>> Live from Boston, Massachusetts, it's theCUBE, Covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back to theCUBE's live coverage of IBM Chief Data Strategy Summit. My name is Rebecca Knight, and I'm here with my co-host Dave Vellante, we are joined by Christopher Penn, the VP of Marketing Technology at SHIFT Communications, here in Boston. >> Yes. >> Thanks so much for joining us. >> Thank you for having me. >> So we're going to talk about cognitive marketing. Tell our viewers: what is cognitive marketing, and what your approach to it is. >> Sure, so cognitive marketing essentially is applying machine learning and artificial intelligence strategies, tactics and technologies to the discipline of marketing. For a really long time marketing has been kind of known as the arts and crafts department, which was fine, and there's certainly, creativity is an essential part of the discipline, that's never going away. But we have been tasked with proving our value. What's the ROI of things, is a common question. Where's the data live? The chief data officer would be asking, like, who's responsible for this? And if we don't have good answers to those things, we kind of get shown the door. >> Well it sort of gets back to that old adage in advertising, I know half my marketing budget is wasted, I just don't know which half. >> Exactly. >> So now we're really able to know which half is working. >> Yeah, so I mean, one of the more interesting things that I've been working on recently is using what's called Markov chains, which is a type of very primitive machine learning, to do attribution analysis, to say what actually caused someone to become a new viewer of theCUBE, for example. And you would take all this data that you have from your analytics. Most of it that we have, we don't really do anything with. You might pull up your Google Analytics console, and go, "Okay, I got more visitors today than yesterday." but you don't really get a lot of insights from the stock software. But using a lot of tools, many of which are open source and free of financial cost, if you have technical skills you can get much deeper insights into your marketing. >> So I wonder, just if we can for our audience... When we talk about machine learning, and deep learning, and A.I., we're talking about math, right, largely? >> Well so let's actually go through this, because this is important. A.I. is a bucket category. It means teaching a machine to behave as though it had human intelligence. So if your viewers can see me, and disambiguate me from the background, they're using vision, right? If you're hearing sounds coming out of my mouth and interpreting them into words, that's natural language processing. Humans do this naturally. It is now trying to teach machines to do these things, and we've been trying to do this for centuries, in a lot of ways, right? You have the old Mechanical Turks and stuff like that. Machine learning is based on algorithms, and it is mostly math. And there's two broad categories, supervised and unsupervised. Supervised is you put a bunch of blocks on the table, kids blocks, and you hold the red one, and you show the machine over and over again this is red, this is red, and eventually you train it, that's red. Unsupervised is- >> Not a hot dog. (Laughter) >> This is an apple, not a banana. Sorry CNN. >> Silicon Valley fans. >> Unsupervised is there's a whole bunch of blocks on the table, "Machine, make as many different sequences as possible," some are big, some are small, some are red, some are blue, and so on, and so forth. You can sort, and then you figure out what's in there, and that's a lot of what we do. So if you were to take, for example, all of the comments on every episode of theCUBE, that's a lot, right? No humans going to be able to get through that, but you can take a machine and digest through, just say, what's in the bag? And then there's another category, beyond machine learning, called deep learning, and that's where you hear a lot of talk today. Deep learning, if you think of machine learning as a pancake, now deep learnings like a stack of pancakes, where the data gets passed from one layer to the next, until what you get at the bottom is a much better, more tuned out answer than any human can deliver, because it's like having a hundred humans all at once coming up with the answer. >> So when you hear about, like, rich neural networks, and deep neural networks, that's what we're talking about. >> Exactly, generative adversarial networks. All those things are ... Any kind of a lot of the neural network stuff is deep learning. It's tying all these piece together, so that in concert, they're greater than the sum of any one. >> And the math, I presume, is not new math, right? >> No. >> SVM and, it's stuff that's been around forever, it's just the application of that math. And why now? Cause there's so much data? Cause there's so much processing power? What are the factors that enable this? >> The main factor's cloud. There's a great shirt that says: "There's no cloud, it's just somebody else's computer." Well it's absolutely true, it's all somebody else's computer but because of the scale of this, all these tech companies have massive server farms that are kind of just waiting for something to do. And so they offer this as a service, so now you have computational power that is significantly greater than we've ever had in human history. You have the internet, which is a major contributor, the ability to connect machines and people. And you have all these devices. I mean, this little laptop right here, would have been a supercomputer twenty years ago, right? And the fact that you can go to a service like GitHub or Stack Exchange, and copy and paste some code that someone else has written that's open source, you can run machine learning stuff right on this machine, and get some incredible answers. So that's why now, because you've got this confluence of networks, and cloud, and technology, and processing power that we've never had before. >> Well with this emphasis on math and science in marketing, how does this change the composition of the marketing department at companies around the world? >> So, that's a really interesting question because it means very different skill sets for people. And a lot of people like to say, well there's the left brain and then there's a right brain. The right brains the creative, the left brains the quant, and you can't really do that anymore. You actually have to be both brained. You have to be just as creative as you've always been, but now you have to at least have an understanding of this technology and what to do with it. You may not necessarily have to write code, but you'd better know how to think like a coder, and say, how can I approach this problem systematically? This is kind of a popular culture joke: Is there an app for that, right? Well, think about that with every business problem you face. Is there an app for that? Is there an algorithm for that? Can I automate this? And once you go down that path of thinking, you're on the path towards being a true marketing technologist. >> Can you talk about earned, paid, and owned media? How those lines are blurring, or not, and the relationship between sort of those different forms of media, and results in PR or advertising. >> Yeah, there is no difference, media is media, because you can take a piece of content that this media, this interview that we're doing here on theCUBE is technically earned media. If I go and embed this on my website, is that owned media? Well it's still the same thing, and if I run some ads to it, is it technically now paid media? It's the thing, it's content that has value, and then what we do with it, how we distribute it, is up to us, and who our audience is. One of the things that a lot of veteran marketing and PR practitioners have to overcome is this idea that the PR folks sit over there, and they just smile and dial and get hits, go get another hit. And then the ad folks are over here... No, it's all the same thing. And if we don't, as an industry realize that those silos are artificially imposed, basically to keep people in certain jobs, we will eventually end up turning over all of it to the machines, because the machines will be able to cross those organizational barriers much faster. When you have the data, and whatever the data says that's what you do. So if the data says this channels going to be more effective, yes it's a CUBE interview, but actually it's better off as a paid YouTube video. So the machine will just go do that for us. >> I want to go back to something you were talking about at the very beginning of the conversation, which is really understanding, companies understanding, how their marketing campaigns and approaches are effectively working or not working. So without naming names of clients, can you talk about some specific examples of what you've seen, and how it's really changed the way companies are reaching customers? >> The number one thing that does not work, is for any business executive to have a pre-conceived idea of the way things should be, right? "Well we're the industry leader in this, we should have all the market share." Well no, the world doesn't work like that anymore. This lovely device that we all carry around in our pockets is literally a slot-machine for your attention. >> I like it, you've got to copyright that. A slot machine for your attention. >> And there's a million and a half different options, cause that's how many apps there are in the app store. There's a million and half different options that are more exciting than your white paper. (Laughter) Right, so for companies that are successful, they realize this, they realize they can't boil the ocean, that you are competing every single day with the Pope, the president, with Netflix, you know, all these things. So it's understanding: When is my audience interested in something? Then, what are they interested in? And then, how do I reach those people? There was a story on the news relatively recently, Facebook is saying, "Oh brand pages, we're not going to show "your stuff in the regular news feed anymore, "there will be a special feed over here "that no one will ever look at, unless you pay up." So understanding that if we don't understand our audiences, and recruit these influencers, these people who have the ability to reach these crowds, our ability to do so through the "free" social media continues to dwindle, and that's a major change. >> So the smart companies get this, where are we though, in terms of the journey? >> We're in still very early days. I was at major Fortune 50, not too long ago, who just installed Google Analytics on their website, and this is a company that if I named the name you would know it immediately. They make billions of dollars- >> It would embarrass them. >> They make billions of dollars, and it's like, "Yeah, we're just figuring out this whole internet thing." And I'm like, "Cool, we'd be happy to help you, but why, what took so long?" And it's a lot of organizational inertia. Like, "Well, this is the way we've always done it, and it's gotten us this far." But what they don't realize is the incredible amount of danger they're in, because their more agile competitors are going to eat them for lunch. >> Talking about organizational inertia, and this is a very big problem, we're here at a CDO summit to share best practices, and what to learn from each other, what's your advice for a viewer there who's part of an organization that isn't working fast enough on this topic? >> Update your LinkedIn profile. (Laughter) >> Move on, it's a lost cause. >> One of the things that you have to do an honest assessment of, is whether the organization you're in is capable of pivoting quickly enough to outrun its competition. And in some cases, you may be that laboratory inside, but if you don't have that executive buy in, you're going to be stymied, and your nearest competitor that does have that willingness to pivot, and bet big on a relatively proven change, like hey data is important, yeah, you make want to look for greener pastures. >> Great, well Chris thanks so much for joining us. >> Thank you for having me. >> I'm Rebecca Knight, for Dave Vellante, we will have more of theCUBE's coverage of the IBM Chief Data Strategy Officer Summit, after this.

>>Good morning, brilliant humans. Good afternoon or good evening, depending on your time zone. My name is Savannah Peterson and I'm here live with the Cube. We are at CubeCon in Detroit, Michigan. And joining me is my beautiful co-host, Lisa, how you feeling? Afternoon of day three. >>Afternoon day three. We've had such great conversations. We have's been fantastic. The momentum has just been going like this. I love it. >>Yes. You know, sometimes we feel a little low when we're at the end of a conference. Not today. Don't feel that that way at all, which is very exciting. Just like the guests that we have up for you next. Kind of an unexpected player when we think about technology. However, since every company, one of the themes is every company is trying to be a software company. I love that we're talking to I n G. Joining us today is Ty Evers and Arno vk. Welcome to the show gentlemen. Thank >>You very much. Glad to be you. Thank you. >>Yes, it's wonderful. All the way in from Amsterdam. Probably some of the farthest flying folks here for this adventure. Starting off. I forgot what's going on with the shirts guys. You match very well. Tell, tell everyone. >>Well these are our VR code shirts. VR code is basically the player of our company to get people interested as an IT person in banking. Right? Actually, people don't think banking is a good place to work as an IT professional, but actually this, and we are using the OC went with these nice logos to get it attention. >>I love that. So let's actually, let's just talk about that for a second. Why is it such an exciting role to be working in technology at a company like I N G or traditional bank? >>I N G is a challenging environment. That's how do you make an engineer happy, basically give them a problem to solve. So we have lots and lots of problems to solve. So that makes it challenging. But yeah, also rewarding. And you can say a lot of things about banks and with looking at the IT perspective, we are doing amazing things in I and that's what we talked about. Can >>You, can you tell us any of those amazing things or are they secrets? >>Think we talked about last Tuesday at S shift commons conference. Yeah, so we had two, two presentations I presented with my coho sand on my journey over the last three years. So what has IG done? Basically building a secure container hosting platform. Yeah. How do we live a banking cot with cloud native technology and together with our coho young villa presented actually showed it by demo making life and >>Awesome >>In person. So we were not just presenting, >>It's not all smoke and mirrors. It's >>Not smoke and mirror, which we're not presenting our fufu marketing block now. We actually doing it today. And that's what we wanted to share here. >>Well, and as consumers we expect we can access our banking on any device 24 by seven. I wanna be able to do all my transactions in a way that I know is secure. Obviously security's a huge thing there, but talk about I n G Bank aren't always been around for a very long time. Talk about this financial institution as a software company. Really obviously a lot of challenges to solve, a lot of opportunity. But talk about what it's like working for a history and bank that's really now a tech company. >>Yes. It's been really changing as a bank to a tech company. Yeah. We have a lot of developers and operators and we do deliver offer. We OnPrem, we run in the public. So we have a huge engineers and people around to make our software. Yes. And I am responsible for the i Container Ocean platform and we deliver that the name space as a surface and as a real, real secure environment. So our developers, all our developers in, I can request it, but they only get a name space. Yeah, that's very important there. They >>Have >>Resources and all sort of things. Yeah. And it is, they cannot access it. They can only access it by one wifi. So, >>So Lisa and I were chatting before we brought you up here. Name space as a service. This is a newer term for us. Educate us. What does that mean? >>Basically it means we don't give a full cluster to our consumers, right? We only give them basically cpu, memory networking. That's all they need to host application. Everything else we abstract away. And especially in a banking context where compliance is a big thing, you don't need to do compliance for an entire s clusterized developer. It's really saves development time for the colleagues in the bank. It >>Decreases the complexity of projects, which is a huge theme here, especially at scale. I can imagine. I mean, my gosh, you're serving so many different people, it probably saves you time. Let's talk about regulation. What, how challenging is that for you as technologists to balance in all the regulations around banking and FinTech? It's, it's, it's, it's not like some of these kind of wild, wild west industries where we can just go out and play and prototype and do whatever we want. There's a lot of >>Rules. There's a lot of rules. And the problem is you have legislation and you have the real world. Right. And you have to find something in, they're >>Not the same thing. >>You have to find something in between with both parties on the stands and cannot adhere to. Yeah. So the challenge we had, basically we had to wide our, in our own container security standards to prove that the things we were doing were the white things to be in control as a bank because there was no market standard for container security. So basically we took some input from this. So n did a lot of good work. We basically added some things on top to be valid for a bank in Europe. So yeah, that's what we did. And the nice thing is today we take all the boxes we defined back in 2019. >>Hey, so you what it's, I guess, I guess the rules are a little bit easier when you get to help define them. Yep. Yeah. That it feels like a very good strategic call >>And they makes sense. Yeah. Right. Because the hardest problem is try to be compliant for something which doesn't make sense. Right, >>Right. Arnold, talk about, let's double click on namespace as a service. You talked about what that is, but give us a little bit of information on why I N G really believes this is the right approach for this company. >>It's protects for the security that developers doing things they don't shoot. Yeah. They cannot access their store anymore when it is running in production. And that is the most, most important. That is, it is immutable running in our platform. >>Excellent. Talk about both of you. How long have you, have you both been at I n G for a long time? >>I've been with I N G since September, 2001. So that's more than 20 years >>Now. Long time. Ana, what about you? >>Before 2000 already before. >>So both of your comment on that's a long time. Yeah. Talk about the culture of innovation that's at I N G to be able to move at such speed and be groundbreaking in what you're, how you're using technology, what, what's the appetite like at the bank to embrace new and emerging technologies? >>So we are really looking, basically the, the mantra of the bank is to help our customers get a step ahead in life and in business. And we do that by one superior customer service and secondly, sustainability at the heart. So anything which contributes to those targets, you can go to your manager and if you can make goods case why it contributes most of the cases you get some time or some budgets or even some additional colleagues to help you out and give it a try require from a culture perspective required open to trying things out before we reach production. Once you go to production. Yeah. Then we are back to being a bank and you need to take all the boxes to make really sure that we are confident with our customers data and basically we're still a bank but a lot of is possible. >>A lot. It is possible. And there's the customer on the other end who's expecting, like I said earlier, that they can access their data any time that they want, be able to do any transaction they want, making sure the content that's delivered to them is relevant, that it's secure. Obviously with, that's the biggest challenge especially is we think about how many generations are alive today and and those that aren't tech savvy. Yeah. Have challenges with that. Talk about what the bank's dedication is to ensuring from a security perspective that its customers don't have anything to worry about. >>That's always a thin line between security and the user experience. So I n g, like every other bank needs to make choices. Yes. We want the really ease of customers and take the risk that somebody abuses it or do we make it really, really secure and alienate part of our customer base. And that's an ongoing, that's a, that's a a hard, >>It's a trade off. That's >>A line. >>So it's really hard. Interesting part is in Netherlands we had some debates about banks closing down locations, but the moment we introduced our mobile weapon iPads, basically the debates became a lot quieter because a lot of elderly people couldn't work with an iPhone. It turned out they were perfectly fine with a well-designed iPad app to do their banking. Really? >>Okay. >>But that's already learning from like 15 years ago. >>What was the, what was the product roadmap on that? So how, I mean I can imagine you released a mobile app, you're not really thinking that. >>That's basically, I think that was a heavy coincidence. We just, Yeah, okay. Went out to design a very good mobile app. Yeah. And then looking out afterwards at the statistics we say, hey, who was using this way? We've got somebody who's signing on and I dunno the exact age, but it was something like somebody of 90 plus who signed on to use that mobile app. >>Wow. Wow. I mean you really are the five different generations living and working right now. Designing technology. Everybody has to go to the bank whether we are fans of our bank or we're not. Although now I'm thinking about IG as a bank in general. Y'all have a a very good attitude about it. What has kept you at the company for over 20 years? That is we, we see people move around, especially in this technology industry. Yes. Yeah. You know, every two to three years. Sometimes obviously you're in positions of leadership, they're obviously taking good care of you. But I mean multiple decades. Why have you stuck? >>Well first I didn't have the same job in I N D for two decades. Nice. So I went around the infrastructure domain. I did storage initially I did security, I did solution design and in the end I ended up in enterprise architecture. So yeah, it's not like I stuck 20 years in the same role. So every so years >>Go up the ladder but also grow your own skill sets. >>Explore. Yeah. >>So basically I think that's what's every, everybody should be thinking in these days. If you're in a cloud head industry, if you're good at it, you can out quite a nice salary. But it also means that you have some kind of obligation to society to make a difference. And I think, yeah, >>I wouldn't say that everybody feels that way. I >>Need to make a difference with I N G A difference for being more available to our consumers, be more secure to, to our consumers. I, I think that's what's driving me to stick with the company. >>What about you R Now? >>Yes, for me it's very important. Every two, three years are doing new things. I can work with the latest technology so I become really, really innovative so that it is the place to be. >>Yeah. You sort of get that rotation every two to three years with the different tools that you're using. Speaking of or here we're at Cuan, we're talking cloud native, we're talking Kubernetes. Do you think it's possible to, I'm coming back to the regulations. Do you think it's possible to get to banking grade security with cloud native Tech? >>Initially I said we would be at least as secure traditional la but last Tuesday we've proven we can get more secure than situational it. So yeah, definitely. Yes. >>Awesome. I mean, sounds like you proved it to yourself too, which is really saying something. >>Well we actually have Penta results and of course I cannot divulge those, but I about pretty good. >>Can you define, I wanna kind of double book on thanking great security, define what that is, thanking great security and how could other industries aim to Yeah, >>Hit that, that >>Standard. I want security everywhere. Especially my bank. The >>Architecture is zero privilege. So you hear a lot about lease privilege in all the security talks. That's not what you should be aiming for. Zero privilege is what you should be aiming for. And once you're at zero privileged environments, okay, who can leak data because no natural person has access to it. Even if you have somebody invading your infrastructure, there are no privileges. They cannot do privilege escalations. Yeah. So the answer for me is really clear. If you are handling customer data, if you're and customer funds aim for zero privilege architecture, >>What, what are you most excited about next? What's next for you guys? What's next for I n G? What are we gonna be talking about when we're chatting to you Right here? Atan next year or in Amsterdam actually, since we're headed that way in the spring, which is fun. Yes. >>Happy to be your host in Amsterdam. The >>Other way around. We're holding you to that. You've talked about how fun the culture is. Now you're gonna ask, she and I we need, but we need the tee-shirts. We, we obviously need a matching outfit. >>Definitely. We'll arrange some teachers for you as well. Yeah, no, for me, two highlights from this com. The first one was kcp. That can potentially be a paradigm change on how we deal with workloads on Kubernetes. So that's very interesting. I don't know if you see any implementations by next year, but it's definitely something. Looks >>Like we had them on the show as well. Yeah. So it's, it's very fun. I'm sure, I'm sure they'll be very flattered that you just just said. What about you Arnoldo that got you most excited? >>The most important for me was talking to a lot of Asian is other people. What if they thinking how we go forward? So the, the, the community and talk to each other. And also we found those and people how we go forward. >>Yeah, that's been a big thing for us here on the cube and just the energy, the morale. I mean the open source community is so collaborative. It creates an entirely different ethos. Arna. Ty, thank you so much for being here. It's wonderful to have you and hear what I n g is doing in the technology space. Lisa, always a pleasure to co-host with you. Of course. And thank you Cube fans for hanging out with us here on day three of Cuban Live from Detroit, Michigan. My name is Savannah Peterson and we'll see you up next for a great chat coming soon.

