(Gentle music) >> Hello and welcome to the Cube here in Palo Alto, California. I'm John Furrier here, joined by Platform nine, Amelia Bell the Chief Revenue Officer, really digging into the conversation around Kubernetes Cloud native and the journey this next generation cloud. Amelia, thanks for coming in and joining me today. >> Thank you, thank you. Great pleasure to be here. >> So, CRO, chief Revenue Officer. So you're mainly in charge of serving the customers, making sure they're they're happy with the solution you guys have. >> That's right. >> And this market must be pretty exciting. >> Oh, it's very exciting and we are seeing a lot of new use cases coming up all the time. So part of my job is to obtain new customers but then of course, service our existing customers and then there's a constant evolution. Nothing is standing still right now. >> We've had all your co-founders on, on the show here and we've kind of talked about the trends and where you guys have come from, where you guys are going now. And it's interesting, if you look at the cloud native market, the scale is still huge. You seeing now this next wave of AI coming on, which I call that's the real web three in my mind in terms of like the next experiences really still points to data infrastructure scale. These next gen apps are coming. And so that's being built on the previous generation of DevSecOps. >> Right >> And so a lot of enterprises are having to grow up really, really fast >> Right. >> And figure out, okay, I got to have scale I got large scale data, I got horizontal scalability I got to apply machine learning now the new software engineering practice. And then, oh, by the way I got the Kubernetes clusters I got to manage >> Right. >> I got what's containers weather, the security problems. This is a really complicated but important area of build out right now in the marketplace. >> Right. What are you seeing? >> So it's, it's really important that the infrastructure is not the hindrance in these cases. And we, one of our customers is in fact a large AI company and we, I met with them yesterday and asked them, you know, why are you giving that to us? You've got really smart engineers. They can run and create the infrastructure, you know in a custom way that you want it. And they said, we've got to be core to our business. There's plenty of work to do just on delivering the AI capabilities, and there's plenty of work to do. We can't get bogged down in the infrastructure. We don't want to have people running the engine we want them driving the car. We want them creating value on top of that. so they can't have the infrastructure being the bottleneck for them. >> It's interesting, the AI companies, that's their value proposition to their customers is that they don't want the technical talent. >> Right. >> Working on, you know, non-differentiated heavy lifting things. >> Right. >> And automate those and scale it up. Can you talk about the problem that you guys are solving? Because there's a lot going on here. >> Yeah. >> You can look at all aspects of the DevOps scale. There's a lot of little problems, some big problems. What are you guys focusing on? What's the bullseye for Platform known? >> Okay, so the bullseye is that Kubernetes infrastructure is really hard, right? It's really hard to create and run. So we introduce a time to market efficiency, let's get this up and running and let's get you into production and and producing results for your customers fast. But at the same time, let's reduce your cost and complexity and increase reliability. So, >> And what are some of the things that they're having problems with that are breaking? Is it more of updates on code? Is it size of the, I mean clusters they have, what what is it more operational? What are the, what are some of the things that are that kind of get them to call you guys up? What's the main thing? >> It's the operations. It's all operations. So what, what happens is that if you have a look at Kubernetes platform it's made up of many, many components. And that's where it gets complex. It's not just Kubernetes. There's load balances, networking, there's observability. All these things have to operate together. And all the piece parts have to be upgraded and maintained. The integrations need to work, you need to have probes into the system to predict where problems can be coming. So the operational part of it is complex. So you need to be observing not only your clusters in the health of the clusters and the nodes and so on but the health of the platform itself. >> We're going to get Peter Frey in on here after I talk about some of the technical issues on deployments. But what's the, what's the big decision for the customer? Because there's kind of, there's two schools of thought. One is, I'm going to build my own and have my team build it or I'm going to go with a partner >> Right. >> Say platform nine, what's the trade offs there? Because it seems to me that, that there's a there's a certain area of where it's core competency but I can outsource it or partner with it and, and work with platform nine versus trying to take it all on internally >> Right. >> Of which requires more costs. So there's a, there's a line where you kind of like figure out that customers have to figure out that, that piece >> Right >> What do, what's your view on that? Because I'm hearing that more people are saying, hey I want to, I want to focus my people on solutions. The app side, not so much the ops >> Right. >> What's the trade off? How do you talk about? >> It's a really interesting question because most companies think they have two options. It's either a DIY option and they love that engineers love playing with the new and on the latest. And then they think the other option is going to cloud, public cloud and have it semi managed by them. And you get very different out of those. So in the DIY you get flexibility coz you get to choose your infrastructure but then you've got all the complexities of the DIY piece. You've got to not only choose all your components but you've got to keep them working. Now if you go to public cloud option, you lose flexibility because a lot of those choices are made for you but you gain agility because quite frankly it's really easy to spin up clusters. So what we are, is that in the middle we bring the agility and the flexibility because we bring the control plane that allows you to spin up clusters and and lifecycle manage them very quickly. So the agility's there but you can do it on the infrastructure of your choice. And in the DIY culture, one of the hardest things to do actually is to convince them they don't have to do it themselves. They can focus on higher value activities, which are more focused on delivering outcomes to their customers. >> So you provide the solution that allows them to feel like they're billing it themselves. >> Correct. >> And get these scale and speed and the efficiencies of the op side. So it's kind of the best of both worlds. It's not a full outsource. >> Right, right. >> You're bringing them in to make their jobs easier >> Right, That's right. So they get choices. >> Yeah. >> We, we, they get choices on how they build it and then we run and operate it for them. But they, they have all the observability. The benefit is that if we are managing their operations and most of our customers choose the managed operations piece of it, then they don't. If something goes wrong, we fix that and they, they they get told, oh, by the way, you had a problem. We've dealt with it. But in the other model is they've got to create all that observability themselves and they've got to get ahead of the issues themselves, and then they've got to raise tickets to whoever they need to raise tickets to. Whereas we have things like auto ticket generation and so on where, look, just drive the car let us worry about the engine and all of that. Let us deal with that. And you can choose whatever you want about the engine but let us manage it for you. So >> What do you, what do you say to folks out there that are may have a need for platform nine? What's the signals inside their company that they should be calling you guys up and, and leaning in with platform nine? >> Right. >> Is it more sprawl on on clusters? Is it more errors? Is it more tickets? Is it more hassle? What are some of the signs? If someone's watching this say, hey I have, I have an issue with this. >> I would say, if there's operational inefficiencies you can't get things to market fast enough because you are building this and it's just taking too long you're spending way too much time operationally on the infrastructure, then you are, you are not using your resources where they should best be used. And, and that is delivering services to the customer. >> Ed me Hora on for International Women's Day. And she was talking about how they love to solve complex problems on the engineering team at Platform nine. It's going to get pretty complex with the edge emerging >> Indeed >> and cloud native on-premises distributed computing. >> Indeed. >> essentially is what it is. That's kind of the core DNA of the team. >> Yeah. >> What, how does that translate to the customers? Because IT seems to be, okay, I have virtual machines were great, now I got to scale up and and convert over a transform to containers, Kubernetes >> Right. >> And then large scale app, app applications. >> Right, so when it comes to Edge it gets complex pretty fast because it's highly distributed. So how do you have standardization and governance across all the different edge locations? So what we bring into play is an ability to, um, at each edge, location eh, provision from bare metal up all the way up to the application. So let's say you have thousands of stores and you want to modernize those stores, you know rather than having a server being sent somewhere to have an image loaded up and then sent that and then you've got to send a technical guide to the store and you've got to implement it all there. Forget all that. That's just, that's just a ridiculous waste of time. So what we've done is we've created the ability where the server can just be sent to the store. You can get your barista or your chef just to plug it in, right? You don't need to send any technical person over there. As long as we have access to it, we get access to it and we provision the whole thing from bare metal up and then we can maintain it according to the standards that are needed and upgrade accordingly. And that gives standardization across all your stores or edge locations or 5G towers or whatever it is, distribution centers. And we can create nice governance and good standardization which allows them to innovate fast as well. >> So this is a real opportunity for you guys. >> Yeah. >> This is an advantage from your expertise. >> Yes. >> The edge piece, dropping in a box, self-provisioning. >> That's right. So yeah. >> Can people do that? What's the, >> No, actually it, it's, it's very difficult to do. I I, from my understanding, we're the only people that can provision it from bare metal up, right? So if anyone has a different story, I'd love to hear about that. But that's my understanding today. >> That's a good value purpose. So talk about the value of the customer. What kind of scope do you got? Can you scope some of the customer environments you have from >> Sure. >> From, you know, small to the large, how give us an idea of the order of magnitude of the >> Yeah, so, so small customers may have 20 clusters or something like that. 20 nodes, I beg your pardon. Our large customers, like we're we are scaling one particular distributed environment from 2200 nodes to 10,000 nodes by the end of this year and 26,000 nodes next year. We have another customer that's scaling up to 10,000 nodes this year as well. So we have some very large scale, but some smaller ones too. And we're, we're happy to work with either end. >> Okay, so pretend I'm a customer. I'm really, I got pain and Kubernetes like I want to, I can't hire enough people. I want to have my all focus. What's the pitch? >> Okay. So skill shortage is something that that everyone is facing right now. And if, if you've got skill shortage it's going to be really hard to hire if you are competing against really, you know, high salary you know, offering companies that are out there. So the pitch is, let us do it for you. We have, we have a team of excellent probably the best Kubernetes engineers on the planet. We will create your environment for you. We will get it up and running. We will allow you to, you know, run your applica, just consume the platform, we'll run it for you. We'll have SLAs and up times guaranteed and you can just focus on delivering the software and the value needed to your customers. >> What are some of the testimonials that you get from people? Just anecdotally, what do they say? Oh my god, you guys save. >> Yeah. >> Our butts. >> Yeah. >> This is amazing. We just shipped our code out much faster. >> Yeah. >> What are some of the things that you hear? >> So, so the number one thing I hear is it just works right? It's, we don't have to worry about it, it just works. So that, that's a really great feedback that we get. The other thing I hear is if we do have issues that your team are amazing, they they fix things, they're proactive, you know, they're we really enjoy working with you. So from, from that perspective, that's great. But the other side of it is we hear things like if we were to do that ourselves we would've taken six to 12 months to build that. And you guys have just saved us six to 12 months. The other thing that we hear is with the same two engineers we started on, you know, a hundred nodes we're now running thousands of nodes. We have not had to increase the size of the team and expand and scale exponentially. >> Awesome. What's next for you guys? What's on your, your plate? >> Yeah. >> With CRO, what's some of the goals you have? >> Yeah, so growth of course as a CRO, you don't get away from that. We've got some very exciting, actually, initiatives coming up. One of the things that we are seeing a lot of demand for and is, is in the area of virtualization bringing virtual machine, virtual virtual containers, sorry I'm saying that all wrong. Bringing virtual machine, the virtual machines onto the cloud native infrastructure using Kubernetes technology. So that provides a, an excellent stepping stone for those guys who are in the virtualization world. And they can't move to containers, they can't refactor their applications and workloads fast enough. So just bring your virtual machine and put it onto the container infrastructure. So we're seeing a lot of demand for that, because it provides an excellent stepping stone. Why not use Kubernetes to orchestrate virtual the virtual world? And then we've got some really interesting cost optimization. >> So a lot of migration kind of thinking around VMs and >> Oh, tremendous. The, the VM world is just massively bigger than the container world right now. So you can't ignore that. So we are providing basically the evolution, the the journey for the customers to utilize the greatest of technologies without having to do that in a, in a in a way that just breaks the bank and they can't get there fast enough. So we provide those stepping stones for them. Yeah. >> Amelia thank you for coming on. Sharing. >> Thank you. >> The update on platform nine. Congratulations on your big accounts you have and >> thank you. >> And the world could get more complex, which Means >> indeed >> have more customers. >> Thank you, thank you John. Appreciate that. Thank you. >> I'm John Furry. You're watching Platform nine and the Cube Conversations here. Thanks for watching. (gentle music)

(upbeat music) >> Morning guys and girls, welcome back to theCUBE's live coverage of Women in Data Science WIDS 2023 live at Stanford University. Lisa Martin here with my co-host for this segment, Tracy Zhang. We're really excited to be talking with a great female rockstar. You're going to learn a lot from her next, Jacqueline Kuo, solutions engineer at Dataiku. Welcome, Jacqueline. Great to have you. >> Thank you so much. >> Thank for being here. >> I'm so excited to be here. >> So one of the things I have to start out with, 'cause my mom Kathy Dahlia is watching, she's a New Yorker. You are a born and raised New Yorker and I learned from my mom and others. If you're born in New York no matter how long you've moved away, you are a New Yorker. There's you guys have like a secret club. (group laughs) >> I am definitely very proud of being born and raised in New York. My family immigrated to New York, New Jersey from Taiwan. So very proud Taiwanese American as well. But I absolutely love New York and I can't imagine living anywhere else. >> Yeah, yeah. >> I love it. >> So you studied, I was doing some research on you you studied mechanical engineering at MIT. >> Yes. >> That's huge. And you discovered your passion for all things data-related. You worked at IBM as an analytics consultant. Talk to us a little bit about your career path. Were you always interested in engineering STEM-related subjects from the time you were a child? >> I feel like my interests were ranging in many different things and I ended up landing in engineering, 'cause I felt like I wanted to gain a toolkit like a toolset to make some sort of change with or use my career to make some sort of change in this world. And I landed on engineering and mechanical engineering specifically, because I felt like I got to, in my undergrad do a lot of hands-on projects, learn every part of the engineering and design process to build products which is super-transferable and transferable skills sort of is like the trend in my career so far. Where after undergrad I wanted to move back to New York and mechanical engineering jobs are kind of few and fall far in between in the city. And I ended up landing at IBM doing analytics consulting, because I wanted to understand how to use data. I knew that data was really powerful and I knew that working with it could allow me to tell better stories to influence people across different industries. And that's also how I kind of landed at Dataiku to my current role, because it really does allow me to work across different industries and work on different problems that are just interesting. >> Yeah, I like the way that, how you mentioned building a toolkit when doing your studies at school. Do you think a lot of skills are still very relevant to your job at Dataiku right now? >> I think that at the core of it is just problem solving and asking questions and continuing to be curious or trying to challenge what is is currently given to you. And I think in an engineering degree you get a lot of that. >> Yeah, I'm sure. >> But I think that we've actually seen that a lot in the panels today already, that you get that through all different types of work and research and that kind of thoughtfulness comes across in all different industries too. >> Talk a little bit about some of the challenges, that data science is solving, because every company these days, whether it's an enterprise in manufacturing or a small business in retail, everybody has to be data-driven, because the end user, the end customer, whoever that is whether it's a person, an individual, a company, a B2B, expects to have a personalized custom experience and that comes from data. But you have to be able to understand that data treated properly, responsibly. Talk about some of the interesting projects that you're doing at Dataiku or maybe some that you've done in the past that are really kind of transformative across things climate change or police violence, some of the things that data science really is impacting these days. >> Yeah, absolutely. I think that what I love about coming to these conferences is that you hear about those really impactful social impact projects that I think everybody who's in data science wants to be working on. And I think at Dataiku what's great is that we do have this program called Ikig.AI where we work with nonprofits and we support them in their data and analytics projects. And so, a project I worked on was with the Clean Water, oh my goodness, the Ocean Cleanup project, Ocean Cleanup organization, which was amazing, because it was sort of outside of my day-to-day and it allowed me to work with them and help them understand better where plastic is being aggregated across the world and where it appears, whether that's on beaches or in lakes and rivers. So using data to help them better understand that. I feel like from a day-to-day though, we, in terms of our customers, they're really looking at very basic problems with data. And I say basic, not to diminish it, but really just to kind of say that it's high impact, but basic problems around how do they forecast sales better? That's a really kind of, sort of basic problem, but it's actually super-complex and really impactful for people, for companies when it comes to forecasting how much headcount they need to have in the next year or how much inventory to have if they're retail. And all of those are going to, especially for smaller companies, make a huge impact on whether they make profit or not. And so, what's great about working at Dataiku is you get to work on these high-impact projects and oftentimes I think from my perspective, I work as a solutions engineer on the commercial team. So it's just, we work generally with smaller customers and sometimes talking to them, me talking to them is like their first introduction to what data science is and what they can do with that data. And sort of using our platform to show them what the possibilities are and help them build a strategy around how they can implement data in their day-to-day. >> What's the difference? You were a data scientist by title and function, now you're a solutions engineer. Talk about the ascendancy into that and also some of the things that you and Tracy will talk about as those transferable, those transportable skills that probably maybe you learned in engineering, you brought data science now you're bringing to solutions engineering. >> Yeah, absolutely. So data science, I love working with data. I love getting in the weeds of things and I love, oftentimes that means debugging things or looking line by line at your code and trying to make it better. I found that on in the data science role, while those things I really loved, sometimes it also meant that I didn't, couldn't see or didn't have visibility into the broader picture of well like, well why are we doing this project? And who is it impacting? And because oftentimes your day-to-day is very much in the weeds. And so, I moved into sales or solutions engineering at Dataiku to get that perspective, because what a sales engineer does is support the sale from a technical perspective. And so, you really truly understand well, what is the customer looking for and what is going to influence them to make a purchase? And how do you tell the story of the impact of data? Because oftentimes they need to quantify well, if I purchase a software like Dataiku then I'm able to build this project and make this X impact on the business. And that is really powerful. That's where the storytelling comes in and that I feel like a lot of what we've been hearing today about connecting data with people who can actually do something with that data. That's really the bridge that we as sales engineers are trying to connect in that sales process. >> It's all about connectivity, isn't it? >> Yeah, definitely. We were talking about this earlier that it's about making impact and it's about people who we are analyzing data is like influencing. And I saw that one of the keywords or one of the biggest thing at Dataiku is everyday AI, so I wanted to just ask, could you please talk more about how does that weave into the problem solving and then day-to-day making an impact process? >> Yes, so I started working on Dataiku around three years ago and I fell in love with the product itself. The product that we have is we allow for people with different backgrounds. If you're coming from a data analyst background, data science, data engineering, maybe you are more of like a business subject matter expert, to all work in one unified central platform, one user interface. And why that's powerful is that when you're working with data, it's not just that data scientist working on their own and their own computer coding. We've heard today that it's all about connecting the data scientists with those business people, with maybe the data engineers and IT people who are actually going to put that model into production or other folks. And so, they all use different languages. Data scientists might use Python and R, your business people are using PowerPoint and Excel, everyone's using different tools. How do we bring them all in one place so that you can have conversations faster? So the business people can understand exactly what you're building with the data and can get their hands on that data and that model prediction faster. So that's what Dataiku does. That's the product that we have. And I completely forgot your question, 'cause I got so invested in talking about this. Oh, everyday AI. Yeah, so the goal of of Dataiku is really to allow for those maybe less technical people with less traditional data science backgrounds. Maybe they're data experts and they understand the data really well and they've been working in SQL for all their career. Maybe they're just subject matter experts and want to get more into working with data. We allow those people to do that through our no and low-code tools within our platform. Platform is very visual as well. And so, I've seen a lot of people learn data science, learn machine learning by working in the tool itself. And that's sort of, that's where everyday AI comes in, 'cause we truly believe that there are a lot of, there's a lot of unutilized expertise out there that we can bring in. And if we did give them access to data, imagine what we could do in the kind of work that they can do and become empowered basically with that. >> Yeah, we're just scratching the surface. I find data science so fascinating, especially when you talk about some of the real world applications, police violence, health inequities, climate change. Here we are in California and I don't know if you know, we're experiencing an atmospheric river again tomorrow. Californians and the rain- >> Storm is coming. >> We are not good... And I'm a native Californian, but we all know about climate change. People probably don't associate all of the data that is helping us understand it, make decisions based on what's coming what's happened in the past. I just find that so fascinating. But I really think we're truly at the beginning of really understanding the impact that being data-driven can actually mean whether you are investigating climate change or police violence or health inequities or your a grocery store that needs to become data-driven, because your consumer is expecting a personalized relevant experience. I want you to offer me up things that I know I was doing online grocery shopping, yesterday, I just got back from Europe and I was so thankful that my grocer is data-driven, because they made the process so easy for me. And but we have that expectation as consumers that it's going to be that easy, it's going to be that personalized. And what a lot of folks don't understand is the data the democratization of data, the AI that's helping make that a possibility that makes our lives easier. >> Yeah, I love that point around data is everywhere and the more we have, the actually the more access we actually are providing. 'cause now compute is cheaper, data is literally everywhere, you can get access to it very easily. And so, I feel like more people are just getting themselves involved and that's, I mean this whole conference around just bringing more women into this industry and more people with different backgrounds from minority groups so that we get their thoughts, their opinions into the work is so important and it's becoming a lot easier with all of the technology and tools just being open source being easier to access, being cheaper. And that I feel really hopeful about in this field. >> That's good. Hope is good, isn't it? >> Yes, that's all we need. But yeah, I'm glad to see that we're working towards that direction. I'm excited to see what lies in the future. >> We've been talking about numbers of women, percentages of women in technical roles for years and we've seen it hover around 25%. I was looking at some, I need to AnitaB.org stats from 2022 was just looking at this yesterday and the numbers are going up. I think the number was 26, 27.6% of women in technical roles. So we're seeing a growth there especially over pre-pandemic levels. Definitely the biggest challenge that still seems to be one of the biggest that remains is attrition. I would love to get your advice on what would you tell your younger self or the previous prior generation in terms of having the confidence and the courage to pursue engineering, pursue data science, pursue a technical role, and also stay in that role so you can be one of those females on stage that we saw today? >> Yeah, that's the goal right there one day. I think it's really about finding other people to lift and mentor and support you. And I talked to a bunch of people today who just found this conference through Googling it, and the fact that organizations like this exist really do help, because those are the people who are going to understand the struggles you're going through as a woman in this industry, which can get tough, but it gets easier when you have a community to share that with and to support you. And I do want to definitely give a plug to the WIDS@Dataiku team. >> Talk to us about that. >> Yeah, I was so fortunate to be a WIDS ambassador last year and again this year with Dataiku and I was here last year as well with Dataiku, but we have grown the WIDS effort so much over the last few years. So the first year we had two events in New York and also in London. Our Dataiku's global. So this year we additionally have one in the west coast out here in SF and another one in Singapore which is incredible to involve that team. But what I love is that everyone is really passionate about just getting more women involved in this industry. But then also what I find fortunate too at Dataiku is that we have a strong female, just a lot of women. >> Good. >> Yeah. >> A lot of women working as data scientists, solutions engineer and sales and all across the company who even if they aren't doing data work in a day-to-day, they are super-involved and excited to get more women in the technical field. And so. that's like our Empower group internally that hosts events and I feel like it's a really nice safe space for all of us to speak about challenges that we encounter and feel like we're not alone in that we have a support system to make it better. So I think from a nutrition standpoint every organization should have a female ERG to just support one another. >> Absolutely. There's so much value in a network in the community. I was talking to somebody who I'm blanking on this may have been in Barcelona last week, talking about a stat that showed that a really high percentage, 78% of people couldn't identify a female role model in technology. Of course, Sheryl Sandberg's been one of our role models and I thought a lot of people know Sheryl who's leaving or has left. And then a whole, YouTube influencers that have no idea that the CEO of YouTube for years has been a woman, who has- >> And she came last year to speak at WIDS. >> Did she? >> Yeah. >> Oh, I missed that. It must have been, we were probably filming. But we need more, we need to be, and it sounds like Dataiku was doing a great job of this. Tracy, we've talked about this earlier today. We need to see what we can be. And it sounds like Dataiku was pioneering that with that ERG program that you talked about. And I completely agree with you. That should be a standard program everywhere and women should feel empowered to raise their hand ask a question, or really embrace, "I'm interested in engineering, I'm interested in data science." Then maybe there's not a lot of women in classes. That's okay. Be the pioneer, be that next Sheryl Sandberg or the CTO of ChatGPT, Mira Murati, who's a female. We need more people that we can see and lean into that and embrace it. I think you're going to be one of them. >> I think so too. Just so that young girls like me like other who's so in school, can see, can look up to you and be like, "She's my role model and I want to be like her. And I know that there's someone to listen to me and to support me if I have any questions in this field." So yeah. >> Yeah, I mean that's how I feel about literally everyone that I'm surrounded by here. I find that you find role models and people to look up to in every conversation whenever I'm speaking with another woman in tech, because there's a journey that has had happen for you to get to that place. So it's incredible, this community. >> It is incredible. WIDS is a movement we're so proud of at theCUBE to have been a part of it since the very beginning, since 2015, I've been covering it since 2017. It's always one of my favorite events. It's so inspiring and it just goes to show the power that data can have, the influence, but also just that we're at the beginning of uncovering so much. Jacqueline's been such a pleasure having you on theCUBE. Thank you. >> Thank you. >> For sharing your story, sharing with us what Dataiku was doing and keep going. More power to you girl. We're going to see you up on that stage one of these years. >> Thank you so much. Thank you guys. >> Our pleasure. >> Our pleasure. >> For our guests and Tracy Zhang, this is Lisa Martin, you're watching theCUBE live at WIDS '23. #EmbraceEquity is this year's International Women's Day theme. Stick around, our next guest joins us in just a minute. (upbeat music)

(low-key music) >> The cube's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (uplifting electronic music) (crowd chattering in background) >> Welcome back to Spain, everybody. My name's Dave Vellante. I'm here with Dave Nicholson. We are live at the Fira in Barcelona, covering MWC23 day four. We've been talking about, you know, 5G all week. We're going to talk about it some more. Jillian Kaplan is here. She's the head of Global Telecom Thought Leadership at Dell Technologies, and we're pleased to have Meg Knauth, who's the Vice President for Digital Platform Engineering at T-Mobile. Ladies, welcome to theCUBE. Thanks for coming on. >> Thanks for having us. >> Yeah, thank you. >> All right, Meg, can you explain 5G and edge to folks that may not be familiar with it? Give us the 101 on 5G and edge. >> Sure, I'd be happy to. So, at T-Mobile, we want businesses to be able to focus on their business outcomes and not have to stress about network technology. So we're here to handle the networking behind the scenes for you to achieve your business goals. The main way to think about 5G is speed, reduced latency, and heightened security. And you can apply that to so many different business goals and objectives. You know, some of the use cases that get touted out the most are in the retail manufacturing sectors with sensors and with control of inventory and things of that nature. But it can be applied to pretty much any industry because who doesn't need more (chuckles) more speed and lower latency. >> Yeah. And reliability, right? >> Exactly. >> I mean, that's what you're going to have there. So it's not like it's necessarily going to- you know, you think about 5G and these private networks, right? I mean, it's not going to, oh, maybe it is going to eat into, there's a Venn there, I know, but it's not going to going to replace wireless, right? I mean, it's new use cases. >> Yeah. >> Maybe you could talk about that a little bit. >> Yeah, they definitely coexist, right? And Meg touched a little bit on like all the use cases that are coming to be, but as we look at 5G, it's really the- we call it like the Enterprise G, right? It's where the enterprise is going to be able to see changes in their business and the way that they do things. And for them, it's going to be about reducing costs and heightening ROI, and safety too, right? Like being able to automate manufacturing facilities where you don't have workers, like, you know, getting hit by various pieces of equipment and you can take them out of harm's way and put robots in their place. And having them really work in an autonomous situation is going to be super, super key. And 5G is just the, it's the backbone of all future technologies if you look at it. We have to have a network like that in order to build things like AI and ML, and we talk about VR and the Metaverse. You have to have a super reliable network that can handle the amount of devices that we're putting out today, right? So, extremely important. >> From T-Mobile's perspective, I mean we hear a lot about, oh, we spent a lot on CapEx, we know that. You know, trillion and a half over the next seven years, going into 5G infrastructure. We heard in the early keynotes at MWC, we heard the call to you know, tax the over the top vendors. We heard the OTT, Netflix shot back, they said, "Why don't you help us pay for the content that we're creating?" But, okay, so I get that, but telcos have a great business. Where's T-Mobile stand on future revenue opportunities? Are you looking to get more data and monetize that data? Are you looking to do things like partner with Dell to do, you know, 5G networks? Where are the opportunities for T-Mobile? >> I think it's more, as Jillian said, it's the opportunities for each business and it's unique to those businesses. So we're not in it just for ourselves. We're in it to help others achieve their business goals and to do more with all of the new capabilities that this network provides. >> Yeah, man, I like that answer because again, listening to some of the CEOs of the large telcos, it's like, hmm, what's in it for me as the customer or the business? I didn't hear enough of that. And at least in the early keynotes, I'm hearing it more, you know, as the show goes on. But I don't know, Dave, what do you think about what you've heard at the event? >> Well, I'm curious from T-Mobile's perspective, you know when a consumer thinks about 5G, we think of voice, text, and data. And if we think about the 5G network that you already have in place, I'm curious, if you can share this kind of information, what percentage of that's being utilized now? How much is available for the, you know, for the Enterprise G that we're talking about, and maybe, you know, in five years in the future, do you have like a projected mix of consumer use versus all of these back office, call them processes that a consumer's not aware of, but you know the factory floor being connected via 5G, that frontiers that emerges, where are we now and what are you looking towards? Does that make sense? Kind of the mixed question? >> Hand over the business plan! (all laugh) >> Yeah! Yeah, yeah, yeah. >> Yeah, I- >> I want numbers Meg, numbers! >> Wow. (Dave and Dave laugh) I'm probably actually not the right person to speak to that. But as you know, T-Mobile has the largest 5G network in North America, and we just say, bring it, right? Let's talk- >> So you got room, you got room for Jillian's stuff? >> Yeah, let's solve >> Well, we can build so many >> business problems together. >> private 5G networks, right? Like I would say like the opportunities are... There's not a limit, right? Because as we build out these private networks, right? We're not on a public network when we're talking about like connecting these massive factories or connecting like a retail store to you and your house to be able to basically continue to try on the clothes remotely, something like that. It's limitless and what we can build- >> So they're related, but they're not necessarily mutually exclusive in the sense that what you are doing in the factory example is going to interfere with my ability to get my data through T-mobile. >> No, no, I- >> These are separated. >> Yeah. Yeah. >> Okay. >> As we build out these private networks and these private facilities, and there are so many applications in the consumer space that haven't even been realized yet. Like, when we think about 4G, when 4G launched, there were no applications that needed 4G to run on our cell phones, right? But then the engineers got to work, right? And we ended up with Uber and Instagram stories and all these applications that require 4G to launch. And that's what's going to happen with 5G too, it's like, as the network continues to get built, in the consumer space as well as the enterprise space, there's going to be new applications realized on this is all the stuff that we can do with this amazing network and look how many more devices and look how much faster it is, and the lower latency and the higher bandwidth, and you know, what we can really build. And I think what we're seeing at this show compared to last year is this stuff actually in practice. There was a lot of talk last year, like about, oh, this is what we can build, but now we're building it. And I think that's really key to show that companies like T-Mobile can help the enterprise in this space with cooperation, right? Like, we're not just talking about it now, we're actually putting it into practice. >> So how does it work? If I put in a private network, what are you doing? You slice out a piece of the network and charge me for it and then I get that as part of my private network. How does it actually work for the customer? >> You want to take that one? >> So I was going to say, yeah, you can do a network slice. You can actually physically build