>> Live from San Francisco, it's theCUBE covering IBM Think 2019, brought to you by IBM. >> Welcome back everyone, live here in Moscone North in San Francisco, it's theCUBE's exclusive coverage of IBM Think 2019. I'm John Furrier, Dave Vellante. We're bringing down all the action, four days of live coverage. We've got two great guests here, Elenita Elinon, Executive Director of Quantitative Research at JP Morgan Chase, and John Thomas, Distinguished Engineer and Director of the Data Science Elite Team... great team, elite data science team at IBM, and of course, JP Morgan Chase, great innovator. Welcome to theCUBE. >> Welcome. >> Thank you very much. >> Thank you, thank you, guys. >> So I like to dig in, great use case here real customer on the cutting edge, JP Morgan Chase, known for being on the bleeding edge sometimes, but financial, money, speed... time is money, insights is money. >> Absolutely. Yes. >> Tell us what you do at the Quantitative Group. >> Well, first of all, thank you very much for having me here, I'm quite honored. I hope you get something valuable out of what I say here. At the moment, I have two hats on, I am co-head of Quantitative Research Analytics. It's a very small SWAT, very well selected group of technologists who are also physicists and mathematicians, statisticians, high-performance compute experts, machine learning experts, and we help the larger organization of Quantitative Research which is about 700-plus strong, as well as some other technology organizations in the firm to use the latest, greatest technologies. And how we do this is we actually go in there, we're very hands-on, we're working with the systems, we're working with the tools, and we're applying it to real use cases and real business problems that we see in Quantitative Research, and we prove out the technology. We make sure that we're going to save millions of dollars using this thing, or we're going to be able to execute a lot on this particular business that was difficult to execute on before because we didn't have the right compute behind it. So we go in there, we try out these various technologies, we have lots of partnerships with the different vendors, and IBM's been obviously one of few, very major vendors that we work with, and we find the ones that work. We have an influencing role as well in the organization, so we go out and tell people, "Hey, look, "this particular tool, perfect for this type of problem. "You should try it out." We help them set it up. They can't figure out the technology? We help them out. We're kind of like what I said, we're a SWAT team, very small compared to the rest of the organization, but we add a lot of value. >> You guys are the brain trust too. You've got the math skills, you've got the quantitative modeling going on, and it's a competitive advantage for your business. This is like a key thing, a lot of new things are emerging. One of things we're seeing here in the industry, certainly at this show, it's not your yesterday's machine learning. There's certainly math involved, you've got cognition and math kind of coming together, deterministic, non-deterministic elements, you guys are seeing these front edge, the problems, opportunities, for you guys. How do you see that world evolving because you got the classic math, school of math machine learning, and then the school of learning machines coming together? What kind of problems do you see these things, this kind of new model attacking? >> So we're making a very, very large investment in machine learning and data science as a whole in the organization. You probably heard in the press that we've brought in the Head of Machine Learning from CMU, Manuela Veloso. She's now heading up the AI Research Organization, JP Morgan, and she's making herself very available to the rest of the firm, setting strategies, trying different things out, partnering with the businesses, and making sure that she understands the use case of where machine learning will be a success. We've also put a lot of investments in tooling and hiring the right kinds of people from the right kinds of universities. My organization, we're changing the focus in our recruiting efforts to bring in more data science and machine learning. But, I think the most important thing, in addition to all that investment is that we, first and foremost, understand our own problems, we work with researchers, we work with IBM, we work with the vendors, and say, "Okay, this is the types of problems, "what is the best thing to throw at it?" And then we PoC, we prove it out, we look for the small wins, we try to strategize, and then we come up with the recommendations for a full-out, scalable architecture. >> John, talk about the IBM Elite Program. You guys roll your sleeves up. It's a service that you guys provide with your top clients. You bring in the best and you just jump in, co-create opportunities together, solving problems. >> That is exactly right. >> How does this work? What's your relationship with JP Morgan Chase? What specific use case are you going after? What are the opportunities? >> Yeah, so the Data Science Elite Team was setup to really help our top clients in their AI journey, in terms of bringing skills, tools, expertise to work collaboratively with clients like JP Morgan Chase. It's been a great partnership working with Elenita and her team. We've had some very interesting use cases related to her model risk management platform, and some interesting challenges in that space about how do you apply machine learning and deep learning to solve those problems. >> So what exactly is model risk management? How does that all work? >> Good question. (laughing) That's why we're