressure on gross margins. It was a product mix thing. And revenue because PC chips are less expensive than server chips was affected as were margins. Now we shared this chart in our last Intel update showing, spending momentum over time for Dell's laptop business from ETR. And you can see in the inset, the unit growth and the market data from IDC, yes, Dell's laptop business is growing, everybody's laptop business is growing. Thank you COVID. But you see the numbers from IDC, Gartner, et cetera. Now, as we pointed out last time, PC volumes had peaked in 2011 and that's when the long arm of rights law began to eat into Intel's dominance. Today ARM wafer production as we said is far greater than Intel's and well, you know the story. Here's the irony, the very bucket that conferred volume adventures to Intel PCs, yes, it had a slight uptick last year, which was great news for Dell. But according to Intel it pulled down its margins. The point is Intel is loving the high end of the market because it's higher margin and more profitable. I wonder what Clay Christensen would say to that. Now there's more to this story. Intel's CFO blame the supply constraints on Intel's revenue and profit pressures yet AMD's revenue and profits are booming. So RTSMCs. Only Intel can't seem to thrive when there's this massive chip shortage. Now let's get back to Pat's announcement. Intel is for sure, going forward investing $20 billion in two new US-based fabrication facilities. This chart shows Intel's investments in US R&D, US CapEx and the job growth that's created as a result, as well as R&D and CapEx investments in Ireland and Israel. Now we added the bar on the right hand side from a Wall Street journal article that compares TSMC CapEx in the dark green to that of Intel and the light green. You can see TSMC surpass the CapEx investment of Intel in 2015, and then Intel took the lead back again. And in 2017 was, hey it on in 2018. But last year TSMC took the lead, again. And appears to be widening that lead quite substantially. Leading us to our conclusion that this will not be enough. These moves by Intel will not be enough. They need to do more. And a big part of this announcement was partnerships and packaging. Okay. So here's where it gets interesting. Intel, as you may know was late to the party with SOC system on a chip. And it's going to use its packaging prowess to try and leap frog the competition. SOC bundles things like GPU, NPU, DSUs, accelerators caches on a single chip. So better use the real estate if you will. Now Intel wants to build system on package which will dis-aggregate memory from compute. Now remember today, memory is very poorly utilized. What Intel is going to do is to create a package with literally thousands of nodes comprising small processors, big processors, alternative processors, ARM processors, custom Silicon all sharing a pool of memory. This is a huge innovation and we'll come back to this in a moment. Now as part of the announcement, Intel trotted out some big name customers, prospects and even competitors that it wants to turn into prospects and customers. Amazon, Google, Satya Nadella gave a quick talk from Microsoft to Cisco. All those guys are designing their own chips as does Ericsson and look even Qualcomm is on the list, a competitor. Intel wants to earn the right to make chips for these firms. Now many on the list like Microsoft and Google they'd be happy to do so because they want more competition. And Qualcomm, well look if Intel can do a good job and be a strong second sourced, why not? Well, one reason is they compete aggressively with Intel but we don't like Intel so much but it's very possible. But the two most important partners on this slide are one IBM and two, the US government. Now many people were going to gloss over IBM in this announcement, but we think it's one of the most important pieces of the puzzle. Yes. IBM and semiconductors. IBM actually has some of the best semiconductor technology in the world. It's got great architecture and is two to three years ahead of Intel with POWER10. Yes, POWER. IBM is the world's leader in terms of dis-aggregating compute from memory with the ability to scale to thousands of nodes, sound familiar? IBM leads in power density, efficiency and it can put more stuff closer together. And it's looking now at a 20x increase in AI inference performance. We think Pat has been thinking about this for a while and he said, how can I leave leap frog system on chip. And we think he thought and said, I'll use our outstanding process manufacturing and I'll tap IBM as a partner for R&D and architectural chips to build the next generation of systems that are more flexible and performant than anything that's out there. Now look, this is super high end stuff. And guess who needs really high end massive supercomputing capabilities? Well, the US military. Pat said straight up, "We've talked to the government and we're honored to be competing for the government/military chips boundary." I mean, look Intel in my view was going to have to fall down into face not win this business. And by making the commitment to Foundry Services we think they will get a huge contract from the government, as large, perhaps as $10 billion or more to build a secure government Foundry and serve the military for decades to come. Now Pat was specifically asked in the Q&A section is this Foundry strategy that you're embarking on viable without the help of the US government? Kind of implying that it was a handout or a bailout. And Pat of course said all the right things. He said, "This is the right thing for Intel. Independent of the government, we haven't received any commitment or subsidies or anything like that from the US government." Okay, cool. But they have had conversations and I have no doubt, and Pat confirmed this, that those conversations were very, very positive that Intel should head in this direction. Well, we know what's happening here. The US government wants Intel to win. It needs Intel to win and its participation greatly