(light airy music) >> Hello, everyone, welcome to theCUBE's presentation of the AWS Startup Showcase, AI and machine learning. "Top Startups Building Generative AI on AWS." This is season three, episode one of the ongoing series covering the exciting startups from the AWS ecosystem, talking about AI machine learning. We have three great guests Bratin Saha, VP, Vice President of Machine Learning and AI Services at Amazon Web Services. Tom Mason, the CTO of Stability AI, and Aidan Gomez, CEO and co-founder of Cohere. Two practitioners doing startups and AWS. Gentlemen, thank you for opening up this session, this episode. Thanks for coming on. >> Thank you. >> Thank you. >> Thank you. >> So the topic is hype versus reality. So I think we're all on the reality is great, hype is great, but the reality's here. I want to get into it. Generative AI's got all the momentum, it's going mainstream, it's kind of come out of the behind the ropes, it's now mainstream. We saw the success of ChatGPT, opens up everyone's eyes, but there's so much more going on. Let's jump in and get your early perspectives on what should people be talking about right now? What are you guys working on? We'll start with AWS. What's the big focus right now for you guys as you come into this market that's highly active, highly hyped up, but people see value right out of the gate? >> You know, we have been working on generative AI for some time. In fact, last year we released Code Whisperer, which is about using generative AI for software development and a number of customers are using it and getting real value out of it. So generative AI is now something that's mainstream that can be used by enterprise users. And we have also been partnering with a number of other companies. So, you know, stability.ai, we've been partnering with them a lot. We want to be partnering with other companies as well. In seeing how we do three things, you know, first is providing the most efficient infrastructure for generative AI. And that is where, you know, things like Trainium, things like Inferentia, things like SageMaker come in. And then next is the set of models and then the third is the kind of applications like Code Whisperer and so on. So, you know, it's early days yet, but clearly there's a lot of amazing capabilities that will come out and something that, you know, our customers are starting to pay a lot of attention to. >> Tom, talk about your company and what your focus is and why the Amazon Web Services relationship's important for you? >> So yeah, we're primarily committed to making incredible open source foundation models and obviously stable effusions been our kind of first big model there, which we trained all on AWS. We've been working with them over the last year and a half to develop, obviously a big cluster, and bring all that compute to training these models at scale, which has been a really successful partnership. And we're excited to take it further this year as we develop commercial strategy of the business and build out, you know, the ability for enterprise customers to come and get all the value from these models that we think they can get. So we're really excited about the future. We got hugely exciting pipeline for this year with new modalities and video models and wonderful things and trying to solve images for once and for all and get the kind of general value and value proposition correct for customers. So it's a really exciting time and very honored to be part of it. >> It's great to see some of your customers doing so well out there. Congratulations to your team. Appreciate that. Aidan, let's get into what you guys do. What does Cohere do? What are you excited about right now? >> Yeah, so Cohere builds large language models, which are the backbone of applications like ChatGPT and GPT-3. We're extremely focused on solving the issues with adoption for enterprise. So it's great that you can make a super flashy demo for consumers, but it takes a lot to actually get it into billion user products and large global enterprises. So about six months ago, we released our command models, which are some of the best that exist for large language models. And in December, we released our multilingual text understanding models and that's on over a hundred different languages and it's trained on, you know, authentic data directly from native speakers. And so we're super excited to continue pushing this into enterprise and solving those barriers for adoption, making this transformation a reality. >> Just real quick, while I got you there on the new products coming out. Where are we in the progress? People see some of the new stuff out there right now. There's so much more headroom. Can you just scope out in your mind what that looks like? Like from a headroom standpoint? Okay, we see ChatGPT. "Oh yeah, it writes my papers for me, does some homework for me." I mean okay, yawn, maybe people say that, (Aidan chuckles) people excited or people are blown away. I mean, it's helped theCUBE out, it helps me, you know, feed up a little bit from my write-ups but it's not always perfect. >> Yeah, at the moment it's like a writing assistant, right? And it's still super early in the technologies trajectory. I think it's fascinating and it's interesting but its impact is still really limited. I think in the next year, like within the next eight months, we're going to see some major changes. You've already seen the very first hints of that with stuff like Bing Chat, where you augment these dialogue models with an external knowledge base. So now the models can be kept up to date to the millisecond, right? Because they can search the web and they can see events that happened a millisecond ago. But that's still limited in the sense that when you ask the question, what can these models actually do? Well they can just write text back at you. That's the extent of what they can do. And so the real project, the real effort, that I think we're all working towards is actually taking action. So what happens when you give these models the ability to use tools, to use APIs? What can they do when they can actually affect change out in the real world, beyond just streaming text back at the user? I think that's the really exciting piece. >> Okay, so I wanted to tee that up early in the segment 'cause I want to get into the customer applications. We're seeing early adopters come in, using the technology because they have a lot of data, they have a lot of large language model opportunities and then there's a big fast follower wave coming behind it. I call that the people who are going to jump in the pool early and get into it. They might not be advanced. Can you guys share what customer applications are being used with large language and vision models today and how they're using it to transform on the early adopter side, and how is that a tell sign of what's to come? >> You know, one of the things we have been seeing both with the text models that Aidan talked about as well as the vision models that stability.ai does, Tom, is customers are really using it to change the way you interact with information. You know, one example of a customer that we have, is someone who's kind of using that to query customer conversations and ask questions like, you know, "What was the customer issue? How did we solve it?" And trying to get those kinds of insights that was previously much harder to do. And then of course software is a big area. You know, generating software, making that, you know, just deploying it in production. Those have been really big areas that we have seen customers start to do. You know, looking at documentation, like instead of you know, searching for stuff and so on, you know, you just have an interactive way, in which you can just look at the documentation for a product. You know, all of this goes to where we need to take the technology. One of which is, you know, the models have to be there but they have to work reliably in a production setting at scale, with privacy, with security, and you know, making sure all of this is happening, is going to be really key. That is what, you know, we at AWS are looking to do, which is work with partners like stability and others and in the open source and really take all of these and make them available at scale to customers, where they work reliably. >> Tom, Aidan, what's your thoughts on this? Where are customers landing on this first use cases or set of low-hanging fruit use cases or applications? >> Yeah, so I think like the first group of adopters that really found product market fit were the copywriting companies. So one great example of that is HyperWrite. Another one is Jasper. And so for Cohere, that's the tip of the iceberg, like there's a very long tail of usage from a bunch of different applications. HyperWrite is one of our customers, they help beat writer's block by drafting blog posts, emails, and marketing copy. We also have a global audio streaming platform, which is using us the power of search engine that can comb through podcast transcripts, in a bunch of different languages. Then a global apparel brand, which is using us to transform how they interact with their customers through a virtual assistant, two dozen global news outlets who are using us for news summarization. So really like, these large language models, they can be deployed all over the place into every single industry sector, language is everywhere. It's hard to think of any company on Earth that doesn't use language. So it's, very, very- >> We're doing it right now. We got the language coming in. >> Exactly. >> We'll transcribe this puppy. All right. Tom, on your side, what do you see the- >> Yeah, we're seeing some amazing applications of it and you know, I guess that's partly been, because of the growth in the open source community and some of these applications have come from there that are then triggering this secondary wave of innovation, which is coming a lot from, you know, controllability and explainability of the model. But we've got companies like, you know, Jasper, which Aidan mentioned, who are using stable diffusion for image generation in block creation, content creation. We've got Lensa, you know, which exploded, and is built on top of stable diffusion for fine tuning so people can bring themselves and their pets and you know, everything into the models. So we've now got fine tuned stable diffusion at scale, which is democratized, you know, that process, which is really fun to see your Lensa, you know, exploded. You know, I think it was the largest growing app in the App Store at one point. And lots of other examples like NightCafe and Lexica and Playground. So seeing lots of cool applications. >> So much applications, we'll probably be a customer for all you guys. We'll definitely talk after. But the challenges are there for people adopting, they want to get into what you guys see as the challenges that turn into opportunities. How do you see the customers adopting generative AI applications? For example, we have massive amounts of transcripts, timed up to all the videos. I don't even know what to do. Do I just, do I code my API there. So, everyone has this problem, every vertical has these use cases. What are the challenges for people getting into this and adopting these applications? Is it figuring out what to do first? Or is it a technical setup? Do they stand up stuff, they just go to Amazon? What do you guys see as the challenges? >> I think, you know, the first thing is coming up with where you think you're going to reimagine your customer experience by using generative AI. You know, we talked about Ada, and Tom talked about a number of these ones and you know, you pick up one or two of these, to get that robust. And then once you have them, you know, we have models and we'll have more models on AWS, these large language models that Aidan was talking about. Then you go in and start using these models and testing them out and seeing whether they fit in use case or not. In many situations, like you said, John, our customers want to say, "You know, I know you've trained these models on a lot of publicly available data, but I want to be able to customize it for my use cases. Because, you know, there's some knowledge that I have created and I want to be able to use that." And then in many cases, and I think Aidan mentioned this. You know, you need these models to be up to date. Like you can't have it staying. And in those cases, you augmented with a knowledge base, you know you have to make sure that these models are not hallucinating. And so you need to be able to do the right kind of responsible AI checks. So, you know, you start with a particular use case, and there are a lot of them. Then, you know, you can come to AWS, and then look at one of the many models we have and you know, we are going to have more models for other modalities as well. And then, you know, play around with the models. We have a playground kind of thing where you can test these models on some data and then you can probably, you will probably want to bring your own data, customize it to your own needs, do some of the testing to make sure that the model is giving the right output and then just deploy it. And you know, we have a lot of tools. >> Yeah. >> To make this easy for our customers. >> How should people think about large language models? Because do they think about it as something that they tap into with their IP or their data? Or is it a large language model that they apply into their system? Is the interface that way? What's the interaction look like? >> In many situations, you can use these models out of the box. But in typical, in most of the other situations, you will want to customize it with your own data or with your own expectations. So the typical use case would be, you know, these are models are exposed through APIs. So the typical use case would be, you know you're using these APIs a little bit for testing and getting familiar and then there will be an API that will allow you to train this model further on your data. So you use that AI, you know, make sure you augmented the knowledge base. So then you use those APIs to customize the model and then just deploy it in an application. You know, like Tom was mentioning, a number of companies that are using these models. So once you have it, then you know, you again, use an endpoint API and use it in an application. >> All right, I love the example. I want to ask Tom and Aidan, because like most my experience with Amazon Web Service in 2007, I would stand up in EC2, put my code on there, play around, if it didn't work out, I'd shut it down. Is that a similar dynamic we're going to see with the machine learning where developers just kind of log in and stand up infrastructure and play around and then have a cloud-like experience? >> So I can go first. So I mean, we obviously, with AWS working really closely with the SageMaker team, do fantastic platform there for ML training and inference. And you know, going back to your point earlier, you know, where the data is, is hugely important for companies. Many companies bringing their models to their data in AWS on-premise for them is hugely important. Having the models to be, you know, open sources, makes them explainable and transparent to the adopters of those models. So, you know, we are really excited to work with the SageMaker team over the coming year to bring companies to that platform and make the most of our models. >> Aidan, what's your take on developers? Do they just need to have a team in place, if we want to interface with you guys? Let's say, can they start learning? What do they got to do to set up? >> Yeah, so I think for Cohere, our product makes it much, much easier to people, for people to get started and start building, it solves a lot of the productionization problems. But of course with SageMaker, like Tom was saying, I think that lowers a barrier even further because it solves problems like data privacy. So I want to underline what Bratin was saying earlier around when you're fine tuning or when you're using these models, you don't want your data being incorporated into someone else's model. You don't want it being used for training elsewhere. And so the ability to solve for enterprises, that data privacy and that security guarantee has been hugely important for Cohere, and that's very easy to do through SageMaker. >> Yeah. >> But the barriers for using this technology are coming down super quickly. And so for developers, it's just becoming completely intuitive. I love this, there's this quote from Andrej Karpathy. He was saying like, "It really wasn't on my 2022 list of things to happen that English would become, you know, the most popular programming language." And so the barrier is coming down- >> Yeah. >> Super quickly and it's exciting to see. >> It's going to be awesome for all the companies here, and then we'll do more, we're probably going to see explosion of startups, already seeing that, the maps, ecosystem maps, the landscape maps are happening. So this is happening and I'm convinced it's not yesterday's chat bot, it's not yesterday's AI Ops. It's a whole another ballgame. So I have to ask you guys for the final question before we kick off the company's showcasing here. How do you guys gauge success of generative AI applications? Is there a lens to look through and say, okay, how do I see success? It could be just getting a win or is it a bigger picture? Bratin we'll start with you. How do you gauge success for generative AI? >> You know, ultimately it's about bringing business value to our customers. And making sure that those customers are able to reimagine their experiences by using generative AI. Now the way to get their ease, of course to deploy those models in a safe, effective manner, and ensuring that all of the robustness and the security guarantees and the privacy guarantees are all there. And we want to make sure that this transitions from something that's great demos to actual at scale products, which means making them work reliably all of the time not just some of the time. >> Tom, what's your gauge for success? >> Look, I think this, we're seeing a completely new form of ways to interact with data, to make data intelligent, and directly to bring in new revenue streams into business. So if businesses can use our models to leverage that and generate completely new revenue streams and ultimately bring incredible new value to their customers, then that's fantastic. And we hope we can power that revolution. >> Aidan, what's your take? >> Yeah, reiterating Bratin and Tom's point, I think that value in the enterprise and value in market is like a huge, you know, it's the goal that we're striving towards. I also think that, you know, the value to consumers and actual users and the transformation of the surface area of technology to create experiences like ChatGPT that are magical and it's the first time in human history we've been able to talk to something compelling that's not a human. I think that in itself is just extraordinary and so exciting to see. >> It really brings up a whole another category of markets. B2B, B2C, it's B2D, business to developer. Because I think this is kind of the big trend the consumers have to win. The developers coding the apps, it's a whole another sea change. Reminds me everyone use the "Moneyball" movie as example during the big data wave. Then you know, the value of data. There's a scene in "Moneyball" at the end, where Billy Beane's getting the offer from the Red Sox, then the owner says to the Red Sox, "If every team's not rebuilding their teams based upon your model, there'll be dinosaurs." I think that's the same with AI here. Every company will have to need to think about their business model and how they operate with AI. So it'll be a great run. >> Completely Agree >> It'll be a great run. >> Yeah. >> Aidan, Tom, thank you so much for sharing about your experiences at your companies and congratulations on your success and it's just the beginning. And Bratin, thanks for coming on representing AWS. And thank you, appreciate for what you do. Thank you. >> Thank you, John. Thank you, Aidan. >> Thank you John. >> Thanks so much. >> Okay, let's kick off season three, episode one. I'm John Furrier, your host. Thanks for watching. (light airy music)

(intense music) >> Hello, I'm John Furrier with "SiliconANGLE News" and the host of theCUBE here in Palo Alto, with coverage of MWC 2023. theCUBE is onsite in Barcelona, four days of wall to wall coverage. Here is a news update from MWC and in the news here is Google. Google Cloud targets cloud native network transformation for all the carriers or cloud service providers, and the communication service providers. They announced three new products to help communications service providers, also known as CSPs, build, deploy and operate hybrid cloud native networks, as well as collect and manage network data. The new products, when combined with Unified Cloud, enables the CSPs to improve customer experience, artificial intelligence, and data analytics. This is a big move, because 70% of communication service providers are expected to adopt cloud native network functions by the end of this year, making it a big, big wave. One of the key features of Google's products is the telecom network automation. This cloud service accelerates CSPs network and edge deployments through the use of Kubernetes based cloud native automation tools. It's managed by a cloud version of open source Nephio, project that Google founded in 2022. Of course, other key product announcements with Google, the Telecom Data Fabric, a tool that helps CSPs generate insights. That's the data driven piece, to target and optimize their network performance and reliability, works by simplifying the collection, normalization, correlation through an adaptive framework. This is kind of where AI shines. Finally, Google has telecom subscriber insights, a powerful AI tool that enables CSPs to extract insights from existing data sources in a privacy safe environment. Let's see if this is better than Bing search, we'll see. But CSPs are moving to the cloud across all channels. This is a really important trend, as cloud native scale, AI, data, configuration, automation all come to the edge of the network. That's an update from "SiliconANGLE News". Check out the coverage on siliconangle.com. Of course, thecube.net, four days, Dave Vellante and Lisa Martin are there. I'm here in Palo Alto. Thanks for watching. (slow music) (upbeat music)

(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)

>> TheCUBE presents Ignite 22 brought to you by Palo Alto Networks. >> Welcome back to Las Vegas, everyone. We're glad you're with us. This is theCUBE live at Palo Alto Ignite 22 at the MGM Grant in Las Vegas. Lisa Martin here with Dave Vellante, day one of our coverage. We've had great conversations. The cybersecurity landscape is so interesting Dave, it's such a challenging problem to solve but it's so diverse and dynamic at the same time. >> You know, Lisa theCUBE started in May of 2010 in Boston. We called it the chowder event, chowder and Lobster. It was a EMC world, 2010. BJ Jenkins, who's here, of course, was a longtime friend of theCUBE and made the, made the transition into from, well, it's still data, data to, to cyber. So >> True. And BJ is back with us. BJ Jenkins, president Palo Alto Networks great to have you back on theCUBE. >> It is great to be here in person on theCube >> Isn't it great? >> In Vegas. It's awesome. >> And we can tell by your voice will be, will be gentle. You, you've been in Vegas typical Vegas occupational hazard of losing the voice. >> Yeah. It was one of the benefits of Covid. I didn't lose my voice at home sitting talking to a TV. You lose it when you come to Vegas. >> Exactly. >> But it's a small price to pay. >> So things kick off yesterday with the partner summit. You had a keynote then, you had a customer, a CISO on stage. You had a keynote today, which we didn't get to see. But talk to us a little bit about the lay of the land. What are you hearing from CISOs, from CIOs as we know security is a board level conversation. >> Yeah, I, you know it's been an interesting three or four months here. Let me start with that. I think, cybersecurity in general is still front and center on CIOs and CISO's minds. It has to be, if you saw Wendy's presentation today and the threats out there companies have to have it front and center. I do think it's been interesting though with the macro uncertainty. We've taken to calling this year the revenge of the CFO and you know these deals in cybersecurity are still a top priority but they're getting finance and procurements, scrutiny which I think in this environment is a necessity but it's still a, you know, number one number two imperative no matter who you talked to, in my mind >> It was interesting what Nikesh was saying in the last conference call that, hey we just have to get more approvals. We know this. We're, we're bringing more go-to-market people on board. We, we have, we're filling the pipeline 'cause we know they're going to split up deals big deals go into smaller chunks. So the question I have for you is is how are you able to successfully integrate those people so that you can get ahead of that sort of macro transition? >> Yeah I, you know, I think there's two things I'd say about uncertain macro situations and Dave, you know how old I am. I'm pretty old. I've been through a lot of cycles. And in those cycles I've always found stronger companies with stronger value proposition separate themselves actually in uncertain, economic times. And so I think there's actually an opportunity here. The message tilts a little bit though where it's been about innovation and new threat vectors to one of you have 20, 30, 40 vendors you can consolidate become more effective in your security posture and save money on your TCOs. So one of the things as we bring people on board it's training them on that business value proposition. How do you take a customer who's got 20 or 30 tools take 'em down to 5 or 10 where Palo is more central and strategic and be able to demonstrate that value. So we do that through, we're making a huge investment in our people but macroeconomic times also puts some stronger people back on the market and we're able to incorporate them into the business. >> What are the conditions that are necessary for that consolidation? Like I would imagine if you're, if you're a big customer of a big, you know, competitor of yours that that migration is going to be harder than if you're dealing with lots of little point tools. Do those, do those point tools, are they sort of is it the end of the subscription? Is it just stuff that's off the books now? What's, the condition that is ripe for that kind of consolidation? >> Look, I think the challenge coming into this year was skills. And so customers had all of these point products. It required a lot more human intervention as Nikesh was talking about to integrate them or make them work. And as all of us know finding people with cybersecurity skills over the last 12 months has been incredibly hard. That drove, if you know, if you think about that a CIO and a CISO sitting there going, I have all all this investment in tools. I don't have the people to operate 'em. What do I need to do? What we tried to do is elevate that conversation because in a customer, everybody who's bought one of those, they they bought it to solve a problem. And there's people with affinity for that tool. They're not just going to say I want to get consolidated and give up my tool. They're going to wrap their arms around it. And so what we needed to do and this changed our ecosystem strategy too how we leverage partners. We needed to get into the CIO and CISO and say look at this chaos you have here and the challenges around people that it's, it's presenting you. We can help solve that by, by standardizing, consolidating taking that integration away from you as Nikesh talked about, and making it easier for your your high skill people to work on high skill, you know high challenges in there. >> Let chaos reign, and then reign in the chaos. >> Yes. >> Andy Grove. >> I was looking at some stats that there's 26 million developers but less than 3 million cybersecurity professionals. >> Talked about that skills gap and what CISOs and CIOs are facing is do you consider from a value prop perspective Palo Alto Networks to be a, a facilitator of helping organizations deal with that skills gap? >> I think there's a short term and a long term. I think Nikesh today talked about the long term that we'll never win this battle with human beings. We're going to have to win it with automation. That, that's the long term the short term right here and now is that people need people with cybersecurity skills. Now what we're trying to do, you know, is multifaceted. We work with universities to standardize programs to develop skills that people can come into the marketplace with. We run our own programs inside the company. We have a cloud academy program now where we take people high aptitude for sales and technical aptitude and we will put them through a six month boot camp on cloud and they'll come out of that ready to really work with the leading experts in cloud security. The third angle is partners, right, there are partners in the marketplace