building a very large platform around it. So model risk is one of several types of risk that we worry about and keep us awake at night. There's a long history of risk management in the banks. Of course, there's credit risk, there's market risk, these are all very well-known, very quantified risks. Model risk isn't a number, right? You can't say, "this model, which is some stochastic model "it's going to cost us X million dollars today," right? We currently... it's so somewhat new, and at the moment, it's more prescriptive and things like, you can't do that, or you can use that model in this context, or you can't use it for this type of trade. It's very difficult to automate that type of model risk in the banks, so I'm attempting to put together a platform that captures all of the prescriptive, and the conditions, and the restrictions around what to do, and what to use models for in the bank. Making sure that we actually know this in real time, or at least when the trade is being booked, We have an awareness of where these models are getting somewhat abused, right? We look out for those types of situations, and we make sure that we alert the correct stakeholders, and they do something about it. >> So in essence, you're governing the application of the model, and then learning as you go on, in terms of-- >> That's the second phase. So we do want to learn at the moment, what's in production today. Morpheus running in production, it's running against all of the trading systems in the firm, inside the investment bank. We want to make sure that as these trades are getting booked from day to day, we understand which ones are risky, and we flag those. There's no learning yet in that, but what we've worked with John on are the potential uses of machine learning to help us manage all those risks because it's difficult. There's a lot of data out there. I was just saying, "I don't want our Quants to do stupid things," 'cause there's too much stupidity happening right now. We're looking at emails, we're looking at data that doesn't make sense, so Morpheus is an attempt to make all of that understandable, and make the whole workflow efficient. >> So it's financial programming in a way, that's come with a whole scale of computing, a model gone astray could be very dangerous? >> Absolutely. >> This is what you're getting at right? >> It will cost real money to the firm. This is all the use-- >> So a model to watch the model? So policing the models, kind of watching-- >> Yes, another model. >> When you have to isolate the contribution of the model not like you saying before, "Are there market risks "or other types of risks--" >> Correct. >> You isolate it to the narrow component. >> And there's a lot of work. We work with the Model Governance Organization, another several hundred person organization, and that's all they do. They figure out, they review the models, they understand what the risk of the models are. Now, it's the job of my team to take what they say, which could be very easy to interpret or very hard, and there's a little bit of NLP that I think is potentially useful there, to convert what they say about a model, and what controls around the model are to something that we can systematize and run everyday, and possibly even in real time. >> This is really about getting it right and not letting it get out of control, but also this is where the scale comes in so when you get the model right, you can deploy it, manage it in a way that helps the business, versus if someone throws the wrong number in there, or the classic "we've got a model for that." >> Right, exactly. (laughing) There's two things here, right? There's the ability to monitor a model such that we don't pay fines, and we don't go out of compliance, and there's the ability to use the model exactly to the extreme where we're still within compliance, and make money, right? 'Cause we want to use these models and make our business stronger. >> There's consequences too, I mean, if it's an opportunity, there's upside, it's a problem, there's downside. You guys look at the quantification of those kinds of consequences where the risk management comes in? >> Yeah, absolutely. And there's real money that's at stake here, right? If the regulators decide that a model's too risky, you have to set aside a certain amount of capital so that you're basically protecting your investors and your business, and the stakeholders. If that's done incorrectly, we end up putting a lot more capital in reserve than we should be, and that's a bad thing. So quantifying the risks correctly and accurately is a very important part of what we do. >> So a lot of skillsets obviously, and I always say, "In the money business, you want the best nerds." Don't hate me for saying that... the smartest people. What are some of the challenges that are unique to model risk management that you might not see in sort of other risk management approaches? >> There are some technical challenges, right? The volume of data that you're dealing with is very large. If you are building... so at the very simplistic level, you have classification problems that you're addressing with data that might not actually be all there, so that is one. When you get into time series analysis for exposure prediction and so on, these are complex problems to handle. The training time for these models, especially deep learning models, if you are doing time series analysis, can be pretty challenging. Data volume, training time for models, how do you turn this around quickly? We use a combination of technologies for some of these use cases. Watson Studio running on power hardware with GPUs. So the idea here is you can cut down your model