increases the probability of success. But unfortunately, we still don't think it's enough for Intel to regain its number one position. Let's look at that in a little bit more detail. The headwinds for Intel are many. Look it can't just flick a switch and catch up on manufacturing leadership. It's going to take four years. And lots can change in that time. It tells market momentum as well as we pointed out earlier is headed in the wrong direction from a financial perspective. Moreover, where is the volume going to come from? It's going to take years for Intel to catch up for ARMS if it never can. And it's going to have to fight to win that business from its current competitors. Now I have no doubt. It will fight hard under Pat's excellent leadership. But the Foundry business is different. Consider this, Intel's annual CapEx expenditures, if you divide that by their yearly revenue it comes out to about 20% of revenue. TSMC spends 50% of its revenue each year on CapEx. This is a different animal, very service oriented. So look, we're not pounding the table saying Intel's worst days are over. We don't think they are. Now, there are some positives, I'm showing those in the right-hand side. Pat Gelsinger was born for this job. He proved that the other day, even though we already knew it. I have never seen him more excited and more clearheaded. And we agreed that the chip demand dynamic is going to have legs in this decade and beyond with Digital, Edge, AI and new use cases that are going to power that demand. And Intel is too strategic to fail. And the US government has huge incentives to make sure that it succeeds. But it's still not enough in our opinion because like the steel manufacturers Intel's real advantage today is increasingly in the high end high margin business. And without volume, China is going to win this battle. So we continue to believe that a new joint venture is going to emerge. Here's our prediction. We see a triumvirate emerging in a new joint venture that is led by Intel. It brings x86, that volume associated with that. It brings cash, manufacturing prowess, R&D. It brings global resources, so much more than we show in this chart. IBM as we laid out brings architecture, it's R&D, it's longstanding relationships. It's deal flow, it can funnel its business to the joint venture as can of course, parts of Intel. We see IBM getting a nice licensed deal from Intel and or the JV. And it has to get paid for its contribution and we think it'll also get a sweet deal and the manufacturing fees from this Intel Foundry. But it's still not enough to beat China. Intel needs volume. And that's where Samsung comes in. It has the volume with ARM, has the experience and a complete offering across products. We also think that South Korea is a more geographically appealing spot in the globe than Taiwan with its proximity to China. Not to mention that TSMC, it doesn't need Intel. It's already number one. Intel can get a better deal from number two, Samsung. And together these three we think, in this unique structure could give it a chance to become number one by the end of the decade or early in the 2030s. We think what's happening is our take, is that Intel is going to fight hard to win that government business, put itself in a stronger negotiating position and then cut a deal with some supplier. We think Samsung makes more sense than anybody else. Now finally, we want to leave you with some comments and some thoughts from the community. First, I want to thank David Foyer. His decade plus of work and knowledge of this industry along with this collaboration made this work possible. His fingerprints are all over this research in case you didn't notice. And next I want to share comments from two of my colleagues. The first is Serbjeet Johal. He sent this to me last night. He said, "We are not in our grandfather's compute era anymore. Compute is getting spread into every aspect of our economy and lives. The use of processors is getting more and more specialized and will intensify with the rise in edge computing, AI inference and new workloads." Yes, I totally agree with Sarbjeet. And that's the dynamic which Pat is betting and betting big. But the bottom line is summed up by my friend and former IDC mentor, Dave Moschella. He says, "This is all about China. History suggests that there are very few second acts, you know other than Microsoft and Apple. History also will say that the antitrust pressures that enabled AMD to thrive are the ones, the very ones that starved Intel's cash. Microsoft made the shift it's PC software cash cows proved impervious to competition. The irony is the same government that attacked Intel's monopoly now wants to be Intel's protector because of China. Perhaps it's a cautionary tale to those who want to break up big tech." Wow. What more can I add to that? Okay. That's it for now. Remember I publish each week on wikibon.com and siliconangle.com. These episodes are all available as podcasts. All you got to do is search for Braking Analysis podcasts and you can always connect with me on Twitter @dvellante or email me at david.vellante, siliconangle.com As always I appreciate the comments on LinkedIn and in clubhouse please follow me so that you're notified when we start a room and start riffing on these topics. And don't forget to check out etr.plus for all the survey data. This is Dave Vellante for theCUBE insights powered by ETR, be well, and we'll see you next time. (upbeat music)

Published Date : Mar 26 2021

SUMMARY :

in Palo Alto in Boston, in the dark green to that of

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A Day in the Life of Data with the HPE Ezmeral Data Fabric


 