>>Oracle continues to enhance my sequel Heatwave at a very rapid pace. The company is now in its fourth major release since the original announcement in December 2020. 1 of the main criticisms of my sequel, Heatwave, is that it only runs on O. C I. Oracle Cloud Infrastructure and as a lock in to Oracle's Cloud. Oracle recently announced that heat wave is now going to be available in AWS Cloud and it announced its intent to bring my sequel Heatwave to Azure. So my secret heatwave on AWS is a significant TAM expansion move for Oracle because of the momentum AWS Cloud continues to show. And evidently the Heatwave Engineering team has taken the development effort from O. C I. And is bringing that to A W S with a number of enhancements that we're gonna dig into today is senior vice president. My sequel Heatwave at Oracle is back with me on a cube conversation to discuss the latest heatwave news, and we're eager to hear any benchmarks relative to a W S or any others. Nippon has been leading the Heatwave engineering team for over 10 years and there's over 100 and 85 patents and database technology. Welcome back to the show and good to see you. >>Thank you. Very happy to be back. >>Now for those who might not have kept up with the news, uh, to kick things off, give us an overview of my sequel, Heatwave and its evolution. So far, >>so my sequel, Heat Wave, is a fully managed my secret database service offering from Oracle. Traditionally, my secret has been designed and optimised for transaction processing. So customers of my sequel then they had to run analytics or when they had to run machine learning, they would extract the data out of my sequel into some other database for doing. Unlike processing or machine learning processing my sequel, Heat provides all these capabilities built in to a single database service, which is my sequel. He'd fake So customers of my sequel don't need to move the data out with the same database. They can run transaction processing and predicts mixed workloads, machine learning, all with a very, very good performance in very good price performance. Furthermore, one of the design points of heat wave is is a scale out architecture, so the system continues to scale and performed very well, even when customers have very large late assignments. >>So we've seen some interesting moves by Oracle lately. The collaboration with Azure we've we've covered that pretty extensively. What was the impetus here for bringing my sequel Heatwave onto the AWS cloud? What were the drivers that you considered? >>So one of the observations is that a very large percentage of users of my sequel Heatwave, our AWS users who are migrating of Aurora or so already we see that a good percentage of my secret history of customers are migrating from GWS. However, there are some AWS customers who are still not able to migrate the O. C. I to my secret heat wave. And the reason is because of, um, exorbitant cost, which was charges. So in order to migrate the workload from AWS to go see, I digress. Charges are very high fees which becomes prohibitive for the customer or the second example we have seen is that the latency of practising a database which is outside of AWS is very high. So there's a class of customers who would like to get the benefits of my secret heatwave but were unable to do so and with this support of my secret trip inside of AWS, these customers can now get all the grease of the benefits of my secret he trip without having to pay the high fees or without having to suffer with the poorly agency, which is because of the ws architecture. >>Okay, so you're basically meeting the customer's where they are. So was this a straightforward lifted shift from from Oracle Cloud Infrastructure to AWS? >>No, it is not because one of the design girls we have with my sequel, Heatwave is that we want to provide our customers with the best price performance regardless of the cloud. So when we decided to offer my sequel, he headed west. Um, we have optimised my sequel Heatwave on it as well. So one of the things to point out is that this is a service with the data plane control plane and the console are natively running on AWS. And the benefits of doing so is that now we can optimise my sequel Heatwave for the E. W s architecture. In addition to that, we have also announced a bunch of new capabilities as a part of the service which will also be available to the my secret history of customers and our CI, But we just announced them and we're offering them as a part of my secret history of offering on AWS. >>So I just want to make sure I understand that it's not like you just wrapped your stack in a container and stuck it into a W s to be hosted. You're saying you're actually taking advantage of the capabilities of the AWS cloud natively? And I think you've made some other enhancements as well that you're alluding to. Can you maybe, uh, elucidate on those? Sure. >>So for status, um, we have taken the mind sequel Heatwave code and we have optimised for the It was infrastructure with its computer network. And as a result, customers get very good performance and price performance. Uh, with my secret he trade in AWS. That's one performance. Second thing is, we have designed new interactive counsel for the service, which means that customers can now provision there instances with the council. But in addition, they can also manage their schemas. They can. Then court is directly from the council. Autopilot is integrated. The council we have introduced performance monitoring, so a lot of capabilities which we have introduced as a part of the new counsel. The third thing is that we have added a bunch of new security features, uh, expose some of the security features which were part of the My Secret Enterprise edition as a part of the service, which gives customers now a choice of using these features to build more secure applications. And finally, we have extended my secret autopilot for a number of old gpus cases. In the past, my secret autopilot had a lot of capabilities for Benedict, and now we have augmented my secret autopilot to offer capabilities for elderly people. Includes as well. >>But there was something in your press release called Auto thread. Pooling says it provides higher and sustained throughput. High concerns concerns concurrency by determining Apple number of transactions, which should be executed. Uh, what is that all about? The auto thread pool? It seems pretty interesting. How does it affect performance? Can you help us understand that? >>Yes, and this is one of the capabilities of alluding to which we have added in my secret autopilot for transaction processing. So here is the basic idea. If you have a system where there's a large number of old EP transactions coming into it at a high degrees of concurrency in many of the existing systems of my sequel based systems, it can lead to a state where there are few transactions executing, but a bunch of them can get blocked with or a pilot tried pulling. What we basically do is we do workload aware admission control and what this does is it figures out, what's the right scheduling or all of these algorithms, so that either the transactions are executing or as soon as something frees up, they can start executing, so there's no transaction which is blocked. The advantage to the customer of this capability is twofold. A get significantly better throughput compared to service like Aurora at high levels of concurrency. So at high concurrency, for instance, uh, my secret because of this capability Uh oh, thread pulling offers up to 10 times higher compared to Aurora, that's one first benefit better throughput. The second advantage is that the true part of the system never drops, even at high levels of concurrency, whereas in the case of Aurora, the trooper goes up, but then, at high concurrency is, let's say, starting, uh, level of 500 or something. It depends upon the underlying shit they're using the troopers just dropping where it's with my secret heatwave. The truth will never drops. Now, the ramification for the customer is that if the truth is not gonna drop, the user can start off with a small shape, get the performance and be a show that even the workload increases. They will never get a performance, which is worse than what they're getting with lower levels of concurrency. So this let's leads to customers provisioning a shape which is just right for them. And if they need, they can, uh, go with the largest shape. But they don't like, you know, over pay. So those are the two benefits. Better performance and sustain, uh, regardless of the level of concurrency. >>So how do we quantify that? I know you've got some benchmarks. How can you share comparisons with other cloud databases especially interested in in Amazon's own databases are obviously very popular, and and are you publishing those again and get hub, as you have done in the past? Take us through the benchmarks. >>Sure, So benchmarks are important because that gives customers a sense of what performance to expect and what price performance to expect. So we have run a number of benchmarks. And yes, all these benchmarks are available on guitar for customers to take a look at. So we have performance results on all the three castle workloads, ol DB Analytics and Machine Learning. So let's start with the Rdp for Rdp and primarily because of the auto thread pulling feature. We show that for the IPCC for attended dataset at high levels of concurrency, heatwave offers up to 10 times better throughput and this performance is sustained, whereas in the case of Aurora, the performance really drops. So that's the first thing that, uh, tend to alibi. Sorry, 10 gigabytes. B B C c. I can come and see the performance are the throughput is 10 times better than Aurora for analytics. We have done a comparison of my secret heatwave in AWS and compared with Red Ship Snowflake Googled inquiry, we find that the price performance of my secret heatwave compared to read ship is seven times better. So my sequel, Heat Wave in AWS, provides seven times better price performance than red ship. That's a very, uh, interesting results to us. Which means that customers of Red Shift are really going to take the service seriously because they're gonna get seven times better price performance. And this is all running in a W s so compared. >>Okay, carry on. >>And then I was gonna say, compared to like, Snowflake, uh, in AWS offers 10 times better price performance. And compared to Google, ubiquity offers 12 times better price performance. And this is based on a four terabyte p PCH workload. Results are available on guitar, and then the third category is machine learning and for machine learning, uh, for training, the performance of my secret heatwave is 25 times faster compared to that shit. So all the three workloads we have benchmark's results, and all of these scripts are available on YouTube. >>Okay, so you're comparing, uh, my sequel Heatwave on AWS to Red Shift and snowflake on AWS. And you're comparing my sequel Heatwave on a W s too big query. Obviously running on on Google. Um, you know, one of the things Oracle is done in the past when you get the price performance and I've always tried to call fouls you're, like, double your price for running the oracle database. Uh, not Heatwave, but Oracle Database on a W s. And then you'll show how it's it's so much cheaper on on Oracle will be like Okay, come on. But they're not doing that here. You're basically taking my sequel Heatwave on a W s. I presume you're using the same pricing for whatever you see to whatever else you're using. Storage, um, reserved instances. That's apples to apples on A W s. And you have to obviously do some kind of mapping for for Google, for big query. Can you just verify that for me, >>we are being more than fair on two dimensions. The first thing is, when I'm talking about the price performance for analytics, right for, uh, with my secret heat rape, the cost I'm talking about from my secret heat rape is the cost of running transaction processing, analytics and machine learning. So it's a fully loaded cost for the case of my secret heatwave. There has been I'm talking about red ship when I'm talking about Snowflake. I'm just talking about the cost of these databases for running, and it's only it's not, including the source database, which may be more or some other database, right? So that's the first aspect that far, uh, trip. It's the cost for running all three kinds of workloads, whereas for the competition, it's only for running analytics. The second thing is that for these are those services whether it's like shit or snowflakes, That's right. We're talking about one year, fully paid up front cost, right? So that's what most of the customers would pay for. Many of the customers would pay that they will sign a one year contract and pay all the costs ahead of time because they get a discount. So we're using that price and the case of Snowflake. The costs were using is their standard edition of price, not the Enterprise edition price. So yes, uh, more than in this competitive. >>Yeah, I think that's an important point. I saw an analysis by Marx Tamer on Wiki Bond, where he was doing the TCO comparisons. And I mean, if you have to use two separate databases in two separate licences and you have to do et yelling and all the labour associated with that, that that's that's a big deal and you're not even including that aspect in in your comparison. So that's pretty impressive. To what do you attribute that? You know, given that unlike, oh, ci within the AWS cloud, you don't have as much control over the underlying hardware. >>So look hard, but is one aspect. Okay, so there are three things which give us this advantage. The first thing is, uh, we have designed hateful foreign scale out architecture. So we came up with new algorithms we have come up with, like, uh, one of the design points for heat wave is a massively partitioned architecture, which leads to a very high degree of parallelism. So that's a lot of hype. Each were built, So that's the first part. The second thing is that although we don't have control over the hardware, but the second design point for heat wave is that it is optimised for commodity cloud and the commodity infrastructure so we can have another guys, what to say? The computer we get, how much network bandwidth do we get? How much of, like objects to a brand that we get in here? W s. And we have tuned heat for that. That's the second point And the third thing is my secret autopilot, which provides machine learning based automation. So what it does is that has the users workload is running. It learns from it, it improves, uh, various premieres in the system. So the system keeps getting better as you learn more and more questions. And this is the third thing, uh, as a result of which we get a significant edge over the competition. >>Interesting. I mean, look, any I SV can go on any cloud and take advantage of it. And that's, uh I love it. We live in a new world. How about machine learning workloads? What? What did you see there in terms of performance and benchmarks? >>Right. So machine learning. We offer three capabilities training, which is fully automated, running in France and explanations. So one of the things which many of our customers told us coming from the enterprise is that explanations are very important to them because, uh, customers want to know that. Why did the the system, uh, choose a certain prediction? So we offer explanations for all models which have been derailed by. That's the first thing. Now, one of the interesting things about training is that training is usually the most expensive phase of machine learning. So we have spent a lot of time improving the performance of training. So we have a bunch of techniques which we have developed inside of Oracle to improve the training process. For instance, we have, uh, metal and proxy models, which really give us an advantage. We use adaptive sampling. We have, uh, invented in techniques for paralysing the hyper parameter search. So as a result of a lot of this work, our training is about 25 times faster than that ship them health and all the data is, uh, inside the database. All this processing is being done inside the database, so it's much faster. It is inside the database. And I want to point out that there is no additional charge for the history of customers because we're using the same cluster. You're not working in your service. So all of these machine learning capabilities are being offered at no additional charge inside the database and as a performance, which is significantly faster than that, >>are you taking advantage of or is there any, uh, need not need, but any advantage that you can get if two by exploiting things like gravity. John, we've talked about that a little bit in the past. Or trainee. Um, you just mentioned training so custom silicon that AWS is doing, you're taking advantage of that. Do you need to? Can you give us some insight >>there? So there are two things, right? We're always evaluating What are the choices we have from hybrid perspective? Obviously, for us to leverage is right and like all the things you mention about like we have considered them. But there are two things to consider. One is he is a memory system. So he favours a big is the dominant cost. The processor is a person of the cost, but memory is the dominant cost. So what we have evaluated and found is that the current shape which we are using is going to provide our customers with the best price performance. That's the first thing. The second thing is that there are opportunities at times when we can use a specialised processor for vaccinating the world for a bit. But then it becomes a matter of the cost of the customer. Advantage of our current architecture is on the same hardware. Customers are getting very good performance. Very good, energetic performance in a very good machine learning performance. If you will go with the specialised processor, it may. Actually, it's a machine learning, but then it's an additional cost with the customers we need to pay. So we are very sensitive to the customer's request, which is usually to provide very good performance at a very low cost. And we feel is that the current design we have as providing customers very good performance and very good price performance. >>So part of that is architectural. The memory intensive nature of of heat wave. The other is A W s pricing. If AWS pricing were to flip, it might make more sense for you to take advantage of something like like cranium. Okay, great. Thank you. And welcome back to the benchmarks benchmarks. Sometimes they're artificial right there. A car can go from 0 to 60 in two seconds. But I might not be able to experience that level of performance. Do you? Do you have any real world numbers from customers that have used my sequel Heatwave on A W s. And how they look at performance? >>Yes, absolutely so the my Secret service on the AWS. This has been in Vera for, like, since November, right? So we have a lot of customers who have tried the service. And what actually we have found is that many of these customers, um, planning to migrate from Aurora to my secret heat rape. And what they find is that the performance difference is actually much more pronounced than what I was talking about. Because with Aurora, the performance is actually much poorer compared to uh, like what I've talked about. So in some of these cases, the customers found improvement from 60 times, 240 times, right? So he travels 100 for 240 times faster. It was much less expensive. And the third thing, which is you know, a noteworthy is that customers don't need to change their applications. So if you ask the top three reasons why customers are migrating, it's because of this. No change to the application much faster, and it is cheaper. So in some cases, like Johnny Bites, what they found is that the performance of their applications for the complex storeys was about 60 to 90 times faster. Then we had 60 technologies. What they found is that the performance of heat we have compared to Aurora was 100 and 39 times faster. So, yes, we do have many such examples from real workloads from customers who have tried it. And all across what we find is if it offers better performance, lower cost and a single database such that it is compatible with all existing by sequel based applications and workloads. >>Really impressive. The analysts I talked to, they're all gaga over heatwave, and I can see why. Okay, last question. Maybe maybe two and one. Uh, what's next? In terms of new capabilities that customers are going to be able to leverage and any other clouds that you're thinking about? We talked about that upfront, but >>so in terms of the capabilities you have seen, like they have been, you know, non stop attending to the feedback from the customers in reacting to it. And also, we have been in a wedding like organically. So that's something which is gonna continue. So, yes, you can fully expect that people not dressed and continue to in a way and with respect to the other clouds. Yes, we are planning to support my sequel. He tripped on a show, and this is something that will be announced in the near future. Great. >>All right, Thank you. Really appreciate the the overview. Congratulations on the work. Really exciting news that you're moving my sequel Heatwave into other clouds. It's something that we've been expecting for some time. So it's great to see you guys, uh, making that move, and as always, great to have you on the Cube. >>Thank you for the opportunity. >>All right. And thank you for watching this special cube conversation. I'm Dave Volonte, and we'll see you next time.

>>Okay. Okay. We're back at Caesar's Forum. The Snowflake summit 2022. The cubes. Continuous coverage this day to wall to wall coverage. We're so excited to have the analyst panel here, some of my colleagues that we've done a number. You've probably seen some power panels that we've done. David McGregor is here. He's the senior vice president and research director at Ventana Research. To his left is Tony Blair, principal at DB Inside and my in the co host seat. Sanjeev Mohan Sanremo. Guys, thanks so much for coming on. I'm glad we can. Thank you. You're very welcome. I wasn't able to attend the analyst action because I've been doing this all all day, every day. But let me start with you, Dave. What have you seen? That's kind of interested you. Pluses, minuses. Concerns. >>Well, how about if I focus on what I think valuable to the customers of snowflakes and our research shows that the majority of organisations, the majority of people, do not have access to analytics. And so a couple of things they've announced I think address those are helped to address those issues very directly. So Snow Park and support for Python and other languages is a way for organisations to embed analytics into different business processes. And so I think that will be really beneficial to try and get analytics into more people's hands. And I also think that the native applications as part of the marketplace is another way to get applications into people's hands rather than just analytical tools. Because most most people in the organisation or not, analysts, they're doing some line of business function. Their HR managers, their marketing people, their salespeople, their finance people right there, not sitting there mucking around in the data. They're doing a job and they need analytics in that job. So, >>Tony, I thank you. I've heard a lot of data mesh talk this week. It's kind of funny. Can't >>seem to get away from it. You >>can't see. It seems to be gathering momentum, but But what have you seen? That's been interesting. >>What I have noticed. Unfortunately, you know, because the rooms are too small, you just can't get into the data mesh sessions, so there's a lot of interest in it. Um, it's still very I don't think there's very much understanding of it, but I think the idea that you can put all the data in one place which, you know, to me, stuff like it seems to be kind of sort of in a way, it sounds like almost like the Enterprise Data warehouse, you know, Clouded Cloud Native Edition, you know, bring it all in one place again. Um, I think it's providing, sort of, You know, it's I think, for these folks that think this might be kind of like a a linchpin for that. I think there are several other things that actually that really have made a bigger impression on me. Actually, at this event, one is is basically is, um we watch their move with Eunice store. Um, and it's kind of interesting coming, you know, coming from mongo db last week. And I see it's like these two companies seem to be going converging towards the same place at different speeds. I think it's not like it's going to get there faster than Mongo for a number of different reasons, but I see like a number of common threads here. I mean, one is that Mongo was was was a company. It's always been towards developers. They need you know, start cultivating data, people, >>these guys going the other way. >>Exactly. Bingo. And the thing is that but they I think where they're converging is the idea of operational analytics and trying to serve all constituencies. The other thing, which which also in terms of serving, you know, multiple constituencies is how snowflake is laid out Snow Park and what I'm finding like. There's an interesting I economy. On one hand, you have this very ingrained integration of Anaconda, which I think is pretty ingenious. On the other hand, you speak, let's say, like, let's say the data robot folks and say, You know something our folks wanna work data signs us. We want to work in our environment and use snowflake in the background. So I see those kind of some interesting sort of cross cutting trends. >>So, Sandy, I mean, Frank Sullivan, we'll talk about there's definitely benefits into going into the walled garden. Yeah, I don't think we dispute that, but we see them making moves and adding more and more open source capabilities like Apache iceberg. Is that a Is that a move to sort of counteract the narrative that the data breaks is put out there. Is that customer driven? What's your take on that? >>Uh, primarily I think it is to contract this whole notion that once you move data into snowflake, it's a proprietary format. So I think that's how it started. But it's hugely beneficial to the customers to the users, because now, if you have large amounts of data in parquet files, you can leave it on s three. But then you using the the Apache iceberg table format. In a snowflake, you get all the benefits of snowflakes. Optimizer. So, for example, you get the, you know, the micro partitioning. You get the meta data. So, uh, in a single query, you can join. You can do select from a snowflake table union and select from iceberg table, and you can do store procedures, user defined functions. So I think they what they've done is extremely interesting. Uh, iceberg by itself still does not have multi table transactional capabilities. So if I'm running a workload, I might be touching 10 different tables. So if I use Apache iceberg in a raw format, they don't have it. But snowflake does, >>right? There's hence the delta. And maybe that maybe that closes over time. I want to ask you as you look around this I mean the ecosystems pretty vibrant. I mean, it reminds me of, like reinvent in 2013, you know? But then I'm struck by the complexity of the last big data era and a dupe and all the different tools. And is this different, or is it the sort of same wine new new bottle? You guys have any thoughts on that? >>I think it's different and I'll tell you why. I think it's different because it's based around sequel. So if back to Tony's point, these vendors are coming at this from different angles, right? You've got data warehouse vendors and you've got data lake vendors and they're all going to meet in the middle. So in your case, you're taught operational analytical. But the same thing is true with Data Lake and Data Warehouse and Snowflake no longer wants to be known as the Data Warehouse. There a data cloud and our research again. I like to base everything off of that. >>I love what our >>research shows that organisation Two thirds of organisations have sequel skills and one third have big data skills, so >>you >>know they're going to meet in the middle. But it sure is a lot easier to bring along those people who know sequel already to that midpoint than it is to bring big data people to remember. >>Mrr Odula, one of the founders of Cloudera, said to me one time, John Kerry and the Cube, that, uh, sequel is the killer app for a Yeah, >>the difference at this, you know, with with snowflake, is that you don't have to worry about taming the zoo. Animals really have thought out the ease of use, you know? I mean, they thought about I mean, from the get go, they thought of too thin to polls. One is ease of use, and the other is scale. And they've had. And that's basically, you know, I think very much differentiates it. I mean, who do have the scale, but it didn't have the ease of use. But don't I >>still need? Like, if I have, you know, governance from this vendor or, you know, data prep from, you know, don't I still have to have expertise? That's sort of distributed in those those worlds, right? I mean, go ahead. Yeah. >>So the way I see it is snowflake is adding more and more capabilities right into the database. So, for example, they've they've gone ahead and added security and privacy so you can now create policies and do even set level masking, dynamic masking. But most organisations have more than snowflake. So what we are starting to see all around here is that there's a whole series of data catalogue companies, a bunch of companies that are doing dynamic data masking security and governance data observe ability, which is not a space snowflake has gone into. So there's a whole ecosystem of companies that that is mushrooming, although, you know so they're using the native capabilities of snowflake, but they are at a level higher. So if you have a data lake and a cloud data warehouse and you have other, like relational databases, you can run these cross platform capabilities in that layer. So so that way, you know, snowflakes done a great job of enabling that ecosystem about >>the stream lit acquisition. Did you see anything here that indicated there making strong progress there? Are you excited about that? You're sceptical. Go ahead. >>And I think it's like the last mile. Essentially. In other words, it's like, Okay, you have folks that are basically that are very, very comfortable with tableau. But you do have developers who don't want to have to shell out to a separate tool. And so this is where Snowflake is essentially working to address that constituency, um, to San James Point. I think part of it, this kind of plays into it is what makes this different from the ado Pere is the fact that this all these capabilities, you know, a lot of vendors are taking it very seriously to make put this native obviously snowflake acquired stream. Let's so we can expect that's extremely capabilities are going to be native. >>And the other thing, too, about the Hadoop ecosystem is Claudia had to help fund all those different projects and got really, really spread thin. I want to ask you guys about this super cloud we use. Super Cloud is this sort of metaphor for the next wave of cloud. You've got infrastructure aws, azure, Google. It's not multi cloud, but you've got that infrastructure you're building a layer on top of it that hides the underlying complexities of the primitives and the a p I s. And you're adding new value in this case, the data cloud or super data cloud. And now we're seeing now is that snowflake putting forth the notion that they're adding a super path layer. You can now build applications that you can monetise, which to me is kind of exciting. It makes makes this platform even less discretionary. We had a lot of talk on Wall Street about discretionary spending, and that's not discretionary. If you're monetising it, um, what do you guys think about that? Is this something that's that's real? Is it just a figment of my imagination, or do you see a different way of coming any thoughts on that? >>So, in effect, they're trying to become a data operating system, right? And I think that's wonderful. It's ambitious. I think they'll experience some success with that. As I said, applications are important. That's a great way to deliver information. You can monetise them, so you know there's there's a good economic model around it. I think they will still struggle, however, with bringing everything together onto one platform. That's always the challenge. Can you become the platform that's hard, hard to predict? You know, I think this is This is pretty exciting, right? A lot of energy, a lot of large ecosystem. There is a network effect already. Can they succeed in being the only place where data exists? You know, I think that's going to be a challenge. >>I mean, the fact is, I mean, this is a classic best of breed versus the umbrella play. The thing is, this is nothing new. I mean, this is like the you know, the old days with enterprise applications were basically oracle and ASAP vacuumed up all these. You know, all these applications in their in their ecosystem, whereas with snowflake is. And if you look at the cloud, folks, the hyper scale is still building out their own portfolios as well. Some are, You know, some hyper skills are more partner friendly than others. What? What Snowflake is saying is that we're going to give all of you folks who basically are competing against the hyper skills in various areas like data catalogue and pipelines and all that sort of wonderful stuff will make you basically, you know, all equal citizens. You know the burden is on you to basically we will leave. We will lay out the A P. I s Well, we'll allow you to basically, you know, integrate natively to us so you can provide as good experience. But the but the onus is on your back. >>Should the ecosystem be concerned, as they were back to reinvent 2014 that Amazon was going to nibble away at them or or is it different? >>I find what they're doing is different. Uh, for example, data sharing. They were the first ones out the door were data sharing at a large scale. And then everybody has jumped in and said, Oh, we also do data sharing. All the hyper scholars came in. But now what snowflake has done is they've taken it to the next level. Now they're saying it's not just data sharing. It's up sharing and not only up sharing. You can stream the thing you can build, test deploy, and then monetise it. Make it discoverable through, you know, through your marketplace >>you can monetise it. >>Yes. Yeah, so So I I think what they're doing is they are taking it a step further than what hyper scale as they are doing. And because it's like what they said is becoming like the data operating system You log in and you have all of these different functionalities you can do in machine learning. Now you can do data quality. You can do data preparation and you can do Monetisation. Who do you >>think is snowflakes? Biggest competitor? What do you guys think? It's a hard question, isn't it? Because you're like because we all get the we separate computer from storage. We have a cloud data and you go, Okay, that's nice, >>but there's, like, a crack. I think >>there's uniqueness. I >>mean, put it this way. In the old days, it would have been you know, how you know the prime household names. I think today is the hyper scholars and the idea what I mean again, this comes down to the best of breed versus by, you know, get it all from one source. So where is your comfort level? Um, so I think they're kind. They're their co op a Titian the hyper scale. >>Okay, so it's not data bricks, because why they're smaller. >>Well, there is some okay now within the best of breed area. Yes, there is competition. The obvious is data bricks coming in from the data engineering angle. You know, basically the snowflake coming from, you know, from the from the data analyst angle. I think what? Another potential competitor. And I think Snowflake, basically, you know, admitted as such potentially is mongo >>DB. Yeah, >>Exactly. So I mean, yes, there are two different levels of sort >>of a on a longer term collision course. >>Exactly. Exactly. >>Sort of service now and in salesforce >>thing that was that we actually get when I say that a lot of people just laughed. I was like, No, you're kidding. There's no way. I said Excuse me, >>But then you see Mongo last week. We're adding some analytics capabilities and always been developers, as you say, and >>they trashed sequel. But yet they finally have started to write their first real sequel. >>We have M c M Q. Well, now we have a sequel. So what >>were those numbers, >>Dave? Two thirds. One third. >>So the hyper scale is but the hyper scale urz are you going to trust your hyper scale is to do your cross cloud. I mean, maybe Google may be I mean, Microsoft, perhaps aws not there yet. Right? I mean, how important is cross cloud, multi cloud Super cloud Whatever you want to call it What is your data? >>Shows? Cloud is important if I remember correctly. Our research shows that three quarters of organisations are operating in the cloud and 52% are operating across more than one cloud. So, uh, two thirds of the organisations are in the cloud are doing multi cloud, so that's pretty significant. And now they may be operating across clouds for different reasons. Maybe one application runs in one cloud provider. Another application runs another cloud provider. But I do think organisations want that leverage over the hyper scholars right they want they want to be able to tell the hyper scale. I'm gonna move my workloads over here if you don't give us a better rate. Uh, >>I mean, I I think you know, from a database standpoint, I think you're right. I mean, they are competing against some really well funded and you look at big Query barely, you know, solid platform Red shift, for all its faults, has really done an amazing job of moving forward. But to David's point, you know those to me in any way. Those hyper skills aren't going to solve that cross cloud cloud problem, right? >>Right. No, I'm certainly >>not as quickly. No. >>Or with as much zeal, >>right? Yeah, right across cloud. But we're gonna operate better on our >>Exactly. Yes. >>Yes. Even when we talk about multi cloud, the many, many definitions, like, you know, you can mean anything. So the way snowflake does multi cloud and the way mongo db two are very different. So a snowflake says we run on all the hyper scalar, but you have to replicate your data. What Mongo DB is claiming is that one cluster can have notes in multiple different clouds. That is right, you know, quite something. >>Yeah, right. I mean, again, you hit that. We got to go. But, uh, last question, um, snowflake undervalued, overvalued or just about right >>in the stock market or in customers. Yeah. Yeah, well, but, you know, I'm not sure that's the right question. >>That's the question I'm asking. You know, >>I'll say the question is undervalued or overvalued for customers, right? That's really what matters. Um, there's a different audience. Who cares about the investor side? Some of those are watching, but But I believe I believe that the from the customer's perspective, it's probably valued about right, because >>the reason I I ask it, is because it has so hyped. You had $100 billion value. It's the past service now is value, which is crazy for this student Now. It's obviously come back quite a bit below its IPO price. So But you guys are at the financial analyst meeting. Scarpelli laid out 2029 projections signed up for $10 billion.25 percent free time for 20% operating profit. I mean, they better be worth more than they are today. If they do >>that. If I If I see the momentum here this week, I think they are undervalued. But before this week, I probably would have thought there at the right evaluation, >>I would say they're probably more at the right valuation employed because the IPO valuation is just such a false valuation. So hyped >>guys, I could go on for another 45 minutes. Thanks so much. David. Tony Sanjeev. Always great to have you on. We'll have you back for sure. Having us. All right. Thank you. Keep it right there. Were wrapping up Day two and the Cube. Snowflake. Summit 2022. Right back. Mm. Mhm.