a private network, right? It depends, there's so many different ways to engineer it. So I think you can do it either way, basically. >> We just, we don't want it to be scary, right? >> Yep. >> So it starts with having a conversation about the business challenges that you're facing and then backing it into the technology and letting the technology power those solutions. But we don't want it to be scary for people because there's so much buzz around 5G, around edge, and it can be overwhelming and you can feel like you need a PhD in engineering to have a conversation. And we just want to kind of simplify things and talk in your language, not in our language. We'll figure out the tech behind the scenes. Just tell us what problems we can solve together. >> And so many non-technical companies are having to transform, right? Like retail, like manufacturing, that haven't had to be tech companies before. But together with T-Mobile and Dell, we can help enable that and make it not scary like Meg said. >> Right, so you come into my factory, I say, okay, look around. I got all these people there, and they're making hoses and they're physically putting 'em together. And we go and we have to take a physical measurement as to, you know, is it right? And because if we don't do that, then we have to rework it. Okay, now that's a problem. Okay, can you help me digitize that business? I need a network to do that. I'm going to put in some robots to do that. This is, I mean, I'm making this up but this has got to be a common use case, right? >> Yeah. >> So how do you simplify that for the business owner? >> So we start with what we can provide, and then in some cases you need additional solution providers. You might need a robotics company, you might need a sensor company. But we have those contacts to bring that together for you so that you don't have to be the expert in all those things. >> And what do I do with all the data that I'm collecting? Because, you know, I'm not really a data expert. Maybe, you know, I'm good at putting hoses together, but what's the data layer look like here? (all laughing) >> It's a hose business! >> I know! >> Great business. >> Back to the hoses again. >> There's a lot of different things you can do with it, right? You can collect it in a database, you can send it up to a cloud, you can, you know, use an edge device. It depends how we build the network. >> Dave V.: Can you guys help me do that? Can you guys- >> Sure, yeah. >> Help me figure that out. Should I put it into cloud? Should I use this database or that data? What kind of skills do I need? >> And it depends on the size of the network, right? And the size of the business. Like, you know, there's very simple. You don't have to be a massive manufacturer in order to install this stuff. >> No, I'm asking small business questions. >> Yeah. >> Right, I might not have this giant IT team. I might not have somebody who knows how to do ETL and PBA. >> Exactly. And we can talk to you too about what data matters, right? And we can, together, talk about what data might be the most valuable to you. We can talk to you about how we use data. But again, simplifying it down and making it personal to your business. >> Your point about scary is interesting, because no one has mentioned that until you did in four days. Three? Four days. Somebody says, let's do a private 5G network. That sounds like you're offering, you know, it's like, "Hey, you know what we should do Dave? We'll build you a cruise ship." It's like, I don't need a cruise ship, I just want to go bass fishing. >> Right, right, right. >> But in fact, these things are scalable in the sense that it can be scaled down from the trillions of dollars of infrastructure investment. >> Yeah. >> Yeah. It needs to be focused on your outcome, right? And not on the tech. >> When I was at the Dell booth I saw this little private network, it was about this big. I'm like, how much is that? I want one of those. (all laugh) >> I'm not the right person to talk about that! >> The little black one? >> Yes. >> I wanted one of those, too! >> I saw it, it had a little case to carry it around. I'm like, that could fit in my business. >> Just take it with you. >> theCUBE could use that! (all laugh) >> Anything that could go in a pelican case, I want. >> It's true. Like, it's so incredibly important, like you said, to focus on outcomes, right? Not just tech for the sake of tech. What's the problem? Let's solve the problem together. And then you're getting the outcome you want. You'll know what data you need. If you know what the problem is, you're like, okay this is the data I need to know if this problem is solved or not. >> So it sounds like 2022 was the year of talking about it. 2023, I'm inferring is the year of seeing it. >> Yep. >> And 2024 is going to be the year of doing it? >> I think we're doing it now. >> We're doing it now. >> Yeah. >> Okay. >> Yeah, yeah. We're definitely doing it now. >> All right. >> I see a lot of this stuff being put into place and a lot more innovation and a lot more working together. And Meg mentioned working with other partners. No one's going to do this alone. You've got to like, you know, Dell especially, we're focused on open and making sure that, you know, we have the right software partners. We're bringing in smaller players, right? Like ISVs too, as well as like the big software guys. Incredibly, incredibly important. The sensor companies, whatever we need you've got to be able to solve your customer's issue, which in this case, we're looking to help the enterprise together to transform their space. And Dell knows a little bit about the enterprise, so. >> So if we are there in 2023, then I assume 2024 will be the year that each of your companies sets up a dedicated vertical to address the hose manufacturing market. (Meg laughing) >> Oh, the hose manufacturing market. >> Further segmentation is usually a hallmark of the maturity of an industry. >> I got a lead for you. >> Yeah, there you go. >> And that's one thing we've done at Dell, too. We've built like this use case directory to help the service providers understand what, not just say like, oh, you can help manufacturers. Yeah, but how, what are the use cases to do that? And we worked with a research firm to figure out, like, you know these are the most mature, these are the best ROIs. Like to really help hone in on exactly what we can deploy for 5G and edge solutions that make the most sense, not only for service providers, right, but also for the enterprises. >> Where do you guys want to see this partnership go? Give us the vision. >> To infinity and beyond. To 5G! (Meg laughing) To 5G and beyond. >> I love it. >> It's continuation. I love that we're partnering together. It's incredibly important to the future of the business. >> Good deal. >> To bring the strengths of both together. And like Jillian said, other partners in the ecosystem, it has to be approached from a partnership perspective, but focused on outcomes. >> Jillian: Yep. >> To 5G and beyond. I love it. >> To 5G and beyond. >> Folks, thanks for coming on theCUBE. >> Thanks for having us. >> Appreciate your insights. >> Thank you. >> All right. Dave Vellante for Dave Nicholson, keep it right there. You're watching theCUBE. Go to silliconANGLE.com. John Furrier is banging out all the news. theCUBE.net has all the videos. We're live at the Fira in Barcelona, MWC23. We'll be right back. (uplifting electronic music)

>> Narrator: theCUBE's live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (bright upbeat music plays) >> Welcome back to Barcelona, everybody. You're watching theCUBE's coverage of MWC '23, my name is Dave Vellante. Just left a meeting with the CEO of Cisco, Chuck Robbins, to meet with Jeetu Patel, who's our Executive Vice President and General Manager of security and collaboration at Cisco. Good to see you. >> You never leave a meeting with Chuck Robbins to meet with Jeetu Patel. >> Well, I did. >> That's a bad idea. >> Walked right out. I said, hey, I got an interview to do, right? So, and I'm excited about this. Thanks so much for coming on. >> Thank you for having me. It's a pleasure. >> So, I mean you run such an important part of the business. I mean, obviously the collaboration business but also security. So many changes going on in the security market. Maybe we could start there. I mean, there hasn't been a ton of security talk here Jeetu, because I think it's almost assumed. It was 45 minutes into the keynote yesterday before anybody even mentioned security. >> Huh. >> Right? And so, but it's the most important topic in the enterprise IT world. And obviously is important here. So why is it you think that it's not the first topic that people mention. >> You know, it's a complicated subject area and it's intimidating. And actually that's one of the things that the industry screwed up on. Where we need to simplify security so it actually gets to be relatable for every person on the planet. But, if you think about what's happening in security, it's not just important for business it's critical infrastructure that if you had a breach, you know lives are cost now. Because hospitals could go down, your water supply could go down, your electricity could go down. And so it's one of these things that we have to take pretty seriously. And, it's 51% of all breaches happen because of negligence, not because of malicious intent. >> It's that low. Interesting. I always- >> Someone else told me the same thing, that they though it'd be higher, yeah. >> I always say bad user behavior is going to trump good security every time. >> Every single time. >> You can't beat it. But, you know, it's funny- >> Jeetu: Every single time. >> Back, the earlier part of last decade, you could see that security was becoming a board level issue. It became, it was on the agenda every quarter. And, I remember doing some research at the time, and I asked, I was interviewing Robert Gates, former Defense Secretary, and I asked him, yeah, but we're getting attacked but don't we have the best offense? Can't we have the best technology? He said, yeah but we have so much critical infrastructure the risks to United States are higher. So we have to be careful about how we use security as an offensive weapon, you know? And now you're seeing the future of war involves security and what's going on in Ukraine. It's a whole different ballgame. >> It is, and the scales always tip towards the adversary, not towards the defender, because you have to be right every single time. They have to be right once. >> Yeah. And, to the other point, about bad user behavior. It's going now beyond the board level, to it's everybody's responsibility. >> That's right. >> And everybody's sort of aware of it, everybody's been hacked. And, that's where it being such a complicated topic is problematic. >> It is, and it's actually, what got us this far will not get us to where we need to get to if we don't simplify security radically. You know? The experience has to be almost invisible. And what used to be the case was sophistication had to get to a certain level, for efficacy to go up. But now, that sophistication has turned to complexity. And there's an inverse relationship between complexity and efficacy. So the simpler you make security, the more effective it gets. And so I'll give you an example. We have this great kind of innovation we've done around passwordless, right? Everyone hates passwords. You shouldn't have passwords in 2023. But, when you get to passwordless security, not only do you reduce a whole lot of friction for the user, you actually make the system safer. And that's what you need to do, is you have to make it simpler while making it more effective. And, I think that's what the future is going to hold. >> Yeah, and CISOs tell me that they're, you know zero trust before the pandemic was like, yeah, yeah zero trust. And now it's like a mandate. >> Yeah. >> Every CISO you talk to says, yes we're implementing a zero trust architecture. And a big part of that is that, if they can confirm zero trust, they can get to market a lot faster with revenue generating or critical projects. And many projects as we know are being pushed back, >> Yeah. >> you know? 'Cause of the macro. But, projects that drive revenue and value they want to accelerate, and a zero trust confirmation allows people to rubber stamp it and go faster. >> And the whole concept of zero trust is least privileged access, right? But what we want to make sure that we get to is continuous assessment of least privileged access, not just a one time at login. >> Dave: 'Cause things change so frequently. >> So, for example, if you happen to be someone that's logged into the system and now you start doing some anomalous behavior that doesn't sound like Dave, we want to be able to intercept, not just do it at the time that you're authenticating Dave to come in. >> So you guys got a good business. I mentioned the macro before. >> Yeah. >> The big theme is consolidating redundant vendors. So a company with a portfolio like Cisco's obviously has an advantage there. You know, you guys had great earnings. Palo Alto is another company that can consolidate. Tom Gillis, great pickup. Guy's amazing, you know? >> Love Tom. >> Great respect. Just had a little webinar session with him, where he was geeking out with the analyst and so- >> Yeah, yeah. >> Learned a lot there. Now you guys have some news, at the event event with Mercedes? >> We do. >> Take us through that, and I want to get your take on hybrid work and what's happening there. But what's going on with Mercedes? >> Yeah so look, it all actually stems from the hybrid work story, which is the future is going to be hybrid, people are going to work in mixed mode. Sometimes you'll be in the office, sometimes at home, sometimes somewhere in the middle. One of the places that people are working more and more from is their cars. And connected cars are getting to be a reality. And in fact, cars sometimes become an extension of your home office. And many a times I have found myself in a parking lot, because I didn't have enough time to get home and I was in a parking lot taking a conference call. And so we've made that section easier, because we have now partnered with Mercedes. And they aren't the first partner, but they're a very important partner where we are going to have Webex available, through the connected car, natively in Mercedes. >> Ah, okay. So I could take a call, I can do it all the time. I find good service, pull over, got to take the meeting. >> Yeah. >> I don't want to be driving. I got to concentrate. >> That's right. >> You know, or sometimes, I'll have the picture on and it's not good. >> That's right. >> Okay, so it'll be through the console, and all through the internet? >> It'll be through the console. And many people ask me like, how's safety going to work over that? Because you don't want to do video calls while you're driving. Exactly right. So when you're driving, the video automatically turns off. And you'll have audio going on, just like a conference call. But the moment you stop and put it in park, you can have video turned on. >> Now, of course the whole hybrid work trend, we, seems like a long time ago but it doesn't, you know? And it's really changed the security dynamic as well, didn't it? >> It has, it has. >> I mean, immediately you had to go protect new endpoints. And those changes, I felt at the time, were permanent. And I think it's still the case, but there's an equilibrium now happening. People as they come back to the office, you see a number of companies are mandating back to work. Maybe the central offices, or the headquarters, were underfunded. So what's going on out there in terms of that balance? >> Well firstly, there's no unanimous consensus on the way that the future is going to be, except that it's going to be hybrid. And the reason I say that is some companies mandate two days a week, some companies mandate five days a week, some companies don't mandate at all. Some companies are completely remote. But whatever way you go, you want to make sure that regardless of where you're working from, people can have an inclusive experience. You know? And, when they have that experience, you want to be able to work from a managed device or an unmanaged device, from a corporate network or from a Starbucks, from on the road or stationary. And whenever you do any of those things, we want to make sure that security is always handled, and you don't have to worry about that. And so the way that we say it is the company that created the VPN, which is Cisco, is the one that's going to kill it. Because what we'll do is we'll make it simple enough so that you don't, you as a user, never have to worry about what connection you're going to use to dial in to what app. You will have one, seamless way to dial into any application, public application, private application, or directly to the internet. >> Yeah, I got a love, hate with my VPN. I mean, it's protecting me, but it's in the way a lot. >> It's going to be simple as ever. >> Do you have kids? >> I do, I have a 12 year old daughter. >> Okay, so not quite high school age yet. She will be shortly. >> No, but she's already, I'm not looking forward to high school days, because she has a very, very strong sense of debate and she wins 90% of the arguments. >> So when my kids were that age, I've got four kids, but the local high school banned Wikipedia, they can't use Wikipedia for research. Many colleges, I presume high schools as well, they're banning Chat GPT, can't use it. Now at the same time, I saw recently on Medium a Wharton school professor said he's mandating Chat GPT to teach his students how to prompt in progressively more sophisticated prompts, because the future is interacting with machines. You know, they say in five years we're all going to be interacting in some way, shape, or form with AI. Maybe we already are. What's the intersection between AI and security? >> So a couple very, very consequential things. So firstly on Chat GPT, the next generation skill is going to be to learn how to go out and have the right questions to ask, which is the prompt revolution that we see going on right now. But if you think about what's happening in security, and there's a few areas which are, firstly 3,500 hundred vendors in this space. On average, most companies have 50 to 70 vendors in security. Not a single vendor owns more than 10% of the market. You take out a couple vendors, no one owns more than 5%. Highly fractured market. That's a problem. Because it's untenable for companies to go out and manage 70 policy engines. And going out and making sure that there's no contention. So as you move forward, one of the things that Chat GPT will be really good for is it's fundamentally going to change user experiences, for how software gets built. Because rather than it being point and click, it's going to be I'm going to provide an instruction and it's going to tell me what to do in natural language. Imagine Dave, when you joined a company if someone said, hey give Dave all the permissions that he needs as a direct report to Chuck. And instantly you would get all of the permissions. And it would actually show up in a screen that says, do you approve? And if you hit approve, you're done. The interfaces of the future will get more natural language kind of dominated. The other area that you'll see is the sophistication of attacks and the surface area of attacks is increasing quite exponentially. And we no longer can handle this with human scale. You have to handle it in machine scale. So detecting breaches, making sure that you can effectively and quickly respond in real time to the breaches, and remediate those breaches, is all going to happen through AI and machine learning. >> So, I agree. I mean, just like Amazon turned the data center into an API, I think we're now going to be interfacing with technology through human language. >> That's right. >> I mean I think it's a really interesting point you're making. Now, from a security standpoint as well, I mean, the state of the art today in my email is be careful, this person's outside your organization. I'm like, yeah I know. So it's a good warning sign, but it's really not automated in any way. So two part question. One is, can AI help? You know, with the phishing, obviously it can, but the bad guys have AI too. >> Yeah. >> And they're probably going to be smarter than I am about using it. >> Yeah, and by the way, Talos is our kind of threat detection and response >> Yes. >> kind of engine. And, they had a great kind of piece that came out recently where they talked about this, where Chat GPT, there is going to be more sophistication of the folks that are the bad actors, the adversaries in using Chat GPT to have more sophisticated phishing attacks. But today it's not something that is fundamentally something that we can't handle just yet. But you still need to do the basic hygiene. That's more important. Over time, what you will see is attacks will get more bespoke. And in order, they'll get more sophisticated. And, you will need to have better mechanisms to know that this was actually not a human being writing that to you, but it was actually a machine pretending to be a human being writing something to you. And that you'll have to be more clever about it. >> Oh interesting. >> And so, you will see attacks get more bespoke and we'll have to get smarter and smarter about it. >> The other thing I wanted to ask you before we close is you're right on. I mean you take the top security vendors and they got a single digit market share. And it's like it's untenable for organizations, just far too many tools. We have a partner at ETR, they do quarterly survey research and one of the things they do is survey emerging technology companies. And when we look at in the security sector just the number of emerging technology companies that are focused on cybersecurity is as many as there are out there already. And so, there's got to be consolidation. Maybe that's through M & A. I mean, what do you think happens? Are company's going to go out of business? There's going to be a lot of M & A? You've seen a lot of companies go private. You know, the big PE companies are sucking up all these security companies and may be ready to spit 'em out and go back public. How do you see the landscape? You guys are obviously an inquisitive company. What are your thoughts on that? >> I think there will be a little bit of everything. But the biggest change that you'll see is a shift that's going to happen with an integrated platform, rather than point solution vendors. So what's going to happen is the market's going to consolidate towards very few, less than a half a dozen, integrated platforms. We believe Cisco is going to be one. Microsoft will be one. There'll be others over there. But these, this platform will essentially be able to provide a unified kind of policy engine across a multitude of different services to protect multiple different entities within the organization. And, what we found is that platform will also be something that'll provide, through APIs, the ability for third parties to be able to get their technology incorporated in, and their telemetry ingested. So we certainly intend to do that. We don't believe, we are not arrogant enough to think that every single new innovation will be built by us. When there's someone else who has built that, we want to make sure that we can ingest that telemetry as well, because the real enemy is not the competitor. The real enemy is the adversary. And we all have to get together, so that we can keep humanity safe. >> Do you think there's been enough collaboration in the industry? I mean- >> Jeetu: Not nearly enough. >> We've seen companies, security companies try to monetize private data before, instead of maybe sharing it with competitors. And so I think the industry can do better there. >> Well I think the industry can do better. And we have this concept called the security poverty line. And the security poverty line is the companies that fall below the security poverty line don't have either the influence or the resources or the know how to keep themselves safe. And when they go unsafe, everyone else that communicates with them also gets that exposure. So it is in our collective interest for all of us to make sure that we come together. And, even if Palo Alto might be a competitor of ours, we want to make sure that we invite them to say, let's make sure that we can actually exchange telemetry between our companies. And we'll continue to do that with as many companies that are out there, because actually that's better for the market, that's better for the world. >> The enemy of the enemy is my friend, kind of thing. >> That's right. >> Now, as it relates to, because you're right. I mean I, I see companies coming up, oh, we do IOT security. I'm like, okay, but what about cloud security? Do you that too? Oh no, that's somebody else. But, so that's another stove pipe. >> That's a huge, huge advantage of coming with someone like Cisco. Because we actually have the entire spectrum, and the broadest portfolio in the industry of anyone else. From the user, to the device, to the network, to the applications, we provide the entire end-to-end story for security, which then has the least amount of cracks that you can actually go out and penetrate through. The biggest challenges that happen in security is you've got way too many policy engines with way too much contention between the policies from these different systems. And eventually there's a collision course. Whereas with us, you've actually got a broad portfolio that operates as one platform. >> We were talking about the cloud guys earlier. You mentioned Microsoft. They're obviously a big competitor in the security space. >> Jeetu: But also a great partner. >> So that's right. To my opinion, the cloud has been awesome as a first line of defense if you will. But the shared responsibility model it's different for each cloud, right? So, do you feel that those guys are working together or will work together to actually improve? 'Cause I don't see that yet. >> Yeah so if you think about, this is where we feel like we have a structural advantage in this, because what does a company like Cisco become in the future? I think as the world goes multicloud and hybrid cloud, what'll end up happening is there needs to be a way, today all the CSPs provide everything from storage to computer network, to security, in their own stack. If we can abstract networking and security above them, so that we can acquire and steer any and all traffic with our service providers and steer it to any of those CSPs, and make sure that the security policy transcends those clouds, you would actually be able to have the public cloud economics without the public cloud lock-in. >> That's what we call super cloud Jeetu. It's securing the super cloud. >> Yeah. >> Hey, thanks so much for coming to theCUBE. >> Thank you for having me. >> Really appreciate you coming on our editorial program. >> Such a pleasure. >> All right, great to see you again. >> Cheers. >> All right, keep it right there. Dave Vellante with David Nicholson and Lisa Martin. We'll be back, right after this short break from MWC '23 live, in the Fira, in Barcelona. (bright music resumes) (music fades out)

(upbeat music) >> Hello, everyone, welcome to theCUBE. I'm John Furrier, the host of theCUBE in Palo Alto, California. Also it's SiliconANGLE News. Got two great guests here to talk about AI, the impact of the future of the internet, the applications, the people. Amr Awadallah, the founder and CEO, Ed Alban is the CEO of Vectara, a new startup that emerged out of the original Cloudera, I would say, 'cause Amr's known, famous for the Cloudera founding, which was really the beginning of the big data movement. And now as AI goes mainstream, there's so much to talk about, so much to go on. And plus the new company is one of the, now what I call the wave, this next big wave, I call it the fifth wave in the industry. You know, you had PCs, you had the internet, you had mobile. This generative AI thing is real. And you're starting to see startups come out in droves. Amr obviously was founder of Cloudera, Big Data, and now Vectara. And Ed Albanese, you guys have a new company. Welcome to the show. >> Thank you. It's great to be here. >> So great to see you. Now the story is theCUBE started in the Cloudera office. Thanks to you, and your friendly entrepreneurship views that you have. We got to know each other over the years. But Cloudera had Hadoop, which was the beginning of what I call the big data wave, which then became what we now call data lakes, data oceans, and data infrastructure that's developed from that. It's almost interesting to look back 12 plus years, and see that what AI is doing now, right now, is opening up the eyes to the mainstream, and the application's almost mind blowing. You know, Sati Natel called it the Mosaic Moment, didn't say Netscape, he built Netscape (laughing) but called it the Mosaic Moment. You're seeing companies in startups, kind of the alpha geeks running here, because this is the new frontier, and there's real meat on the bone, in terms of like things to do. Why? Why is this happening now? What's is the confluence of the forces happening, that are making this happen? >> Yeah, I mean if you go back to the Cloudera days, with big data, and so on, that was more about data processing. Like how can we process data, so we can extract numbers from it, and do reporting, and maybe take some actions, like this is a fraud transaction, or this is not. And in the meanwhile, many of the researchers working in the neural network, and deep neural network space, were trying to focus on data understanding, like how can I understand the data, and learn from it, so I can take actual actions, based on the data directly, just like a human does. And we were only good at doing that at the level of somebody who was five years old, or seven years old, all the way until about 2013. And starting in 2013, which is only 10 years ago, a number of key innovations started taking place, and each one added on. It was no major innovation that just took place. It was a couple of really incremental ones, but they added on top of each other, in a very exponentially additive way, that led to, by the end of 2019, we now have models, deep neural network models, that can read and understand human text just like we do. Right? And they can reason about it, and argue with you, and explain it to you. And I think that's what is unlocking this whole new wave of innovation that we're seeing right now. So data understanding would be the essence of it. >> So it's not a Big Bang kind of theory, it's been evolving over time, and I think that the tipping point has been the advancements and other things. I mean look at cloud computing, and look how fast it just crept up on AWS. I mean AWS you back three, five years ago, I was talking to Swami yesterday, and their big news about AI, expanding the Hugging Face's relationship with AWS. And just three, five years ago, there wasn't a model training models out there. But as compute comes out, and you got more horsepower,, these large language models, these foundational models, they're flexible, they're not monolithic silos, they're interacting. There's a whole new, almost fusion of data happening. Do you see that? I mean is that part of this? >> Of course, of course. I mean this wave is building on all the previous waves. We wouldn't be at this point if we did not have hardware that can scale, in a very efficient way. We wouldn't be at this point, if we don't have data that we're collecting about everything we do, that we're able to process in this way. So this, this movement, this motion, this phase we're in, absolutely builds on the shoulders of all the previous phases. For some of the observers from the outside, when they see chatGPT for the first time, for them was like, "Oh my god, this just happened overnight." Like it didn't happen overnight. (laughing) GPT itself, like GPT3, which is what chatGPT is based on, was released a year ahead of chatGPT, and many of us were seeing the power it can provide, and what it can do. I don't know if Ed agrees with that. >> Yeah, Ed? >> I do. Although I would acknowledge that the possibilities now, because of what we've hit from a maturity standpoint, have just opened up in an incredible way, that just wasn't tenable even three years ago. And that's what makes it, it's true that it developed incrementally, in the same way that, you know, the possibilities of a mobile handheld device, you know, in 2006 were there, but when the iPhone came out, the possibilities just exploded. And that's the moment we're in. >> Well, I've had many conversations over the past couple months around this area with chatGPT. John Markoff told me the other day, that he calls it, "The five dollar toy," because it's not that big of a deal, in context to what AI's doing behind the scenes, and all the work that's done on ethics, that's happened over the years, but it has woken up the mainstream, so everyone immediately jumps to ethics. "Does it work? "It's not factual," And everyone who's inside the industry is like, "This is amazing." 'Cause you have two schools of thought there. One's like, people that think this is now the beginning of next gen, this is now we're here, this ain't your grandfather's chatbot, okay?" With NLP, it's got reasoning, it's got other things. >> I'm in that camp for sure. >> Yeah. Well I mean, everyone who knows what's going on is in that camp. And as the naysayers start to get through this, and they go, "Wow, it's not just plagiarizing homework, "it's helping me be better. "Like it could rewrite my memo, "bring the lead to the top." It's so the format of the user interface is interesting, but it's still a data-driven app. >> Absolutely. >> So where does it go from here? 'Cause I'm not even calling this the first ending. This is like pregame, in my opinion. What do you guys see this going, in terms of scratching the surface to what happens next? >> I mean, I'll start with, I just don't see how an application is going to look the same in the next three years. Who's going to want to input data manually, in a form field? Who is going to want, or expect, to have to put in some text in a search box, and then read through 15 different possibilities, and try to figure out which one of them actually most closely resembles the question they asked? You know, I don't see that happening. Who's going to start with an absolute blank sheet of paper, and expect no help? That is not how an application will work in the next three years, and it's going to fundamentally change how people interact and spend time with opening any element on their mobile phone, or on their computer, to get something done. >> Yes. I agree with that. Like every single application, over the next five years, will be rewritten, to fit within this model. So imagine an HR application, I don't want to name companies, but imagine an HR application, and you go into application and you clicking on buttons, because you want to take two weeks of vacation, and menus, and clicking here and there, reasons and managers, versus just telling the system, "I'm taking two weeks of vacation, going to Las Vegas," book it, done. >> Yeah. >> And the system just does it for you. If you weren't completing in your input, in your description, for what you want, then the system asks you back, "Did you mean this? "Did you mean that? "Were you trying to also do this as well?" >> Yeah. >> "What was the reason?" And that will fit it for you, and just do it for you. So I think the user interface that we have with apps, is going to change to be very similar to the user interface that we have with each other. And that's why all these apps will need to evolve. >> I know we don't have a lot of time, 'cause you guys are very busy, but I want to definitely have multiple segments with you guys, on this topic, because there's so much to talk about. There's a lot of parallels going on here. I was talking again with Swami who runs all the AI database at AWS, and I asked him, I go, "This feels a lot like the original AWS. "You don't have to provision a data center." A lot of this heavy lifting on the back end, is these large language models, with these foundational models. So the bottleneck in the past, was the energy, and cost to actually do it. Now you're seeing it being stood up faster. So there's definitely going to be a tsunami of apps. I would see that clearly. What is it? We don't know yet. But also people who are going to leverage the fact that I can get started building value. So I see a startup boom coming, and I see an application tsunami of refactoring things. >> Yes. >> So the replatforming is already kind of happening. >> Yes, >> OpenAI, chatGPT, whatever. So that's going to be a developer environment. I mean if Amazon turns this into an API, or a Microsoft, what you guys are doing. >> We're turning it into API as well. That's part of what we're doing as well, yes. >> This is why this is exciting. Amr, you've lived the big data dream, and and we used to talk, if you didn't have a big data problem, if you weren't full of data, you weren't really getting it. Now people have all the data, and they got to stand this up. >> Yeah. >> So the analogy is again, the mobile, I like the mobile movement, and using mobile as an analogy, most companies were not building for a mobile environment, right? They were just building for the web, and legacy way of doing apps. And as soon as the user expectations shifted, that my expectation now, I need to be able to do my job on this small screen, on the mobile device with a touchscreen. Everybody had to invest in re-architecting, and re-implementing every single app, to fit within that model, and that model of interaction. And we are seeing the exact same thing happen now. And one of the core things we're focused on at Vectara, is how to simplify that for organizations, because a lot of them are overwhelmed by large language models, and ML. >> They don't have the staff. >> Yeah, yeah, yeah. They're understaffed, they don't have the skills. >> But they got developers, they've got DevOps, right? >> Yes. >> So they have the DevSecOps going on. >> Exactly, yes. >> So our goal is to simplify it enough for them that they can start leveraging this technology effectively, within their applications. >> Ed, you're the COO of the company, obviously a startup. You guys are growing. You got great backup, and good team. You've also done a lot of business development, and technical business development in this area. If you look at the landscape right now, and I agree the apps are coming, every company I talk to, that has that jet chatGPT of, you know, epiphany, "Oh my God, look how cool this is. "Like magic." Like okay, it's code, settle down. >> Mm hmm. >> But everyone I talk to is using it in a very horizontal way. I talk to a very senior person, very tech alpha geek, very senior person in the industry, technically. they're using it for log data, they're using it for configuration of routers. And in other areas, they're using it for, every vertical has a use case. So this is horizontally scalable from a use case standpoint. When you hear horizontally scalable, first thing I chose in my mind is cloud, right? >> Mm hmm. >> So cloud, and scalability that way. And the data is very specialized. So now you have this vertical specialization, horizontally scalable, everyone will be refactoring. What do you see, and what are you seeing from customers, that you talk to, and prospects? >> Yeah, I mean put yourself in the shoes of an application developer, who is actually trying to make their application a bit more like magic. And to have that soon-to-be, honestly, expected experience. They've got to think about things like performance, and how efficiently that they can actually execute a query, or a question. They've got to think about cost. Generative isn't cheap, like the inference of it. And so you've got to be thoughtful about how and when you take advantage of it, you can't use it as a, you know, everything looks like a nail, and I've got a hammer, and I'm going to hit everything with it, because that will be wasteful. Developers also need to think about how they're going to take advantage of, but not lose their own data. So there has to be some controls around what they feed into the large language model, if anything. Like, should they fine tune a large language model with their own data? Can they keep it logically separated, but still take advantage of the powers of a large language model? And they've also got to take advantage, and be aware of the fact that when data is generated, that it is a different class of data. It might not fully be their own. >> Yeah. >> And it may not even be fully verified. And so when the logical cycle starts, of someone making a request, the relationship between that request, and the output, those things have to be stored safely, logically, and identified as such. >> Yeah. >> And taken advantage of in an ongoing fashion. So these are mega problems, each one of them independently, that, you know, you can think of it as middleware companies need to take advantage of, and think about, to help the next wave of application development be logical, sensible, and effective. It's not just calling some raw API on the cloud, like openAI, and then just, you know, you get your answer and you're done, because that is a very brute force approach. >> Well also I will point, first of all, I agree with your statement about the apps experience, that's going to be expected, form filling. Great point. The interesting about chatGPT. >> Sorry, it's not just form filling, it's any action you would like to take. >> Yeah. >> Instead of clicking, and