>>Welcome everyone to a day in the life of data with HPE as well. Data fabric, the session is being recorded and will be available for replay at a later time. When you want to come back and view it again, feel free to add any questions that you have into the chat. And Chad and I joined stark. We'll, we'll be more than willing to answer your questions. And now let me turn it over to Jimmy Bates. >>Thanks. Uh, let me go ahead and share my screen here and we'll get started. >>Hey everyone. Uh, once again, my name is Jimmy Bates. I'm a director of solutions architecture here for HPS Merle in the Americas. Uh, today I'd like to walk you through a journey on how our everyday life is evolving, how everything about our world continues to grow more connected about, and about how here at HPE, how we support the data that represents that digital evolution for our customers, with the HPE as rural data fabric to start with, let's define that term data. The concept of that data can be simplified to a record of life's events. No matter if it's personal professional or mechanical in nature, data is just records that represent and describe what has happened, what is happening or what we think will happen. And it turns out the more complete record we have of these events, the easier it is to figure out what comes next. >>Um, I like to refer to that as the omnipotence protocol. Um, let's look at this from a personal perspective of two very different people. Um, let me introduce you to James. He's a native citizen of the digital world. He's, he's been, he's been a citizen of this, uh, an a career professional in the it world for years. He's always on always connected. He loves to get all the information he needs on a smartphone. He works constantly with analytics. He predicts what his customers need, what they want, where they are, uh, and how best to reach them. Um, he's fully embraced the use of data in his life. This is Sue SCA. She's, she's a bit of a, um, of an opposite to James. She's not yet immigrated to our digital world. She's been dealing with the changes that are prevalent in our times. And she started a new business that allows her customers, the option of, um, of expressing their personalities and the mask that they wear. She wants to make sure her customers can upload images, logos, and designs in order to deliver that customized mask, uh, to brighten their interactions with others while being safe as they go about their day. But she needs a crash course in digital and the digital journey. She's recently as, as most of us have as transitioned from an office culture to a work from home culture, and she wants to continue to grow that revenue venture on the side >>At the core of these personalities is a journey that is, that is representative common challenge that we're all facing today. Our world has been steadily shrinking as our ability to reach out to one another has steadily increased. We're all on that journey together to know more about what is happening to be connected to what our business is doing to be instantly responsive to our customer needs and to deliver that personalized service to every individual. And it as moral, we see this across every industry, the challenge of providing tailored experiences to potential customers in a connected world to provide constant information on deliveries that we requested or provide an easier commute to our destination to, to change the inventories, um, to the just-in-time arrival for our fabrications to identify quality issues in real time to alter the production of each product. So it's tailored to the request of the end user to deliver energy in, in smarter, more efficient ways, uh, without injury w while protecting the environment and to identify those, those, uh, medical emerging threats, and to deliver those personalized treatments safely. >>And at the core of all of these changes, all of these different industries is data. Um, if you look at the major technology trends, um, they've been evolving down this path for some time now, we're we're well into our cloud journey. The mobile platform world is, is now just part of our core strategies. IOT is feeding constant streams of data often over those mobile, uh, platforms. And the edge is increasingly just part of our core, all of this combined with the massive amounts of data that's becoming, becoming available through it is driving autonomous solutions with machine learning and AI. Uh, this is, this is just one aspect of this, this data journey that we're on, but for success, it's got, uh, sorry for success. It's got to be paired. Um, it's gotta be paired with action. >>Um, >>Well, when you look at the, uh, um, if we take a look at James and Cisco, right, we can start to see, um, with the investments in those actions, um, how their travel they're realizing >>Their goals, >>Services, efforts, you know, uh, focused, deliver new data-driven applications are done in new ways that are smaller in nature and kind of rapidly iterate, um, to respond to the digital needs of, of our new world, um, containerization to deploy and manage those apps anywhere in our connected world, they need to be secure we'll time streaming architecture, um, from, from the, from the beginning to allow for continual interactions with our changing customer demands and all of this, especially in our current environment, while running cost reduction initiatives. This is just the current world that, that our solutions must live in. Um, with that framework in mind, um, I'd like to take the remainder of our time and kind of walk through some of the use cases where, where we at HPE helped organizations through this journey with, with, with the ASML data fabrics, >>Let's >>Start with what's happening in the mobile world. In fact, the HPE as moral data fabric is being used by a number of companies to provide infinitely personalized experiences. In this case, it could be James could be sushi. It could be anyone that opens up their smartphone in the morning, uh, quickly checking what's transpiring in the world with a selection of curated, relative relevant articles, images, and videos provided by data-driven algorithm workloads, all that data, the logs, the recommendations, and the delivery of those recommendations are done through a variety of companies using HP as rural software, um, that provides a very personalized experience for our users. In addition, other companies