(upbeat music) >> Hello, and welcome to the special Cube conversation that we're launching in conjunction with Pure Accelerate. Prakash Darji is here, is the general manager of Digital Experience. They actually have a business unit dedicated to this at Pure Storage. Prakash, welcome back, good to see you. >> Yeah Dave, happy to be here. >> So a few weeks back, you and I were talking about the Shift 2 and as a service economy and which is a good lead up to Accelerate, held today, we're releasing this video in LA. This is the fifth in person Accelerate. It's got a new tagline techfest so you're making it fun, but still hanging out to the tech, which we love. So this morning you guys made some announcements expanding the portfolio. I'm really interested in your reaffirmed commitment to Evergreen. That's something that got this whole trend started in the introduction of Evergreen Flex. What is that all about? What's your vision for Evergreen Flex? >> Well, so look, this is one of the biggest moments that I think we have as a company now, because we introduced Evergreen and that was and probably still is one of the largest disruptions to happen to the industry in a decade. Now, Evergreen Flex takes the power of modernizing performance and capacity to storage beyond the box, full stop. So we first started on a project many years ago to say, okay, how can we bring that modernization concept to our entire portfolio? That means if someone's got 10 boxes, how do you modernize performance and capacity across 10 boxes or across maybe FlashBlade and FlashArray. So with Evergreen Flex, we first are starting to hyper disaggregate performance and capacity and the capacity can be moved to where you need it. So previously, you could have thought of a box saying, okay, it has this performance or capacity range or boundary, but let's think about it beyond the box. Let's think about it as a portfolio. My application needs performance or capacity for storage, what if I could bring the resources to it? So with Evergreen Flex within the QLC family with our FlashBlade and our FlashArray QLC projects, you could actually move QLC capacity to where you need it. And with FlashArray X and XL or TLC family, you could move capacity to where you need it within that family. Now, if you're enabling that, you have to change the business model because the capacity needs to get build where you use it. If you use it in a high performance tier, you could build at a high performance rate. If you use it as a lower performance tier, you could build at a lower performance rate. So we changed the business model to enable this technology flexibility, where customers can buy the hardware and they get a pay per use consumption model for the software and services, but this enables the technology flexibility to use your capacity wherever you need. And we're just continuing that journey of hyper disaggregated. >> Okay, so you solve the problem of having to allocate specific capacity or performance to a particular workload. You can now spread that across whatever products in the portfolio, like you said, you're disaggregating performance and capacity. So that's very cool. Maybe you could double click on that. You obviously talk to customers about doing this. They were in pain a little bit, right? 'Cause they had this sort of stovepipe thing. So talk a little bit about the customer feedback that led you here. >> Well, look, let's just say today if you're an application developer or you haven't written your app yet, but you know you're going to. Well, you need that at least say I need something, right? So someone's going to ask you what kind of storage do you need? How many IOPS, what kind of performance capacity, before you've written your code. And you're going to buy something and you're going to spend that money. Now at that point, you're going to go write your application, run it on that box and then say, okay, was I right or was I wrong? And you know what? You were guessing before you wrote the software. After you wrote the software, you can test it and decide what you need, how it's going to scale, et cetera. But if you were wrong, you already bought something. In a hyper disaggregated world, that capacity is not a sunk cost, you can use it wherever you want. You can use capacity of somewhere else and bring it over there. So in the world of application development and in the world of storage, today people think about, I've got a workload, it's SAP, it's Oracle, I've built this custom app. I need to move it to a tier of storage, a performance class. Like you think about the application and you think about moving the application. And it takes time to move the application, takes performance, takes loan, it's a scheduled event. What if you said, you know what? You don't have to do any of that. You just move the capacity to where you need it, right? >> Yep. >> So the application's there and you actually have the ability to instantaneously move the capacity to where you need it for the application. And eventually, where we're going is we're looking to do the same thing across the performance hearing. So right now, the biggest benefit is the agility and flexibility a customer has across their fleet. So Evergreen was great for the customer with one array, but Evergreen Flex now brings that power to the entire fleet. And that's not tied to just FlashArray or FlashBlade. We've engineered a data plane in our direct flash fabric software to be able to take on the personality of the system it needs to go into. So when a data pack goes into a FlashBlade, that data pack is optimized for use in that scale out architecture with the metadata for FlashBlade. When it goes into a FlashArray C it's optimized for that metadata structure. So our Purity software has made this transformative to be able to do this. And we created a business model that allowed us to take advantage of this technology flexibility. >> Got it. Okay, so you got this mutually interchangeable performance and capacity across the portfolio beautiful. And I want to come back to sort of the Purity, but help me understand how this is different from just normal Evergreen, existing evergreen options. You mentioned the one array, but help us understand that more fully. >> Well, look, so in addition to this, like we had Evergreen Gold historically. We introduced Evergreen Flex and we had Pure as a service. So you had kind of two spectrums previously. You had Evergreen Gold on one hand, which modernized the performance and capacity of a box. You had Pure as a service that said don't worry about the box, tell me how many IOPS you have and will run and operate and manage that service for you. I think we've spoken about that previously on theCUBE. >> Yep. >> Now, we have this model where it's not just about the box, we have this model where we say, you know what, it's your fleet. You're going to run and operate and manage your fleet and you could move the capacity to where you need it. So as we started thinking about this, we decided to unify our entire portfolio of sub software and subscription services under the Evergreen brand. Evergreen Gold we're renaming to Evergreen Forever. We've actually had seven customers just crossed a decade of updates Forever Evergreen within a box. So Evergreen Forever is about modernizing a box. Evergreen Flex is about modernizing your fleet and Evergreen one, which is our rebrand of Pure as a service is about modernizing your labor. Instead of you worrying about it, let us do it for you. Because if you're an application developer and you're trying to figure out, where should I put my capacity? Where should I do it? You can just sign up for the IOPS you need and let us actually deliver and move the components to where you need it for performance, capacity, management, SLAs, et cetera. So as we think about this, for us this is a spectrum and a continuum of where you're at in the modernization journey to software subscription and services. >> Okay, got it. So why did you feel like now was the right time for the rebranding and the renaming convention, what's behind? What was the thing? Take us inside the internal conversations and the chalkboard discussion? >> Well, look, the chalkboard discussion's simple. It's everything was built on the Evergreen stateless architecture where within a box, right? We disaggregated the performance and capacity within the box already, 10 years ago within Evergreen. And that's what enabled us to build Pure as a service. That's why I say like when companies say they built a service, I'm like it's not a service if you have to do a data migration. You need a stateless architecture that's disaggregated. You can almost think of this as the anti hyper-converge, right? That's going the other way. It's hyper disaggregated. >> Right. >> And that foundation is true for our whole portfolio. That was fundamental, the Evergreen architecture. And then if Gold is modernizing a box and Flex is modernizing your fleet and your portfolio and Pure as a service is modernizing the labor, it is more of a continuation in the spectrum of how do you ensure you get better with age, right? And it's like one of those things when you think about a car. Miles driven on a car means your car's getting older and it doesn't necessarily get better with age, right? What's interesting when you think about the human body, yeah, you get older and some people deteriorate with age and some people it turns out for a period of time, you pick up some muscle mass, you get a little bit older, you get a little bit wiser and you get a little bit better with age for a while because you're putting in the work to modernize, right? But where in infrastructure and hardware and technology are you at the point where it always just gets better with age, right? We've introduced that concept 10 years ago. And we've now had proven industry success over a decade, right? As I mentioned, our first seven customers who've had a decade of Evergreen update started with an FA-300 way back when, and since then performance and capacity has been getting better over time with Evergreen Forever. So this is the next 10 years of it getting better and better for the company and not just tying it to the box because now we've grown up, we've got customers with like large fleets. I think one of our customers just hit 900 systems, right? >> Wow. >> So when you have 900 systems, right? And you're running a fleet you need to think about, okay, how am I using these resources? And in this day and age in that world, power becomes a big thing because if you're using resources inefficiently and the cost of power and energy is up, you're going to be in a world of hurt. So by using Flex where you can move the capacity to where it's needed, you're creating the most efficient operating environment, which is actually the lowest power consumption environment as well. >> Right. >> So we're really excited about this journey of modernizing, but that rebranding just became kind of a no brainer to us because it's all part of the spectrum on your journey of whether you're a single array customer, you're a fleet customer, or you don't want to even run, operate and manage. You can actually just say, you know what, give me the guarantee in the SLA. So that's the spectrum that informed the rebranding. >> Got it. Yeah, so to your point about the human body, all you got to do is look at Tom Brady's NFL combine videos and you'll see what a transformation. Fine wine is another one. I like the term hyper disaggregated because that to me is consistent with what's happening with the cloud and edge. We're building this hyper distributed or disaggregated system. So I want to just understand a little bit about you mentioned Purity so there's this software obviously is the enabler here, but what's under the covers? Is it like a virtualizer or megaload balancer, metadata manager, what's the tech behind this? >> Yeah, so we'll do a little bit of a double tech, right? So we have this concept of drives where in Purity, we build our own software for direct flash that takes the NAND and we do the NAND management as we're building our drives in Purity software. Now ,that advantage gives us the ability to say how should this drive behave? So in a FlashArray C system, it can behave as part of a FlashArray C and its usable capacity that you can write because the metadata and some of the system information is in NVRAM as part of the controller, right? So you have some metadata capability there. In a legend architecture for example, you have a distributed Blade architecture. So you need parts of that capacity to operate almost like a single layer chip where you can actually have metadata operations independent of your storage operations that operate like QLC. So we actually manage the NAND in a very very different way based on the persona of the system it's going into, right? So this capacity to make it usable, right? It's like saying a competitor could go ahead name it, Dell that has power max in Isilon, HPE that has single store and three power and nimble and like you name, like can you really from a technology standpoint say your capacity can be used anywhere or all these independent systems. Everyone's thinking about the world like a system, like here's this system, here's that system, here's that system. And your capacity is locked into a system. To be able to unlock that capacity to the system, you need to behave differently with the media type in the operating environment you're going into and that's what Purity does, right? So we are doing that as part of our direct Flex software around how we manage these drives to enable this. >> Well, it's the same thing in the cloud precaution, right? I mean, you got different APIs and primitive for object, for block, for file. Now, it's all programmable infrastructure so that makes it easier, but to the point, it's still somewhat stovepipe. So it's funny, it's good to see your commitment to Evergreen, I think you're right. You lay down the gauntlet a decade plus ago. First everybody ignored you and then they kind of laughed at you, then they criticized you, and then they said, oh, then you guys reached the escape velocity. So you had a winning hand. So I'm interested in that sort of progression over the past decade where you're going, why this is so important to your customers, where you're trying to get them ultimately. >> Well, look, the thing that's most disappointing is if I bought 100 terabytes still have to re-buy it every three or five years. That seems like a kind of ridiculous proposition, but welcome to storage. You know what I mean? That's what most people do with Evergreen. We want to end data migrations. We want to make sure that every software updates, hardware updates, non disruptive. We want to make it easy to deploy and run at scale for your fleet. And eventually we want everyone to move to our Evergreen one, formerly Pure as a service where we can run and operate and manage 'cause this is all about trust. We're trying to create trust with the customer to say, trust us, to run and operate and scale for you and worry about your business because we make tech easy. And like think about this hyper disaggregated if you go further. If you're going further with hyper disaggregated, you can think about it as like performance and capacity is your Lego building blocks. Now for anyone, I have a son, he wants to build a Lego Death Star. He didn't have that manual, he's toast. So when you move to at scale and you have this hyper disaggregated world and you have this unlimited freedom, you have unlimited choice. It's the problem of the cloud today, too much choice, right? There's like hundreds of instances of this, what do I even choose? >> Right. >> Well, so the only way to solve that problem and create simplicity when you have so much choice is put data to work. And that's where Pure one comes in because we've been collecting and we can scan your landscape and tell you, you should move these types of resources here and move those types of resources there, right? In the past, it was always about you should move this application there or you should move this application there. We're actually going to turn the entire industry on it's head. It's not like applications and data have gravity. So let's think about moving resources to where that are needed versus saying resources are a fixed asset, let's move the applications there. So that's a concept that's new to the industry. Like we're creating that concept, we're introducing that concept because now we have the technology to make that reality a new efficient way of running storage for the world. Like this is that big for the company. >> Well, I mean, a lot of the failures in data analytics and data strategies are a function of trying to jam everything into a single monolithic system and hyper centralize it. Data by its very nature is distributed. So hyper disaggregated fits that model and the pendulum's clearly swinging to that. Prakash, great to have you, purestorage.com I presume is where I can learn more? >> Oh, absolutely. We're super excited and our pent up by demand I think in this space is huge so we're looking forward to bringing this innovation to the world. >> All right, hey, thanks again. Great to see you, I appreciate you coming on and explaining this new model and good luck with it. >> All right, thank you. >> All right, and thanks for watching. This is David Vellante, and appreciate you watching this Cube conversation, we'll see you next time. (upbeat music)

(upbeat music) >> Welcome back. We're winding down theCUBE's coverage of Red Hat Summit 2022. We're here at the Seaport in Boston. It's been two days of a little different Red Hat Summit. We're used to eight, 9,000 people. It's much smaller event this year, fewer developers or actually in terms of the mix, a lot more suits this year, which is kind of interesting to see that evolution and a big virtual audience. And I love the way, the keynotes we've noticed are a lot tighter. They're pithy, on time, they're not keeping us in the hall for three hours. So we appreciate that kind of catering to the virtual audience. Dave Vellante here with my co-host, Paul Gillin. As to say things are winding down, there was an analyst event here today, that's ended, but luckily we have Jim Mercer here as a research director at IDC. He's going to share maybe some of the learnings from that event today and this event overall, we're going to talk about DevSecOps. And Kirsten Newcomer is director of security, product management and hybrid platforms at Red Hat. Folks, welcome. >> Thank you. >> Thank you. >> Great to see you. >> Great to be here. >> Security's everywhere, right? You and I have spoken about the supply chain hacks, we've done some sort of interesting work around that and reporting around that. I feel like SolarWinds created a new awareness. You see these moments, it's Stuxnet, or WannaCry and now is SolarWinds very insidious, but security, Red Hat, it's everywhere in your portfolio. Maybe talk about the strategy. >> Sure, absolutely. We feel strongly that it's really important that security be something that is managed in a holistic way present throughout the application stack, starting with the operating system and also throughout the life cycle, which is partly where DevSecOps comes in. So Red Hat has kind of had a long history here, right? Think SELinux and Red Hat Enterprise Linux for mandatory access control. That's been a key component of securing containers in a Kubernetes environment. SELinux has demonstrated the ability to prevent or mitigate container escapes to the file system. And we just have continued to work up the stack as we go, our acquisition of stack rocks a little over a year ago, now known as Red Hat Advanced Cluster Security, gives us the opportunity to really deliver on that DevSecOps component. So Kubernetes native security solution with the ability to both help shift security left for the developers by integrating in the supply chain, but also providing a SecOps perspective for the operations and the security team and feeding information between the two to really try and do that closed infinity loop and then an additional investment more recently in sigstore and some technologies. >> Interesting. >> Yeah, is interesting. >> Go ahead. >> But Shift Left, explain to people what you mean by Shift Left for people might not be familiar with that term. >> Fair enough. For many, many years, right, IT security has been something that's largely been part of an operations environment and not something that developers tended to need to be engaged in with the exception of say source code static analysis tools. We started to see vulnerability management tools get added, but even then they tend to come after the application has been built. And I even ran a few years ago, I ran into a customer who said my security team won't let me get this information early. So Shift Left is all about making sure that there are security gates in the app dev process and information provided to the developer as early as possible. In fact, even in the IDE, Red Hat code ready dependency analytics does that, so that the developers are part of the solution and don't have to wait and get their apps stalled just before it's ready to go into deployment. >> Thank you. You've also been advocating for supply chain security, software supply chain. First of all, explain what a software supply chain is and then, what is unique about the security needs of that environment? >> Sure. And the SolarWinds example, as Dave said, really kind of has raised awareness around this. So just like we use the term supply chain, most people given kind of what's been happening with the pandemic, they've started hearing that term a lot more than they used to, right? So there's a supply chain to get your groceries, to the grocery store, food to the grocery store. There's a supply chain for manufacturing, where do the parts come for the laptops that we're all using, right? And where do they get assembled? Software has a supply chain also, right? So for years and even more so now, developers have been including open source components into the applications they build. So some of the supplies for the applications, the components of those applications, they can come from anywhere in the world. They can come from a wide range of open source projects. Developers are adding their custom code to that. All of this needs to be built together, delivered together and so when we think about a supply chain and the SolarWinds hack, right, there are a couple of elements of supply chain security that are particularly key. The executive order from May of last year, I think was partly in direct response to the SolarWinds hack. And it calls out that we need a software bill of materials. Now again, in manufacturing that's something folks are used to, I actually had the opportunity to contribute to the software package data exchange format, SPDX when it was first started, I've lost track of when that was. But an S-bomb is all about saying, what are all of those components that I'm delivering in my solution? It might be an application layer. It might be the host operating system layer, but at every layer. And if I know what's in what I'm delivering, I have the opportunity to learn more information about those components to track where does Log4Shell, right? When the Log4j or Spring4Shell, which followed shortly thereafter. When those hit, how do I find out which solutions that I'm running have the vulnerable components in them and where are they? The software bill of materials helps with that but you also have to know where, right. And that's the Ops side. I feel like I missed a piece of your question. >> No, it's not a silver bullet though, to your point and Log4j very widely used, but let's bring Jim into the conversation. So Jim, we've been talking about some of these trends, what's your focus area of research? What are you seeing as some of the mega trends in this space? >> I mean, I focus in DevOps and DevSecOps and it's interesting just talking about trends. Kirsten was mentioning the open source and if you look back five, six, seven years ago and you went to any major financial institution, you asked them if they use an open source. Oh, no. >> True. >> We don't use that, right. We wrote it all here. It's all from our developers-- >> Witchcraft. >> Yeah, right, exactly. But the reality is, they probably use a little open source back then but they didn't realize it. >> It's exactly true. >> However, today, not only are they not on versed to open source, they're seeking it out, right. So we have survey data that kind of indicates... A survey that was run kind of in late 2021 that shows that 70% of those who responded said that within the next two years 90% of their applications will be made up of open source. In other words, the content of an application, 10% will be written by themselves and 90% will come from other sources. So we're seeing these more kind of composite applications. Not, everybody's kind of, if you will, at that 90%, but applications are much more composite than they were before. So I'm pulling in pieces, but I'm taking the innovation of the community. So I not only have the innovation of my developers, but I can expand that. I can take the innovation to the community and bring that in and do things much quicker. I can also not have my developers worry about things that, maybe just kind of common stuff that's out there that might have already been written. In other words, just focus on the business logic, don't focus on, how to get orders or how to move widgets and those types of things that everybody does 'cause that's out there in open source. I'll just take that, right. I'll take it, somebody's perfected it, better than I'll ever do. I'll take that in and then I'll just focus and build my business logic on top of that. So open source has been a boom for growth. And I think we've heard a little bit of that (Kirsten laughs) in the last two days-- >> In the Keynotes. >> From Red Hat, right. But talking about the software bill of materials, and then you think about now I taking all that stuff in, I have my first level open source that I took in, it's called it component A. But behind component A is all these transitive dependencies. In other words, open source also uses open source, right? So there's this kind of this, if you will, web or nest, if you want to call it that, of transitive dependencies that need to be understood. And if I have five, six layers deep, I have a vulnerability in another component and I'm over here. Well, guess what? I picked up that vulnerability, right. Even though I didn't explicitly go for that component. So that's where understanding that software bill of materials is really important. I like to explain it as, during the pandemic, we've all experienced, there was all this contact tracing. It was a term where all came to mind. The software bill of materials is like the contact tracing for your open source, right. >> Good analogy. >> Anything that I've come in contact with, just because I came in contact with it, even though I didn't explicitly go looking for COVID, if you will, I got it, right. So in the same regard, that's how I do the contact tracing for my software. >> That 90% figure is really striking. 90% open source use is really striking, considering that it wasn't that long ago that one of the wraps on open source was it's insecure because anybody can see the code, therefore anybody can see the vulnerabilities. What changed? >> I'll say that, what changed is kind of first, the understanding that I can leapfrog and innovate with open source, right? There's more open source content out there. So as organizations had to digitally transform themselves and we've all heard the terminology around, well, hey, with the pandemic, we've leapfrog up five years of digital transformation or something along those lines, right? Open source is part of what helps those teams to do that type of leapfrog and do that type of innovation. You had to develop all of that natively, it just takes too long, or you might not have the talent to do it, right. And to find that talent to do it. So it kind of gives you that benefit. The interesting thing about what you mentioned there was, now we're hearing about all these vulnerabilities, right, in open source, that we need to contend with because the bad guys realize that I'm taking a lot of open source and they're saying, geez, that's a great way to get myself into applications. If I get myself into this one open source component, I'll get into thousands or more applications. So it's a fast path into the supply chain. And that's why it's so important that you understand where your vulnerabilities are in the software-- >> I think the visibility cuts two ways though. So when people say, it's insecure because it's visible. In fact, actually the visibility helps with security. The reality that I can go see the code, that there is a community working on finding and fixing vulnerabilities in that code. Whereas in code that is not open source it's a little bit more security by obscurity, which isn't really security. And there could well be vulnerabilities that a good hacker is going to find, but are not disclosed. So one of the other things we feel strongly about at Red Hat, frankly, is if there is a CVE that affects our code, we disclose that publicly, we have a public CVE database. And it's actually really important to us that we share that, we think we share way more information about issues in our code than most other users or consumers of open source and we work that through the broad community as well. And then also for our enterprise customers, if an issue needs to be fixed, we don't just fix it in the most recent version of the open source. We will backport that fix. And one of the challenges, if you're only addressing the most recent version, that may not be well tested, it might have other bugs, it might have other issues. When we backport a security vulnerability fix, we're able to do that to a stable version, give the customers the benefit of all the testing and use that's gone on while also fixing. >> Kirsten, can you talk about the announcements 'cause everybody's wondering, okay, now what do I do about this? What technology is there to help me? Obviously this framework, you got to follow the right processes, skill sets, all that, not to dismiss that, that's the most important part, but the announcements that you made at Red Hat Summit and how does the StackRox acquisition fit into those? >> Sure. So in particular, if we stick with DevSecOps a minute, but again, I'll do. Again for me, DevSecOps is the full life cycle and many people think of it as just that Shift Left piece. But for me, it's the whole thing. So StackRox ACS has had the ability to integrate into the CI/CD pipeline before we bought them. That continues. They don't just assess for vulnerabilities, but also for application misconfigurations, excess proof requests and helm charts, deployment YAML. So kind of the big, there are two sort of major things in the DevSecOps angle of the announcement or the supply chain angle of the announcement, which is the investment that we've been making in sigstore, signing, getting integrity of the components, the elements you're deploying is important. I have been asked for years about the ability to sign container images. The reality is that the signing technology and Red Hat signs everything we ship and always have, but the signing technology wasn't designed to be used in a CI/CD pipeline and sigstore is explicitly designed for that use case to make it easy for developers, as well as you can back it with full CO, you can back it with an OIDC based signing, keyless signing, throw away the key. Or if you want that enterprise CA, you can have that backing there too. >> And you can establish that as a protocol where you must. >> You can, right. So our pattern-- >> So that would've helped with SolarWinds. >> Absolutely. >> Because they were putting in malware and then taking it out, seeing what happened. My question was, could sigstore help? I always evaluate now everything and I'm not a security expert, but would this have helped with SolarWinds? A lot of times the answer is no. >> It's a combination. So a combination of sigstore integrated with Tekton Chains. So we ship Tekton, which is a Kubernetes supply chain pipeline. As OpenShift pipelines, we added chains to that. Chains allows you to attest every step in your pipeline. And you're doing that attestation by signing those steps so that you can validate that those steps have not changed. And in fact, the folks at SolarWinds are using Tekton Chains. They did a great talk in October at KubeCon North America on the changes they've made to their supply chain. So they're using both Tekton Chains and sigstore as part of their updated pipeline. Our pattern will allow our customers to deploy OpenShift, advanced cluster manager, advanced cluster security and Quay with security gates in place. And that include a pipeline built on Tekton with Tekton Chains there to sign those steps in the pipeline to enable signing of the code that's moving through that pipeline to store that signature in Quay and to validate the image signature upon deployment with advanced cluster security. >> So Jim, your perspective on this, Red Hat's, I mean, you care about security, security's everywhere, but you're not a security company. You follow security companies. There's like far too many of them. CISOs all say my number one challenge is lack of talent, but I have all these tools to deal with. You see new emerging companies that are doing pretty well. And then you see a company that's highly respected, like an Okta screw up the communications on a pretty benign hack. Actually, when you peel the onion on that, it's just this mess (chuckles) and it doesn't seem like it's going to get any simpler. Maybe the answer is companies like Red Hat kind of absorbing that and taking care of it. What do you see there? I mean, maybe it's great for business 'cause you've got so many companies. >> There's a lot of companies and there's certainly a lot of innovation out there and unique ways to make security easier, right. I mean, one of the keys here is to be able to make security easier for developers, right. One of the challenges with adopting DevSecOps is if DevSecOps creates a lot of friction in the process, it's hard to really... I can do it once, but I can't keep doing that and get the same kind of velocity. So I need to take the friction out of the process. And one of the challenges a lot of organizations have, and I've heard this from the development side, but I've also heard it from the InfoSec side, right. Because I take inquiry for people on InfoSec, and they're like, how do I get these developers to do what I want? And part of the challenge they have is like, I got these teams using these tools. I got those teams using those tools. And it's a similar challenge that we saw on DevOps where there's just too many, if you will, too many dang tools, right. So that is a challenge for organizations is, they're trying to kind of normalize the tools. Interestingly, we did a survey, I think around last August or something. And one of the questions was around, where do you want your security? Where do you want to get your DevSecOps security from, do you want to get it from individual vendors? Or do you want to get it from like, your platforms that you're using and deploying changes in Kubernetes. >> Great question. What did they say? >> The majority of them, they're hoping they can get it built into the platform. That's really what they want. And you see a lot of the security vendors are trying to build security platforms. Like we're not just assess tool, we're desk, we're this, whatever. And they're building platforms to kind of be that end-to-end security platform, trying to solve that problem, right, to make it easier to kind of consume the product overall, without a bunch of individual tools along the way. But certainly tool sprawl is definitely a challenge out there. Just one other point around the sigstore stuff which I love. Because that goes back to the supply chain and talking about digital providence, right. Understanding where things... How do I validate that what I gave you is what you thought it was, right. And what I like about it with Tekton Chains is because there's a couple things. Well, first of all, I don't want to just sign things after I built the binary. Well, I mean, I do want to sign it, but I want to just sign things once, right. Because all through the process, I think of it as a manufacturing plant, right. I'm making automobiles. If I check the quality of the automobile at one stage and I don't check it to the other, things have changed, right. How do I know that I did something wasn't compromised, right. So with sigstore kind of tied in with Tekton Chains, kind of gives me that view. And the other aspect I like it about is, this kind of transparency in the log, right-- >> The report component. >> Exactly. So I can see what was going on. So there is some this kind of like public scrutiny, like if something bad happened, you could go back and see what happened there and it wasn't as you were expected. >> As with most discussions on this topic, we could go for an hour because it's really important. And thank you guys for coming on and sharing your perspectives, the data. >> Our pleasure. >> And keep up the good work. Kirsten, it's on you. >> Thanks so much. >> The IDC survey said it, they want it in platforms. You're up. >> (laughs) That's right. >> All right. Good luck to both you. >> Thank you both so much. >> All right. And thank you for watching. We're back to wrap right after this short break. This is Dave Vellante for Paul Gill. You're watching theCUBE. (upbeat music)