dragging, and dropping, and doing it on a menu, or on a touch screen, you just say it, and it's and it happens perfectly. >> Yeah. It's a different interface. And that's why I love that UIUX experiences, that's the people falling out of their chair moment with chatGPT, right? But a lot of the things with chatGPT, if you feed it right, it works great. If you feed it wrong and it goes off the rails, it goes off the rails big. >> Yes, yes. >> So the the Bing catastrophes. >> Yeah. >> And that's an example of garbage in, garbage out, classic old school kind of comp-side phrase that we all use. >> Yep. >> Yes. >> This is about data in injection, right? It reminds me the old SQL days, if you had to, if you can sling some SQL, you were a magician, you know, to get the right answer, it's pretty much there. So you got to feed the AI. >> You do, Some people call this, the early word to describe this as prompt engineering. You know, old school, you know, search, or, you know, engagement with data would be, I'm going to, I have a question or I have a query. New school is, I have, I have to issue it a prompt, because I'm trying to get, you know, an action or a reaction, from the system. And the active engineering, there are a lot of different ways you could do it, all the way from, you know, raw, just I'm going to send you whatever I'm thinking. >> Yeah. >> And you get the unintended outcomes, to more constrained, where I'm going to just use my own data, and I'm going to constrain the initial inputs, the data I already know that's first party, and I trust, to, you know, hyper constrain, where the application is actually, it's looking for certain elements to respond to. >> It's interesting Amr, this is why I love this, because one we are in the media, we're recording this video now, we'll stream it. But we got all your linguistics, we're talking. >> Yes. >> This is data. >> Yep. >> So the data quality becomes now the new intellectual property, because, if you have that prompt source data, it makes data or content, in our case, the original content, intellectual property. >> Absolutely. >> Because that's the value. And that's where you see chatGPT fall down, is because they're trying to scroll the web, and people think it's search. It's not necessarily search, it's giving you something that you wanted. It is a lot of that, I remember in Cloudera, you said, "Ask the right questions." Remember that phrase you guys had, that slogan? >> Mm hmm. And that's prompt engineering. So that's exactly, that's the reinvention of "Ask the right question," is prompt engineering is, if you don't give these models the question in the right way, and very few people know how to frame it in the right way with the right context, then you will get garbage out. Right? That is the garbage in, garbage out. But if you specify the question correctly, and you provide with it the metadata that constrain what that question is going to be acted upon or answered upon, then you'll get much better answers. And that's exactly what we solved Vectara. >> Okay. So before we get into the last couple minutes we have left, I want to make sure we get a plug in for the opportunity, and the profile of Vectara, your new company. Can you guys both share with me what you think the current situation is? So for the folks who are now having those moments of, "Ah, AI's bullshit," or, "It's not real, it's a lot of stuff," from, "Oh my god, this is magic," to, "Okay, this is the future." >> Yes. >> What would you say to that person, if you're at a cocktail party, or in the elevator say, "Calm down, this is the first inning." How do you explain the dynamics going on right now, to someone who's either in the industry, but not in the ropes? How would you explain like, what this wave's about? How would you describe it, and how would you prepare them for how to change their life around this? >> Yeah, so I'll go first and then I'll let Ed go. Efficiency, efficiency is the description. So we figured that a way to be a lot more efficient, a way where you can write a lot more emails, create way more content, create way more presentations. Developers can develop 10 times faster than they normally would. And that is very similar to what happened during the Industrial Revolution. I always like to look at examples from the past, to read what will happen now, and what will happen in the future. So during the Industrial Revolution, it was about efficiency with our hands, right? So I had to make a piece of cloth, like this piece of cloth for this shirt I'm wearing. Our ancestors, they had to spend month taking the cotton, making it into threads, taking the threads, making them into pieces of cloth, and then cutting it. And now a machine makes it just like that, right? And the ancestors now turned from the people that do the thing, to manage the machines that do the thing. And I think the same thing is going to happen now, is our efficiency will be multiplied extremely, as human beings, and we'll be able to do a lot more. And many of us will be able to do things they couldn't do before. So another great example I always like to use is the example of Google Maps, and GPS. Very few of us knew how to drive a car from one location to another, and read a map, and get there correctly. But once that efficiency of an AI, by the way, behind these things is very, very complex AI, that figures out how to do that for us. All of us now became amazing navigators that can go from any point to any point. So that's kind of how I look at the future. >> And that's a great real example of impact. Ed, your take on how you would talk to a friend, or colleague, or anyone who asks like, "How do I make sense of the current situation? "Is it real? "What's in it for me, and what do I do?" I mean every company's rethinking their business right now, around this. What would you say to them? >> You know, I usually like to show, rather than describe. And so, you know, the other day I just got access, I've been using an application for a long time, called Notion, and it's super popular. There's like 30 or 40 million users. And the new version of Notion came out, which has AI embedded within it. And it's AI that allows you primarily to create. So if you could break down the world of AI into find and create, for a minute, just kind of logically separate those two things, find is certainly going to be massively impacted in our experiences as consumers on, you know, Google and Bing, and I can't believe I just said the word Bing in the same sentence as Google, but that's what's happening now (all laughing), because it's a good example of change. >> Yes. >> But also inside the business. But on the crate side, you know, Notion is a wiki product, where you try to, you know, note down things that you are thinking about, or you want to share and memorialize. But sometimes you do need help to get it down fast. And just in the first day of using this new product, like my experience has really fundamentally changed. And I think that anybody who would, you know, anybody say for example, that is using an existing app, I would show them, open up the app. Now imagine the possibility of getting a starting point right off the bat, in five seconds of, instead of having to whole cloth draft this thing, imagine getting a starting point then you can modify and edit, or just dispose of and retry again. And that's the potential for me. I can't imagine a scenario where, in a few years from now, I'm going to be satisfied if I don't have a little bit of help, in the same way that I don't manually spell check every email that I send. I automatically spell check it. I love when I'm getting type ahead support inside of Google, or anything. Doesn't mean I always take it, or when texting. >> That's efficiency too. I mean the cloud was about developers getting stuff up quick. >> Exactly. >> All that heavy lifting is there for you, so you don't have to do it. >> Right? >> And you get to the value faster. >> Exactly. I mean, if history taught us one thing, it's, you have to always embrace efficiency, and if you don't fast enough, you will fall behind. Again, looking at the industrial revolution, the companies that embraced the industrial revolution, they became the leaders in the world, and the ones who did not, they all like. >> Well the AI thing that we got to watch out for, is watching how it goes off the rails. If it doesn't have the right prompt engineering, or data architecture, infrastructure. >> Yes. >> It's a big part. So this comes back down to your startup, real quick, I know we got a couple minutes left. Talk about the company, the motivation, and we'll do a deeper dive on on the company. But what's the motivation? What are you targeting for the market, business model? The tech, let's go. >> Actually, I would like Ed to go first. Go ahead. >> Sure, I mean, we're a developer-first, API-first platform. So the product is oriented around allowing developers who may not be superstars, in being able to either leverage, or choose, or select their own large language models for appropriate use cases. But they that want to be able to instantly add the power of large language models into their application set. We started with search, because we think it's going to be one of the first places that people try to take advantage of large language models, to help find information within an application context. And we've built our own large language models, focused on making it very efficient, and elegant, to find information more quickly. So what a developer can do is, within minutes, go up, register for an account, and get access to a set of APIs, that allow them to send data, to be converted into a format that's easy to understand for large language models, vectors. And then secondarily, they can issue queries, ask questions. And they can ask them very, the questions that can be asked, are very natural language questions. So we're talking about long form sentences, you know, drill down types of questions, and they can get answers that either come back in depending upon the form factor of the user interface, in list form, or summarized form, where summarized equals the opportunity to kind of see a condensed, singular answer. >> All right. I have a. >> Oh okay, go ahead, you go. >> I was just going to say, I'm going to be a customer for you, because I want, my dream was to have a hologram of theCUBE host, me and Dave, and have questions be generated in the metaverse. So you know. (all laughing) >> There'll be no longer any guests here. They'll all be talking to you guys. >> Give a couple bullets, I'll spit out 10 good questions. Publish a story. This brings the automation, I'm sorry to interrupt you. >> No, no. No, no, I was just going to follow on on the same. So another way to look at exactly what Ed described is, we want to offer you chatGPT for your own data, right? So imagine taking all of the recordings of all of the interviews you have done, and having all of the content of that being ingested by a system, where you can now have a conversation with your own data and say, "Oh, last time when I met Amr, "which video games did we talk about? "Which movie or book did we use as an analogy "for how we should be embracing data science, "and big data, which is moneyball," I know you use moneyball all the time. And you start having that conversation. So, now the data doesn't become a passive asset that you just have in your organization. No. It's an active participant that's sitting with you, on the table, helping you make decisions. >> One of my favorite things to do with customers, is to go to their site or application, and show them me using it. So for example, one of the customers I talked to was one of the biggest property management companies in the world, that lets people go and rent homes, and houses, and things like that. And you know, I went and I showed them me searching through reviews, looking for information, and trying different words, and trying to find out like, you know, is this place quiet? Is it comfortable? And then I put all the same data into our platform, and I showed them the world of difference you can have when you start asking that question wholeheartedly, and getting real information that doesn't have anything to do with the words you asked, but is really focused on the meaning. You know, when I asked like, "Is it quiet?" You know, answers would come back like, "The wind whispered through the trees peacefully," and you know, it's like nothing to do with quiet in the literal word sense, but in the meaning sense, everything to do with it. And that that was magical even for them, to see that. >> Well you guys are the front end of this big wave. Congratulations on the startup, Amr. I know you guys got great pedigree in big data, and you've got a great team, and congratulations. Vectara is the name of the company, check 'em out. Again, the startup boom is coming. This will be one of the major waves, generative AI is here. I think we'll look back, and it will be pointed out as a major inflection point in the industry. >> Absolutely. >> There's not a lot of hype behind that. People are are seeing it, experts are. So it's going to be fun, thanks for watching. >> Thanks John. (soft music)

(inspirational music) (music ends) >> Okay, so now that we've heard Scott talk about smart data fabrics, it's time to see this in action. Right now we're joined by Jess Jowdy, who's the manager of Healthcare Field Engineering at InterSystems. She's going to give a demo of how smart data fabrics actually work, and she's going to show how embedding a wide range of analytics capabilities, including data exploration business intelligence, natural language processing and machine learning directly within the fabric makes it faster and easier for organizations to gain new insights and power intelligence predictive and prescriptive services and applications. Now, according to InterSystems, smart data fabrics are applicable across many industries from financial services to supply chain to healthcare and more. Jess today is going to be speaking through the lens of a healthcare focused demo. Don't worry, Joe Lichtenberg will get into some of the other use cases that you're probably interested in hearing about. That will be in our third segment, but for now let's turn it over to Jess. Jess, good to see you. >> Hi, yeah, thank you so much for having me. And so for this demo, we're really going to be bucketing these features of a smart data fabric into four different segments. We're going to be dealing with connections, collections, refinements, and analysis. And so we'll see that throughout the demo as we go. So without further ado, let's just go ahead and jump into this demo, and you'll see my screen pop up here. I actually like to start at the end of the demo. So I like to begin by illustrating what an end user's going to see, and don't mind the screen 'cause I gave you a little sneak peek of what's about to happen. But essentially what I'm going to be doing is using Postman to simulate a call from an external application. So we talked about being in the healthcare industry. This could be, for instance, a mobile application that a patient is using to view an aggregated summary of information across that patient's continuity of care or some other kind of application. So we might be pulling information in this case from an electronic medical record. We might be grabbing clinical history from that. We might be grabbing clinical notes from a medical transcription software, or adverse reaction warnings from a clinical risk grouping application, and so much more. So I'm really going to be simulating a patient logging in on their phone and retrieving this information through this Postman call. So what I'm going to do is I'm just going to hit send, I've already preloaded everything here, and I'm going to be looking for information where the last name of this patient is Simmons, and their medical record number or their patient identifier in the system is 32345. And so as you can see, I have this single JSON payload that showed up here of, just, relevant clinical information for my patient whose last name is Simmons, all within a single response. So fantastic, right? Typically though, when we see responses that look like this there is an assumption that this service is interacting with a single backend system, and that single backend system is in charge of packaging that information up and returning it back to this caller. But in a smart data fabric architecture, we're able to expand the scope to handle information across different, in this case, clinical applications. So how did this actually happen? Let's peel back another layer and really take a look at what happened in the background. What you're looking at here is our mission control center for our smart data fabric. On the left we have our APIs that allow users to interact with particular services. On the right we have our connections to our different data silos. And in the middle here, we have our data fabric coordinator which is going to be in charge of this refinement and analysis, those key pieces of our smart data fabric. So let's look back and think about the example we just showed. I received an inbound request for information for a patient whose last name is Simmons. My end user is requesting to connect to that service, and that's happening here at my patient data retrieval API location. Users can define any number of different services and APIs depending on their use cases. And to that end, we do also support full life cycle API management within this platform. When you're dealing with APIs, I always like to make a little shout out on this, that you really want to make sure you have enough, like a granular enough security model to handle and limit which APIs and which services a consumer can interact with. In this IRIS platform, which we're talking about today we have a very granular role-based security model that allows you to handle that, but it's really important in a smart data fabric to consider who's accessing your data and in what context. >> Can I just interrupt you for a second, Jess? >> Yeah, please. >> So you were showing on the left hand side of the demo a couple of APIs. I presume that can be a very long list. I mean, what do you see as typical? >> I mean you could have hundreds of these APIs depending on what services an organization is serving up for their consumers. So yeah, we've seen hundreds of these services listed here. >> So my question is, obviously security is critical in the healthcare industry, and API securities are like, really hot topic these days. How do you deal with that? >> Yeah, and I think API security is interesting 'cause it can happen at so many layers. So, there's interactions with the API itself. So can I even see this API and leverage it? And then within an API call, you then have to deal with all right, which end points or what kind of interactions within that API am I allowed to do? What data am I getting back? And with healthcare data, the whole idea of consent to see certain pieces of data is critical. So, the way that we handle that is, like I said, same thing at different layers. There is access to a particular API, which can happen within the IRIS product, and also we see it happening with an API management layer, which has become a really hot topic with a lot of organizations. And then when it comes to data security, that really happens under the hood within your smart data fabric. So, that role-based access control becomes very important in assigning, you know, roles and permissions to certain pieces of information. Getting that granular becomes the cornerstone of the security. >> And that's been designed in, it's not a bolt on as they like to say. >> Absolutely. >> Okay, can we get into collect now? >> Of course, we're going to move on to the collection piece at this point in time, which involves pulling information from each of my different data silos to create an overall aggregated record. So commonly, each data source requires a different method for establishing connections and collecting this information. So for instance, interactions with an EMR may require leveraging a standard healthcare messaging format like Fire. Interactions with a homegrown enterprise data warehouse for instance, may use SQL. For a cloud-based solutions managed by a vendor, they may only allow you to use web service calls to pull data. So it's really important that your data fabric platform that you're using has the flexibility to connect to all of these different systems and applications. And I'm about to log out, so I'm going to (chuckles) keep my session going here. So therefore it's incredibly important that your data fabric has the flexibility to connect to all these different kinds of applications and data sources, and all these different kinds of formats and over all of these different kinds of protocols. So let's think back on our example here. I had four different applications that I was requesting information for to create that payload that we saw initially. Those are listed here under this operations section. So these are going out and connecting to downstream systems to pull information into my smart data fabric. What's great about the IRIS platform is, it has an embedded interoperability platform. So there's all of these native adapters that can support these common connections that we see for different kinds of applications. So using REST, or SOAP, or SQL, or FTP, regardless of that protocol, there's an adapter to help you work with that. And we also think of the types of formats that we typically see data coming in as in healthcare we have HL7, we have Fire, we have CCDs, across the industry, JSON is, you know, really hitting a market strong now, and XML payloads, flat files. We need to be able to handle all of these different kinds of formats over these different kinds of protocols. So to illustrate that, if I click through these when I select a particular connection on the right side panel, I'm going to see the different settings that are associated with that particular connection that allows me to collect information back into my smart data fabric. In this scenario, my connection to my chart script application in this example, communicates over a SOAP connection. When I'm grabbing information from my clinical risk grouping application I'm using a SQL based connection. When I'm connecting to my EMR, I'm leveraging a standard healthcare messaging format known as Fire, which is a REST based protocol. And then when I'm working with my health record management system, I'm leveraging a standard HTTP adapter. So you can see how we can be flexible when dealing with these different kinds of applications and systems. And then it becomes important to be able to validate that you've established those connections correctly, and be able to do it in a reliable and quick way. Because if you think about it, you could have hundreds of these different kinds of applications built out and you want to make sure that you're maintaining and understanding those connections. So I can actually go ahead and test one of these applications and put in, for instance my patient's last name and their MRN, and make sure that I'm actually getting data back from that system. So it's a nice little sanity check as we're building out that data fabric to ensure that we're able to establish these connections appropriately. So turnkey adapters are fantastic, as you can see we're leveraging them all here, but sometimes these connections are going to require going one step further and building something really specific for an application. So why don't we go one step further here and talk about doing something custom or doing something innovative. And so it's important for users to have the ability to develop and go beyond what's an out-of-the box or black box approach to be able to develop things that are specific to their data fabric, or specific to their particular connection. In this scenario, the IRIS data platform gives users access to the entire underlying code base. So you not only get an opportunity to view how we're establishing these connections or how we're building out these processes, but you have the opportunity to inject your own kind of processing, your own kinds of pipelines into this. So as an example, you can leverage any number of different programming languages right within this pipeline. And so I went ahead and I injected Python. So Python is a very up and coming language, right? We see more and more developers turning towards Python to do their development. So it's important that your data fabric supports those kinds of developers and users that have standardized on these kinds of programming languages. This particular script here, as you can see actually calls out to our turnkey adapters. So we see a combination of out-of-the-box code that is provided in this data fabric platform from IRIS, combined with organization specific or user specific customizations that are included in this Python method. So it's a nice little combination of how do we bring the developer experience in and mix it with out-of-the-box capabilities that we can provide in a smart data fabric. >> Wow. >> Yeah, I'll pause. (laughs) >> It's a lot here. You know, actually- >> I can pause. >> If I could, if we just want to sort of play that back. So we went to the connect and the collect phase. >> Yes, we're going into refine. So it's a good place to stop. >> So before we get there, so we heard a lot about fine grain security, which is crucial. We heard a lot about different data types, multiple formats. You've got, you know, the ability to bring in different dev tools. We heard about Fire, which of course big in healthcare. And that's the standard, and then SQL for traditional kind of structured data, and then web services like HTTP you mentioned. And so you have a rich collection of capabilities within this single platform. >> Absolutely. And I think that's really important when you're dealing with a smart data fabric because what you're effectively doing is you're consolidating all of your processing, all of your collection, into a single platform. So that platform needs to be able to handle any number of different kinds of scenarios and technical challenges. So you've got to pack that platform with as many of these features as you can to consolidate that processing. >> All right, so now we're going into refinement. >> We're going into refinement. Exciting. (chuckles) So how do we actually do refinement? Where does refinement happen? And how does this whole thing end up being performant? Well the key to all of that is this SDF coordinator, or stands for Smart Data Fabric coordinator. And what this particular process is doing is essentially orchestrating all of these calls to all of these different downstream systems. It's aggregating, it's collecting that information, it's aggregating it, and it's refining it into that single payload that we saw get returned to the user. So really this coordinator is the main event when it comes to our data fabric. And in the IRIS platform we actually allow users to build these coordinators using web-based tool sets to make it intuitive. So we can take a sneak peek at what that looks like. And as you can see, it follows a flow chart like structure. So there's a start, there is an end, and then there are these different arrows that point to different activities throughout the business process. And so there's all these different actions that are being taken within our coordinator. You can see an action for each of the calls to each of our different data sources to go retrieve information. And then we also have the sync call at the end that is in charge of essentially making sure that all of those responses come back before we package them together and send them out. So this becomes really crucial when we're creating that data fabric. And you know, this is a very simple data fabric example where we're just grabbing data and we're consolidating it together. But you can have really complex orchestrators and coordinators that do any number of different things. So for instance, I could inject SQL logic into this or SQL code, I can have conditional logic, I can do looping, I can do error trapping and handling. So we're talking about a whole number of different features that can be included in this coordinator. So like I said, we have a really very simple process here that's just calling out, grabbing all those different data elements from all those different data sources and consolidating it. We'll look back at this coordinator in a second when we introduce, or we make this data fabric a bit smarter, and we start introducing that analytics piece to it. So this is in charge of the refinement. And so at this point in time we've looked at connections, collections, and refinements. And just to summarize what we've seen 'cause I always like to go back and take a look at everything that we've seen. We have our initial API connection, we have our connections to our individual data sources and we have our coordinators there in the middle that are in charge of collecting the data and refining it into a single payload. As you can imagine, there's a lot going on behind the scenes of a smart data fabric, right? There's all these different processes that are interacting. So it's really important that your smart data fabric platform has really good traceability, really good logging, 'cause you need to be able to know, you know, if there was an issue, where did that issue happen in which connected process, and how did it affect the other processes that are related to it? In IRIS, we have this concept called a visual trace. And what our clients use this for is basically to be able to step through the entire history of a request from when it initially came into the smart data fabric, to when data was sent back out from that smart data fabric. So I didn't record the time, but I bet if you recorded the time it was this time that we sent that request in and you can see my patient's name and their medical record number here, and you can see that that instigated four different calls to four different systems, and they're represented by these arrows going out. So we sent something to chart script, to our health record management system, to our clinical risk grouping application, into my EMR through their Fire server. So every request, every outbound application gets a request and we pull back all of those individual pieces of information from all of those different systems, and we bundle them together. And from my Fire lovers, here's our Fire bundle that we got back from our Fire server. So this is a really good way of being able to validate that I am appropriately grabbing the data from all these different applications and then ultimately consolidating it into one payload. Now we change this into a JSON format before we deliver it, but this is those data elements brought together. And this screen would also be used for being able to see things like error trapping, or errors that were thrown, alerts, warnings, developers might put log statements in just to validate that certain pieces of code are executing. So this really becomes the one stop shop for understanding what's happening behind the scenes with your data fabric. >> Sure, who did what when where, what did the machine do what went wrong, and where did that go wrong? Right at your fingertips. >> Right. And I'm a visual person so a bunch of log files to me is not the most helpful. While being able to see this happened at this time in this location, gives me that understanding I need to actually troubleshoot a problem. >> This business orchestration piece, can you say a little bit more about that? How people are using it? What's the business impact of the business orchestration? >> The business orchestration, especially in the smart data fabric, is really that crucial part of being able to create a smart data fabric. So think of your business orchestrator as doing the heavy lifting of any kind of processing that involves data, right? It's bringing data in, it's analyzing that information it's transforming that data, in a format that your consumer's not going to understand. It's doing any additional injection of custom logic. So really your coordinator or that orchestrator that sits in the middle is the brains behind your smart data fabric. >> And this is available today? It all works? >> It's all available today. Yeah, it all works. And we have a number of clients that are using this technology to support these kinds of use cases. >> Awesome demo. Anything else you want to show us? >> Well, we can keep going. I have a lot to say, but really this is our data fabric. The core competency of IRIS is making it smart, right? So I won't spend too much time on this, but essentially if we go back to our coordinator here, we can see here's that original, that pipeline that we saw where we're pulling data from all these different systems and we're collecting it and we're sending it out. But then we see two more at the end here, which involves getting a readmission prediction and then returning a prediction. So we can not only deliver data back as part of a smart data fabric, but we can also deliver insights back to users and consumers based on data that we've aggregated as part of a smart data fabric. So in this scenario, we're actually taking all that data that we just looked at, and we're running it through a machine learning model that exists within the smart data fabric pipeline, and producing a readmission score to determine if this particular patient is at risk for readmission within the next 30 days. Which is a typical problem that we see in the healthcare space. So what's really exciting about what we're doing in the IRIS world, is we're bringing analytics close to the data with integrated ML. So in this scenario we're actually creating the model, training the model, and then executing the model directly within the IRIS platform. So there's no shuffling of data, there's no external connections to make this happen. And it doesn't really require having a PhD in data science to understand how to do that. It leverages all really basic SQL-like syntax to be able to construct and execute these predictions. So, it's going one step further than the traditional data fabric example to introduce this ability to define actionable insights to our users based on the data that we've brought together. >> Well that readmission probability is huge, right? Because it directly affects the cost for the provider and the patient, you know. So if you can anticipate the probability of readmission and either do things at that moment, or, you know, as an outpatient perhaps, to minimize the probability then that's huge. That drops right to the bottom line. >> Absolutely. And that really brings us from that data fabric to that smart data fabric at the end of the day, which is what makes this so exciting. >> Awesome demo. >> Thank you! >> Jess, are you cool if people want to get in touch with you? Can they do that? >> Oh yes, absolutely. So you can find me on LinkedIn, Jessica Jowdy, and we'd love to hear from you. I always love talking about this topic so we'd be happy to engage on that. >> Great stuff. Thank you Jessica, appreciate it. >> Thank you so much. >> Okay, don't go away because in the next segment, we're going to dig into the use cases where data fabric is driving business value. Stay right there. (inspirational music) (music fades)

(upbeat music) >> Okay, welcome back everyone. I'm John Furrier here in theCUBE, in Palo Alto for "CUBE Conversation" with Chris Jones, Director of Product Management at Platform9. I've got a series of questions, had a great conversation earlier. Chris, I have a couple questions for you, what do you think? >> Let's do it, John. >> Okay, how does Platform9 Solution, you- can it be used on any infrastructure anywhere, cloud, edge, on-premise? >> It can, that's the beauty of our control plane, right? It was born in the cloud, and we primarily deliver that SaaS, which allows it to work in your data center, on bare metal, on VMs, or with public cloud infrastructure. We now give you the ability to take that control plane, install it in your data center, and then use it with anything, or even in air gap. And that includes capabilities with bare metal orchestration as well. >> Second question. How does Platform9 ensure maximum uptime, and proactive issue resolution? >> Oh, that's a good question. So if you come to Platform nine we're going to talk about always on assurance. What is driving that is a system of three components around self-healing, monitoring, and proactive assistance. So our software will heal broken things on nodes, right? If something stops running that should be running, it will attempt to restart that. We also have monitoring that's deployed with everything. So you build a cluster in AWS, well, we put open source monitoring agents, that are actually Prometheus, on every single node. That means it's resilient, right? So if you lose a node, you don't lose monitoring. But that data importantly comes back to our control plane, and that's the control plane that you can put in your data center as well. That data is what alerts us, and you as a user, anytime of the day that something's going wrong. Let's say etcd latency, good example, etcd is going slow. We'll find out, we might not be able to take restorative action immediately, but we're definitely going to reach out and say,, "You have a problem, let's get ahead of this and let's prevent that from becoming a bigger problem." And that's what we're delivering. When we say always on assurance, we're talking about self-healing, we're talking about remote monitoring, we're talking about being proactive with our customers, not waiting for the phone call or the support desk ticket saying, "Oh we think something's not working." Or worse, the customer has an outage. >> Awesome. Thanks for sharing. Can you explain the process for implementing Platform9 within a company's existing infrastructure. >> Are we doing air gap, or on-prem or SaaS approached? SaaS approach I think is by far the easiest, right? We can build a dedicated Platform9 control plane instance in a manner of minutes, for any customer. So when we do a proof of concept or onboarding, we just literally put in an email address, put in the name you want for your fully qualified domain name, and your instance is up. From that point onwards, the user can just log in, and using our CLI, talk to any number of, say, virtual machines, or physical servers in their environment for, you know, doing this in a data center or colo, and say, "I want these to be my Kubernetes control plane nodes. Here's the five of them. Here's the VIP for the load balancing, the API server and here are all of my compute nodes." And that CLI will work with the SaaS control plane, and go and build the cluster. That's as simple as it, CentOS, Ubuntu, just plain old operating system. Our software takes care of all the prerequisites, installing all the pieces, putting down MetalLB, CoreDNS, Metrics Server, Kubernetes dashboard, etcd backups. You built some servers. That's essentially what you've done, and the rest is being handled by Platform9. It's as simple as that. >> Great, thanks for that. What are the two traditional paths for companies considering the cloud native journey? The two paths. >> The traditional paths. I think that's your engineering team running so fast that before you even realize that you've got, you know, 10 EKS clusters. Or, hey, we can do this. You know, I've got the I can build it mentality. Let's go DIY completely open source Kubernetes on our infrastructure, and we're going to piecemeal build it all up together. They're, I think the pathways that people traditionally look at this journey, as opposed to having that third alternative saying can I just consume it on my infrastructure, be it cloud or on-premise or at the edge. >> Third is the new way, you guys do that. >> That's been our focus since the company was, you know, brought together back in the open OpenStack days. >> Awesome, what's the makeup of your customer base? Is there a certain pattern to the size or environments that you guys work with? Is there a pattern or consistency to your customer base? >> It's a spread, right? We've got large enterprises like Juniper, and we go all the way down to people with 20, 30, 50 nodes in total. We've got people in banking and finance, we've got things all the way through to telecommunications and storage infrastructure. >> What's your favorite feature of Platform9? >> My favorite feature? You know, if I ask, should I say this as a pre-sales engineer, let me show you a favorite thing. My immediate response is, I should never do this. (John laughs) To me it's just being able to define my cluster and say, go. And in five minutes I have that environment, I can see everything that's running, right? It's all unified, it's one spot, right? I'm a cluster admin. I said I wanted three control plane, 25 workers. Here's the infrastructure, it creates it, and once it's built, I can see everything that's running, right? All the applications that are there. One UI, I don't have to go click around. I'm not trying to solve things or download things. It's the fact that it's unified and just delivered in one hit. >> What is the one thing that people should know about Platform9 that they might not know about it? >> I think it's that we help developers and engineers as much as we can help our operations teams. I think, for a long time we've sort of targeted that user and said, hey, we, we really help you. It's like, but why are they doing this? Why are they building any infrastructure or any cloud platform? Well, it's to run applications and services, to help their customers, but how do they get there? There's people building and writing those things, and we're helping them, right? For the last two years, we've been really focused on making it simple, and I think that's an important thing to know. >> Chris, thanks so much, appreciate it. >> Yeah, thank you, John. >> Okay, that's theCUBE Q&A session here with Platform9. I'm John Furrier, thanks for watching. (light music)