monitor the service quality of those mobile devices to ensure optimize connectivity as they move throughout their day. The same is true for digital communication for that video communication, what we're doing right now, especially in these days where it's our primary method of connecting as we deal with limited physical engagements. Um, there's been a clear spike in the usage of these types of services. HPE, as Merle is helping a number of these companies deliver on real time telemetry analysis, predicting demand, latency, monitoring, user experience, and analyzing in real time, responding with autonomous adjustments to maintain pleasant experiences for all participants involved. >>Um, >>Another area, um, we're eight or HBS ML data fabric is playing a crucial role in the daily experience inside our automobiles. We invest a lot of ourselves in our cars. We expect tailored experiences that help us stay safe and connected as we move from one destination to another, in the areas of autonomous driving connected car, a number of major car companies in the world are using our data fabric to take autonomous driving to the next level where it should be effectively collecting all data from sensors and cameras, and then feeding that back into a global data fabric. So that engineers that develop cars can train next generation, future driving algorithms that make our driving experience safer and more autonomy going forward. >>Now let's take a look at a different mode of travel. Uh, the airline industry is being impaired. Varied is being impacted very differently today from, from the car companies, with our software, uh, we help airlines travel agencies, and even us as consumers deal with pricing, calculations and challenges, uh, with, um, air traffic services. We, we deal with, um, um, uh, delivering services around route predictions on time arrivals, weather patterns, and tagging and tracking luggage. We help people with flight connections and finding out what the figuring out what the best options are for your, for your travel. Uh, we collect mountains of data, secure it in a global data fabric, so it can provide, be provided back in an analyzed form with it. The stressed industry can contain some very interesting insights, provide competitive offerings and better services to us as travelers. >>This is also true for powering biometrics. At scale, we work with the biggest biometrics databases in the world, providing the back end for their enormous biometric authentication pursuit. Just to kind of give you a rough idea. A biometric authentication is done with a number of different data points from fingerprints. I re scans numerous facial features. All of these data points are captured for every individual and uploaded into the database, such that when the user is requesting services, their biometric metrics can be pooled and validated in seconds. From a scale perspective, they're onboarding 1 million people a day more than 200 million a year with a hundred percent business continuity and the options do multi-master and a global data fabric as needed ensuring that users will have no issues in securely accessing their pension payouts medical services or what other types of services. They may be guaranteed >>Pivoting >>To a very different industry. Even agriculture was being impacted in digital ways. Using HPE as well, data fabric, we help farmers become more digital. We help them predict weather patterns, optimize sea production. We even helped see producers create custom seed for very specific weather and ground conditions. We combine all of these things to help optimize production and ensure we can feed future generations. In some cases, all of these data sources collected at the edge can be provided back to insurance companies to help farmers issue claims when micro patterns affect farmers in negative ways, we all benefit from optimized farming and the HBS Modena fabric is there to assist in that journey. We provide the framework and the workload guidance to collect relevant data, analyze it and optimize food production. Our customers demonstrate the agricultural industry is most definitely my immigrating to our digital world. >>Now >>That we've got the food, we need to ship it along with everything else, all over the world, as well as offer can be found in action in many of the largest logistics companies in the world. I mean, just tracking things with greater efficiency can lead to astounding insights. What flights and ships did the package take? What Hans held it along its journey, what weather conditions did it encounter? What, what customs office did it go through and, and how much of it's requested and being delivered this along with hundreds of other telemetry points can be used to provide very accurate trade and economic predictions around what's going on with trade in the world. These data sets are being used very intensively to understand economy conditions and plan for future event consequences. We also help answer, uh, questions for shipping containers that are, that are more basic. Uh, like where is my container located at is my container still on the correct ship? Uh, surprisingly, uh, this helps cut down on those pesky little events like lost containers. >>Um, it's astounding the amount of data that's in DNA, and it's not just the pairs. It's, it's the never ending patterns found with other patterns that none of it can be fully understood unless the micro is maintained in context to the macro. You can't really understand these small patterns unless you maintain that overall understanding of the entire DNA structure to help the HVS mold data fabric can be found across every aspect of the medical field. Most recently was there providing the software framework to collect genomic sequencing, landing it in the data fabric, empowering connected availability for analysis to predict and find patterns of significance to shorten the effort it takes to identify those potential triggers and make things like vaccines become becoming available. In record time. >>Data is about people