who want to drive their business into high services areas. They have people, they know how to train. We give them, we partner with them to give them training. Hopefully that helps solve some of the short-term gaps that are out there today. >> So you made the jump from data storage to security and >> Yeah. >> You know, network security, all kinds of security. What was that like? What you must have learned a lot in the last better part of a decade? >> Yeah. >> Take us through that. >> You know, so the first jump was from EMC. I was 15 years there to be CEO of Barracuda. And you know, it was interesting because EMC was, you know large enterprise for the most part. At Barracuda we had, you know 250,000 small and mid-size enterprises. And it was, it's interesting to get into security in small and mid-size businesses because, you know Wendy today was talking about nation states. For small and mid-size business, it's common thievery right? It's ransomware, it's, and, those customers don't have, you know, the human and financial resources to keep up with the threat factor. So, you know, Nikesh talked about how it's taken 'em four and a half years to get into cybersecurity. I remember my first week at Barracuda, I was talking with a customer who had, you know, breached data shut down. There wasn't much bitcoin back then so it was just a pure ransom. And I'm like, wow, this is, you know, incredible industry. So it's been a good, you know, transition for me. I still think data is at the heart of all of this. Right? And I have always believed there's a strong connection between the things I learned growing up at EMC and what I put into practice today at Palo Alto Networks. >> And how about a culture because I, you know I know have observed the EMC culture >> Yeah. >> And you were there in really the heyday. >> Yeah. >> Right? Which was an awesome place. And it seems like Palo Alto obviously, different times but you know, similar like laser focus on solving problems, you know, obviously great, you know value sellers, you know, you guys aren't the commodity >> Yeah. For Product. But there seemed to be some similarities from afar. I don't know Palo Alto as well as I know EMC. >> I think there's a lot. When I joined EMC, it was about, it was 2 billion in in revenue and I think when I left it was over 20, 20, 21. And, you know, we're at, you know hopefully 5, 5 5 in revenue. I feel like it's this very similar, there's a sense of urgency, there's an incredible focus on the customer. you know, Near and Moche are definitely different individuals but the both same kind of disruptive, Israeli force out there driving the business. There are a lot of similarities. I, you know, the passion, I feel privileged as a, you know go to market person that I have this incredible portfolio to go, you know, work with customers on. It's a lucky position to be in, but very I feel like it is a movie I've seen before. >> Yeah. And but, and the course, the challenges from the, the target that you're disrupting is different. It was, you know, EMC had a lot of big, you know IBM obviously was, you know, bigger target whereas you got thousands of, you know, smaller companies. >> Yes. >> And, and so that's a different dynamic but that's why the consolidation play is so important. >> Look at, that's why I joined Palo Alto Networks when I was at Barracuda for nine years. It just fascinated me, that there was 3000 plus players in security and why didn't security evolve like the storage market did or the server market or network where working >> Yeah, right. >> You know, two or three big gorillas came to, to dominate those markets. And it's, I think it's what Nikesh talked about today. There was a new problem in best of breed. It was always best of breed. You can never in security go in and, you know, say, Hey it's good I saved us some money but I got the third best product in the marketplace. And there was that kind of gap between products. I, believe in why I joined here I think this is my last gig is we have a chance to change that. And this is the first company as I look from the outside in that had best of breed as, you know Nikesh said 13 categories. >> Yeah. >> And you know, we're in the leaders quadrant and it's a conversation I have with customers. You don't have to sacrifice best of breed but get the benefits of a platform. And I, think that resonates today. I think we have a chance to change the industry from that viewpoint. >> Give us a little view of the voice of the customer. You had, was it Sabre? >> Yeah. >> That was on >> Scott Moser, The CISO from Sabre. >> Give us a view, what are you hearing from the voice of the customer? Obviously they're quite a successful customer but challenges, concerns, the partnership. >> Yeah. Look, I think security is similar to industries where we come up with magic marketing phrases and, you know, things to you know, make you want to procure our solutions. You know, zero trust is one. And you know, you'll talk to customers and they're like, okay, yes. And you know, the government, right? Joe, Joe Biden's putting out zero trust executive orders. And the, the problem is if you talk to customers, it's a journey. They have legacy infrastructure they have business drivers that you know they just don't deal with us. They've got to deal with the business side who's trying to make the money that keeps the, the company going. it's really helped them draw a map from where they're at today to zero trust or to a better security architecture. Or, you know, they're moving their apps into the cloud. How am I going to migrate? Right? Again, that discussion three years ago was around lift and shift, right? Today it's about, well, no I need cloud native developed apps to service the business the way I want to, I want to service it. How do I, so I, I think there's this element of a trusted partner and relationship. And again, I think this is why you can't have 40 or 50 of those. You got to start narrowing it down if you want to be able to meet and beat the threats that are out there for you. So I, you know, the customers, I see a lot of 'em. It's, here's where I'm at help me get here to a better position. And they know it's, you know Scott said in our keynote today, you don't just, you know have layer three firewall policies and decide, okay tomorrow I'm going to go to layer seven. That, that's not how it works. Right? There's, and, and by the way these things are a mission critical type areas. So there's got to be a game plan that you help customers go through to get there. >> Definitely. Last question, my last question for you is, is security being a board level conversation I was reading some stats from a survey I think it was the what's new in Cypress survey that that Palo Alto released today that showed that while significant numbers of organizations think they've got a cyber resiliency playbook, there's a lot of disconnect or lack of alignment at the boardroom. Are you in those conversations? How can you help facilitate that alignment between the executive team and the board when it comes to security being so foundational to any business? >> Yeah, it's, I've been on three, four public company boards. I'm on, I'm on two today. I would say four years ago, this was a almost a taboo topic. It was a, put your head in the sand and pray to God nothing happened. And you know, the world has changed significantly. And because of the number of breaches the impact it's had on brand, boards have to think about this in duty of care and their fiduciary duty. Okay. So then you start with a board that may not have the technical skills. The first problem the security industry had is how do I explain your risk profile in a way you can understand it. I'm, I'm on the board of Generac that makes home generators. It's a manufacturing, you know, company but they put Wifi modules in their boxes so that the dealers could help do the maintenance on 'em. And all of a sudden these things were getting attacked. Right? And they're being used for bot attacks. >> Yeah. >> Everybody on their board had a manufacturing background. >> Ah. >> So how do you help that board understand the risk they have that's what's changed over the last four years. It's a constant discussion. It's one I have with CISOs where they're like help us put it in layman's terms so they understand they know what we're doing and they feel confident but at the same time understand the marketplace better. And that's a journey for us. >> That Generac example is a great one because, you know, think about IOT Technologies. They've historically been air gaped >> Yes. >> By design. And all of a sudden the business comes in and says, "Hey we can put wifi in there", you know >> Connect it to a home Wifi system that >> Make our lives so much easier. Next thing you know, it's being used to attack. >> Yeah. >> So that's why, as you go around the world are you discerning, I know you were just in Japan are you discerning significant differences in sort of attitudes toward, towards cyber? Whether it's public policy, you know things like regulation where you, they don't want you sharing data, but as as a cyber company, you want to share that data with you know, public and private? >> Look it, I, I think around the world we see incredible government activity first of all. And I think given the position we're in we get to have some unique conversations there. I would say worldwide security is an imperative. I, no matter where I go, you know it's in front of everybody's mind. The, on the, the governance side, it's really what do we need to adapt to make sure we meet local regulations. And I, and I would just tell you Dave there's ways when you do that, and we talk with governments that because of how they want to do it reduce our ability to give them full insight into all the threats and how we can help them. And I do think over time governments understand that we can anonymize the data. There's, but that, that's a work in process. Definitely there is a balance. We need to have privacy, we need to have, you know personal security for people. But there's ways to collect that data in an anonymous way and give better security insight back into the architectures that are out there. >> All right. A little shift the gears here. A little sports question. We've had some great Boston's sports guests on theCUBE right? I mean, Randy Seidel, we were talking about him. Peter McKay, Snyk, I guess he's a competitor now but you know, there's no question got >> He got a little funding today. I saw that. >> Down round. But they still got a lot of money. Not of a down round, but they were, but yeah, but actually, you know, he was on several years ago and it was around the time they were talking about trading Brady. He said Never trade Brady. And he got that right. We, I think we can agree Brady's the goat. >> Yes. >> The big question I have for you is, Belichick. Do you ever question Has your belief in him as the greatest coach of all time wavered, you know, now that- No. Okay. >> Never. >> Weigh in on that. >> Never, he says >> Still the Goat. >> I'll give you my best. You know, never In Bill we trust. >> Okay. Still. >> All right >> I, you know, the NFL is a unique property that's designed for parody and is designed, I mean actively designed to not let Mr. Craft and Bill Belichick do what they do every year. I feel privileged as a Boston sports fan that in our worst years we're in the seventh playoff spot. And I have a lot of family in Chicago who would kill for that position, by the way. And you know, they're in perpetual rebuilding. And so look, and I think he, you know the way he's been able to manage the cap and the skill levels, I think we have a top five defense. There's different ways to win titles. And if I, you know, remember in Brady's last title with Boston, the defense won us that Super Bowl. >> Well thanks for weighing in on that because there's a lot of crazy talk going on. Like, 'Hey, if he doesn't beat Arizona, he's got to go.' I'm like, what? So, okay, I'm sometimes it takes a good good loyal fan who's maybe, you know, has >> The good news in Boston is we're emotional fans too so I understand you got to keep the long term long term in mind. And we're, we're in a privileged position in Boston. We've got Celtics, we've got Bruins we've got the Patriots right on the edge of the playoffs and we need the Red Sox to get to work. >> Yeah, no, you know they were last, last year so maybe they're going to win it all like they usually do. So >> Fingers crossed. >> Crazy worst to first. >> Exactly. Well you said, in Bill we trust it sounds like from our conversation in BJ we trust from the customers, the partners. >> I hope so. >> Thank you so much BJ, for coming back on theCUBE giving us the lay of the land, what's new, the voice of the customer and how Palo Alto was really differentiated in the market. We always appreciate your, coming on the show you >> Honor and privilege seeing you here. Thanks. >> You may be thinking that you were watching ESPN just now but you know, we call ourselves the ESPN at Tech News. This is Lisa Martin for Dave Vellante and our guest. You're watching theCUBE, the Leader and live emerging in enterprise tech coverage. (upbeat music)

>>Hey everyone. Welcome to Las Vegas. The Cube is here, live at the Venetian Expo Center for AWS Reinvent 2022. Amazing attendance. This is day one of our coverage. Lisa Martin here with Day Ante. David is great to see so many people back. We're gonna be talk, we've been having great conversations already. We have a wall to wall coverage for the next three and a half days. When we talk to companies, customers, every company has to be a data company. And one of the things I think we learned in the pandemic is that access to real time data and real time analytics, no longer a nice to have that is a differentiator and a competitive all >>About data. I mean, you know, I love the topic and it's, it's got so many dimensions and such texture, can't get enough of data. >>I know we have a great guest joining us. One of our alumni is back, Evan Kaplan, the CEO of Influx Data. Evan, thank you so much for joining us. Welcome back to the Cube. >>Thanks for having me. It's great to be here. So here >>We are, day one. I was telling you before we went live, we're nice and fresh hosts. Talk to us about what's new at Influxed since the last time we saw you at Reinvent. >>That's great. So first of all, we should acknowledge what's going on here. This is pretty exciting. Yeah, that does really feel like, I know there was a show last year, but this feels like the first post Covid shows a lot of energy, a lot of attention despite a difficult economy. In terms of, you know, you guys were commenting in the lead into Big data. I think, you know, if we were to talk about Big Data five, six years ago, what would we be talking about? We'd been talking about Hadoop, we were talking about Cloudera, we were talking about Hortonworks, we were talking about Big Data Lakes, data stores. I think what's happened is, is this this interesting dynamic of, let's call it if you will, the, the secularization of data in which it breaks into different fields, different, almost a taxonomy. You've got this set of search data, you've got this observability data, you've got graph data, you've got document data and what you're seeing in the market and now you have time series data. >>And what you're seeing in the market is this incredible capability by developers as well and mostly open source dynamic driving this, this incredible capability of developers to assemble data platforms that aren't unicellular, that aren't just built on Hado or Oracle or Postgres or MySQL, but in fact represent different data types. So for us, what we care about his time series, we care about anything that happens in time, where time can be the primary measurement, which if you think about it, is a huge proportion of real data. Cuz when you think about what drives ai, you think about what happened, what happened, what happened, what happened, what's going to happen. That's the functional thing. But what happened is always defined by a period, a measurement, a time. And so what's new for us is we've developed this new open source engine called IOx. And so it's basically a refresh of the whole database, a kilo database that uses Apache Arrow, par K and data fusion and turns it into a super powerful real time analytics platform. It was already pretty real time before, but it's increasingly now and it adds SQL capability and infinite cardinality. And so it handles bigger data sets, but importantly, not just bigger but faster, faster data. So that's primarily what we're talking about to show. >>So how does that affect where you can play in the marketplace? Is it, I mean, how does it affect your total available market? Your great question. Your, your customer opportunities. >>I think it's, it's really an interesting market in that you've got all of these different approaches to database. Whether you take data warehouses from Snowflake or, or arguably data bricks also. And you take these individual database companies like Mongo Influx, Neo Forge, elastic, and people like that. I think the commonality you see across the volume is, is many of 'em, if not all of them, are based on some sort of open source dynamic. So I think that is an in an untractable trend that will continue for on. But in terms of the broader, the broader database market, our total expand, total available tam, lots of these things are coming together in interesting ways. And so the, the, the wave that will ride that we wanna ride, because it's all big data and it's all increasingly fast data and it's all machine learning and AI is really around that measurement issue. That instrumentation the idea that if you're gonna build any sophisticated system, it starts with instrumentation and the journey is defined by instrumentation. So we view ourselves as that instrumentation tooling for understanding complex systems. And how, >>I have to follow quick follow up. Why did you say arguably data bricks? I mean open source ethos? >>Well, I was saying arguably data bricks cuz Spark, I mean it's a great company and it's based on Spark, but there's quite a gap between Spark and what Data Bricks is today. And in some ways data bricks from the outside looking in looks a lot like Snowflake to me looks a lot like a really sophisticated data warehouse with a lot of post-processing capabilities >>And, and with an open source less >>Than a >>Core database. Yeah. Right, right, right. Yeah, I totally agree. Okay, thank you for that >>Part that that was not arguably like they're, they're not a good company or >>No, no. They got great momentum and I'm just curious. Absolutely. You know, so, >>So talk a little bit about IOx and, and what it is enabling you guys to achieve from a competitive advantage perspective. The key differentiators give us that scoop. >>So if you think about, so our old storage engine was called tsm, also open sourced, right? And IOx is open sourced and the old storage engine was really built around this time series measurements, particularly metrics, lots of metrics and handling those at scale and making it super easy for developers to use. But, but our old data engine only supported either a custom graphical UI that you'd build yourself on top of it or a dashboarding tool like Grafana or Chronograph or things like that. With IOCs. Two or three interventions were important. One is we now support, we'll support things like Tableau, Microsoft, bi, and so you're taking that same data that was available for instrumentation and now you're using it for business intelligence also. So that became super important and it kind of answers your question about the expanded market expands the market. The second thing is, when you're dealing with time series data, you're dealing with this concept of cardinality, which is, and I don't know if you're familiar with it, but the idea that that it's a multiplication of measurements in a table. And so the more measurements you want over the more series you have, you have this really expanding exponential set that can choke a database off. And the way we've designed IIS to handle what we call infinite cardinality, where you don't even have to think about that design point of view. And then lastly, it's just query performance is dramatically better. And so it's pretty exciting. >>So the unlimited cardinality, basically you could identify relationships between data and different databases. Is that right? Between >>The same database but different measurements, different tables, yeah. Yeah. Right. Yeah, yeah. So you can handle, so you could say, I wanna look at the way, the way the noise levels are performed in this room according to 400 different locations on 25 different days, over seven months of the year. And that each one is a measurement. Each one adds to cardinality. And you can say, I wanna search on Tuesdays in December, what the noise level is at 2:21 PM and you get a very quick response. That kind of instrumentation is critical to smarter systems. How are >>You able to process that data at at, in a performance level that doesn't bring the database to its knees? What's the secret sauce behind that? >>It's AUM database. It's built on Parque and Apache Arrow. But it's, but to say it's nice to say without a much longer conversation, it's an architecture that's really built for pulling that kind of data. If you know the data is time series and you're looking for a time measurement, you already have the ability to optimize pretty dramatically. >>So it's, it's that purpose built aspect of it. It's the >>Purpose built aspect. You couldn't take Postgres and do the same >>Thing. Right? Because a lot of vendors say, oh yeah, we have time series now. Yeah. Right. So yeah. Yeah. Right. >>And they >>Do. Yeah. But >>It's not, it's not, the founding of the company came because Paul Dicks was working on Wall Street building time series databases on H base, on MyQ, on other platforms and realize every time we do it, we have to rewrite the code. We build a bunch of application logic to handle all these. We're talking about, we have customers that are adding hundreds of millions to billions of points a second. So you're talking about an ingest level. You know, you think about all those data points, you're talking about ingest level that just doesn't, you know, it just databases aren't designed for that. Right? And so it's not just us, our competitors also build good time series databases. And so the category is really emergent. Yeah, >>Sure. Talk about a favorite customer story they think really articulates the value of what Influx is doing, especially with IOx. >>Yeah, sure. And I love this, I love this story because you know, Tesla may not be in favor because of the latest Elon Musker aids, but, but, but so we've had about a four year relationship with Tesla where they built their power wall technology around recording that, seeing your device, seeing the stuff, seeing the charging on your car. It's all captured in influx databases that are reporting from power walls and mega power packs all over the world. And they report to a central place at, at, at Tesla's headquarters and it reports out to your phone and so you can see it. And what's really cool about this to me is I've got two Tesla cars and I've got a Tesla solar roof tiles. So I watch this date all the time. So it's a great customer story. And actually if you go on our website, you can see I did an hour interview with the engineer that designed the system cuz the system is super impressive and I just think it's really cool. Plus it's, you know, it's all the good green stuff that we really appreciate supporting sustainability, right? Yeah. >>Right, right. Talk about from a, what's in it for me as a customer, what you guys have done, the change to IOCs, what, what are some of the key features of it and the key values in it for customers like Tesla, like other industry customers as well? >>Well, so it's relatively new. It just arrived in our cloud product. So Tesla's not using it today. We have a first set of customers starting to use it. We, the, it's in open source. So it's a very popular project in the open source world. But the key issues are, are really the stuff that we've kind of covered here, which is that a broad SQL environment. So accessing all those SQL developers, the same people who code against Snowflake's data warehouse or data bricks or Postgres, can now can code that data against influx, open up the BI market. It's the cardinality, it's the performance. It's really an architecture. It's the next gen. We've been doing this for six years, it's the next generation of everything. We've seen how you make time series be super performing. And that's only relevant because more and more things are becoming real time as we develop smarter and smarter systems. The journey is pretty clear. You instrument the system, you, you let it run, you watch for anomalies, you correct those anomalies, you re instrument the system. You do that 4 billion times, you have a self-driving car, you do that 55 times, you have a better podcast that is, that is handling its audio better, right? So everything is on that journey of getting smarter and smarter. So >>You guys, you guys the big committers to IOCs, right? Yes. And how, talk about how you support the, develop the surrounding developer community, how you get that flywheel effect going >>First. I mean it's actually actually a really kind of, let's call it, it's more art than science. Yeah. First of all, you you, you come up with an architecture that really resonates for developers. And Paul Ds our founder, really is a developer's developer. And so he started talking about this in the community about an architecture that uses Apache Arrow Parque, which is, you know, the standard now becoming for file formats that uses Apache Arrow for directing queries and things like that and uses data fusion and said what this thing needs is a Columbia database that sits behind all of this stuff and integrates it. And he started talking about it two years ago and then he started publishing in IOCs that commits in the, in GitHub commits. And slowly, but over time in Hacker News and other, and other people go, oh yeah, this is fundamentally right. >>It addresses the problems that people have with things like click cows or plain databases or Coast and they go, okay, this is the right architecture at the right time. Not different than original influx, not different than what Elastic hit on, not different than what Confluent with Kafka hit on and their time is you build an audience of people who are committed to understanding this kind of stuff and they become committers and they become the core. Yeah. And you build out from it. And so super. And so we chose to have an MIT open source license. Yeah. It's not some secondary license competitors can use it and, and competitors can use it against us. Yeah. >>One of the things I know that Influx data talks about is the time to awesome, which I love that, but what does that mean? What is the time to Awesome. Yeah. For developer, >>It comes from that original story where, where Paul would have to write six months of application logic and stuff to build a time series based applications. And so Paul's notion was, and this was based on the original Mongo, which was very successful because it was very easy to use relative to most databases. So Paul developed this commitment, this idea that I quickly joined on, which was, hey, it should be relatively quickly for a developer to build something of import to solve a problem, it should be able to happen very quickly. So it's got a schemaless background so you don't have to know the schema beforehand. It does some things that make it really easy to feel powerful as a developer quickly. And if you think about that journey, if you feel powerful with a tool quickly, then you'll go deeper and deeper and deeper and pretty soon you're taking that tool with you wherever you go, it becomes the tool of choice as you go to that next job or you go to that next application. And so that's a fundamental way we think about it. To be honest with you, we haven't always delivered perfectly on that. It's generally in our dna. So we do pretty well, but I always feel like we can do better. >>So if you were to put a bumper sticker on one of your Teslas about influx data, what would it >>Say? By the way, I'm not rich. It just happened to be that we have two Teslas and we have for a while, we just committed to that. The, the, so ask the question again. Sorry. >>Bumper sticker on influx data. What would it say? How, how would I >>Understand it be time to Awesome. It would be that that phrase his time to Awesome. Right. >>Love that. >>Yeah, I'd love it. >>Excellent time to. Awesome. Evan, thank you so much for joining David, the >>Program. It's really fun. Great thing >>On Evan. Great to, you're on. Haven't Well, great to have you back talking about what you guys are doing and helping organizations like Tesla and others really transform their businesses, which is all about business transformation these days. We appreciate your insights. >>That's great. Thank >>You for our guest and Dave Ante. I'm Lisa Martin, you're watching The Cube, the leader in emerging and enterprise tech coverage. We'll be right back with our next guest.