welcome back to the seaport in boston massachusetts with cities crazy with bruins and celtics talk but we're here we're talking red hat linux open shift ansible and ashesh badani is here he's the senior vice president and the head of products at red hat fresh off the keynotes had amex up in the state of great to see you face to face amazing that we're here now after two years of of the isolation economy welcome back thank you great to see you again as well and you as well paul yeah so no shortage of announcements uh from red hat this week paul wrote a piece on siliconangle.com i got my yellow highlights i've been through all the announcements which is your favorite baby hard for me to choose hard for me to choose um i'll talk about real nine right well nine's exciting um and in a weird way it's exciting because it's boring right because it's consistent three years ago we committed to releasing a major well uh every three years right so customers partners users can plan for it so we released the latest version of rel in between we've been delivering releases every six months as well minor releases a lot of capabilities that are bundled in around security automation edge management and then rel is also the foundation of the work we announced with gm with the in-vehicle operating system so you know that's extremely exciting news for us as well and the collaboration that we're doing with them and then a whole host of other announcements around you know cloud services work around devsecops and so on so yeah a lot of news a lot of announcements i would say rel nine and the work with gm probably you know comes right up to the top i wanted to get to one aspect of the rail 9 announcement that is the the rose centos streams in that development now in december i think it was red hat discontinued development or support for for centos and moved to central streams i'm still not clear what the difference is between the two can you clarify that i think we go into a situation especially with with many customers many partners as well that you know didn't sort of quite exactly uh get a sense of you know where centos was from a life cycle perspective so was it upstream to rel was it downstream to rel what's the life cycle for itself as well and then there became some sort of you know implied notions around what that looked like and so what we decided was to say well we'll make a really clean break and we'll say centos stream is the upstream for enterprise linux from day one itself partners uh you know software partners hardware partners can collaborate with us to develop rel and then take it all the way through life cycle right so now it becomes a true upstream a true place for development for us and then rel essentially comes uh out as a series of releases based on the work that we do in a fast-moving center-os environment but wasn't centos essentially that upstream uh development environment to begin with what's the difference between centos stream yeah it wasn't wasn't um it wasn't quite upstream it was actually a little bit downstream yeah it was kind of bi-directional yeah and yeah and so then you know that sort of became an implied life cycle to it when there really wasn't one but it was just became one because of some usage and adoption and so now this really clarifies the relationship between the two we've heard feedback for example from software partners users saying hey what do i do for development because i used you know centervis in the past we're like yup we have real for developers available we have rel for small teams available we have rel available for non-profit organizations up and so we've made rail now available in various form factors for the needs that folks had and they were perhaps using centos for because there was no such alternative or rel history so language so now it's this clarity so that's really the key point there so language matters a lot in the technology business we've seen it over the years the industry coalesces around you know terminology whether it was the pc era everything was pc this pc that the internet era and and certainly the cloud we we learned a lot of language from the likes of you know aws two pizza teams and working backwards and things like that became common commonplace hybrid and multi-cloud are kind of the the parlance of the day you guys use hybrid you and i have talked about this i feel like there's something new coming i don't think my term of super cloud is the right necessary terminology but it signifies something different and i feel like your announcements point to that within your hybrid umbrella point being so much talk about the edge and it's we heard paul cormier talk about new hardware architectures and you're seeing that at the edge you know what you're doing with the in-vehicle operating system these are new the cloud isn't just a a bunch of remote services in the cloud anymore it's on-prem it's a cloud it's cross-clouds it's now going out to the edge it's something new and different i think hybrid is your sort of term for that but it feels like it's transcending hybrid are your thoughts you know really really great question actually since you and i talked dave i've been spending some time you know sort of noodling just over that right and you're right right there's probably some terminology something sort of you know that will get developed you know either by us or you know in collaboration with the industry you know where we sort of almost have the connection almost like a meta cloud right that we're sort of working our way towards because there's if you will you know the cloud right so you know on premise you know virtualized uh bare metal by the way you know increasingly interesting and important you know we do a lot of work with nvidia folks want to run specific workloads there we announced support for arm right another now popular architecture especially as we go out to the edge so obviously there's private cloud public cloud then the edge becomes a continuum now you know on that process we actually have a major uh uh shipping company so uh a cruise lines that's talking about using openshift on cruise lines right so you know that's the edge right last year we had verizon talking about you know 5g and you know ran in the next generation there to then that's the edge when we talk to retail the store front's the edge right you talk to a bank you know the bank environments here so everyone's got a different kind of definition of edge we're working with them and then when we you know announce this collaboration with gm right now the edge there becomes the automobile so if you think of this as a continuum right you know bare metal private cloud public cloud take it out to the edge now we're sort of almost you know living in a world of you know a little bit of abstractions and making sure that we are focused on where uh data is being generated and then how can we help ensure that we're providing a consistent experience regardless of you know where meta meta cloud because i can work in nfts i can work a little bit we're going to get through this whole thing without saying metaverse i was hoping i do want to ask you about about the edge and the proliferation of hardware platforms paul comey mentioned this during the keynote today hardware is becoming important yeah there's a lot of people building hardware it's in development now for areas like uh like intelligent devices and ai how does this influence your development priorities you have all these different platforms that you need to support yeah so um we think about that a lot mostly because we have engagements with so many partners hardware right so obviously there's more traditional partners i'd say like the dell and the hpes that we work with we've historically worked with them also working with them in in newer areas uh with regard to appliances that are being developed um and then the work that we do with partners like nvidia or new architectures like arm and so our perspective is this will be uh use case driven more than anything else right so there are certain environments right where you have arm-based devices other environments where you've got specific workloads that can take advantage of being built on gpus that we'll see increasingly being used especially to address that problem and then provide a solution towards that so our belief has always been look we're going to give you a consistent platform a consistent abstraction across all these you know pieces of hardware um and so you mr miss customer make the best choice for yourself a couple other areas we have to hit on i want to talk about cloud services we've got to talk about security leave time to get there but why the push to cloud services what's driving that it's actually customers they're driving right so we have um customers consistently been asking us say you know love what you give us right want to make sure that's available to us when we consume in the cloud so we've made rel available for example on demand right you can consume this directly via public cloud consoles we are now making available via marketplaces uh talked about ansible available as a managed service on azure openshift of course available as a managed service in multiple clouds um all of this also is because you know we've got customers who've got these uh committed spends that they have you know with cloud providers they want to make sure that the environments that they're using are also counting towards that at the same time give them flexibility give them the choice right if in certain situations they want to run in the data center great we have that solution for them other cases they want to procure from the cloud and run it there we're happy to support them there as well let's talk about security because you have a lot of announcements like security everywhere yeah um and then some specific announcements as well i i always think about these days in the context of the solar wind supply chain hack would this have you know how would this have affected it but tell us about what's going on in security your philosophy there and the announcements that you guys made so our secure announcements actually span our entire portfolio yeah right and and that's not an accident right that's by design because you know we've really uh been thinking and emphasizing you know how we ensure that security profile is raised for users both from a malicious perspective and also helping accidental issues right so so both matters so one huge amounts of open source software you know out of the world you know and then estimates are you know one in ten right has some kind of security vulnerability um in place a massive amount of change in where software is being developed right so rate of change for example in kubernetes is dramatic right much more than even than linux right entire parts of kubernetes get rewritten over over a three-year period of time so as you introduce all that right being able to think for example about you know what's known as shift left security or devsec ops right how do we make sure we move security closer to where development is actually done how do we ensure we give you a pattern so you know we introduced a software supply chain pattern uh via openshift delivers complete stack of code that you know you can go off and run that follows best practices uh including for example for developers you know with git ops and support on the pipelines front a whole bunch of security capabilities in rel um a new image integrity measurement architecture which allows for a better ability to see in a post install environment what the integrity of the packages are signing technology they're incorporating open shift as well as an ansible so it's it's a long long list of cables and features and then also more and more defaults that we're putting in place that make it easier for example for someone not to hurt themselves accidentally on security front i noticed that uh this today's batch of announcements included support within openshift pipelines for sigstor which is an open source project that was birthed actually at red hat right uh we haven't heard a whole lot about it how important is zig store to to you know your future product direction yeah so look i i think of that you know as you know work that's you know being done out of our cto's office and obviously security is a big focus area for them um six store's great example of saying look how can we verify content that's in uh containers make sure it's you know digitally signed that's appropriate uh to be deployed across a bunch of environments but that thinking isn't maybe unique uh for us uh in the container side mostly because we have you know two decades or more of thinking about that on the rel side and so fundamentally containers are being built on linux right so a lot of the lessons that we've learned a lot of the expertise that we've built over the years in linux now we're starting to you know use that same expertise trying to apply it to containers and i'm my guess is increasingly we're going to see more of the need for that you know into the edge as well i i i picked up on that too let me ask a follow-up question on sigstor so if i'm a developer and i and i use that capability it it ensures the provenance of that code is it immutable the the signature uh and the reason i ask is because again i think of everything in the context of the solar winds where they were putting code into the the supply chain and then removing it to see what happened and see how people reacted and it's just a really scary environment yeah the hardest part you know in in these environments is actually the behavior change so what's an example of that um packages built verified you know by red hat when it went from red hat to the actual user have we been able to make sure we verify the integrity of all of those when they were put into use um and unless we have behavior that you know make sure that we do that then we find ourselves in trouble in the earliest days of open shift uh we used to get knocked a lot by by developers because i said hey this platform's really hard to use we investigate hey look why is that happening so by default we didn't allow for root access you know and so someone's using you know the openshift platform they're like oh my gosh i can't use it right i'm so used to having root access we're like no that's actually sealed by default because that's not a good security best practice now over a period of time when we you know randomly enough times explained that enough times now behavior changes like yeah that makes sense now right so even just kind of you know there's behaviors the more that we can do for example in in you know the shift left which is one of the reasons by the way why we bought uh sac rocks a year right right for declarative security contain native security so threat detection network segmentation uh watching intrusions you know malicious behavior is something that now we can you know essentially make native into uh development itself all right escape key talk futures a little bit so i went downstairs to the expert you know asked the experts and there was this awesome demo i don't know if you've seen it of um it's like a design thinking booth with what happened how you build an application i think they were using the who one of their apps um during covet and it's you know shows the the granularity of the the stack and the development pipeline and all the steps that have to take place and it strikes me of something we've talked about so you've got this application development stack if you will and the database is there to support that and then over here you've got this analytics stack and it's separate and we always talk about injecting more ai into apps more data into apps but there's separate stacks do you see a day where those two stacks can come together and if not how do we inject more data and ai into apps what are your thoughts on that so great that's another area we've talked about dave in the past right um so we definitely agree with that right and and what final shape it takes you know i think we've got some ideas around that what we started doing is starting to pick up specific areas where we can start saying let's go and see what kind of usage we get from customers around it so for example we have openshift data science which is basically a way for us to talk about ml ops right and you know how can we have a platform that allows for different models that you can use we can uh test and train data different frameworks that you can then deploy in an environment of your choice right and we run that uh for you up and assist you in in uh making sure that you're able to take the next steps you want with with your machine learning algorithms um there's work that we've uh introduced at summit around databases service so essentially our uh a cloud service that allows for deep as an easy way for customers to access either mongodb or or cockroach in a cloud native fashion and all of these things that we're sort of you know experimenting with is to be able to say look how do we sort of bring the world's closer together right off database of data of analytics with a core platform and a core stack because again right this will become part of you know one continuum that we're going to work with it's not i'd like your continuum that's that's i think really instructive it's not a technical barrier is what i'm hearing it's maybe organizational mindset i can i should be able to insert a column into my my my application you know development pipeline and insert the data i mean kafka tensorflow in there there's no technical reason i can't can't do that it's just we've created these sort of separate stovepipe organizations 100 right right so they're different teams right you've got the platform team or the ops team and you're a separate dev team there's a separate data team there's a separate storage team and each of them will work you know slightly differently independently right so the question then is i mean that's sort of how devops came along then you're like oh wait a minute yeah don't forget security and now we're at devsecops right so the more of that that we can kind of bring together i think the more convergence that we'll see when i think about the in-vehicle os i see the the that is a great use case for real-time ai inferencing streaming data i wanted to ask you that about that real quickly because at the very you know just before the conference began we got an announcement about gm but your partnership with gm it seems like this came together very quickly why is it so important for red hat this is a whole new category of application that you're going to be working on yeah so we've been working with gm not publicly for a while now um and it was very clear that look you know gm believes this is the future right you know electric vehicles into autonomous driving and we're very keen to say we believe that a lot of attributes that we've got in rel that we can bring to bear in a different form factor to assist with the different needs that exist in this industry so one it's interesting for us because we believe that's a use case that you know we can add value to um but it's also the future of automotive right so the opportunity to be able to say look we can get open source technology we can collaborate out with the community to fundamentally help transform that industry uh towards where it wants to go you know that that's just the passion that we have that you know is what wakes us up every morning you're opening into that yeah thank you for coming on the cube really appreciate your time and your insights and uh have a great rest of rest of the event thank you for having me metacloud it's a thing it's a thing right it's it's it's kind of there we're gonna we're gonna see it emerge over the next decade all right you're watching the cube's coverage of red hat summit 2022 from boston keep it right there be right back you

>>Hello, everyone. Welcome to the cubes presentation of the 80 minutes startup showcase. Season two, episode two, the theme is data as code, the future of analytics. I'm John furry, your host. We had a great day lineup for you. Fast growing startups, great lineup of companies, founders, and stories around data as code. And we're going to kick it off here with our opening keynote with Rahul Pathak VP of analytics at AWS cube alumni. Right? We'll thank you for coming on and being the opening keynote for this awesome event. >>Yeah. And it's great to see you, and it's great to be part of this event, uh, excited to, um, to help showcase some of the great innovation that startups are doing on top of AWS. >>Yeah. We last spoke at AWS reinvent and, uh, a lot's happened there, service loss of serverless as the center of the, of the action, but all these start-ups rock set Dremio Cribble monks next Liccardo, a HANA imply all doing great stuff. Data as code has a lot of traction. So a lot of still momentum going on in the marketplace. Uh, pretty exciting. >>No, it's, uh, it's awesome. I mean, I think there's so much innovation happening and you know, the, the wonderful part of working with data is that the demand for services and products that help customers drive insight from data is just skyrocketing and has no sign of no sign of slowing down. And so it's a great time to be in the data business. >>It's interesting to see the theme of the show getting traction, because you start to see data being treated almost like how developers write software, taking things out of branches, working on them, putting them back in, uh, machine learnings, uh, getting iterated on you, seeing more models, being trained differently with better insights, action ones that all kind of like working like code. And this is a whole nother way. People are reinventing their businesses. This has been a big, huge wave. What's your reaction to that? >>Uh, I think it's spot on, I mean, I think the idea of data's code and bringing some of the repeatability of processes from software development into how people built it, applications is absolutely fundamental and especially so in machine learning where you need to think about the explainability of a model, what version of the world was it trained on? When you build a better model, you need to be able to explain and reproduce it. So I think your insights are spot on and these ideas are showing up in all stages of the data work flow from ingestion to analytics to I'm out >>This next way is about modernization and going to the next level with cloud-scale. Uh, thank you so much for coming on and being the keynote presenter here for this great event. Um, I'll let you take it away. Reinventing businesses, uh, with ads analytics, right? We'll take it away. >>Okay, perfect. Well, folks, we're going to talk about, uh, um, reinventing your business with, uh, data. And if you think about it, the first wave of reinvention was really driven by the cloud. As customers were able to really transform how they thought about technology and that's well on her way. Although if you stop and think about it, I think we're only about five to 10% of the way done in terms of it span being on the cloud. So lots of work to do there, but we're seeing another wave of reinvention, which is companies reinventing their businesses with data and really using data to transform what they're doing to look for new opportunities and look for ways to operate more efficiently. And I think the past couple of years of the pandemic, it really only accelerated that trend. And so what we're seeing is, uh, you know, it's really about the survival of the most informed folks for the best data are able to react more quickly to what's happening. >>Uh, we've seen customers being able to scale up if they're in, say the delivery business or scale down, if they were in the travel business at the beginning of all of this, and then using data to be able to find new opportunities and new ways to serve customers. And so it's really foundational and we're seeing this across the board. And so, um, you know, it's great to see the innovation that's happening to help customers make sense of all of this. And our customers are really looking at ways to put data to work. It's about making better decisions, finding new efficiencies and really finding new opportunities to succeed and scale. And, um, you know, when it comes to, uh, good examples of this FINRA is a great one. You may not have heard of them, but that the U S equities regulators, all trading that happens in equities, they keep track of they're look at about 250 billion records per day. >>Uh, the examiner, I was only EMR, which is our spark and Hadoop service, and they're processing 20 terabytes of data running across tens of thousands of nodes. And they're looking for fraud and bad actors in the market. So, um, you know, huge, uh, transformation journey for FINRA over the years of customer I've gotten to work with personally since really 2013 onward. So it's been amazing to see their journey, uh, Pinterest, not a great customer. I'm sure everyone's familiar with, but, um, you know, they're about visual search and discovery and commerce, and, um, they're able to scale their daily lot searches, um, really a factor of three X or more, uh, drive down their costs. And they're using the Amazon Opus search service. And really what we're trying to do at AWS is give our customers the most comprehensive set of services for the end-to-end journey around, uh, data from ingestion to analytics and machine learning. And we will want to provide a comprehensive set of capabilities for ingestion, cataloging analytics, and then machine learning. And all of these are things that our partners and the startups that are run on us have available to them to build on as they build and deliver value for their customers. >>And, you know, the way we think about this is we want customers to be able to modernize what they're doing and their infrastructure. And we provide services for that. It's about unifying data, wherever it lives, connecting it. So the customers can build a complete picture of their customers and business. And then it's about innovation and really using machine learning to bring all of this unified data, to bear on driving new innovation and new opportunities for customers. And what we're trying to do AWS is really provide a scalable and secure cloud platform that customers and partners can build on a unifying is about connecting data. And it's also about providing well-governed access to data. So one of the big trends that we see is customers looking for the ability to make self-service data available to that customer there and use. And the key to that is good foundational governance. >>Once you can define good access controls, you then are more comfortable setting data free. And, um, uh, the other part of it is, uh, data lakes play a huge role because you need to be able to think about structured and unstructured data. In fact, about 80% of the data being generated today, uh, is unstructured. And you want to be able to connect data that's in data lakes with data that's in purpose-built data stores, whether that's databases on AWS databases, outside SAS products, uh, as well as things like data warehouses and machine learning systems, but really connecting data as key. Uh, and then, uh, innovation, uh, how can we bring to bear? And we imagine all processes with new technologies like AI and machine learning, and AI is also key to unlocking a lot of the value that's in unstructured data. If you can figure out what's in an imagine the sentiment of audio and do that in real-time that lets you then personalize and dynamically tailor experiences, all of which are super important to getting an edge, um, in, uh, in the modern marketplace. And so at AWS, we, when we think about connecting the dots across sources of data, allowing customers to use data, lakes, databases, analytics, and machine learning, we want to provide a common catalog and governance and then use these to help drive new experiences for customers and their apps and their devices. And then this, you know, in an ideal world, we'll create a closed loop. So you create a new experience. You observe our customers interact with it, that generates more data, which is a data source that feeds into the system. >>And, uh, you know, on AWS, uh, thinking about a modern data strategy, uh, really at the core is a data lakes built on us three. And I'll talk more about that in a second. Then you've got services like Athena included, lake formation for managing that data, cataloging it and querying it in place. And then you have the ability to use the right tool for the right job. And so we're big believers in purpose-built services for data because that's where you can avoid compromising on performance functionality or scale. Uh, and then as I mentioned, unification and inter interconnecting, all of that data. So if you need to move data between these systems, uh, there's well-trodden pathways that allow you to do that, and then features built into services that enable that. >>And, um, you know, some of the core ideas that guide the work that we do, um, scalable data lakes at key, um, and you know, this is really about providing arbitrarily scalable high throughput systems. It's about open format data for future-proofing. Uh, then we talk about purpose-built systems at the best possible functionality, performance, and cost. Uh, and then from a serverless perspective, this has been another big trend for us. We announced a bunch of serverless services and reinvented the goal here is to really take away the need to manage infrastructure from customers. They can really focus about driving differentiated business value, integrated governance, and then machine learning pervasively, um, not just as an end product for data scientists, but also machine learning built into data, warehouses, visualization and a database. >>And so it's scalable data lakes. Uh, data three is really the foundation for this. One of our, um, original services that AWS really the backbone of so much of what we do, uh, really unmatched your ability, availability, and scale, a huge portfolio of analytics services, uh, both that we offer, but also that our partners and customers offer and really arbitrary skin. We've got individual customers and estimator in the expert range, many in the hundreds of petabytes. And that's just growing. You know, as I mentioned, we see roughly a 10 X increase in data volume every five years. So that's a exponential increase in data volumes, Uh, from a purpose-built perspective, it's the right tool for the right job, the red shift and data warehousing Athena for querying all your data. Uh, EMR is our managed sparking to do, uh, open search for log analytics and search, and then Kinesis and Amex care for CAFCA and streaming. And that's been another big trend is, uh, real time. Data has been exploding and customers wanting to make sense of that data in real time, uh, is another big deal. >>Uh, some examples of how we're able to achieve differentiated performance and purpose-built systems. So with Redshift, um, using managed storage and it's led us and since types, uh, the three X better price performance, and what's out there available to all our customers and partners in EMR, uh, with things like spark, we're able to deliver two X performance of open source with a hundred percent compatibility, uh, almost three X and Presto, uh, with on two, which is our, um, uh, new Silicon chips on AWS, better price performance, about 10 to 12% better price performance, and 20% lower costs. And then, uh, all compatible source. So drop your jobs, then have them run faster and cheaper. And that translates to customer benefits for better margins for partners, uh, from a serverless perspective, this is about simplifying operations, reducing total cost of ownership and freeing customers from the need to think about capacity management. If we invent, we, uh, announced serverless redshifts EMR, uh, serverless, uh, Kinesis and Kafka, um, and these are all game changes for customers in terms of freeing our customers and partners from having to think about infrastructure and allowing them to focus on data. >>And, um, you know, when it comes to several assumptions in analytics, we've really got a very full and complete set. So, uh, whether that's around data warehousing, big data processing streaming, or cataloging or governance or visualization, we want all of our customers to have an option to run something struggles as well as if they have specialized needs, uh, uh, instances are available as well. And so, uh, really providing a comprehensive deployment model, uh, based on the customer's use cases, uh, from a governance perspective, uh, you know, like information is about easy build and management of data lakes. Uh, and this is what enables data sharing and self service. And, um, you know, with you get very granular access controls. So rule level security, uh, simple data sharing, and you can tag data. So you can tag a group of analysts in the year when you can say those only have access to the new data that's been tagged with the new tags, and it allows you to very, scaleably provide different secure views onto the same data without having to make multiple copies, another big win for customers and partners, uh, support transactions on data lakes. >>So updates and deletes. And time-travel, uh, you know, John talked about data as code and with time travel, you can look at, um, querying on different versions of data. So that's, uh, a big enabler for those types of strategies. And with blue, you're able to connect data in multiple places. So, uh, whether that's accessing data on premises in other SAS providers or, uh, clouds, uh, as well as data that's on AWS and all of this is, uh, serverless and interconnected. And, um, and really it's about plugging all of your data into the AWS ecosystem and into our partner ecosystem. So this API is all available for integration as well, but then from an AML perspective, what we're really trying to do is bring machine learning closer to data. And so with our databases and warehouses and lakes and BI tools, um, you know, we've infused machine learning throughout our, by, um, the state of the art machine running that we offer through SageMaker. >>And so you've got a ML in Aurora and Neptune for broths. Uh, you can train machine learning models from SQL, directly from Redshift and a female. You can use free inference, and then QuickSight has built in forecasting built in natural language, querying all powered by machine learning, same with anomaly detection. And here are the ideas, you know, how can we up our systems get smarter at the surface, the right insights for our customers so that they don't have to always rely on smart people asking the right questions, um, and you know, uh, really it's about bringing data back together and making it available for innovation. And, uh, thank you very much. I appreciate your attention. >>Okay. Well done reinventing the business with AWS analytics rural. That was great. Thanks for walking through that. That was awesome. I have to ask you some questions on the end-to-end view of the data. That seems to be a theme serverless, uh, in there, uh, Mel integration. Um, but then you also mentioned picking the right tool for the job. So then you've got like all these things moving on, simplify it for me right now. So from a business standpoint, how do they modernize? What's the steps that the clients are taking with analytics, what's the best practice? How do they, what's the what's the high order bit here? >>Uh, so the basic hierarchy is, you know, historically legacy systems are rigid and inflexible, and they weren't really designed for the scale of modern data or the variety of it. And so what customers are finding is they're moving to the cloud. They're moving from legacy systems with punitive licensing into more flexible, more systems. And that allows them to really think about building a decoupled, scalable future proof architecture. And so you've got the ability to combine data lakes and databases and data warehouses and connect them using common KPIs and common data protection. And that sets you up to deal with arbitrary scale and arbitrary types. And it allows you to evolve as the future changes since it makes it easy to add in a new type of engine, as we invent a better one a few years from now. Uh, and then, uh, once you've kind of got your data in a cloud and interconnected in this way, you can now build complete pictures of what's going on. You can understand all your touch points with customers. You can understand your complete supply chain, and once you can build that complete picture of your business, you can start to use analytics and machine learning to find new opportunities. So, uh, think about modernizing, moving to the cloud, setting up for the future, connecting data end to end, and then figuring out how to use that to your advantage. >>I know as you mentioned, modern data strategy gives you the best of both worlds. And you've mentioned, um, briefly, I want to get a little bit more, uh, insight from you on this. You mentioned