at HPE asthma. We keep people connected all around the world. We do this in a variety of ways. We we've already looked at several of the ways that that happens. We help you find data. You need, we help you get from point a to point B. We help make sure those birthday gifts show up on time. Some other interesting ways we connect people via recipes, through social platforms and online services. We help people connect to that new recipe that is unexpected, but may just be the kind of thing you need for dinner tonight at HPDs where we provide our customers with the power to deliver services that are tailored to the individual from edge to core, from containers to cloud. Many of the services you encounter everyday are delivered to you through an HV as oral global data fabric. You may not see it, but we're there in the morning in the morning when you get up and we're there in the evening. Um, when you wind down, um, at HPE as role, we make data globally available across everywhere that your business needs to go. Um, I'd like to thank everyone, uh, for the time that you've given us today. And I'd like to turn it back over and open up the floor for questions at this time, >>Jimmy, here's a question. What are the ways consumers can get started with HPS >>The fabric? Well, um, uh, there's several ways to get started, right? We, we, uh, first off we have software available that you can download that there's extensive documentation and use cases posted on our website. Um, uh, we have services that we offer, like, um, assessment services that can come in and help you assess the, the data challenges that you're having, whether you're, you're just dealing with a scale issue, a security issue, or trying to migrate to a more containerized approach. We have a services to help you come in, assess that aspect. Um, we have a getting started bundles, um, and we have, um, so there's all kinds of services that, that help you get started on your journey. So what >>Does a typical first deployment look like? >>Well, that's, that's a very, very interesting question. Um, a typical first deployment, it really kind of varies depending on where you're at in the material. Are you James? Are you, um, um, Cisco, right? It really depends on, on where you're at in your journey. Um, but a typical deployment, um, is, is, is involved. Uh, we, we like to come in, we we'd like to do workshops, really understand your specific challenges and problems so that we can determine what solutions are best for you. Um, that to take a look at when we kind of settle on that we, we, um, the first deployment, uh, is, um, there's typically, um, a deployment of, uh, a, uh, a service offering, um, w with a software to kind of get you started along the way we kind of bundle that aspect. Um, as you move forward, if you're more mature and you already have existing container solutions, you already have existing, large scale data aspects of it. Um, it's really about the specific use case of your current problem that you're dealing with. Um, every solution, um, is tailored towards the individual challenges and problems that, that each one of us are facing. >>I break, they mentioned as part of the asthma family. So how does data fabric pair with the other solutions within Israel? >>Well, so I like to say there's, um, there, there's, there's three main areas, um, from a software standpoint, um, for when you count some of our, um, offerings with the GreenLake solution, but there are, so there are really four main areas with ESMO. There's the data fabric offering, which is really focused on, on, on, on delivering that data at scale for AI ML workloads for big data workloads for containerized workloads. There is the ESMO container platform, which really solves a lot of, um, some of the same problems, but really focus more on a compute delivery, uh, and a hundred percent Kubernetes environment. We also have security offerings, um, which, which help you take in this containerized world, uh, that help you take the different aspects of, um, securing those applications. Um, so that when the application, the containerized applications move from one framework or one infrastructure from one to the other, it really helps those, the security go with those applications so that they can operate in a zero trust environment. And of course, all of this, uh, options of being available to you, where everything has a service, including the hardware through some of our GreenLake offerings. So those are kind of the areas that, uh, um, that pair with the HPE, um, data fabric, uh, when you look at the entire ESMO pro portfolio. >>Well, thanks, Jimmy really appreciate it. That's all the questions we have right now. So is there anything that you'd like to close with? >>Uh, you know, the, um, I I'm, I find it I'm very, uh, I'm honored to be here at HPE. Um, I, I really find it, it's amazing. Uh, as we work with our customers solving some really challenging problems that are core to their business, um, it's, it's always an interesting, um, interesting, um, day in the office because, uh, every problem is different because every problem is tailored to the specific challenges that our customers face. Um, while they're all will well, we will, what we went over today is a lot of the general areas and the general concepts that we're all on together in a journey, but the devil's always in the details. It's about understanding the specific challenges in the organization and, and as moral software is designed to help adapt, um, and, and empower your growth in your, in your company. So that you're focused on your business, in the complexity of delivering services across this connected world. That's what as will takes off your plate so that you don't have to worry about that. It just works, and you can focus on the things that impact your business more directly. >>Okay. Well, we really thank everyone for coming today and hope you learned, uh, an idea about how data fabric can begin to help your business with it. All of a sudden analytics, thank you for coming. Thanks.