training time dramatically and we saw that as part of the-- >> Talk about how that works because this is something that we're seeing people move from manual to automated machine learning and deep learning, it give you augmented assistance to get this to the market. How does it actually work? >> So there is a training part of this, and then there is the operationalizing part of this, right? At the training part itself, you have a challenge, which is you're dealing with very large data volumes, you're dealing with training times that need to be shrunk down. And having a platform that allows you to do that, so you build models quickly, your data science folks can iterate through model creation very quickly is essential. But then, once the models have been built, how do you operationalize those models? How do you actually invoke the models at scale? How do you do workflow management of those models? How do you make sure that a certain exposure model is not thrashing some other models that are also essential to the business? How do you do policies and workflow management? >> And on top of that, we need to be very transparent, right? If the model is used to make certain decisions that have obvious impact financially on the bottom line, and an auditor comes back and says, "Okay, you made this trade so and so, why? What was happening at that time?" So we need to be able to capture and snapshot and understand what the model was doing at that particular instant in time, and go back and understand the inputs that went into that model and made it operate the way it did. >> It can't be a black box. >> It cannot be, yeah. >> Holistically, you got to look at the time series in real time, when things were happening and happened, happening, and then holistically tie that together. Is that kind of the impact analysis? >> We have to make our regulars happy. (laughing) That's number one, and we have to make our traders happy. We, as quantitative researchers, we're the ones that give them the hard math and the models, and then they use it. They use their own skillsets too to apply them, but-- >> What's the biggest needs that your stakeholders on the trading side want, and what's the needs on the compliance side, the traders want more, they want to move quickly? >> They're coming from different sides of it. Traders want to make more money, right? And they want to make decisions quickly. They want all the tools to tell them what to do, and for them to exercise whatever they normally exercise-- >> They want a competitive advantage. >> They want that competitive advantage, and they're also... we've got algo-trades as well, we want to have the best algo behind our trading. >> And the regulator side, we just want to make sure laws aren't broken, that there's auditing-- >> We use the phrase, "model explainability," right? Can you explain how the model came to a conclusion, right? Can you make sure that there is no bias in the model? How can you ensure the models are fair? And if you can detect there is a drift, what do you do to correct that? So that is very important. >> Do you have means of detecting sort of misuse of the model? Is that part of the governance process? >> That is exactly what Morpheus is doing. The unique thing about Morpheus is that we're tied into the risk management systems in the investment bank. We're actually running the same exact code that's pricing these trades, and what that brings is the ability to really understand pretty much the full stack trace of what's going into the price of a trade. We also have captured the restrictions and the conditions. It's in the Python script, it's essentially Python. And we can marry the two, and we can do all the checks that the governance person indicated we should be doing, and so we know, okay, if this trade is operating beyond maturity or a certain maturity, or beyond a certain expiry, we'll know that, and then we'll tag that information. >> And just for clarification, Morpheus is the name of the platform that does the-- >> Morpheus is the name of the model risk platform that I'm building out, yes. >> A final question for you, what's the biggest challenge that you guys have seen from a complexity standpoint that you're solving? What's the big complex... You don't want to just be rubber-stamping models. You want to solve big problems. What are the big problems that you guys are going after? >> I have many big problems. (laughing) >> Opportunities. >> The one that is right now facing me, is the problem of metadata, data ingestion, getting disparate sources, getting different disparate data from different sources. One source calls it a delta, this other source calls it something else. We've got a strategic data warehouse, that's supposed to take all of these exposures and make sense out of it. I'm in the middle because they're there, probably at the ten-year roadmap, who knows? And I have a one-month roadmap, I have something that was due last week and I need to come up with these regulatory reports today. So what I end up doing is a mix of a tactical strategic data ingestion, and I have to make sense of the data that I'm getting. So I need tools out there that will help support that type of data ingestion problem that will also lead the way towards the more strategic one, where we're better integrated with this-- >> John, talk about how you solve the problems? What are some of the things that you guys do? Give the plug for IBM real quick, 'cause I know you guys got the Studio. Explain how you guys are helping and working with JP Morgan Chase. >> Yeah, I touched upon this briefly earlier, which is from the model training perspective, Watson Studio running on Power hardware is