open, open formats. One of the themes that's come out of some of the interviews, these companies we're going to be hearing from today is open source. The role opens playing. Um, how do you see that integrating in? Because again, this is just like software, right? Open, uh, open source software, open source data. It seems to be a trend. What does open look like to you? How do you see that progressing? >>Uh, it's a great question. Uh, open operates on multiple dimensions, John, as you point out, there's open data formats. These are things like JSI and our care for analytics. This allows multiple engines tend to operate on data and it'll, it, it creates option value for customers. If you're going to data in an open format, you can use it with multiple technologies and that'll be future-proofed. You don't have to migrate your data. Now, if you're thinking about using a different technology. So that's one piece now that sort of software, um, also, um, really a big enabler for innovation and for customers. And you've got things like squat arc and Presto, which are popular. And I know some of the startups, um, you know, that we're talking about as part of the showcase and use these technologies, and this allows for really the world to contribute, to innovating and these engines and moving them forward together. And we're big believers in that we've got open source services. We contribute to open-source, we support open source projects, and that's another big part of what we do. And then there's open API is things like SQL or Python. Uh, again, uh, common ways of interacting with data that are broadly adopted. And this one, again, create standardization. It makes it easier for customers to inter-operate and be flexible. And so open is really present all the way through. And it's a big part, I think, of, uh, the present and the future. >>Yeah. It's going to be fun to watch and see how that grows. It seems to be a lot of traction there. I want to ask you about, um, the other comment I thought was cool. You had the architectural slides out there. One was data lakes built on S3, and you had a theme, the glue in lake formation kind of around S3. And then you had the constellation of, you know, Kinesis SageMaker and other things around it. And you said, you know, pick the tool for the right job. And then you had the other slide on the analytics at the center and you had Redshift and all the other, other, other services around it around serverless. So one was more about the data lake with Athena glue and lake formation. The other one's about serverless. Explain that a little bit more for me, because I'm trying to understand where that fits. I get the data lake piece. Okay. Athena glue and lake formation enables it, and then you can pick and choose what you need on the serverless side. What does analytics in the center mean? >>So the idea there is that really, we wanted to talk about the fact that if you zoom into the analytics use case within analytics, everything that we offer, uh, has a serverless option for our customers. So, um, you could look at the bucket of analytics across things like Redshift or EMR or Athena, or, um, glue and league permission. You have the option to use instances or containers, but also to just not worry about infrastructure and just think declaratively about the data that you want to. >>Oh, so basically you're saying the analytics is going serverless everywhere. Talking about volumes, you mentioned 10 X volumes. Um, what are other stats? Can you share in terms of volumes? What are people seeing velocity I've seen data warehouses can't move as fast as what we're seeing in the cloud with some of your customers and how they're using data. How does the volume and velocity community have any kind of other kind of insights into those numbers? >>Yeah, I mean, I think from a stats perspective, um, you know, take Redshift, for example, customers are processing. So reading and writing, um, multiple exabytes of data there across from each shift. And, uh, you know, one of the things that we've seen in, uh, as time has progressed as, as data volumes have gone up and did a tapes have exploded, uh, you've seen data warehouses get more flexible. So we've added things like the ability to put semi-structured data and arbitrary, nested data into Redshift. Uh, we've also seen the seamless integration of data warehouses and data lakes. So, um, actually Redshift was one of the first to enable a straightforward acquiring of data. That's sitting in locally and drives as well as feed and that's managed on a stream and, uh, you know, those trends will continue. I think you'll kind of continue to see this, um, need to query data wherever it lives and, um, and, uh, allow, uh, leaks and warehouses and purpose-built stores to interconnect. >>You know, one of the things I liked about your presentation was, you know, kind of had the theme of, you know, modernize, unify, innovate, um, and we've been covering a lot of companies that have been, I won't say stumbling, but like getting to the future, some go faster than others, but they all kind of get stuck in an area that seems to be the same spot. It's the silos, breaking down the silos and get in the data lakes and kind of blending that purpose built data store. And they get stuck there because they're so used to silos and their teams, and that's kind of holding back the machine learning side of it because the machine learning can't do its job if they don't have access to all the data. And that's where we're seeing machine learning kind of being this new iterative model where the models are coming in faster. And so the silo brake busting is an issue. So what's your take on this part of the equation? >>Uh, so there's a few things I plan it. So you're absolutely right. I think that transition from some old data to interconnected data is always straightforward and it operates on a number of levels. You want to have the right technology. So, um, you know, we enable things like queries that can span multiple stores. You want to have good governance, you can connect across multiple ones. Uh, then you need to be able to get data in and out of these things and blue plays that role. So there's that interconnection on the technical side, but the other piece is also, um, you know, you want to think through, um, organizationally, how do you organize, how do you define it once data when they share it? And one of the asylees for enabling that sharing and, um, think about, um, some of the processes that need to get put in place and create the right incentives in your company to enable that data sharing. And then the foundational piece is good guardrails. You know, it's, uh, it can be scary to open data up. And, uh, the key to that is to put good governance in place where you can ensure that data can be shared and distributed while remaining protected and adhering to the privacy and compliance and security regulations that you have for that. And once you can assert that level of protection, then you can set that data free. And that's when, uh, customers really start to see the benefits of connecting all of it together, >>Right? And then we have a batch of startups here on this episode that are doing a lot of different things. Uh, some have, you know, new lake new lakes are forming observability lakes. You have CQL innovation on the front end data, tiering innovation at the data tier side, just a ton of innovation around this new data as code. How do you see as executive at AWS? You're enabling all this, um, where's the action going? Where are the white spaces? Where are the opportunities as this architecture continues to grow, um, and get traction because of the relevance of machine learning and AI and the apps are embedding data in there now as code where's the opportunities for these startups and how can they continue to grow? >>Yeah, the, I mean, the opportunity is it's amazing, John, you know, we talked a little bit about this at the beginning, but the, there is no slow down insight for the volume of data that we're generating pretty much everything that we have, whether it's a watch or a phone or the systems that we interact with are generating data and, uh, you know, customers, uh, you know, we talk a lot about the things that'll stay the same over time. And so, you know, the data volumes will continue to go up. Customers are gonna want to keep analyzing that data to make sense of it. They're going to want to be able to do it faster and more cheaply than they were yesterday. And then we're going to want to be able to make decisions and innovate, uh, in a shorter cycle and run more experiments than they were able to do. >>And so I think as long as, and they're always going to want this data to be secure and well-protected, and so I think as long as we, and the startups that we work with can continue to push on making these things better. Can I deal with more data? Can I deal with it more cheaply? Can I make it easier to get insight? And can I maintain a super high bar in security investments in these areas will just be off. Um, because, uh, the demand side of this equation is just in a great place, given what we're seeing in terms of theater and the architect for forum. >>I also love your comment about, uh, ML integration being the last leg of the equation here or less likely the journey, but you've got that enablement of the AIP solves a lot of problems. People can see benefits from good machine learning and AI is creating opportunities. Um, and also you also have mentioned the end to end with security piece. So data and security are kind of going hand in hand these days, not just the governments and the compliance stuff we're talking about security. So machine learning integration kind of connects all of this. Um, what's it all mean for the customers, >>For customers. That means that with machine learning and really enabling themselves to use machine learning, to make sense of data, they're able to find patterns that can represent new opportunities, um, quicker than ever before. And they're able to do it, uh, dynamically. So, you know, in a prior version of the world, we'd have little bit of systems and they would be relatively rigid and then we'd have to improve them. Um, with machine learning, this can be dynamic and near real time and you can customize them. So, uh, that just represents an opportunity to deepen relationships with customers and create more value and to find more efficiency in how businesses are run. So that piece is there. Um, and you know, your ideas around, uh, data's code really come into play because machine learning needs to be repeatable and explainable. And that means versioning, uh, keeping track of everything that you've done from a code and data and learning and training perspective >>And data sets are updating the machine learning. You got data sets growing, they become code modules that can be reused and, uh, interrogated, um, security okay. Is a big as a big theme data, really important security is seen as one of our top use cases. Certainly now in this day and age, we're getting a lot of, a lot of breaches and hacks coming in, being defended. It brings up the open, brings up the data as code security is a good proxy for kind of where this is going. What's your what's take on that and your reaction to that. >>So I'm, I'm security. You can, we can never invest enough. And I think one of the things that we, um, you know, guide us in AWS is security, availability, durability sort of jobs, you know, 1, 2, 3, and, um, and it operates at multiple levels. You need to protect data and rest with encryption, good key management and good practices though. You need to protect data on the wire. You need to have a good sense of what data is allowed to be seen by whom. And then you need to keep track of who did what and be able to verify and come back and prove that, uh, you know, uh, only the things that were allowed to happen actually happened. And you can actually then use machine learning on top of all of this apparatus to say, uh, you know, can I detect things that are happening that shouldn't be happening in near real time so they could put a stop to them. So I don't think any of us can ever invest enough in securing and protecting my data and our systems, and it is really fundamental or adding customer trust and it's just good business. So I think it is absolutely crucial. And we think about it all the time and are always looking for ways to raise >>Well, I really appreciate you taking the time to give the keynote final word here for the folks watching a lot of these startups that are presenting, they're doing well. Business wise, they're being used by large enterprises and people buying their products and using their services for customers are implementing more and more of the hot startups products they're relevant. What's your advice to the customer out there as they go on this journey, this new data as code this new future of analytics, what's your recommendation. >>So for customers who are out there, uh, recommend you take a look at, um, what, uh, the startups on AWS are building. I think there's tremendous innovation and energy, uh, and, um, there's really great technology being built on top of a rock solid platform. And so I encourage customers thinking about it to lean forward, to think about new technology and to embrace, uh, move to the cloud suite, modernized, you know, build a single picture of our data and, and figure out how to innovate and when >>Well, thanks for coming on. Appreciate your keynote. Thanks for the insight. And thanks for the conversation. Let's hand it off to the show. Let the show begin. >>Thank you, John pleasure, as always.

(upbeat music) >> Hello, everyone. Welcome to theCUBE's coverage of the "AWS Startup Showcase", season two, episode one. I'm Lisa Martin, and I'm excited to be joined by Snyk, next in this episode. Liran Tal joins me, the director of developer advocacy. Liran, welcome to the program. >> Lisa, thank you for having me. This is so cool. >> Isn't it cool? (Liran chuckles) All the things that we can do remotely. So I had the opportunity to speak with your CEO, Peter McKay, just about a month or so ago at AWS re:Invent. So much growth and momentum going on with Snyk, it's incredible. But I wanted to talk to you about specifically, let's start with your role from a developer advocate perspective, 'cause Snyk is saying modern development is changing, so traditional AppSec gatekeeping doesn't apply anymore. Talk to me about your role as a developer advocate. >> It is definitely. The landscape is changing, both developer and security, it's just not what it was before, and what we're seeing is developers need to be empowered. They need some help, just working through all of those security issues, security incidents happening, using open source, building cloud native applications. So my role is basically about making them successful, helping them any way we can. And so getting that security awareness out, or making sure people are having those best practices, making sure we understand what are the frustrations developers have, what are the things that we can help them with, to be successful day to day. And how they can be a really good part of the organization in terms of fixing security issues, not just knowing about it, but actually being proactively on it. >> And one of the things also that I was reading is, Shift Left is not a new concept. We've been talking about it for a long time. But Snyk's saying it was missing some things and proactivity is one of those things that it was missing. What else was it missing and how does Snyk help to fix that gap? >> So I think Shift Left is a good idea. In general, the idea is we want to fix security issues as soon as we can. We want to find them. Which I think that is a small nuance that what's kind of missing in the industry. And usually what we've seen with traditional security before was, 'cause notice that, the security department has like a silo that organizations once they find some findings they push it over to the development team, the R&D leader or things like that, but until it actually trickles down, it takes a lot of time. And what we needed to do is basically put those developer security tools, which is what Snyk is building, this whole security platform. Is putting that at the hands and at the scale of, and speed of modern development into developers. So, for example, instead of just finding security issues in your open source dependencies, what we actually do at Snyk is not just tell you about them, but you actually open a poll request to your source codes version and management system. And through that we are able to tell you, now you can actually merge it, you can actually review it, you can actually have it as part of your day-to-day workflows. And we're doing that through so many other ways that are really helpful and actually remediating the problem. So another example would be the IDE. So we are actually embedding an extension within your IDEs. So, once you actually type in your own codes, that is when we actually find the vulnerabilities that could exist within your own code, if that's like insecure code, and we can tell you about it as you hit Command + S and you will save the file. Which is totally different than what SaaS tools starting up application security testing was before because, when things started, you usually had SaaS tools running in the background and like CI jobs at the weekend and in deltas of code bases, because they were so slow to run, but developers really need to be at speed. They're developing really fast. They need to deploy. One development is deployed to production several times a day. So we need to really enable developers to find and fix those security issues as fast as we can. >> Yeah, that speed that you mentioned is absolutely critical to their workflow and what they're expecting. And one of the unique things about Snyk, you mentioned, the integration into how this works within development workflow with IDE, CIDC, they get environment enabling them to work at speed and not have to be security experts. I imagine are two important elements to the culture of the developer environment, right? >> Correct, yes. It says, a large part is we don't expect developers to be security experts. We want to help them, we want to, again, give them the tools, give them the knowledge. So we do it in several ways. For example, that IDE extension has a really cool thing that's like kind of unique to it that I really like, and that is, when we find, for example, you're writing code and maybe there's a batch traversal vulnerability in the function that you just wrote, what we'll actually do when we tell you about it, it will actually tell you, hey, look, these are some other commits made by other open source projects where we found the same vulnerability and those commits actually fixed it. So actually giving you example cases of what potentially good code looks like. So if you think about it, like who knows what patch reversal is, but prototype pollution like many types of vulnerabilities, but at the same time, we don't expect developers to actually know, the deep aspects of security. So they're left off with, having some findings, but not really, they want to fix them, but they don't really have the expertise to do it. So what we're doing is we're bridging that gap and we're being helpful. So I think this is what really proactive security is for developers, that says helping them remediate it. And I can give like more examples, like the security database, it's like a wonderful place where we also like provide examples and references of like, where does their vulnerability come from if there's like, what's fogging in open-source package? And we highlight that with a lot of references that provide you with things, the pull requests that fixed date, or the issue with where this was discussed. You have like an entire context of what is the... What made this vulnerability happen. So you have like a little bit more context than just specifically, emerging some stuff and updating, and there's a ton more. I'm happy to like dive more into this. >> Well, I can hear your enthusiasm for it, a developer advocate it seems like you are. But talking about the burdens of the gaps that you guys are filling it also seems like the developers and the security folks that this is also a bridge for those teams to work better together. >> Correct. I think that is not siloed anymore. I think the idea of having security champions or having threat modeling activities are really, really good, or like insightful both like developers and security, but more than just being insightful, useful practices that organizations should actually do actually bringing a discussion together to actually creating a more cohesive environment for both of those kind of like expertise, development and security to work together towards some of these aspects of like just mitigating security issues. And one of the things that actually Snyk is doing in that, in bringing their security into the developer mindset is also providing them with the ability to prioritize and understand what policies to put in place. So a lot of the times security organizations actually, the security org wants to do is put just, guardrails to make sure that developers have a good leeway to work around, but they're not like doing things that like, they definitely shouldn't do that, like prior to bringing a big risk into today organizations. And that's what I think we're doing also like great, which is the fact that we're providing the security folks to like put the policies in place and then developers who actually like, work really well within those understand how to prioritize vulnerabilities is an important part. And we kind of like quantify that, we put like an urgency score that says, hey, you should fix this vulnerability first. Why? Because it has, first of all, well, you can upgrade really quickly. It has a fix right there. Secondly, there's like an exploit in the wild. It means potentially an attacker can weaponize this vulnerability and like attack your organizations, in an automated fashion. So you definitely want to put that put like a lead on that, on that broken window, if so to say. So we ended up other kind of metrics that we can quantify and put this as like an urgency score, which we called a priority score that helps again, developers really know what to fix first, because like they could get a scan of like hundreds of vulnerabilities, but like, what do I start first with? So I find that like very useful for both the security and the developers working together. >> Right, and especially now, as we've seen such changes in the last couple of years to the threat landscape, the vulnerabilities, the security issues that are impacting every industry. The ability to empower developers to not only work at the speed with which they are accustomed and need to work, but also to be able to find those vulnerabilities faster prioritize which ones need to be fixed. I mean, I think of Log4Shell, for example, and when the challenge is going on with the supply chain, that this is really a critical capability from a developer empowerment perspective, but also from a overall business health and growth perspective. >> Definitely. I think, first of all, like if you want to step just a step back in terms of like, what has changed. Like what is the landscape? So I think we're seeing several things happening. First of all, there's this big, tremendous... I would call it a trend, but now it's like the default. Like of the growth of open source software. So first of all as developers are using more and more open source and that's like a growing trend of have like drafts of this. And it's like always increasing across, by the way, every ecosystem go, rust, .net, Java, JavaScript, whatever you're building, that's probably like on a growing trend, more open source. And that is, we will talk about it in a second what are the risks there. But that is one trend that we're saying. The other one is cloud native applications, which is also worth to like, I think dive deep into it in terms of the way that we're building applications today has completely shifted. And I think what AWS is doing in that sense is also creating a tremendous shift in the mindset of things. For example, out of the cloud infrastructure has basically democratized infrastructure. I do not need to, own my servers and own my monitoring and configure everything out. I can actually write codes that when I deploy it, when something parses this and runs this, it actually creates servers and monitoring, logging, different kinds of things for me. So it democratize the whole sense of building applications from what it was decades ago. And this whole thing is important and really, really fast. It makes things scalable. It also introduces some rates. For example, some of these configuration. So there's a lot that has been changed. And in that landscape of like what modern developer is and I think in that sense, we kind of can need a lead to a little bit more, be helpful to developers and help them like avoid all those cases. And I'm like happy to dive into like the open source and the cloud native. That was like follow-ups on this one. >> I want to get into a little bit more about your relationship with AWS. When I spoke with Peter McKay for re:Invent, he talked about the partnership being a couple of years old, but there's some kind of really interesting things that AWS is doing in terms of leveraging, Snyk. Talk to me about that. >> Indeed. So Snyky integrates with almost, I think probably a lot of services, but probably almost all of those that are unique and related to developers building on top of the AWS platform. And for example, that would be, if you actually are building your code, it connects like the source code editor. If you are pushing that code over, it integrates with code commits. As you build and CIS are running, maybe code build is something you're using that's in code pipeline. That is something that you have like native integrations. At the end of the day, like you have your container registry or Lambda. If you're using like functions as a service for your obligations, what we're doing is integrating with all of that. So at the end of the day, you really have all of that... It depends where you're integrating, but on all of those points of integration, you have like Snyk there to help you out and like make sure that if we find on any of those, any potential issues, anything from like licenses to vulnerabilities in your containers or just your code or your open source code in those, they actually find it at that point and mitigate the issue. So this kind of like if you're using Snyk, when you're a development machine, it kind of like accompanies you through this journey all over what a CIC kind of like landscape looks like as an architectural landscape for development, kind of like all the way there. And I think what you kind of might be I think more interested, I think to like put your on and an emphasis would be this recent integration with the Amazon Inspector. Which is as it's like very pivotal parts on the AWS platform to provide a lot of, integrate a lot of services and provide you with those insights on security. And I think the idea that now that is able to leverage vulnerability data from the Snyk's security intelligence database that says that's tremendous. And we can talk about that. We'd look for shell and recent issues. >> Yeah. Let's dig into that. We've have a few minutes left, but that was obviously a huge issue in November of 2021, when obviously we're in a very dynamic global situation period, but it's now not a matter of if an organization is going to be hit by vulnerabilities and security threats. It's a matter of when. Talk to me about really how impactful Snyk was in the Log4Shell vulnerability and how you help customers evade probably some serious threats, and that could have really impacted revenue growth, customer satisfaction, brand reputation. >> Definitely. The Log4Shell is, well, I mean was a vulnerability that was disclosed, but it's probably still a major part and going to be probably for the foreseeable future. An issue for organizations as they would need to deal with us. And we'll dive in a second and figure out like why, but in like a summary here, Log4Shell was the vulnerability that actually was found in Java library called Log4J. A logging library that is so popular today and used. And the thing is having the ability to react fast to those new vulnerabilities being disclosed is really a vital part of the organizations, because when it is asking factful, as we've seen Log4Shell being that is when, it determines where the security tool you're using is actually helping you, or is like just an added thing on like a checkbox to do. And that is what I think made Snyk's so unique in the sense. We have a team of those folks that are really boats, manually curating the ecosystem of CVEs and like finding by ourselves, but also there's like an entire, kind of like an intelligence platform beyond us. So we get a lot of notifications on chatter that happens. And so when someone opens an issue on an open source repository says, Hey, I found an issue here. Maybe that's an XSS or code injection or something like that. We find it really fast. And we at that point, before it goes to CVE requirement and stuff like that through like a miter and NVD, we find it really fast and can add it to the database. So this has been something that we've done with Log4Shell, where we found that as it was disclosed, not on the open source, but just on the open source system, but it was generally disclosed to everyone at that point. But not only that, because look for J as the library had several iterations of fixes they needed. So they fixed one version. Then that was the recommendation to upgrade to then that was actually found as vulnerable. So they needed to fix the another time and then another time and so on. So being able to react fast, which is, what I think helped a ton of customers and users of Snyk is that aspect. And what I really liked in the way that this has been received very well is we were very fast on creating those command line tools that allow developers to actually find cases of the Log4J library, embedded into (indistinct) but not true a package manifest. So sometimes you have those like legacy applications, deployed somewhere, probably not even legacy, just like the Log4J libraries, like bundled into a net or Java source code base. So you may not even know that you're using it in a sense. And so what we've done is we've like exposed with Snyk CLI tool and a command line argument that allows you to search for all of those cases. Like we can find them and help you, try and mitigate those issues. So that has been amazing. >> So you've talked in great length, Liran about, and detail about how Snyk is really enabling and empowering developers. One last question for you is when I spoke with Peter last month at re:Invent, he talked about the goal of reaching 28 million developers. Your passion as a director of developer advocacy is palpable. I can feel it through the screen here. Talk to me about where you guys are on that journey of reaching those 28 million developers and what personally excites you about what you're doing here. >> Oh, yeah. So many things. (laughs) Don't know where to start. We are constantly talking to developers on community days and things like that. So it's a couple of examples. We have like this dev site community, which is a growing and kicking community of developers and security people coming together and trying to work and understand, and like, just learn from each other. We have those events coming up. We actually have this, "The Big Fix". It's a big security event that we're launching on February 25th. And the idea is, want to help the ecosystem secure security obligations, open source or even if it's closed source. We like help you fix that though that yeah, it's like helping them. We've launched this Snyk ambassadors program, which is developers and security people, CSOs are even in there. And the idea is how can we help them also be helpful to the community? Because they are like known, they are passionate as we are, on application security and like helping developers code securely, build securely. So we launching all of those programs. We have like social impact related programs and the way that we like work with organizations, like maybe non-profit maybe they just need help, like getting, the security part of things kind of like figured out, students and things like that. Like, there's like a ton of those initiatives all over the boards, helping basically the world be a little bit more secure. >> Well, we could absolutely use Snyk's help in making the world more secure. Liran it's been great talking to you. Like I said, your passion for what you do and what Snyk is able to facilitate and enable is palpable. And it was a great conversation. I appreciate that. And we look forward to hearing what transpires during 2022 for Snyk so you got to come back. >> I will. Thank you. Thank you, Lisa. This has been fun. >> All right. Excellent. Liran Tal, I'm Lisa Martin. You're watching theCUBE's second season, season two of the "AWS Startup Showcase". This has been episode one. Stay tuned for more great episodes, full of fantastic content. We'll see you soon. (upbeat music)