>>Collibra is a company that was founded in 2008 right before the so-called modern big data era kicked into high gear. The company was one of the first to focus its business on data governance. Now, historically, data governance and data quality initiatives, they were back office functions and they were largely confined to regulatory regulated industries that had to comply with public policy mandates. But as the cloud went mainstream, the tech giants showed us how valuable data could become and the value proposition for data quality and trust. It evolved from primarily a compliance driven issue to becoming a lynchpin of competitive advantage. But data in the decade of the 2010s was largely about getting the technology to work. You had these highly centralized technical teams that were formed and they had hyper specialized skills to develop data architectures and processes to serve the myriad data needs of organizations. >>And it resulted in a lot of frustration with data initiatives for most organizations that didn't have the resources of the cloud guys and the social media giants to really attack their data problems and turn data into gold. This is why today for example, this quite a bit of momentum to rethinking monolithic data architectures. You see, you hear about initiatives like data mesh and the idea of data as a product. They're gaining traction as a way to better serve the the data needs of decentralized business Uni users, you hear a lot about data democratization. So these decentralization efforts around data, they're great, but they create a new set of problems. Specifically, how do you deliver like a self-service infrastructure to business users and domain experts? Now the cloud is definitely helping with that, but also how do you automate governance? This becomes especially tricky as protecting data privacy has become more and more important. >>In other words, while it's enticing to experiment and run fast and loose with data initiatives kinda like the Wild West, to find new veins of gold, it has to be done responsibly. As such, the idea of data governance has had to evolve to become more automated. And intelligence governance and data lineage is still fundamental to ensuring trust as data. It moves like water through an organization. No one is gonna use data that isn't trusted. Metadata has become increasingly important for data discovery and data classification. As data flows through an organization, the continuously ability to check for data flaws and automating that data quality, they become a functional requirement of any modern data management platform. And finally, data privacy has become a critical adjacency to cyber security. So you can see how data governance has evolved into a much richer set of capabilities than it was 10 or 15 years ago. >>Hello and welcome to the Cube's coverage of Data Citizens made possible by Calibra, a leader in so-called Data intelligence and the host of Data Citizens 2022, which is taking place in San Diego. My name is Dave Ante and I'm one of the hosts of our program, which is running in parallel to data citizens. Now at the Cube we like to say we extract the signal from the noise, and over the, the next couple of days, we're gonna feature some of the themes from the keynote speakers at Data Citizens and we'll hear from several of the executives. Felix Von Dala, who is the co-founder and CEO of Collibra, will join us along with one of the other founders of Collibra, Stan Christians, who's gonna join my colleague Lisa Martin. I'm gonna also sit down with Laura Sellers, she's the Chief Product Officer at Collibra. We'll talk about some of the, the announcements and innovations they're making at the event, and then we'll dig in further to data quality with Kirk Hasselbeck. >>He's the vice president of Data quality at Collibra. He's an amazingly smart dude who founded Owl dq, a company that he sold to Col to Collibra last year. Now many companies, they didn't make it through the Hado era, you know, they missed the industry waves and they became Driftwood. Collibra, on the other hand, has evolved its business. They've leveraged the cloud, expanded its product portfolio, and leaned in heavily to some major partnerships with cloud providers, as well as receiving a strategic investment from Snowflake earlier this year. So it's a really interesting story that we're thrilled to be sharing with you. Thanks for watching and I hope you enjoy the program. >>Last year, the Cube Covered Data Citizens Collibra's customer event. And the premise that we put forth prior to that event was that despite all the innovation that's gone on over the last decade or more with data, you know, starting with the Hado movement, we had data lakes, we'd spark the ascendancy of programming languages like Python, the introduction of frameworks like TensorFlow, the rise of ai, low code, no code, et cetera. Businesses still find it's too difficult to get more value from their data initiatives. And we said at the time, you know, maybe it's time to rethink data innovation. While a lot of the effort has been focused on, you know, more efficiently storing and processing data, perhaps more energy needs to go into thinking about the people and the process side of the equation, meaning making it easier for domain experts to both gain insights for data, trust the data, and begin to use that data in new ways, fueling data, products, monetization and insights data citizens 2022 is back and we're pleased to have Felix Van Dema, who is the founder and CEO of Collibra. He's on the cube or excited to have you, Felix. Good to see you again. >>Likewise Dave. Thanks for having me again. >>You bet. All right, we're gonna get the update from Felix on the current data landscape, how he sees it, why data intelligence is more important now than ever and get current on what Collibra has been up to over the past year and what's changed since Data Citizens 2021. And we may even touch on some of the product news. So Felix, we're living in a very different world today with businesses and consumers. They're struggling with things like supply chains, uncertain economic trends, and we're not just snapping back to the 2010s. That's clear, and that's really true as well in the world of data. So what's different in your mind, in the data landscape of the 2020s from the previous decade, and what challenges does that bring for your customers? >>Yeah, absolutely. And, and I think you said it well, Dave, and and the intro that that rising complexity and fragmentation in the broader data landscape, that hasn't gotten any better over the last couple of years. When when we talk to our customers, that level of fragmentation, the complexity, how do we find data that we can trust, that we know we can use has only gotten kinda more, more difficult. So that trend that's continuing, I think what is changing is that trend has become much more acute. Well, the other thing we've seen over the last couple of years is that the level of scrutiny that organizations are under respect to data, as data becomes more mission critical, as data becomes more impactful than important, the level of scrutiny with respect to privacy, security, regulatory compliance, as only increasing as well, which again, is really difficult in this environment of continuous innovation, continuous change, continuous growing complexity and fragmentation. >>So it's become much more acute. And, and to your earlier point, we do live in a different world and and the the past couple of years we could probably just kind of brute for it, right? We could focus on, on the top line. There was enough kind of investments to be, to be had. I think nowadays organizations are focused or are, are, are, are, are, are in a very different environment where there's much more focus on cost control, productivity, efficiency, How do we truly get value from that data? So again, I think it just another incentive for organization to now truly look at data and to scale it data, not just from a a technology and infrastructure perspective, but how do you actually scale data from an organizational perspective, right? You said at the the people and process, how do we do that at scale? And that's only, only only becoming much more important. And we do believe that the, the economic environment that we find ourselves in today is gonna be catalyst for organizations to really dig out more seriously if, if, if, if you will, than they maybe have in the have in the best. >>You know, I don't know when you guys founded Collibra, if, if you had a sense as to how complicated it was gonna get, but you've been on a mission to really address these problems from the beginning. How would you describe your, your, your mission and what are you doing to address these challenges? >>Yeah, absolutely. We, we started Colli in 2008. So in some sense and the, the last kind of financial crisis, and that was really the, the start of Colli where we found product market fit, working with large finance institutions to help them cope with the increasing compliance requirements that they were faced with because of the, of the financial crisis and kind of here we are again in a very different environment, of course 15 years, almost 15 years later. But data only becoming more important. But our mission to deliver trusted data for every user, every use case and across every source, frankly, has only become more important. So what has been an incredible journey over the last 14, 15 years, I think we're still relatively early in our mission to again, be able to provide everyone, and that's why we call it data citizens. We truly believe that everyone in the organization should be able to use trusted data in an easy, easy matter. That mission is is only becoming more important, more relevant. We definitely have a lot more work ahead of us because we are still relatively early in that, in that journey. >>Well, that's interesting because, you know, in my observation it takes seven to 10 years to actually build a company and then the fact that you're still in the early days is kind of interesting. I mean, you, Collibra's had a good 12 months or so since we last spoke at Data Citizens. Give us the latest update on your business. What do people need to know about your, your current momentum? >>Yeah, absolutely. Again, there's, there's a lot of tail organizations that are only maturing the data practices and we've seen it kind of transform or, or, or influence a lot of our business growth that we've seen, broader adoption of the platform. We work at some of the largest organizations in the world where it's Adobe, Heineken, Bank of America, and many more. We have now over 600 enterprise customers, all industry leaders and every single vertical. So it's, it's really exciting to see that and continue to partner with those organizations. On the partnership side, again, a lot of momentum in the org in, in the, in the markets with some of the cloud partners like Google, Amazon, Snowflake, data bricks and, and others, right? As those kind of new modern data infrastructures, modern data architectures that are definitely all moving to the cloud, a great opportunity for us, our partners and of course our customers to help them kind of transition to the cloud even faster. >>And so we see a lot of excitement and momentum there within an acquisition about 18 months ago around data quality, data observability, which we believe is an enormous opportunity. Of course, data quality isn't new, but I think there's a lot of reasons why we're so excited about quality and observability now. One is around leveraging ai, machine learning, again to drive more automation. And the second is that those data pipelines that are now being created in the cloud, in these modern data architecture arch architectures, they've become mission critical. They've become real time. And so monitoring, observing those data pipelines continuously has become absolutely critical so that they're really excited about about that as well. And on the organizational side, I'm sure you've heard a term around kind of data mesh, something that's gaining a lot of momentum, rightfully so. It's really the type of governance that we always believe. Then federated focused on domains, giving a lot of ownership to different teams. I think that's the way to scale data organizations. And so that aligns really well with our vision and, and from a product perspective, we've seen a lot of momentum with our customers there as well. >>Yeah, you know, a couple things there. I mean, the acquisition of i l dq, you know, Kirk Hasselbeck and, and their team, it's interesting, you know, the whole data quality used to be this back office function and, and really confined to highly regulated industries. It's come to the front office, it's top of mind for chief data officers, data mesh. You mentioned you guys are a connective tissue for all these different nodes on the data mesh. That's key. And of course we see you at all the shows. You're, you're a critical part of many ecosystems and you're developing your own ecosystem. So let's chat a little bit about the, the products. We're gonna go deeper in into products later on at, at Data Citizens 22, but we know you're debuting some, some new innovations, you know, whether it's, you know, the, the the under the covers in security, sort of making data more accessible for people just dealing with workflows and processes as you talked about earlier. Tell us a little bit about what you're introducing. >>Yeah, absolutely. We're super excited, a ton of innovation. And if we think about the big theme and like, like I said, we're still relatively early in this, in this journey towards kind of that mission of data intelligence that really bolts and compelling mission, either customers are still start, are just starting on that, on that journey. We wanna make it as easy as possible for the, for our organization to actually get started because we know that's important that they do. And for our organization and customers that have been with us for some time, there's still a tremendous amount of opportunity to kind of expand the platform further. And again, to make it easier for really to, to accomplish that mission and vision around that data citizen that everyone has access to trustworthy data in a very easy, easy way. So that's really the theme of a lot of the innovation that we're driving. >>A lot of kind of ease of adoption, ease of use, but also then how do we make sure that lio becomes this kind of mission critical enterprise platform from a security performance architecture scale supportability that we're truly able to deliver that kind of an enterprise mission critical platform. And so that's the big theme from an innovation perspective, From a product perspective, a lot of new innovation that we're really excited about. A couple of highlights. One is around data marketplace. Again, a lot of our customers have plans in that direction, how to make it easy. How do we make, how do we make available to true kind of shopping experience that anybody in your organization can, in a very easy search first way, find the right data product, find the right dataset, that data can then consume usage analytics. How do you, how do we help organizations drive adoption, tell them where they're working really well and where they have opportunities homepages again to, to make things easy for, for people, for anyone in your organization to kind of get started with ppia, you mentioned workflow designer, again, we have a very powerful enterprise platform. >>One of our key differentiators is the ability to really drive a lot of automation through workflows. And now we provided a new low code, no code kind of workflow designer experience. So, so really customers can take it to the next level. There's a lot more new product around K Bear Protect, which in partnership with Snowflake, which has been a strategic investor in kib, focused on how do we make access governance easier? How do we, how do we, how are we able to make sure that as you move to the cloud, things like access management, masking around sensitive data, PII data is managed as much more effective, effective rate, really excited about that product. There's more around data quality. Again, how do we, how do we get that deployed as easily and quickly and widely as we can? Moving that to the cloud has been a big part of our strategy. >>So we launch more data quality cloud product as well as making use of those, those native compute capabilities in platforms like Snowflake, Data, Bricks, Google, Amazon, and others. And so we are bettering a capability, a capability that we call push down. So actually pushing down the computer and data quality, the monitoring into the underlying platform, which again, from a scale performance and ease of use perspective is gonna make a massive difference. And then more broadly, we, we talked a little bit about the ecosystem. Again, integrations, we talk about being able to connect to every source. Integrations are absolutely critical and we're really excited to deliver new integrations with Snowflake, Azure and Google Cloud storage as well. So there's a lot coming out. The, the team has been work at work really hard and we are really, really excited about what we are coming, what we're bringing to markets. >>Yeah, a lot going on there. I wonder if you could give us your, your closing thoughts. I mean, you, you talked about, you know, the marketplace, you know, you think about data mesh, you think of data as product, one of the key principles you think about monetization. This is really different than what we've been used to in data, which is just getting the technology to work has been been so hard. So how do you see sort of the future and, you know, give us the, your closing thoughts please? >>Yeah, absolutely. And I, and I think we we're really at this pivotal moment, and I think you said it well. We, we all know the constraint and the challenges with data, how to actually do data at scale. And while we've seen a ton of innovation on the infrastructure side, we fundamentally believe that just getting a faster database is important, but it's not gonna fully solve the challenges and truly kind of deliver on the opportunity. And that's why now is really the time to deliver this data intelligence vision, this data intelligence platform. We are still early, making it as easy as we can. It's kind of, of our, it's our mission. And so I'm really, really excited to see what we, what we are gonna, how the marks gonna evolve over the next, next few quarters and years. I think the trend is clearly there when we talk about data mesh, this kind of federated approach folks on data products is just another signal that we believe that a lot of our organization are now at the time. >>The understanding need to go beyond just the technology. I really, really think about how do we actually scale data as a business function, just like we've done with it, with, with hr, with, with sales and marketing, with finance. That's how we need to think about data. I think now is the time given the economic environment that we are in much more focus on control, much more focused on productivity efficiency and now's the time. We need to look beyond just the technology and infrastructure to think of how to scale data, how to manage data at scale. >>Yeah, it's a new era. The next 10 years of data won't be like the last, as I always say. Felix, thanks so much and good luck in, in San Diego. I know you're gonna crush it out there. >>Thank you Dave. >>Yeah, it's a great spot for an in-person event and, and of course the content post event is gonna be available@collibra.com and you can of course catch the cube coverage@thecube.net and all the news@siliconangle.com. This is Dave Valante for the cube, your leader in enterprise and emerging tech coverage. >>Hi, I'm Jay from Collibra's Data Office. Today I want to talk to you about Collibra's data intelligence cloud. We often say Collibra is a single system of engagement for all of your data. Now, when I say data, I mean data in the broadest sense of the word, including reference and metadata. Think of metrics, reports, APIs, systems, policies, and even business processes that produce or consume data. Now, the beauty of this platform is that it ensures all of your users have an easy way to find, understand, trust, and access data. But how do you get started? Well, here are seven steps to help you get going. One, start with the data. What's data intelligence? Without data leverage the Collibra data catalog to automatically profile and classify your enterprise data wherever that data lives, databases, data lakes or data warehouses, whether on the cloud or on premise. >>Two, you'll then wanna organize the data and you'll do that with data communities. This can be by department, find a business or functional team, however your organization organizes work and accountability. And for that you'll establish community owners, communities, make it easy for people to navigate through the platform, find the data and will help create a sense of belonging for users. An important and related side note here, we find it's typical in many organizations that data is thought of is just an asset and IT and data offices are viewed as the owners of it and who are really the central teams performing analytics as a service provider to the enterprise. We believe data is more than an asset, it's a true product that can be converted to value. And that also means establishing business ownership of data where that strategy and ROI come together with subject matter expertise. >>Okay, three. Next, back to those communities there, the data owners should explain and define their data, not just the tables and columns, but also the related business terms, metrics and KPIs. These objects we call these assets are typically organized into business glossaries and data dictionaries. I definitely recommend starting with the topics that are most important to the business. Four, those steps that enable you and your users to have some fun with it. Linking everything together builds your knowledge graph and also known as a metadata graph by linking or relating these assets together. For example, a data set to a KPI to a report now enables your users to see what we call the lineage diagram that visualizes where the data in your dashboards actually came from and what the data means and who's responsible for it. Speaking of which, here's five. Leverage the calibra trusted business reporting solution on the marketplace, which comes with workflows for those owners to certify their reports, KPIs, and data sets. >>This helps them force their trust in their data. Six, easy to navigate dashboards or landing pages right in your platform for your company's business processes are the most effective way for everyone to better understand and take action on data. Here's a pro tip, use the dashboard design kit on the marketplace to help you build compelling dashboards. Finally, seven, promote the value of this to your users and be sure to schedule enablement office hours and new employee onboarding sessions to get folks excited about what you've built and implemented. Better yet, invite all of those community and data owners to these sessions so that they can show off the value that they've created. Those are my seven tips to get going with Collibra. I hope these have been useful. For more information, be sure to visit collibra.com. >>Welcome to the Cube's coverage of Data Citizens 2022 Collibra's customer event. My name is Dave Valante. With us is Kirk Hasselbeck, who's the vice president of Data Quality of Collibra Kirk, good to see you. Welcome. >>Thanks for having me, Dave. Excited to be here. >>You bet. Okay, we're gonna discuss data quality observability. It's a hot trend right now. You founded a data quality company, OWL dq, and it was acquired by Collibra last year. Congratulations. And now you lead data quality at Collibra. So we're hearing a lot about data quality right now. Why is it such a priority? Take us through your thoughts on that. >>Yeah, absolutely. It's, it's definitely exciting times for data quality, which you're right, has been around for a long time. So why now and why is it so much more exciting than it used to be? I think it's a bit stale, but we all know that companies use more data than ever before and the variety has changed and the volume has grown. And, and while I think that remains true, there are a couple other hidden factors at play that everyone's so interested in as, as to why this is becoming so important now. And, and I guess you could kind of break this down simply and think about if Dave, you and I were gonna build, you know, a new healthcare application and monitor the heartbeat of individuals, imagine if we get that wrong, you know, what the ramifications could be, what, what those incidents would look like, or maybe better yet, we try to build a, a new trading algorithm with a crossover strategy where the 50 day crosses the, the 10 day average. >>And imagine if the data underlying the inputs to that is incorrect. We will probably have major financial ramifications in that sense. So, you know, it kind of starts there where everybody's realizing that we're all data companies and if we are using bad data, we're likely making incorrect business decisions. But I think there's kind of two other things at play. You know, I, I bought a car not too long ago and my dad called and said, How many cylinders does it have? And I realized in that moment, you know, I might have failed him because, cause I didn't know. And, and I used to ask those types of questions about any lock brakes and cylinders and, and you know, if it's manual or, or automatic and, and I realized I now just buy a car that I hope works. And it's so complicated with all the computer chips, I, I really don't know that much about it. >>And, and that's what's happening with data. We're just loading so much of it. And it's so complex that the way companies consume them in the IT function is that they bring in a lot of data and then they syndicate it out to the business. And it turns out that the, the individuals loading and consuming all of this data for the company actually may not know that much about the data itself, and that's not even their job anymore. So we'll talk more about that in a minute, but that's really what's setting the foreground for this observability play and why everybody's so interested. It, it's because we're becoming less close to the intricacies of the data and we just expect it to always be there and be correct. >>You know, the other thing too about data quality, and for years we did the MIT CDO IQ event, we didn't do it last year, Covid messed everything up. But the observation I would make there thoughts is, is it data quality? Used to be information quality used to be this back office function, and then it became sort of front office with financial services and government and healthcare, these highly regulated industries. And then the whole chief data officer thing happened and people were realizing, well, they sort of flipped the bit from sort of a data as a, a risk to data as a, as an asset. And now as we say, we're gonna talk about observability. And so it's really become front and center just the whole quality issue because data's so fundamental, hasn't it? >>Yeah, absolutely. I mean, let's imagine we pull up our phones right now and I go to my, my favorite stock ticker app and I check out the NASDAQ market cap. I really have no idea if that's the correct number. I know it's a number, it looks large, it's in a numeric field. And, and that's kind of what's going on. There's, there's so many numbers and they're coming from all of these different sources and data providers and they're getting consumed and passed along. But there isn't really a way to tactically put controls on every number and metric across every field we plan to monitor, but with the scale that we've achieved in early days, even before calibra. And what's been so exciting is we have these types of observation techniques, these data monitors that can actually track past performance of every field at scale. And why that's so interesting and why I think the CDO is, is listening right intently nowadays to this topic is, so maybe we could surface all of these problems with the right solution of data observability and with the right scale and then just be alerted on breaking trends. So we're sort of shifting away from this world of must write a condition and then when that condition breaks, that was always known as a break record. But what about breaking trends and root cause analysis? And is it possible to do that, you know, with less human intervention? And so I think most people are seeing now that it's going to have to be a software tool and a computer system. It's, it's not ever going to be based on one or two domain experts anymore. >>So, So how does data observability relate to data quality? Are they sort of two sides of the same coin? Are they, are they cousins? What's your perspective on that? >>Yeah, it's, it's super interesting. It's an emerging market. So the language is changing a lot of the topic and areas changing the way that I like to say it or break it down because the, the lingo is constantly moving is, you know, as a target on this space is really breaking records versus breaking trends. And I could write a condition when this thing happens, it's wrong and when it doesn't it's correct. Or I could look for a trend and I'll give you a good example. You know, everybody's talking about fresh data and stale data and, and why would that matter? Well, if your data never arrived or only part of it arrived or didn't arrive on time, it's likely stale and there will not be a condition that you could write that would show you all the good in the bads. That was kind of your, your traditional approach of data quality break records. But your modern day approach is you lost a significant portion of your data, or it did not arrive on time to make that decision accurately on time. And that's a hidden concern. Some people call this freshness, we call it stale data, but it all points to the same idea of the thing that you're observing may not be a data quality condition anymore. It may be a breakdown in the data pipeline. And with thousands of data pipelines in play for every company out there there, there's more than a couple of these happening every day. >>So what's the Collibra angle on all this stuff made the acquisition, you got data quality observability coming together, you guys have a lot of expertise in, in this area, but you hear providence of data, you just talked about, you know, stale data, you know, the, the whole trend toward real time. How is Calibra approaching the problem and what's unique about your approach? >>Well, I think where we're fortunate is with our background, myself and team, we sort of lived this problem for a long time, you know, in, in the Wall Street days about a decade ago. And we saw it from many different angles. And what we came up with before it was called data observability or reliability was basically the, the underpinnings of that. So we're a little bit ahead of the curve there when most people evaluate our solution, it's more advanced than some of the observation techniques that that currently exist. But we've also always covered data quality and we believe that people want to know more, they need more insights, and they want to see break records and breaking trends together so they can correlate the root cause. And we hear that all the time. I have so many things going wrong, just show me the big picture, help me find the thing that if I were to fix it today would make the most impact. So we're really focused on root cause analysis, business impact, connecting it with lineage and catalog metadata. And as that grows, you can actually achieve total data governance at this point with the acquisition of what was a Lineage company years ago, and then my company Ldq now Collibra, Data quality Collibra may be the best positioned for total data governance and intelligence in the space. >>Well, you mentioned financial services a couple of times and some examples, remember the flash crash in 2010. Nobody had any idea what that was, you know, they just said, Oh, it's a glitch, you know, so they didn't understand the root cause of it. So this is a really interesting topic to me. So we know at Data Citizens 22 that you're announcing, you gotta announce new products, right? You're yearly event what's, what's new. Give us a sense as to what products are coming out, but specifically around data quality and observability. >>Absolutely. There's this, you know, there's always a next thing on the forefront. And the one right now is these hyperscalers in the cloud. So you have databases like Snowflake and Big Query and Data Bricks is Delta Lake and SQL Pushdown. And ultimately what that means is a lot of people are storing in loading data even faster in a SaaS like model. And we've started to hook in to these databases. And while we've always worked with the the same databases in the past, they're supported today we're doing something called Native Database pushdown, where the entire compute and data activity happens in the database. And why that is so interesting and powerful now is everyone's concerned with something called Egress. Did your, my data that I've spent all this time and money with my security team securing ever leave my hands, did it ever leave my secure VPC as they call it? >>And with these native integrations that we're building and about to unveil, here's kind of a sneak peek for, for next week at Data Citizens. We're now doing all compute and data operations in databases like Snowflake. And what that means is with no install and no configuration, you could log into the Collibra data quality app and have all of your data quality running inside the database that you've probably already picked as your your go forward team selection secured database of choice. So we're really excited about that. And I think if you look at the whole landscape of network cost, egress, cost, data storage and compute, what people are realizing is it's extremely efficient to do it in the way that we're about to release here next week. >>So this is interesting because what you just described, you know, you mentioned Snowflake, you mentioned Google, Oh actually you mentioned yeah, data bricks. You know, Snowflake has the data cloud. If you put everything in the data cloud, okay, you're cool, but then Google's got the open data cloud. If you heard, you know, Google next and now data bricks doesn't call it the data cloud, but they have like the open source data cloud. So you have all these different approaches and there's really no way up until now I'm, I'm hearing to, to really understand the relationships between all those and have confidence across, you know, it's like Jak Dani, you should just be a note on the mesh. And I don't care if it's a data warehouse or a data lake or where it comes from, but it's a point on that mesh and I need tooling to be able to have confidence that my data is governed and has the proper lineage, providence. And, and, and that's what you're bringing to the table, Is that right? Did I get that right? >>Yeah, that's right. And it's, for us, it's, it's not that we haven't been working with those great cloud databases, but it's the fact that we can send them the instructions now, we can send them the, the operating ability to crunch all of the calculations, the governance, the quality, and get the answers. And what that's doing, it's basically zero network costs, zero egress cost, zero latency of time. And so when you were to log into Big Query tomorrow using our tool or like, or say Snowflake for example, you have instant data quality metrics, instant profiling, instant lineage and access privacy controls, things of that nature that just become less onerous. What we're seeing is there's so much technology out there, just like all of the major brands that you mentioned, but how do we make it easier? The future is about less clicks, faster time to value, faster scale, and eventually lower cost. And, and we think that this positions us to be the leader there. >>I love this example because, you know, Barry talks about, wow, the cloud guys are gonna own the world and, and of course now we're seeing that the ecosystem is finding so much white space to add value, connect across cloud. Sometimes we call it super cloud and so, or inter clouding. All right, Kirk, give us your, your final thoughts and on on the trends that we've talked about and Data Citizens 22. >>Absolutely. Well, I think, you know, one big trend is discovery and classification. Seeing that across the board, people used to know it was a zip code and nowadays with the amount of data that's out there, they wanna know where everything is, where their sensitive data is. If it's redundant, tell me everything inside of three to five seconds. And with that comes, they want to know in all of these hyperscale databases how fast they can get controls and insights out of their tools. So I think we're gonna see more one click solutions, more SAS based solutions and solutions that hopefully prove faster time to value on, on all of these modern cloud platforms. >>Excellent. All right, Kurt Hasselbeck, thanks so much for coming on the Cube and previewing Data Citizens 22. Appreciate it. >>Thanks for having me, Dave. >>You're welcome. Right, and thank you for watching. Keep it right there for more coverage from the Cube. Welcome to the Cube's virtual Coverage of Data Citizens 2022. My name is Dave Valante and I'm here with Laura Sellers, who's the Chief Product Officer at Collibra, the host of Data Citizens. Laura, welcome. Good to see you. >>Thank you. Nice to be here. >>Yeah, your keynote at Data Citizens this year focused on, you know, your mission to drive ease of use and scale. Now when I think about historically fast access to the right data at the right time in a form that's really easily consumable, it's been kind of challenging, especially for business users. Can can you explain to our audience why this matters so much and what's actually different today in the data ecosystem to make this a reality? >>Yeah, definitely. So I think what we really need and what I hear from customers every single day is that we need a new approach to data management and our product teams. What inspired me to come to Calibra a little bit a over a year ago was really the fact that they're very focused on bringing trusted data to more users across more sources for more use cases. And so as we look at what we're announcing with these innovations of ease of use and scale, it's really about making teams more productive in getting started with and the ability to manage data across the entire organization. So we've been very focused on richer experiences, a broader ecosystem of partners, as well as a platform that delivers performance, scale and security that our users and teams need and demand. So as we look at, Oh, go ahead. >>I was gonna say, you know, when I look back at like the last 10 years, it was all about getting the technology to work and it was just so complicated. But, but please carry on. I'd love to hear more about this. >>Yeah, I, I really, you know, Collibra is a system of engagement for data and we really are working on bringing that entire system of engagement to life for everyone to leverage here and now. So what we're announcing from our ease of use side of the world is first our data marketplace. This is the ability for all users to discover and access data quickly and easily shop for it, if you will. The next thing that we're also introducing is the new homepage. It's really about the ability to drive adoption and have users find data more quickly. And then the two more areas of the ease of use side of the world is our world of usage analytics. And one of the big pushes and passions we have at Collibra is to help with this data driven culture that all companies are trying to create. And also helping with data literacy, with something like usage analytics, it's really about driving adoption of the CLE platform, understanding what's working, who's accessing it, what's not. And then finally we're also introducing what's called workflow designer. And we love our workflows at Libra, it's a big differentiator to be able to automate business processes. The designer is really about a way for more people to be able to create those workflows, collaborate on those workflow flows, as well as people to be able to easily interact with them. So a lot of exciting things when it comes to ease of use to make it easier for all users to find data. >>Y yes, there's definitely a lot to unpack there. I I, you know, you mentioned this idea of, of of, of shopping for the data. That's interesting to me. Why this analogy, metaphor or analogy, I always get those confused. I let's go with analogy. Why is it so important to data consumers? >>I think when you look at the world of data, and I talked about this system of engagement, it's really about making it more accessible to the masses. And what users are used to is a shopping experience like your Amazon, if you will. And so having a consumer grade experience where users can quickly go in and find the data, trust that data, understand where the data's coming from, and then be able to quickly access it, is the idea of being able to shop for it, just making it as simple as possible and really speeding the time to value for any of the business analysts, data analysts out there. >>Yeah, I think when you, you, you see a lot of discussion about rethinking data architectures, putting data in the hands of the users and business people, decentralized data and of course that's awesome. I love that. But of course then you have to have self-service infrastructure and you have to have governance. And those are really challenging. And I think so many organizations, they're facing adoption challenges, you know, when it comes to enabling teams generally, especially domain experts to adopt new data technologies, you know, like the, the tech comes fast and furious. You got all these open source projects and get really confusing. Of course it risks security, governance and all that good stuff. You got all this jargon. So where do you see, you know, the friction in adopting new data technologies? What's your point of view and how can organizations overcome these challenges? >>You're, you're dead on. There's so much technology and there's so much to stay on top of, which is part of the friction, right? It's just being able to stay ahead