very powerful, in terms of cutting down training time, right? But you've got to go beyond model building to how do you operationalize these models? How do I deploy these models at scale? How do I define workload management policies for these models, and connecting to their backbone. So that is part of this, and model explainability, we touched upon that, to eliminate this problem of how do I ingest data from different sources without having to manually oversee all of that. We need to manually apply auto-classification at the time of ingestion. Can I capture metadata around the model and reconcile data from different data sources as the data is being brought in? And can I apply ML to solve that problem, right? There is multiple applications of ML along this workflow. >> Talk about real quick, comment before we break, I want to get this in, machine learning has been around for a while now with compute and scale. It really is a renaissance in AI, it's great things are happening. But what feeds machine learning is data, the cleaner the data, the better the AI, the better the machine learning, so data cleanliness now has to be more real-time, it's less of a cleaning group, right? It used to be clean the data, bring it in, wrangle it, now you got to be much more agile, use speed of compute to make sure that you're qualifying data before it comes in, these machine learning. How do you guys see that rolling out, is that impacting you now? Are you thinking about it? How should people think about data quality as an input in machine learning? >> Well, I think the whole problem of setting up an application properly for data science and machine learning is really making sure that from the beginning, you're designing, and you're thinking about all of these problems of data quality, if it's the speed of ingestion, the speed of publication, all of that stuff. You need to think about the beginning, set yourself up to have the right elements, and it may not all be built out, and that's been a big strategy I've had with Morpheus. I've had a very small team working on it, but we think ahead and we put elements of the right components in place so data quality is just one of those things, and we're always trying to find the right tool sets that will enable use to do that better, faster, quicker. One of the things I'd like to do is to upscale and uplift the skillsets on my team, so that we are building the right things in the system from the beginning. >> A lot of that's math too, right? I mean, you talk about classification, getting that right upfront. Mathematics is-- >> And we'll continue to partner with Elenita and her team on this, and this helps us shape the direction in which our data science offerings go because we need to address complex enterprise challenges. >> I think you guys are really onto something big. I love the elite program, but I think having the small team, thinking about the model, thinking about the business model, the team model before you build the technology build-out, is super important, that seems to be the new model versus the old days, build some great technology and then, we'll put a team around it. So you see the world kind of being a little bit more... it's easier to build out and acquire technology, than to get it right, that seems to be the trend here. Congratulations. >> Thank you. >> Thanks for coming on. I appreciate it. theCUBE here, CUBE Conversations here. We're live in San Francisco, IBM Think. I'm John Furrier, Dave Vellante, stay with us for more day two coverage. Four days we'll be here in the hallway and lobby of Moscone North, stay with us.

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> Spark became a top level Apache project in 2014, and then shortly thereafter, burst onto the big data scene. Spark, along with the cloud, transformed and in many ways, disrupted the big data market. Databricks optimized its tech stack for Spark and took advantage of the cloud to really cleverly deliver a managed service that has become a leading AI and data platform among data scientists and data engineers. However, emerging customer data requirements are shifting into a direction that will cause modern data platform players generally and Databricks, specifically, we think, to make some key directional decisions and perhaps even reinvent themselves. Hello and welcome to this week's wikibon theCUBE Insights, powered by ETR. In this Breaking Analysis, we're going to do a deep dive into Databricks. We'll explore its current impressive market momentum. We're going to use some ETR survey data to show that, and then we'll lay out how customer data requirements are changing and what the ideal data platform will look like in the midterm future. We'll then evaluate core elements of the Databricks portfolio against that vision, and then we'll close with some strategic decisions that we think the company faces. And to do so, we welcome in our good friend, George Gilbert, former equities analyst, market analyst, and current Principal at TechAlpha Partners. George, good to see you. Thanks for coming on. >> Good to see you, Dave. >> All right, let me set this up. We're going to start by taking a look at where Databricks sits in the market in terms of how customers perceive the company and what it's momentum looks like. And this chart that we're showing here is data from ETS, the emerging technology survey of private companies. The N is 1,421. What we did is we cut the data on three sectors, analytics, database-data warehouse, and AI/ML. The vertical axis is a measure of customer sentiment, which evaluates an IT decision maker's awareness of the firm and the likelihood of engaging and/or purchase intent. The