(bright music) >> Hi, everybody, this is Dave Vellante, welcome back to Supercloud 2. Last August, at the first Supercloud event, we invited the broader community to help further define Supercloud, we assessed its viability, and identified the critical elements and deployment models of the concept. The objectives here at Supercloud too are, first of all, to continue to tighten and test the concept, the second is, we want to get real world input from practitioners on the problems that they're facing and the viability of Supercloud in terms of applying it to their business. So on the program, we got companies like Walmart, Sachs, Western Union, Ionis Pharmaceuticals, NASDAQ, and others. And the third thing that we want to do is we want to drill into the intersection of cloud and data to project what the future looks like in the context of Supercloud. So in this segment, we want to explore the concept of data architectures and what's going to be required for Supercloud. And I'm pleased to welcome one of our Supercloud sponsors, ChaosSearch, Ed Walsh is the CEO of the company, with Thomas Hazel, who's the Founder, CTO, and Chief Scientist. Guys, good to see you again, thanks for coming into our Marlborough studio. >> Always great. >> Great to be here. >> Okay, so there's a little debate, I'm going to put you right in the spot. (Ed chuckling) A little debate going on in the community started by Bob Muglia, a former CEO of Snowflake, and he was at Microsoft for a long time, and he looked at the Supercloud definition, said, "I think you need to tighten it up a little bit." So, here's what he came up with. He said, "A Supercloud is a platform that provides a programmatically consistent set of services hosted on heterogeneous cloud providers." So he's calling it a platform, not an architecture, which was kind of interesting. And so presumably the platform owner is going to be responsible for the architecture, but Dr. Nelu Mihai, who's a computer scientist behind the Cloud of Clouds Project, he chimed in and responded with the following. He said, "Cloud is a programming paradigm supporting the entire lifecycle of applications with data and logic natively distributed. Supercloud is an open architecture that integrates heterogeneous clouds in an agnostic manner." So, Ed, words matter. Is this an architecture or is it a platform? >> Put us on the spot. So, I'm sure you have concepts, I would say it's an architectural or design principle. Listen, I look at Supercloud as a mega trend, just like cloud, just like data analytics. And some companies are using the principle, design principles, to literally get dramatically ahead of everyone else. I mean, things you couldn't possibly do if you didn't use cloud principles, right? So I think it's a Supercloud effect, you're able to do things you're not able to. So I think it's more a design principle, but if you do it right, you get dramatic effect as far as customer value. >> So the conversation that we were having with Muglia, and Tristan Handy of dbt Labs, was, I'll set it up as the following, and, Thomas, would love to get your thoughts, if you have a CRM, think about applications today, it's all about forms and codifying business processes, you type a bunch of stuff into Salesforce, and all the salespeople do it, and this machine generates a forecast. What if you have this new type of data app that pulls data from the transaction system, the e-commerce, the supply chain, the partner ecosystem, et cetera, and then, without humans, actually comes up with a plan. That's their vision. And Muglia was saying, in order to do that, you need to rethink data architectures and database architectures specifically, you need to get down to the level of how the data is stored on the disc. What are your thoughts on that? Well, first of all, I'm going to cop out, I think it's actually both. I do think it's a design principle, I think it's not open technology, but open APIs, open access, and you can build a platform on that design principle architecture. Now, I'm a database person, I love solving the database problems. >> I'm waited for you to launch into this. >> Yeah, so I mean, you know, Snowflake is a database, right? It's a distributed database. And we wanted to crack those codes, because, multi-region, multi-cloud, customers wanted access to their data, and their data is in a variety of forms, all these services that you're talked about. And so what I saw as a core principle was cloud object storage, everyone streams their data to cloud object storage. From there we said, well, how about we rethink database architecture, rethink file format, so that we can take each one of these services and bring them together, whether distributively or centrally, such that customers can access and get answers, whether it's operational data, whether it's business data, AKA search, or SQL, complex distributed joins. But we had to rethink the architecture. I like to say we're not a first generation, or a second, we're a third generation distributed database on pure, pure cloud storage, no caching, no SSDs. Why? Because all that availability, the cost of time, is a struggle, and cloud object storage, we think, is the answer. >> So when you're saying no caching, so when I think about how companies are solving some, you know, pretty hairy problems, take MySQL Heatwave, everybody thought Oracle was going to just forget about MySQL, well, they come out with Heatwave. And the way they solve problems, and you see their benchmarks against Amazon, "Oh, we crush everybody," is they put it all in memory. So you said no caching? You're not getting performance through caching? How is that true, and how are you getting performance? >> Well, so five, six years ago, right? When you realize that cloud object storage is going to be everywhere, and it's going to be a core foundational, if you will, fabric, what would you do? Well, a lot of times the second generation say, "We'll take it out of cloud storage, put in SSDs or something, and put into cache." And that adds a lot of time, adds a lot of costs. But I said, what if, what if we could actually make the first read hot, the first read distributed joins and searching? And so what we went out to do was said, we can't cache, because that's adds time, that adds cost. We have to make cloud object storage high performance, like it feels like a caching SSD. That's where our patents are, that's where our technology is, and we've spent many years working towards this. So, to me, if you can crack that code, a lot of these issues we're talking about, multi-region, multicloud, different services, everybody wants to send their data to the data lake, but then they move it out, we said, "Keep it right there." >> You nailed it, the data gravity. So, Bob's right, the data's coming in, and you need to get the data from everywhere, but you need an environment that you can deal with all that different schema, all the different type of technology, but also at scale. Bob's right, you cannot use memory or SSDs to cache that, that doesn't scale, it doesn't scale cost effectively. But if you could, and what you did, is you made object storage, S3 first, but object storage, the only persistence by doing that. And then we get performance, we should talk about it, it's literally, you know, hundreds of terabytes of queries, and it's done in seconds, it's done without memory caching. We have concepts of caching, but the only caching, the only persistence, is actually when we're doing caching, we're just keeping another side-eye track of things on the S3 itself. So we're using, actually, the object storage to be a database, which is kind of where Bob was saying, we agree, but that's what you started at, people thought you were crazy. >> And maybe make it live. Don't think of it as archival or temporary space, make it live, real time streaming, operational data. What we do is make it smart, we see the data coming in, we uniquely index it such that you can get your use cases, that are search, observability, security, or backend operational. But we don't have to have this, I dunno, static, fixed, siloed type of architecture technologies that were traditionally built prior to Supercloud thinking. >> And you don't have to move everything, essentially, you can do it wherever the data lands, whatever cloud across the globe, you're able to bring it together, you get the cost effectiveness, because the only persistence is the cheapest storage persistent layer you can buy. But the key thing is you cracked the code. >> We had to crack the code, right? That was the key thing. >> That's where the plans are. >> And then once you do that, then everything else gets easier to scale, your architecture, across regions, across cloud. >> Now, it's a general purpose database, as Bob was saying, but we use that database to solve a particular issue, which is around operational data, right? So, we agree with Bob's. >> Interesting. So this brings me to this concept of data, Jimata Gan is one of our speakers, you know, we talk about data fabric, which is a NetApp, originally NetApp concept, Gartner's kind of co-opted it. But so, the basic concept is, data lives everywhere, whether it's an S3 bucket, or a SQL database, or a data lake, it's just a node on the data mesh. So in your view, how does this fit in with Supercloud? Ed, you've said that you've built, essentially, an enabler for that, for the data mesh, I think you're an enabler for the Supercloud-like principles. This is a big, chewy opportunity, and it requires, you know, a team approach. There's got to be an ecosystem, there's not going to be one Supercloud to rule them all, so where does the ecosystem fit into the discussion, and where do you fit into the ecosystem? >> Right, so we agree completely, there's not one Supercloud in effect, but we use Supercloud principles to build our platform, and then, you know, the ecosystem's going to be built on leveraging what everyone else's secret powers are, right? So our power, our superpower, based upon what we built is, we deal with, if you're having any scale, or cost effective scale issues, with data, machine generated data, like business observability or security data, we are your force multiplier, we will take that in singularly, just let it, simply put it in your object storage wherever it sits, and we give you uniformity access to that using OpenAPI access, SQL, or you know, Elasticsearch API. So, that's what we do, that's our superpower. So I'll play it into data mesh, that's a perfect, we are a node on a data mesh, but I'll play it in the soup about how, the ecosystem, we see it kind of playing, and we talked about it in just in the last couple days, how we see this kind of possibly. Short term, our superpowers, we deal with this data that's coming at these environments, people, customers, building out observability or security environments, or vendors that are selling their own Supercloud, I do observability, the Datadogs of the world, dot dot dot, the Splunks of the world, dot dot dot, and security. So what we do is we fit in naturally. What we do is a cost effective scale, just land it anywhere in the world, we deal with ingest, and it's a cost effective, an order of magnitude, or two or three order magnitudes more cost effective. Allows them, their customers are asking them to do the impossible, "Give me fast monitoring alerting. I want it snappy, but I want it to keep two years of data, (laughs) and I want it cost effective." It doesn't work. They're good at the fast monitoring alerting, we're good at the long-term retention. And yet there's some gray area between those two, but one to one is actually cheaper, so we would partner. So the first ecosystem plays, who wants to have the ability to, really, all the data's in those same environments, the security observability players, they can literally, just through API, drag our data into their point to grab. We can make it seamless for customers. Right now, we make it helpful to customers. Your Datadog, we make a button, easy go from Datadog to us for logs, save you money. Same thing with Grafana. But you can also look at ecosystem, those same vendors, it used to be a year ago it was, you know, its all about how can you grow, like it's growth at all costs, now it's about cogs. So literally we can go an environment, you supply what your customer wants, but we can help with cogs. And one-on one in a partnership is better than you trying to build on your own. >> Thomas, you were saying you make the first read fast, so you think about Snowflake. Everybody wants to talk about Snowflake and Databricks. So, Snowflake, great, but you got to get the data in there. All right, so that's, can you help with that problem? >> I mean we want simple in, right? And if you have to have structure in, you're not simple. So the idea that you have a simple in, data lake, schema read type philosophy, but schema right type performance. And so what I wanted to do, what we have done, is have that simple lake, and stream that data real time, and those access points of Search or SQL, to go after whatever business case you need, security observability, warehouse integration. But the key thing is, how do I make that click, click, click answer, and do it quickly? And so what we want to do is, that first read has to be fast. Why? 'Cause then you're going to do all this siloing, layers, complexity. If your first read's not fast, you're at a disadvantage, particularly in cost. And nobody says I want less data, but everyone has to, whether they say we're going to shorten the window, we're going to use AI to choose, but in a security moment, when you don't have that answer, you're in trouble. And that's why we are this service, this Supercloud service, if you will, providing access, well-known search, well-known SQL type access, that if you just have one access point, you're at a disadvantage. >> We actually talked about Snowflake and BigQuery, and a different platform, Data Bricks. That's kind of where we see the phase two of ecosystem. One is easy, the low-hanging fruit is observability and security firms. But the next one is, what we do, our super power is dealing with this messy data that schema is changing like night and day. Pipelines are tough, and it's changing all the time, but you want these things fast, and it's big data around the world. That's the next point, just use us alongside, or inside, one of their platforms, and now we get the best of both worlds. Our superpower is keeping this messy data as a streaming, okay, not a batch thing, allow you to do that. So, that's the second one. And then to be honest, the third one, which plays you to Supercloud, it also plays perfectly in the data mesh, is if you really go to the ultimate thing, what we have done is made object storage, S3, GCS, and blob storage, we made it a database. Put, get, complex query with big joins. You know, so back to your original thing, and Muglia teed it up perfectly, we've done that. Now imagine if that's an ecosystem, who would want that? If it's, again, it's uniform available across all the regions, across all the clouds, and it's right next to where you are building a service, or a client's trying, that's where the ecosystem, I think people are going to use Superclouds for their superpowers. We're really good at this, allows that short term. I think the Snowflakes and the Data Bricks are the medium term, you know? And then I think eventually gets to, hey, listen if you can make object storage fast, you can just go after it with simple SQL queries, or elastic. Who would want that? I think that's where people are going to leverage it. It's not going to be one Supercloud, and we leverage the super clouds. >> Our viewpoint is smart object storage can be programmable, and so we agree with Bob, but we're not saying do it here, do it here. This core, fundamental layer across regions, across clouds, that everyone has? Simple in. Right now, it's hard to get data in for access for analysis. So we said, simply, we'll automate the entire process, give you API access across regions, across clouds. And again, how do you do a distributed join that's fast? How do you do a distributed join that doesn't cost you an arm or a leg? And how do you do it at scale? And that's where we've been focused. >> So prior, the cloud object store was a niche. >> Yeah. >> S3 obviously changed that. How standard is, essentially, object store across the different cloud platforms? Is that a problem for you? Is that an easy thing to solve? >> Well, let's talk about it. I mean we've fundamentally, yeah we've extracted it, but fundamentally, cloud object storage, put, get, and list. That's why it's so scalable, 'cause it doesn't have all these other components. That complexity is where we have moved up, and provide direct analytical API access. So because of its simplicity, and costs, and security, and reliability, it can scale naturally. I mean, really, distributed object storage is easy, it's put-get anywhere, now what we've done is we put a layer of intelligence, you know, call it smart object storage, where access is simple. So whether it's multi-region, do a query across, or multicloud, do a query across, or hunting, searching. >> We've had clients doing Amazon and Google, we have some Azure, but we see Amazon and Google more, and it's a consistent service across all of them. Just literally put your data in the bucket of choice, or folder of choice, click a couple buttons, literally click that to say "that's hot," and after that, it's hot, you can see it. But we're not moving data, the data gravity issue, that's the other. That it's already natively flowing to these pools of object storage across different regions and clouds. We don't move it, we index it right there, we're spinning up stateless compute, back to the Supercloud concept. But now that allows us to do all these other things, right? >> And it's no longer just cheap and deep object storage. Right? >> Yeah, we make it the same, like you have an analytic platform regardless of where you're at, you don't have to worry about that. Yeah, we deal with that, we deal with a stateless compute coming up -- >> And make it programmable. Be able to say, "I want this bucket to provide these answers." Right, that's really the hope, the vision. And the complexity to build the entire stack, and then connect them together, we said, the fabric is cloud storage, we just provide the intelligence on top. >> Let's bring it back to the customers, and one of the things we're exploring in Supercloud too is, you know, is Supercloud a solution looking for a problem? Is a multicloud really a problem? I mean, you hear, you know, a lot of the vendor marketing says, "Oh, it's a disaster, because it's all different across the clouds." And I talked to a lot of customers even as part of Supercloud too, they're like, "Well, I solved that problem by just going mono cloud." Well, but then you're not able to take advantage of a lot of the capabilities and the primitives that, you know, like Google's data, or you like Microsoft's simplicity, their RPA, whatever it is. So what are customers telling you, what are their near term problems that they're trying to solve today, and how are they thinking about the future? >> Listen, it's a real problem. I think it started, I think this is a a mega trend, just like cloud. Just, cloud data, and I always add, analytics, are the mega trends. If you're looking at those, if you're not considering using the Supercloud principles, in other words, leveraging what I have, abstracting it out, and getting the most out of that, and then build value on top, I think you're not going to be able to keep up, In fact, no way you're going to keep up with this data volume. It's a geometric challenge, and you're trying to do linear things. So clients aren't necessarily asking, hey, for Supercloud, but they're really saying, I need to have a better mechanism to simplify this and get value across it, and how do you abstract that out to do that? And that's where they're obviously, our conversations are more amazed what we're able to do, and what they're able to do with our platform, because if you think of what we've done, the S3, or GCS, or object storage, is they can't imagine the ingest, they can't imagine how easy, time to glass, one minute, no matter where it lands in the world, querying this in seconds for hundreds of terabytes squared. People are amazed, but that's kind of, so they're not asking for that, but they are amazed. And then when you start talking on it, if you're an enterprise person, you're building a big cloud data platform, or doing data or analytics, if you're not trying to leverage the public clouds, and somehow leverage all of them, and then build on top, then I think you're missing it. So they might not be asking for it, but they're doing it. >> And they're looking for a lens, you mentioned all these different services, how do I bring those together quickly? You know, our viewpoint, our service, is I have all these streams of data, create a lens where they want to go after it via search, go after via SQL, bring them together instantly, no e-tailing out, no define this table, put into this database. We said, let's have a service that creates a lens across all these streams, and then make those connections. I want to take my CRM with my Google AdWords, and maybe my Salesforce, how do I do analysis? Maybe I want to hunt first, maybe I want to join, maybe I want to add another stream to it. And so our viewpoint is, it's so natural to get into these lake platforms and then provide lenses to get that access. >> And they don't want it separate, they don't want something different here, and different there. They want it basically -- >> So this is our industry, right? If something new comes out, remember virtualization came out, "Oh my God, this is so great, it's going to solve all these problems." And all of a sudden it just got to be this big, more complex thing. Same thing with cloud, you know? It started out with S3, and then EC2, and now hundreds and hundreds of different services. So, it's a complex matter for a lot of people, and this creates problems for customers, especially when you got divisions that are using different clouds, and you're saying that the solution, or a solution for the part of the problem, is to really allow the data to stay in place on S3, use that standard, super simple, but then give it what, Ed, you've called superpower a couple of times, to make it fast, make it inexpensive, and allow you to do that across clouds. >> Yeah, yeah. >> I'll give you guys the last word on that. >> No, listen, I think, we think Supercloud allows you to do a lot more. And for us, data, everyone says more data, more problems, more budget issue, everyone knows more data is better, and we show you how to do it cost effectively at scale. And we couldn't have done it without the design principles of we're leveraging the Supercloud to get capabilities, and because we use super, just the object storage, we're able to get these capabilities of ingest, scale, cost effectiveness, and then we built on top of this. In the end, a database is a data platform that allows you to go after everything distributed, and to get one platform for analytics, no matter where it lands, that's where we think the Supercloud concepts are perfect, that's where our clients are seeing it, and we're kind of excited about it. >> Yeah a third generation database, Supercloud database, however we want to phrase it, and make it simple, but provide the value, and make it instant. >> Guys, thanks so much for coming into the studio today, I really thank you for your support of theCUBE, and theCUBE community, it allows us to provide events like this and free content. I really appreciate it. >> Oh, thank you. >> Thank you. >> All right, this is Dave Vellante for John Furrier in theCUBE community, thanks for being with us today. You're watching Supercloud 2, keep it right there for more thought provoking discussions around the future of cloud and data. (bright music)

(bright upbeat music) >> Welcome back, everybody, to the Supercloud 2. My name is Dave Vellante. And I'm pleased to welcome Nir Zuk. He's the founder and CTO of Palo Alto Networks. Nir, good to see you again. Welcome. >> Same here. Good to see you. >> So let's start with the right security architecture in the context of today's fragmented market. You've got a lot of different tools, you've got different locations, on-prem, you've got hardware and software. Tell us about the right security architecture from your standpoint. What's that look like? >> You know, the funny thing is using the word security in architecture rarely works together. (Dave chuckles) If you ask a typical information security person to step up to a whiteboard and draw their security architecture, they will look at you as if you fell from the moon. I mean, haven't you been here in the last 25 years? There's no security architecture. The architecture today is just buying a bunch of products and dropping them into the infrastructure at some relatively random way without really any guiding architecture. And that's a huge challenge in cybersecurity. It's always been, we've always tried to find ways to put an architecture into writing blueprints, whatever you want to call it, and it's always been difficult. Luckily, two things. First, there's something called zero trust, which we can talk a little bit about more, if you want, and zero trust among other things is really a way to create a security architecture, and second, because in the cloud, in the supercloud, we're starting from scratch, we can do things differently. We don't have to follow the way we've always done cybersecurity, again, buying random products, okay, maybe not random, maybe there is some thinking going into it by buying products, one of the other, dropping them in, and doing it over 20 years and ending up with a mess in the cloud, we have an opportunity to do it differently and really have an architecture. >> You know, I love talking to founders and particularly technical founders from StartupNation. I think I saw an article, I think it was Erie Levine, one of the founders or co-founders of Waze, and he had a t-shirt on, it said, "Fall in love with the problem, not the solution." Is that how you approached architecture? You talk about zero trust, it's a relatively new term, but was that in your head when you thought about forming the company? >> Yeah, so when I started Palo Alto Networks, exactly, by the way, 17 years ago, we got funded January, 2006, January 18th, 2006. The idea behind Palo Alto Networks was to create a security platform and over time take more and more cybersecurity functions and deliver them on top of that platform, by the way, as a service, SaaS. Everybody thought we were crazy trying to combine many functions into one platform, best of breed and defense in death and putting all your eggs in the same basket and a bunch of other slogans were flying around, and also everybody thought we were crazy asking customers to send information to the cloud in order to secure themselves. Of course, step forward 17 years, everything is now different. We changed the market. Almost all of cybersecurity today is delivered as SaaS and platforms are ruling more and more the world. And so again, the idea behind the platform was to over time take more and more cybersecurity functions and deliver them together, one brain, one decision being made for each and every packet or system call or file or whatever it is that you're making the decision about and it works really, really well. As a side effect, when you combine that with zero trust and you end up with, let's not call it an architecture yet. You end up with with something where any user, any location, both geographically as well as any location in terms of branch office, headquarters, home, coffee shop, hotel, whatever, so any user, any geographical location, any location, any connectivity method, whether it is SD1 or IPsec or Client VPN or Client SVPN or proxy or browser isolation or whatever and any application deployed anywhere, public cloud, private cloud, traditional data center, SaaS, you secure the same way. That's really zero trust, right? You secure everything, no matter who the user is, no matter where they are, no matter where they go, you secure them exactly the same way. You don't make any assumptions about the user or the application or the location or whatever, just because you trust nothing. And as a side effect, when you do that, you end up with a security architecture, the security architecture I just described. The same thing is true for securing applications. If you try to really think and not just act instinctively the way we usually do in cybersecurity and you say, I'm going to secure my traditional data center applications or private cloud applications and public cloud applications and my SaaS applications the same way, I'm not going to trust something just because it's deployed in the private data center. I'm not going to trust two components of an application or two applications talking to each other just because they're deployed in the same place versus if one component is deployed in one public cloud and the other component is deployed in another public cloud or private cloud or whatever. I'm going to secure all of them the same way without making any trust assumptions. You end up with an architecture for securing your applications, which is applicable for the supercloud. >> It was very interesting. There's a debate I want to pick up on what you said because you said don't call it an architecture yet. So Bob Muglia, I dunno if you know Bob, but he sort of started the debate, said, "Supercloud, think of it as a platform, not an architecture." And there are others that are saying, "No, no, if we do that, then we're going to have a bunch of more stove pipes. So there needs to be standard, almost a purist view. There needs to be a supercloud architecture." So how do you think about it? And it's a bit academic, I know, but do you think of this idea of a supercloud, this layer of value on top of the hyperscalers, do you think of that as a platform approach that each of the individual vendors are responsible for the architecture? Or is there some kind of overriding architecture of standards that needs to emerge to enable the supercloud? >> So we can talk academically or we can talk practically. >> Yeah, let's talk practically. That's who you are. (Dave laughs) >> Practically, this world is ruled by financial interests and none of the public cloud providers, especially the bigger they are has any interest of making it easy for anyone to go multi-cloud, okay? Also, on top of that, if we want to be even more practical, each of those large cloud providers, cloud scale providers have engineers and all these engineers think they're the best in the world, which they are and they all like to do things differently. So you can't expect things in AWS and in Azure and GCP and in the other clouds like Oracle and Ali and so on to be the same. They're not going to be the same. And some things can be abstracted. Maybe cloud storage or bucket storage can be abstracted with the layer that makes them look the same no matter where you're running. And some things cannot be abstracted and unfortunately will not be abstracted because the economical interest and the way engineers work won't let it happen. We as a third party provider, cybersecurity provider, and I'm sure other providers in other areas as well are trying or we're doing our best. We're not trying, we are doing our best, and it's pretty close to being the way you describe the top of your supercloud. We're building something that abstracts the underlying cloud such that securing each of these clouds, and by the way, I would add private cloud to it as well, looks exactly the same. So we use, almost always, whenever possible, the same terminology, no matter which cloud we're securing and the same policy and the same alerts and the same information and so on. And that's also very important because when you look at the people that actually end up using the product, security engineers and more importantly, SOC, security operations center analysts, they're not going to study the details of each and every cloud. It's just going to be too much. So we need to abstract it for them. >> Yeah, we agree by the way that the supercloud definition is inclusive of on-prem, you know, what you call private cloud. And I want to pick up on something else you said. I think you're right that abstracting and making consistent across clouds something like object storage, get put, you know, whether it's an S3 bucket or an Azure Blob, relatively speaking trivial. When you now bring that supercloud concept to something more complex like security, first of all, as a technically feasible and inferring the answer there is yes, and if so, what do you see as the main technical challenges of doing so? >> So it is feasible to the extent that the different cloud provide the same functionality. Then you step into a territory where different cloud providers have different paths services and different cloud providers do things a little bit differently and they have different sets of permissions and different logging that sometimes provides all the information and sometimes it doesn't. So you end up with some differences. And then the question is, do you abstract the lowest common dominator and that's all you support? Or do you find a way to be smarter than that? And yeah, whatever can be abstracted is abstracted and whatever cannot be abstracted, you find an easy way to represent that to your users, security engineers, security analysts, and so on, which is what I believe we do. >> And you do that by what? Inventing or developing technology that presents that experience to users? Could you be more specific there? >> Yeah, so different cloud providers call their storage in different names and you use different ways to configure them and the logs come out the same. So we normalize it. I mean, the keyword is probably normalization. Normalize it. And we try to, you know, then you have to pick a winner here and to use someone's terminology or you need to invent new terminology. So we try to use the terminology of the largest cloud provider so that we have a better chance of doing that but we can't always do that because they don't support everything that other cloud providers provide, but the important thing is, with or thanks to that normalization, our customers both on the engineering side and on the user side, operations side end up having to learn one terminology in order to set policies and understand attacks and investigate incidents. >> I wonder if I could pick your brain on what you see as the ideal deployment model to achieve this supercloud experience. For example, do you think instantiating your stack in multiple regions and multiple clouds is the right way to do it? Or is building a single global instance on top of the clouds a more preferable way? Are maybe other models we should consider? What do you see as the trade off of these different deployment models and which one is ideal in your view? >> Yeah, so first, when you deploy cloud security, you have to decide whether you're going to use agents or not. By agents, I mean something working, something running inside the workload. Inside a virtual machine on the container host attached to function, serverless function and so on and I, of course, recommend using agents because that enables prevention, it enables functionality you cannot get without agents but you have to choose that. Now, of course, if you choose agent, you need to deploy AWS agents in AWS and GCP agents in GCP and Azure agents in Azure and so on. Of course, you don't do it manually. You do it through the CICD pipeline. And then the second thing that you need to do is you need to connect with the consoles. Of course, that can be done over the internet no matter where your security instances is running. You can run it on premise, you can run it in one of the other different clouds. Of course, we don't run it on premise. We prefer not to run it on premise because if you're secured in cloud, you might as well run in the cloud. And then the question is, for example, do you run a separate instance for AWS for GCP or for Azure, or you want to run one instance for all of them in one of these clouds? And there are advantages and disadvantages. I think that from a security perspective, it's always better to run in one place because then when you collect the information, you get information from all the clouds and you can start looking for cross-cloud issues, incidents, attacks, and so on. The downside of that is that you need to send all the information to one of the clouds and you probably know that sending data out of the cloud costs a lot of money versus keeping it in the cloud. So theoretically, you can build an architecture where you keep the data for AWS in AWS, Azure in Azure, GCP in GCP, and then you try to run distributed queries. When you do that, you find out you'd end up paying more for the compute to do that than you would've paid for sending all the data to a central location. So we prefer the approach of running in one place, bringing all the data there, and running all the security, the machine learning or whatever, the rules or whatever it is that you're running in one place versus trying to create a distributed deployment in order to try to save some money on the data, the network data transfers. >> Yeah, thank you for that. That makes a lot of sense. And so basically, should we think about the next layer building security data lake, if you will, and then running machine learning on top of that if I can use that term of a data lake or a lake house? Is that sort of where you're headed? >> Yeah, look, the world is headed in that direction, not just the cybersecurity world. The world is headed from being rule-based to being data-based. So cybersecurity is not different and what we used to do with rules in the past, we're now doing with machine learning. So in the past, you would define rules saying, if you see this, this, and this, it's an attack. Now you just throw the data at the machine, I mean, I'm simplifying it, but you throw data at a machine. You'll tell the machine, find the attack in the data. It's not that simple. You need to build the right machine learning models. It needs to be done by people that are both cybersecurity experts and machine learning experts. We do it mostly with ex-military offensive people that take their offensive knowledge and translate it into machine learning models. But look, the world is moving in that direction and cybersecurity is moving in that direction as well. You need to collect a lot of data. Like I said, I prefer to see all the data in one place so that the machine learning can be much more efficient, pay for transferring the data, save money on the compute. >> I think the drop the mic quote it ignite that you had was within five years, your security operation is going to be AI-powered. And so you could probably apply that to virtually any job over the next five years. >> I don't know if any job. Certainly writing essays for school is automated already as we've seen with ChatGPT and potentially other things. By the way, we need to talk at some point about ChatGPT security. I don't want to think what happens when someone spends a lot of money on creating a lot of fake content and teaches ChatGPT the wrong answer to a question. We start seeing ChatGPT as the oracle of everything. We need to figure out what to do with the security of that. But yeah, things have to be automated in cybersecurity. They have to be automated. They're just too much data to deal with and it's just not even close to being good enough to wait for an incident to happen and then going investigate the incident based on the data that we have. It's better to look at all the data all the time, millions of events per second, and find those incidents before they happen. There's no way to do that without machine learning. >> I'd love to have you back and talk about ChatGPT. I know they're trying to put in some guardrails but there are a lot of unintended consequences, aren't there? >> Look, if they're not going to have a person filtering the data, then with enough money, you can create thousands or tens of thousands of pieces of articles or whatever that look real and teach the machine something that is totally wrong. >> We were talking about the hyper skills before and I agree with you. It's very unlikely they're going to get together, band together, and create these standards. But it's not a static market. It's a moving train, if you will. So assuming you're building this cross cloud experience which you are, what do you want from the hyperscalers? What do you want them to bring to the table? What is a technology supplier like Palo Alto Networks bring? In other words, where do you see ongoing as your unique value add and that moat that you're building and how will that evolve over time vis-a-vis the hyperscaler evolution? >> Yeah, look, we need APIs. The more data we have, the more access we have to more data, the less restricted the access is and the cheaper the access is to the data because someone has to pay today for some reason for accessing that data, the more secure their customers are going to be. So we need help and are helping by the way a lot, all of them in finding easy ways for customers to deploy things in the cloud, access data, and again, a lot of data, very diversified data and do it in a cost-effective way. >> And when we talk about the edge, I presume you look at the edge as just another data center or maybe it's the reverse. Maybe the data center is just another edge location, but you're seeing specific edge security solutions come out. I'm guessing that you would say, that's not what we want. Edge should be part of that architecture that we talked about earlier. Do you agree? >> Correct, it should be part of the architecture. I would also say that the edge provides an opportunity specifically for network security, whereas traditional network security would be deployed on premise. I'm talking about internet security but half network security market, and not just network security but also the other network intelligent functions like routing and QS. We're seeing a trend of pushing those to the edge of the cloud. So what you deploy on premise is technology for bringing packets to the edge of the cloud and then you run your security at the edge, whatever that edge is, whether it's a private edge or public edge, you run it in the edge. It's called SASE, Secure Access Services Edge, pronounced SASE. >> Nir, I got to thank you so much. You're such a clear thinker. I really appreciate you participating in Supercloud 2. >> Thank you. >> All right, keep it right there for more content covering the future of cloud and data. This is Dave Vellante for John Furrier. I'll be right back. (bright upbeat music)