of, of and understand all the technologies that are coming. You also look at as there's so many more sources of data and people are migrating data to the cloud and they're migrating to new sources. Where the friction comes is really that ability to understand where the data came from, where it's moving to, and then also to be able to put the access controls on top of it. So people are only getting access to the data that they should be getting access to. So one of the other things we're announcing with, with all of the innovations that are coming is what we're doing around performance and scale. So with all of the data movement, with all of the data that's out there, the first thing we're launching in the world of performance and scale is our world of data quality. >>It's something that Collibra has been working on for the past year and a half, but we're launching the ability to have data quality in the cloud. So it's currently an on-premise offering, but we'll now be able to carry that over into the cloud for us to manage that way. We're also introducing the ability to push down data quality into Snowflake. So this is, again, one of those challenges is making sure that that data that you have is d is is high quality as you move forward. And so really another, we're just reducing friction. You already have Snowflake stood up. It's not another machine for you to manage, it's just push down capabilities into Snowflake to be able to track that quality. Another thing that we're launching with that is what we call Collibra Protect. And this is that ability for users to be able to ingest metadata, understand where the PII data is, and then set policies up on top of it. So very quickly be able to set policies and have them enforced at the data level. So anybody in the organization is only getting access to the data they should have access to. >>Here's Topica data quality is interesting. It's something that I've followed for a number of years. It used to be a back office function, you know, and really confined only to highly regulated industries like financial services and healthcare and government. You know, you look back over a decade ago, you didn't have this worry about personal information, g gdpr, and, you know, California Consumer Privacy Act all becomes, becomes so much important. The cloud is really changed things in terms of performance and scale and of course partnering for, for, with Snowflake it's all about sharing data and monetization, anything but a back office function. So it was kind of smart that you guys were early on and of course attracting them and as a, as an investor as well was very strong validation. What can you tell us about the nature of the relationship with Snowflake and specifically inter interested in sort of joint engineering or, and product innovation efforts, you know, beyond the standard go to market stuff? >>Definitely. So you mentioned there were a strategic investor in Calibra about a year ago. A little less than that I guess. We've been working with them though for over a year really tightly with their product and engineering teams to make sure that Collibra is adding real value. Our unified platform is touching pieces of our unified platform or touching all pieces of Snowflake. And when I say that, what I mean is we're first, you know, able to ingest data with Snowflake, which, which has always existed. We're able to profile and classify that data we're announcing with Calibra Protect this week that you're now able to create those policies on top of Snowflake and have them enforce. So again, people can get more value out of their snowflake more quickly as far as time to value with, with our policies for all business users to be able to create. >>We're also announcing Snowflake Lineage 2.0. So this is the ability to take stored procedures in Snowflake and understand the lineage of where did the data come from, how was it transformed with within Snowflake as well as the data quality. Pushdown, as I mentioned, data quality, you brought it up. It is a new, it is a, a big industry push and you know, one of the things I think Gartner mentioned is people are losing up to $15 million without having great data quality. So this push down capability for Snowflake really is again, a big ease of use push for us at Collibra of that ability to, to push it into snowflake, take advantage of the data, the data source, and the engine that already lives there and get the right and make sure you have the right quality. >>I mean, the nice thing about Snowflake, if you play in the Snowflake sandbox, you, you, you, you can get sort of a, you know, high degree of confidence that the data sharing can be done in a safe way. Bringing, you know, Collibra into the, into the story allows me to have that data quality and, and that governance that I, that I need. You know, we've said many times on the cube that one of the notable differences in cloud this decade versus last decade, I mean ob there are obvious differences just in terms of scale and scope, but it's shaping up to be about the strength of the ecosystems. That's really a hallmark of these big cloud players. I mean they're, it's a key factor for innovating, accelerating product delivery, filling gaps in, in the hyperscale offerings cuz you got more stack, you know, mature stack capabilities and you know, it creates this flywheel momentum as we often say. But, so my question is, how do you work with the hyperscalers? Like whether it's AWS or Google, whomever, and what do you see as your role and what's the Collibra sweet spot? >>Yeah, definitely. So, you know, one of the things I mentioned early on is the broader ecosystem of partners is what it's all about. And so we have that strong partnership with Snowflake. We also are doing more with Google around, you know, GCP and kbra protect there, but also tighter data plex integration. So similar to what you've seen with our strategic moves around Snowflake and, and really covering the broad ecosystem of what Collibra can do on top of that data source. We're extending that to the world of Google as well and the world of data plex. We also have great partners in SI's Infosys is somebody we spoke with at the conference who's done a lot of great work with Levi's as they're really important to help people with their whole data strategy and driving that data driven culture and, and Collibra being the core of it. >>Hi Laura, we're gonna, we're gonna end it there, but I wonder if you could kind of put a bow on, you know, this year, the event your, your perspectives. So just give us your closing thoughts. >>Yeah, definitely. So I, I wanna say this is one of the biggest releases Collibra's ever had. Definitely the biggest one since I've been with the company a little over a year. We have all these great new product innovations coming to really drive the ease of use to make data more valuable for users everywhere and, and companies everywhere. And so it's all about everybody being able to easily find, understand, and trust and get access to that data going forward. >>Well congratulations on all the pro progress. It was great to have you on the cube first time I believe, and really appreciate you, you taking the time with us. >>Yes, thank you for your time. >>You're very welcome. Okay, you're watching the coverage of Data Citizens 2022 on the cube, your leader in enterprise and emerging tech coverage. >>So data modernization oftentimes means moving some of your storage and computer to the cloud where you get the benefit of scale and security and so on. But ultimately it doesn't take away the silos that you have. We have more locations, more tools and more processes with which we try to get value from this data. To do that at scale in an organization, people involved in this process, they have to understand each other. So you need to unite those people across those tools, processes, and systems with a shared language. When I say customer, do you understand the same thing as you hearing customer? Are we counting them in the same way so that shared language unites us and that gives the opportunity for the organization as a whole to get the maximum value out of their data assets and then they can democratize data so everyone can properly use that shared language to find, understand, and trust the data asset that's available. >>And that's where Collibra comes in. We provide a centralized system of engagement that works across all of those locations and combines all of those different user types across the whole business. At Collibra, we say United by data and that also means that we're united by data with our customers. So here is some data about some of our customers. There was the case of an online do it yourself platform who grew their revenue almost three times from a marketing campaign that provided the right product in the right hands of the right people. In other case that comes to mind is from a financial services organization who saved over 800 K every year because they were able to reuse the same data in different kinds of reports and before there was spread out over different tools and processes and silos, and now the platform brought them together so they realized, oh, we're actually using the same data, let's find a way to make this more efficient. And the last example that comes to mind is that of a large home loan, home mortgage, mortgage loan provider where they have a very complex landscape, a very complex architecture legacy in the cloud, et cetera. And they're using our software, they're using our platform to unite all the people and those processes and tools to get a common view of data to manage their compliance at scale. >>Hey everyone, I'm Lisa Martin covering Data Citizens 22, brought to you by Collibra. This next conversation is gonna focus on the importance of data culture. One of our Cube alumni is back, Stan Christians is Collibra's co-founder and it's Chief Data citizens. Stan, it's great to have you back on the cube. >>Hey Lisa, nice to be. >>So we're gonna be talking about the importance of data culture, data intelligence, maturity, all those great things. When we think about the data revolution that every business is going through, you know, it's so much more than technology innovation. It also really re requires cultural transformation, community transformation. Those are challenging for customers to undertake. Talk to us about what you mean by data citizenship and the role that creating a data culture plays in that journey. >>Right. So as you know, our event is called Data Citizens because we believe that in the end, a data citizen is anyone who uses data to do their job. And we believe that today's organizations, you have a lot of people, most of the employees in an organization are somehow gonna to be a data citizen, right? So you need to make sure that these people are aware of it. You need that. People have skills and competencies to do with data what necessary and that's on, all right? So what does it mean to have a good data culture? It means that if you're building a beautiful dashboard to try and convince your boss, we need to make this decision that your boss is also open to and able to interpret, you know, the data presented in dashboard to actually make that decision and take that action. Right? >>And once you have that why to the organization, that's when you have a good data culture. Now that's continuous effort for most organizations because they're always moving, somehow they're hiring new people and it has to be continuous effort because we've seen that on the hand. Organizations continue challenged their data sources and where all the data is flowing, right? Which in itself creates a lot of risk. But also on the other set hand of the equation, you have the benefit. You know, you might look at regulatory drivers like, we have to do this, right? But it's, it's much better right now to consider the competitive drivers, for example, and we did an IDC study earlier this year, quite interesting. I can recommend anyone to it. And one of the conclusions they found as they surveyed over a thousand people across organizations worldwide is that the ones who are higher in maturity. >>So the, the organizations that really look at data as an asset, look at data as a product and actively try to be better at it, don't have three times as good a business outcome as the ones who are lower on the maturity scale, right? So you can say, ok, I'm doing this, you know, data culture for everyone, awakening them up as data citizens. I'm doing this for competitive reasons, I'm doing this re reasons you're trying to bring both of those together and the ones that get data intelligence right, are successful and competitive. That's, and that's what we're seeing out there in the market. >>Absolutely. We know that just generally stand right, the organizations that are, are really creating a, a data culture and enabling everybody within the organization to become data citizens are, We know that in theory they're more competitive, they're more successful. But the IDC study that you just mentioned demonstrates they're three times more successful and competitive than their peers. Talk about how Collibra advises customers to create that community, that culture of data when it might be challenging for an organization to adapt culturally. >>Of course, of course it's difficult for an organization to adapt but it's also necessary, as you just said, imagine that, you know, you're a modern day organization, laptops, what have you, you're not using those, right? Or you know, you're delivering them throughout organization, but not enabling your colleagues to actually do something with that asset. Same thing as through with data today, right? If you're not properly using the data asset and competitors are, they're gonna to get more advantage. So as to how you get this done, establish this. There's angles to look at, Lisa. So one angle is obviously the leadership whereby whoever is the boss of data in the organization, you typically have multiple bosses there, like achieve data officers. Sometimes there's, there's multiple, but they may have a different title, right? So I'm just gonna summarize it as a data leader for a second. >>So whoever that is, they need to make sure that there's a clear vision, a clear strategy for data. And that strategy needs to include the monetization aspect. How are you going to get value from data? Yes. Now that's one part because then you can leadership in the organization and also the business value. And that's important. Cause those people, their job in essence really is to make everyone in the organization think about data as an asset. And I think that's the second part of the equation of getting that right, is it's not enough to just have that leadership out there, but you also have to get the hearts and minds of the data champions across the organization. You, I really have to win them over. And if you have those two combined and obviously a good technology to, you know, connect those people and have them execute on their responsibilities such as a data intelligence platform like s then the in place to really start upgrading that culture inch by inch if you'll, >>Yes, I like that. The recipe for success. So you are the co-founder of Collibra. You've worn many different hats along this journey. Now you're building Collibra's own data office. I like how before we went live, we were talking about Calibra is drinking its own champagne. I always loved to hear stories about that. You're speaking at Data Citizens 2022. Talk to us about how you are building a data culture within Collibra and what maybe some of the specific projects are that Collibra's data office is working on. >>Yes, and it is indeed data citizens. There are a ton of speaks here, are very excited. You know, we have Barb from m MIT speaking about data monetization. We have Dilla at the last minute. So really exciting agen agenda. Can't wait to get back out there essentially. So over the years at, we've doing this since two and eight, so a good years and I think we have another decade of work ahead in the market, just to be very clear. Data is here to stick around as are we. And myself, you know, when you start a company, we were for people in a, if you, so everybody's wearing all sorts of hat at time. But over the years I've run, you know, presales that sales partnerships, product cetera. And as our company got a little bit biggish, we're now thousand two. Something like people in the company. >>I believe systems and processes become a lot important. So we said you CBRA isn't the size our customers we're getting there in of organization structure, process systems, et cetera. So we said it's really time for us to put our money where is and to our own data office, which is what we were seeing customers', organizations worldwide. And they organizations have HR units, they have a finance unit and over time they'll all have a department if you'll, that is responsible somehow for the data. So we said, ok, let's try to set an examples that other people can take away with it, right? Can take away from it. So we set up a data strategy, we started building data products, took care of the data infrastructure. That's sort of good stuff. And in doing all of that, ISA exactly as you said, we said, okay, we need to also use our product and our own practices and from that use, learn how we can make the product better, learn how we make, can make the practice better and share that learning with all the, and on, on the Monday mornings, we sometimes refer to eating our dog foods on Friday evenings. >>We referred to that drinking our own champagne. I like it. So we, we had a, we had the driver to do this. You know, there's a clear business reason. So we involved, we included that in the data strategy and that's a little bit of our origin. Now how, how do we organize this? We have three pillars, and by no means is this a template that everyone should, this is just the organization that works at our company, but it can serve as an inspiration. So we have a pillar, which is data science. The data product builders, if you'll or the people who help the business build data products. We have the data engineers who help keep the lights on for that data platform to make sure that the products, the data products can run, the data can flow and you know, the quality can be checked. >>And then we have a data intelligence or data governance builders where we have those data governance, data intelligence stakeholders who help the business as a sort of data partner to the business stakeholders. So that's how we've organized it. And then we started following the CBRA approach, which is, well, what are the challenges that our business stakeholders have in hr, finance, sales, marketing all over? And how can data help overcome those challenges? And from those use cases, we then just started to build a map and started execution use of the use case. And a important ones are very simple. We them with our, our customers as well, people talking about the cata, right? The catalog for the data scientists to know what's in their data lake, for example, and for the people in and privacy. So they have their process registry and they can see how the data flows. >>So that's a starting place and that turns into a marketplace so that if new analysts and data citizens join kbra, they immediately have a place to go to, to look at, see, ok, what data is out there for me as an analyst or a data scientist or whatever to do my job, right? So they can immediately get access data. And another one that we is around trusted business. We're seeing that since, you know, self-service BI allowed everyone to make beautiful dashboards, you know, pie, pie charts. I always, my pet pee is the pie chart because I love buy and you shouldn't always be using pie charts. But essentially there's become proliferation of those reports. And now executives don't really know, okay, should I trust this report or that report the reporting on the same thing. But the numbers seem different, right? So that's why we have trusted this reporting. So we know if a, the dashboard, a data product essentially is built, we not that all the right steps are being followed and that whoever is consuming that can be quite confident in the result either, Right. And that silver browser, right? Absolutely >>Decay. >>Exactly. Yes, >>Absolutely. Talk a little bit about some of the, the key performance indicators that you're using to measure the success of the data office. What are some of those KPIs? >>KPIs and measuring is a big topic in the, in the data chief data officer profession, I would say, and again, it always varies with to your organization, but there's a few that we use that might be of interest. Use those pillars, right? And we have metrics across those pillars. So for example, a pillar on the data engineering side is gonna be more related to that uptime, right? Are the, is the data platform up and running? Are the data products up and running? Is the quality in them good enough? Is it going up? Is it going down? What's the usage? But also, and especially if you're in the cloud and if consumption's a big thing, you have metrics around cost, for example, right? So that's one set of examples. Another one is around the data sciences and products. Are people using them? Are they getting value from it? >>Can we calculate that value in ay perspective, right? Yeah. So that we can to the rest of the business continue to say we're tracking all those numbers and those numbers indicate that value is generated and how much value estimated in that region. And then you have some data intelligence, data governance metrics, which is, for example, you have a number of domains in a data mesh. People talk about being the owner of a data domain, for example, like product or, or customer. So how many of those domains do you have covered? How many of them are already part of the program? How many of them have owners assigned? How well are these owners organized, executing on their responsibilities? How many tickets are open closed? How many data products are built according to process? And so and so forth. So these are an set of examples of, of KPIs. There's a, there's a lot more, but hopefully those can already inspire the audience. >>Absolutely. So we've, we've talked about the rise cheap data offices, it's only accelerating. You mentioned this is like a 10 year journey. So if you were to look into a crystal ball, what do you see in terms of the maturation of data offices over the next decade? >>So we, we've seen indeed the, the role sort of grow up, I think in, in thousand 10 there may have been like 10 achieve data officers or something. Gartner has exact numbers on them, but then they grew, you know, industries and the number is estimated to be about 20,000 right now. Wow. And they evolved in a sort of stack of competencies, defensive data strategy, because the first chief data officers were more regulatory driven, offensive data strategy support for the digital program. And now all about data products, right? So as a data leader, you now need all of those competences and need to include them in, in your strategy. >>How is that going to evolve for the next couple of years? I wish I had one of those balls, right? But essentially I think for the next couple of years there's gonna be a lot of people, you know, still moving along with those four levels of the stack. A lot of people I see are still in version one and version two of the chief data. So you'll see over the years that's gonna evolve more digital and more data products. So for next years, my, my prediction is it's all products because it's an immediate link between data and, and the essentially, right? Right. So that's gonna be important and quite likely a new, some new things will be added on, which nobody can predict yet. But we'll see those pop up in a few years. I think there's gonna be a continued challenge for the chief officer role to become a real executive role as opposed to, you know, somebody who claims that they're executive, but then they're not, right? >>So the real reporting level into the board, into the CEO for example, will continue to be a challenging point. But the ones who do get that done will be the ones that are successful and the ones who get that will the ones that do it on the basis of data monetization, right? Connecting value to the data and making that value clear to all the data citizens in the organization, right? And in that sense, they'll need to have both, you know, technical audiences and non-technical audiences aligned of course. And they'll need to focus on adoption. Again, it's not enough to just have your data office be involved in this. It's really important that you're waking up data citizens across the organization and you make everyone in the organization think about data as an asset. >>Absolutely. Because there's so much value that can be extracted. Organizations really strategically build that data office and democratize access across all those data citizens. Stan, this is an exciting arena. We're definitely gonna keep our eyes on this. Sounds like a lot of evolution and maturation coming from the data office perspective. From the data citizen perspective. And as the data show that you mentioned in that IDC study, you mentioned Gartner as well, organizations have so much more likelihood of being successful and being competitive. So we're gonna watch this space. Stan, thank you so much for joining me on the cube at Data Citizens 22. We appreciate it. >>Thanks for having me over >>From Data Citizens 22, I'm Lisa Martin, you're watching The Cube, the leader in live tech coverage. >>Okay, this concludes our coverage of Data Citizens 2022, brought to you by Collibra. Remember, all these videos are available on demand@thecube.net. And don't forget to check out silicon angle.com for all the news and wiki bod.com for our weekly breaking analysis series where we cover many data topics and share survey research from our partner ETR Enterprise Technology Research. If you want more information on the products announced at Data Citizens, go to collibra.com. There are tons of resources there. You'll find analyst reports, product demos. It's really worthwhile to check those out. Thanks for watching our program and digging into Data Citizens 2022 on the Cube, your leader in enterprise and emerging tech coverage. We'll see you soon.

>>from >>around the globe. It's the cube with digital >>Coverage of IBM think 2021 brought to you by IBM. >>Welcome back to IBM Think 2021 The virtual edition. My name is Dave Volonte and you're watching the cubes continuous coverage of think 21. And right now we're gonna talk about banking in the post isolation economy. I'm very pleased to welcome our next guest. Look at wag lee is the general manager, Global banking financial markets at IBM and john Degnan is the global ceo and vice president and distinguished engineer for banking and financial services. Gentlemen, welcome to the cube. >>Thank you. Yeah >>that's my pleasure. Look at this current economic upheaval. It's quite a bit different from the last one, isn't it? I mean liquidity doesn't seem to be a problem for most pecs these days. I mean if anything they're releasing loan loss reserves that they didn't need. What's from your perspective, what's the state of banking today and hopefully as we exit this pandemic soon. >>So so dave, I think, like you say, it's, you know, it's a it's a state and a picture that in a significantly different from what people were expecting. And I think some way, in some ways you're seeing the benefits of a number of the regulations that were put into into place after the, you know, the financial crisis last time around, right? And therefore this time, you know, a health crisis did not become a financial crisis, because I think the banks were in better shape. And also, you know, governments clearly have put worldwide a lot of liquidity into the, into the system. I think if you look at it though, maybe two or three things ready to call out firstly, there's a there's a massive regional variation. So if you look at the U. S. Banking industry, it's extremely buoyant and I'll come back to that in a minute in the way in which is performing, you know, the banks that are starting to report their first quarter results are going to show profitability. That's you know significantly ahead of where they were last year and probably some of the some of their best performance for quite a long time. If you go into europe, it's a completely different picture. I think the banks are extremely challenged out there and I think you're going to see a much bleaker outlook in terms of what those banks report and as far as Asia pacific is concerned again, you know because they they have come out of the pandemic much faster than consumer businesses back into growth. Again, I think they're showing some pretty buoyant performance as far as as far as banking performance is concerned. I think the piece that's particularly interesting and I think him as a bit of a surprise to most is what we've seen in the U. S. Right. And in the US what's actually happened is uh the investment banking side of banking businesses has been doing better than they've ever done before. There's been the most unbelievable amount of acquisition activity. You've seen a lot of what's going on with this facts that's driving deal raised, you know, deal based fee income for the banks. The volatility in the marketplace is meaning that trading income is much much higher than it's ever been. And therefore the banks are very much seeing a profitability on that investment banking side. That was way ahead of what I think they were. They were expecting consumer businesses definitely down. If you look at the credit card business, it's down. If you look at, you know, lending activity that's going down going out is substantially less than where it was before. There's hardly any lending growth because the economy clearly is flat at this moment in time. But again, the good news that, and I think this is a worldwide which are not just in us, the good news here is that because of the liquidity and and some of the special measures the government put out there, there has not been the level of bankruptcies that people were expecting, right. And therefore most of the provisioning that the banks did um in expectation of non performing loans has been, I think, a much more, much greater than what they're going to need, which is why you're starting to see provisions being released as well, which are kind of flattering, flattering the income, flattering the engine. I think going forward that you're going to see a different picture >>is the re thank you for the clarification on the regional divergence, is that and you're right on, I mean, european central banks are not the same, the same position uh to to affect liquidity. But is that nuances that variation across the globe? Is that a is that a blind spot? Is that a is that a concern or the other other greater concerns? You know, inflation and and and the the pace of the return to the economy? What are your thoughts on that? >>So, I think, I think the concern, um, you know, as far as the european marketplace is concerned is um you know, whether whether the performance that and particularly, I don't think the level of provisions in there was quite a generous, as we saw in other parts of the world, and therefore, you know, is the issue around non performing loans in in europe, going to hold the european uh european banks back? And are they going to, you know, therefore, constrain the amount of lending that they put into the economy and that then, um, you know, reduces the level of economic growth that we see in europe. Right? I think, I think that is certainly that is certainly a concern. Um I would be surprised and I've been looking at, you know, forecasts that have been put forward by various people around the world around inflation. I would be surprised if inflation starts to become a genuine problem in the, in the kind of short to medium term, I think in the industry that are going to be two or three other things that are probably going to be more, you know, going to be more issues. Right. I think the first one which is becoming top of mind for chief executives, is this whole area around operational resiliency. So, you know, regulators universally are making very very sure that banks do not have a technical debt or a complexity of legacy systems issue. They are and you know, the U. K. Has taken the lead on this and they are going so far as even requiring non executive directors to be liable if banks are found to not have the right policies in place. This is now being followed by other regulators around the world. Right. So so that is very much drop in mind at this moment in time. So I think discretionary investment is going to be put you know, towards solving that particular problem. I think that's that's one issue. I think the other issue is what the pandemic has shown is that and and and this was very evident to me and I mean I spent the last three years out in Singapore where you know, banks have become very digital businesses. Right? When I came into the U. S. In my current role, it was somewhat surprising to me as to where the U. S. Market place was in terms of digitization of banking. But if you look in the last 12 months, you know, I think more has been achieved in terms of banks becoming digital businesses and they've probably done in the last two or three years. Right. And that the real acceleration of that digitization which is going to continue to happen. But the downside of that has been that the threat to the banking industry from essentially fintech and big tex has exactly, it's really accelerated. Right, Right. Just to give you an example, Babel is the second largest financial services institutions in the US. Right. So that's become a real problem I think with the banking industry is going to have to deal with >>and I want to come back to that. But now let's bring john into the conversation. Let's talk about the tech stack. Look, it was talking about whether it was resiliency going digital, We certainly saw over the pandemic, remote work, huge, huge volumes of things like TPP and and and and and mortgages and with dropping rates, etcetera. So john, how is the tech stack Been altered in the past 14 months? >>Great question. Dave. And it's top of mind for almost every single financial services firm, regardless of the sector within the overall industry, every single business has been taking stock of how they handled the pandemic and the economic conditions thereafter and all of the business needs that were driven by the pandemic. In so many situations, firms were unable to service their clients or we're not competitive in serving their clients. And as a result they've had to do very deep uh architectural transformation and digital transformation around their core platforms. Their systems of analytics and their systems different end systems of engagement In terms of the core processing systems that many of these institutions, some in many cases there are 50 years old And with any 50 year old application platform there are inherent limitations. There's an in flex itty inflexibility. There's an inability to innovate for the future. There's a speed of delivery issue. In other words, it can be very hard to accelerate the delivery of new capabilities onto an aging platform. And so in every single case um institutions are looking to hybrid cloud and public cloud technology and pre packaged a ai and prepackaged solutions from an I. S. V. Ecosystem of software vendor ecosystem to say. As long as we can crack open many of these old monolithic cause and surround them with new digitalization, new user experience that spans every channel and automation from the front to back of every interaction. That's where most institutions are prioritizing. >>Banks aren't going to migrate, they're gonna they're gonna build an abstraction layer. I want to come back to the disruption is so interesting. The coin base I. P. O. Last month see Tesla and microstrategy. They're putting Bitcoin on their balance sheets. Jamie diamonds. Traditional banks are playing a smaller role in the financial system because of the new fin text. Look at, you mentioned Paypal, the striped as Robin Hood, you get the Silicon Valley giants have this dual disrupt disruption agenda. Apple amazon even walmart facebook. The question is, are traditional banks going to lose control of the payment systems? >>Yeah. I mean I think to a large extent that is that has already happened, right? Because I think if you look at, you know, if you look at the experience in ASia, right? And you look at particularly organizations like and financial, you know, in India, you look at organizations like A T. M. You know, very substantial chance, particularly on the consumer payments side has actually moved away from the banks. And I think you're starting to see that in the west as well, right? With organizations like, you know, cloud, No, that's coming out with this, you know, you know, buying out a later type of schemes. You've got great. Um, and then so you've got paper and as you said, strike, uh and and others as well, but it's not just, you know, in the payment side. Right. I think, I think what's starting to happen is that there are very core part of the banking business. You know, especially things like lending for instance, where again, you are getting a number of these Frontex and big, big tech companies entering the marketplace. And and I think the threat for the banks is this is not going to be small chunks of market share that you're going to actually lose. Right? It's it's actually, it could actually be a Kodak moment. Let me give you an example. Uh, you know, you will have just seen that grab is going to be acquired by one of these facts for about $40 billion. I mean, this organization started like the Uber in Singapore. It very rapidly got into both the payment site. Right? So it actually went to all of these moment pop shops and then offered q are based um, 12 code based payment capabilities to these very small retailers, they were charging about half or a third or world Mastercard or Visa were charging to run those payment rails. They took market share overnight. You look at the Remittance business, right? They went into the Remittance business. They set up these wallets in 28 countries around the Asean region. They took huge chunks of business completely away from DBS, which is the local bank out there from Western Union and all of these, all of these others. So, so I think it's a real threat. I think Jamie Dimon is saying what the banking industry has said always right, which is the reason we're losing is because the playing field is not even, this is not about playing fields. Been even write, all of these businesses have been subject to exactly the same regulation that the banks are subject to. Regulations in Singapore and India are more onerous than maybe in other parts of the world. This is about the banking business, recognizing that this is a threat and exactly as john was saying, you've got to get to delivering the customer experience that consumers are wanting at the level of cost that they're prepared to pay. And you're not going to do that by purely sorting out the channels and having a cool app on somebody's smartphone, Right? If that's not funny reported by arcade processes and legacy systems when I, you know, like, like today, you know, you make a payment, your payment does not clear for five days, right? Whereas in Singapore, I make a payment. The payment is instantaneously clear, right? That's where the banking system is going to have to get to. In order to get to that. You need to water the whole stack. And the really good news is that many examples where this has been done very successfully by incumbent banks. You don't have to set up a digital bank on the site to do it. And incumbent bank can do it and it can do it in a sensible period of time at a sensible level of investment. A lot of IBM s business across our consulting as well as our technology stack is very much trying to do that with our clients. So I am personally very bullish about what the industry >>yeah, taking friction out of the system, sometimes with a case of crypto taking the middle person out of the system. But I think you guys are savvy, you understand that, you know, you yeah, Jamie Diamond a couple years ago said he'd fire anybody doing crypto Janet Yellen and says, I don't really get a Warren Buffett, but I think it's technology people we look at and say, okay, wait a minute. This is an interesting Petri dish. There's, there's a fundamental technology here that has massive funding that is going to inform, you know, the future. And I think, you know, big bags are gonna lean in some of them and others, others won't john give you the last word here >>for sure, they're leaning in. Uh so to just to to think about uh something that lick it said a moment ago, the reason these startups were able to innovate fast was because they didn't have the legacy, They didn't have the spaghetti lying around. They were able to be relentlessly laser focused on building new, using the app ecosystem going straight to public and hybrid cloud and not worrying about everything that had been built for the last 50 years or so. The benefit for existing institutions, the incumbents is that they can use all of the same techniques and tools and hybrid cloud accelerators in terms And we're not just thinking about uh retail banking here. Your question around the industry that disruption from Bitcoin Blockchain technologies, new ways of processing securities. It is playing out in every single securities processing and capital markets organization right now. I'm working with several organizations right now exactly on how to build custody systems to take advantage of these non fungible digital assets. It's a hard, hard topic around which there's an incredible appetite to invest. An incredible appetite to innovate. And we know that the center of all these technologies are going to be cloud forward cloud ready. Ai infused data infused technologies >>Guys, I want to have you back. I wish I had more time. I want to talk about SPAC. So I want to talk about N. F. T. S. I want to talk about technology behind all this. You really great conversation. I really appreciate your time. I'm sorry. We got to go. >>Thank you. Thanks very much indeed for having us. It was a real pleasure. >>Really. Pleasure was mine. Thank you for watching everybody's day. Volonte for IBM think 2021. You're watching the Cube. Mhm.