horizontal axis shows mindshare in the dataset, and we've highlighted Databricks, which has been a consistent high performer in this survey over the last several quarters. And as we, by the way, just as aside as we previously reported, OpenAI, which burst onto the scene this past quarter, leads all names, but Databricks is still prominent. You can see that the ETR shows some open source tools for reference, but as far as firms go, Databricks is very impressively positioned. Now, let's see how they stack up to some mainstream cohorts in the data space, against some bigger companies and sometimes public companies. This chart shows net score on the vertical axis, which is a measure of spending momentum and pervasiveness in the data set is on the horizontal axis. You can see that chart insert in the upper right, that informs how the dots are plotted, and net score against shared N. And that red dotted line at 40% indicates a highly elevated net score, anything above that we think is really, really impressive. And here we're just comparing Databricks with Snowflake, Cloudera, and Oracle. And that squiggly line leading to Databricks shows their path since 2021 by quarter. And you can see it's performing extremely well, maintaining an elevated net score and net range. Now it's comparable in the vertical axis to Snowflake, and it consistently is moving to the right and gaining share. Now, why did we choose to show Cloudera and Oracle? The reason is that Cloudera got the whole big data era started and was disrupted by Spark. And of course the cloud, Spark and Databricks and Oracle in many ways, was the target of early big data players like Cloudera. Take a listen to Cloudera CEO at the time, Mike Olson. This is back in 2010, first year of theCUBE, play the clip. >> Look, back in the day, if you had a data problem, if you needed to run business analytics, you wrote the biggest check you could to Sun Microsystems, and you bought a great big, single box, central server, and any money that was left over, you handed to Oracle for a database licenses and you installed that database on that box, and that was where you went for data. That was your temple of information. >> Okay? So Mike Olson implied that monolithic model was too expensive and inflexible, and Cloudera set out to fix that. But the best laid plans, as they say, George, what do you make of the data that we just shared? >> So where Databricks has really come up out of sort of Cloudera's tailpipe was they took big data processing, made it coherent, made it a managed service so it could run in the cloud. So it relieved customers of the operational burden. Where they're really strong and where their traditional meat and potatoes or bread and butter is the predictive and prescriptive analytics that building and training and serving machine learning models. They've tried to move into traditional business intelligence, the more traditional descriptive and diagnostic analytics, but they're less mature there. So what that means is, the reason you see Databricks and Snowflake kind of side by side is there are many, many accounts that have both Snowflake for business intelligence, Databricks for AI machine learning, where Snowflake, I'm sorry, where Databricks also did really well was in core data engineering, refining the data, the old ETL process, which kind of turned into ELT, where you loaded into the analytic repository in raw form and refine it. And so people have really used both, and each is trying to get into the other. >> Yeah, absolutely. We've reported on this quite a bit. Snowflake, kind of moving into the domain of Databricks and vice versa. And the last bit of ETR evidence that we want to share in terms of the company's momentum comes from ETR's Round Tables. They're run by Erik Bradley, and now former Gartner analyst and George, your colleague back at Gartner, Daren Brabham. And what we're going to show here is some direct quotes of IT pros in those Round Tables. There's a data science head and a CIO as well. Just make a few call outs here, we won't spend too much time on it, but starting at the top, like all of us, we can't talk about Databricks without mentioning Snowflake. Those two get us excited. Second comment zeros in on the flexibility and the robustness of Databricks from a data warehouse perspective. And then the last point is, despite competition from cloud players, Databricks has reinvented itself a couple of times over the year. And George, we're going to lay out today a scenario that perhaps calls for Databricks to do that once again. >> Their big opportunity and their big challenge for every tech company, it's managing a technology transition. The transition that we're talking about is something that's been bubbling up, but it's really epical. First time in 60 years, we're moving from an application-centric view of the world to a data-centric view, because decisions are becoming more important than automating processes. So let me let you sort of develop. >> Yeah, so let's talk about that here. We going to put up some bullets on precisely that point and the changing sort of customer environment. So you got IT stacks are shifting is George just said, from application centric silos to data centric stacks where the priority is shifting from automating processes to automating decision. You know how look at RPA and there's still a lot of automation going on, but from the focus of that application centricity and the data locked into those apps, that's changing. Data has historically been on the outskirts in silos, but organizations, you think of Amazon, think Uber, Airbnb, they're putting data at the core, and logic is increasingly being embedded in the data