(upbeat music) >> Today more than ever before, organizations are striving to gain a competitive advantage, deliver more value to customers, reduce risk, and respond more quickly to the needs of businesses. Now, to achieve these goals, organizations need easy access to a single view of accurate, consistent and very importantly, trusted data. If it's not trusted, nobody's going to use it and all in near real time. However, the growing volumes and complexities of data make this difficult to achieve in practice. Not to mention the organizational challenges that have evolved as data becomes increasingly important to winning in the marketplace. Specifically as data grows, so does the prevalence of data silos, making, integrating and leveraging data from internal and external sources a real challenge. Now, in this final segment, we'll hear from Joe Lichtenberg who's the global head of product and industry marketing, and he's going to discuss how smart data fabrics can be applied to different industries. And by way of these use cases, we'll probe Joe's vast knowledge base and ask him to highlight how InterSystems, which touts a next gen approach to Customer 360, how the company leverages a smart data fabric to provide organizations of varying sizes and sectors in financial services, supply chain, logistics and healthcare with a better, faster and easier way to deliver value to the business. Joe welcome, great to have you here. >> Thank you, it's great to be here. That was some intro. I could not have said it better myself, so thank you for that. >> Thank you. Well, we're happy to have you on this show now. I understand- >> It's great to be here. >> You you've made a career helping large businesses with technology solutions, small businesses, and then scale those solutions to meet whatever needs they had. And of course, you're a vocal advocate as is your company of data fabrics. We talked to Scott earlier about data fabrics, how it relates to data mesh big discussions in the industry. So tell us more about your perspective. >> Sure, so first I would say that I have been in this industry for a very long time so I've been like you, I'm sure, for decades working with customers and with technology, really to solve these same kinds of challenges. So for decades, companies have been working with lots and lots of data and trying to get business value to solve all sorts of different challenges. And I will tell you that I've seen many different approaches and different technologies over the years. So, early on, point to point connections with custom coding, and I've worked with integration platforms 20 years ago with the advent of web services and service-oriented architectures and exposing endpoints with wisdom and getting access to disparate data from across the organization. And more recently, obviously with data warehouses and data lakes and now moving workloads to the cloud with cloud-based data marts and data warehouses. Lots of approaches that I've seen over the years but yet still challenges remain in terms of getting access to a single trusted real-time view of data. And so, recently, we ran a survey of more than 500 different business users across different industries and 86% told us that they still lack confidence in using their data to make decisions. That's a huge number, right? And if you think about all of the work and all of the technology and approaches over the years, that is a surprising number and drilling into why that is, there were three main reasons. One is latency. So the amount of time that it takes to access the data and process the data and make it fit for purpose by the time the business has access to the data and the information that they need, the opportunity has passed. >> Elapsed time, not speed a light, right? But that too maybe. >> But it takes a long time if you think about these processes and you have to take the data and copy it and run ETL processes and prepare it. So that's one, one is just the amount of data that's disparate in data silos. So still struggling with data that is dispersed across different systems in different formats. And the third, is data democratization. So the business really wants to have access to the data so that they can drill into the data and ask ad hoc questions and the next question and drill into the information and see where it leads them rather than having sort of pre-structured data and pre-structured queries and having to go back to IT and put the request back on the queue again and waiting. >> So it takes too long, the data's too hard to get to 'cause it's in silos and the data lacks context because it's technical people that are serving up the data to the business people. >> Exactly. >> And there's a mismatch. >> Exactly right. So they call that data democratization or giving the business access to the data and the tools that they need to get the answers that they need in the moment. >> So the skeptic in me, 'cause you're right I have seen this story before and the problems seem like they keep coming up, year after year, decade after decade. But I'm an optimist and so. >> As am I. >> And so I sometimes say, okay, same wine new bottle, but it feels like it's different this time around with data fabrics. You guys talk about smart data fabrics from your perspective, what's different? >> Yeah, it's very exciting and it's a fundamentally different approach. So if you think about all of these prior approaches, and by the way, all of these prior approaches have added value, right? It's not like they were bad, but there's still limitations and the business still isn't getting access to all the data that they need in the moment, right? So data warehouses are terrific if you know the questions that you want answered and you take the data and you structure the data in advance. And so now you're serving the business with sort of pre-planned answers to pre-planned queries, right? The data fabric, what we call a smart data fabric is fundamentally different. It's a fundamentally different approach in that rather than sort of in batch mode, taking the data and making it fit for purpose with all the complexity and delays associated with it, with a data fabric where accessing the data on demand as it's needed, as it's requested, either by the business or by applications or by the data scientists directly from the source systems. >> So you're not copying it necessarily to that to make that you're not FTPing it, for instance. I've got it, you take it, you're basically using the same source. >> You're pulling the data on demand as it's being requested by the consumers. And then all of the data management processes that need to be applied for integration and transformation to get the data into a consistent format and business rules and analytic queries. And with Jess showed with machine learning, predictive prescriptive analytics all sorts of powerful capabilities are built into the fabric so that as you're pulling the data on demand, right, all of these processes are being applied and the net result is you're addressing these limitations around latency and silos that we've seen in the past. >> Okay, so you've talked about you have a lot of customers, InterSystems does in different industries supply chain, financial services, manufacturing. We heard from just healthcare. What are you seeing in terms of applications of smart data fabrics in the real world? >> Yeah, so we see it in every industry. So InterSystems, as you know, has been around now for 43 years, and we have tens of thousands of customers in every industry. And this architectural pattern now is providing value for really critical use cases in every industry. So I'm happy to talk to you about some that we're seeing. I could actually spend like three hours here and there but I'm very passionate about working with customers and there's all sorts of exciting. >> What are some of your favorites? >> So, obviously supply chain right now is going through a very challenging time. So the combination of what's happening with the pandemic and disruptions and now I understand eggs are difficult to come by I just heard on NPR. >> Yeah and it's in part a data problem and a big part of data problem, is that fair? >> Yeah and so, in supply chain, first there's supply chain visibility. So organizations want a real time or near real time expansive view of what's happening across the entire supply chain from a supply all the way through distribution, right? So that's only part of the issue but that's a huge sort of real-time data silos problem. So if you think about your extended supply chain, it's complicated enough with all the systems and silos inside your firewall, before all of your suppliers even just thinking about your tier one suppliers let alone tier two and tier three. And then building on top of real-time visibility is what the industry calls a control tower, what we call the ultimate control tower. And so it's built in analytics to be able to sense disruptions and exceptions as they occur and predict the likelihood of these disruptions occurring. And then having data driven and analytics driven guidance in terms of the best way to deal with these disruptions. So for example, an order is missing line items or a cargo ship is stuck off port somewhere. What do you do about it? Do you reroute a different cargo ship, right? Do you take an order that's en route to a different client and reroute that? What's the cost associated? What's the impact associated with it? So that's a huge issue right now around control towers for supply chain. So that's one. >> Can I ask you a question about that? Because you and I have both seen a lot but we've never seen, at least I haven't the economy completely shut down like it was in March of 2020, and now we're seeing this sort of slingshot effect almost like you're driving on the highway sometimes you don't know why, but all of a sudden you slow down and then you speed up, you think it's okay then you slow down again. Do you feel like you guys can help get a handle on that product because it goes on both sides. Sometimes you can't get the product, sometimes there's too much of a product as well and that's not good for business. >> Yeah, absolutely. You want to smooth out the peaks and valleys. >> Yeah. >> And that's a big business goal, business challenge for supply chain executives, right? So you want to make sure that you can respond to demand but you don't want to overstock because there's cost associated with that as well. So how do you optimize the supply chains and it's very much a data silo and a real time challenge. So it's a perfect fit for this new architectural pattern. >> All right, what else? >> So if we look at financial services, we have many, many customers in financial services and that's another industry where they have many different sources of data that all have information that organizations can use to really move the needle if they could just get to that single source of truth in real time. So we sort of bucket many different implementations and use cases that we do around what we call Business 360 and Customer 360. So Business 360, there's all sorts of ways to add business value in terms of having a real-time operational view across all of the different GOs and parts of the business, especially in these very large global financial services institutions like capital markets and investment firms and so forth. So around Business 360, having a realtime view of risk, operational performance regulatory compliance, things like that. Customer 360, there's a whole set of use cases around Customer 360 around hyper-personalization of customers and in realtime next best action looking to see how you can sell more increase share of wallet, cross-sell, upsell to customers. We also do a lot in terms of predicting customer churn. So if you have all the historical data and what's the likelihood of customers churning to be able to proactively intercede, right? It's much more cost effective to keep assets under management and keep clients rather than going and getting new clients to come to the firm. A very interesting use case from one of our customers in Latin America, so Banco do Brasil largest bank in all of Latin America and they have a very innovative CTO who's always looking for new ways to move the needle for the bank. And so one of their ideas and we're working with them to do this is how can they generate net new revenue streams by bringing in new business to the bank? And so they identified a large percentage of the population in Latin America that does no banking. So they have no banking history not only with Banco do Brasil, but with any bank. So there's a fair amount of risk associated with offering services to this segment of the population that's not associated with any banks or financial institutions. >> There is no historical data on them, there's no. >> So it's a data challenge. And so, they're bringing in data from a variety of different sources, social media, open source data that they find online and so forth. And with us running risk models to identify which are the citizens that there's acceptable risk to offer their services. >> It's going to be huge market of unbanked people in vision Latin America. >> Wow, that's interesting. >> Yeah, yeah, totally vision. >> And if you can lower the risk and you could tap that market and be first >> And they are, yeah. >> Yeah. >> So very exciting. Manufacturing, we know industry 4.0 which is about taking the OT data, so the data from the MES systems and the streaming data, real-time streaming data from the machine controllers and integrating it with the IT data, so your data warehouses and your ERP systems and so forth to have not only a real-time view of manufacturing from supply and source all the way through demand but also predictive maintenance and things like that. So that's very big right now in manufacturing. >> Kind of cool to hear these use cases beyond your healthcare, which is obviously, your wheelhouse, Scott defined this term of smart data fabrics, different than data fabrics, I guess. So when we think about these use cases what's the value add of so-called smart data fabrics? >> Yeah, it's a great question. So we did not define the term data fabric or enterprise data fabric. The analysts now are all over it. They're all saying it's the future of data management. It's a fundamentally different approach this architectural approach to be able to access the data on demand. The canonical definition of a data fabric is to access the data where it lies and apply a set of data management processes, but it does not include analytics, interestingly. And so we firmly believe that most of these use cases gain value from having analytics built directly into the fabric. So whether that's business rules or predictive analytics to predict the likelihood of a customer churn or a machine on the shop floor failing or prescriptive analytics. So if there's a problem in the supply chain, what's the guidance for the supply chain managers to take the best action, right? Prescriptive analytics based on data. So rather than taking the data and the data fabric and moving it to another environment to run those analytics where you have complexity and latency, having tall of those analytics capabilities built directly into the fabric, which is why we call it a smart data fabric, brings a lot of value to our customers. >> So simplifies the whole data lifecycle, data pipelining, the hyper-specialized roles that you have to have, you can really just focus on one platform, is that? >> Exactly, basically, yeah. And it's a simplicity of architecture and faster speed to production. So a big differentiator for our technology, for InterSystems, Iris, is most if not all of the capabilities that are needed are built into one engine, right? So you don't need to stitch together 10 or 15 or 20 different data management services for relational database in a non-relational database and a caching layer and a data warehouse and security and so forth. And so you can do that. There's many ways to build this data fabric architecture, right? InterSystems is not the only way. >> Right? >> But if you can speed and simplify the implementation of the fabric by having most of what you need in one engine, one product that gets you to where you need to go much, much faster. >> Joe, how can people learn more about smart data Fabric some of the use cases that you've presented here? >> Yeah, come to our website, intersystems.com. If you go to intersystems.com/smartdatafabric that'll take you there. >> I know that you have like probably dozens more examples but it would be cool- >> I do. >> If people reach out to you, how can they get in touch? >> Oh, I would love that. So feel free to reach out to me on LinkedIn. It's Joe Lichtenberg I think it's linkedin.com/joeLichtenberg and I'd love to connect. >> Awesome. Joe, thanks so much for your time. Really appreciate it. >> It was great to be here. Thank you, Dave. >> All right, I hope you've enjoyed our program today. You know, we heard Scott now he helped us understand this notion of data fabrics and smart data fabrics and how they can address the data challenges faced by the vast majority of organizations today. Jess Jody's demo was awesome. It was really a highlight of the program where she showed the smart data fabrics inaction and Joe Lichtenberg, we just heard from him dug in to some of the prominent use cases and proof points. We hope this content was educational and inspires you to action. Now, don't forget all these videos are available on Demand to watch, rewatch and share. Go to theCUBE.net, check out siliconangle.com for all the news and analysis and we'll summarize the highlights of this program and go to intersystems.com because there are a ton of resources there. In particular, there's a knowledge hub where you'll find some excellent educational content and online learning courses. There's a resource library with analyst reports, technical documentation videos, some great freebies. So check it out. This is Dave Vellante. On behalf of theCUBE and our supporter, InterSystems, thanks for watching and we'll see you next time. (upbeat music)

>> Okay, so now that we've heard Scott talk about smart data fabrics, it's time to see this in action. Right now we're joined by Jess Jowdy, who's the manager of Healthcare Field Engineering at InterSystems. She's going to give a demo of how smart data fabrics actually work, and she's going to show how embedding a wide range of analytics capabilities including data exploration, business intelligence natural language processing, and machine learning directly within the fabric, makes it faster and easier for organizations to gain new insights and power intelligence, predictive and prescriptive services and applications. Now, according to InterSystems, smart data fabrics are applicable across many industries from financial services to supply chain to healthcare and more. Jess today is going to be speaking through the lens of a healthcare focused demo. Don't worry, Joe Lichtenberg will get into some of the other use cases that you're probably interested in hearing about. That will be in our third segment, but for now let's turn it over to Jess. Jess, good to see you. >> Hi. Yeah, thank you so much for having me. And so for this demo we're really going to be bucketing these features of a smart data fabric into four different segments. We're going to be dealing with connections, collections, refinements and analysis. And so we'll see that throughout the demo as we go. So without further ado, let's just go ahead and jump into this demo and you'll see my screen pop up here. I actually like to start at the end of the demo. So I like to begin by illustrating what an end user's going to see and don't mind the screen 'cause I gave you a little sneak peek of what's about to happen. But essentially what I'm going to be doing is using Postman to simulate a call from an external application. So we talked about being in the healthcare industry. This could be for instance, a mobile application that a patient is using to view an aggregated summary of information across that patient's continuity of care or some other kind of application. So we might be pulling information in this case from an electronic medical record. We might be grabbing clinical history from that. We might be grabbing clinical notes from a medical transcription software or adverse reaction warnings from a clinical risk grouping application and so much more. So I'm really going to be assimilating a patient logging on in on their phone and retrieving this information through this Postman call. So what I'm going to do is I'm just going to hit send, I've already preloaded everything here and I'm going to be looking for information where the last name of this patient is Simmons and their medical record number their patient identifier in the system is 32345. And so as you can see I have this single JSON payload that showed up here of just relevant clinical information for my patient whose last name is Simmons all within a single response. So fantastic, right? Typically though when we see responses that look like this there is an assumption that this service is interacting with a single backend system and that single backend system is in charge of packaging that information up and returning it back to this caller. But in a smart data fabric architecture we're able to expand the scope to handle information across different, in this case, clinical applications. So how did this actually happen? Let's peel back another layer and really take a look at what happened in the background. What you're looking at here is our mission control center for our smart data fabric. On the left we have our APIs that allow users to interact with particular services. On the right we have our connections to our different data silos. And in the middle here we have our data fabric coordinator which is going to be in charge of this refinement and analysis those key pieces of our smart data fabric. So let's look back and think about the example we just showed. I received an inbound request for information for a patient whose last name is Simmons. My end user is requesting to connect to that service and that's happening here at my patient data retrieval API location. Users can define any number of different services and APIs depending on their use cases. And to that end we do also support full lifecycle API management within this platform. When you're dealing with APIs I always like to make a little shout out on this that you really want to make sure you have enough like a granular enough security model to handle and limit which APIs and which services a consumer can interact with. In this IRIS platform, which we're talking about today we have a very granular role-based security model that allows you to handle that, but it's really important in a smart data fabric to consider who's accessing your data and in what contact. >> Can I just interrupt you for a second? >> Yeah, please. >> So you were showing on the left hand side of the demo a couple of APIs. I presume that can be a very long list. I mean, what do you see as typical? >> I mean you can have hundreds of these APIs depending on what services an organization is serving up for their consumers. So yeah, we've seen hundreds of these services listed here. >> So my question is, obviously security is critical in the healthcare industry and API securities are really hot topic these days. How do you deal with that? >> Yeah, and I think API security is interesting 'cause it can happen at so many layers. So there's interactions with the API itself. So can I even see this API and leverage it? And then within an API call, you then have to deal with all right, which end points or what kind of interactions within that API am I allowed to do? What data am I getting back? And with healthcare data, the whole idea of consent to see certain pieces of data is critical. So the way that we handle that is, like I said, same thing at different layers. There is access to a particular API, which can happen within the IRIS product and also we see it happening with an API management layer, which has become a really hot topic with a lot of organizations. And then when it comes to data security, that really happens under the hood within your smart data fabric. So that role-based access control becomes very important in assigning, you know, roles and permissions to certain pieces of information. Getting that granular becomes the cornerstone of security. >> And that's been designed in, >> Absolutely, yes. it's not a bolt-on as they like to say. Okay, can we get into collect now? >> Of course, we're going to move on to the collection piece at this point in time, which involves pulling information from each of my different data silos to create an overall aggregated record. So commonly each data source requires a different method for establishing connections and collecting this information. So for instance, interactions with an EMR may require leveraging a standard healthcare messaging format like FIRE, interactions with a homegrown enterprise data warehouse for instance may use SQL for a cloud-based solutions managed by a vendor. They may only allow you to use web service calls to pull data. So it's really important that your data fabric platform that you're using has the flexibility to connect to all of these different systems and and applications. And I'm about to log out so I'm going to keep my session going here. So therefore it's incredibly important that your data fabric has the flexibility to connect to all these different kinds of applications and data sources and all these different kinds of formats and over all of these different kinds of protocols. So let's think back on our example here. I had four different applications that I was requesting information for to create that payload that we saw initially. Those are listed here under this operations section. So these are going out and connecting to downstream systems to pull information into my smart data fabric. What's great about the IRIS platform is it has an embedded interoperability platform. So there's all of these native adapters that can support these common connections that we see for different kinds of applications. So using REST or SOAP or SQL or FTP regardless of that protocol there's an adapter to help you work with that. And we also think of the types of formats that we typically see data coming in as, in healthcare we have H7, we have FIRE we have CCDs across the industry. JSON is, you know, really hitting a market strong now and XML, payloads, flat files. We need to be able to handle all of these different kinds of formats over these different kinds of protocols. So to illustrate that, if I click through these when I select a particular connection on the right side panel I'm going to see the different settings that are associated with that particular connection that allows me to collect information back into my smart data fabric. In this scenario, my connection to my chart script application in this example communicates over a SOAP connection. When I'm grabbing information from my clinical risk grouping application I'm using a SQL based connection. When I'm connecting to my EMR I'm leveraging a standard healthcare messaging format known as FIRE, which is a rest based protocol. And then when I'm working with my health record management system I'm leveraging a standard HTTP adapter. So you can see how we can be flexible when dealing with these different kinds of applications and systems. And then it becomes important to be able to validate that you've established those connections correctly and be able to do it in a reliable and quick way. Because if you think about it, you could have hundreds of these different kinds of applications built out and you want to make sure that you're maintaining and understanding those connections. So I can actually go ahead and test one of these applications and put in, for instance my patient's last name and their MRN and make sure that I'm actually getting data back from that system. So it's a nice little sanity check as we're building out that data fabric to ensure that we're able to establish these connections appropriately. So turnkey adapters are fantastic, as you can see we're leveraging them all here, but sometimes these connections are going to require going one step further and building something really specific for an application. So let's, why don't we go one step further here and talk about doing something custom or doing something innovative. And so it's important for users to have the ability to develop and go beyond what's an out of the box or black box approach to be able to develop things that are specific to their data fabric or specific to their particular connection. In this scenario, the IRIS data platform gives users access to the entire underlying code base. So you cannot, you not only get an opportunity to view how we're establishing these connections or how we're building out these processes but you have the opportunity to inject your own kind of processing your own kinds of pipelines into this. So as an example, you can leverage any number of different programming languages right within this pipeline. And so I went ahead and I injected Python. So Python is a very up and coming language, right? We see more and more developers turning towards Python to do their development. So it's important that your data fabric supports those kinds of developers and users that have standardized on these kinds of programming languages. This particular script here, as you can see actually calls out to our turnkey adapters. So we see a combination of out of the box code that is provided in this data fabric platform from IRIS combined with organization specific or user specific customizations that are included in this Python method. So it's a nice little combination of how do we bring the developer experience in and mix it with out of the box capabilities that we can provide in a smart data fabric. >> Wow. >> Yeah, I'll pause. >> It's a lot here. You know, actually, if I could >> I can pause. >> If I just want to sort of play that back. So we went through the connect and the collect phase. >> And the collect, yes, we're going into refine. So it's a good place to stop. >> Yeah, so before we get there, so we heard a lot about fine grain security, which is crucial. We heard a lot about different data types, multiple formats. You've got, you know the ability to bring in different dev tools. We heard about FIRE, which of course big in healthcare. >> Absolutely. >> And that's the standard and then SQL for traditional kind of structured data and then web services like HTTP you mentioned. And so you have a rich collection of capabilities within this single platform. >> Absolutely, and I think that's really important when you're dealing with a smart data fabric because what you're effectively doing is you're consolidating all of your processing, all of your collection into a single platform. So that platform needs to be able to handle any number of different kinds of scenarios and technical challenges. So you've got to pack that platform with as many of these features as you can to consolidate that processing. >> All right, so now we're going into refine. >> We're going into refinement, exciting. So how do we actually do refinement? Where does refinement happen and how does this whole thing end up being performant? Well the key to all of that is this SDF coordinator or stands for smart data fabric coordinator. And what this particular process is doing is essentially orchestrating all of these calls to all of these different downstream systems. It's aggregating, it's collecting that information it's aggregating it and it's refining it into that single payload that we saw get returned to the user. So really this coordinator is the main event when it comes to our data fabric. And in the IRIS platform we actually allow users to build these coordinators using web-based tool sets to make it intuitive. So we can take a sneak peek at what that looks like and as you can see it follows a flow chart like structure. So there's a start, there is an end and then there are these different arrows that point to different activities throughout the business process. And so there's all these different actions that are being taken within our coordinator. You can see an action for each of the calls to each of our different data sources to go retrieve information. And then we also have the sync call at the end that is in charge of essentially making sure that all of those responses come back before we package them together and send them out. So this becomes really crucial when we're creating that data fabric. And you know, this is a very simple data fabric example where we're just grabbing data and we're consolidating it together. But you can have really complex orchestrators and coordinators that do any number of different things. So for instance, I could inject SQL Logic into this or SQL code, I can have conditional logic, I can do looping, I can do error trapping and handling. So we're talking about a whole number of different features that can be included in this coordinator. So like I said, we have a really very simple process here that's just calling out, grabbing all those different data elements from all those different data sources and consolidating it. We'll look back at this coordinator in a second when we introduce or we make this data fabric a bit smarter and we start introducing that analytics piece to it. So this is in charge of the refinement. And so at this point in time we've looked at connections, collections, and refinements. And just to summarize what we've seen 'cause I always like to go back and take a look at everything that we've seen. We have our initial API connection we have our connections to our individual data sources and we have our coordinators there in the middle that are in charge of collecting the data and refining it into a single payload. As you can imagine, there's a lot going on behind the scenes of a smart data fabric, right? There's all these different processes that are interacting. So it's really important that your smart data fabric platform has really good traceability, really good logging 'cause you need to be able to know, you know, if there was an issue, where did that issue happen, in which connected process and how did it affect the other processes that are related to it. In IRIS, we have this concept called a visual trace. And what our clients use this for is basically to be able to step through the entire history of a request from when it initially came into the smart data fabric to when data was sent back out from that smart data fabric. So I didn't record the time but I bet if you recorded the time it was this time that we sent that request in. And you can see my patient's name and their medical record number here and you can see that that instigated four different calls to four different systems and they're represented by these arrows going out. So we sent something to chart script to our health record management system, to our clinical risk grouping application into my EMR through their FIRE server. So every request, every outbound application gets a request and we pull back all of those individual pieces of information from all of those different systems and we bundle them together. And for my FIRE lovers, here's our FIRE bundle that we got back from our FIRE server. So this is a really good way of being able to validate that I am appropriately grabbing the data from all these different applications and then ultimately consolidating it into one payload. Now we change this into a JSON format before we deliver it, but this is those data elements brought together. And this screen would also be used for being able to see things like error trapping or errors that were thrown alerts, warnings, developers might put log statements in just to validate that certain pieces of code are executing. So this really becomes the one stop shop for understanding what's happening behind the scenes with your data fabric. >> Etcher, who did what, when, where what did the machine do? What went wrong and where did that go wrong? >> Exactly. >> Right in your fingertips. >> Right, and I'm a visual person so a bunch of log files to me is not the most helpful. Well, being able to see this happened at this time in this location gives me that understanding I need to actually troubleshoot a problem. >> This business orchestration piece, can you say a little bit more about that? How people are using it? What's the business impact of the business orchestration? >> The business orchestration, especially in the smart data fabric is really that crucial part of being able to create a smart data fabric. So think of your business orchestrator as doing the heavy lifting of any kind of processing that involves data, right? It's bringing data in, it's analyzing that information, it's transforming that data, in a format that your consumer's not going to understand it's doing any additional injection of custom logic. So really your coordinator or that orchestrator that sits in the middle is the brains behind your smart data fabric. >> And this is available today? This all works? >> It's all available today. Yeah, it all works. And we have a number of clients that are using this technology to support these kinds of use cases. >> Awesome demo. Anything else you want to show us? >> Well we can keep going. 'Cause right now, I mean we can, oh, we're at 18 minutes. God help us. You can cut some of this. (laughs) I have a lot to say, but really this is our data fabric. The core competency of IRIS is making it smart, right? So I won't spend too much time on this but essentially if we go back to our coordinator here we can see here's that original that pipeline that we saw where we're pulling data from all these different systems and we're collecting it and we're sending it out. But then we see two more at the end here which involves getting a readmission prediction and then returning a prediction. So we can not only deliver data back as part of a smart data fabric but we can also deliver insights back to users and consumers based on data that we've aggregated as part of a smart data fabric. So in this scenario, we're actually taking all that data that we just looked at and we're running it through a machine learning model that exists within the smart data fabric pipeline and producing a readmission score to determine if this particular patient is at risk for readmission within the next 30 days. Which is a typical problem that we see in the healthcare space. So what's really exciting about what we're doing in the IRIS world is we're bringing analytics close to the data with integrated ML. So in this scenario we're actually creating the model, training the model, and then executing the model directly within the IRIS platform. So there's no shuffling of data, there's no external connections to make this happen. And it doesn't really require having a PhD in data science to understand how to do that. It leverages all really basic SQL like syntax to be able to construct and execute these predictions. So it's going one step further than the traditional data fabric example to introduce this ability to define actionable insights to our users based on the data that we've brought together. >> Well that readmission probability is huge. >> Yes. >> Right, because it directly affects the cost of for the provider and the patient, you know. So if you can anticipate the probability of readmission and either do things at that moment or you know, as an outpatient perhaps to minimize the probability then that's huge. That drops right to the bottom line. >> Absolutely, absolutely. And that really brings us from that data fabric to that smart data fabric at the end of the day which is what makes this so exciting. >> Awesome demo. >> Thank you. >> Fantastic people, are you cool? If people want to get in touch with you? >> Oh yes, absolutely. So you can find me on LinkedIn, Jessica Jowdy and we'd love to hear from you. I always love talking about this topic, so would be happy to engage on that. >> Great stuff, thank you Jess, appreciate it. >> Thank you so much. >> Okay, don't go away because in the next segment we're going to dig into the use cases where data fabric is driving business value. Stay right there.