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hey welcome back here writer jeff rick here with the cube we're in our Palo Alto studios you know kind of continuing our leadership coverage reaching out to the community for people that we've got in our community to get their take on you know how they're dealing with the Kovach crisis how they're helping to contribute back to the community to to bring their resources to bear and you know just some general good tips and tricks of getting through these kind of challenging times and we're really excited to have one of my favorite guests he's being used to come on all the time we haven't had them on for three years which I can't believe it sabi Mehta the CEO of true SATA founder to say to obby I checked the record I can't believe it's been three years since we last that down great to see you Jeff there's well first of all it's always a pleasure and I think the only person to blame for that is you Jeff well I will make sure that it doesn't happen again so in just a check-in how's things going with the family the company thank you for asking you know family is great we have I've got two young kids who have become video conferencing experts and they don't teach me the tricks for it which I'm sure is happening a lot of families around the world and the team is great we vent remote at this point almost almost two months ago down and can't complain I think their intellectual property business like you are so it's been a little easier for us to go remote compared to a lot of other businesses in the world and in America but no complaints it'll be very fortunate we are glad that we have a business and a company that can withstand the the economic uncertainty and the family's great I hope the same for the queue family I haven't seen Dave and John and it's good to see you again and I hope all of you guys are helped happy and healthy great I think in we're good so thank you for asking so let's jump into it you know one of the things that I've always loved about you is you know really your sense of culture and this kind of constant reinforcing of culture in your social media posts and the company blog post at true SATA you know celebrating your interns and and you really have a good pulse for that and you know I just I think we may even talked about it before about you know kind of the CEOs and leadership and and social media those that do and that and those that don't and you know I think it's it's probably from any kind of a risk reward trade-off you know I could say something group it versus what am I getting at it but really it's super important and in these times with the distributed workforce that the the importance and value of communicating and culture and touching your people frequently across a lot of different mediums and topic areas is is more important than ever before share with us kind of your strategy why did you figure this out early how have you you know kind of adjusted you know your method of keeping your team up and communicating absolutely like I guess I owe you guys a little bit of gratitude for it which is we launched our company and you know I'm showing a member on the cube it was a social media launch you know if you say that say it like that I think there are two or three things that are very important Jeff and you hit on all of them one is the emphasis on information sharing it becomes more important than times like these and we as as a society value the ability to share a positive conversation of positive perspective and a positive outlook more but since day zero at the seder we've had this philosophy that there are no secrets it is important to be open and transparent both inside and outside the company and that our legacy is going to be defined by what we do for the community and not just what we do for our shareholders and by its very nature the fact that you know I grew up in a different continent now live and call America now a different continent my home I guess I was it's very important for me to stay connected to my roots it is a good memory or reminder that the world is very interconnected unfortunately the pandemic is the is the best or worst example of it in a really weird way but I think it's also a very important point Jeff that I believe we learned early and I hope coming out from this is something that we don't lose the point you made about kindness social media and social networking has a massively in my opinion massively positive binding force for the world at the same time there were certain business models it tried to capitalize on the negative aspects of it you know whether they are the the commercialized versions of slam books or not so nice business models that capitalize on the ability for people to complain I hope that people society and us humans coming out of it learn from people like yourself or you know the small voice that I have on social media or the messages we share and we are kinda in what we do online because the ability to have networks that are viral and can propagate or self propagate is a very positive unifying force and I hope out of this pandemic we all realize the positive nature's of it more than the negative nature's of it because unfortunately as you know that our business models built on the negative forces of social media and I really really hope they're coming out of this are positive voices drown out the negative voices that's great point and and it's a great I want to highlight a quote from one of your blog's again I think you're just a phenomenal communicator and in relationship to what's going on with kovat and and I quote we are fighting fear pain and anxiety as much as we are fighting the virus this is our humble attempt to we'll get into what you guys did to help the thousands of first responders clerks rockstars but I just really want to stick with that kindness theme you know I used to or I still joke right that the greatest smile in technology today is our G from signal FX the guys are gonna throw up a picture of him he's a great guy he looks like everybody's favorite I love that guy but therefore signal effects and actually it's funny signal FX also launched on the cube at big data a big data show I used to say the greatest smile intact is avi Mehta I mean how can I go wrong and and what I when I reached out to you I I do I consciously thought what what more important time do we have than to see people like you with a big smile with the great positive attitude focusing on on the positives and and I just think it's so important and it segues nicely into what we used to talk about it the strata shows and the big data shows all the time everyone wanted to talk about Hadoop and big data you always stress is never about the technology it's about the application of the technology and you focus your company on that very where that laser focus from day one now it's so great to see is we think you know the bad news about kovat a lot of bad news but one of the good news is is you know there's never been as much technology compute horsepower big data analytics smart people like yourself to bring a whole different set of tools to the battle than just building Liberty ships or building playing planes or tanks so you guys have a very aggressive thing that you're doing tell us a little bit about is the kovat active transmission the coat if you will tell us about what that is how did it come to be and what are you hoping to accomplish of course so first of all you're too kind you know thank you so much I think you also were the first people to give me a hard time about my new or Twitter picture I put on and he said what are you doing RV you know you have a good smile come on give me the smile die so thank you you're very kind Jeff I think as I as we as you know and I know I think you've a lot to be thankful for in life and there's no reason why we should not smile no matter what the circumstance we have so much to be thankful for and also I am remiss happy Earth Day you know I'm rocking my green for Earth Day as well as Ramadan Kareem today is the first day of Ramadan and you know I I wish everybody in the world Ramadan Kareem and on that friend right on that trend of how does do we as a community come together when faced with crisis so Court was a very simple thing you know it's I'm thank you for recognizing the hard work of the team that led it it was an idea I came up with it you know in the shower I'm like there are two kinds of people or to your you can we have we as humans have a choice when history is being made which I do believe I do believe history is being made right whether you look at it economically and a economic shock and that we have not felt as humanity since the depression so you look at it socially and again something we haven't seen sin the Spanish blue history is being made in in these times and I think we as humans have a choice we can either be witnesses to it or play our part in helping shape it and coat was our humble tiny attempt to when we look back when history was being made we chose to not just sit on the sidelines but be a part of trying to be part of the solution so all riddled with code was take a small idea I had team gets the entire credit read they ran with it and the idea was there was a lot of data being open sourced around co-ed a lot of work being done around reporting what is happening but nothing was being done around reporting or thinking through using the data to predict what could happen with it and that was code with code we try to make the first code wonder oh that came out almost two weeks ago now when you first contacted us was predicting the spread and the idea around breaking the spread wasn't just saying here is the number of cases a number of deaths and know what to be very off we wanted to provide like you know how firefighters do can we predict where it may go to next at a county by county level so we could create a little bit of a firewall to help it from stop you know have the spread of it to be slower in no ways are we claiming that if you did port you can stop it but if he could create firewalls around it and distribute tests not just in areas and cities and counties where it is you know spiking but look at the areas and counties where it's about to go to so we use a inner inner in-house Network algorithm we call that Orion and we were able to start predicting where the virus is gonna go to we also then quickly realize that this could be an interesting where an extra you know arrow and the quiver in our fight we should also think about where are there green shoots around where can recovery be be helped so before you know the the president email announced this it was surrender serendipitous before the the president came and said I want to start finding the green shoes to open the country we then did quote $2 which we announced a week ago with the green shoots around a true sailor recovery index and the recovery index is looking at its car like a meta algorithm we're looking at the rates of change of the rates of change so if you're seeing the change of the rates of change you know the meta part we're declining we're saying there are early shoots that we if as we plan to reopen our economy in our country these are the counties to look at first that was the second attempt of code and the third attempt we have done is we calling it the odd are we there yet index it got announced yesterday and now - you're the first public announcement of it and the are we there yet index is using the government's definition of the phase 1 phase 2 phase 3 and we are making a prediction on where which are the counties that are ready to be open up and there's good news everywhere in the country but we we are predicting there are 73 different counties that ask for the government's definition of ready to open are ready to open that's all you know we were able to launch the app in five days it is free for all first responders all hospital chains all not-for-profit organizations trying to help the country through this pandemic and poor profit operations who want to use the data to get tests out to get antibodies out and to get you know the clinical trials out so we have made a commitment that we will not charge for code through - for any of those organizations to have the country open are very very small attempt to add another dimension to the fight you know it's data its analytics I'm not a first responder this makes me sleep well at night that I'm at least we're trying to help you know right well just for the true heroes right the true heroes this is our our humble attempt to help them and recognize that their effort should not go to its hobby that that's great because you know there is data and there is analytics and there is you know algorithms and the things that we've developed to help people you know pick they're better next purchase at Amazon or where they gonna watch next on Netflix and it's such a great application no it's funny I just finished a book called ghost Bob and is a story of the cholera epidemic in London in like 1850 something or other about four but what's really interesting at that point in time is they didn't know about waterborne diseases they thought everything kind of went through the air and and it was really a couple of individuals in using data in a new and more importantly mapping different types of datasets on top of it and now this is it's as this map that were they basically figured out where the the pump was that was polluting everybody but it was a great story and you know kind of changing the narrative by using data in a new novel and creative way to get to an answer that they couldn't and you know they're there's so much data out there but then they're so short a date I'm just curious from a data science point of view you know um you know there there aren't enough tests for you know antibodies who's got it there aren't enough tests for just are you sick and then you know we're slowly getting the data on the desk which is changing all the time you know recently announced that the first Bay Area deaths were actually a month were they before they thought they were so as you look at what you're trying to accomplish what are some of the great datasets out there and how are you working around some of the the lack of data in things like you know test results are you kind of organizing pulling that together what would you like to see more of that's why I like talking to you so I missed you you are these good questions of me excellent point I think there are three things I would like to highlight number one it doesn't take your point that you made with the with the plethora of technical advances and this S curve shift that these first spoke at the cube almost eleven years ago to the date now or ten years ago just the idea of you know population level or modeling that cluster computing is finally democratized so everybody can run complicated tests and a unique segment or one and this is the beauty of what we should be doing in the pandemic I'm coming I'm coming I'm quite surprised actually and given the fact we've had this S curve shift where the world calls a combination of cloud computing so on-demand IO and technical resources for processing data and then the on-demand ability to store and run algorithms at massive scale we haven't really combined our forces to predict more you know that the point you made about the the the waterborne pandemic in the eighteen eighteen hundreds we have an ability as humanity right now to actually see history play out rather than write a book about it you know it has a past tense and it's important to do are as follows number one luckily for you and I the cost of computing an algorithm to predict is manageable so I am surprised why the large cloud players haven't come out and said you know what anybody who wants to distribute anything around predictions lay to the pandemic should get cloud resources for free I we are running quote on all three cloud platforms and I'm paying for all of it right that doesn't really make sense but I'm surprised that they haven't really you know joined the debate or contribute to it and said in a way to say let's make compute free for anybody who would like to add a new dimension to our fight against the pandemic number one but the good news is it's available number two there is luckily for us an open data movement you know that was started on the Obama administration and hasn't stopped because you can't stop open movements allows people companies like ours to go leverage know whether it's John Hancock Carnegie Mellon or the new data coming out of you know California universities a lot of those people are opening up the data not every single piece is at the level we would like to see you know it's not zip plus 4 is mostly county level it's available the third innovation is what we have done with code but not it's not an innovation for the world right which is the give get model so we have said we will curate everything is available lie and boo cost anybody is used but they're for purposes and computations you want to enrich it every organization who gives code data will get more out of it so we have enabled a data exchange keep our far-off purple form and the open up the rail exchange that my clients use but you know we've opened up our data exchange part of our software platform and we have open source for this particular case a give get model but the more you give to it the more you get out of there and our first installations this was the first week that we have users of the platform you know the state of Nevada is using it there are no our state in North Carolina is using it already and we're trying to see the first asks for the gift get model to be used but that's the three ways you're trying to address the that's great and and and and so important you know in this again when this whole thing started I couldn't help but think of the Ford plant making airplanes and and Keiser making Liberty ships in in World War two but you know now this is a different battle but we have different tools and to your point luckily we have a lot of the things in place right and we have mobile phones and you know we can do zoom and well you know we can we can talk as we're talking now so I want to shift gears a little bit and just talk about digital transformation right we've been talking about this for ad nauseam and then and then suddenly right there's this light switch moment for people got to go home and work and people got to communicate via via online tools and you know kind of this talk and this slow movement of getting people to work from home kind of a little bit and digital transformation a little bit and data-driven decision making a little bit but now it's a light switch moment and you guys are involved in some really critical industries like healthcare like financial services when you kind of look at this not from a you know kind of business opportunity peer but really more of an opportunity for people to get over the hump and stop you can't push back anymore you have to jump in what are you kind of seeing in the marketplace Howard you know some of your customers dealing with this good bad and ugly there are two towers to start my response to you with using two of my favorite sayings that you know come to mind as we started the pandemic one is you know someone very smart said and I don't know who's been attributed to but a crisis is a terrible thing to waste so I do believe this move to restoring the world back to a natural state where there's not much fossil fuels being burnt and humans are not careful about their footprint but even if it's forced is letting us enjoy the earth in its glory which is interesting and I hope you don't waste an opportunity number one number two Warren Buffett came out and said that it's only when the tide goes out you realize who's swimming naked and this is a culmination of both those phenomenal phrases you know which is one this is the moment I do believe this is something that is deep both in the ability for us to realize the virtuosity of humanity as a society as social species as well as a reality check on what a business model looks like visa vie a presentation that you can put some fancy words on even what has been an 11-year boom cycle and blitzscale your way to disaster you know I have said publicly that this the peak of the cycle was when mr. Hoffman mr. Reid Hoffman wrote the book bit scaling so we should give him a lot of credit for calling the peak in the cycle so what we are seeing is a kind of coming together of those two of those two big trends crises is going to force industry as you've heard me say many for many years now do not just modernize what we have seen happen chef in the last few years or decades is modernization not transformation and they are different is the big difference as you know transformation is taking a business model pulling it apart understanding the economics that drive it and then not even reassembling it recreating how you can either recapture that value or recreate that value completely differently or by the way blow up the value create even more value that hasn't happened yet digital transformation you know data and analytics AI cloud have been modernizing trends for the last ten years not transformative trends in fact I've also gone and said publicly that today the very definition of technology transformation is run a sequel engine in the cloud and you get a big check off as a technology organization saying I'm good I've transformed how I look at data analytics I'm doing what I was doing on Prem in the cloud there's still sequel in the cloud you know there's a big a very successful company it has made a businessman out of it you don't need to talk about the company today but I think this becomes that moment where those business models truly truly get a chance to transform number one number two I think there's going to be less on the industry side on the new company side I think the the error of anointing winners by saying grow at all cost economics don't matter is fundamentally over I believe that the peak of that was the book let's called blitzscaling you know the markets always follow the peaks you know little later but you and I in our lifetimes will see the return to fundamentals fundamentals as you know never go out of fashion Jeff whether it's good conversations whether it's human values or its economic models if you do not have a par to being a profitable contributing member of society whether that is running a good balance sheet individually and not driven by debt or running a good balance sheet as a company you know we call it financial jurisprudence financial jurisprudence never goes out of fashion and the fact that even men we became the mythical animal which is not the point that we became a unicorn we were a profitable company three years ago and two years ago and four years ago and today and will end this year as a profitable company I think it's a very very nice moment for the world to realize that within the realm of digital transformation even the new companies that can leverage and push that trend forward can build profitable business models from it and if you don't it doesn't matter if you have a billion users as my economic professor told me selling a watermelon that you buy for a dollar or fifty cents even if you sell that a billion times you cannot make it up in volume I think those are two things that will fundamentally change the trend from modernization the transformation it is coming and this will be the moment when we look back and when you write a book about it that people say you know what now Jeff called it and now and the cry and the pandemic is what drove the economic jurisprudence as much as the social jurisprudence obvious on so many things here we can we're gonna be we're gonna go Joe Rogan we're gonna be here for four hours so hopefully hopefully you're in a comfortable chair but uh-huh but I don't I don't sit anymore I love standing on a DD the stand-up desk but I do the start of my version of your watermelon story was you know I dad a couple of you know kind of high-growth spend a lot of money raised a lot of money startups back in the day and I just know finally we were working so hard I'm Michael why don't we just go up to the street and sell dollars for 90 cents with a card table and a comfy chair maybe some iced tea and we'll drive revenue like there's nobody's business and lose less money than we're losing now not have to work so hard I mean it's so interesting I think you said everyone's kind of Punt you know kind of this pump the brakes moment as well growth at the ethic at the cost of everything else right there used to be a great concept called triple-line accounting right which is not just shareholder value to this to the sacrifice of everything else but also your customers and your employees and-and-and your community and being a good steward and a good participant in what's going on and I think that a lot of that got lost another you know to your point about pumping the brakes and the in the environment I mean we've been kind of entertaining on the oil side watching an unprecedented supply shock followed literally within days by an unprecedented demand shock but but the fact now that when everyone's not driving to work at 9:00 in the morning we actually have a lot more infrastructure than we thought and and you know kind of goes back to the old mob capacity planning issue but why are all these technology workers driving to work every morning at nine o'clock it means one thing if you're a service provider or you got to go work at a restaurant or you're you're carrying a truck full of tools but for people that just go sit on a laptop all day makes absolutely no sense and and I'd love your point that people are now you know seeing things a little bit slowed down you know that you can hear birds chirp you're not just stuck in traffic and into your point on the digital transformation right I mean there's been revolution and evolution and revolution people get killed and you know the fact that digital is not the same as physical but it's different had Ben Nelson on talking about the changes in education he had a great quote I've been using it for weeks now right that a car is not a is not a mechanical horse right it's really an opportunity to rethink the you know rethink the objective and design a new solution so it is a really historical moment I think it is it's real interesting that we're all going through it together as well right it's not like there quake in 89 or I was in Mount st. Helens and that blew up in in 1980 where you had kind of a population that was involved in the event now it's a global thing where were you in March 20 20 and we've all gone through this indeed together so hopefully it is a little bit of a more of a unifying factor in kind of the final thought since we're referencing great books and authors and quotes right as you've all know Harare and sapiens talked about what is culture right cultures is basically it's it's a narrative that we all have bought into it I find it so ironic that in the year 2020 that we always joke is 20/20 hindsight we quickly found out that everything we thought was suddenly wasn't and the fact that the global narrative changed literally within days you know really a lot of spearhead is right here in Santa Clara County with with dr. Sarah Cody shutting down groups of more than 150 people which is about four days before they went to the full shutdown it is a really interesting time but as you said you know if you're fortunate enough as we are to you know have a few bucks in the bank and have a business that can be digital which you can if you're in the sports business or the travel business the hotel business and restaurant business a lot of a lot of a lot of not not good stuff happening there but for those of us that can it is an opportunity to do this nice you know kind of a reset and use the powers that we've developed for recommendation engines for really a much more power but good for good and you're doing a lot more stuff too right with banking and in in healthcare telemedicine is one of my favorite things right we've been talking about telemedicine and electronic medicine for now well guess what now you have to cuz the hospitals are over are overflowing Jeff to your point three stories and you know then at some point I know you have you I will let you go you can let me go I can talk to you for four hours I can talk to you for but days my friend you know the three stories that there have been very relevant to me through this crisis I know one is first I think I guess in a way all are personal but the first one you know that I always like to remind people on there were business models built around allowing people to complain online and then using that as almost like a a stick to find a way to commercialize it and I look at that all of our friends I'm sure you have friends have lots of friend the restaurant is big and how much they are struggling right they are honest working the hardest thing to do in life as I've been told and I've witnessed through my friends is to run a restaurant the hours the effort you put into it making sure that what you produce this is not just edible but it's good quality is enjoyed by people is sanitary is the hard thing to do and there was yet there were all of these people you know who would not find in their heart and their minds for two seconds to go post a review if something wasn't right and be brutal in those reviews and if they were the same people were to look back now and think about how they assort the same souls then anything to be supportive for our restaurant workers you know it's easy to go and slam them online but this is our chance to let a part of the industry that we all depend on food right critical to humanity's success what have we done to support them as easy as it was for us to complain about them what have we done to support them and I truly hope and I believe they're coming out of it those business models don't work anymore and before we are ready to go on and online on our phones and complain about well it took time for the bread to come to my table we think twice how hard are they working right number one that's my first story I really hope you do tell me about that my second story is to your have you chained to baby with Mark my kids I'm sure as your kids get up every morning get dressed and launch you know their online version of a classroom do you think when they enter the workforce or when they go to college you and me are going to try and convince them to get in a oil burning combustion engine but by the way can't have current crash and breakdown and impact your health impact the environment and show up to work and they'll say what do you talk about are you talking about I can be effective I can learn virtually why can't I contribute virtually so I think there'll be a generation of the next class of you know contribute to society who are now raised to live in an environment where the choice of making sure we preserve the planet and yet contribute towards the growth of it is no longer a binary choice both can be done so I completely agree with you we have fundamentally changed how our kids when they grew up will go to work and contribute right my third story is the thing you said about how many industries are suffering we have clients you know in the we have health care customers we have banking customers you know we have whoever paying the bills like we are are doing everything they can to do right by society and then we have customers in the industry of travel hospitality and one of my most humbling moments Jeff there's one of the no sea level executives sent us an email early in this in this crisis and said this is a moment where a strong David can help AV Goliath and just reading that email had me very emotional because they're not very many moments that we get as corporations as businesses where we can be there for our customers when they ask us to be their father and if we as companies and help our customers our clients who area today are flying people are feeding people are taking care of their health and they're well if V in this moment and be there for them we we don't forget those moments you know those as humans have long-term memories right that was one of the kindest gentlest reminders to me that what was more important to me my co-founder Richard you know my leadership team every single person at Reseda that have tried very hard to build automations because as an automation company to automate complex human process so we can make humans do higher order activities in the moment when our customers asked us to contribute and be there for them I said yes they said yes you said yes and I hope I hope people don't forget that that unicorns aren't important there are mythical animals there's nothing all about profits there's nothing mythical about fortress balance sheet and there's nothing mythical about a strong business model that is built for sustainable growth not good at all cost and those are my three stories that you know bring me a lot of lot of calm in this tremendous moment of strife and and in the piece that wraps up all those is ultimately it's about relationships right people don't do business I mean companies don't do business with companies people do business with people and it's those relationships and and in strong relationships through the bad times which really set us up for when things start to come back I me as always it's I'm not gonna let it be three years to the next time I hear me pounding on your door great to catch up you know love to love to watch really your your culture building and your community engagement good luck I mean great success on the company but really that's one thing I think you really do a phenomenal job of just keeping this positive drumbeat you always have you always will and really appreciate you taking some time on a Friday to sit down with us well first of all thank you I wish I could tell you I just up to you but we celebrate formal Fridays that to Seder and that's what this is all so I want to end on a good on a positive bit of news I was gonna give you a demo of it but if you want to go to our website and look at what everything we're doing we have a survival kit around a data survival kit around kovat how am I using buzzwords you know a is let's not use that buzzword right now but in your in your lovely state but on my favorite places on the planet when we ran the algorithm on who is ready as per the government definition of opening up we have five counties that are ready to be open you know between Santa Clara to LA Sacramento Kern and San Francisco the metrics today the data today with our algorithm there are meta algorithm is saying that those five counties those five regions look like I've done a lot of positive activities if the country was to open under all the right circumstances those five look you know the first as we were men at on cream happy Earth Day a pleasure to see you so good to know your family is doing well and I hope we see we talk to each other soon thanks AVI great conversation with avi Mehta terrific guy thanks for watching everybody stay safe have a good weekend Jeff Rick checking out from the cube [Music]

[Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] [Music] hello and welcome to the Palo Alto students leaky bomb John free we're here for a special cube conversation and special report big news from VMware to discuss the launch of the availability of vSphere seven I'm here with Chris Prasad SVP and general manager of the vSphere business and cloud platform business unit and Paul Turner VP a VP of Product Management guys thanks for coming in and talking about the big news thank you for having us you guys announced some interesting things back in march around containers kubernetes and the vSphere there's just about the hard news what's being announced today we are announcing the general availability of vSphere 7 John it's by far the biggest release that we have done in the last 10 years we previewed it this project Pacific a few months ago with this release we are putting kubernetes native support into the vSphere platform what that allows us to do is give customers the ability to run both modern applications based on kubernetes and containers as well as traditional VM based applications on the same platform and it also allows the IT departments to provide their developers cloud operating model using the VMware cloud foundation that is powered by this release this is a key part of our tansu portfolio of solutions and products that we announced this year and it is targeted fully at the developers of modern applications and the specific news is vSphere 7 is general available you know really vSphere 7 yes ok that so let's on the trend line here the relevance is what what's the big trend line that this is riding obviously we saw the announcements at VMworld last year and throughout the year there's a lot of buzz Pat Keller says there's a big wave here with kubernetes what does this announcement mean for you guys with the marketplace trend yeah so what kubernetes is really about is people trying to have an agile operation they're trying to modernize their IT applications and they the best way to do that is build off your current platform expanded and and make it a an innovative a agile platform for you to run kubernetes applications and VM applications together I'm not just that customers are also looking at being able to manage a hybrid cloud environment both on Prem and public cloud together so they want to be able to evolve and modernize their application stack but modernize their infrastructure stack which means hybrid cloud operations with innovative applications kubernetes or container based applications and VMs what's excited about this trend Chris we were talking with us at VMworld last year and we've had many conversations around cloud native but you're seeing cloud native becoming the operating model for modern business I mean this is really the move to the cloud if you look at the successful enterprises even the suppliers the on-premises piece if not move to the cloud native marketplace technologies the on premise isn't effective so it's not so much on premises going away we know it's not but it's turning into cloud native this is the move to the cloud generally this is a big wave yeah absolutely I mean if Jon if you think about it on-premise we have significant market share by far the leader in the market and so what we are trying to do with this is to allow customers to use the current platform they are using but bring their application modern application development on top of the same platform today customers tend to set up stacks which are different right so you have a kubernetes stack you have a stack for the traditional applications you have operators and administrators who are specialized in kubernetes on one side and you have the traditional VM operators on the other side with this move what we are saying is that you can be on the same common platform you can have the same administrators who are used to administering the environment that you already had and at the same time offer the developers what they like which is kubernetes dial-tone that they can come and deploy their applications on the same platform that you use for traditional applications yep all Pat said Cuba is gonna be the dial tone on the internet most Millennials might even know what dial tone is but a buddy mince is is that's the key fabric there's gonna work a straight and you know we've heard over the years skill gap skill gap not a lot of skills out there but when you look at the reality of skills gap it's really about skills gaps and shortages not enough people most CIOs and chief and major security are so that we talk to you say I don't want to fork my development teams I don't want to have three separate teams so I don't have to I want to have automation I want an operating model that's not gonna be fragmented this kind of speaks to this whole idea of you know interoperability and multi-cloud this seems to be the next big way behind ibrid I think it I think it is the next big wake the the thing that customers are looking for is a cloud operating model they like the ability for developers to be able to invoke new services on demand in a very agile way and we want to bring that cloud operating model to on-prem to Google cloud to Amazon Cloud to Microsoft cloud to any of our VC peepee partners you get the same cloud operating experience and it's all driven by a kubernetes based dial-tone it's effective and available within this platform so by bringing a single infrastructure platform that can one run in this hybrid manner and give you the cloud operating agility that developers are looking for that's what's key in version seven says Pat Kelsey near me when he says dial tone of the internet kubernetes does he mean always on or what does he mean specifically just that it's always available what's what says what's the meaning behind that that phrase the the first thing he means is that developers can come to the infrastructure which is the VMware cloud foundation and be able to work with a set of api's that are kubernetes api s-- so developers understand that they're looking for that they understand that dial tone right and you come to our VMware cloud foundation that runs across all these clouds you get the same API said that you can use to deploy their application okay so let's get into the value here of vSphere seven how does VMware vSphere 7 specifically help customers isn't just bolting on kubernetes to vSphere some will say is it that's simple or are you running product management no it's not that easy it's yeah some people say hey just Bolton kubernetes on vSphere it's it's not that easy so so one of the things if if anybody's actually tried deploying kubernetes first it's it's highly complicated um so so definitely one of the things that we're bringing is you call it a bolt on but it's certainly not like that we are making it incredibly simple you talked about IT operational shortages customers want to be able to deploy kubernetes environments in a very simple way the easiest way that we can you can do that is take your existing environment that are out ninety percent of IT and just turn on turn on the kubernetes dial tone and it is as simple as that now it's much more than that in version 7 as well we're bringing in a couple things that are very important you also have to be able to manage at scale just like you would in the cloud you want to be able to have infrastructure almost self-managed and upgrade and lifecycle manage itself and so we're bringing in a new way of managing infrastructure so that you can manage just large scale environments both on-premise and public cloud environments and scale and then associated with that as well is you must make it secure so there's a lot of enhancements we're building into the platform around what we call intrinsic security which is how can we actually build in truly a trusted platform for your developers and IIT yeah I mean I I was just going to touch on your point about the shortage of IT staff and how we are addressing that here the the way we are addressing that is that the IT administrators that are used to administering vSphere can continue to administer this enhanced platform with kubernetes the same way administered the older laces so they don't have to learn anything new they're just working the same way we are not changing any tools process technologies so same as it was before same as it was before more capable dealer and developers can come in and they see new capabilities around kubernetes so it's best of both worlds and what was the pain point that you guys are so obviously the ease-of-use is critical Asti operationally I get that as you look at the cloud native developer Saiga's infrastructure as code means as app developers on the other side taking advantage of it what's the real pain point that you guys are solving with vSphere 7 so I think it's it's it's multiple factors so so first is we've we've talked about agility a few times right there is DevOps as a real trend inside an IT organizations they need to be able to build and deliver applications much quicker they need to be able to respond to the business and to do that what they are doing is is they need infrastructure that is on demand so what what we're really doing in the core kubernetes kind of enablement is allowing that on-demand fulfillment of infrastructure so you get that agility that you need but it's it's not just tied to modern applications it's also your all of your existing business applications and your monitoring applications on one platform which means that you know you've got a very simple and and low-cost way of managing large-scale IT infrastructure so that's a that's a huge piece as well and and then I I do want to emphasize a couple of other things it's we're also bringing in new capabilities for AI and m/l applications for sa P Hana databases where we can actually scale to some of the largest business applications out there and you have all of the capabilities like like the GPU awareness and FPGA were FPGA awareness that we built into the platform so that you can truly run this as the fastest accelerated platform for your most extreme applications so you've got the ability to run those applications as well as your kubernetes and container based applications that's the accelerated application innovation piece of the announcement right that's right yeah it's it's it's quite powerful that we've actually brought in you know basically new hardware awareness into the product and expose that to your developers whether that's through containers or through VMs Chris I want to get your thoughts on the ecosystem and then the community but I want to just dig into one feature you mentioned I get the lifestyle improvement a life cycle improvement I get the application acceleration innovation but the intrinsic security is interesting could you take a minute explain what that is yeah so there's there's a few different aspects one is looking at how can we actually provide a trusted environment and that means that you need to have a way that the the key management that even your administrator is not able to get keys to the kingdom as we would call it you you want to have a controlled environment that you know some of the worst security challenges inside and some of the companies has been your Intel or internal IT staff so you've got to have a way that you can run a trusted environment in independent we've got these fair trust Authority that we released in version 7 that actually gives you a a secure environment for actually managing your keys to the kingdom effectively your certificates so you've got this you know continuous runtime now not only that we've actually gone and taken our carbon black features and we're actually building in full support for carbon black into the platform so that you've got negative security of even your application ecosystem yeah that's been coming up a lot conversations the carbon black in the security piece Chris obviously have vsphere everywhere having that operating model makes a lot of sense but you have a lot of touch points you got cloud hyper scale is got the edge you got partners so the other dominant market share and private cloud we are on Amazon as you well know as your Google IBM cloud Oracle cloud so all the major clouds there is a vSphere stack running so it allows customers if you think about it right it allows customers to have the same operating model irrespective where their workload is residing they can set policies compliance security they said it once it applies to all their environments across this hybrid cloud and it's all for a supported by our VMware cloud foundation which is powered by vSphere 7 yeah I think having that the cloud is API based having connection points and having that reliable easy to use is critical operating model all right guys so let's summarize the announcement what do you guys take Derek take away from this vSphere 7 what is the bottom line what's what's it really mean I think what we're if we look at it for developers we are democratizing kubernetes we already are in 90% of IT environments out there are running vSphere we are bringing to every one of those be sphere environments and all of the virtual infrastructure administrators they can now manage kubernetes environments you can you can manage it by simply upgrading your environment that's a really nice position rather than having independent kind of environments you need to manage so so I think that's that is one of the key things that's in here the other thing though is there is I don't think any other platform out there that other than vSphere that can run in your data center in Google's in Amazon's in Microsoft's in you know thousands of VC PP partners you have one hybrid platform that you can run with and that's got operational benefits that's got efficiency benefits that's got agility benefits yeah I just add to that and say that look we want to meet customers where they are in their journey and we want to enable them to make business decisions without technology getting in the way and I think the announcement that we made today with vSphere 7 is going to help them accelerate their digital transformation journey without making trade-offs on people process and technology and there's more to come that we're laser focused on making our platform the best in the industry for running all kinds of applications and the best platform for a hybrid and multi cloud and so you'll see more capabilities coming in the future stay tuned oh one final question on this news announcement which is this awesome vSphere core product for you guys if I'm the customer tell me why it's gonna be important five years from now because of what I just said it is the only platform that is going to be running across all the public clouds right which will allow you to have an operational model that is consistent across the clouds so think about it if you go to Amazon native and then you have orc Lord and as your you're going to have different tools different processes different people trained to work with those clouds but when you come to VMware and you use our cloud foundation you have one operating model across all these environments and that's going to be game-changing great stuff great stuff thanks for unpacking that for us graduates on the insulin Thank You Vera bees fear 7 News special report here inside the cube conversation I'm John Farrar your thanks for watch [Music] and welcome back everybody Jeff Rick here with the cube we are having a very special Q conversation and kind of the the ongoing unveil if you will of the new VMware vSphere 7 dot gonna get a little bit more of a technical deep dive here today we're excited to have a longtime cube alumni kit Kolbert here is the vp and CTO cloud platform at being work it great to see you yeah and and new to the cube jared rose off he's a senior director of product management at VMware and I'm guessin had a whole lot to do with this build so Jared first off congratulations for birthing this new release and great to have you on board alright so let's just jump into it from kind of a technical aspect what is so different about vSphere seven yeah great so vSphere seven baek's kubernetes right into the virtualization platform and so this means that as a developer I can now use kubernetes to actually provision and control workloads inside of my vSphere environment and it means as an IT admin I'm actually able to deliver kubernetes and containers to my developers really easily right on top of the platform I already run so I think we had kind of a sneaking suspicion that that might be coming when the with the acquisition of the hefty Oh team so really exciting news and I think it you tease it out quite a bit at VMware last year about really enabling customers to deploy workloads across environments regardless of whether that's on Prem public cloud this public cloud that public cloud so this really is the the realization of that vision yes yeah so we talked at VMworld about project Pacific right this technology preview and as Jared mentioned of what that was was how do we take kubernetes and really build it into vSphere as you know we had a hybrid cloud vision for quite a while now how do we proliferate vSphere to as many different locations as possible now part of the broader VMware cloud foundation portfolio and you know as we've gotten more and more of these instances in the cloud on-premises at the edge with service providers there's a secondary question how do we actually evolve that platform so it can support not just the existing workloads but also modern work clothes as well right all right so I think you brought some pictures for us a little demo so why don't ya well into there and let's see what it looks like you guys can cube the demo yes we're gonna start off looking at a developer actually working with the new VMware cloud foundation for an vSphere 7 so what you're seeing here is the developers actually using kubernetes to deploy kubernetes the self eating watermelon right so the developer uses this kubernetes declarative syntax where they can describe a whole kubernetes cluster and the whole developer experience now is driven by kubernetes they can use the coop control tool and all of the ecosystem of kubernetes api is and tool chains to provision workloads right into vSphere and so you know that's not just provisioning workloads though this is also key to the developer being able to explore the things they've already deployed so go look at hey what's the IP address that got allocated to that or what's the CPU load on this you know workload I just deployed on top of kubernetes we've integrated a container registry into vSphere so here we see a developer pushing and pulling container images and you know one of the amazing things about this is from an infrastructure as code standpoint now the developers infrastructure as well as their software is all unified in source control I can check in not just my code but also the description of the kubernetes environment and storage and networking and all the things that are required to run that app so now we're looking at a sort of a side-by-side view where on the right hand side is the developer continuing to deploy some pieces of their application and on the left-hand side we see V Center and what's key here is that as the developer deploys new things through kubernetes those are showing up right inside of the V center console and so the developer and IT are seeing exactly the same things with the same names and so this means what a developer calls their IT department says hey I got a problem with my database we don't spend the next hour trying to figure out which VM they're talking about they got the same name they say they see the same information so what we're gonna do is that you know we're gonna push the the developer screen aside and start digging into the vSphere experience and you know what you'll see here is that V Center is the V Center you've already known and loved but what's different is that now it's much more application focused so here we see a new screen inside of V Center vSphere namespaces and so these vSphere namespaces represent whole logical applications like a whole distributed system now as a single object inside a V Center and when I click into one of these apps this is a managed object inside of e spear I can click on permissions and I can decide which developers have the permission to deploy or read the configuration of one of these namespaces I can hook this into my Active Directory infrastructure so I can use the same you know corporate credentials to access the system I tap into all my existing storage so you know this platform works with all of the existing vSphere storage providers can use storage policy based management to provide storage for kubernetes and it's hooked in with things like DRS right so I can define quotas and limits for CPU and memory and all that's going to be enforced by Drs inside the cluster and again as an as an admin I'm just using vSphere but to the developer they're getting a whole kubernetes experience out of this platform now vSphere also now sucks in all this information from the kubernetes environment so besides you know seeing the VMS and and things that developers have deployed I can see all of the desired state specifications all the different kubernetes objects that the developers have created the compute network and storage objects they're all integrated right inside the the vCenter console and so once again from a diagnostics and troubleshooting perspective this data is invaluable it often saves hours just in trying to figure out what what we're even talking about when we're trying to resolve an issue so the you know as you can see this is all baked right into V Center the V Center experience isn't transformed a lot we get a lot of VI admins who look at this and say where's the kubernetes and they're surprised that like they've been managing kubernetes all this time it just looks it looks like the vSphere experience they've already got but all those kubernetes objects the pods and containers kubernetes clusters load balancer stores they're all represented right there natively in the V Center UI and so we're able to take all of that and make it work for your existing VI admins well that's a it's pretty it's pretty wild you know it really builds off the vision that again I think you kind of outlined kid teased out it at VMworld which was you know the IT still sees vSphere which is what they want to see when they're used to seeing but devs siku Nettie's and really bringing those together in a unified environment so that depending on what your job is and what you're working on that's what you're gonna see in this kind of unified environment yeah yeah as the demo showed it is still vSphere at the center but now there's two different experiences that you can have interacting with vSphere the kubernetes base one which is of course great for developers and DevOps type folks as well as the traditional vSphere interface API is which is great for VI admins and IT operations right and then and really it was interesting to you tease that a lot that was a good little preview of people knew they're watching but you talked about really cloud journey and and kind of this bifurcation of kind of classical school apps that are that are running in their classic memes and then kind of the modern you know county cloud native applications built on kubernetes and youyou outlined a really interesting thing that people often talk about the two ends of the spectrum and getting from one to the other but not really about kind of the messy middle if you will and this is really enabling people to pick where along that spectrum they can move their workloads or move their apps ya know I think we think a lot about it like that that we look at we talk to customers and all of them have very clear visions on where they want to go their future state architecture and that involves embracing cloud it involves modernizing applications and you know as you mentioned that it's it's challenging for them because I think what a lot of customers see is this kind of these two extremes either you're here where you are kind of the old current world and you got the bright Nirvana future on the far end there and they believe it's the only way to get there is to kind of make a leap from one side to the other that you have to kind of change everything out from underneath you and that's obviously very expensive very time-consuming and very error-prone as well there's a lot of things that can go wrong there and so I think what we're doing differently at VMware is really to your point as you call it the the messy middle I would say it's more like how do we offer stepping stones along that journey rather than making this one giant leap we had to invest all this time and resources how come you able people to make smaller incremental steps each of which have a lot of business value but don't have a huge amount of cost right and its really enabling kind of this next gen application where there's a lot of things that are different about about one of the fundamental things is we're now the application defines a reach sources that it needs to operate versus the resources defining kind of the capabilities of what the what the application can't do and that's where everybody is moving as quickly as as makes sense you said not all applications need to make that move but most of them should and most of them are and most of them are at least making that journey you see that yeah definitely I mean I think that you know certainly this is one of the big evolutions we're making in vSphere from you know looking historically at how we managed infrastructure one of things we enable in VCR 7 is how we manage applications right so a lot of the things you would do in infrastructure management of setting up security rules or encryption settings or you know your your resource allocation you would do this in terms of your physical and virtual infrastructure you talk about it in terms of this VM is going to be encrypted or this VM is gonna have this firewall rule and what we do in vSphere 7 is elevate all of that to application centric management so you actually look at an application and say I want this application to be constrained to this much CPU or I want this application to be have these security rules on it and so that shifts the focus of management really up to the application level right yeah and like I kind of even zoom back a little bit there and say you know if you look back one thing we did was something like V San before that people had to put policies on a LUN you know an actual storage LUN and a storage array and then by virtue of a workload being placed on that array it inherited certain policies right and so these have turned that around allows you to put the policy on the VM but what jerez talking about now is that for a modern workload a modern were close not a single VM it's it's a collection of different things you've got some containers in there some VMs probably distributed maybe even some on-prem some in the cloud and so how do you start managing that more holistically and this notion of really having an application as a first-class entity that you can now manage inside of vSphere it's really powerful and very simplifying one right and why this is important is because it's this application centric point of view which enables the digital transformation that people are talking about all the time that's it's a nice big word but the rubber hits the road is how do you execute and deliver applications and more importantly how do you continue to evolve them and change them you know based on either customer demands or competitive demands or just changes in the marketplace yeah well you look at something like a modern app that maybe has a hundred VMs that are part of it and you take something like compliance right so today if I want to check of this app is compliant I got to go look at every individual VM and make sure it's locked down and hardened and secured the right way but now instead what I can do is I can just look at that one application object inside of each Center set the right security settings on that and I can be assured that all the different objects inside of it are gonna inherit that stuff so it really simplifies that it also makes it so that that admin can handle much larger applications you know if you think about vCenter today you might log in and see a thousand VMs in your inventory when you log in with vSphere seven what you see is a few dozen applications so a single admin can manage a much larger pool of infrastructure many more applications and they could before because we automate so much of that operation and it's not just the scale part which is obviously really important but it's also the rate of change and this notion of how do we enable developers to get what they want to get done done ie building applications well at the same time enabling the IT operations teams to put the right sort of guardrails in place around compliance and security performance concerns these sorts of elements and so being by being able to have the IT operations team really manage that logical application at that more abstract level and then have the developer be able to push in new containers or new VMs or whatever they need inside of that abstraction it actually allows those two teams to work actually together and work together better they're not stepping over each other but in fact now they can both get what they need to get done done and do so as quickly as possible but while also being safe and in compliance is ready fourth so there's a lot more to this is a very significant release right again a lot of foreshadowing if you go out and read the tea leaves that's a pretty significant you know kind of RER context or many many parts of ease of beer so beyond the kubernetes you know kind of what are some of the other things that are coming out and there's a very significant release yeah it's a great question because we tend to talk a lot about kubernetes what was project Pacific but is now just part of vSphere and certainly that is a very large aspect of it but to your point you know vSphere 7 is a massive release with all sorts of other features and so instead of a demo here let's pull up with some slides right look at what's there so outside of kubernetes there's kind of three main categories that we think about when we look at vSphere seven so the first first one is simplified lifecycle management and then really focus on security it's a second one and then applications as well out both including you know the cloud native apps that don't fit in the kubernetes bucket as well as others and so we go on the first one the first column there there's a ton of stuff that we're doing around simplifying life cycle so let's go to the next slide here where we can dive in a little bit more to the specifics so we have this new technology vSphere lifecycle management VL cm and the idea here is how do we dramatically simplify upgrades lifecycle management of the ESX clusters and ESX hosts how do we make them more declarative with a single image you can now specify for an entire cluster we find that a lot of our vSphere admins especially at larger scales have a really tough time doing this there's a lot of in and out today it's somewhat tricky to do and so we want to make it really really simple and really easy to automate as well so if you're doing kubernetes on kubernetes I suppose you're gonna have automation on automation right because they're upgrading to the sevens is probably not any consequent inconsequential tasks mm-hm and yeah and going forward and allowing you know as we start moving to deliver a lot of this great VCR functionality at a more rapid clip how do we enable our customers to take advantage of all those great things we're putting out there as well right next big thing you talk about is security yep we just got back from RSA thank goodness we got that that show in before all the badness started yeah but everyone always talked about security's got to be baked in from the bottom to the top yeah talk about kind of the the changes in the security so done a lot of things around security things around identity Federation things around simplifying certificate management you know dramatic simplifications there across the board one I want to focus on here on the next slide is actually what we call vSphere trust Authority and so with that one what we're looking at here is how do we reduce the potential attack surfaces and really ensure there's a trusted computing base when we talk to customers what we find is that they're nervous about a lot of different threats including even internal ones right how do they know all the folks that work for them can be fully trusted and obviously if you're hiring someone you somewhat trust them but you know what what's how do you implement that the concept of least privilege right or zero trust right yeah topic exactly so the idea with trust authorities that we can specify a small number of physical ESX hosts that you can really lock down and sure fully secure those can be managed by a special vCenter server which is in turn very lockdown only a few people have access to it and then those hosts and that vCenter can then manage other hosts that are untrusted and can use attestation to actually prove that okay these untrusted hosts haven't been modified we know they're okay so they're okay to actually run workloads on they're okay to put data on and that sort of thing so is this kind of like building block approach to ensure that businesses can have a very small trust base off of which they can build to include their entire vSphere environment right and then the third kind of leg of the stool is you know just better leveraging you know kind of a more complex asset ecosystem if you know what things like FPGAs and GPUs and you know kind of all of the various components that power these different applications which now the application could draw the appropriate resources as needed so you've done a lot of work here as well yeah there's a ton of innovation happening in the hardware space as you mentioned all sort of accelerators coming out we all know about GPUs and obviously what they can do for machine learning and AI type use cases not to mention 3d rendering but you know FPGAs and all sorts of other things coming down the pike as well there and so what we found is that as customers try to roll these out they have a lot of the same problems that we saw in the very early days of virtualization ie silos of specialized hardware that different teams were using and you know what you find is all things we found before you found we find very low utilization rates inability to automate that inability to manage that well putting security and compliance and so forth and so this is really the reality that we see at most customers and it's funny because and some ones you think well well shouldn't we be past this as an industry shouldn't we have solved this already you know we did this with virtualization but as it turns out the virtualization we did was for compute and then storage and network but now we really needed to virtualize all these accelerators and so that's where this bit fusion technology that we're including now with vSphere it really comes to the forefront so if you see in the current slide we're showing here the challenge is that just these separate pools of infrastructure how do you manage all that and so if you go to the we go to the next slide what we see is that with bit fusion you can do the same thing that we saw with compute virtualization you can now pool all these different silos infrastructure together so they become one big pool of GPUs of infrastructure that anyone in an organization can use we can you know have multiple people sharing a GPU we can do it very dynamically and the great part of it is is that it's really easy for these folks to use they don't even need to think about it in fact integrates seamlessly with their existing workflows so it's pretty it's pretty trick is because the classifications of the assets now are much much larger much varied and much more workload specific right that's really the opportunities flash they are they're good guys are diverse yeah and so like you know a couple other things just I don't have a slide on it but just things we're doing to our base capabilities things around DRS and vmotion really massive evolutions there as well to support a lot of these bigger workloads right so you look at some of the massive sa P Hana or Oracle databases and how do we ensure that the emotion can scale to handle those without impacting their performance or anything else they're making DRS smarter about how it does load balancing and so forth right now a lot of this stuff not just kind of brand new cool new accelerator stuff but it's also how do we ensure the core ass people have already been running for many years we continue to keep up with the innovation and scale there as well right all right so do I give you the last word you've been working on this for a while there's a whole bunch of admins that have to sit and punch keys what do you what do you tell them what should they be excited about what are you excited for them in this new release I think what I'm excited about is how you know IT can really be an enabler of the transformation of modern apps right I think today you look at a lot of these organizations and what ends up happening is the app team ends up sort of building their own infrastructure on top of IT infrastructure right and so now I think we can shift that story around I think that there's you know there's an interesting conversation that a lot of IT departments and appdev teams are gonna be having over the next couple years about how do we really offload some of these infrastructure tasks from the dev team make you more productive give you better performance availability disaster recovery and these kinds of capabilities awesome well Jared congratulations that get both of you for forgetting to release out I'm sure it was a heavy lift and it's always good to get it out in the world and let people play with it and thanks for for sharing a little bit more of a technical deep dive I'm sure there's ton more resources from people I even want to go down into the weeds so thanks for stopping by thank you thank you all right ease Jared he's kid I'm Jeff you're watching the cube we're in the Palo Alto studios thanks for watching we'll see you next time [Music] hi and welcome to a special cube conversation I'm Stu min a minute and we're digging into VMware vSphere seven announcement we've had conversations with some of the executives some of the technical people but we know that there's no better way to really understand a technology than to talk to some of the practitioners that are using it so really happy to have joined me for the program I have Bill Buckley Miller who is in infrastructure designer with British Telecom joining me digitally from across the pond bill thanks so much for joining us nice - all right so Phil let's start of course British Telecom I think most people know you know what BT is and it's a you know a really sprawling company tell us a little bit about you know your group your role and what's your mandate okay so my group it's called service platforms it's the bit of BT that services all of our multi millions of our customers so they we have broadband we have TV we have mobile we have DNS and email systems and one and it's all about our customers it's not a B to be part of BT you with me we we specifically focus on those kind of multi million customers that we've got in those various services I'm in particular my group is for we do infrastructure so we really do from data center all the way up to really about boot time or so we'll just past boot time and the application developers look after that stage and above okay great we definitely gonna want to dig in and talk about that that boundary between the infrastructure teams and the application teams but let's talk a little bit first you know we're talking about VMware so you know how long's your organization been doing VMware and tell us you know what you see with the announcement that VMware's making work BC or seven sure well I mean we've had a really great relationship with VMware for about twelve thirteen years something like that and it's a absolutely key part of our of our infrastructure it's written throughout BT really in every part of our operations design development and the whole ethos of the company is based around a lot of VMware products and so one of the challenges that we've got right now is application architectures are changing quite significantly at the moment and as you know in particular with serving us and with containers and a whole bunch of other things like that we're very comfortable with our ability to manage VMs and have been for a while we currently use extensively we use vSphere NSX t.v raps log insight network insight and a whole bunch of other VMware constellation applications and our operations teams know how to use that they know how to optimize they know how to capacity plan and troubleshoot so that's that's great and that's been like that for a half a decade at least we've been really really confident with our ability to still with Yemen where environments and Along Came containers and like I say multi cloud as well and what we were struggling with was the inability to have a cell pane a glass really on all of that and to use the same people and the same same processes to manage a different kind of technology so we we'd be working pretty closely with VMware on a number of different containerization products for several years now I would really closely with the b-string integrated containers guys in particular and now with the Pacific guys with really the idea that when we we bring in version 7 and the containerization aspects of version 7 we'll be in a position to have that single pane of glass to