instead of the reverse. In other words, today, the data's locked inside the app, which is why you need to extract that data is sticking it to a data warehouse. The point, George, is we're putting forth this new vision for how data is going to be used. And you've used this Uber example to underscore the future state. Please explain? >> Okay, so this is hopefully an example everyone can relate to. The idea is first, you're automating things that are happening in the real world and decisions that make those things happen autonomously without humans in the loop all the time. So to use the Uber example on your phone, you call a car, you call a driver. Automatically, the Uber app then looks at what drivers are in the vicinity, what drivers are free, matches one, calculates an ETA to you, calculates a price, calculates an ETA to your destination, and then directs the driver once they're there. The point of this is that that cannot happen in an application-centric world very easily because all these little apps, the drivers, the riders, the routes, the fares, those call on data locked up in many different apps, but they have to sit on a layer that makes it all coherent. >> But George, so if Uber's doing this, doesn't this tech already exist? Isn't there a tech platform that does this already? >> Yes, and the mission of the entire tech industry is to build services that make it possible to compose and operate similar platforms and tools, but with the skills of mainstream developers in mainstream corporations, not the rocket scientists at Uber and Amazon. >> Okay, so we're talking about horizontally scaling across the industry, and actually giving a lot more organizations access to this technology. So by way of review, let's summarize the trend that's going on today in terms of the modern data stack that is propelling the likes of Databricks and Snowflake, which we just showed you in the ETR data and is really is a tailwind form. So the trend is toward this common repository for analytic data, that could be multiple virtual data warehouses inside of Snowflake, but you're in that Snowflake environment or Lakehouses from Databricks or multiple data lakes. And we've talked about what JP Morgan Chase is doing with the data mesh and gluing data lakes together, you've got various public clouds playing in this game, and then the data is annotated to have a common meaning. In other words, there's a semantic layer that enables applications to talk to the data elements and know that they have common and coherent meaning. So George, the good news is this approach is more effective than the legacy monolithic models that Mike Olson was talking about, so what's the problem with this in your view? >> So today's data platforms added immense value 'cause they connected the data that was previously locked up in these monolithic apps or on all these different microservices, and that supported traditional BI and AI/ML use cases. But now if we want to build apps like Uber or Amazon.com, where they've got essentially an autonomously running supply chain and e-commerce app where humans only care and feed it. But the thing is figuring out what to buy, when to buy, where to deploy it, when to ship it. We needed a semantic layer on top of the data. So that, as you were saying, the data that's coming from all those apps, the different apps that's integrated, not just connected, but it means the same. And the issue is whenever you add a new layer to a stack to support new applications, there are implications for the already existing layers, like can they support the new layer and its use cases? So for instance, if you add a semantic layer that embeds app logic with the data rather than vice versa, which we been talking about and that's been the case for 60 years, then the new data layer faces challenges that the way you manage that data, the way you analyze that data, is not supported by today's tools. >> Okay, so actually Alex, bring me up that last slide if you would, I mean, you're basically saying at the bottom here, today's repositories don't really do joins at scale. The future is you're talking about hundreds or thousands or millions of data connections, and today's systems, we're talking about, I don't know, 6, 8, 10 joins and that is the fundamental problem you're saying, is a new data error coming and existing systems won't be able to handle it? >> Yeah, one way of thinking about it is that even though we call them relational databases, when we actually want to do lots of joins or when we want to analyze data from lots of different tables, we created a whole new industry for analytic databases where you sort of mung the data together into fewer tables. So you didn't have to do as many joins because the joins are difficult and slow. And when you're going to arbitrarily join thousands, hundreds of thousands or across millions of elements, you need a new type of database. We have them, they're called graph databases, but to query them, you go back to the prerelational era in terms of their usability. >> Okay, so we're going to come back to that and talk about how you get around that problem. But let's first lay out what the ideal data platform of the future we think looks like. And again, we're going to come back to use this Uber example. In this graphic that George put together, awesome. We got three layers. The application layer is where the data products reside. The example here is drivers, rides, maps, routes, ETA, et cetera. The digital version of what we were talking about in the previous slide, people, places and things. The next layer is the data layer, that breaks down the silos and connects the data elements through semantics and everything