(gentle music playing) >> For centuries, we've been captivated by the concept of machines doing the job of humans. And over the past decade or so, we've really focused on AI and the possibility of intelligent machines that can perform cognitive tasks. Now in the past few years, with the popularity of machine learning models ranging from recent ChatGPT to Bert, we're starting to see how AI is changing the way we interact with the world. How is AI transforming the way we do business? And what does the future hold for us there. At theCube, we've covered Oracle's AI and ML strategy for years, which has really been used to drive automation into Oracle's autonomous database. We've talked a lot about MySQL HeatWave in database machine learning, and AI pushed into Oracle's business apps. Oracle, it tends to lead in AI, but not competing as a direct AI player per se, but rather embedding AI and machine learning into its portfolio to enhance its existing products, and bring new services and offerings to the market. Now, last October at Cloud World in Las Vegas, Oracle partnered with Nvidia, which is the go-to AI silicon provider for vendors. And they announced an investment, a pretty significant investment to deploy tens of thousands more Nvidia GPUs to OCI, the Oracle Cloud Infrastructure and build out Oracle's infrastructure for enterprise scale AI. Now, Oracle CEO, Safra Catz said something to the effect of this alliance is going to help customers across industries from healthcare, manufacturing, telecoms, and financial services to overcome the multitude of challenges they face. Presumably she was talking about just driving more automation and more productivity. Now, to learn more about Oracle's plans for AI, we'd like to welcome in Elad Ziklik, who's the vice president of AI services at Oracle. Elad, great to see you. Welcome to the show. >> Thank you. Thanks for having me. >> You're very welcome. So first let's talk about Oracle's path to AI. I mean, it's the hottest topic going for years you've been incorporating machine learning into your products and services, you know, could you tell us what you've been working on, how you got here? >> So great question. So as you mentioned, I think most of the original four-way into AI was on embedding AI and using AI to make our applications, and databases better. So inside mySQL HeatWave, inside our autonomous database in power, we've been driving AI, all of course are SaaS apps. So Fusion, our large enterprise business suite for HR applications and CRM and ELP, and whatnot has built in AI inside it. Most recently, NetSuite, our small medium business SaaS suite started using AI for things like automated invoice processing and whatnot. And most recently, over the last, I would say two years, we've started exposing and bringing these capabilities into the broader OCI Oracle Cloud infrastructure. So the developers, and ISVs and customers can start using our AI capabilities to make their apps better and their experiences and business workflow better, and not just consume these as embedded inside Oracle. And this recent partnership that you mentioned with Nvidia is another step in bringing the best AI infrastructure capabilities into this platform so you can actually build any type of machine learning workflow or AI model that you want on Oracle Cloud. >> So when I look at the market, I see companies out there like DataRobot or C3 AI, there's maybe a half dozen that sort of pop up on my radar anyway. And my premise has always been that most customers, they don't want to become AI experts, they want to buy applications and have AI embedded or they want AI to manage their infrastructure. So my question to you is, how does Oracle help its OCI customers support their business with AI? >> So it's a great question. So I think what most customers want is business AI. They want AI that works for the business. They want AI that works for the enterprise. I call it the last mile of AI. And they want this thing to work. The majority of them don't want to hire a large and expensive data science teams to go and build everything from scratch. They just want the business problem solved by applying AI to it. My best analogy is Lego. So if you think of Lego, Lego has these millions Lego blocks that you can use to build anything that you want. But the majority of people like me or like my kids, they want the Lego death style kit or the Lego Eiffel Tower thing. They want a thing that just works, and it's very easy to use. And still Lego blocks, you still need to build some things together, which just works for the scenario that you're looking for. So that's our focus. Our focus is making it easy for customers to apply AI where they need to, in the right business context. So whether it's embedding it inside the business applications, like adding forecasting capabilities to your supply chain management or financial planning software, whether it's adding chat bots into the line of business applications, integrating these things into your analytics dashboard, even all the way to, we have a new platform piece we call ML applications that allows you to take a machine learning model, and scale it for the thousands of tenants that you would be. 'Cause this is a big problem for most of the ML use cases. It's very easy to build something for a proof of concept or a pilot or a demo. But then if you need to take this and then deploy it across your thousands of customers or your thousands of regions or facilities, then it becomes messy. So this is where we spend our time making it easy to take these things into production in the context of your business application or your business use case that you're interested in right now. >> So you mentioned chat bots, and I want to talk about ChatGPT, but my question here is different, we'll talk about that in a minute. So when you think about these chat bots, the ones that are conversational, my experience anyway is they're just meh, they're not that great. But the ones that actually work pretty well, they have a conditioned response. Now they're limited, but they say, which of the following is your problem? And then if that's one of the following is your problem, you can maybe solve your problem. But this is clearly a trend and it helps the line of business. How does Oracle think about these use cases for your customers? >> Yeah, so I think the key here is exactly what you said. It's about task completion. The general purpose bots are interesting, but as you said, like are still limited. They're getting much better, I'm sure we'll talk about ChatGPT. But I think what most enterprises want is around task completion. I want to automate my expense report processing. So today inside Oracle we have a chat bot where I submit my expenses the bot ask a couple of question, I answer them, and then I'm done. Like I don't need to go to our fancy application, and manually submit an expense report. I do this via Slack. And the key is around managing the right expectations of what this thing is capable of doing. Like, I have a story from I think five, six years ago when technology was much inferior than it is today. Well, one of the telco providers I was working with wanted to roll a chat bot that does realtime translation. So it was for a support center for of the call centers. And what they wanted do is, Hey, we have English speaking employees, whatever, 24/7, if somebody's calling, and the native tongue is different like Hebrew in my case, or Chinese or whatnot, then we'll give them a chat bot that they will interact with and will translate this on the fly and everything would work. And when they rolled it out, the feedback from customers was horrendous. Customers said, the technology sucks. It's not good. I hate it, I hate your company, I hate your support. And what they've done is they've changed the narrative. Instead of, you go to a support center, and you assume you're going to talk to a human, and instead you get a crappy chat bot, they're like, Hey, if you want to talk to a Hebrew speaking person, there's a four hour wait, please leave your phone and we'll call you back. Or you can try a new amazing Hebrew speaking AI powered bot and it may help your use case. Do you want to try it out? And some people said, yeah, let's try it out. Plus one to try it out. And the feedback, even though it was the exact same technology was amazing. People were like, oh my God, this is so innovative, this is great. Even though it was the exact same experience that they hated a few weeks earlier on. So I think the key lesson that I picked from this experience is it's all about setting the right expectations, and working around the right use case. If you are replacing a human, the level is different than if you are just helping or augmenting something that otherwise would take a lot of time. And I think this is the focus that we are doing, picking up the tasks that people want to accomplish or that enterprise want to accomplish for the customers, for the employees. And using chat bots to make those specific ones better rather than, hey, this is going to replace all humans everywhere, and just be better than that. >> Yeah, I mean, to the point you mentioned expense reports. I'm in a Twitter thread and one guy says, my favorite part of business travel is filling out expense reports. It's an hour of excitement to figure out which receipts won't scan. We can all relate to that. It's just the worst. When you think about companies that are building custom AI driven apps, what can they do on OCI? What are the best options for them? Do they need to hire an army of machine intelligence experts and AI specialists? Help us understand your point of view there. >> So over the last, I would say the two or three years we've developed a full suite of machine learning and AI services for, I would say probably much every use case that you would expect right now from applying natural language processing to understanding customer support tickets or social media, or whatnot to computer vision platforms or computer vision services that can understand and detect objects, and count objects on shelves or detect cracks in the pipe or defecting parts, all the way to speech services. It can actually transcribe human speech. And most recently we've launched a new document AI service. That can actually look at unstructured documents like receipts or invoices or government IDs or even proprietary documents, loan application, student application forms, patient ingestion and whatnot and completely automate them using AI. So if you want to do one of the things that are, I would say common bread and butter for any industry, whether it's financial services or healthcare or manufacturing, we have a suite of services that any developer can go, and use easily customized with their own data. You don't need to be an expert in deep learning or large language models. You could just use our automobile capabilities, and build your own version of the models. Just go ahead and use them. And if you do have proprietary complex scenarios that you need customer from scratch, we actually have the most cost effective platform for that. So we have the OCI data science as well as built-in machine learning platform inside the databases inside the Oracle database, and mySQL HeatWave that allow data scientists, python welding people that actually like to build and tweak and control and improve, have everything that they need to go and build the machine learning models from scratch, deploy them, monitor and manage them at scale in production environment. And most of it is brand new. So we did not have these technologies four or five years ago and we've started building them and they're now at enterprise scale over the last couple of years. >> So what are some of the state-of-the-art tools, that AI specialists and data scientists need if they're going to go out and develop these new models? >> So I think it's on three layers. I think there's an infrastructure layer where the Nvidia's of the world come into play. For some of these things, you want massively efficient, massively scaled infrastructure place. So we are the most cost effective and performant large scale GPU training environment today. We're going to be first to onboard the new Nvidia H100s. These are the new super powerful GPU's for large language model training. So we have that covered for you in case you need this 'cause you want to build these ginormous things. You need a data science platform, a platform where you can open a Python notebook, and just use all these fancy open source frameworks and create the models that you want, and then click on a button and deploy it. And it infinitely scales wherever you need it. And in many cases you just need the, what I call the applied AI services. You need the Lego sets, the Lego death style, Lego Eiffel Tower. So we have a suite of these sets for typical scenarios, whether it's cognitive services of like, again, understanding images, or documents all the way to solving particular business problems. So an anomaly detection service, demand focusing service that will be the equivalent of these Lego sets. So if this is the business problem that you're looking to solve, we have services out there where we can bring your data, call an API, train a model, get the model and use it in your production environment. So wherever you want to play, all the way into embedding this thing, inside this applications, obviously, wherever you want to play, we have the tools for you to go and engage from infrastructure to SaaS at the top, and everything in the middle. >> So when you think about the data pipeline, and the data life cycle, and the specialized roles that came out of kind of the (indistinct) era if you will. I want to focus on two developers and data scientists. So the developers, they hate dealing with infrastructure and they got to deal with infrastructure. Now they're being asked to secure the infrastructure, they just want to write code. And a data scientist, they're spending all their time trying to figure out, okay, what's the data quality? And they're wrangling data and they don't spend enough time doing what they want to do. So there's been a lack of collaboration. Have you seen that change, are these approaches allowing collaboration between data scientists and developers on a single platform? Can you talk about that a little bit? >> Yeah, that is a great question. One of the biggest set of scars that I have on my back from for building these platforms in other companies is exactly that. Every persona had a set of tools, and these tools didn't talk to each other and the handoff was painful. And most of the machine learning things evaporate or die on the floor because of this problem. It's very rarely that they are unsuccessful because the algorithm wasn't good enough. In most cases it's somebody builds something, and then you can't take it to production, you can't integrate it into your business application. You can't take the data out, train, create an endpoint and integrate it back like it's too painful. So the way we are approaching this is focused on this problem exactly. We have a single set of tools that if you publish a model as a data scientist and developers, and even business analysts that are seeing a inside of business application could be able to consume it. We have a single model store, a single feature store, a single management experience across the various personas that need to play in this. And we spend a lot of time building, and borrowing a word that cellular folks used, and I really liked it, building inside highways to make it easier to bring these insights into where you need them inside applications, both inside our applications, inside our SaaS applications, but also inside custom third party and even first party applications. And this is where a lot of our focus goes to just because we have dealt with so much pain doing this inside our own SaaS that we now have built the tools, and we're making them available for others to make this process of building a machine learning outcome driven insight in your app easier. And it's not just the model development, and it's not just the deployment, it's the entire journey of taking the data, building the model, training it, deploying it, looking at the real data that comes from the app, and creating this feedback loop in a more efficient way. And that's our focus area. Exactly this problem. >> Well thank you for that. So, last week we had our super cloud two event, and I had Juan Loza on and he spent a lot of time talking about how open Oracle is in its philosophy, and I got a lot of feedback. They were like, Oracle open, I don't really think, but the truth is if you think about database Oracle database, it never met a hardware platform that it didn't like. So in that sense it's open. So, but my point is, a big part of of machine learning and AI is driven by open source tools, frameworks, what's your open source strategy? What do you support from an open source standpoint? >> So I'm a strong believer that you don't actually know, nobody knows where the next slip fog or the next industry shifting innovation in AI is going to come from. If you look six months ago, nobody foreseen Dali, the magical text to image generation and the exploding brought into just art and design type of experiences. If you look six weeks ago, I don't think anybody's seen ChatGPT, and what it can do for a whole bunch of industries. So to me, assuming that a customer or partner or developer would want to lock themselves into only the tools that a specific vendor can produce is ridiculous. 'Cause nobody knows, if anybody claims that they know where the innovation is going to come from in a year or two, let alone in five or 10, they're just wrong or lying. So our strategy for Oracle is to, I call this the Netflix of AI. So if you think about Netflix, they produced a bunch of high quality shows on their own. A few years ago it was House of Cards. Last month my wife and I binge watched Ginny and Georgie, but they also curated a lot of shows that they found around the world and bought them to their customers. So it started with things like Seinfeld or Friends and most recently it was Squid games and those are famous Israeli TV series called Founder that Netflix bought in, and they bought it as is and they gave it the Netflix value. So you have captioning and you have the ability to speed the movie and you have it inside your app, and you can download it and watch it offline and everything, but nobody Netflix was involved in the production of these first seasons. Now if these things hunt and they're great, then the third season or the fourth season will get the full Netflix production value, high value budget, high value location shooting or whatever. But you as a customer, you don't care whether the producer and director, and screenplay writing is a Netflix employee or is somebody else's employee. It is fulfilled by Netflix. I believe that we will become, or we are looking to become the Netflix of AI. We are building a bunch of AI in a bunch of places where we think it's important and we have some competitive advantage like healthcare with Acellular partnership or whatnot. But I want to bring the best AI software and hardware to OCI and do a fulfillment by Oracle on that. So you'll get the Oracle security and identity and single bill and everything you'd expect from a company like Oracle. But we don't have to be building the data science, and the models for everything. So this means both open source recently announced a partnership with Anaconda, the leading provider of Python distribution in the data science ecosystem where we are are doing a joint strategic partnership of bringing all the goodness into Oracle customers as well as in the process of doing the same with Nvidia, and all those software libraries, not just the Hubble, both for other stuff like Triton, but also for healthcare specific stuff as well as other ISVs, other AI leading ISVs that we are in the process of partnering with to get their stuff into OCI and into Oracle so that you can truly consume the best AI hardware, and the best AI software in the world on Oracle. 'Cause that is what I believe our customers would want the ability to choose from any open source engine, and honestly from any ISV type of solution that is AI powered and they want to use it in their experiences. >> So you mentioned ChatGPT, I want to talk about some of the innovations that are coming. As an AI expert, you see ChatGPT on the one hand, I'm sure you weren't surprised. On the other hand, maybe the reaction in the market, and the hype is somewhat surprising. You know, they say that we tend to under or over-hype things in the early stages and under hype them long term, you kind of use the internet as example. What's your take on that premise? >> So. I think that this type of technology is going to be an inflection point in how software is being developed. I truly believe this. I think this is an internet style moment, and the way software interfaces, software applications are being developed will dramatically change over the next year two or three because of this type of technologies. I think there will be industries that will be shifted. I think education is a good example. I saw this thing opened on my son's laptop. So I think education is going to be transformed. Design industry like images or whatever, it's already been transformed. But I think that for mass adoption, like beyond the hype, beyond the peak of inflected expectations, if I'm using Gartner terminology, I think certain things need to go and happen. One is this thing needs to become more reliable. So right now it is a complete black box that sometimes produce magic, and sometimes produce just nonsense. And it needs to have better explainability and better lineage to, how did you get to this answer? 'Cause I think enterprises are going to really care about the things that they surface with the customers or use internally. So I think that is one thing that's going to come out. And the other thing that's going to come out is I think it's going to come industry specific large language models or industry specific ChatGPTs. Something like how OpenAI did co-pilot for writing code. I think we will start seeing this type of apps solving for specific business problems, understanding contracts, understanding healthcare, writing doctor's notes on behalf of doctors so they don't have to spend time manually recording and analyzing conversations. And I think that would become the sweet spot of this thing. There will be companies, whether it's OpenAI or Microsoft or Google or hopefully Oracle that will use this type of technology to solve for specific very high value business needs. And I think this will change how interfaces happen. So going back to your expense report, the world of, I'm going to go into an app, and I'm going to click on seven buttons in order to get some job done like this world is gone. Like I'm going to say, hey, please do this and that. And I expect an answer to come out. I've seen a recent demo about, marketing in sales. So a customer sends an email that is interested in something and then a ChatGPT powered thing just produces the answer. I think this is how the world is going to evolve. Like yes, there's a ton of hype, yes, it looks like magic and right now it is magic, but it's not yet productive for most enterprise scenarios. But in the next 6, 12, 24 months, this will start getting more dependable, and it's going to change how these industries are being managed. Like I think it's an internet level revolution. That's my take. >> It's very interesting. And it's going to change the way in which we have. Instead of accessing the data center through APIs, we're going to access it through natural language processing and that opens up technology to a huge audience. Last question, is a two part question. And the first part is what you guys are working on from the futures, but the second part of the question is, we got data scientists and developers in our audience. They love the new shiny toy. So give us a little glimpse of what you're working on in the future, and what would you say to them to persuade them to check out Oracle's AI services? >> Yep. So I think there's two main things that we're doing, one is around healthcare. With a new recent acquisition, we are spending a significant effort around revolutionizing healthcare with AI. Of course many scenarios from patient care using computer vision and cameras through automating, and making better insurance claims to research and pharma. We are making the best models from leading organizations, and internal available for hospitals and researchers, and insurance providers everywhere. And we truly are looking to become the leader in AI for healthcare. So I think that's a huge focus area. And the second part is, again, going back to the enterprise AI angle. Like we want to, if you have a business problem that you want to apply here to solve, we want to be your platform. Like you could use others if you want to build everything complicated and whatnot. We have a platform for that as well. But like, if you want to apply AI to solve a business problem, we want to be your platform. We want to be the, again, the Netflix of AI kind of a thing where we are the place for the greatest AI innovations accessible to any developer, any business analyst, any user, any data scientist on Oracle Cloud. And we're making a significant effort on these two fronts as well as developing a lot of the missing pieces, and building blocks that we see are needed in this space to make truly like a great experience for developers and data scientists. And what would I recommend? Get started, try it out. We actually have a shameless sales plug here. We have a free deal for all of our AI services. So it typically cost you nothing. I would highly recommend to just go, and try these things out. Go play with it. If you are a python welding developer, and you want to try a little bit of auto mail, go down that path. If you're not even there and you're just like, hey, I have these customer feedback things and I want to try out, if I can understand them and apply AI and visualize, and do some cool stuff, we have services for that. My recommendation is, and I think ChatGPT got us 'cause I see people that have nothing to do with AI, and can't even spell AI going and trying it out. I think this is the time. Go play with these things, go play with these technologies and find what AI can do to you or for you. And I think Oracle is a great place to start playing with these things. >> Elad, thank you. Appreciate you sharing your vision of making Oracle the Netflix of AI. Love that and really appreciate your time. >> Awesome. Thank you. Thank you for having me. >> Okay. Thanks for watching this Cube conversation. This is Dave Vellante. We'll see you next time. (gentle music playing)

(upbeat music) >> Hello, this is Dave Valente with theCUBE, and one of the most gratifying aspects of my role as a host of "theCUBE TV" is I get to cover a wide range of topics. And quite often, we're able to bring to our program a level of expertise that allows us to more deeply explore and unpack some of the topics that we cover throughout the year. And one of our favorite topics, of course, is data. Now, in 2021, after being in isolation for the better part of two years, a group of industry analysts met up at AWS re:Invent and started a collaboration to look at the trends in data and predict what some likely outcomes will be for the coming year. And it resulted in a very popular session that we had last year focused on the future of data management. And I'm very excited and pleased to tell you that the 2023 edition of that predictions episode is back, and with me are five outstanding market analyst, Sanjeev Mohan of SanjMo, Tony Baer of dbInsight, Carl Olofson from IDC, Dave Menninger from Ventana Research, and Doug Henschen, VP and Principal Analyst at Constellation Research. Now, what is it that we're calling you, guys? A data pack like the rat pack? No, no, no, no, that's not it. It's the data crowd, the data crowd, and the crowd includes some of the best minds in the data analyst community. They'll discuss how data management is evolving and what listeners should prepare for in 2023. Guys, welcome back. Great to see you. >> Good to be here. >> Thank you. >> Thanks, Dave. (Tony and Dave faintly speaks) >> All right, before we get into 2023 predictions, we thought it'd be good to do a look back at how we did in 2022 and give a transparent assessment of those predictions. So, let's get right into it. We're going to bring these up here, the predictions from 2022, they're color-coded red, yellow, and green to signify the degree of accuracy. And I'm pleased to report there's no red. Well, maybe some of you will want to debate that grading system. But as always, we want to be open, so you can decide for yourselves. So, we're going to ask each analyst to review their 2022 prediction and explain their rating and what evidence they have that led them to their conclusion. So, Sanjeev, please kick it off. Your prediction was data governance becomes key. I know that's going to knock you guys over, but elaborate, because you had more detail when you double click on that. >> Yeah, absolutely. Thank you so much, Dave, for having us on the show today. And we self-graded ourselves. I could have very easily made my prediction from last year green, but I mentioned why I left it as yellow. I totally fully believe that data governance was in a renaissance in 2022. And why do I say that? You have to look no further than AWS launching its own data catalog called DataZone. Before that, mid-year, we saw Unity Catalog from Databricks went GA. So, overall, I saw there was tremendous movement. When you see these big players launching a new data catalog, you know that they want to be in this space. And this space is highly critical to everything that I feel we will talk about in today's call. Also, if you look at established players, I spoke at Collibra's conference, data.world, work closely with Alation, Informatica, a bunch of other companies, they all added tremendous new capabilities. So, it did become key. The reason I left it as yellow is because I had made a prediction that Collibra would go IPO, and it did not. And I don't think anyone is going IPO right now. The market is really, really down, the funding in VC IPO market. But other than that, data governance had a banner year in 2022. >> Yeah. Well, thank you for that. And of course, you saw data clean rooms being announced at AWS re:Invent, so more evidence. And I like how the fact that you included in your predictions some things that were binary, so you dinged yourself there. So, good job. Okay, Tony Baer, you're up next. Data mesh hits reality check. As you see here, you've given yourself a bright green thumbs up. (Tony laughing) Okay. Let's hear why you feel that was the case. What do you mean by reality check? >> Okay. Thanks, Dave, for having us back again. This is something I just wrote and just tried to get away from, and this just a topic just won't go away. I did speak with a number of folks, early adopters and non-adopters during the year. And I did find that basically that it pretty much validated what I was expecting, which was that there was a lot more, this has now become a front burner issue. And if I had any doubt in my mind, the evidence I would point to is what was originally intended to be a throwaway post on LinkedIn, which I just quickly scribbled down the night before leaving for re:Invent. I was packing at the time, and for some reason, I was doing Google search on data mesh. And I happened to have tripped across this ridiculous article, I will not say where, because it doesn't deserve any publicity, about the eight (Dave laughing) best data mesh software companies of 2022. (Tony laughing) One of my predictions was that you'd see data mesh washing. And I just quickly just hopped on that maybe three sentences and wrote it at about a couple minutes saying this is hogwash, essentially. (laughs) And that just reun... And then, I left for re:Invent. And the next night, when I got into my Vegas hotel room, I clicked on my computer. I saw a 15,000 hits on that post, which was the most hits of any single post I put all year. And the responses were wildly pro and con. So, it pretty much validates my expectation in that data mesh really did hit a lot more scrutiny over this past year. >> Yeah, thank you for that. I remember that article. I remember rolling my eyes when I saw it, and then I recently, (Tony laughing) I talked to Walmart and they actually invoked Martin Fowler and they said that they're working through their data mesh. So, it takes a really lot of thought, and it really, as we've talked about, is really as much an organizational construct. You're not buying data mesh >> Bingo. >> to your point. Okay. Thank you, Tony. Carl Olofson, here we go. You've graded yourself a yellow in the prediction of graph databases. Take off. Please elaborate. >> Yeah, sure. So, I realized in looking at the prediction that it seemed to imply that graph databases could be a major factor in the data world in 2022, which obviously didn't become the case. It was an error on my part in that I should have said it in the right context. It's really a three to five-year time period that graph databases will really become significant, because they still need accepted methodologies that can be applied in a business context as well as proper tools in order for people to be able to use them seriously. But I stand by the idea that it is taking off, because for one thing, Neo4j, which is the leading independent graph database provider, had a very good year. And also, we're seeing interesting developments in terms of things like AWS with Neptune and with Oracle providing graph support in Oracle database this past year. Those things are, as I said, growing gradually. There are other companies like TigerGraph and so forth, that deserve watching as well. But as far as becoming mainstream, it's going to be a few years before we get all the elements together to make that happen. Like any new technology, you have to create an environment in which ordinary people without a whole ton of technical training can actually apply the technology to solve business problems. >> Yeah, thank you for that. These specialized databases, graph databases, time series databases, you see them embedded into mainstream data platforms, but there's a place for these specialized databases, I would suspect we're going to see new types of databases emerge with all this cloud sprawl that we have and maybe to the edge. >> Well, part of it is that it's not as specialized as you might think it. You can apply graphs to great many workloads and use cases. It's just that people have yet to fully explore and discover what those are. >> Yeah. >> And so, it's going to be a process. (laughs) >> All right, Dave Menninger, streaming data permeates the landscape. You gave yourself a yellow. Why? >> Well, I couldn't think of a appropriate combination of yellow and green. Maybe I should have used chartreuse, (Dave laughing) but I was probably a little hard on myself making it yellow. This is another type of specialized data processing like Carl was talking about graph databases is a stream processing, and nearly every data platform offers streaming capabilities now. Often, it's based on Kafka. If you look at Confluent, their revenues have grown at more than 50%, continue to grow at more than 50% a year. They're expected to do more than half a billion dollars in revenue this year. But the thing that hasn't happened yet, and to be honest, they didn't necessarily expect it to happen in one year, is that streaming hasn't become the default way in which we deal with data. It's still a sidecar to data at rest. And I do expect that we'll continue to see streaming become more and more mainstream. I do expect perhaps in the five-year timeframe that we will first deal with data as streaming and then at rest, but the worlds are starting to merge. And we even see some vendors bringing products to market, such as K2View, Hazelcast, and RisingWave Labs. So, in addition to all those core data platform vendors adding these capabilities, there are new vendors approaching this market as well. >> I like the tough grading system, and it's not trivial. And when you talk to practitioners doing this stuff, there's still some complications in the data pipeline. And so, but I think, you're right, it probably was a yellow plus. Doug Henschen, data lakehouses will emerge as dominant. When you talk to people about lakehouses, practitioners, they all use that term. They certainly use the term data lake, but now, they're using lakehouse more and more. What's your thoughts on here? Why the green? What's your evidence there? >> Well, I think, I was accurate. I spoke about it specifically as something that vendors would be pursuing. And we saw yet more lakehouse advocacy in 2022. Google introduced its BigLake service alongside BigQuery. Salesforce introduced Genie, which is really a lakehouse architecture. And it was a safe prediction to say vendors are going to be pursuing this in that AWS, Cloudera, Databricks, Microsoft, Oracle, SAP, Salesforce now, IBM, all advocate this idea of a single platform for all of your data. Now, the trend was also supported in 2023, in that we saw a big embrace of Apache Iceberg in 2022. That's a structured table format. It's used with these lakehouse platforms. It's open, so it ensures portability and it also ensures performance. And that's a structured table that helps with the warehouse side performance. But among those announcements, Snowflake, Google, Cloud Era, SAP, Salesforce, IBM, all embraced Iceberg. But keep in mind, again, I'm talking about this as something that vendors are pursuing as their approach. So, they're advocating end users. It's very cutting edge. I'd say the top, leading edge, 5% of of companies have really embraced the lakehouse. I think, we're now seeing the fast followers, the next 20 to 25% of firms embracing this idea and embracing a lakehouse architecture. I recall Christian Kleinerman at the big Snowflake event last summer, making the announcement about Iceberg, and he asked for a show of hands for any of you in the audience at the keynote, have you heard of Iceberg? And just a smattering of hands went up. So, the vendors are ahead of the curve. They're pushing this trend, and we're now seeing a little bit more mainstream uptake. >> Good. Doug, I was there. It was you, me, and I think, two other hands were up. That was just humorous. (Doug laughing) All right, well, so I liked the fact that we had some yellow and some green. When you think about these things, there's the prediction itself. Did it come true or not? There are the sub predictions that you guys make, and of course, the degree of difficulty. So, thank you for that open assessment. All right, let's get into the 2023 predictions. Let's bring up the predictions. Sanjeev, you're going first. You've got a prediction around unified metadata. What's the prediction, please? >> So, my prediction is that metadata space is currently a mess. It needs to get unified. There are too many use cases of metadata, which are being addressed by disparate systems. For example, data quality has become really big in the last couple of years, data observability, the whole catalog space is actually, people don't like to use the word data catalog anymore, because data catalog sounds like it's a catalog, a museum, if you may, of metadata that you go and admire. So, what I'm saying is that in 2023, we will see that metadata will become the driving force behind things like data ops, things like orchestration of tasks using metadata, not rules. Not saying that if this fails, then do this, if this succeeds, go do that. But it's like getting to the metadata level, and then making a decision as to what to orchestrate, what to automate, how to do data quality check, data observability. So, this space is starting to gel, and I see there'll be more maturation in the metadata space. Even security privacy, some of these topics, which are handled separately. And I'm just talking about data security and data privacy. I'm not talking about infrastructure security. These also need to merge into a unified metadata management piece with some knowledge graph, semantic layer on top, so you can do analytics on it. So, it's no longer something that sits on the side, it's limited in its scope. It is actually the very engine, the very glue that is going to connect data producers and consumers. >> Great. Thank you for that. Doug. Doug Henschen, any thoughts on what Sanjeev just said? Do you agree? Do you disagree? >> Well, I agree with many aspects of what he says. I think, there's a huge opportunity for consolidation and streamlining of these as aspects of governance. Last year, Sanjeev, you said something like, we'll see more people using catalogs than BI. And I have to disagree. I don't think this is a category that's headed for mainstream adoption. It's a behind the scenes activity for the wonky few, or better yet, companies want machine learning and automation to take care of these messy details. We've seen these waves of management technologies, some of the latest data observability, customer data platform, but they failed to sweep away all the earlier investments in data quality and master data management. So, yes, I hope the latest tech offers, glimmers that there's going to be a better, cleaner way of addressing these things. But to my mind, the business leaders, including the CIO, only want to spend as much time and effort and money and resources on these sorts of things to avoid getting breached, ending up in headlines, getting fired or going to jail. So, vendors bring on the ML and AI smarts and the automation of these sorts of activities. >> So, if I may say something, the reason why we have this dichotomy between data catalog and the BI vendors is because data catalogs are very soon, not going to be standalone products, in my opinion. They're going to get embedded. So, when you use a BI tool, you'll actually use the catalog to find out what is it that you want to do, whether you are looking for data or you're looking for an existing dashboard. So, the catalog becomes embedded into the BI tool. >> Hey, Dave Menninger, sometimes you have some data in your back pocket. Do you have any stats (chuckles) on this topic? >> No, I'm glad you asked, because I'm going to... Now, data catalogs are something that's interesting. Sanjeev made a statement that data catalogs are falling out of favor. I don't care what you call them. They're valuable to organizations. Our research shows that organizations that have adequate data catalog technologies are three times more likely to express satisfaction with their analytics for just the reasons that Sanjeev was talking about. You can find what you want, you know you're getting the right information, you know whether or not it's trusted. So, those are good things. So, we expect to see the capabilities, whether it's embedded or separate. We expect to see those capabilities continue to permeate the market. >> And a lot of those catalogs are driven now by machine learning and things. So, they're learning from those patterns of usage by people when people use the data. (airy laughs) >> All right. Okay. Thank you, guys. All right. Let's move on to the next one. Tony Bear, let's bring up the predictions. You got something in here about the modern data stack. We need to rethink it. Is the modern data stack getting long at the tooth? Is it not so modern anymore? >> I think, in a way, it's got almost too modern. It's gotten too, I don't know if it's being long in the tooth, but it is getting long. The modern data stack, it's traditionally been defined as basically you have the data platform, which would be the operational database and the data warehouse. And in between, you have all the tools that are necessary to essentially get that data from the operational realm or the streaming realm for that matter into basically the data warehouse, or as we might be seeing more and more, the data lakehouse. And I think, what's important here is that, or I think, we have seen a lot of progress, and this would be in the cloud, is with the SaaS services. And especially you see that in the modern data stack, which is like all these players, not just the MongoDBs or the Oracles or the Amazons have their database platforms. You see they have the Informatica's, and all the other players there in Fivetrans have their own SaaS services. And within those SaaS services, you get a certain degree of simplicity, which is it takes all the housekeeping off the shoulders of the customers. That's a good thing. The problem is that what we're getting to unfortunately is what I would call lots of islands of simplicity, which means that it leads it (Dave laughing) to the customer to have to integrate or put all that stuff together. It's a complex tool chain. And so, what we really need to think about here, we have too many pieces. And going back to the discussion of catalogs, it's like we have so many catalogs out there, which one do we use? 