(upbeat electronic music plays) >> Hey everyone, welcome back to theCube's coverage of .NEXT 2021 Virtual. I'm John Furrier, hosts of theCube. We have two great guests, Rajiv Mirani, who's the Chief Technology Officer, and Thomas Cornely, SVP of Product Management. Day Two keynote product, the platform, announcements, news. A lot of people, Rajiv, are super excited about the, the platform, uh, moving to a subscription model. Everything's kind of coming into place. How are the customers, uh, seeing this? How they adopted hybrid cloud as a hybrid, hybrid, hybrid, data, data, data? Those are the, those are the, that's the, that's where the puck is right now. You guys are there. How are customers seeing this? >> Mirani: Um, um, great question, John, by the way, great to be back here on theCube again this year. So when we talk to our customers, pretty much, all of them agreed that for them, the ideal state that they want to be in is a hybrid world, right? That they want to essentially be able to run both of those, both on the private data center and the public cloud, and sort of have a common platform, common experience, common, uh, skillset, same people managing, managing workloads across both locations. And unfortunately, most of them don't have that that tooling available today to do so, right. And that's where the platform, the Nutanix platform's come a long way. We've always been great at running in the data center, running every single workload, we continue to make great strides on our core with the increased performance for, for the most demanding, uh, workloads out there. But what we have done in the last couple of years has also extended this platform to run in the public cloud and essentially provide the same capabilities, the same operational behavior across locations. And that's when you're seeing a lot of excitement from our customers because they really want to be in that state, for it to have the common tooling across work locations, as you can imagine, we're getting traction. Customers who want to move workloads to public cloud, they don't want to spend the effort to refactor them. Or for customers who really want to operate in a hybrid mode with things like disaster recovery, cloud bursting, workloads like that. So, you know, I think we've made a great step in that direction. And we look forward to doing more with our customers. >> Furrier: What is the big challenge that you're seeing with this hybrid transition from your customers and how are you solving that specifically? >> Mirani: Yeah. If you look at how public and private operate today, they're very different in the kind of technologies used. And most customers today will have two separate teams, like one for their on-prem workloads, using a certain set of tooling, a second completely different team, managing a completely different set of workloads, but with different technologies. And that's not an ideal state in some senses, that's not true hybrid, right? It's like creating two new silos, if anything. And our vision is that you get to a point where both of these operate in the same manner, you've got the same people managing all of them, the same workloads anyway, but similar performance, similar SaaS. So they're going to literally get to point where applications and data can move back and forth. And that's, that's, that's where I think the real future is for hybrid >> Furrier: I have to ask you a personal question. As the CTO, you've got be excited with the architecture that's evolving with hybrid and multi-cloud, I mean, I mean, it's pretty, pretty exciting from a tech standpoint, what is your reaction to that? >> Mirani: %100 and it's been a long time coming, right? We have been building pieces of this over years. And if you look at all the product announcements, Nutanix has made over the last few years and the acquisitions that made them and so on, there's been a purpose behind them. That's been a purpose to get to this model where we can operate a customer's workloads in a hybrid environment. So really, really happy to see all of that come together. Years and years of work finally finally bearing fruit. >> Furrier: Well, we've had many conversations in the past, but it congratulates a lot more to do with so much more action happening. Thomas, you get the keys to the kingdom, okay, and the product management you've got to prioritize, you've got to put it together. What are the key components of this Nutanix cloud platform? The hybrid cloud, multi-cloud strategy that's in place, because there's a lot of headroom there, but take us through the key components today and then how that translates into hybrid multi-cloud for the future. >> Cornely: Certainly, John, thank you again and great to be here, and kind of, Rajiv, you said really nicely here. If you look at our portfolio at Nutanix, what we have is great technologies. They've been sold as a lot of different products in the past, right. And what we've done last few months is we kind of bring things together, simplify and streamline, and we align everything around a cloud platform, right? And this is really the messaging that we're going after is look, it's not about the price of our solutions, but business outcomes for customers. And so are we focusing on pushing the cloud platform, which we encompasses five key areas for us, which we refer to as cloud infrastructure, no deficiencies running your workloads. Cloud management, which is how you're going to go and actually manage, operate, automate, and get governance. And then services on top that started on all around data, right? So we have unified storage, finding the objects, data services. We have database services. Now we have outset of desktop services, which is for EMC. So all of this, the big change for us is this is something that, you know, you can consume in terms of solutions and consume on premises. As Rajiv discussed, you know, we can take the same platform and deploy it in public cloud regions now, right? So you can now get no seamless hybrid cloud, same operating model. But increasingly what we're doing is taking your solutions and re-targeting issues and problems at workers running native public clouds. So think of this as going, after automating more governance, security, you know, finding objects, database services, wherever you're workload is running. So this is taking this portfolio and reapplying it, and targeting on prem at the edge in hybrid and in christening public cloud in ATV. >> Furrier: That's awesome. I've been watching some of the footage and I was noticing quite a lot of innovation around virtualized, networking, disaster, recovery security, and data services. It's all good. You guys were, and this is in your wheelhouse. I know you guys are doing this for many, many years. I want to dive deeper into that because the theme right now that we've been reporting on, you guys are hitting right here what the keynote is cloud scale is about faster development, right? Cloud native is about speed, it's about not waiting for these old departments, IT or security to get back to them in days or weeks and responding to either policy or some changes, you got to move faster. And data, data is critical in all of this. So we'll start with virtualized networking because networking again is a key part of it. The developers want to go faster. They're shifting left, take us through the virtualization piece of how important that is. >> Mirani: Yeah, that's actually a great question as well. So if you think about it, virtual networking is the first step towards building a real cloud like infrastructure on premises that extends out to include networking as well. So one of the key components of any cloud is automation. Another key component is self service and with the API, is it bigger on virtual networking All of that becomes much simpler, much more possible than having to, you know, qualify it, work with someone there to reconfigure physical networks and slots. We can, we can do that in a self service way, much more automated way. But beyond that, the, the, the notion of watching networks is really powerful because it helps us to now essentially extend networks and, and replicate networks anywhere on the private data center, but in the public cloud as well. So now when customers move their workloads, we'd already made that very simple with our clusters offering. But if you're only peek behind the layers a little bit, it's like, well, yea, but the network's not the same on the side. So now it, now it means that a go re IP, my workloads create new subnets and all of that. So there was a little bit of complication left in that process. So to actual network that goes away also. So essentially you can repeat the same network in both locations. You can literally move your workloads, no redesign of your network acquired and still get that self service and automation capabilities of which cookies so great step forward, it really helps us complete the infrastructure as a service stack. We had great storage capabilities before, we create compute capabilities before, and sort of networking the third leg and all of that. >> Furrier: Talk about the complexity here, because I think a lot of people will look at dev ops movement and say, infrastructure is code when you go to one cloud, it's okay. You can, you can, you know, make things easier. Programmable. When, when you start getting into data center, private data centers, or essentially edges now, cause if it's distributed cloud environment or cloud operations, it's essentially one big cloud operation. So the networks are different. As you said, this is a big deal. Okay. This is sort of make infrastructure as code happen in multiple environments across multiple clouds is not trivial. Could you talk about the main trends and how you guys see this evolving and how you solve that? >> Mirani: Yeah. Well, the beauty here is that we are actually creating the same environment everywhere, right? From, from, from point of view of networking, compute, and storage, but also things like security. So when you move workloads, things with security, posture also moves, which is also super important. It's a really hard problem, and something a lot of CIO's struggle with, but having the same security posture in public and private clouds reporting as well. So with this, with this clusters offering and our on-prem offering competing with the infrastructure service stack, you may not have this capability where your operations really are unified across multicloud hybrid cloud in any way you run. >> Furrier: Okay, so if I have multiple cloud vendors, there are different vendors. You guys are creating a connection unifying those three. Is that right? >> Mirani: Essentially, yes, so we're running the same stack on all of them and abstracting away the differences between the clouds that you can run operations. >> Furrier: And when the benefits, the benefits of the customers are what? What's the main, what's the main benefit there? >> Mirani: Essentially. They don't have to worry about, about where their workloads are running. Then they can pick the best cloud for their workloads. It can seamlessly move them between Cloud. They can move their data over easily, and essentially stop worrying about getting locked into a single, into a single cloud either in a multi-cloud scenario or in a hybrid cloud scenario, right. There many, many companies now were started on a cloud first mandate, but over time realized that they want to move workloads back to on-prem or the other way around. They have traditional workloads that they started on prem and want to move them to public cloud now. And we make that really simple. >> Furrier: Yeah. It's kind of a trick question. I wanted to tee that up for Thomas, because I love that kind of that horizontal scales, what the cloud's all about, but when you factor data into it, this is the sweet spot, because this is where, you know, I think it gets really exciting and complicated too, because, you know, data's got, can get unwieldy pretty quickly. You got state got multiple applications, Thomas, what's your, what can you share the data aspect of this? This is super, super important. >> Absolutely. It's, you know, it's really our core source of differentiation, when you think about it. That's what makes Nutanix special right? In, in the market. When we talk about cloud, right. Actually, if you've been following Nutanix for years, you know, we've been talking a lot about making infrastructure invisible, right? The new way for us to talk about what we're doing, with our vision is, is to make clouds invisible so that in the end, you can focus on your own business, right? So how do you make Cloud invisible? Lots of technology is at the application layer to go and containerize applications, you know, make them portable, modernize them, make them cloud native. That's all fine when you're not talking of state class containers, that the simplest thing to move around. Right. But as we all know, you know, applications end of the day, rely on data and measure the data across all of these different locations. I'm not even going to go seconds. Cause that's almost a given, you're talking about attribution. You can go straight from edge to on-prem to hybrid, to different public cloud regions. You know, how do you go into the key control of that and get consistency of all of this, right? So that's part of it is being aware of where your data is, right? But the other part is that inconsistency of set up data services regardless of where you're running. And so this is something that we look at the cloud platform, where we provide you the cloud infrastructure go and run the applications. But we also built into the cloud platform. You get all of your core data services, whether you have to consume file services, object services, or database services to really support your application. And that will move with your application, that is the key thing here by bringing everything onto the same platform. You now can see all operations, regardless of where you're running the application. The last thing that we're adding, and this is a new offering that we're just launching, which is a service, it's called, delete the dead ends. Which is a solution that gives you visibility and allow you to go and get better governance around all your data, wherever it may live, across on-prem edge and public clouds. That's a big deal again, because to manage it, you first have to make sense of it and get control over it. And that's what data answer's is going to be all about. >> Furrier: You know, one of the things we've we've been reporting on is data is now a competitive advantage, especially when you have workflows involved, um, super important. Um, how do you see customers going to the edge? Because if you have this environment, how does the data equation, Thomas, go to the edge? How do you see that evolving? >> Cornely: So it's yeah. I mean, edge is not one thing. And that's actually the biggest part of the challenge of defining what the edge is depending on the customer that you're working with. But in many cases you get data ingesting or being treated at the edge that you then have to go move to either your private cloud or your public cloud environment to go and basically aggregate it, analyze it and get insights from it. Right? So this is where a lot of our technologies, whether it's, I think the object's offering built in, we'll ask you to go and make the ingest over great distances over the network, right? And then have your common data to actually do an ethics audit over our own object store. Right? Again, announcements, we brought into our storage solutions here, we want to then actually organize it then actually organize it directly onto the objects store solution. Nope. Using things, things like or SG select built into our protocols. So again, make it easy for you to go in ingest anywhere, consolidate your data, and then get value out of it. Using some of the latest announcements on the API forms. >> Furrier: Rajiv databases are still the heart of most applications in the enterprise these days, but databases are not just the data is a lot of different data. Moving around. You have a lot a new data engineering platforms coming in. A lot of customers are scratching their head and, and they want to kind of be, be ready and be ready today. Talk about your view of the database services space and what you guys are doing to help enterprise, operate, manage their databases. >> Mirani: Yeah, it's a super important area, right? I mean, databases are probably the most important workload customers run on premises and pretty close on the public cloud as well. And if you look at it recently, the tooling that's available on premises, fairly traditional, but the clouds, when we integrate innovation, we're going to be looking at things like Amazon's relational database service makes it an order of magnitude simpler for our customers to manage the database. At the same time, also a proliferation of databases and we have the traditional Oracle and SQL server. But if you have open source Mongo, DB, and my SQL, and a lot of post-grads, it's a lot of different kinds of databases that people have to manage. And now it just becomes this cable. I have the spoke tooling for each one of them. So with our Arab product, what we're doing is essentially creating a data management layer, a database management layer that unifies operations across your databases and across locations, public cloud and private clouds. So all the operations that you need, you do, which are very complicated in, in, in, in with traditional tooling now, provisioning of databases backing up and restoring them providing a true time machine capabilities, so you can pull back transactions. We can copy data management for your data first. All of that has been tested in Era for a wide variety of database engines, your choice of database engine at the back end. And so the new capabilities are adding sort of extend that lead that we have in that space. Right? So, so one of the things we announced at .Next is, is, is, is one-click storage scaling. So one of the common problems with databases is as they grow over time, it's not running out of storage capacity. Now re-provisions to storage for a database, migrate all the data where it's weeks and months of look, right? Well, guess what? With Era, you can do that in one click, it uses the underlying AOS scale-out architecture to provision more storage and it does it have zero downtime. So on the fly, you can resize your databases that speed, you're adding some security capabilities. You're adding some capabilities around resilience. Era continues to be a very exciting product for us. And one of the things, one of the real things that we are really excited about is that it can really unify database operations between private and public. So in the future, we can also offer an aversion of Era, which operates on native public cloud instances and really excited about that. >> Furrier: Yeah. And you guys got that two X performance on scaling up databases and analytics. Now the big part point there, since you brought up security, I got to ask you, how are you guys talking about security? Obviously it's embedded in from the beginning. I know you guys continue to talk about that, but talk about, Rajiv, the security on, on that's on everyone's mind. Okay. It goes evolving. You seeing ransomware are continuing to happen more and more and more, and that's just the tip of the iceberg. What do you guys, how are you guys helping customers stay secure? >> Mirani: Security is something that you always have to think about as a defense in depth when it comes to security, right? There's no one product that, that's going to do everything for you. That said, what we are trying to do is to essentially go with the gamut of detection, prevention, and response with our security, and ransom ware is a great example of that, right. We've partnered with Qualys to essentially be able to do a risk assessment of your workloads, to basically be able to look into your workloads, see whether they have been bashed, whether they have any known vulnerabilities and so on. To try and prevent malware from infecting your workloads in the first place, right? So that's, that's the first line of defense. Now not systems will be perfect. Some, some, some, some malware will probably get in anyway But then you detect it, right. We have a database of all the 4,000 ransomware signatures that you can use to prevent ransomware from, uh, detecting ransom ware if it does infect the system. And if that happens, we can prevent it from doing any damage by putting your fire systems and storage into read-only mode, right. We can also prevent lateral spread of, of your ransomware through micro-segmentation. And finally, if you were, if you were to invade, all those defenses that you were actually able to encrypt data on, on, on a filer, we have immutable snapshots, they can recover from those kinds of attacks. So it's really a defense in depth approach. And in keeping with that, you know, we also have a rich ecosystem of partners while this is one of them, but older networks market sector that we work with closely to make sure that our customers have the best tooling around and the simplest way to manage security of their infrastructure. >> Furrier: Well, I got to say, I'm very impressed guys, by the announcements from the team I've been, we've been following Nutanix in the beginning, as you know, and now it's back in the next phase of the inflection point. I mean, looking at my notebook here from the announcements, the VPC virtual networking, DR Observability, zero trust security, workload governance, performance expanded availability, and AWS elastic DR. Okay, we'll get to that in a second, clusters on Azure preview cloud native ecosystem, cloud control plane. I mean, besides all the buzzword bingo, that's going on there, this is cloud, this is a cloud native story. This is distributed computing. This is virtualization, containers, cloud native, kind of all coming together around data. >> Cornely: What you see here is, I mean, it is clear that it is about modern applications, right? And this is about shifting strategy in terms of focusing on the pieces where we're going to be great at. And a lot of these are around data, giving you data services, data governance, not having giving you an invisible platform that can be running in any cloud. And then partnering, right. And this is just recognizing what's going on in the world, right? People want options, customers and options. When it comes to cloud, they want options to where they're running the reports, what options in terms of, whether it be using to build the modern applications. Right? So our big thing here is providing and being the best platform to go and actually support for Devers to come in and build and run their new and modern applications. That means that for us supporting a broad ecosystem of partners, entrepreneur platform, you know, we announced our partnership with Red Hat a couple of months ago, right? And this is going to be a big deal for us because again, we're bringing two leaders in the industry that are eminently complimentary when it comes to providing you a complete stack to go and build, run, and manage your client's applications. When you do that on premises, utilizing like the preferred ATI environment to do that. Using the Red Hat Open Shift, or, you're doing this open to public cloud and again, making it seamless and easy, to move the applications and their supporting data services around, around them that support them, whether they're running on prem in hybrid winter mechanic. So client activity is a big deal, but when it comes to client activity, the way we look at this, it's all about giving customers choice, choice of that from services and choice of infrastructure service. >> Furrier: Yeah. Let's talk to the red hat folks, Rajiv, it's you know, it's, they're an operating system thinking company. You know, you look at the internet now in the cloud and edge, and on-premise, it's essentially an operating system. you need your backup and recovery needs to disaster recovery. You need to have the HCI, you need to have all of these elements part of the system. It's, it's, it's, it's building on top of the existing Nutanix legacy, then the roots and the ecosystem with new stuff. >> Mirani: Right? I mean, it's, in fact, the Red Hat part is a great example of, you know, the perfect marriage, if you will, right? It's, it's, it's the best in class platform for running the cloud-native workloads and the best in class platform with a service offering in there. So two really great companies coming together. So, so really happy that we could get that done. You know, the, the point here is that cloud native applications still need infrastructure to run off, right? And then that infrastructure, if anything, the demands on that and growing it since it's no longer that hail of, I have some box storage, I have some filers and, you know, just don't excite them, set. People are using things like object stores, they're using databases increasingly. They're using the Kafka and Map Reduce and all kinds of data stores out there. And back haul must be great at supporting all of that. And that's where, as Thomas said, earlier, data services, data storage, those are our strengths. So that's certainly a building from platform to platform. And then from there onwards platform services, great to have right out of the pocket. >> Furrier: People still forget this, you know, still hardware and software working together behind the scenes. The old joke we have here on the cube is server less is running on a bunch of servers. So, you know, this is the way that is going. It's really the innovation. This is the infrastructure as code truly. This is what's what's happened is super exciting. Rajiv, Thomas, thank you guys for coming on. Always great to talk to you guys. Congratulations on an amazing platform. You guys are developing. Looks really strong. People are giving it rave reviews and congratulations on, on, on your keynotes. >> Cornely: Thank you for having us >> Okay. This is theCube's coverage of.next global virtual 2021 cube coverage day two keynote review. I'm John Furrier Furrier with the cube. Thanks for watching.

(upbeat music) >> Okay, welcome back to theCUBES's coverage of Dockercon 2021. I'm John Furrier, your host of theCUBE. We have Justin Cormack, CTO of Docker. Was also involved in the CNCF technical oversight and variety of other technical activities. Justin, great to see you. Thanks for coming on theCUBE Virtual this year, again, twice in a row and maybe next year will be in person but certainly hybrid, great to see you. >> Yeah, great to see you too. Yeah, in person would be nice one of these days, yes. >> Yeah, when we get real life back. It's almost there, I can feel it, but there's so much activity. One of the things that we've been talking about, certainly in theCUBE and even here at DockerCon, same story. The pandemic really hasn't truly impacted developer community, because most of the people have been working remotely and virtually for many, many decades. And if you think about just in the past 10 years, all the innovation in cloud has come from virtual teams, open-source softwares, always had good kind of governance and a democratization of kind of how it becomes built. So not a bit's been skipped during the pandemic. In fact, if anything supply chain of software development has increased. So- >> Yeah, I think that it's definitely true that open-source was really the place that pioneered remote working. And a lot of the work methods the people worked out to do open-source as in communication and things like that, were things that people have adopted. It's a slightly different community. I'd say open-source projects like meetings less than some other organizations, but there was definitely that pioneering thing. And a lot of the companies that started off remote first, were in open-source software, and they started off for those reasons as well because developers were already working like that, and they could just hire them and they could continue to work like that. >> Yeah, one of the upsides of all this is that people won't tolerate even zoom or in person meetings that just go on, 15, 30 minutes good call. Why do we have a meeting? What's the purpose? (faintly speaking) the way to go. Let's get into the developer community. One of the things I love about DockerCon this year 2021 is the envelopes being pushed again almost to another level, it's almost a new level, this next level of containers is bringing more innovation to the table and productivity and simplicity. Some of the same messages last year but now more than ever, stuff's going on. What are you hearing directly from the community? You talk to a lot of the developers out of the millions of developers in the Docker ecosystem. What are they saying now in 2021? What's going on in their mind? >> Yeah, I think it's an area... More and more people are using Docker, and they're using it every day and it's a change that's been going on, obviously for a while, but it begins to sort of, as it spreads, the kind of developers using Docker, so different from... When I started at Docker, coming up for six years ago, it was a very bleeding edge type thing for early adopters. Now it's everywhere, millions and millions of ordinary developers are using Docker every day. And the kinds of things that's telling us is, well, some of this stuff that we thought, well, five years ago was an amazing breakthrough and simplicity. Now that's on its own still too hard. One of the things I mentioned in my keynote was that, we're talking to developers who just primarily have been working windows all their life but more and more applications being shipped on Linux. And they using Linux containers, but they find Docker files really hard because they have really, Linux shell scrapes and not a windows developer doesn't know how to use a Linux shell script. And it's bringing it down to that next level of use where you can adopt these things more easily, the pitched to the kind of level of developer who is just thinking about their language, their APIs and they don't want to have to learn kind of lots of new things to do Docker. They'll learn some, but they really wanted to kind of integrate better into the environments they work in and help them more. We've been working on a lot of detailed instructions about like how to use Docker better with JavaScript and Python, because people have told us, be specific about these things, tell us exactly how I do make things work well with the way I'm doing things now. >> What is the big upside for containers for the folks watching? And last year, one of the most popular sessions was the one-on-one Peter McKay did, which was fascinating, packed with people. And the adoption of containers is going everywhere and enabling a lot of growth. What's the main message to these new developers that are coming on board to ecosystem. >> I think what's happening is that people are gradually, very slowly starting to think about containers in a different way. When we started, the question everyone kept asking was about containers and VMS, what's the difference? That question didn't really, kind of really address what the big fundamental changes that containers made to how people work was. I'd like to think about it in terms of the physical shipping containers, like people are concerned about like, can you escape from the box? Can I get out of a container? These kinds of questions. This is not really the important question about containers is kind of escape from the box. The question is, what does it enable you to build? The shipping container let us build the supply chains that let people build products and factories and things that would never have been possible without the ability to actually just ship things in a routine and predictable and reliable and secure way, getting that content and the things that come in the container and you actually work more effectively. And, so I think that now we're talking about like what's the effect of containers on the industry as a whole? What are the things that we can learn about repeatability and documentation and metadata and reliability, that we kind of talked about a little bit before, but these are becoming the important use cases for containers. Containers are really about, they're not about that kind of security and escape piece, there're about the content, the supply chain and your actual process of working. >> What do you, first of all, great call out on the security piece. I want to get that in a second. I think that's a killer one. You've mentioned supply chain, can you define software supply chain, and is that where the automation value comes in? Because a lot of people are talking about automation is improving the developer experience. So can you clarify quickly, what do you mean by the software supply chain? And is that where automation comes in? Am I getting that right? >> Yeah, so the software supply chain is really that process by which you get components of software to build your applications. Around 99% of companies are using open-source software to build applications. And the vast majority of the pieces of any modern application art consists mainly of open-source software and some tries source software, and some software that people are writing themselves. But you've got to get these components in, you've got to make sure that they're updated and scanned and they're reliable. And that's the software supply chain is that process for bringing in components that you're using to build your applications. And so, the way automation comes in, is just because there's so much of the software dealing with it manually is just difficult, and it's an ongoing process of build and test and CI and all those scanning and all those processes. And I think as software developers, we fundamentally know that the most valuable things are the things that we automate. They're the things that we do all the time and they're important. And that a lot of building a software is about building repeatable processes, rather than just doing things one by one, because we know that we have to keep updating software, we have to keep fixing bags, we have to keep improving software. And so you've got to be able to keep doing these things, and automation is what helps us do that. >> I was talking to Dana Lawson the VP of Engineering at GitHub, and she and I were chatting about this one topic. I want to get your thoughts on it, because she was definitely of the camp of automation helps with productivity. No doubt, check, double check there. The question I have for you is how do you see the impact on say the developer experience and innovation specifically? Because, okay, I can see the productivity, okay, something happens a bunch of times automated. Then you start thinking about supply chain, then you thought about developer experience and ultimately with Kubernetes around the corner, with the relationship with containers, you can see the cloud-native benefits from an innovation standpoint. Can you share your thoughts on the automation impact to experience for the developer and the innovation strategies they need? >> Well, I think that one of the ways we're trying to think about everything we do at Docker is that we should be helping build processes rather than helping you do something once, because, if you do something three times, you want to automate it, but what if the first time you did it, that could also build that automated process. And if it was, why isn't it as easy to make something automated as it is to do it once? There's no real reason why it shouldn't be. And I think that kind of... I was having a conversation with someone the other day about how they would... They had kind of reversed their thinking and they found that often it was easier to start with automation and harder to do things manually. And that's a kind of real reversal of that kind of role between automation and doing stuff run, so, and it's not how we think about it, but I think it's really interesting to think about that kind of thing, and how could we make automation really, really simple. >> Well, that's a great example when you have that kind of environment, and certainly the psychology is better to have automation but if everyone's saying it's hard to do manual, that means they're at some sort of scale, right? So scale matters, right? So as you start getting the SRE vibes going, and you start getting Cloud Scale in cloud-native apps, that's going to be cool. Now, the question I want to ask you, because while the other thing that's happening is more people are coming into open-source than ever before, not just young developers, but also end users. Not like the hardcore-end users, looking like classic enterprises are coming in. So as more developers come in and increase over the year, what does that mean for the experience for developers? Now you have, does that change it? How do you view that? Because as more developers come in, you have institutional knowledge, you have scale, you have learnings, what's your thoughts on on the impact as the population of developers increase? How does Docker view that? >> Yeah, now, I think it's really interesting trend. It's been very visible in CNCF for the last few years. We've been seeing a lot more active end-user, company's doing open-source. Spotify has been one of the examples with a backstage project they brought into CNCF and other areas where they work. And I think it's part of this growing trend that's really important to Docker, Docker is a bottom up technology adoption company. Developers are using Docker because it works for them and they love it. And developers are doing open-source in their companies because open-source works for them and they love it. And it works for their business as well. And whereas historically like the the model was, you would buy kind of large enterprise products, with big procurement deals that were often not what the developers wanted, but now you're getting developers saying, what we want to do is adopt these open-source projects, because we know how they work, we already understand that we know how to integrate them better into our processes. And I think it's that developer lad demand that's really important, and it's the kind of integration that developers want to do, the kind of products that they want to work with, because they understand them and love them, and they had targeted at developers and that's incredibly important. And I think that's very much where Docker's focused and we really want to... Open-source is of the core of everything we've always done. We've built with the open-source community, and we've kind of come from that kind of environment. And we built things that we love as developers and that other developers love. >> Talk about your thoughts on security. Obviously it's always built in from the beginning, Shift-Left is the ethos, day two operations, AI apps, whatever people want to call that. Post-deployment mode, security has to be at the center of this, containers can be a great solution and give some great flexibility for developers. Can you talk about your view and Docker view on the security posture and situation? >> Yeah, I think Shift-Left is incredibly important because just doing things late, everyone knows is the wrong thing from the point of view of productivity. But I think Shift-Left can just mean, ask the developers to do everything, which is really a bit too much. I think that sometimes things need to be shifted even further left than people have actually thought. So like, why are you expecting developers to scan components to see if they're allowed to use? If they should be using them or they should be updated, why hasn't that happened before the developer even gets there? I think there's a, I sorted my keynote about this whole piece, about trusted content. And it's really important that we really shift that even further left, so it's long before it gets to the developer, those things that are happening. Security, it's a huge area, of course, but it's very much, we need to help developers because security is non-obvious. I think the more you understand about security, the more you understand that it doesn't come naturally to people and they need to be helped with it, and they need to learn a lot about things in a way to, I found myself that, learning how to think like an attacker is a really important way of thinking about how to secure softwares, like what what would they do rather than just thinking about the normal kind of, oh, this works in the (faintly speaking) What happens if things go wrong? That you have to think about as well. So there's a lot of work to do to educate and help and build tools that help developers there. And it's been really good working with Snyk, cause they're a very developer focused security company, that's why we chose to work with them. Whereas historically, security companies have been very oriented towards kind of the operator side of it, not the development side, not the developer experience. And the other piece is really around supply chain security. That's just kind of a new security area. And it's very important from the container point of view, because one of the things containers let you do is really control the components that you're using to build applications and manage them better. And so we can really build tooling that helps you manage, that helps you