is coherent. And then the bottom layers, the legacy operational systems feed that data layer. George, explain what's different here, the graph database element, you talk about the relational query capabilities, and why can't I just throw memory at solving this problem? >> Some of the graph databases do throw memory at the problem and maybe without naming names, some of them live entirely in memory. And what you're dealing with is a prerelational in-memory database system where you navigate between elements, and the issue with that is we've had SQL for 50 years, so we don't have to navigate, we can say what we want without how to get it. That's the core of the problem. >> Okay. So if I may, I just want to drill into this a little bit. So you're talking about the expressiveness of a graph. Alex, if you'd bring that back out, the fourth bullet, expressiveness of a graph database with the relational ease of query. Can you explain what you mean by that? >> Yeah, so graphs are great because when you can describe anything with a graph, that's why they're becoming so popular. Expressive means you can represent anything easily. They're conducive to, you might say, in a world where we now want like the metaverse, like with a 3D world, and I don't mean the Facebook metaverse, I mean like the business metaverse when we want to capture data about everything, but we want it in context, we want to build a set of digital twins that represent everything going on in the world. And Uber is a tiny example of that. Uber built a graph to represent all the drivers and riders and maps and routes. But what you need out of a database isn't just a way to store stuff and update stuff. You need to be able to ask questions of it, you need to be able to query it. And if you go back to prerelational days, you had to know how to find your way to the data. It's sort of like when you give directions to someone and they didn't have a GPS system and a mapping system, you had to give them turn by turn directions. Whereas when you have a GPS and a mapping system, which is like the relational thing, you just say where you want to go, and it spits out the turn by turn directions, which let's say, the car might follow or whoever you're directing would follow. But the point is, it's much easier in a relational database to say, "I just want to get these results. You figure out how to get it." The graph database, they have not taken over the world because in some ways, it's taking a 50 year leap backwards. >> Alright, got it. Okay. Let's take a look at how the current Databricks offerings map to that ideal state that we just laid out. So to do that, we put together this chart that looks at the key elements of the Databricks portfolio, the core capability, the weakness, and the threat that may loom. Start with the Delta Lake, that's the storage layer, which is great for files and tables. It's got true separation of compute and storage, I want you to double click on that George, as independent elements, but it's weaker for the type of low latency ingest that we see coming in the future. And some of the threats highlighted here. AWS could add transactional tables to S3, Iceberg adoption is picking up and could accelerate, that could disrupt Databricks. George, add some color here please? >> Okay, so this is the sort of a classic competitive forces where you want to look at, so what are customers demanding? What's competitive pressure? What are substitutes? Even what your suppliers might be pushing. Here, Delta Lake is at its core, a set of transactional tables that sit on an object store. So think of it in a database system, this is the storage engine. So since S3 has been getting stronger for 15 years, you could see a scenario where they add transactional tables. We have an open source alternative in Iceberg, which Snowflake and others support. But at the same time, Databricks has built an ecosystem out of tools, their own and others, that read and write to Delta tables, that's what makes the Delta Lake and ecosystem. So they have a catalog, the whole machine learning tool chain talks directly to the data here. That was their great advantage because in the past with Snowflake, you had to pull all the data out of the database before the machine learning tools could work with it, that was a major shortcoming. They fixed that. But the point here is that even before we get to the semantic layer, the core foundation is under threat. >> Yep. Got it. Okay. We got a lot of ground to cover. So we're going to take a look at the Spark Execution Engine next. Think of that as the refinery that runs really efficient batch processing. That's kind of what disrupted the DOOp in a large way, but it's not Python friendly and that's an issue because the data science and the data engineering crowd are moving in that direction, and/or they're using DBT. George, we had Tristan Handy on at Supercloud, really interesting discussion that you and I did. Explain why this is an issue for Databricks? >> So once the data lake was in place, what people did was they refined their data batch, and Spark has always had streaming support and it's gotten better. The underlying storage as we've talked about is an issue. But basically they took raw data, then they refined it into tables that were like customers and products and partners. And then they refined that again into what was like gold artifacts, which might be business intelligence metrics or dashboards, which were collections of metrics. But they were running it on the Spark Execution Engine, which it's a Java-based engine or it's running on a Java-based virtual machine, which means all the data scientists and the data engineers who