'Cause chances are of most organizations do not rely on a single catalog at this point. What I'm calling on all the data providers or all the SaaS service providers, is to literally get it together and essentially make this modern data stack less of a stack, make it more of a blending of an end-to-end solution. And that can come in a number of different ways. Part of it is that we're data platform providers have been adding services that are adjacent. And there's some very good examples of this. We've seen progress over the past year or so. For instance, MongoDB integrating search. It's a very common, I guess, sort of tool that basically, that the applications that are developed on MongoDB use, so MongoDB then built it into the database rather than requiring an extra elastic search or open search stack. Amazon just... AWS just did the zero-ETL, which is a first step towards simplifying the process from going from Aurora to Redshift. You've seen same thing with Google, BigQuery integrating basically streaming pipelines. And you're seeing also a lot of movement in database machine learning. So, there's some good moves in this direction. I expect to see more than this year. Part of it's from basically the SaaS platform is adding some functionality. But I also see more importantly, because you're never going to get... This is like asking your data team and your developers, herding cats to standardizing the same tool. In most organizations, that is not going to happen. So, take a look at the most popular combinations of tools and start to come up with some pre-built integrations and pre-built orchestrations, and offer some promotional pricing, maybe not quite two for, but in other words, get two products for the price of two services or for the price of one and a half. I see a lot of potential for this. And it's to me, if the class was to simplify things, this is the next logical step and I expect to see more of this here. >> Yeah, and you see in Oracle, MySQL heat wave, yet another example of eliminating that ETL. Carl Olofson, today, if you think about the data stack and the application stack, they're largely separate. Do you have any thoughts on how that's going to play out? Does that play into this prediction? What do you think? >> Well, I think, that the... I really like Tony's phrase, islands of simplification. It really says (Tony chuckles) what's going on here, which is that all these different vendors you ask about, about how these stacks work. All these different vendors have their own stack vision. And you can... One application group is going to use one, and another application group is going to use another. And some people will say, let's go to, like you go to a Informatica conference and they say, we should be the center of your universe, but you can't connect everything in your universe to Informatica, so you need to use other things. So, the challenge is how do we make those things work together? As Tony has said, and I totally agree, we're never going to get to the point where people standardize on one organizing system. So, the alternative is to have metadata that can be shared amongst those systems and protocols that allow those systems to coordinate their operations. This is standard stuff. It's not easy. But the motive for the vendors is that they can become more active critical players in the enterprise. And of course, the motive for the customer is that things will run better and more completely. So, I've been looking at this in terms of two kinds of metadata. One is the meaning metadata, which says what data can be put together. The other is the operational metadata, which says basically where did it come from? Who created it? What's its current state? What's the security level? Et cetera, et cetera, et cetera. The good news is the operational stuff can actually be done automatically, whereas the meaning stuff requires some human intervention. And as we've already heard from, was it Doug, I think, people are disinclined to put a lot of definition into meaning metadata. So, that may be the harder one, but coordination is key. This problem has been with us forever, but with the addition of new data sources, with streaming data with data in different formats, the whole thing has, it's been like what a customer of mine used to say, "I understand your product can make my system run faster, but right now I just feel I'm putting my problems on roller skates. (chuckles) I don't need that to accelerate what's already not working." >> Excellent. Okay, Carl, let's stay with you. I remember in the early days of the big data movement, Hadoop movement, NoSQL was the big thing. And I remember Amr Awadallah said to us in theCUBE that SQL is the killer app for big data. So, your prediction here, if we bring that up is SQL is back. Please elaborate. >> Yeah. So, of course, some people would say, well, it never left. Actually, that's probably closer to true, but in the perception of the marketplace, there's been all this noise about alternative ways of storing, retrieving data, whether it's in key value stores or document databases and so forth. We're getting a lot of messaging that for a while had persuaded people that, oh, we're not going to do analytics in SQL anymore. We're going to use Spark for everything, except that only a handful of people know how to use Spark. Oh, well, that's a problem. Well, how about, and for ordinary conventional business analytics, Spark is like an over-engineered solution to the problem. SQL works just great. What's happened in the past couple years, and what's going to continue to happen is that SQL is insinuating itself into everything we're seeing. We're seeing all the major data lake providers offering SQL support, whether it's Databricks or... And of course, Snowflake is loving this, because that is what they do, and their success is certainly points to the success of SQL, even MongoDB. And we were all, I think, at the MongoDB conference where on one day, we hear SQL is dead. They're not teaching SQL in schools anymore, and this kind of thing. And then, a couple days later at the same conference, they announced we're adding a new analytic capability-based on SQL. But didn't you just say SQL is dead? So, the reality is that SQL is better understood than most other methods of certainly of retrieving and finding data in a data collection, no matter whether it happens to be relational or non-relational. And even in systems that are very non-relational, such as graph and document databases, their query languages are being built or extended to resemble SQL, because SQL is something people understand. >> Now, you remember when we were in high school and you had had to take the... Your debating in the class and you were forced to take one side and defend it. So, I was was at a Vertica conference one time up on stage with Curt Monash, and I had to take the NoSQL, the world is changing paradigm shift. And so just to be controversial, I said to him, Curt Monash, I said, who really needs acid compliance anyway? Tony Baer. And so, (chuckles) of course, his head exploded, but what are your thoughts (guests laughing) on all this? >> Well, my first thought is congratulations, Dave, for surviving being up on stage with Curt Monash. >> Amen. (group laughing) >> I definitely would concur with Carl. We actually are definitely seeing a SQL renaissance and if there's any proof of the pudding here, I see lakehouse is being icing on the cake. As Doug had predicted last year, now, (clears throat) for the record, I think, Doug was about a year ahead of time in his predictions that this year is really the year that I see (clears throat) the lakehouse ecosystems really firming up. You saw the first shots last year. But anyway, on this, data lakes will not go away. I've actually, I'm on the home stretch of doing a market, a landscape on the lakehouse. And lakehouse will not replace data lakes in terms of that. There is the need for those, data scientists who do know Python, who knows Spark, to go in there and basically do their thing without all the restrictions or the constraints of a pre-built, pre-designed table structure. I get that. Same thing for developing models. But on the other hand, there is huge need. Basically, (clears throat) maybe MongoDB was saying that we're not teaching SQL anymore. Well, maybe we have an oversupply of SQL developers. Well, I'm being facetious there, but there is a huge skills based in SQL. Analytics have been built on SQL. They came with lakehouse and why this really helps to fuel a SQL revival is that the core need in the data lake, what brought on the lakehouse was not so much SQL, it was a need for acid. And what was the best way to do it? It was through a relational table structure. So, the whole idea of acid in the lakehouse was not to turn it into a transaction database, but to make the data trusted, secure, and more granularly governed, where you could govern down to column and row level, which you really could not do in a data lake or a file system. So, while lakehouse can be queried in a manner, you can go in there with Python or whatever, it's built on a relational table structure. And so, for that end, for those types of data lakes, it becomes the end state. You cannot bypass that table structure as I learned the hard way during my research. So, the bottom line I'd say here is that lakehouse is proof that we're starting to see the revenge of the SQL nerds. (Dave chuckles) >> Excellent. Okay, let's bring up back up the predictions. Dave Menninger, this one's really thought-provoking and interesting. We're hearing things like data as code, new data applications, machines actually generating plans with no human involvement. And your prediction is the definition of data is expanding. What do you mean by that? >> So, I think, for too long, we've thought about data as the, I would say facts that we collect the readings off of devices and things like that, but data on its own is really insufficient. Organizations need to manipulate that data and examine derivatives of the data to really understand what's happening in their organization, why has it happened, and to project what might happen in the future. And my comment is that these data derivatives need to be supported and managed just like the data needs to be managed. We can't treat this as entirely separate. Think about all the governance discussions we've had. Think about the metadata discussions we've had. If you separate these things, now you've got more moving parts. We're talking about simplicity and simplifying the stack. So, if these things are treated separately, it creates much more complexity. I also think it creates a little bit of a myopic view on the part of the IT organizations that are acquiring these technologies. They need to think more broadly. So, for instance, metrics. Metric stores are becoming much more common part of the tooling that's part of a data platform. Similarly, feature stores are gaining traction. So, those are designed to promote the reuse and consistency across the AI and ML initiatives. The elements that are used in developing an AI or ML model. And let me go back to metrics and just clarify what I mean by that. So, any type of formula involving the data points. I'm distinguishing metrics from features that are used in AI and ML models. And the data platforms themselves are increasingly managing the models as an element of data. So, just like figuring out how to calculate a metric. Well, if you're going to have the features associated with an AI and ML model, you probably need to be managing the model that's associated with those features. The other element where I see expansion is around external data. Organizations for decades have been focused on the data that they generate within their own organization. We see more and more of these platforms acquiring and publishing data to external third-party sources, whether they're within some sort of a partner ecosystem or whether it's a commercial distribution of that information. And our research shows that when organizations use external data, they derive even more benefits from the various analyses that they're conducting. And the last great frontier in my opinion on this expanding world of data is the world of driver-based planning. Very few of the major data platform providers provide these capabilities today. These are the types of things you would do in a spreadsheet. And we all know the issues associated with spreadsheets. They're hard to govern, they're error-prone. And so, if we can take that type of analysis, collecting the occupancy of a rental property, the projected rise in rental rates, the fluctuations perhaps in occupancy, the interest rates associated with financing that property, we can project forward. And that's a very common thing to do. What the income might look like from that property income, the expenses, we can plan and purchase things appropriately. So, I think, we need this broader purview and I'm beginning to see some of those things happen. And the evidence today I would say, is more focused around the metric stores and the feature stores starting to see vendors offer those capabilities. And we're starting to see the ML ops elements of managing the AI and ML models find their way closer to the data platforms as well. >> Very interesting. When I hear metrics, I think of KPIs, I think of data apps, orchestrate people and places and things to optimize around a set of KPIs. It sounds like a metadata challenge more... Somebody once predicted they'll have more metadata than data. Carl, what are your thoughts on this prediction? >> Yeah, I think that what Dave is describing as data derivatives is in a way, another word for what I was calling operational metadata, which not about the data itself, but how it's used, where it came from, what the rules are governing it, and that kind of thing. If you have a rich enough set of those things, then not only can you do a model of how well your vacation property rental may do in terms of income, but also how well your application that's measuring that is doing for you. In other words, how many times have I used it, how much data have I used and what is the relationship between the data that I've used and the benefits that I've derived from using it? Well, we don't have ways of doing that. What's interesting to me is that folks in the content world are way ahead of us here, because they have always tracked their content using these kinds of attributes. Where did it come from? When was it created, when was it modified? Who modified it? And so on and so forth. We need to do more of that with the structure data that we have, so that we can track what it's used. And also, it tells us how well we're doing with it. Is it really benefiting us? Are we being efficient? Are there improvements in processes that we need to consider? Because maybe data gets created and then it isn't used or it gets used, but it gets altered in some way that actually misleads people. (laughs) So, we need the mechanisms to be able to do that. So, I would say that that's... And I'd say that it's true that we need that stuff. I think, that starting to expand is probably the right way to put it. It's going to be expanding for some time. I think, we're still a distance from having all that stuff really working together. >> Maybe we should say it's gestating. (Dave and Carl laughing) >> Sorry, if I may- >> Sanjeev, yeah, I was going to say this... Sanjeev, please comment. This sounds to me like it supports Zhamak Dehghani's principles, but please. >> Absolutely. So, whether we call it data mesh or not, I'm not getting into that conversation, (Dave chuckles) but data (audio breaking) (Tony laughing) everything that I'm hearing what Dave is saying, Carl, this is the year when data products will start to take off. I'm not saying they'll become mainstream. They may take a couple of years to become so, but this is data products, all this thing about vacation rentals and how is it doing, that data is coming from different sources. I'm packaging it into our data product. And to Carl's point, there's a whole operational metadata associated with it. The idea is for organizations to see things like developer productivity, how many releases am I doing of this? What data products are most popular? I'm actually in right now in the process of formulating this concept that just like we had data catalogs, we are very soon going to be requiring data products catalog. So, I can discover these data products. I'm not just creating data products left, right, and center. I need to know, do they already exist? What is the usage? If no one is using a data product, maybe I want to retire and save cost. But this is a data product. Now, there's a associated thing that is also getting debated quite a bit called data contracts. And a data contract to me is literally just formalization of all these aspects of a product. How do you use it? What is the SLA on it, what is the quality that I am prescribing? So, data product, in my opinion, shifts the conversation to the consumers or to the business people. Up to this point when, Dave, you're talking about data and all of data discovery curation is a very data producer-centric. So, I think, we'll see a shift more into the consumer space. >> Yeah. Dave, can I just jump in there just very quickly there, which is that what Sanjeev has been saying there, this is really central to what Zhamak has been talking about. It's basically about making, one, data products are about the lifecycle management of data. Metadata is just elemental to that. And essentially, one of the things that she calls for is making data products discoverable. That's exactly what Sanjeev was talking about. >> By the way, did everyone just no notice how Sanjeev just snuck in another prediction there? So, we've got- >> Yeah. (group laughing) >> But you- >> Can we also say that he snuck in, I think, the term that we'll remember today, which is metadata museums. >> Yeah, but- >> Yeah. >> And also comment to, Tony, to your last year's prediction, you're really talking about it's not something that you're going to buy from a vendor. >> No. >> It's very specific >> Mm-hmm. >> to an organization, their own data product. So, touche on that one. Okay, last prediction. Let's bring them up. Doug Henschen, BI analytics is headed to embedding. What does that mean? >> Well, we all know that conventional BI dashboarding reporting is really commoditized from a vendor perspective. It never enjoyed truly mainstream adoption. Always that 25% of employees are really using these things. I'm seeing rising interest in embedding concise analytics at the point of decision or better still, using analytics as triggers for automation and workflows, and not even necessitating human interaction with visualizations, for example, if we have confidence in the analytics. So, leading companies are pushing for next generation applications, part of this low-code, no-code movement we've seen. And they want to build that decision support right into the app. So, the analytic is right there. Leading enterprise apps vendors, Salesforce, SAP, Microsoft, Oracle, they're all building smart apps with the analytics predictions, even recommendations built into these applications. And I think, the progressive BI analytics vendors are supporting this idea of driving insight to action, not necessarily necessitating humans interacting with it if there's confidence. So, we want prediction, we want embedding, we want automation. This low-code, no-code development movement is very important to bringing the analytics to where people are doing their work. We got to move beyond the, what I call swivel chair integration, between where people do their work and going off to separate reports and dashboards, and having to interpret and analyze before you can go back and do take action. >> And Dave Menninger, today, if you want, analytics or you want to absorb what's happening in the business, you typically got to go ask an expert, and then wait. So, what are your thoughts on Doug's prediction? >> I'm in total agreement with Doug. I'm going to say that collectively... So, how did we get here? I'm going to say collectively as an industry, we made a mistake. We made BI and analytics separate from the operational systems. Now, okay, it wasn't really a mistake. We were limited by the technology available at the time. Decades ago, we had to separate these two systems, so that the analytics didn't impact the operations. You don't want the operations preventing you from being able to do a transaction. But we've gone beyond that now. We can bring these two systems and worlds together and organizations recognize that need to change. As Doug said, the majority of the workforce and the majority of organizations doesn't have access to analytics. That's wrong. (chuckles) We've got to change that. And one of the ways that's going to change is with embedded analytics. 2/3 of organizations recognize that embedded analytics are important and it even ranks higher in importance than AI and ML in those organizations. So, it's interesting. This is a really important topic to the organizations that are consuming these technologies. The good news is it works. Organizations that have embraced embedded analytics are more comfortable with self-service than those that have not, as opposed to turning somebody loose, in the wild with the data. They're given a guided path to the data. And the research shows that 65% of organizations that have adopted embedded analytics are comfortable with self-service compared with just 40% of organizations that are turning people loose in an ad hoc way with the data. So, totally behind Doug's predictions. >> Can I just break in with something here, a comment on what Dave said about what Doug said, which (laughs) is that I totally agree with what you said about embedded analytics. And at IDC, we made a prediction in our future intelligence, future of intelligence service three years ago that this was going to happen. And the thing that we're waiting for is for developers to build... You have to write the applications to work that way. It just doesn't happen automagically. Developers have to write applications that reference analytic data and apply it while they're running. And that could involve simple things like complex queries against the live data, which is through something that I've been calling analytic transaction processing. Or it could be through something more sophisticated that involves AI operations as Doug has been suggesting, where the result is enacted pretty much automatically unless the scores are too low and you need to have a human being look at it. So, I think that that is definitely something we've been watching for. I'm not sure how soon it will come, because it seems to take a long time for people to change their thinking. But I think, as Dave was saying, once they do and they apply these principles in their application development, the rewards are great. >> Yeah, this is very much, I would say, very consistent with what we were talking about, I was talking about before, about basically rethinking the modern data stack and going into more of an end-to-end solution solution. I think, that what we're talking about clearly here is operational analytics. There'll still be a need for your data scientists to go offline just in their data lakes to do all that very exploratory and that deep modeling. But clearly, it just makes sense to bring operational analytics into where people work into their workspace and further flatten that modern data stack. >> But with all this metadata and all this intelligence, we're talking about injecting AI into applications, it does seem like we're entering a new era of not only data, but new era of apps. Today, most applications are about filling forms out or codifying processes and require a human input. And it seems like there's enough data now and enough intelligence in the system that the system can actually pull data from, whether it's the transaction system, e-commerce, the supply chain, ERP, and actually do something with that data without human involvement, present it to humans. Do you guys see this as a new frontier? >> I think, that's certainly- >> Very much so, but it's going to take a while, as Carl said. You have to design it, you have to get the prediction into the system, you have to get the analytics at the point of decision has to be relevant to that decision point. >> And I also recall basically a lot of the ERP vendors back like 10 years ago, we're promising that. And the fact that we're still looking at the promises shows just how difficult, how much of a challenge it is to get to what Doug's saying. >> One element that could be applied in this case is (indistinct) architecture. If applications are developed that are event-driven rather than following the script or sequence that some programmer or designer had preconceived, then you'll have much more flexible applications. You can inject decisions at various points using this technology much more easily. It's a completely different way of writing applications. And it actually involves a lot more data, which is why we should all like it. (laughs) But in the end (Tony laughing) it's more stable, it's easier to manage, easier to maintain, and it's actually more efficient, which is the result of an MIT study from about 10 years ago, and still, we are not seeing this come to fruition in most business applications. >> And do you think it's going to require a new type of data platform database? Today, data's all far-flung. We see that's all over the clouds and at the edge. Today, you cache- >> We need a super cloud. >> You cache that data, you're throwing into memory. I mentioned, MySQL heat wave. There are other examples where it's a brute force approach, but maybe we need new ways of laying data out on disk and new database architectures, and just when we thought we had it all figured out. >> Well, without referring to disk, which to my mind, is almost like talking about cave painting. I think, that (Dave laughing) all the things that have been mentioned by all of us today are elements of what I'm talking about. In other words, the whole improvement of the data mesh, the improvement of metadata across the board and improvement of the ability to track data and judge its freshness the way we judge the freshness of a melon or something like that, to determine whether we can still use it. Is it still good? That kind of thing. Bringing together data from multiple sources dynamically and real-time requires all the things we've been talking about. All the predictions that we've talked about today add up to elements that can make this happen. >> Well, guys, it's always tremendous to get these wonderful minds together and get your insights, and I love how it shapes the outcome here of the predictions, and let's see how we did. We're going to leave it there. I want to thank Sanjeev, Tony, Carl, David, and Doug. Really appreciate the collaboration and thought that you guys put into these sessions. Really, thank you. >> Thank you. >> Thanks, Dave. >> Thank you for having us. >> Thanks. >> Thank you. >> All right, this is Dave Valente for theCUBE, signing off for now. Follow these guys on social media. Look for coverage on siliconangle.com, theCUBE.net. Thank you for watching. (upbeat music)

Hello and welcome back to Supercloud 2 where we examine the intersection of cloud and data in the 2020s. My name is Dave Vellante. Our Supercloud panel, our power panel is back. Maribel Lopez is the founder and principal analyst at Lopez Research. Sanjeev Mohan is former Gartner analyst and principal at Sanjeev Mohan. And Keith Townsend is the CTO advisor. Folks, welcome back and thanks for your participation today. Good to see you. >> Okay, great. >> Great to see you. >> Thanks. Let me start, Maribel, with you. Bob Muglia, we had a conversation as part of Supercloud the other day. And he said, "Dave, I like the work, you got to simplify this a little bit." So he said, quote, "A Supercloud is a platform." He said, "Think of it as a platform that provides programmatically consistent services hosted on heterogeneous cloud providers." And then Nelu Mihai said, "Well, wait a minute. This is just going to create more stove pipes. We need more standards in an architecture," which is kind of what Berkeley Sky Computing initiative is all about. So there's a sort of a debate going on. Is supercloud an architecture, a platform? Or maybe it's just another buzzword. Maribel, do you have a thought on this? >> Well, the easy answer would be to say it's just a buzzword. And then we could just kill the conversation and be done with it. But I think the term, it's more than that, right? The term actually isn't new. You can go back to at least 2016 and find references to supercloud in Cornell University or assist in other documents. So, having said this, I think we've been talking about Supercloud for a while, so I assume it's more than just a fancy buzzword. But I think it really speaks to that undeniable trend of moving towards an abstraction layer to deal with the chaos of what we consider managing multiple public and private clouds today, right? So one definition of the technology platform speaks to a set of services that allows companies to build and run that technology smoothly without worrying about the underlying infrastructure, which really gets back to something that Bob said. And some of the question is where that lives. And you could call that an abstraction layer. You could call it cross-cloud services, hybrid cloud management. So I see momentum there, like legitimate momentum with enterprise IT buyers that are trying to deal with the fact that they have multiple clouds now. So where I think we're moving is trying to define what are the specific attributes and frameworks of that that would make it so that it could be consistent across clouds. What is that layer? And maybe that's what the supercloud is. But one of the things I struggle with with supercloud is. What are we really trying to do here? Are we trying to create differentiated services in the supercloud layer? Is a supercloud just another variant of what AWS, GCP, or others do? You spoken to Walmart about its cloud native platform, and that's an example of somebody deciding to do it themselves because they need to deal with this today and not wait for some big standards thing to happen. So whatever it is, I do think it's something. I think we're trying to maybe create an architecture out of it would be a better way of saying it so that it does get to those set of principles, but it also needs to be edge aware. I think whenever we talk about supercloud, we're always talking about like the big centralized cloud. And I think we need to think about all the distributed clouds that we're looking at in edge as well. So that might be one of the ways that supercloud evolves. >> So thank you, Maribel. Keith, Brian Gracely, Gracely's law, things kind of repeat themselves. We've seen it all before. And so what Muglia brought to the forefront is this idea of a platform where the platform provider is really responsible for the architecture. Of course, the drawback is then you get a a bunch of stove pipes architectures. But practically speaking, that's kind of the way the industry has always evolved, right? >> So if we look at this from the practitioner's perspective and we talk about platforms, traditionally vendors have provided the platforms for us, whether it's distribution of lineage managed by or provided by Red Hat, Windows, servers, .NET, databases, Oracle. We think of those as platforms, things that are fundamental we can build on top. Supercloud isn't today that. It is a framework or idea, kind of a visionary goal to get to a point that we can have a platform or a framework. But what we're seeing repeated throughout the industry in customers, whether it's the Walmarts that's kind of supersized the idea of supercloud, or if it's regular end user organizations that are coming out with platform groups, groups who normalize cloud native infrastructure, AWS multi-cloud, VMware resources to look like one thing internally to their developers. We're seeing this trend that there's a desire for a platform that provides the capabilities of a supercloud. >> Thank you for that. Sanjeev, we often use Snowflake as a supercloud example, and now would presumably would be a platform with an architecture that's determined by the vendor. Maybe Databricks is pushing for a more open architecture, maybe more of that nirvana that we were talking about before to solve for supercloud. But regardless, the practitioner discussions show. At least currently, there's not a lot of cross-cloud data sharing. I think it could be a killer use case, egress charges or a barrier. But how do you see it? Will that change? Will we hide that underlying complexity and start sharing data across cloud? Is that something that you think Snowflake or others will be able to achieve? >> So I think we are already starting to see some of that happen. Snowflake is definitely one example that gets cited a lot. But even we don't talk about MongoDB in this like, but you could have a MongoDB cluster, for instance, with nodes sitting in different cloud providers. So there are companies that are starting to do it. The advantage that these companies have, let's take Snowflake as an example, it's a centralized proprietary platform. And they are building the capabilities that are needed for supercloud. So they're building things like you can push down your data transformations. They have the entire security and privacy suite. Data ops, they're adding those capabilities. And if I'm not mistaken, it'll be very soon, we will see them offer data observability. So it's all works great as long as you are in one platform. And if you want resilience, then Snowflake, Supercloud, great example. But if your primary goal is to choose the most cost-effective service irrespective of which cloud it sits in, then things start falling sideways. For example, I may be a very big Snowflake user. And I like Snowflake's resilience. I can move from one cloud to another cloud. Snowflake does it for me. But what if I want to train a very large model? Maybe Databricks is a better platform for that. So how do I do move my workload from one platform to another platform? That tooling does not exist. So we need server hybrid, cross-cloud, data ops platform. Walmart has done a great job, but they built it by themselves. Not every company is Walmart. Like Maribel and Keith said, we need standards, we need reference architectures, we need some sort of a cost control. I was just reading recently, Accenture has been public about their AWS bill. Every time they get the bill is tens of millions of lines, tens of millions 'cause there are over thousand teams using AWS. If we have not been able to corral a usage of a single cloud, now we're talking about supercloud, we've got multiple clouds, and hybrid, on-prem, and edge. So till we've got some cross-platform tooling in place, I think this will still take quite some time for it to take shape. >> It's interesting. Maribel, Walmart would tell you that their on-prem infrastructure is cheaper to run than the stuff in the cloud. but at the same time, they want the flexibility and the resiliency of their three-legged stool model. So the point as Sanjeev was making about hybrid. It's an interesting balance, isn't it, between getting your lowest cost and at the same time having best of breed and scale? >> It's basically what you're trying to optimize for, as you said, right? And by the way, to the earlier point, not everybody is at Walmart's scale, so it's not actually cheaper for everybody to have the purchasing power to make the cloud cheaper to have it on-prem. But I think what you see almost every company, large or small, moving towards is this concept of like, where do I find the agility? And is the agility in building the infrastructure for me? And typically, the thing that gives you outside advantage as an organization is not how you constructed your cloud computing infrastructure. It might be how you structured your data analytics as an example, which cloud is related to that. But how do you marry those two things? And getting back to sort of Sanjeev's point. We're in a real struggle now where one hand we want to have best of breed services and on the other hand we want it to be really easy to manage, secure, do data governance. And those two things are really at odds with each other right now. So if you want all the knobs and switches of a service like geospatial analytics and big query, you're going to have to use Google tools, right? Whereas if you want visibility across all the clouds for your application of state and understand the security and governance of that, you're kind of looking for something that's more cross-cloud tooling at that point. But whenever you talk to somebody about cross-cloud tooling, they look at you like that's not really possible. So it's a very interesting time in the market. Now, we're kind of layering this concept of supercloud on it. And some people think supercloud's about basically multi-cloud tooling, and some people think it's about a whole new architectural stack. So we're just not there yet. But it's not all about cost. I mean, cloud has not been about cost for a very, very long time. Cloud has been about how do you really make the most of your data. And this gets back to cross-cloud services like Snowflake. Why did they even exist? They existed because we had data everywhere, but we need to treat data as a unified object so that we can analyze it and get insight from it. And so that's where some of the benefit of these cross-cloud services are moving today. Still a long way to go, though, Dave. >> Keith, I reached out to my friends at ETR given the macro headwinds, And you're right, Maribel, cloud hasn't really been about just about cost savings. But I reached out to the ETR, guys, what's your data show in terms of how customers are dealing with the economic headwinds? And they said, by far, their number one strategy to cut cost is consolidating redundant vendors. And a distant second, but still notable was optimizing cloud costs. Maybe using reserve instances, or using more volume buying. Nowhere in there. And I asked them to, "Could you go look and see if you can find it?" Do we see repatriation? And you hear this a lot. You hear people whispering as analysts, "You better look into that repatriation trend." It's pretty big. You can't find it. But some of the Walmarts in the world, maybe even not repatriating, but they maybe have better cost structure on-prem. Keith, what are you seeing from the practitioners that you talk to in terms of how they're dealing with these headwinds? >> Yeah, I just got into a conversation about this just this morning with (indistinct) who is an analyst over at GigaHome. He's reading the same headlines. Repatriation is happening at large scale. I think this is kind of, we have these quiet terms now. We have quiet quitting, we have quiet hiring. I think we have quiet repatriation. Most people haven't done away with their data centers. They're still there. Whether they're completely on-premises data centers, and they own assets, or they're partnerships with QTX, Equinix, et cetera, they have these private cloud resources. What I'm seeing practically is a rebalancing of workloads. Do I really need to pay AWS for this instance of SAP that's on 24 hours a day versus just having it on-prem, moving it back to my data center? I've talked to quite a few customers who were early on to moving their static SAP workloads onto the public cloud, and they simply moved them back. Surprising, I was at VMware Explore. And we can talk about this a little bit later on. But our customers, net new, not a lot that were born in the cloud. And they get to this point where their workloads are static. And they look at something like a Kubernetes, or a OpenShift, or VMware Tanzu. And they ask the question, "Do I need the scalability of cloud?" I might consider being a net new VMware customer to deliver this base capability. So are we seeing repatriation as the number one reason? No, I think internal IT operations are just naturally come to this realization. Hey, I have these resources on premises. The private cloud technologies have moved far along enough that I can just simply move this workload back. I'm not calling it repatriation, I'm calling it rightsizing for the operating model that I have. >> Makes sense. Yeah. >> Go ahead. >> If I missed something, Dave, why we are on this topic of repatriation. I'm actually surprised that we are talking about repatriation as a very big thing. I think repatriation is happening, no doubt, but