understand what's in a container, helps you understand where it came from, how it was built and automate those processes and sign and authenticate them as well. And we've been working with CNCF on Nature V2, which is for signing revamp of the container signing process, because people really want to know who originated this container? Where did it come from? What did they say is in it? There's a lot of work about build up materials and composition analysis and all those things that you need to know about. What's in a container, and the... >> Everyone wants to know what's in a container. If you've got a Kubernetes cluster for instance, that's all highly secure and in comes a container, how do you know what the... There's no perimeter, right? So again, as you said, thinking like an attack vector there, you got to understand that, this is where the action is, right? This is where a lot of work's being done on this idea of always on security. You don't know when the container's coming in. during the run stage, you're running a business now, it's not just build and share, your running infrastructure. >> Absolutely, you really want full control about everything that goes into it, and you want to know where everything that you're running in production came from, and you pretty tired of this, and that's your end to end supply chain. It's everything from developer inputs through the build process and grow to production. And in production, understanding whether it needs to be updated and whether there's new discover vulnerabilities and whether it's being attacked and how that relates back to what came into it in the first place. >> Lot more intelligence, lot more monitoring. You guys are enabling all that I know it's cool. Great stuff. Hey, I want to get your thoughts on just what got you here on the calendar, looking at the DockerCon '21 event, and we're having a fun time here with, we're on theCUBE track, get the keynote track. But if you look at the sessions that's going on, you got, and I'll get your comment on this, cause it's really interesting how it's cleverly laid out this is. You've got the classic run share build and then you've got a track called accelerate, interesting metadata around these labels. Take us through, because this basically shows the maturation of containers. We already talked about the relationship, somewhat with Kubernetes, everyone kind of sees that direction clearly, but you got acceleration, which is a key new track, but run, share, build, what's your reaction to that? Share your observations of what the layout of those names and what it means to an enterprise and people building. >> Yeah, (faintly speaking) has been Docker's kind of motto for a long time. It kind of encapsulates that kind of process of like, the developer building application, the collaborative piece that's really important about sharing content in containers and then obviously putting into production because that's the aim. But, accelerate is incredibly important too. Developers are just being asked to do a lot. Everything is software, there's a lot of software, and a lot of software has to be created and we've got to make it easier to do this. And that kind of getting quickly from idea to business outcomes and results is what modern software teams are really driving at. And, I think we've really been focused this last year on what the team needs to succeed, and especially, small focused teams delivering business value. It's how we're structured internally as well and is how our customers, to a large extent are structured. And there's that kind of focus on accelerating those business outcomes and the feedback loops from your ideas to what the feedback that your customers give you at helping you understand that it's really important. >> Talk about final question for you in terms of the topic here, cloud, hybrid cloud, multicloud, this is, put multicloud asides more hype. Everyone has multiple clouds, but it speaks to the general distributed computing architecture when you talk about public cloud and on-premises cloud operations. So modern developers looking at that as, okay, distributed environment, edge, whatever you're going to call it. What's your view of Docker as it goes forward for the folks watching, who have experience with Docker, loved the vibe, loved the open-source, but now I've got to start thinking about putting the containers everywhere. What's the Docker pitch, so to speak, with a tech story that they should walk away with from you? What's the story, what's the pitch? >> Yeah, so containers everywhere has been a sort of emerging trend for a while, the last year or so. The whole Kubernetes at the edge thing has really exploded with people experimenting with lots and lots of different architectures for different kinds of environments at the edge. What's totally clear is that people want to be able to update software really easily at the edge the way you can in the cloud. We can't have the sort of, there's no point in shipping a modern piece of manufacturing equipment that you can't update the software on, because the software is how it works, more and more equipment is becoming very general purpose, people making general purpose robots, general purpose factories, general purpose everything which need to be specialized into the application they're going to run that week. And also people are getting more and more feedback and data and feedback from the data. So if you're building something that runs on a farm, you're getting permanent feedback about how well it's doing and how well the crops are growing was coming back. And so everywhere you've got this, we need to update. And everywhere you need to update, you want containers because containers are the simple reliable way to update software. >> I know you talked about CNCF and your role there. Also the CTO of Docker, I have to ask cause we were just covered Coop con and cloud-native con just last month and this month. And it's clear that Kubernetes is becoming boringly good in a way that's good to be boring, right? It means it's working. And it's becoming more cloud-native con than Coop-con. That has been kind of editorial observation, which speaks to what we feel is a trend towards more cloud-native discussions, less about Kubernetes. So, it's still Kubernetes stuff going on, don't get me wrong, just saying it's not as controversial in the sense that people kind of clearly understand why that's important, and all the discussions now seem to be on cloud-native modern developer workflows. What's your reaction to that? Do you agree, if not, what's your take? >> Yeah, I think that's definitely true. Kubernetes is definitely much more boring. Everyone is using it. They're using it in production now vastly more than they were a few years ago, when it was just experiment, experiment, experiment, now it's production scale out. The ecosystem in CNCF is kind of huge. There's so many little bits that have to be filled in storage and networking and all that. So there's actually a lot of pieces that are around Kubernetes, but, there's definitely more of a focus coming on the developer experience there. Compared to DockerCon, the audience at Coop Con is incarnated kind of still much more operator focused rather than developer focused. And it's very nice coming to DockerCon, just to feel like being amongst that developer community, Coop Con still has a way to gauge to have more of a real developer audience, but the project is starting to pair with a more developer focused kind of aim or things like backstage from Spotify is a really interesting one where it's about operations, but it's a developer portal focused things. So, I think it's happening, and there's a lot more talk about that. There's a whole bunch of infrastructure, there's a lot more security projects in CNCF than they were before. And we're doing a lot of work on supply chain security and CNCF just released a white paper on that few days ago. So there's a lot of work there that touches on developer needs. I still think that audience (faintly speaking) that much different from DockerCon which is I think 80% developers and maybe 10% infrastructure rather than the other way round. >> I think if you're going to get operators it can be SRE/platformleads. The platform leads are definitely inside DockerCon now than they've ever been before from my observation. So, but that speaks to the sign of the times. Most development teams have an SRE in the team, not an SRE team. They're just starting to see much more integration amongst the kind of a threaded or threaded teams or whatnot. So... >> Yeah. (faintly speaking) Operate your apps is the model. And I think that it's going to lead to more and more crossover between these communities. It's what DevOps was supposed to be about, somehow got diverted into building DevOps teams instead of working together, but we'll get there. >> It's clear from my standpoint, at least from reporting here is that, from the DockerCon and community at large, cloud-native community, having end-to-end work-load visibility on developer test run, everything seems to be the consensus, without a doubt. And then having multiple teams, and then having some platform, have some flexing people moving between teams for the most part, but built insecurity, built in SRE, built in DevOps, DevSecOps, all the way from end-to-end. >> Absolutely, we know that that's what does work best, it's where most organizations are heading at different speeds, because it's very different from the traditional architecture. It takes time to get there, but that's the model that has come out of microservices that really containers enabled and allow that model to happen. And it's the team architecture of containers. >> Hey, monolithic applications have monolithic organizations, microservices have microservices teams. Justin, great to have you on theCUBE for this conversation. If folks watching this interview, check out Justin's keynote, came from the main stage, great stuff. Justin, thanks for coming on theCUBE, we really appreciate your time and insight. >> Thank you, good to see you again. >> Okay, this is theCUBES's coverage of DockerCon 2021 Virtual. I'm John Furrier, your host. Thanks for watching. (upbeat music)

>> Narrator: From around the globe It's theCUBE with digital coverage of IBM Think 2021. Brought to you by IBM. >> Welcome back everyone to theCUBE's coverage of IBM Think 2021 virtual, I'm John Furrier, your host. I've got a great guest here Robin Hernandez, vice president Hybrid Cloud Management and Watson AIOps. Robin, great to see you. Thanks for coming on theCUBE. >> Thanks so much for having me, John. >> You know, Hybrid Cloud, the CEO of IBM Arvind loves Cloud. We know that we've talked to him all the time about it. And Cloud is now part of the entire DNA of the company. Hybrid Cloud is validated multi clouds around the corner. This is the underlying pinnings of the new operating system of business. And with that, that's massive change that we've seen IT move to large scale. You're seeing transformation, driving innovation, driving scale, and AI is the center of it. So AIOps is a huge topic. I want to jump right into it. Can you just tell me about your day to day IT operations teams what you guys are doing? How are you guys organized? How you guys bring in value to the customers? What are your teams responsible for? >> Yeah, so for a few years we've been working with our IT customers, our enterprise customers in this transformation that they're going through. As they move more workloads to cloud, and they still have some of their workloads on premise, or they have a strategy of using multiple public clouds, each of those cloud vendors have different tools. And so they're forced with, how do I keep up with the changing rate and pace of this technology? How do I build skills on a particular public cloud vendor when, you know, maybe six months from now we'll have another cloud vendor that will be introduced or another technology that will be introduced. And it's almost impossible for an it team to keep up with the rate and pace of the change. So we've really been working with IT operations in transforming their processes and their skills within their teams and that looking at what tools do they use to move to this cloud operations model. And then as part of that, how do they leverage the benefits of AI and make that practical and purposeful in this new mode of cloud operations >> And the trend that's been booming is this idea of a site reliability engineer. It's really an IT operations role. It's become kind of a new mix between engineering and IT and development. I mean, classic DevOps, we've seen, you know dev and ops, right? You got to operate the developers and the software modern apps are coming in that's infrastructure as course has been around for a while. But now as the materialization of things like Kubernetes and microservices, people are programming the infrastructure. And so the scale is there, and that's been around for a while. Now it's going to go to a whole enterprise level with containers and other things. How is the site reliability engineering persona if you will, or ITOps changed specifically because that's where the action is. And that's where you hear things like observability and I need more data, break down the silos. What's this all about? What's your view? >> Yeah, so site reliability engineering or SRE practices as we call it has really not changed the processes per se that IT has to do, but it's more accelerated at an enormous rate and pace. Those processes and the tools as you mentioned, the cloud native tools like Kubernetes have accelerated how those processes are executed. Everything from releasing new code and how they work with development to actually code the infrastructure and the policies in that development process to maintaining and observing over the life cycle of an application, the performance, the availability, the response time, and the customer experience. All of those processes that used to happen in silos with separate teams and sort of a waterfall approach, with SRE practices now, they're happening instantaneously. They're being scaled out. They're being... Failback is happening much more quickly so that applications didn't do not have outages. And the rate and pace of this has just accelerated so quickly. This is the transformation of what we call cloud operations. And we believe that as IT teams work more closely with developers and they moved towards this SRE model, that they cannot just do this with their personnel and changing skills and changing tools. They have to do this with modernized tools like AI. And this is where we are recommending applying AI to those processes so that you can then get automation out of the back end that you would not think about in a traditional IT operations, or even in an SRE practice. You have to leverage capabilities and new technologies like AI to even accelerate further. >> Let's unpack the AI operations piece because I think that's where I think I'm in hearing. I'd love you to clarify this because it becomes I think the key important point but also kind of confusing to some folks because IT operations people see that changing. You just pointed out why, honestly, the tools and the culture is changing, but AI becomes a scale point because of the automation piece you mentioned. How does that thread together? How does AIOps specifically change the customer's approach in terms of how they work with their teams and how that automation is being applied? 'Cause I think that's the key thread, right? 'Cause everyone kind of gets the cultural shifts and the tools, if they're not living it and putting it in place, but now they want to scale it. That's where automation comes in. Is that right? Is that the right way to think about it? What's your view on this? This is important. >> It's absolutely right. And I always like to talk about AI in other industries before we apply it to IT to help IT understand. Because a lot of times, IT looks at AI as a buzzword and they say, "Oh, you know, yes, sure. "This is going to help me." But if you think about... We've been doing AI for a long time at many different companies not just at IBM, but if you think about the other industries where we've applied it, healthcare in particular is so tangible for most people, right? It didn't replace a doctor but it helps a doctor see the things that would take them weeks and months of studying and analyzing different patients to say, "Hey, John, I think this may be a symptom "that we overlooked or didn't think about "or a diagnosis that we didn't think about," without manually looking at all this research. AI can accelerate that so rapidly for a doctor, the same notion for IT. If we apply AI properly to IT, we can accelerate things like remediating incidents or finding a performance problem that may take your eye months or weeks or even hours to find, AI applied properly find those issues and diagnose just like they could in healthcare it diagnoses issues correctly much more rapidly. >> Now again, I want to get your thoughts on something while you're here 'cause you've been in the business for many, many decades 20 years experience, you know, cloud cold, you know the new modern area you're managing it now. Clients are having a scenario where they, "Okay, I'm changing over the culture." I'm "Okay, I got some cloud, I got some public "and I got some hybrid and man, "we did some agile things. "We're provisioned, it's all done. "It's out there." And all of a sudden someone adds something new and it crashes (chuckles) And now I've got to get in, "Where's the risks? where's the security holes?" They're seeing this kind of day two operations as some people call, another buzz word but it's becoming more of, "Okay, we got it up and running "but we still now going to still push some code "and things are starting to break. "and that's net new thing." So it's kind of like they're out of their comfort zone. This is where I kind of see the AIOps evolving quickly because there's kind of a DevSecOps piece. There's also data involved, observability. How do you talk to that scenario? Where, okay, you sold me on cloud, I've been doing it. I did some projects. We're not been running. We got a production system and we added something new. Something maybe trivial and it breaks stuff? >> Yes. Yeah, so with the new cloud operations and SRE, the IT teams are much more responsible for business outcomes. And not just as you say, the application being deployed and the application being available, but the life cycle of that application and the results that it's bringing to the end users and the business. And what this means is that it needs to partner much more closely with development. And it is hard for them to keep up with the tools that are being used and the new code and the architectures of microservices that developers are using. So we like to apply AI on what we call the change risk management process. And so everyone's familiar with change management that means a new piece of code is being released. You have to maintain where that code is being released to was part of the application architecture and make sure that it's scaled out and rolled out properly within your enterprise policies. When we apply AI, we then apply what we call a risk factor to that change because we know so often, application outages occur not something new within the environment. So by applying AI, we can then give you a risk rating that says, "There's an 80% probability "that this change that you're about to roll out, "a code change is going to cause a problem "in this application." So it allows you to then go back and work with the development team and say, "Hey, how do we reduce this risk?" Or decide to take that calculated risk and put into the visibility of where those risks may occur. So this is a great example, change risk management of how applying AI can make you more intelligent in your decisions much more tied to the business and tied to the application release team. >> That's awesome. Well, I got you here on this point of change management. The term "Shift Left" has come up a lot in the industry. I'd love to get your quick definition of what that is in your mind. What does Shift Left mean for Ops teams with AIOps? >> Yeah, so in the early days of IT there was a hard line definitely between your development and IT team. It was kind of we always said throwing it over the fence, right? The developers would throw the code over the fence and say, good luck IT, you know, figure out how to deploy it where it needs to be deployed and cross your fingers that nothing bad happens. Well, Shift Left is really about a breaking down that fence. And if you think of your developers on your left-hand side you'd being the IT team, it's really shifting more towards that development team and getting involved in that code release process, getting involved in their CI/CD pipeline to make sure that all of your enterprise policies and what that code needs to run effectively in your enterprise application and architecture, those pieces are coded ahead of time with the developer. So it's really about partnering between it and development, shifting left to have a more collaboration versus throwing things over the fence and playing the blame game, which is what happens a lot in the early days IT. >> Yeah, and you get a smarter team out of it, great point. That's great insight. Thanks for sharing that. I think it's super relevant. That's the hot trend right now making dealers more productive, building security from the beginning. While they're doing it code it right in, make it a security proof if you will. I got to ask you one of the organizational questions as IBM leader. What are some of the roadblocks that you see in organizations that when they embrace AIOps, are trying to embrace AI ops are trying to scale it and how they can overcome those blockers. What are some of the things you're seeing that you could share with other folks that are maybe watching and trying to solve this problem? >> Yeah, so you know, AI in any industry or discipline is only as good as the data you feed it. AI is about learning from past trends and creating a normal baseline for what is normal in your environment. What is most optimal in your environment this being your enterprise application running in steady state. And so if you think back to the healthcare example, if we only have five or six pieces of patient data that we feed the AI, then the AI recommendation to the doctor is going to be pretty limited. We need a broad set of use cases across a wide demographic of people in the healthcare example, it's the same with IT, applying AI to IT. You need a broad set of data. So one of the roadblocks that we hear from many customers is, well I using an analytics tool already and I'm not really getting a lot of good recommendations or automation out of that analytics tool. And we often find it's because they're pulling data from one source, likely they're pulling data from performance metrics, performance of what's happening with the infrastructure, CPU utilization or memory utilization, storage utilization. And those are all good metrics, but without the context of everything else in your environment, without pulling in data from what's happening in your logs, pulling in data from unstructured data, from things like collaboration tools, what are your team saying? What are the customers saying about the experience with your application? You have to pull in many different data sets across IT and the business in order to make that AI recommendation the most useful. And so we recommend a more holistic true AI platform versus a very segregated data approach to applying and eating the analytics or AI engine. >> That's awesome, it's like a masterclass right there. Robin, great stuff. Great insight. We'll quickly wrap. I would love to you to take a quick minute to explain and share what are some of the use cases to get started and really get into AIOps system successes for people that want to explore more, dig in, and get into this fast, what are some use case, what's some low hanging fruit? What would you share? >> Yeah, we know that IT teams like to see results and they hate black boxes. They like to see into everything that's happening and understand deeply. And so this is one of our major focus areas as we do. We say, we're making AI purposeful for IT teams but some of the low hanging fruits, we have visions. And lots of our enterprise customers have visions of applying AI to everything from a customer experience of the application, costs management of the application and infrastructure in many different aspects. But some of the low hanging fruit is really expanding the availability and the service level agreements of your applications. So many people will say, you know I have a 93% uptime availability or an agreement with my business that this application will be up 93% of the time. Applying AI, we can increase those numbers to 99.9% of the time because it learns from past problems and it creates that baseline of what's normal in your environment. And then we'll tell you before an application outage occurs. So avoiding application outages, and then improving performance, recommendations and scalability. What's the number of users coming in versus your normal scale rate and automating that scalability. So, performance improvements and scalability is another low-hanging fruit area where many IT teams are starting. >> Yeah, I mean, why wouldn't you want to have the AIOps? They're totally cool, very relevant. You know, you're seeing hybrid cloud, standardized all across business. You've got to have that data and you got to have that incident management work there. Robin, great insight. Thank you for sharing. Robin Hernandez, vice president of Hybrid Cloud Management in Watson AIOps. Thanks for coming on theCUBE. >> Thank you so much for having me John. >> Okay, this theCUBE's coverage of IBM Think 2021. I'm John Furrier your host. Thanks for watching. (bright upbeat music)

>>Hey, welcome to the Cubes coverage of Red Hat Summit 2021, the virtual experience, I'm lisa martin, I have two guests joining me. One is a cube alum kamal Shah is back, he's now the VP of cloud platforms at Brent had come on, it's great to have you back on the program. You're in a new role, we're going to talk about that. Thank you. And Kirsten newcomer is here as well. She's the Director of cloud and Death stickups strategy at Red Hat, Kirsten, Welcome and thank you for bringing the red hat vibe to the segment. >>Absolutely, very happy to be here. >>So looking forward to this conversation that we're going to be having in the next 20 minutes or so. We're gonna be talking about the last time come on, you were on, you were the ceo of stack rocks In January of 2021. The announcement that red hat plans to acquire stack rocks, it wouldn't be talking all about that. But I'd like to start with Kirsten, give us your perspective from red hats perspective, why is red hat a good fit for stack rocks? >>You know, there are so many reasons first of all as as you know, right? Red hat has been working with product Izing kubernetes since kubernetes one dato. Right, so so open shift three dato shipped with kubernetes one dot Oh, so we've been working with kubernetes for a long time, stack rocks embraces kind of is kubernetes native security embraces the declarative nature of kubernetes and brings that to security. Red hats, Custer's red hat enterprise customers, we have a great set across different verticals that are very security conscious and and during my five years at red hat, that's where I spend the majority of my time is talking with our customers about container and kubernetes security. And while there's a great deal of security built in to open shift as it goes to market out of the box, customers need the additional capabilities that stack rock springs. Historically, we've met those needs with our security partners. We have a great ecosystem of security partners. And with the stack rocks acquisition, we're now in a position to offer additional choice. Right. If a customer wants those capabilities from Red hat tightly integrated with open shift, we'll have those available and we continue to support and work with our broad ecosystem of security partners. >>Excellent customers always want choice. Come on. Give me your perspective. You were at the helm the ceo of stack rocks as you were last time you were on the cube. Talk to me about the redhead acquisition from your seat. >>Yeah. So as as Kirsten mentioned, we were partners of red hat. You're part of the red hat partner ecosystem. And uh, what we found is that was both a great strategic fit and a great cultural fit between our two companies. Right? And so the discussions that we had were how do we go and quickly enable our customers to accelerate their digital transformation initiatives to move workloads to the cloud, to containerized them, to manage them through kubernetes and make sure that we seamlessly addressed their security concerns. Right? Because it continues to be the number one concern for large enterprises and medium sized enterprises and frankly any enterprise that uh, you know, uh, working out today. So, so that was kind of the impetus behind it. And I must say that so far the the acquisition has been going on very smoothly. So we had two months in roughly and everybody and has been very welcoming, very collaborative, very supportive. And we are already working hand in hand to to integrate our companies and to make sure that we are working closely together to make our customers successful. >>Excellent. We're gonna talk about that integration in a second. But I can imagine challenging going through an acquisition during a global pandemic. Um but that is one of the things that I think lends itself to the cultural alignment. Kamal that you talked about, Kirsten. I want to get your perspective. We know we talk about corporate culture and corporate culture has changed a lot in the last year with everybody or so many of us being remote. Talk to me about kind of the core values that red hat and stack rocks share >>actually, you know, that's been one of the great joys doing during the acquisition process in particular, Kamal and and ali shared kind of their key values and how they um how they talked to talk with their team And some of the overlap was just so resonated so much for all of us. In particular the sense of transparency, uh, that the, that the team the stack rocks executive team brings and approaches. That's a that's a clear value for red hat um strongly maintained. Uh, that was one of the key things the interest in um uh, containers and kubernetes. Right. So the technology alignment was very clear. We probably wouldn't have proceeded without that. But again, um and I think the investment in people and the independence and the and the strong drive of the individuals and supporting the individuals as they contribute to the offering so that it really creates that sense of community um and collaboration that is key. Uh and and it's just really strong overlap in in cultural values and we so appreciated that >>community and collaboration couldn't be more important these days. And ultimately the winner is the customers. So let's dig in. Let's talk about what stack rocks brings to open shift Kirsten take it away >>man. So as I said earlier, um so I think we we really believe in continuous security at red hat and in defense and depth. And so when we look at an enterprise kubernetes distribution that involves security at the real core os layer security and kubernetes adding the things into the distribution, making sure they're there by default, that any distribution needs to be secured to be hardened, auditing, logging, identity, access management, just a wealth of things. And Red hat has historically focused on infrastructure and platform security, building those capabilities into what we bring to market stack rocks enhances what we already have and really adds workload protection, which is really when it comes down to it. Especially if you're looking at hybrid cloud, multi cloud, how you secure, not just the platform, but how you secure your workloads changes. And we're moving from a world where, you know, you're deploying anti virus or malware scanners on your VMS and your host operating system to a world where those work clothes may be very short lived. And if they aren't secured from the get go, you miss your opportunity to secure them right? You can't rely on, you know, you do need controls in the infrastructure but they need to be kubernetes native controls and you need to shift that security left. Right? You never patch a running container. You always have to rebuild and redeploy if you patch the running container the next time that container images deployed, you've missed, you've lost that patch. And so the whole ethos the whole shift left. The Deb sec ops capabilities that stack rock springs really adds such value. Right? You can't just do DEF SEc or set cops. You need to do a full infinity loop to really have def SEc ops and stack rocks. I'm gonna let Kamal tell you about it, but they have so many capabilities that that really drive that shift left and enable that closed loop. We're just so excited that they're part of our offerings. >>So can you take us through that? How does stack rocks facilitate the shift left? >>Yeah, absolutely. So stack rocks, which we we announced at summit is now being rebranded as red hat. Advanced cluster security was really purpose built to help our customers address the use cases across the entire application lifecycle. Right? So from bill to deploy to run time. So this is the infinite loop that Kirsten mentioned earlier and one of our foundations was to be kubernetes native to ensure that security is really built into the application is supposed to bolt it on. So specifically, we help our customers shift left by securing the supply chain and we're making sure that we identifying vulnerabilities early during the build process before they make it to a production environment. We helped them secure the infrastructure by preventing miS configurations again early in the process because as we all know, MIS configurations often lead to breaches at at runtime. Right? We help them address uh compliance requirements by ensuring that we can check for CS benchmarks are regulatory requirements around the C I P C I, hip hop and this and and that's uh you know, just focusing on shift left, doesn't really mean that you ignore the right side or ignore the controls you need uh when your applications are running in production. So we help them secure that at runtime by identifying preventing breaches the threat detection, prevention and incident response. >>That built in security is you both mentioned that built in versus bolt on Kirsten? Talk to me about that, that as really kind of a door opener. We talked a lot about security issues, especially in the last year. I don't know how many times we've talked about miS configurations leading to breaches that we've seen so many security challenges present in the last year. We talked to me a little bit Kirsten about >>what >>customers appetites are for going. All right now, I've got cloud native security, I'm going to be able to, I'm going to feel more comfortable with rolling out production deployments. >>It's, it's a great place to go. So there are a number of elements to think about. And if I could, I could, I could start with by building on the example that Kamal said, Right, So when we think about um I need to build security into my pipeline so that when I deliver my containerized workloads, they're secure. What if I miss a step or what if a new vulnerability is discovered after the fact? Right. So one of the things that stack rocks or redhead a CS offers is it has built in policy checks to see whether a container or running image has something like a package manager in it. Well, a package manager can be used to load software that is not delivered with the container. And so the idea of ensuring that you are including workload, built in workload, protect locks with policies that are written for you. So you can focus on building your applications. You don't necessarily have to learn everything there is to know about the new attack vectors that are really just it it's new packaging, it's new technology. It's not so much there are some new attack vectors, but mostly it's a new way of delivering and running your applications. That requires some changes to how you implement your security policies. And so ensuring that you have the tools and the technology that you're running on have those capabilities built in. So that when we have conversations with our security conscious customers, we can talk with them about the attack vectors they care about. We can illustrate how we are addressing those particular concerns. Right? One of them being malware in a container, we can look for stack. Rocks can look for a package manager that could be used to pull in, you know, code that could be exploited and you can stop a running container. Um, we can do deeper data collection with stack rocks. Again, one of the challenges when you're looking at moving your security capabilities from a traditional application environment is containers come and go all the time. In a kubernetes cluster nodes, your servers can come and go in a cloud native kubernetes cluster, right? If you're running on on cloud public cloud infrastructure, um, those things are