Published Date : Mar 17 2021

SUMMARY :

Welcome everyone to a day in the life of data with HPE as well. Uh, let me go ahead and share my screen here and we'll get started. that digital evolution for our customers, with the HPE as rural data fabric to and designs in order to deliver that customized mask, uh, to brighten their interactions with others while protecting the environment and to identify those, those, uh, medical emerging threats, all of this combined with the massive amounts of data that's becoming, becoming available through it is This is just the current world that, that our solutions must live in. the service quality of those mobile devices to ensure optimize connectivity as they move a number of major car companies in the world are using our data fabric to take autonomous uh, we help airlines travel agencies, and even us as consumers deal with pricing, Just to kind of give you a rough idea. from optimized farming and the HBS Modena fabric is there to assist in that journey. and how much of it's requested and being delivered this along with hundreds of other telemetry points landing it in the data fabric, empowering connected availability for analysis to Many of the services you encounter everyday are delivered to you through What are the ways consumers can get started with HPS We have a services to help you uh, a service offering, um, w with a software to kind of get you started with the other solutions within Israel? uh, um, that pair with the HPE, um, data fabric, uh, when you look at the entire ESMO pro portfolio. That's all the questions we have right now. in the organization and, and as moral software is designed to help adapt, an idea about how data fabric can begin to help your business with it.

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3 Quick Wins That Drive Big Gains in Enterprise Workloads


 

hey welcome to analytics unleashed i'm robert christensen your host today thank you for joining us today we have three quick wins that drive big gains in the enterprise workloads and today we have olaf with erickson we have john with orok and we have dragon with dxc welcome thank you for joining me gentlemen yeah good to be here thank you thank you good to have you hey olaf let's start off with you what big problems are you trying to solve today that are doing for those quick wins what are you trying to do today top top of mind yeah when we started looking into this microservices for our financial platform we immediately saw the challenges that we have and we wanted to have a strong partner and we have a good relationship with hp before so we turned to hp because we know that they have the technical support that we need the possibilities that we need in our platform to fulfill our requirements and also the reliability that we would need so tell me i think this is really important you guys are starting into a digital wallet space that correct yeah that's correct so we are in a financial platform so we are spanning across the world and delivering our financial services to our end customers well that's not classically what you hear about ericsson diving into what's really started you guys down that path and specifically these big wins around this digitization no what what we could see earlier was that we have a mobile networks right so we have a lot of a strong user base within them uh both kind of networks and in the where we started in the emerging markets uh you normally they have a lot of unbanked people and that people also were the ones that you want to target so be able to instead of going down and use your cash for example to buy your fruits or your electricity bill etc you could use your mobile wallet and and that's how it all started and now we're also turning into the emerged markets also like the western side part of worlds etc that's fantastic and i hey i want to talk to john here john's with o'rock and he's the one of those early adopters of those container platforms for the uh in the united states here the federal government tell us a little bit about that program and what's going on with that john yeah sure absolutely appreciate it yeah so with orock what we've done is we developed one of the first fedramp authorized container platforms that runs in our moderate and soon to be high cloud and what that does is building on the israel platform gave us the capability of offering customers both commercial as well as federal the capability and the flexibility of running their workloads in a you know as a service model where they can customize and typically what customers have to do is they have to either build it internally or if they go to the cloud they have to be able to take what resources are available then tweak to those designs to make what they need so in this architecture built on open source and with our own infrastructure we offer you know very low cost zero egress capability but the also the workload processing that they would need to run data analytics machine language and other types of high performance processing that typically they would need as we move forward in this computer age so john you you touched on a topic that's i think is really critical and you had mentioned open source why is open source a key aspect for this transformation that we're seeing coming up in like the next decade yeah sure yeah with open source we shifted early on to the company to move to open source only to offer the flexibility we didn't want to be set on one particular platform to operate within so we took and built the cloud infrastructure we went with open source as an open architecture that we can scale and grow within because of that we were one of the very first fedramp authorizations built on open source not on a specific platform and what we've seen from that is the increased performance capability that we would get as well as the flexibility to add additional components that typically you don't get on other