it's such a small percentage of cloud migration that to me it's a rounding error in my opinion. I think there's a bigger problem. The problem is that people don't know where the cost is. If they knew where the cost was being wasted in the cloud, they could do something about it. But if you don't know, then the easy answer is cloud costs a lot and moving it back to on-premises. I mean, take like Capital One as an example. They got rid of all the data centers. Where are they going to repatriate to? They're all in the cloud at this point. So I think my point is that data observability is one of the places that has seen a lot of traction is because of cost. Data observability, when it first came into existence, it was all about data quality. Then it was all about data pipeline reliability. And now, the number one killer use case is FinOps. >> Maribel, you had a comment? >> Yeah, I'm kind of in violent agreement with both Sanjeev and Keith. So what are we seeing here? So the first thing that we see is that many people wildly overspent in the big public cloud. They had stranded cloud credits, so to speak. The second thing is, some of them still had infrastructure that was useful. So why not use it if you find the right workloads to what Keith was talking about, if they were more static workloads, if it was already there? So there is a balancing that's going on. And then I think fundamentally, from a trend standpoint, these things aren't binary. Everybody, for a while, everything was going to go to the public cloud and then people are like, "Oh, it's kind of expensive." Then they're like, "Oh no, they're going to bring it all on-prem 'cause it's really expensive." And it's like, "Well, that doesn't necessarily get me some of the new features and functionalities I might want for some of my new workloads." So I'm going to put the workloads that have a certain set of characteristics that require cloud in the cloud. And if I have enough capability on-prem and enough IT resources to manage certain things on site, then I'm going to do that there 'cause that's a more cost-effective thing for me to do. It's not binary. That's why we went to hybrid. And then we went to multi just to describe the fact that people added multiple public clouds. And now we're talking about super, right? So I don't look at it as a one-size-fits-all for any of this. >> A a number of practitioners leading up to Supercloud2 have told us that they're solving their cloud complexity by going in monocloud. So they're putting on the blinders. Even though across the organization, there's other groups using other clouds. You're like, "In my group, we use AWS, or my group, we use Azure. And those guys over there, they use Google. We just kind of keep it separate." Are you guys hearing this in your view? Is that risky? Are they missing out on some potential to tap best of breed? What do you guys think about that? >> Everybody thinks they're monocloud. Is anybody really monocloud? It's like a group is monocloud, right? >> Right. >> This genie is out of the bottle. We're not putting the genie back in the bottle. You might think your monocloud and you go like three doors down and figure out the guy or gal is on a fundamentally different cloud, running some analytics workload that you didn't know about. So, to Sanjeev's earlier point, they don't even know where their cloud spend is. So I think the concept of monocloud, how that's actually really realized by practitioners is primary and then secondary sources. So they have a primary cloud that they run most of their stuff on, and that they try to optimize. And we still have forked workloads. Somebody decides, "Okay, this SAP runs really well on this, or these analytics workloads run really well on that cloud." And maybe that's how they parse it. But if you really looked at it, there's very few companies, if you really peaked under the hood and did an analysis that you could find an actual monocloud structure. They just want to pull it back in and make it more manageable. And I respect that. You want to do what you can to try to streamline the complexity of that. >> Yeah, we're- >> Sorry, go ahead, Keith. >> Yeah, we're doing this thing where we review AWS service every day. Just in your inbox, learn about a new AWS service cursory. There's 238 AWS products just on the AWS cloud itself. Some of them are redundant, but you get the idea. So the concept of monocloud, I'm in filing agreement with Maribel on this that, yes, a group might say I want a primary cloud. And that primary cloud may be the AWS. But have you tried the licensed Oracle database on AWS? It is really tempting to license Oracle on Oracle Cloud, Microsoft on Microsoft. And I can't get RDS anywhere but Amazon. So while I'm driven to desire the simplicity, the reality is whether be it M&A, licensing, data sovereignty. I am forced into a multi-cloud management style. But I do agree most people kind of do this one, this primary cloud, secondary cloud. And I guarantee you're going to have a third cloud or a fourth cloud whether you want to or not via shadow IT, latency, technical reasons, et cetera. >> Thank you. Sanjeev, you had a comment? >> Yeah, so I just wanted to mention, as an organization, I'm complete agreement, no organization is monocloud, at least if it's a large organization. Large organizations use all kinds of combinations of cloud providers. But when you talk about a single workload, that's where the program arises. As Keith said, the 238 services in AWS. How in the world am I going to be an expert in AWS, but then say let me bring GCP or Azure into a single workload? And that's where I think we probably will still see monocloud as being predominant because the team has developed its expertise on a particular cloud provider, and they just don't have the time of the day to go learn yet another stack. However, there are some interesting things that are happening. For example, if you look at a multi-cloud example where Oracle and Microsoft Azure have that interconnect, so that's a beautiful thing that they've done because now in the newest iteration, it's literally a few clicks. And then behind the scene, your .NET application and your Oracle database in OCI will be configured, the identities in active directory are federated. And you can just start using a database in one cloud, which is OCI, and an application, your .NET in Azure. So till we see this kind of a solution coming out of the providers, I think it's is unrealistic to expect the end users to be able to figure out multiple clouds. >> Well, I have to share with you. I can't remember if he said this on camera or if it was off camera so I'll hold off. I won't tell you who it is, but this individual was sort of complaining a little bit saying, "With AWS, I can take their best AI tools like SageMaker and I can run them on my Snowflake." He said, "I can't do that in Google. Google forces me to go to BigQuery if I want their excellent AI tools." So he was sort of pushing, kind of tweaking a little bit. Some of the vendor talked that, "Oh yeah, we're so customer-focused." Not to pick on Google, but I mean everybody will say that. And then you say, "If you're so customer-focused, why wouldn't you do a ABC?" So it's going to be interesting to see who leads that integration and how broadly it's applied. But I digress. Keith, at our first supercloud event, that was on August 9th. And it was only a few months after Broadcom announced the VMware acquisition. A lot of people, myself included said, "All right, cuts are coming." Generally, Tanzu is probably going to be under the radar, but it's Supercloud 22 and presumably VMware Explore, the company really... Well, certainly the US touted its Tanzu capabilities. I wasn't at VMware Explore Europe, but I bet you heard similar things. Hawk Tan has been blogging and very vocal about cross-cloud services and multi-cloud, which doesn't happen without Tanzu. So what did you hear, Keith, in Europe? What's your latest thinking on VMware's prospects in cross-cloud services/supercloud? >> So I think our friend and Cube, along host still be even more offended at this statement than he was when I sat in the Cube. This was maybe five years ago. There's no company better suited to help industries or companies, cross-cloud chasm than VMware. That's not a compliment. That's a reality of the industry. This is a very difficult, almost intractable problem. What I heard that VMware Europe were customers serious about this problem, even more so than the US data sovereignty is a real problem in the EU. Try being a company in Switzerland and having the Swiss data solvency issues. And there's no local cloud presence there large enough to accommodate your data needs. They had very serious questions about this. I talked to open source project leaders. Open source project leaders were asking me, why should I use the public cloud to host Kubernetes-based workloads, my projects that are building around Kubernetes, and the CNCF infrastructure? Why should I use AWS, Google, or even Azure to host these projects when that's undifferentiated? I know how to run Kubernetes, so why not run it on-premises? I don't want to deal with the hardware problems. So again, really great questions. And then there was always the specter of the problem, I think, we all had with the acquisition of VMware by Broadcom potentially. 4.5 billion in increased profitability in three years is a unbelievable amount of money when you look at the size of the problem. So a lot of the conversation in Europe was about industry at large. How do we do what regulators are asking us to do in a practical way from a true technology sense? Is VMware cross-cloud great? >> Yeah. So, VMware, obviously, to your point. OpenStack is another way of it. Actually, OpenStack, uptake is still alive and well, especially in those regions where there may not be a public cloud, or there's public policy dictating that. Walmart's using OpenStack. As you know in IT, some things never die. Question for Sanjeev. And it relates to this new breed of data apps. And Bob Muglia and Tristan Handy from DBT Labs who are participating in this program really got us thinking about this. You got data that resides in different clouds, it maybe even on-prem. And the machine polls data from different systems. No humans involved, e-commerce, ERP, et cetera. It creates a plan, outcomes. No human involvement. Today, you're on a CRM system, you're inputting, you're doing forms, you're, you're automating processes. We're talking about a new breed of apps. What are your thoughts on this? Is it real? Is it just way off in the distance? How does machine intelligence fit in? And how does supercloud fit? >> So great point. In fact, the data apps that you're talking about, I call them data products. Data products first came into limelight in the last couple of years when Jamal Duggan started talking about data mesh. I am taking data products out of the data mesh concept because data mesh, whether data mesh happens or not is analogous to data products. Data products, basically, are taking a product management view of bringing data from different sources based on what the consumer needs. We were talking earlier today about maybe it's my vacation rentals, or it may be a retail data product, it may be an investment data product. So it's a pre-packaged extraction of data from different sources. But now I have a product that has a whole lifecycle. I can version it. I have new features that get added. And it's a very business data consumer centric. It uses machine learning. For instance, I may be able to tell whether this data product has stale data. Who is using that data? Based on the usage of the data, I may have a new data products that get allocated. I may even have the ability to take existing data products, mash them up into something that I need. So if I'm going to have that kind of power to create a data product, then having a common substrate underneath, it can be very useful. And that could be supercloud where I am making API calls. I don't care where the ERP, the CRM, the survey data, the pricing engine where they sit. For me, there's a logical abstraction. And then I'm building my data product on top of that. So I see a new breed of data products coming out. To answer your question, how early we are or is this even possible? My prediction is that in 2023, we will start seeing more of data products. And then it'll take maybe two to three years for data products to become mainstream. But it's starting this year. >> A subprime mortgages were a data product, definitely were humans involved. All right, let's talk about some of the supercloud, multi-cloud players and what their future looks like. You can kind of pick your favorites. VMware, Snowflake, Databricks, Red Hat, Cisco, Dell, HP, Hashi, IBM, CloudFlare. There's many others. cohesive rubric. Keith, I wanted to start with CloudFlare because they actually use the term supercloud. and just simplifying what they said. They look at it as taking serverless to the max. You write your code and then you can deploy it in seconds worldwide, of course, across the CloudFlare infrastructure. You don't have to spin up containers, you don't go to provision instances. CloudFlare worries about all that infrastructure. What are your thoughts on CloudFlare this approach and their chances to disrupt the current cloud landscape? >> As Larry Ellison said famously once before, the network is the computer, right? I thought that was Scott McNeley. >> It wasn't Scott McNeley. I knew it was on Oracle Align. >> Oracle owns that now, owns that line. >> By purpose or acquisition. >> They should have just called it cloud. >> Yeah, they should have just called it cloud. >> Easier. >> Get ahead. >> But if you think about the CloudFlare capability, CloudFlare in its own right is becoming a decent sized cloud provider. If you have compute out at the edge, when we talk about edge in the sense of CloudFlare and points of presence, literally across the globe, you have all of this excess computer, what do you do with it? First offering, let's disrupt data in the cloud. We can't start the conversation talking about data. When they say we're going to give you object-oriented or object storage in the cloud without egress charges, that's disruptive. That we can start to think about supercloud capability of having compute EC2 run in AWS, pushing and pulling data from CloudFlare. And now, I've disrupted this roach motel data structure, and that I'm freely giving away bandwidth, basically. Well, the next layer is not that much more difficult. And I think part of CloudFlare's serverless approach or supercloud approaches so that they don't have to commit to a certain type of compute. It is advantageous. It is a feature for me to be able to go to EC2 and pick a memory heavy model, or a compute heavy model, or a network heavy model, CloudFlare is taken away those knobs. and I'm just giving code and allowing that to run. CloudFlare has a massive network. If I can put the code closest using the CloudFlare workers, if I can put that code closest to where the data is at or residing, super compelling observation. The question is, does it scale? I don't get the 238 services. While Server List is great, I have to know what I'm going to build. I don't have a Cognito, or RDS, or all these other services that make AWS, GCP, and Azure appealing from a builder's perspective. So it is a very interesting nascent start. It's great because now they can hide compute. If they don't have the capacity, they can outsource that maybe at a cost to one of the other cloud providers, but kind of hiding the compute behind the surplus architecture is a really unique approach. >> Yeah. And they're dipping their toe in the water. And they've announced an object store and a database platform and more to come. We got to wrap. So I wonder, Sanjeev and Maribel, if you could maybe pick some of your favorites from a competitive standpoint. Sanjeev, I felt like just watching Snowflake, I said, okay, in my opinion, they had the right strategy, which was to run on all the clouds, and then try to create that abstraction layer and data sharing across clouds. Even though, let's face it, most of it might be happening across regions if it's happening, but certainly outside of an individual account. But I felt like just observing them that anybody who's traditional on-prem player moving into the clouds or anybody who's a cloud native, it just makes total sense to write to the various clouds. And to the extent that you can simplify that for users, it seems to be a logical strategy. Maybe as I said before, what multi-cloud should have been. But are there companies that you're watching that you think are ahead in the game , or ones that you think are a good model for the future? >> Yes, Snowflake, definitely. In fact, one of the things we have not touched upon very much, and Keith mentioned a little bit, was data sovereignty. Data residency rules can require that certain data should be written into certain region of a certain cloud. And if my cloud provider can abstract that or my database provider, then that's perfect for me. So right now, I see Snowflake is way ahead of this pack. I would not put MongoDB too far behind. They don't really talk about this thing. They are in a different space, but now they have a lakehouse, and they've got all of these other SQL access and new capabilities that they're announcing. So I think they would be quite good with that. Oracle is always a dark forest. Oracle seems to have revived its Cloud Mojo to some extent. And it's doing some interesting stuff. Databricks is the other one. I have not seen Databricks. They've been very focused on lakehouse, unity, data catalog, and some of those pieces. But they would be the obvious challenger. And if they come into this space of supercloud, then they may bring some open source technologies that others can rely on like Delta Lake as a table format. >> Yeah. One of these infrastructure players, Dell, HPE, Cisco, even IBM. I mean, I would be making my infrastructure as programmable and cloud friendly as possible. That seems like table stakes. But Maribel, any companies that stand out to you that we should be paying attention to? >> Well, we already mentioned a bunch of them, so maybe I'll go a slightly different route. I'm watching two companies pretty closely to see what kind of traction they get in their established companies. One we already talked about, which is VMware. And the thing that's interesting about VMware is they're everywhere. And they also have the benefit of having a foot in both camps. If you want to do it the old way, the way you've always done it with VMware, they got all that going on. If you want to try to do a more cross-cloud, multi-cloud native style thing, they're really trying to build tools for that. So I think they have really good access to buyers. And that's one of the reasons why I'm interested in them to see how they progress. The other thing, I think, could be a sleeping horse oddly enough is Google Cloud. They've spent a lot of work and time on Anthos. They really need to create a certain set of differentiators. Well, it's not necessarily in their best interest to be the best multi-cloud player. If they decide that they want to differentiate on a different layer of the stack, let's say they want to be like the person that is really transformative, they talk about transformation cloud with analytics workloads, then maybe they do spend a good deal of time trying to help people abstract all of the other underlying infrastructure and make sure that they get the sexiest, most meaningful workloads into their cloud. So those are two people that you might not have expected me to go with, but I think it's interesting to see not just on the things that might be considered, either startups or more established independent companies, but how some of the traditional providers are trying to reinvent themselves as well. >> I'm glad you brought that up because if you think about what Google's done with Kubernetes. I mean, would Google even be relevant in the cloud without Kubernetes? I could argue both sides of that. But it was quite a gift to the industry. And there's a motivation there to do something unique and different from maybe the other cloud providers. And I'd throw in Red Hat as well. They're obviously a key player and Kubernetes. And Hashi Corp seems to be becoming the standard for application deployment, and terraform, or cross-clouds, and there are many, many others. I know we're leaving lots out, but we're out of time. Folks, I got to thank you so much for your insights and your participation in Supercloud2. Really appreciate it. >> Thank you. >> Thank you. >> Thank you. >> This is Dave Vellante for John Furrier and the entire Cube community. Keep it right there for more content from Supercloud2.

>>The cube presents Ignite 22, brought to you by Palo Alto Networks. >>Good morning. Live from the MGM Grand. It's the cube at Palo Alto Networks Ignite 2022. Lisa Martin here with Dave Valante, day two, Dave of our coverage, or last live day of the year, which I can't believe, lots of good news coming out from Palo Alto Networks. We're gonna sit down with its Chief product officer next and dissect all of that. >>Yeah. You know, oftentimes in, in events like this, day two is product day. And look, it's all about products and sales. Yeah, I mean those, that's the, the, the golden rule. Get the product right, get the sales right, and everything else will take care of itself. So let's talk product. >>Yeah, let's talk product. Lee Claridge joins us, the Chief Product Officer at Palo Alto Networks. Welcome Lee. Great to have >>You. Thank you so much. >>So we didn't get to see your keynote yesterday, but we heard one of the things, you know, we've been talking about the threat landscape, the challenges. We had Unit 42, Wendy on yesterday. We had Nash on and near talking about the massive challenges in the threat landscape. But we understand, despite that you are optimistic. I am. Talk about your optimism given the massive challenges that every organization is facing today. >>Look, cybersecurity's hard and often in cybersecurity in the industry, a lot of people get sort of really focused on what the threat actors are doing, why they're successful. We investigate breaches and we think of it, it just starts to feel somewhat overwhelming for a lot of folks. And I just happen to think a little bit differently. I, I look at it and I think it's actually a solvable problem. >>Talk about cyber resilience. How does Palo Alto Networks define that and how does it help customers achieve that? Cuz that's the, that's the holy grail these days. >>Yes. Look, the, the way I think about cyber resilience is basically in two pieces. One, it's all about how do we prevent the threat actors from actually being successful in the first place. Second, we also have to be prepared for what happens if they happen to find a way to get through, and how do we make sure that that happens? The blast radius is, is as narrowly contained as possible. And so the, the way that we approach this is, you know, I, I kind of think in terms of like threes three core principles. Number one, we have to have amazing technology and we have to constantly be, keep keeping up with and ideally ahead of what attackers are doing. It's a big part of my job as the chief product officer, right? Second is we, you know, one of the, the big transformations that's happened is the advent of, of AI and the opportunity, as long as we can do it, a great job of collecting great data, we can drive AI and machine learning models that can start to be used for our advantage as defenders, and then further use that to drive automation. >>So we take the human out of the response as much as possible. What that allows us to do is actually to start using AI and automation to disrupt attackers as it's happening. The third piece then becomes natively integrating these capabilities into a platform. And when we do that, what allows us to do is to make sure that we are consistently delivering cybersecurity everywhere that it needs to happen. That we don't have gaps. Yeah. So great tech AI and automation deliver natively integrated through platforms. This is how we achieve cyber resilience. >>So I like the positivity. In fact, Steven Schmidt, who's now the CSO of, of Amazon, you know, Steven, and it was the CSO at AWS at the time, the first reinforced, he stood up on stage and said, listen, this narrative that's all gloom and doom is not the right approach. We actually are doing a good job and we have the capability. So I was like, yeah, you know, okay. I'm, I'm down with that. Now when I, my question is around the, the portfolio. I, I was looking at, you know, some of your alternatives and options and the website. I mean, you got network security, cloud security, you got sassy, you got capp, you got endpoint, pretty much everything. You got cider security, which you just recently acquired for, you know, this whole shift left stuff, you know, nothing in there on identity yet. That's good. You partner for that, but, so could you describe sort of how you think about the portfolio from a product standpoint? How you continue to evolve it and what's the direction? Yes. >>So the, the, the cybersecurity industry has long had this, I'm gonna call it a major flaw. And the major flaw of the cybersecurity industry has been that every time there is a problem to be solved, there's another 10 or 20 startups that get funded to solve that problem. And so pretty soon what you have is you're, if you're a customer of this is you have 50, a hundred, the, the record is over 400 different cybersecurity products that as a customer you're trying to operationalize. >>It's not a good record to have. >>No, it's not a good record. No. This is, this is the opposite of Yes. Not a good personal best. So the, so the reason I start there in answering your question is the, the way that, so that's one end of the extreme, the other end of the extreme view to say, is there such a thing as a single platform that does everything? No, there's not. That would be nice. That was, that sounds nice. But the reality is that cybersecurity has to be much broader than any one single thing can do. And so the, the way that we approach this is, is three fundamental areas that, that we, Palo Alto Networks are going to be the best at. One is network security within network security. This includes hardware, NextGen, firewalls, software NextGen, firewalls, sassy, all the different security services that tie into that. All of that makes up our network security platforms. >>So everything to do with network security is integrated in that one place. Second is around cloud security. The shift to the cloud is happening is very real. That's where Prisma Cloud takes center stage. C a P is the industry acronym. If if five letters thrown together can be called an acronym. The, so cloud native application protection platform, right? So this is where we bring all of the different cloud security capabilities integrated together, delivered through one platform. And then security, security operations is the third for us. This is Cortex. And this is where we bring together endpoint security, edr, ndr, attack, surface management automation, all of this. And what we had, what we announced earlier this year is x Im, which is a Cortex product for actually integrating all of that together into one SOC transformation platform. So those are the three platforms, and that's how we deliver much, much, much greater levels of native integration of capabilities, but in a logical way where we're not trying to overdo it. >>And cider will fit into two or three >>Into Prisma cloud into the second cloud to two. Yeah. As part of the shift left strategy of how we secure makes sense applications in the cloud >>When you're in customer conversations. You mentioned the record of 400 different product. That's crazy. Nash was saying yesterday between 30 and 50 and we talked with him and near about what's realistic in terms of getting organizations to, to be able to consolidate. I'd love to understand what does cybersecurity transformation look like for the average organization that's running 30 to 50 point >>Solutions? Yeah, look, 30 to 50 is probably, maybe normal. A hundred is not unusual. Obviously 400 is the extreme example. But all of those are, those numbers are too big right now. I think, I think realistic is high. Single digits, low double digits is probably somewhat realistic for most organizations, the most complex organizations that might go a bit above that if we're really doing a good job. That's, that's what I think. Now second, I do really want to point out on, on the product guy. So, so maybe this is just my way of thinking, consolidation is an outcome of having more tightly and natively integrated capabilities. Got you. And the reason I flip that around is if I just went to you and say, Hey, would you like to consolidate? That just means maybe fewer vendors that that helps the procurement person. Yes. You know, have to negotiate with fewer companies. Yeah. Integration is actually a technology statement. It's delivering better outcomes because we've designed multiple capabilities to work together natively ourselves as the developers so that the customer doesn't have to figure out how to do it. It just happens that by, by doing that, the customer gets all this wonderful technical benefit. And then there's this outcome sitting there called, you've just consolidated your complexity. How >>Specialized is the customer? I think a data pipelines, and I think I have a data engineer, have a data scientists, a data analyst, but hyper specialized roles. If, if, let's say I have, you know, 30 or 40, and one of 'em is an SD wan, you know, security product. Yeah. I'm best of breed an SD wan. Okay, great. Palo Alto comes in as you, you pointed out, I'm gonna help you with your procurement side. Are there hyper specialized individuals that are aligned to that? And how that's kind of part A and B, how, assuming that's the case, how does that integration, you know, carry through to the business case? So >>Obviously there are specializations, this is the, and, and cybersecurity is really important. And so there, this is why there had, there's this tendency in the past to head toward, well I have this problem, so who's the best at solving this one problem? And if you only had one problem to solve, you would go find the specialist. The, the, the, the challenge becomes, well, what do you have a hundred problems to solve? I is the right answer, a hundred specialized solutions for your a hundred problems. And what what I think is missing in this approach is, is understanding that almost every problem that needs to be solved is interconnected with other problems to be solved. It's that interconnectedness of the problems where all of a sudden, so, so you mentioned SD wan. Okay, great. I have Estee wan, I need it. Well what are you connecting SD WAN to? >>Well, ideally our view is you would connect SD WAN and branch to the cloud. Well, would you run in the cloud? Well, in our case, we can take our SD wan, connect it to Prisma access, which is our cloud security solution, and we can natively integrate those two things together such that when you use 'em together, way easier. Right? All of a sudden we took what seemed like two separate problems. We said, no, actually these problems are related and we can deliver a solution where those, those things are actually brought together. And that's just one simple example, but you could, you could extend that across a lot of these other areas. And so that's the difference. And that's how the, the, the mindset shift that is happening. And, and I I was gonna say needs to happen, but it's starting to happen. I'm talking to customers where they're telling me this as opposed to me telling them. >>So when you walk around the floor here, there's a visual, it's called a day in the life of a fuel member. And basically what it has, it's got like, I dunno, six or seven different roles or personas, you know, one is management, one is a network engineer, one's a coder, and it gives you an X and an O. And it says, okay, put the X on things that you spend your time doing, put the o on things that you wanna spend your time doing a across all different sort of activities that a SecOps pro would do. There's Xs and O's in every one of 'em. You know, to your point, there's so much overlap going on. This was really difficult to discern, you know, any kind of consistent pattern because it, it, it, unlike the hyper specialization and data pipelines that I just described, it, it's, it's not, it, it, there's way more overlap between those, those specialization roles. >>And there's a, there's a second challenge that, that I've observed and that we are, we've, we've been trying to solve this and now I'd say we've become, started to become a lot more purposeful in, in, in trying to solve this, which is, I believe cybersecurity, in order for cyber security vendors to become partners, we actually have to start to become more opinionated. We actually have to start, guys >>Are pretty opinionated. >>Well, yes, but, but the industry large. So yes, we're opinionated. We build these products, but that have, that have our, I'll call our opinions built into it, and then we, we sell the, the product and then, and then what happens? Customer says, great, thank you for the product. I'm going to deploy it however I want to, which is fine. Obviously it's their choice at the end of the day, but we actually should start to exert an opinion to say, well, here's what we would recommend, here's why we would recommend that. Here's how we envisioned it providing the most value to you. And actually starting to build that into the products themselves so that they start to guide the customer toward these outcomes as opposed to just saying, here's a product, good luck. >>What's, what's the customer lifecycle, not lifecycle, but really kind of that, that collaboration, like it's one thing to, to have products that you're saying that have opinions to be able to inform customers how to deploy, how to use, but where is their feedback in this cycle of product development? >>Oh, look, my, this, this is, this is my life. I'm, this is, this is why I'm here. This is like, you know, all day long I'm meeting with customers and, and I share what we're doing. But, but it's, it's a, it's a 50 50, I'm half the time I'm listening as well to understand what they're trying to do, what they're trying to accomplish, and how, what they need us to do better in order to help them solve the problem. So the, the, and, and so my entire organization is oriented around not just telling customers, here's what we did, but listening and understanding and bringing that feedback in and constantly making the products better. That's, that's the, the main way in which we do this. Now there's a second way, which is we also allow our products to be customized. You know, I can say, here's our best practices, we see it, but then allowing our customer to, to customize that and tailor it to their environment, because there are going to be uniquenesses for different customers in parti, we need more complex environments. Explain >>Why fire firewalls won't go away >>From your perspective. Oh, Nikesh actually did a great job of explaining this yesterday, and although he gave me credit for it, so this is like a, a circular kind of reference here. But if you think about the firewalls slightly more abstract, and you basically say a NextGen firewalls job is to inspect every connection in order to make sure the connection should be allowed. And then if it is allowed to make sure that it's secure, >>Which that is the definition of an NextGen firewall, by the way, exactly what I just said. Now what you noticed is, I didn't describe it as a hardware device, right? It can be delivered in hardware because there are environments where you need super high throughput, low latency, guess what? Hardware is the best way of delivering that functionality. There's other use cases cloud where you can't, you, you can't ship hardware to a cloud provider and say, can you install this hardware in front of my cloud? No, no, no. You deployed in a software. So you take that same functionality, you instantly in a software, then you have other use cases, branch offices, remote workforce, et cetera, where you say, actually, I just want it delivered from the cloud. This is what sassy is. So when I, when I look at and say, the firewall's not going away, what, what, what I see is the functionality needed is not only not going away, it's actually expanding. But how we deliver it is going to be across these three form factors. And then the customer's going to decide how they need to intermix these form factors for their environment. >>We put forth this notion of super cloud a while about a year ago. And the idea being you're gonna leverage the hyperscale infrastructure and you're gonna build a, a, you're gonna solve a common problem across clouds and even on-prem, super cloud above the cloud. Not Superman, but super as in Latin. But it turned into this sort of, you know, superlative, which is fun. But the, my, my question to you is, is, is, is Palo Alto essentially building a common cross-cloud on-prem, presumably out to the edge consistent experience that we would call a super cloud? >>Yeah, I don't know that we've ever used the term surfer cloud to describe it. Oh, you don't have to, but yeah. But yes, based on how you describe it, absolutely. And it has three main benefits that I describe to customers all the time. The first is the end user experience. So imagine your employee, and you might work from the office, you might work from home, you might work while from, from traveling and hotels and conferences. And, and by the way, in one day you might actually work from all of those places. So, so the first part is the end user experience becomes way better when it doesn't matter where they're working from. They always get the same experience, huge benefit from productivity perspective, no second benefit security operations. You think about the, the people who are actually administering these policies and analyzing the security events. >>Imagine how much better it is for them when it's all common and consistent across everywhere that has to happen. Cloud, on-prem branch, remote workforce, et cetera. So there's a operational benefit that is super valuable. Third, security benefit. Imagine if in this, this platform-based approach, if we come out with some new amazing innovation that is able to detect and block, you know, new types of attacks, guess what, we can deliver that across hardware, software, and sassi uniformly and keep it all up to date. So from a security perspective, way better than trying to figure out, okay, there's some new technology, you know, does my hardware provider have that technology or not? Does my soft provider? So it's bringing that in to one place. >>From a developer perspective, is there a, a, a PAs layer, forgive me super PAs, that a allows the developers to have a common experience across irrespective of physical location with the explicit purpose of serving the objective of your platform. >>So normally when I think of the context of developers, I'm thinking of the context of, of the people who are building the applications that are being deployed. And those applications may be deployed in a data center, increasing the data centers, depending private clouds might be deployed into, into public cloud. It might even be hybrid in nature. And so if you think about what the developer wants, the developer actually wants to not have to think about security, quite frankly. Yeah. They want to think about how do I develop the functionality I need as quickly as possible with the highest quality >>Possible, but they are being forced to think about it more and more. Well, but anyway, I didn't mean to >>Interrupt you. No, it's a, it is a good, it's a, it's, it's a great point. The >>Well we're trying to do is we're trying to enable our security capabilities to work in a way that actually enables what the developer wants that actually allows them to develop faster that actually allows them to focus on the things they want to focus. And, and the way we do that is by actually surfacing the security information that they need to know in the tools that they use as opposed to trying to bring them to our tools. So you think about this, so our customer is a security customer. Yet in the application development lifecycle, the developer is often the user. So we, we we're selling, we're so providing a solution to security and then we're enabling them to surface it in the developer tools. And by, by doing this, we actually make life easier for the developers such that they're not actually thinking about security so much as they're just saying, oh, I pulled down the wrong open source package, it's outdated, it has vulnerabilities. I was notified the second I did it, and I was told which one I should pull down. So I pulled down the right one. Now, if you're a developer, do you think that's security getting your way? Not at all. No. If you're a developer, you're thinking, thank god, thank you, thank, thank you. Yeah. You told me at a point where it was easy as opposed to waiting a week or two and then telling me where it's gonna be really hard to fix it. Yeah. Nothing >>More than, so maybe be talking to Terraform or some other hash corp, you know, environment. I got it. Okay. >>Absolutely. >>We're 30 seconds. We're almost out of time. Sure. But I'd love to get your snapshot. Here we are at the end of calendar 2022. What are you, we know you're optimistic in this threat landscape, which we're gonna see obviously more dynamics next year. What kind of nuggets can you drop about what we might hear and see in 23? >>You're gonna see across everything. We do a lot more focus on the use of AI and machine learning to drive automated outcomes for our customers. And you're gonna see us across everything we do. And that's going to be the big transformation. It'll be a multi-year transformation, but you're gonna see significant progress in the next 12 months. All >>Right, well >>What will be the sign of that progress? If I had to make a prediction, which >>I'm better security with less effort. >>Okay, great. I feel like that's, we can measure that. I >>Feel, I feel like that's a mic drop moment. Lee, it's been great having you on the program. Thank you for walking us through such great detail. What's going on in the organization, what you're doing for customers, where you're meeting, how you're meeting the developers, where they are. We'll have to have you back. There's just, just too much to unpack. Thank you both so much. Actually, our pleasure for Lee Cler and Dave Valante. I'm Lisa Martin. You're watching The Cube Live from Palo Alto Networks Ignite 22, the Cube, the leader in live, emerging and enterprise tech coverage.