the nodes are ephemeral to, they're designed to be shut down and brought back up. So you've got a lot more data that you need to collect and that you need to analyze and you need to correlate the information between these. Right? I no longer have one application stack running on one or more VMS, it's just things are things are moving fast so you want the right type of data collection and the right correlation to have good visibility into your environment. >>And if I can just build on that a little bit. The whole idea here is that these policies really serve as god rails right for the developers. So the it allows developers to move quickly to accelerate the speed of development without having to worry about hundreds of potential security issues because there are guardrails that will notify that with concrete recommendations early in the process. And the analogy I often use is that you know the reason we have breaks in our cars, it's not to slow us down but to allow us to go faster because we know we can slow down when we need to write. So similarly these policies are really it's really designed to accelerate the speed of development and accelerate digital transformation initiatives that our customers are embarking on >>and come on. I want to stick with you on the digital transformation front. We've talked so much about how accelerated that has been in the last year with everything going on in such a dynamic market. Talk to me Kamal about some of the feedback that you've gotten from stack rocks customers about the acquisition and how it is that maybe that facilitator of the many pivots that businesses have had to do in the last year to go from survival mode to thriving business. >>Yeah. Yes, absolutely. The feedback from all of our customers bar none has been very very positive. So it's been it's allowed us to invest more in the business and you know, we publicly stated that we are going to invest more in adding more capabilities. We are more than doubling the size of our teams as an example. And really working hand in hand with our uh the broader team at Red had to uh further accelerate the speed of development and digital transformation initiatives. So it's been extremely positive because we're adding more resources, We're investing more. We're accelerating the product roadmap uh based on uh compared to what we could do as a, as a start up as you can imagine. And and the feedback has been nothing but positive. So that's kind of where we are today. And what we're doing with the summit is rolling out a new bundle called open shift uh, Open shift platform plus, which includes not just Red hat A CS which used to be Stock rocks, but also red hat open shift hybrid cloud platform as well as Red hat advanced uh container cluster management, ACM capabilities as well as create the container registry. So we're making it easier for our customers to get all the capabilities that they need to for the drive digital transformation initiatives to get. It goes back to this whole customer centric city team that red hat has, that was also core value of stack rocks and and the winner and all of this, we believe ultimately is our, our our customers because that's where we exist to serve them, >>right. And I really like that if I could chime in kind of on top of that a little bit. Um so, so I think that one of the things we've seen with the pandemic is more of the red Hat customers are accelerating their move to public cloud and away from on premises data centers. Uh and and you know, that's just part partly because of so many people working remotely. Um it just has really pushed things. And so with Hybrid cloud becoming even more key to our joint customer base and by hybrid cloud, I mean that they have some environments that are on premises as they're making this transition. Some of those environments may stay that footprint may stay on premises, but it might be smaller, they may not have settled on a single public cloud. They could, in fact, they often are picking a public cloud based on where their development focuses. Google is very popular for ai and ml workloads. Amazon of course is just used, you know, by pretty much everybody. Um and then Azzurri is popular with um a subset of customers as well. And so we see our customers investing in all of these environments and stack rocks red hat A CS like open shift runs in all these environments. So with open shift platform plus you get a complete solution that helps with multi cluster management with a C. M with security across all of these environments, right? You can take one approach to how you secure your cluster, how you secure your workloads, how you manage configurations, You get one approach no matter where you're running your containers and kubernetes platform when you're doing this with open shift platform plus. So you also get portability. If today you want to be running an amazon maybe tomorrow you need to spin up a cluster in google, you can do that if you're working with the K s or G K E, you can or a Ks, you can do that with red hat a CS as well. So we really give you everything you need to be successful in this move and we give you back to that choice word, right? We give you the opportunity to choose and to migrate at the speed that works for you. >>So that's simplicity. That streamlining. I gotta ask you the last question here in our last couple of minutes. Come on, what's the integration process been like? as we said the acquisition just a couple of months in. But talk to me about that integration process. What that's been like? >>Yeah, absolutely. So as I mentioned earlier, the process has been very smooth so far, so two months in and it's largely driven by the common set of culture and core values that exists between our two companies. And so uh you know, from a product standpoint, we've been working hand in hand because I mentioned earlier, we were partners are working hand in hand on accelerating the road map the joint roadmap that we have here uh from a go to market perspective teams are well integrated. We are going to be rolling out the rolling out the bundle and we're gonna be rolling out additional uh options for our customers. We've also publicly announced that will be open sourcing uh red hat A. C. S. Uh formerly known as Stock Rock. So stay tuned for further news and that announcement. And, and so you know, uh, again two months and everybody's been super collaborative. Super helpful, super welcoming. And the team is the well settled and we're looking forward to now focusing on our primary objective is just to make sure that our customers are successful. >>Absolutely. That customer focus is absolutely critical. But also so is the employee experience. And it sounds like we both talked about the ethos and the and the core value alignment. They're probably being pretty critical to doing an integration during a very challenging time globally. I appreciate both of you joining me on the program today, sharing what's going on stack rocks now asks the opportunities for customers to have that built in cuBA and the security. Thanks so much for your time. >>Thank you. Thank >>you for Camel shaw and Kirsten newcomer. I'm lisa martin. You're watching the cubes coverage of Red Hat Summit, The virtual experience. Mhm

>>from >>around the globe, it's the cube with digital coverage of IBM Think >>2021 >>brought to you by IBM. >>Hey, welcome to the cubes coverage of IBM Think 2021. I'm lisa martin, I have to IBM alumni with me here today please welcome Brianna frank the director of product management at IBM and Jason McGee is here as well. IBM fellow VP and Cto of the IBM cloud platform, Brianna and Jason welcome back to the cube. >>Thank you so much for having us, >>you guys were here a couple months ago, but I know there's been a whole bunch of things going on. So Brianna, we'll start with you, what's new, what's new with IBM cloud? >>We are, it's just, it's been such a rush of announcements lately, but one of my favorite announcements uh, is the IBM cloud satellite product. We went g a back in March and you know, this has been one of the most fun projects to work on as a product manager because you know, it's all about our clients coming to us and saying, hey look, we're having these are the problems that we're really facing with as we, as we move to cloud in our journey to cloud and can you help us solve them? And I think this has been just an exciting place to be in terms of distributed cloud, this new category that's really emerging where we've taken the IBM cloud, but we've distributed into lots of different locations on prem at the edge and on other public clouds. And it's been a really fun journey and it's such a great fulfilling thing to see it come to life and see clients using it and getting feedback from analysts and um in the industry. So it's been a, it's been a great, you know, a few months. >>That's good. Lots of excitement going on. Jason talk to me a little bit about kind of unpack uh, the cloud satellite from, you see what flashing in Jason's background is an IBM cloud satellite. Me, I'm sorry, I love that. You talk to me a little bit about the genesis of it. What were some of the things that customers were asking for? >>Yeah, absolutely. I mean, so, so look, I think as we've talked about a lot of IBM, you know, as as people have gone on their journey to cloud and been moving workloads in the cloud over the last few years, um, you know, not all workloads have moved right. Maybe 20 of workloads have moved to the cloud and that remaining 80% sometimes that thing that's inhibiting that is regulation compliance data late and see where my data lives. And so people have been kind of struggling with, how do I get the kind of benefits and speed and agility to public cloud, But apply it to all these applications that maybe need to live in my data center or need to live on the edge of the network close to my users or need to live where the data is being generated or in a certain country. And so the genesis of satellite was really to take our hybrid strategy and combine it with the public cloud consumption model and really allow you to have public cloud anywhere you needed it, bring those public cloud services into your data center or bring them to the edge of the network where your data is being generated and let you get the best of both. And we think that really will unlock, you know, the next wave of applications to be able to get the advantages of as a service kind of public consumption um, while retaining the flexibility to run wherever you need, >>curious station, did you see any particular industries in the last year of, I don't want to say mayhem, but you know, mayhem taking really the lead and the edge in wanting to work with you guys to understand how to really facilitate digital business transformation, because we saw a lot of acceleration going on last year. >>Yeah, absolutely. I mean, it's interesting. Cloud is fundamentally pretty horizontal technology. It applies to lots of industries, but I think the past year, especially, um, with, you know, Covid and lockdowns and changes in how we all work have accelerated massively, um, clients adoption of cloud. Um, and they've they've been looking for ways to apply those benefits across more of what they do. All right. And, and I think there's different drivers, you know, there's, you know, security compliance drivers, maybe in, in places like the financial services industry, but there's also the industries like manufacturing and retail that have, you know, they have a geographic footprint, like where things run matters to them. And so they're like, well, how do I get that kind of remote cloud benefit in all those places too. And so I've seen some acceleration in those areas. >>And one of the interesting things that I thought has emerged from an industry focus is this concept of RFs file control. So we have specific control and compliance built into the IBM cloud and one of the most prevalent questions I get from clients, you know, when can I get this FSL controls in satellite, you know, in all of these different locations. And so we built that in that's coming later this year. But I was really surprised to hear every industry and I guess it shouldn't be surprised. I mean every, every industry is trading money so it's important to keep things secure. But those fs cloud controls being extended into the satellite location is something I hear constantly as a need no matter the industry, whether it's, you know, retail or insurance, you know, etcetera. So I think that the security concerns and being able to offload the burden and chores of security is, is huge. >>One of the things we saw a lot last year, Brianna along the security lines is was ransomware booming, ransomware is a service, ransomware getting more personal. Talk to a lot of customers and to your point in different industries that are really focused on, it's not if we get hit by ransomware, it's when. So I'm wondering if that if some of the things that we saw last year or maybe why you're saying this being so such a pervasive need across industries, what do you think? >>Absolutely. I think that it's something that you really have to concentrate on full time and you know, it has to be something you're just maniacally focused on. And we have all kinds of frameworks and actually, uh, groups where we're looking at shaping regulation and compliance and it's really something that we study. Um, so if when we can pass on that expertise to our clients and again offload them. So, you know, not everyone can be an expert in these areas. I find that, you know, relieving, you know, our clients of these operational security tours allows them to get back to what they want to do, which is actually just keep inventing and building better technology for their business. >>I think that's such a, I think that's such an important point that brand is bringing up to those like part of the value of something like satellite is that we can we can run these technology platforms as a service. Right. And well, what does that? The service means? It means you can tap into a team of people who are the industry's best at building and operating that technology platform, right? Like maybe you've decided that, you know, kubernetes and open shift is your go for or platform as a business, but do you have the team and the skills that you need to operate that yourself? You know, you want to use a I you probably don't want to become an expert in how to run like whatever the latest and greatest ai framework is, you want to actually like figure out how to apply that to your business. And so we think that part of what's really attracting people to solutions like satellite, especially now with the threat you described is that they can tap into this expertise by consuming things as a service instead of figuring out how to round all themselves >>to that point. A lot of times we see really talented developers, I really like talking to incubation teams where there, you know, they're building new and they're just trying to figure out how to create, you know, the next new thing and um, it's not that they're not talented enough, they could do whatever they put their mind to, it's just that they don't have enough time and they, you know, then it just becomes too, comes down to, you know, what do you really want to spend your time doing? Is it, you know, security and operational chores, or is it inventing the next big thing for your business? And I think that that's where we're seeing the market really shift, is that, you know, it's not efficient or you know, um you know, a great idea and really no one wants to do that, you know, so we can over, if we can offload those chores, then that becomes really powerful. >>It does resource allocation is key to let those businesses to your point, we're gonna focus on their core competencies, innovating new products, new services, meeting customers where they are as customers like us become more and more demanding of things being readily available. I do want to understand a little bit, Jason, help me understand how this service is differentiated from some of the competitors in the market. >>Yeah, it's a it's a totally fair question. Um so I would answer that in a couple of ways. Um first off, you know, anytime you're talking about extending a cloud into some other environment, you obviously need some infrastructure for that application to run on whether the infrastructure is in your data center or at the edge or somewhere else. And one of the things that we've been able to is by leveraging our hybrid cloud platform by leveraging things like open shift and Lennox, we've been able to build satellite in a way where you can bring almost any clinics infrastructure to the table and use it to run satellite. So we don't require you to buy a certain lack of hardware or particular gear from us. You don't have to replace all your infrastructure. You can kind of use what you have and extend the cloud and that to me is all about, you know, if the goal is to help people build things more quickly and consume cloud, like you don't want step one to be like wheel in a whole new data center full of hardware before you get started. Um the second thing I would say is we have built our whole cloud um on this, this containerized technology, on kubernetes and open ship, which means that we're able to deliver more of our portfolio through satellite. We can deliver application platforms and databases and Deb tools and ai and security functions all as a service via satellite. So the breath of cloud capability that we think we can deliver in this model is much higher than what I think our competitors are going to be able to do. And then finally, I would say the tide to kind of IBM view of enterprise and regulated industries, you know, the work brand I mentioned around things like FS cloud, the work we're doing in telco, like we spent a lot of our energy, I'm like, how do we help, you know, enterprises regulated industries take advantage of cut and we're extending all of that work outside of our cloud data centers with satellite to all these other places. And so you really can move those mission critical applications into a cot environment when you do it with us. >>Let's talk about some successes Brianna tell me about some of the customers that are getting some pretty big business outcomes and this is a new service to talk to me about how it's being used consumed in the benefits. >>Absolutely. You know what I I find a trend that I'm seeing is really uh the cloud being distributed to the edge and there's so many interesting use cases I hear every single day about how to really use machine learning and ai at the edge. And so you know, maybe it's something as simple as, you know, a worker safety app or you're you know, making sure that workers are safe using video cameras in an office building and alerting someone if they're going into a construction area and you're using the Ai and although the the images that's coming, they're coming in through the security cameras, you're doing some analysis and saying this person is wearing a hard hat or not and warning them, but that those use cases can be changed so quickly. And you know, we've we've seen that, I think I've talked about it before with Covid you change that to masks. Um you could change that. You could hook up the application of thermal devices. We've seen situations where you know, um machine learning is used at the manufacturing edge. So you can determine if there's an issue with your um production of, you know, in a factory there's we're seeing uh edge use cases and hospitals in terms of, you know, keeping the waiting room sanitized because of, you know, over usage. So there's all kinds of just really interesting solutions and I think this is kind of the next area where we're really able to um and even partner with folks that have extraordinary vertical expertise in a specific area and you know, bringing that to life at the edge and being able to really process that data at the edge. So there's very little latency and then also you're able to change those use cases so quickly because you're really consuming cloud native best practices in cloud cloud services at the end. So you're not having to install and and manage and operate those services at the edge. It's done for you >>imagine changing the ability to change use cases so quickly in a year that plus that we've seen so much dynamics and pivoting is really key for businesses in any industry Brianna. >>I agree. And that's the thing. You know, there hasn't been one particular industry I think, you know, of course we do see a lot in the financial services industry just probably because we're IBM, but in every industry, you know, we see, you know retail, it's interesting to see sporting goods companies want to have pop up shops in a specific sporting events and how do you, you know, have a van that is a sporting goods shop, but it's just there temporarily. And how do you have a satellite location at, in the van? So there's really interesting use cases that, you know, have emerged, um, you know, just over time due to, um, you know, the need to have this capability at the edge. >>Yeah, it's necessity is the mother of invention as they say, right, well thank you both so much for stopping by sharing what's going on with IBM Cloud Satellite, the new service, the new offerings, the opportunities in it for customers. I'm sure it's going to be another exciting year for IBM because you clearly have been very busy. Thank you both for stopping by the program. >>Thanks. Thanks so much lisa >>for Brianna frank and Jason McGee. I'm lisa martin. You're watching the cube live coverage of IBM, think. >>Mm

(bright upbeat music) >> Narrator: From around the globe. It's theCUBE with digital coverage of IBM Think 2021. Brought to you by IBM. >> Welcome back everyone to theCUBE's coverage of IBM Think 2021 virtual, I'm John Furrier, your host. I've got a great guest here Robin Hernandez, vice president Hybrid Cloud Management and Watson AIOps. Robin, great to see you. Thanks for coming on theCUBE. >> Thanks so much for having me, John. >> You know, Hybrid Cloud, the CEO of IBM Arvind loves Cloud. We know that we've talked to him all the time about it. And Cloud is now part of the entire DNA of the company. Hybrid Cloud is validated multi clouds around the corner. This is the underlying pinnings of the new operating system of business. And with that, that's massive change that we've seen IT move to large scale. You're seeing transformation, driving innovation, driving scale, and AI is the center of it. So AIOps is a huge topic. I want to jump right into it. Can you just tell me about your day to day IT operations teams what you guys are doing? How are you guys organized? How you guys bring in value to the customers? What are your teams responsible for? >> Yeah, so for a few years we've been working with our IT customers, our enterprise customers in this transformation that they're going through. As they move more workloads to cloud, and they still have some of their workloads on premise, or they have a strategy of using multiple public clouds, each of those cloud vendors have different tools. And so they're forced with, how do I keep up with the changing rate and pace of this technology? How do I build skills on a particular public cloud vendor when, you know, maybe six months from now we'll have another cloud vendor that will be introduced or another technology that will be introduced. And it's almost impossible for an it team to keep up with the rate and pace of the change. So we've really been working with IT operations in transforming their processes and their skills within their teams and that looking at what tools do they use to move to this cloud operations model. And then as part of that, how do they leverage the benefits of AI and make that practical and purposeful in this new mode of cloud operations >> And the trend that's been booming is this idea of a site reliability engineer. It's really an IT operations role. It's become kind of a new mix between engineering and IT and development. I mean, classic DevOps, we've seen, you know dev and ops, right? You got to operate the developers and the software modern apps are coming in that's infrastructure as course has been around for a while. But now as the materialization of things like Kubernetes and microservices, people are programming the infrastructure. And so the scale is there, and that's been around for a while. Now it's going to go to a whole enterprise level with containers and other things. How is the site reliability engineering persona if you will, or ITOps changed specifically because that's where the action is. And that's where you hear things like observability and I need more data, break down the silos. What's this all about? What's your view? >> Yeah, so site reliability engineering or SRE practices as we call it has really not changed the processes to say that it has to do, but it's more accelerated at an enormous rate and pace. Those processes and the tools as you mentioned, the cloud native tools like Kubernetes have accelerated how those processes are executed. Everything from releasing new code and how they work with development to actually code the infrastructure and the policies in that development process to maintaining and observing over the life cycle of an application, the performance, the availability, the response time, and the customer experience. All of those processes that used to happen in silos with separate teams and sort of a waterfall approach, with SRA practices now, they're happening instantaneously. They're being scaled out. They're being... Failback is happening much more quickly so that applications didn't do not have outages. And the rate and pace of this has just accelerated so quickly. This is the transformation of what we call cloud operations. And we believe that as IT teams work more closely with developers and they moved towards this SRE model, that they cannot just do this with their personnel and changing skills and changing tools. They have to do this with modernized tools like AI. And this is where we are recommending applying AI to those processes so that you can then get automation out of the back end that you would not think about in a traditional IT operations, or even in an SRE practice. You have to leverage capabilities and new technologies like AI to even accelerate further. >> Let's unpack the AI operations piece because I think that's where I think I'm in hearing. I'd love you to clarify this because it becomes I think the key important point but also kind of confusing to some folks because IT operations people see that changing. You just pointed out why, honestly, the tools and the culture is changing, but AI becomes a scale point because of the automation piece you mentioned. How does that thread together? How does AIOps specifically change the customer's approach in terms of how they work with their teams and how that automation is being applied? 'Cause I think that's the key thread, right? 'Cause everyone kind of gets the cultural shifts and the tools, if they're not living it and putting it in place, but now they want to scale it. That's where automation comes in. Is that right? Is that the right way to think about it? What's your view on this? This is important. >> It's absolutely right. And I always like to talk about AI in other industries before we apply it to IT to help IT understand. Because a lot of times, IT looks at AI as a buzzword and they say, "Oh, you know, yes, sure. "This is going to help me." But if you think about... We've been doing AI for a long time at many different companies not just at IBM, but if you think about the other industries where we've applied it, healthcare in particular is so tangible for most people, right? It didn't replace a doctor but it helps a doctor see the things that would take them weeks and months of studying and analyzing different patients to say, "Hey, John, I think this may be a symptom "that we overlooked or didn't think about "or a diagnosis that we didn't think about," without manually looking at all this research. AI can accelerate that so rapidly for a doctor, the same notion for IT. If we apply AI properly to IT, we can accelerate things like remediating incidents or finding a performance problem that may take your eye months or weeks or even hours to find, AI applied properly find those issues and diagnose just like they could in healthcare it diagnoses issues correctly much more rapidly. >> Now again, I want to get your thoughts on something while you're here 'cause you've been in the business for many, many decades 20 years experience, you know, cloud cold, you know the new modern area you're managing it now. Clients are having a scenario where they, "Okay, I'm changing over the culture." I'm "Okay, I got some cloud, I got some public "and I got some hybrid and man, "we did some agile things. "We're provisioned, it's all done. "It's out there." And all of a sudden someone adds something new and it crashes (chuckles) And now I've got to get in, "Where's the risks? where's the security holes?" They're seeing this kind of day two operations as some people call, another buzz word but it's becoming more of, "Okay, we got it up and running "but we still now going to still push some code "and things are starting to break. "and that's net new thing." So it's kind of like they're out of their comfort zone. This is where I kind of see the AIOps evolving quickly because there's kind of a DevSecOps piece. There's also data involved, observability. How do you talk to that scenario? Where, okay, you sold me on cloud, I've been doing it. I did some projects. We're not been running. We got a production system and we added something new. Something maybe trivial and it breaks stuff? >> Yes. Yeah, so with the new cloud operations and SRE, the IT teams are much more responsible for business outcomes. And not just as you say, the application being deployed and the application being available, but the life cycle of that application and the results that it's bringing to the end users and the business. And what this means is that it needs to partner much more closely with development. And it is hard for them to keep up with the tools that are being used and the new code and the architectures of microservices that developers are using. So we like to apply AI on what we call the change risk management process. And so everyone's familiar with change management that means a new piece of code is being released. You have to maintain where that code is being released to was part of the application architecture and make sure that it's scaled out and rolled out properly within your enterprise policies. When we apply AI, we then apply what we call a risk factor to that change because we know so often, application outages occur not something new within the environment. So by applying AI, we can then give you a risk rating that says, "There's an 80% probability "that this change that you're about to roll out, "a code change is going to cause a problem "in this application." So it allows you to then go back and work with the development team and say, "Hey, how do we reduce this risk?" Or decide to take that calculated risk and put into the visibility of where those risks may occur. So this is a great example, change risk management of how applying AI can make you more intelligent in your decisions much more tied to the business and tied to the application release team. >> That's awesome. Well, I got you here on this point of change management. The term "Shift Left" has come up a lot in the industry. I'd love to get your quick definition of what that is in your mind. What does Shift Left mean for Ops teams with AIOps? >> Yeah, so in the early days of IT there was a hard line definitely between your development and IT team. It was kind of we always said throwing it over the fence, right? The developers would throw the code over the fence and say, good luck IT, you know, figure out how to deploy it where it needs to be deployed and cross your fingers that nothing bad happens. Well, Shift Left is really about a breaking down that fence. And if you think of your developers on your left-hand side you'd being the IT team, it's really shifting more towards that development team and getting involved in that code release process, getting involved in their CI/CD pipeline to make sure that all of your enterprise policies and what that code needs to run effectively in your enterprise application and architecture, those pieces are coded ahead of time with the developer. So it's really about partnering between it and development, shifting left to have a more collaboration versus throwing things over the fence and playing the blame game, which is what happens a lot in the early days IT. >> Yeah, and you get a smarter team out of it, great point. That's great insight. Thanks for sharing that. I think it's super relevant. That's the hot trend right now making dealers more productive, building security from the beginning. While they're doing it code it right in, make it a security proof if you will. I got to ask you one of the organizational questions as IBM leader. What are some of the roadblocks that you see in organizations that when they embrace AIOps, are trying to embrace AI ops are trying to scale it and how they can overcome those blockers. What are some of the things you're seeing that you could share with other folks that are maybe watching and trying to solve this problem? >> Yeah, so you know, AI in any industry or discipline is only as good as the data you feed it. AI is about learning from past trends and creating a normal baseline for what is normal in your environment. What is most optimal in your environment this being your enterprise application running in steady state. And so if you think back to the healthcare example, if we only have five or six pieces of patient data that we feed the AI, then the AI recommendation to the doctor is going to be pretty limited. We need a broad set of use cases across a wide demographic of people in the healthcare example, it's the same with IT, applying AI to IT. You need a broad set of data. So one of the roadblocks that we hear from many customers is, well I using an analytics tool already and I'm not really getting a lot of good recommendations or automation out of that analytics tool. And we often find it's because they're pulling data from one source, likely they're pulling data from performance metrics, performance of what's happening with the infrastructure, CPU utilization or memory utilization, storage utilization. And those are all good metrics, but without the context of everything else in your environment, without pulling in data from what's happening in your logs, pulling in data from unstructured data, from things like collaboration tools, what are your team saying? What are the customers saying about the experience with your application? You have to pull in many different data sets across IT and the business in order to make that AI recommendation the most useful. And so we recommend a more holistic true AI platform versus a very segregated data approach to applying and eating the analytics or AI engine. >> That's awesome, it's like a masterclass right there. Robin, great stuff. Great insight. We'll quickly wrap. I would love to you to take a quick minute to explain and share what are some of the use cases to get started and really get into AIOps system successes for people that want to explore more, dig in, and get into this fast, what are some use case, what's some low hanging fruit? What would you share? >> Yeah, we know that IT teams like to see results and they hate black boxes. They like to see into everything that's happening and understand deeply. And so this is one of our major focus areas as we do. We say, we're making AI purposeful for IT teams but some of the low hanging fruits, we have visions. And lots of our enterprise customers have visions of applying AI to everything from a customer experience of the application, costs management of the application and infrastructure in many different aspects. But some of the low hanging fruit is really expanding the availability and the service level agreements of your applications. So many people will say, you know I have a 93% uptime availability or an agreement with my business that this application will be up 93% of the time. Applying AI, we can increase those numbers to 99.9% of the time because it learns from past problems and it creates that baseline of what's normal in your environment. And then we'll tell you before an application outage occurs. So avoiding application outages, and then improving performance, recommendations and scalability. What's the number of users coming in versus your normal scale rate and automating that scalability. So, performance improvements and scalability is another low-hanging fruit area where many IT teams are starting. >> Yeah, I mean, why wouldn't you want to have the AIOps? They're totally cool, very relevant. You know, you're seeing hybrid cloud, standardized all across business. You've got to have that data and you got to have that incident management work there. Robin, great insight. Thank you for sharing. Robin Hernandez, vice president of Hybrid Cloud Management in Watson AIOps. Thanks for coming on theCUBE. >> Thank you so much for having me John. >> Okay, this theCUBE's coverage of IBM Think 2021. I'm John Furrier your host. Thanks for watching. (bright upbeat music)