platforms so it was a it was a good move we went with and one that the customer will definitely benefit from that that's that's huge actually because performance leads to better cost and better cost leads better performance around that i i'm just super super happy with all the advanced work that you always are doing there is fantastic and dragon so so you're in a space that i think is really interesting you're dealing with what everybody likes to talk about that's autonomous vehicles you're working with automobile manufacturers you're dealing with data at a scale that is unprecedented can you just open that door for us to talk to about these big big wins that you're trying to get over the line with these enterprises yeah absolutely and um thank you robert we approach uh leveraging esmeral from the data fabric angle we practically have a fully integrated the esmeral data fabric into our robotic drive solution rewarding drive solution is actually a game changer as you've mentioned in accelerating the development of autonomous driving vehicles it's a an end-to-end hyper-scale machine learning and ai platform as i mentioned based on the esmeralda data fabric which is used by the some of the largest manufacturers in the world for development of their autonomous driving algorithms and i think we all in technology i think and following up at the same type of news and research right across the globe in in this area so we're pretty proud that we're one of the leaders in actually providing uh hyperscale machine learning platforms for uh kind manufacturers some of them i cannot talk about but bmw is one of uh one of the current manufacturers that we provide uh these type of solutions and they have publicly spoken about their uh d3 platform uh data driven development platform uh just to give you an idea um of the scale as robert mentioned uh daily we collect over 1.5 petabytes of data of raw data did you say daily data daily the storage capacity is over 250 petabytes and growing uh there's over 100 000 cores and over 200 gpus in the in in the compute area um over 50 50 petabytes of data is delivered every two weeks into a hardware in loop right for testing and we have daily uh thousands of engineers and data scientists accessing the relevant data and developing machine learning models on the daily basis right part of it is the simulation right simulation cuts the cost as well as the uh time right for developing of the autonomous uh driving algorithms and uh the the simulations are taking probably 75 percent of the research uh that's being done on this platform that's amazing dragon i i i i the more i get involved with that and i've been part of these conversations with a number of the folks that are involved with it i i computer science me my geekiness my little propeller head starts coming out i might just blows my mind and i think so i'm going to pivot back over to olaf oh left so you're talking about something that is a global network of financial services okay correct and the flow of transactional typically non-relational transactional data flows to actual transactions going through you have issues of potential fraud you have issues a safety and you have multi-geographic regional problems with data and data privacy how are you guys addressing that today so so to answer that question today we have managed to solve that using the container platform to together with the data fabric but as you say we need to span across different regions we need to have the data as secure as possible because we have a lot of legal aspects to look into because if our data disappears but your money is also disappearing so it's a really important area for us with the security and the reliability of the platforms so so that's why we also went this way to make sure that we have this strong partner that could help us with this because just looking at where we are deployed in in more than 23 countries today and and we it's processing more than 900 million us dollars per day in our systems currently so it is a lot of money passing through and you need to take security in a it's as it's a very important point right it really is it really is and so uh john i mean you you uh obviously are dealing with you know a lot of folks that have three letters as acronyms around the government agencies and uh they range in various degrees of certa of security when you say fedramp i mean what could you just uh articulate why the esmerald platform was something that you selected to go to that fedrak compliant container platform because i think that's that that kind of speaks to the to the industrial strength of what we're talking about yeah it all comes down to being able to offer a product that's secure that the customers can trust and when we went with fedramp fedramp has very stringent security requirements that have monthly poems which are performance reviews and and updates that need to be done if not on a daily basis on a monthly basis so the customers there's a lot that goes on behind the scenes that they don't are able to articulate and what by selecting the hp esmerald platform for containers um one of the key strengths that we looked at was the esmo fabric and it's all about the data it's all about securing the data moving the data transferring the data and from a customer's perspective they want to be able to operate in an environment that they can trust no different than being able to turn on their lights or making sure there's water in their utilities you know containers with the israel platform built on orok's infrastructure gives that capability fedramp enables the security tied to the platform that we're able to follow so it's government uh guided which includes this and many and over hundreds of controls that typically you know the customers don't have time or the capability to address so our commercial customers benefit our federal customers you know that you discuss they're able to follow