allow our operations team to really barely differentiate between what's a VM and what's a container that's really the holy grail right so we'll be able to allow our developers to develop our operations team to deploy and to operate and our designers to see the same infrastructure whether that's on premises cloud or off premises and be able to manage the whole piece in that was bad ok so Phil really interesting things you walked through here you've been using containers in a virtualized environment for a number of years want to understand in the organizational piece just a little bit because it sounds I manage all the environment but you know containers are a little bit different than VMs you know if I think back you know from an application standpoint it was you know let's stick it in a vm I don't need to change it and once I spin up a VM often that's gonna sit there for you know months if not years as opposed to you know I think about a containerization environment it's you know I really want a pool of resources I'm gonna create and destroy things all the time so you know bring us inside that organizational piece you know how much will there need to be interaction and more interaction or change in policies between your infrastructure team and your app dev team well yes making absolutely right that's the nature and that the time scales that were talking about between VMs and containers oh he's wildly different as you say we we probably oughta certainly have VMs in place now that were in place in 2000 and 2018 certainly but I imagine I haven't haven't really been touched whereas as you say VMs and a lot of people talk about spinning them all up all the time there are parts of our architecture that require that in particular the very client facing bursty stuff it you know does require spinning up spinning down pretty quickly but some of our smaller the containers do sit around for weeks if not if not months I really just depend on the development cycle aspects of that but the heartbeat that we've we've really had was just the visualizing it and there are a number different products out there that allow you to see the behavior of your containers and understand the resource requirements that they are having at any given moment allows troubleshoot and so on but they are not they need their new products their new things that we we would have to get used to and also it seems that there's an awful lot of competing products quite a Venn diagram if in terms of functionality and user abilities to do that so through again again coming back to being able to manage through vSphere to be able to have a list of VMs and alongside it is a list of containers and to be able to use policies to define how the behave in terms of their networking to be able to essentially put our deployments on Rails by using in particular tag based policies means that we can take the onus of security we can take the onus of performance management and capacity management away from the developers you don't really care about a lot of time and they can just get on with their job which is to develop new functionality and help our customers so that then means that then we have to be really responsible about defining those policies and making sure that they're adhered to but again we know how to do that with VMs new visa so the fact that we can actually apply that straightaway just to add slightly different completely unit which is really what we're talking about here is ideal and then to be able to extend that into multiple clouds as well because we do use multiple cards where AWS and as your customers and were between them is an opportunity that we can't do anything of them be you know excited about take oh yeah still I really like how you described it really the changing roles that are happening there in your organization need to understand right there's things that developers care about you know they want to move fast they want to be able to build new things and there's things that they shouldn't have to worry about and you know we talked about some of the new world and it's like oh can the platform underneath this take care of it well there there's some things platforms take care of there's some things that the software or you know your theme is going to need to understand so maybe if you could dig in a little bit some of those what are the drivers from your application portfolio what is the business asking of your organization that that's driving this change and you know being one of those you know tailwind pushing you towards you know kubernetes and the the vSphere 7 technologies well it all comes down with the customers right our customers want new functionality they want new integrations they want new content and they want better stability and better performance and our ability to extend or contracting capacity as needed as well so they're the real ultimate we want to give our customers the best possible experience of our products and services so we have to address that really from a development perspective it's our developers that have the responsibility to design them to deploy those so we have to in infrastructure we have to act as a firm foundation really underneath all of that that allows them to know that what they spend their time and develop and want to push out to our customers is something that can be trusted as performant we understand where their capacity requirements are coming from in in the short term and in the long term for that and it's secure as well obviously is a big aspect to it so really we're just providing our developers with the best possible chance of giving our customers what will hopefully make them delighted great Phil you've mentioned a couple of times that you're using public clouds as well as you know your your your your VMware farm one of make sure I if you can explain a little bit a couple of things number one is when it comes to your team especially your infrastructure team how much are they involved with setting up some of the the basic pieces or managing things like performance in the public cloud and secondly when you look at your applications are some of your clouds some of your applications hybrid going between the data center and the public cloud and I haven't talked to too many customers that are doing applications that just live in any cloud and move things around but you know maybe if you could clarify those pieces as to you know what cloud really means to your organization and your applications sure well I mean to us climate allows us to accelerate development she's nice because it means we don't have to do on-premises capacity lifts for new pieces of functionality or so we can initially build in the cloud and test in the cloud but very often applications really make better sense especially in the TV environment where people watch TV all the time I mean yes there are peak hours and lighter hours of TV watching same goes for broadband really but we generally we're well more than an eight-hour application profile so what that allows us to do then is to have well it makes sense we run them inside our organization where we have to run them in our organization for you know data protection reasons or whatever then we can do that as well but where we say for instance we have a boxing match on and we're going to be seen enormous spike in the amount of customers that want to sign up into our order journey for to allow them to view that and to gain access to that well why would you spend a lot of money on servers just for that level of additional capacity so we do absolutely have hybrid applications not sorry hybrid blocks we have blocks of suburb locations you know dozens of them really to support oil platform and what you would see is that if you were to look at our full application structure for one of the platform as I mentioned that some of the smoothers application blocks I have to run inside some can run outside and what we want to be able to do is to allow our operations team to define that again by policy as to where they run and to you know have a system that allows us to transparently see where they're running how they're running and the implications of those decisions so that we can tune those maybe in the future as well and that way we best serve our customers we you know we get to get our customers yeah what they need all right great Phil final question I have for you you've been through a few iterations of looking at VMS containers public cloud what what advice would you give your peers with the announcement of vSphere 7 and how they can look at things today in 2020 versus what they might have looked at say a year or two ago well I'll be honest I was a little bit surprised by vSphere so we knew that VMware we're working on trying to make containers on the same level both from a management deployment perspective as we MS I mean they're called VMware after all we knew that they were looking it's no surprise by just quite how quickly they've managed to almost completely reinvent their application really it's you know if you look at the whole tansy stuff from the Mission Control stuff I think a lot of people were blown away by just quite how happy VMware were to reinvent themselves and from an application perspective you know and to really leap forward and this is the very between version six and seven I've been following these since version three at least and it's an absolutely revolutionary change in terms of the overall architecture the aims to - what they want to achieve with the application and you know luckily the nice thing is is that if you're used to version six is not that big a deal it's really not that big a deal to move forward at all it's not such a big change to process and training and things like that but my word there's no awful lot of work underneath that underneath the covers and I'm really excited and I think other people in my position should really just take it as an opportunity to really revisit what they can achieve with them in particular with vSphere and with in combination with and SXT it's it's but you know it's quite hard to put into place unless you've seen the slide or slides about it and useless you've seen the products just how revolutionary the the version 7 is compared to previous revisions which have kind of evolved for a couple of years so yeah I think I'm really excited to run it and know a lot of my peers other companies that I speak with quite often are very excited about seven as well so yeah I'm really excited about the whole ball base well Phil thank you so much absolutely no doubt this is a huge move for VMware the entire company and their ecosystem rallying around helped move to the next phase of where application developers and infrastructure need to go Phil Buckley joining us from British Telecom I'm Stu minimun thank you so much for watching the queue

>> From Boston, Massachusetts. It's theCUBE. Covering Actifio 2019 Data Driven. (upbeat techno music) Brought to you by Actifio. >> Hello everyone and welcome to Boston and theCUBE's special coverage of Actifio Data Driven 19. I'm Dave Vellante. Stu Miniman is here. We've got a special guest, John Furrier is in the house from from Palo Alto. Guys, theCUBE we love to go out on the ground, you know, we go deep. We're here at this data theme, right? We were there in the early days, John, you called me up and say, "Get your butt here, we're going to cover the first of Doop World". And since then things have moved quite fast. Everybody thought, you know, Hadoop Big Data was going to take over the world. Nobody even uses that term anymore, right? It's kind of, now it's AI, and machine intelligence, and block chain, and everything else. So what do you think is happening? Did the early Big Data days fail? You know, Frank Genus this morning called it The experimentation phase. >> I mean, I don't really think Frank has a good handle on what's going on in my opinion, cause I think it's not an experimentation, it's real. That was a wave that was essentially the beginning of, not an experimentation, of realization and reality that data, unstructured data in particular was real and relevant. Hadoop looked good off the tee, mill the fairway as we say, but the thing about the Hadoop ecosystem is that validated big data. Every financial institution jumped on it. Everyone who knew anything about data or had data issues or had a lot of data, knew the value. It's just that the apparatus to build via Hadoop was too expensive. In comes Cloud computing at scale, so, as Cloud was accelerating, you look at the Amazon Web Services Revenue Chart you can almost see the D mark where the inflection point is on the hockey stick of Amazon's revenue numbers. And that is the point in time where Hadoop was on the declining of failure. Hortonworks sold the Cloudera. Cloudera's earnings are at an all-time low. A lot of speculation of their entire strategy, and their venture back company went public, but bet the ranch to be the next data warehouse. That wasn't the business model. The data business was a completely new industry, completely being re-transformed, and, far from experimentation, it is real and definitely growing like a weed, but changing because of the underpinning infrastructure dynamics of Cloud Native, Microservices, and that's only going to get highly accelerated and the people who talk about context of industry like Frank, are going to be off. Their predictions will be off because they don't really see the new picture clear enough, in my opinion, >> So, >> I think he's off. >> So it's not so much of a structural change like it was when we went from, you know, mainframes to PCs, it's more of a sort of flow, evolution into this new area which is being driven, powered by new technologies, we talk about block chain machine intelligence and other things. >> Well, I mean, the make up of companies that were building quote, "Big Data Solutions", were trying to build an apparatus or mechanisms to solve big data problems, but none of them actually had the big data problem. None of them were full of data. None of them had a lot of data. The ones that had problems were the financial institutions, the credit card companies, the people who were doing a lot of large scale, um, with Google, Facebook, and some of the hyperscalers. They were actually dealing with the data tsunami themselves, so the practitioners ended up driving it. You guys at Wikibomb, we pointed this out on theCUBE many times, that the value was going to come from the practitioners not the suppliers of so called technology. So, you know, the Clouderas of the world who thought Hadoop would be relevant and growing as a technology were right on one side, on the other side of the coin was the Cloud decimation of that sector. The Cloud computer just completely blew away that Hadoop market because you didn't have to hire a PhD, you didn't have to hire specialty skills to stand up Hadoop clusters. You could actually throw it in the Cloud and get agile quickly, and get value out of data very very quickly. That has been real, it has not been an experiment. There's been new case studies, new companies born, new brands, so it's not an experiment, it is reality, and it's only going to get more real every day. >> And I add of course now you've got, you mentioned Cloudera and Hortenworks, you also got Matt Bar reeling Stu. Let's talk about Actifio. So they coined the term Copy Data Management, they created the category, of course they do a lot of backup, I mean, everybody in this space does a lot of backup. And then you saw the Silicon Valley companies come in. Particularly Cohesity and Rubric, you know, to a lesser extent he got some other guys like Zerto and Durva, but it was really those two companies, Cohesity and Rubric, they raised more money in their D round than Actifio has since inception. But yet Actifio keeps, you know, plodding along, growing, you know, word is they're profitable, you know, they're not like this really sectioned very East Coast versus kind of West Coast mentality. What's your take on what's going on? >> Yeah, so, Dave right, you look at the early days of Actifio and you say great, Copy Data Management, I have all these copies of data, how do I reduce my cost, get greater utilization than I have and leverage the data? I love the title of the show here, Data Driven. You know, we know at the center of digital transformation if you can't become data driven, like the CMO Brian Regan got up on stage talk about that industrialization of data. How am I going along that journey being this, I collected data versus now, you know, data, you know, is the reason that I make decisions, how I make decisions, I get smarter. The Cloud of course is a huge enabler of this, there's all these services that I can instantly access to be able to get greater insight, and move along with that environment, and if you look underneath all of these backup companies, it's really how I can change that data into business value and drive my business, the metadata underneath and all those pieces, not just the wonky storage and technical solutions that make things better, and I get a faster ROI. It's that data at the core of what we do and how do I get that as a business to accelerate. Because we know IT needs to be able to respond back to the business and data needs to be that rocket fuel. >> Is it the case of data haves and data have-nots? I mean, Amazon has data >> I mean, you're right-- >> and Facebook has data. >> We're talking about Actifio, you brought that up, okay, on this segment, on the inside segment, which is cool, they're here at the event, but they have a good opportunity but they also, they got some challenges. I mean, the thing about Actifio is, to my earlier point, which side of the wave are they on? Are they out too much out front with virtualization and Amazon, the Cloud will take them away, or are they riding the Cloud wave, making that an enabler? And I think what really I like about Actifio is because they have a lot of virtualization capabilities, the question is can they scale that Stu, to containers and microservices, because, the real opportunity in this market, in my opinion, is going to build on the virtualization trend, and make container aware, microservices capabilities because if they don't, then that would be a tell sign. Now either way it's a hot M&A market right now, so I think being in the market, horse on the track as you say. You look at the tableau sales force deal monster numbers we are in clearly a hot IPO market and a major roll up market on the M&A side. I think clearly there's two types of companies, old and new, and that is really what people are looking at, are they part of the old guard, are they the new guard. So, you know, this to me is going to be a tell sign of what they do next, can they make the data driven value proposition, you articulated Stu, actually a reality It's going to come from the technology underneath. >> Well I think it's a really interesting point you're making because, Stu as you probably know, that Amazon announced the Amazon backup service right, and you talked about the backup guys and they're like, "Ah yeah it's backup, but it really doesn't do recovery, it's really not that robust". It's part of me says, "Uh oh"... >> Watch out. >> You better move fast", because Amazon has stated, "Hey if you don't move fast we're going to just keep gobbling", and you've seen Amazon do this. What are your thoughts on that? Can these specialists, can they survive, John's talking about M&A. Can the market support all these guys along with the big, you know, traditional guys like Veritas, and Dell EMC, and IBM and Combol? >> Right, well so Actifio started very much in the data center. They were before this Could wave really took off. It's really only in the last year that they've been sassifying their product. So the question is, does that underlying IP, which wasn't tied to hardware, but, you know, sat at really more of, you know, reminded us of that storage virtualization battles that we talked about for years, Dave, but now they are going in the Cloud. They've got all the partnerships in the Cloud, but they are competing against those new vendors that you talked about like Cohesity and Rubric out there, and there's big money chasing this environment. So, you know, I want to talk to the customers here and find out, you know, where they are using them, and especially some of those first customers using this--. >> Well they clearly need a Cloud play cause that's clearly where the action is. But if you look at what's going on with Amazon, Azure, and Google you see a lot of on premises, Stu, because that's where the customers are. So just because the customers are currently not migrating their existing workloads to the Cloud doesn't mean it's not going to happen. So I think there's an opportunity for any company like Actifio, who may or may not be on the curve on the tech side, one little misfire on a tech bet could cripple the company and also make the company. There's a lot of high risk, reward ratio. How they handle containers. How they build on virtualizations. Virtualization going to to be part of the future with Cloud. These are the kind of the dynamics that are going to be in play, and they got some time on their hands because the on premises growth is because the clients are trying to figure out what to do and they're not going to be migrating, lifting, and shifting workloads all off to the Cloud. New will be Cloud based, but enterprises have proven why we are in multi-Cloud and hybrid-Cloud conversation, that... The enterprise on premises is not going away anytime soon. >> I want to ask you guys, John you specifically, about this sort of new Silicon Valley growth model and how companies are achieving escape velocity. When you and I made our first trip to Barcelona, I was having dinner with David Scott who was the CEO of 3PAR and he said to me, When I came to 3PAR the board said, "Hey we're willing to invest 30 million dollars in this company". And David Scott said to them, "I need way more, I need 80 million dollars". Today 80 million dollars is nothing. You saw, you know, Pure Storage hit escape velocity, was just throwing money, and growing at the problem. You're seeing Cohesity-- >> Well you can debate that. I mean, If you have to build a rocket ship, hit critical mass and you want to fund that, you're going to to need an enterprise. However, there's arguments on the south side that you can actually get fly wheel effect going early with less capital. So again, that's 3PAR-- >> But so that's my point. >> Well so that's 3PAR, that was 2009. >> So, yeah that was early days so that's ancient history. But software is generally supposed to be a capital efficient market, yet these companies are raising many hundreds and hundreds of millions, you know, half a billion dollar raises and they are putting it largely in promotion. Is that the new model, is that sustainable, in your view? >> Well I think you're conflating capital market dynamics with viable companies to invest in. I think there's a robust seed in series A market but the series A market and Silicon Valley is you know, 15 to 25 million, it used to be 3 to 5. So the dynamics are changing on funding. There's just not enough companies, horses on the track, to deploy capital at tranches of 30, 50, 80 million. So the capital markets are clearly going to have the money available so it's a market for the startups and the broke companies. That's separate from actually winning. So you've got slacks going public this weeks, you have other companies who have built business on a sass fly wheel, and then everything else is gravy in terms of the go to market, they got a couple hundred million. I think slack got close to a billion dollars in cash that they've raised. So they're flooded with cash, they'll never spend it all. So there are some companies that can achieve success like that. Others have to buy market share, they got to push and build out a sales force, and it's going to be a function of the role of customer, customization, specialism, and whatnot. But with AI machine leaning there's more efficiencies coming in so I think the modern company can do more with less. >> What do you think of the ride sharing on IPOs, Uber and Lift, do you abol? Do you like 'em or do you think it's just, they're losing too money and can't sustain it? >> I was thinking about that this morning after looking at the article in the Wall Street Journal in our coverage on Silicon angle. You look at Zoom communications, I like models that actually can take a simple concept and an existing mature market and disrupt it by being Cloud efficient and completely sass and data driven. That is an example of success. That to me, Zoom Communications and Zscaler, another company that we talk to, these are companies that were built with a specific value proposition that made the product and they were targeting mature markets with leaders in it. Video conferencing, Webex, Citrix, Zoom came out of nowhere, optimized on simple value proposition, used Cloud scale and data, and crushed it. Uber, Lift, little bit different issue. They're losing money but I would bet on the long term that that is going to be the used case for how people will have transportation. I think that's the long game and I think that without regulatory kind of pressure, without, there's regulatory issues that's really the big risk. But I believe that Uber and Lift absolutely will be long brands and just like Facebook was early on, although they threw off a lot of cash, those guys are building for penetration, and that's where the funding matters. Penetration is critical. Now they're the standard, and people really don't take taxis anymore, but they're really using the ride sharing. And you get the scooters, you get the bikes, they're all sequencing into these adjacent markets which drains more cash but builds the brand, builds the footprint. >> Well that's what I want to ask you. So people compare the early Uber, Lift, Taxi, Ride sharing to Amazon selling books, but there's all these other adjacencies. You have a thought on this? >> Well, just, you know, right, Uber Eats is a huge opportunity for that environment and autonomous vehicles everybody talks about, but it's still quite a ways out. So there are a lot of different- >> Scooters are the same, we're in San Diego, there are 8 gazillion scooters. >> San Diego had fun, you know, going around on their electronic scooters, boy, talk about the gig economy, they pay people at the night, to like go pay by the recharge you do on that, what is the future of work, >> Yeah, that's a great point. >> and how can we have that-- >> Uber going to look a lot like Amazon. You subsidize the front end retail side of the business, but look at the data that they throw up. Uber's data that they're gathering on, not only customer behavior, but just mapping services, 3-D mapping is going to be huge, so you've got these cars that are essentially bots on the road, providing massive mapping and traffic analysis. So you're going to start to see data driven, like Actifio slogan here, be a big part of all design decisions and value proposition from any company out there. And if they're not data driven I think they're going to be toast. >> Probably could because there's that data and that machine learning underneath, that can optimize, you know, where the people are, how I use the system, such a huge wave that we're watching. >> How about one last topic which is heavily data driven, it's Facebook. Facebook is obviously a data driven company, the Facebook crypto play, I love it, I love Facebook. I'm a bull on Facebook, I think it's been beat up. I think, two billion users is hard to replicate, but what's your thoughts on their crypto play? >> Well it's kind of a middle finger to the United States of America but it's a great catalyst for the international market because crypto needed a whale to come in and bring all those users in. Bad timing, in my mind, for Facebook, because given all the anti-trust and regulatory conversations, what better way to show your threat to the world order when you say we're going to run a banking system with a collection of international companies. I think the US is going to look at this and say, "Oh my God! They can't even be trusted to handle personal information and we're going to now let them run a banking system? Run monetary, basically World Bank equivalent infrastructure?" No frickin way! I think this is going to to be a major road to home. I think Facebook has to really make this an ecosystem play if they want to make it work, that's their telegraphic move they're saying, "Hey we want to do for the community but we got our own wallet and we got our own network". But they bring a lot to the table so it's going to be a really interesting dynamic to see the coalescing around Facebook because they could make the market. Look what Instagram did to Snapchat. They literally killed the company, took all their users. That is what's going to happen in the digital money economy when Facebook brings billions of users user experience with money. What happened with Snapchat with Instagram is going to happen to the World Bank if this continues. >> Where do you stand on the government breaking up big tech? >> So Dave, you know, you look in these companies, it's not easy to pull those apart. I don't think our government understands how most of big tech works. You know, take Amazon and AWS, that's one company underneath it. You know, Facebook, Microsoft. You know, Microsoft went through all these issues. Question Dave, we've had lots of debates on Twitter you know, are they breaking the law, are they not doing trust? I have some trust issues with Facebook myself, but most of the big companies up there I don't think the anti-trust kicks in, I don't think it makes sense to pull them apart. >> Stu, the Facebook story and the YouTube story are simply this, they have been hiding under the platform rules, of the Digital Millennium Copyright Act, and they are an editing platform so you can't sue them. Okay, once they become a publisher they could be sued. Just like CNN, Fox News, and everybody else. And we're publishers. So they've been hiding behind the platform. That gig is up. They're going to have to address are you a platform or are you a publisher? You're making editing decisions around what users can see with software, you are essentially editing the feed, that is a publisher role, with that becomes responsibility, and then obviously regulartory. >> Well Facebook is conflicted right now. They're trying to figure out which side of the fence to go on. >> No no no! They want one side! The platform side! They're make billions of dollars! >> Yeah but so they're making decisions about you know, which content to show and whether they monetize it. And when it's controversial content, they'll turn down the ads a little bit but they won't completely eliminate it sometimes. >> So, Dave, the only thing that the partisans in politics seem to agree on though is that big tech has too much power. You know, What's your take on that? >> Well so I think that if they are breaking the law then they should be moderated. But I don't think the answer is to go hard after Elizabeth Warren. Hard after them and break them up. I think you got to start with okay, because you break these companies up what's going to happen is they're going to be worth more, it's going to be AT&T all over again. >> While you guys were at Sysco Live, we covered this at Amazon Web Service and Public Sector Summit. The real issue in government, Stu, is there's too much tech for bad on the PR side, and there's not enough tech for good. Tech is not bad, tech is good. There's not enough promotion around the apps around there. There's real venture funds being created to promote tech for good. That's going to where the tide will turn. When does the tech industry start doing good stuff, not bad stuff. >> All right we've got to wrap. John, thanks for sitting in. Thank you for watching. Be right back, we're here at Actifio Data Driven 2019. From Boston this is theCUBE, be right back. (upbeat techno music)

- So our first prediction relates to how data governance is likely to change in a global basis. If we believe that we need to turn more data into work, businesses haven't generally adopted many of the principles associated with those practices. They haven't optimized to do that better. They haven't elevated those concepts within the business as broadly and successfully as they have, or as they should. We think that's gonna change, in part, by the emergence of GDPR, or the General Data Protection Regulation. It's gonna go in full effect in May 2018. A lot has been written about it. A lot has been talked about. But our core issues ultimately are, is that the dictates associated with GDPR are going to elevate the conversation on a global basis. And it mandates something that's now called the Data Protection Officer. We're gonna talk about that in a second, Dave Elonte. But it is going to have real teeth. So we were talking with one Chief Privacy Officer not too long ago who suggested that had the Equifax breach occurred under the rules of GDPR, that the actual fines that would have been levied would have been in excess of $160 billion dollars, which is a little bit more than the $0 dollars that has been fined thus far. Now we see new bills introduced in Congress, but ultimately our observation and our conversation with a lot of Chief Privacy Officers or Data Protection Officers is that in the B to B world, GDPR is going to strongly influence not just how businesses behave regarding data in Europe, but on a global basis. - A lot of the undertone is, "Cloud, cloud, cloud, governance, governance, governance," is the two, kind of the drivers I've been seeing as the forces this week is a lot of people trying to get their act together on those two fronts. And you can kind of see the scabs on the industry. Some people haven't been paying attention and they're weak in the area. Cloud is absolutely going to be driving the big data world, because data's horizontal, cloud's the power source to that. You guys have been on that. What's your thoughts? What other drivers and currents-- first of all do you agree with what I'm saying? And what else did I miss? I mean, security is obviously in there, but-- - Absolutely, so I think you're exactly right on. So, obviously governance security's a big deal. Largely being driven by the GDPR regulation that's happening in Europe. But I mean, every company today is global, so everybody's essentially affected by it. So I think data up til now has always been a kind of opportunistic thing, that there's a couple guys in the organization who are looking at it as, "Oh, let's do some experimentation, "let's do something interesting here." Now it's becoming government mandate. And so I think there's a lot of organizations who are, like to your point, getting their act together, and that's driving a lot of demand for data management products. So now people say, "Well, if I gotta get my act together, I don't want to have to hire armies of people to do it. Let me look for automated, machine-learning based ways of doing it," so that they can actually deliver on the audit reports that they need to deliver on, ensure the compliance that they need to ensure, but do it in a very scalable way. - Me as a customer come to an enterprise say, "I don't want any of my data stored." It's up to you to go delete that data completely, right? That's the term that's being used, and that goes into effect in May. How do you make sure that that data gets completely deleted by that time the customer has. How do you get that consent from the customer to go do all this? So there's a whole lot of challenges as data as multiplies. How do you deal with the data? How do you create insights to the data? How do you pay the consent on the data? How do you be compliant on the data? You know, how do you create the policies that's needed to generate that data? All those things needs to be, those are the challenges that enterprise is facing. - Digital transformation's accelerating, data protection's being disrupted, millions of jobs are coming in. You guys are playing a role. What is the role that Druva is playing in the digital transformation acceleration? - Absolutely. You think about the world, right, and you think of companies like Domino's or Tesla, they think they are softer companies, right, they deliver, the server they deliver a softer approach of the traditional business model. In the heart of this transformation of enterprise is becoming softer, digitalized, is the data at the core. And data today will outlive most systems. And the more and more fragmented your approach to data becomes, you store data on prem, in the cloud, everywhere in between, the data management has to become more and more centralized. So Druva is in the core of this transformation making a data transformation and making sure your data architextures the future of a better approach of manageablity and protection with a Druva platform. - You guys had a busy month this month. You got a couple big news we're gonna be talking about today. Funding and next generation platform. Walk us through that. - Absolutely, so we have two big news to announce today. The first one being $80 million dollars of capital raised, led by Revolt Capital, followed by most of their investors, including Sequoia. Excellent in iron capital. And then the number two being announcing a whole new Druva cloud platform, which holistically takes our entire product portfolio and puts it together in a nice, simplistic approach to manage your entire information workload in a single platform in the could. - The first question is mind is is everybody ready for GDPR? The answer is "no". Have they started into the journey to get, have they started getting on the racetrack, right? On the road? Yes. Yeah, it depends on the maturity of the organization. Some people have just started building a small strategy around GDPR. Some people have actually started doing assessment to understand how complex is this beast and regulation. And some people have just moved further in the journey of doing assessment, but they're now putting up changes in their infrastructure to handle remediation, right? Things like, for example, consent management. Things about, things like deletion. It could be very big deal to do, right? So they are making changes to the infrastructure that they have or the IT systems to manage it effectively. But I don't think there's any company which probably can claim that they have got it right fully end to end.

>> [John} Hello everyone and welcome to Special Cube Presentation here in Paolo Alto, I'm John Furrier at Silicon Angle, a Special Presentation with Druva. The data protection space is being disrupted big time with a lot of venture capital investment, almost 250 million dollars invested this quarter, in data protection, it's certainly disrupting the Cloud game, we had a great line-up of experts, and thought leaders here, to talk about the news from Druva, and the impact to the industry around digital transformation and my first guest is Jaspreet Singh, who's the CEO and Founder of Druva, great to see you again. >> Good morning John, good to see you again. >> Digital transformation is accelerating, data protection is being disrupted, millions of dollars are coming in, you guys are playing a role, what is the role that Druva's playing, in the digital transformation acceleration? >> Absolutely, to think about the world, right, you think of companies like Domino's or Tesla, the thing that software companies, right, they deliver, the server they should deliver via software of, a software approach of the traditional business model, in the heart of this transformation of enterprises becoming softed and digitalized, is data at the core. And data today, will outlive most systems, and the more and more fragmented their approach to data becomes, you store data on prem, in the Cloud, everywhere in between, the data management has to become more and more centralized, so Druva is in the core of this transformation, making it a data transformation, and making sure the data architects of the future, have a better approach of manageability and protection, with the Druva platform. >> You guys had a busy month this month, you got a couple of big news we're going to be talking about today, funding and next generation platform, walk us through that. >> Absolutely, so we have two big news to announce today, the first one being 80 million dollars of capital raised, led by Riverwood Capital followed by most other investors, including Sequoia, excellent Tenaya Capital, and then the number two, being we're announcing a whole new Druva Cloud platform, which wholistically takes our entire product portfolio and puts it together in a nice, simplistic approach to manage an entire information workload in a single platform in the Cloud. >> 80 million is a lot of funding, that brings you up to 200? >> 200 our total capital raised, it's a great validation for the market, it's a great validation for the Druva product portfolio, and great validation for customers who have trusted Druva so far, to put us towards one of the top, I think, no more than 10 Start-ups have raised capital more than 200 million dollars, in our space, so it's a great place to be, to be here today. >> Talk about the data, as a service, the data management as a service that you guys are doing, on the Druva Cloud platform, how does that solve the customer problems, how does that relate to the growth and Cloud and specifically, private Cloud, or true private Cloud, wherever that you want to slice that out, this is a new segment, talk about that. >> Absolutely, so there's a lot of Cloud washing in the market, about the Cloud data management prediction, the whole nine yards, but eventually, for us, the Cloud is not a technology, it's a business model. When you service the customer, as a predictable assailer across the globe, at a predictable price point, it is consistent throughout the world, right, it's how you build your products, how you build security around it, how you think of the customer experience as the central focal point, of everything you do, and how you drive innovation with customers, you know, and then adopting the product going forward. And then also how you build your ecosystem of partners, and your resellers to sort of adopt this whole motion of servicing a customer, managing data, all in the Cloud, and the core of the innovation is the fact that the more and more data gets decentralized, the more and more centralized the data management has to be, and today Cloud solves great a pain point there by offering simplicity of data management, and offering an assailer, a predictable assailer which the world really needs for data management service, and the hardware, software part of the world, is very, very hard to deliver. >> And what do you guys do specifically that solves that problem and helps in that area? >> So today, Druva delivers a end-to-end platform, this platform you know, think of a traditional enterprise which had to buy a, you know data management was a complicated beast, you had to had a backup play, a archival play, a DR play, eDiscovery play, and for each of these technologies, the solution you had to buy a hardware, a software, a tiering solution like a tape or a cloud, or you had to buy services, and then piecemeal them together. You know, as you have more and more regulations, and you have more and more demands on the data, as data is becoming your new oil of economy, you want to put them together in a way that they talk to each other, not disturb the workflows with the department and the people involved, and managing it as the same data, so Druva does is builds a, it offers a very wholistic platform, a scalable, simple platform on the Cloud, which puts together these multiple workloads of back up DR, archival eDiscovery governance, into a single platform, purely deliver a service without any dedicated hardware or software needed to manage an entire data landscape, with end point servers or cloud data. >> 80 million is a lot of financing, congratulations, great validation to you, by the way and you guys had good funding all along the way, because of this new, fresh financing, how does that change or does it change your competitive position and how do you guys compare from the other Cloud data management companies, we hear about, I mean, there's a lot of people out there, trying to attack this area, how do you guys compare and what's the differentiation? >> I think our differentiation still goes back to the same thesis, our core thought process being that, secondary data or your data management has to live purely in the Cloud, not on appliance, not a software, and Cloud is not a graveyard, you know, where you can just dump your data, and call it Cloud, it's a way for you to store data, use it wholistically, not just for protection, but governance and even for the intelligence. This funding helps us establish ourselves even better in the marketplace, proves validates to your point, our position in the market and you know, as I think of my years being an entrepreneur, of capital is critical for growth, it doesn't replace creativity, so we still have to focus on our core innovation of global market, but funding truly helps in building a firm foot forward in the market. >> Take a minute to describe what the Druva Cloud platform is, and how that address some of these next generation challenges, that are out there. >> So think of Druva Cloud as an Amazon marketplace, an Amazon service console for data management, right, when you think of offer tips to five on Amazon, you think of an experienced to manage your productivity, or in general, enterprise IT, or on the Cloud, the build up management was a piecemeal approach of putting together a software and a hardware together and experience was broken because of so many moving parts. We deliver pure social experience on the Cloud, which not only integrates the front end of, you know, being a simple interface to look at back-up or DR for all your workload, we're also a simplistic way of searching for workloads and you'll see a demo today, in the session of how you can interact with data by simple search, to show you not just the workflow, but the documentation behind it and the whole nine yards. But wholistically, behind the you know, there's a great saying, saying that the complexes compete in, but simple is genius, right, so to make it really simple, behind the whole, the Druva console, is a consolidated or a completely integrated data platform, which lets you take a wholistic approach of storing and managing information all in the Cloud, which is wrapped around security or rather paradigms to really make sure that it's a end-to-end delivered servers and experience, versus just a software wrapped around a legacy hardware approach. >> With the Druva Cloud platform, can organizations embrace more data protection? >> Absolutely, so simplicity is still key to it, right, data management is still something which helps you take care of your data risks and which is pretty pertinent to any organization, with a simple and scalable approach, with a predictable assailer, more organizations can trust Cloud with the corporate data, and they will be more pertinent to pay as you go for a data management play than building a hardware and software story, spending all the money upfront, which we believe will increase adoption, increase trust, in their own data and the Cloud. >> When will the Druva Cloud platform be available? >> So, today we're going a technical preview, for our most important customers, they get to play with it, and give us their feedback of how they feel about it, you know, we're integrating multiple parts, and instilling the feedback around how we can involve giving them more and more control and visibility, we expect a general availability for most of our customers by end of the year. >> Congratulations on the financing, it's a great validation, we'll give you the final word on this segment, to just share with the folks that are watching, what they should squint through all the news, and what does it mean to them, what's the impact of this announcement, these announcements? >> I think a couple of years ago, there's a massive transformation on the primary storage, where you know the EMCs of the world, were vulnerable and so came re-tan excel pure, right, now the whole backlash is going to be on the secondary storage, where the bigger, much bigger market on secondary data and storage, is a lot more vulnerable by the big players, still showing a lot of weakness, and Cloud is a great story here where a very complex solution can be delivered, with the wholistic and simplistic approach, so there's a great time in the market for us to innovate, it's a great time when the customers to trust the Cloud and get a great story all from Druva or other players, purely in the Cloud, and great time for entrepreneurs like us to execute and bring a cutting edge solution to the market. >> We have a lot more to drill down on, thanks so much, and congratulations on your success. >> Thank you. >> Thanks for sharing.

>> Narrator: Live from Las Vegas, it's The Cube covering HPE Discover 2017 brought to you by Hewlett-Packard Enterprise. >> Welcome back everyone, live here in Las Vegas, this is the Cube special presentation of Hewlett-Packard Enterprise, HPE Discover 2017. I'm John Furrier with The Cube, my co-host Dave Vellante for the next three days with wall-to-wall coverage here in Las Vegas. Our next guess is Tom Lattin, Vice President and General Manager of HPE Server Options which is all the good stuff that wrap around the servers. And certainly the big news here at HP is the Gen10, the continuation of the, the generation of servers which is all the rage these days. People are talkin' about servers, are we buying more or buying less? Still a lot of private cloud going on. Nothing really changing in the premise world. Welcome to The Cube. >> Thank you, good to be here. >> Alright, so what's the big news? I mean, to me the, the thing that I've always been watching is the Gen8 stuff, Gen10 now is the new announcement platform for servers. >> Certainly. >> A lot of stuff happening around, a lot of innovations. Give us a quick update on the key things happening around Gen10 and the options. >> Well certainly with Gen10, we've got a whole new level of security that is a big part of the Gen10 story. And with the server options in the memory SEP system and the storage SEP system and the networking SEP system, all of those technologies contribute to building up those layers of security in Gen10. So that's a key part of what we're doing this week at discover. The second thing in the options space, the server options space, is persistent memory. So we introduced persistent memory on Gen9 servers last year with the MB DIMM, an eight gigabyte MB DIMM. We're extending that portfolio this year with the Gen10 servers and increasing the MB DIMM capacity to 16 gigabytes. But the really big news in the persistent memory offering is scalable persistent memory. >> I just saw a great article on the CRN, Computer Reseller News or CRN, really giving you guys some props on this. "Persistent memory combines the performance of D RAM with the persistent of traditional SSDs or spinning disk. So, essentially a huge performance gain, and, which is always good, HP has that, always has some good mojo when it comes to, you know, high quality products and performance. But talk about the impact because one of things that's going on right now is storage has to be invisible but applications now have, going beyond virtual machines. You've got containers, you've got all kinds of cloud things happening with the apps. So the data, the state of the data, the performance of the data is really critical to customers. What's the impact of this to that trend? >> This is, persistent memory unleashes an incredible amount of performance for the server, for the application running on the server. At it's most fundamental level, persistent memory and MB DIMM or a scalable persistent memory implementation can replace a layer of flash storage. And we're seein' performance benefits on the order of doubling the performance of the application just by swapping out an SSD for persistent memory or an MB DIMM. When the applications, when the architects actually modify the applications to take advantage of the fact that there's a persistent memory layer in there, we see performance benefits as small as four times improvement, but in many cases we're seeing 20, 25, 27x performance improvements because the architects of the applications now can dive right into the memory sub-system without going through layers and layers of code and moving data around to get to storage. >> Love, love the options comps that you guys are running because I think one of the things that we here is, you know, flexibility is key and it's kind like goin' to the store and, like, getting some accessories for your suit. Not one size fits all, you need to have a lot of options given the environment. So I got to ask you the competitive question. How do you guys compare vis-a-vis the competition with the persistent memory stuff, for instance? You mentioned the performance, how does that compare with some of your competitors? >> I think persistent memory is a good example of where we're way out ahead of where our competition is right now. It's, you can't just drop in a piece of hardware in the server and get all of the benefit out of it. You've got to be able to integrate that piece of hardware with the system software, the bios in the system, and then work with software application partners especially to go modify, or to optimize their applications to work best with that component that you put in there. >> And you guys do that? >> We do that extensively. >> Not the customer, the customer just, what? Just installs it? >> The customer gets it. It just works. >> So, what's the typical sort of anatomy of a life cycle of a server these days? And, and, I mean, it used to be, in the old days it was called peripherals and peripherals made up probably 60 to 70% of the market so it was very huge, you know, quite a huge opportunity. So how does it work from a customer standpoint? Does, do they, what's their starting point? How do they plan this out? Or is it more reactive? I wonder if you could sort of bring us up to date on that dynamic, Tom. >> I think that's, part of what we bring to the market is a set of server platforms that have an incredible breadth of capability and that breadth comes from the configuration choices that customer can make through the server options portfolios. So, generally, they're, a customer will standardize on a few different platform types and then deploy those servers for a variety of different workloads. And so it's through those configuration choices, storage capacity, memory capacity, the performance of different layers in the memory and storage hierarchy that allow them to be able to fine-tune a small set of servers, really, for a much broader set of workloads. >> So we've commented for a number of years now on The Cube that the pendulum is swinging. Storage is, and servers are coming, and computer coming closer together. >> Absolutely. >> You certainly saw that with Flash and PCIe. Sort of, those trends brought storage and compute together and now you're accelerating that even further with, with persistent memory. >> Tom: Yes. >> So, as that happens, one of the big challenges is data sharing, right? Now you hear things like, you know, NVMe over Fabric and other technologies to, to link these capabilities, these nodes if you will. What's, I mean it sounds like The Machine, so. >> Tom: Exactly. >> What's happening there and help us sort of squint through those two big trends. >> Yeah, I think that as we evolve architectures to focus on the data and recognize that the data really is where the value resides, we're moving from a world where, first of all, sharing the data on storage makes a lot of sense to enable that. If you look at some of the demonstrations that we've done recently with the machine and with memory driven computing, it's about taking that shared presence of the data or shared instance of the data to an entirely new level where the processors, processing capability can get directly at it and operate it and work on that really as a shared body of information, shared body of knowledge. So yes, started in the storage sub-system but absolutely where we're headed is a memory-driven computing world where it's in the memory sub-systems. >> And that is The Machine, but, and which is a, you know, big R&D project that's kind of comin' out of HP Labs, and, you know, Martin Fink showing it off a couple years ago and giving us the roadmap, but now we're seein' it sort of evolve. But as well, I would think your ecosystem can kind of build it's own pseudo-machine, you know? With compute power and all this persistent memory and you know, architecting, I mean, do you see those as two separate vectors that, you know, let's see what happens in the channel? Or, or do you see those two worlds coming together? >> Fundamentally, we are a partnering business, right? We work extensively with the ecosystem of software providers, partners in the industry. So what we're demonstrating with The Machine, necessarily we've done a lot of those parts ourselves to show the capability. But absolutely this memory-centric computing vision and that we're beginning to realize with some of the products that we're releasing today, persistent memory is a great example of that, is all about enabling the industry, the ecosystem in the industry to bring that value for, ultimately for all of our customers. >> And has heading up the sort of options business, if you will, how do you, one of the things we've talked about a lot is this notion of true private cloud which is substantially mimicking public cloud on PRIM because the world is hybrid as we all sort of point out. How do you create that experience for customers? That, that cloud-like experience? >> I think it, well with the simplicity and the agility of what we're doing with the HPE compute experience now is very focused on creating that cloud-like experience in a hybrid cloud world, right? On premises for example. And so that's, a lot of that is about being able to scale up and down the computing capability, to incorporate new financial models so that you can buy compute, rent compute at your, kind of, depending on what your strategic corporate objectives are. So the options themselves then give you that ability to, for example, scalable persistent memory uses the base system in the server and one day you may say, "Hey, I need to use a "portion of that as persistent "because of the workload that I'm running." And then later that night, that same system could translate over to run a completely different workload and change the profile of the persistent memory that's being used because it's a configuration setting of the base system memory. So making the system itself very flexible to adapt to the changing needs of workloads, either overtime or very realtime like that in the course of a day. >> Tom I want to get your thoughts on the trends that we're covering, and certainly the industry's covering. So you have an industry scope and you have, obviously, partners which are going to be critical in getting things certified and or working so the customer just plugs, plugs stuff in, like the memory. >> Tom: Yes. >> Obviously the market place says, well, "Oh server ships are down," but the cloud's happening, servers are actually growing no matter how you look at. But there's more realtime stuff goin' on. There's more processing happening. How do you guys look at the marketplace trends because there is more need, at The Edge for instance, we had Bill Philbin on just earlier before you came on talkin' about how storage and compute are comin' together. This is kind of the, the options world you're in. You're in the middle of all this action so people actually cobbling together and composing solutions, whether it's on PRIM or working with similar architectures in the cloud, same code bases moving back and forth. So this whole world is really not declining. Maybe shifting how it was before, that transformation you're in the middle of, how do you guys look at that and how you do talk to the customers who are like, "I was buying servers and options before, "I still got to do that but I got to "transform and be prepared for realtime analytics, "using multiple clouds, all these kinds of, "these, these important items for the future"? >> Yeah, it's a bit of an architectural revolution if you will, right? As we move to a memory-centric computing world, as we move to a world where everything is cloud-based, cloud architecture-based, whether it's out in a public cloud somewhere or on an on-prep as kind of hybrid IT model, it really is a completely different architectural model. And so to capitalize on that, what we work with customers on is things like the composable capability of synergy. Things like persistent memory and making that scalable to move to a memory-driven computing model. Things like our all-Flash array business and our product offerings to be able to accelerate that storage sub-system well beyond what's been done before. >> It's performance driven too, you got to eek out performance more and more. >> Yes. That's kind of the mandate. Another interesting thing I wanted to get your thoughts on, and, you know, I'm old in the industry these days relative to the average age of most people in the big the big companies, like hyper-scalers are like 28 to 30-something. The trend is systems. I mean, you're seeing, if you look at what the cloud ' doing in the revolution at the architectural level, it's almost a complete crossover to a systems mindset. Systems meaning operating systems or, you know, core systems. So a lot of the people that are really doing well in the industry, who are radically transforming it, are older guys. James Gosling was at, just joined Amazon Web Services. You got guys who are in their 50s who are leading major architectural shifts. This kind of puts HP in an interesting position because you guys have so much experience with systems, servers, you know, just on an isolated basis. But now, as that looks out over the landscape, it's even more important to look at it from a holistic perspective. Your thoughts on this trend? >> Well absolutely, it's a huge trend and by taking the expertise that we've got at a systems level and coupling that with our strategic imperative to partner with other industry leaders in the industry revolution, I think those two together position not just HPE well, but the ecosystem of HPE and our software industry partners to really help advance that, that architectural-- >> I was talking to James Governor last who's the, with Red Monk and one of the research firms we like and I said to him, "It's open bar and open source," was my kind of headline story I was tryin', we were collaborating on and what I mean by that is that, you know, as open source evolves, we saw some of the stuff goin' on with The Machine in mem-store, a lot of that stuffs going to be open source at the core at the system level. So open source is growing, but when I say open bar, it's like there is more goodness being contributed to open source than ever before. You're seeing great machine learning libraries being, you know, given in to collaboration. You're seeing open source being a great recruiting environment. So if you're a young gun in the marketplace right now, you're getting all this contribution so with that kind of as a context, what's the open source strategy that you see? 'Cause you're in kind of a glue layer with options. You're kind of creating some flexibility for customers. At the same time, you've got a glue kind of concept goin' on with software. What do you guys do with open source? Is there a trend there that you'd like to share? I mean, I'm interested to know what your position is vis-a-vis contribution programs and whatnot. >> Yeah, I think, certainly at the hardware layers, right, of what we're doing with server options, it's about enabling new capabilities. And so we work with quite a few open source partners to enable those. So say, for example, Red Hat, they're taking our persistent memory offerings and optimizing those so that they get, certainly the immediate benefit of a layer of high-performance storage but the more radical performance improvements that they can get when they address directly a layer of persistent memory. So it's not so much that we're creating a whole new, at least in the option space, kind of a whole new open source plane. >> But you're intersecting. >> Absolutely. >> I mean, one other thing is networking is hot right now. Certainly, SDM we see that. A lot of the open source projects, and even in Linux foundation you're seeing the network stack just kind of being, kind of decomposed. So a very interesting opportunity. >> Yes. >> Well, and the same thing with storage, right? And you mentioned Flash a couple of times right? You're seeing the whole storage stack just completely morphing and changing. >> Tom: Yeah. >> So you guys are in the center of that, how does a customer engage? Does it happen typically through the channel, do they go to hpe.com, how does that happen? >> For server option kind of products, yeah, certainly. Through our direct sales force, through our partners' sales forces, 'cause, as we said, it's an ecosystem that brings us value forward. So in many cases, it's not us in, even in explaining something like persistent memory. It's Microsoft or it's Red Hat or it's SUSE, or partners there, VMware. We've got a, actually a lot of presence with VMware and some interesting things they're going to be talking about in one of our sessions here later today at Discover. So that's one path. Or two paths. Our sales force, their sales force and then, absolutely, the channel. We've got a very rich channel program and a lot of engagement with them to bring them up on networking technologies, storage technologies, memory, persistent memory technologies so that they can-- >> John: So it's ecosystem is really the key. >> They can effectively engage customers, yeah. >> Alright, Tom Lattin is the Vice President and General Manager at HP Server Options. My final question for you to end the segment here is what should customers know about Gen10 and server options if you had a chance to look right into the camera and say, "Hey, new game in town," or, "Think differently around architecture," what would be, what would be your words? In your words, what should customers know about the world you're building? >> Certainly with Gen10, the server options portfolio unleashes or helps support the overall security capabilities of Gen10, number one. But if I can have a second one. >> John: Of course. >> I've got to play persistent memory high because we've got a terabyte-scale persistent memory capability in the Gen10 platforms which is, opens up a whole new world of opportunity for applications, as I said, early on to develop or increase performance, in many cases, 20, 25, 27 times the capabilities today. >> That's awesome, I mean I think the memories awesome. Dave and I have been talkin' for years that memory used to be the resource that was constrained and unlimited storage, now it's the other way around, people want memory, application developers and programmers. This is The Cube bringing you great content you can put to memory, Flash memory. HPE Discover, I'm John Fullier with Dave Vellante, we'll be back with more live coverage after this short break.