and check the box to meet those requirements and the container platform gives us a capability where now we're able to move files which we'll hear about through the optimal fabric and then we're able to run the workloads in the containers themselves and give isolation and the security element of fed wrapping esmeral gave us that capability in order to paint that environment fedramp authorized that the customers benefit from from security so they have confidence in running their workloads using their data and able to focus on their core job at hand and not worry about their infrastructure the fundamental requirement isn't it that that isolation between that compute and storage and going up a layer there in in a way that provides them a set of services that they can i wouldn't say set it and forget it but really had the confidence that what they're getting is the best performance for the dollars that they're spending uh john my hat's off to what the work that you all do in there thank you we appreciate it yeah yeah and dragon i want to i wanted to pivot a little bit here because you are primarily the the operator what i consider one of the largest data fabrics on the on the planet for that matter um and i just want to talk a little bit about the openness of our architecture right of all the multiple protocols that we support that allow for you know you know some people may have selected a different set of application deployment models and virtualization models that allow to plug into the data fabric you know it did can you talk a little bit about that yeah and i i think um in my mind right um to operate uh such a uh data fabric at scale right um there were three key elements that we were looking for right uh that we found in uh esmeralda fabric ring the first one was a speed cost and scalability right the second one was the globally distributed data lake or ability to distribute data globally and third was certainly the strength of our partnership with with hpe in this case right so if you look at the uh as well data fabric it's it's fast it's cost effective and it's certainly highly scalable because we as you just mentioned stretch the uh sort of the capabilities of the data fabric to hundreds of petabytes and over a million the data points if you will and it important what was important for us was that the esmeralda fabric actually eliminates the need for multiple vendor solutions which would be otherwise required right because it provides integrated file system database or or a data lake right and the data management on top of it right usually you would probably need to incorporate multiple tools right from different vendors and the file system itself it's it's so important right when you're working at scale like this right and honestly in our research maybe there are three file systems in the world that can support uh this kind of size of the auto data fabric the distributed data lake was also important to us and the reason for that is you can imagine that these large car manufacturers are testing and have testing vehicles all around the world right they're not just doing it locally around the uh their data their id centers right so uh collecting the data and this 1.5 petabytes example right uh for for bmw on a daily basis it's it's it's really challenging unless you have the ability to actually leverage the data in a distributed data like fashion right so data can basically reside in different data centers globally or even on-premise and in cloud environments which became uh very important later because a lot of this car manufacturers actually have oems right that would like to get either portions of the data or get access to the data in a in different environments not necessarily in their data center um and truly i think uh to build something at this scale right uh you you need a strong partner and we certainly had that in hpe and uh we got the comprehensive support right for uh for the software um but but more importantly i think uh partner that clearly understood uh criticality of the data fabric trend and the need for the vice fast response right to our clients and you know jointly i think we met all the challenges and it's so doing i think we made the esmo data fabric a much better and stronger product over the over the last few years that's fantastic thank you dragon appreciate it uh hey so if we're going to wrap up here any last words olaf do you want to share with us no looking forward now in from our perspective on helping out with the kobe 19 situation that we have uh enabling people to still be in the market without actually touching each other and and and leaving maybe for action market and being at home etc doing those transactions that's great thank you john in last comment yeah thanks yeah uh look for uh a joint offering announcement coming up between hpe and orok where we're going to be offering sandbox as a service where the data analytics and machine language where people can actually test drive the actual environment as a service and if they like it then they can move into a production-wise environment so stay tuned for that that's great john thank you for that and hey dragon last words yeah last words um we're pretty happy what we have done already for car manufacturers we're taking this solution right in terms of the uh distributed data-like capabilities as well as the uh hyperscale machine learning and ai platform to other industries and we hope to do it jointly with you well we hope that you do it with us as well so thank you very much everybody gentlemen thank you so much for joining us i appreciate it thank you very much thank you very much hey this is robert christensen with analytics unleashed i want to thank all of our guests here today and we'll catch you next time thank you for joining us bye [Music] [Music] [Music] easy [Music] you

Published Date : Mar 17 2021

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