want to work with Python are really working in sort of oil and water. Like if you get an error in Python, you can't tell whether the problems in Python or where it's in Spark. There's just an impedance mismatch between the two. And then at the same time, the whole world is now gravitating towards DBT because it's a very nice and simple way to compose these data processing pipelines, and people are using either SQL in DBT or Python in DBT, and that kind of is a substitute for doing it all in Spark. So it's under threat even before we get to that semantic layer, it so happens that DBT itself is becoming the authoring environment for the semantic layer with business intelligent metrics. But that's again, this is the second element that's under direct substitution and competitive threat. >> Okay, let's now move down to the third element, which is the Photon. Photon is Databricks' BI Lakehouse, which has integration with the Databricks tooling, which is very rich, it's newer. And it's also not well suited for high concurrency and low latency use cases, which we think are going to increasingly become the norm over time. George, the call out threat here is customers want to connect everything to a semantic layer. Explain your thinking here and why this is a potential threat to Databricks? >> Okay, so two issues here. What you were touching on, which is the high concurrency, low latency, when people are running like thousands of dashboards and data is streaming in, that's a problem because SQL data warehouse, the query engine, something like that matures over five to 10 years. It's one of these things, the joke that Andy Jassy makes just in general, he's really talking about Azure, but there's no compression algorithm for experience. The Snowflake guy started more than five years earlier, and for a bunch of reasons, that lead is not something that Databricks can shrink. They'll always be behind. So that's why Snowflake has transactional tables now and we can get into that in another show. But the key point is, so near term, it's struggling to keep up with the use cases that are core to business intelligence, which is highly concurrent, lots of users doing interactive query. But then when you get to a semantic layer, that's when you need to be able to query data that might have thousands or tens of thousands or hundreds of thousands of joins. And that's a SQL query engine, traditional SQL query engine is just not built for that. That's the core problem of traditional relational databases. >> Now this is a quick aside. We always talk about Snowflake and Databricks in sort of the same context. We're not necessarily saying that Snowflake is in a position to tackle all these problems. We'll deal with that separately. So we don't mean to imply that, but we're just sort of laying out some of the things that Snowflake or rather Databricks customers we think, need to be thinking about and having conversations with Databricks about and we hope to have them as well. We'll come back to that in terms of sort of strategic options. But finally, when come back to the table, we have Databricks' AI/ML Tool Chain, which has been an awesome capability for the data science crowd. It's comprehensive, it's a one-stop shop solution, but the kicker here is that it's optimized for supervised model building. And the concern is that foundational models like GPT could cannibalize the current Databricks tooling, but George, can't Databricks, like other software companies, integrate foundation model capabilities into its platform? >> Okay, so the sound bite answer to that is sure, IBM 3270 terminals could call out to a graphical user interface when they're running on the XT terminal, but they're not exactly good citizens in that world. The core issue is Databricks has this wonderful end-to-end tool chain for training, deploying, monitoring, running inference on supervised models. But the paradigm there is the customer builds and trains and deploys each model for each feature or application. In a world of foundation models which are pre-trained and unsupervised, the entire tool chain is different. So it's not like Databricks can junk everything they've done and start over with all their engineers. They have to keep maintaining what they've done in the old world, but they have to build something new that's optimized for the new world. It's a classic technology transition and their mentality appears to be, "Oh, we'll support the new stuff from our old stuff." Which is suboptimal, and as we'll talk about, their biggest patron and the company that put them on the map, Microsoft, really stopped working on their old stuff three years ago so that they could build a new tool chain optimized for this new world. >> Yeah, and so let's sort of close with what we think the options are and decisions that Databricks has for its future architecture. They're smart people. I mean we've had Ali Ghodsi on many times, super impressive. I think they've got to be keenly aware of the limitations, what's going on with foundation models. But at any rate, here in this chart, we lay out sort of three scenarios. One is re-architect the platform by incrementally adopting new technologies. And example might be to layer a graph query engine on top of its stack. They could license key technologies like graph database, they could get aggressive on M&A and buy-in, relational knowledge graphs, semantic technologies, vector database technologies. George, as David Floyer always says, "A lot of ways to skin a cat." We've seen companies like, even think about EMC maintained its relevance through M&A for many, many years. George, give us your thought on each of these strategic options? >> Okay, I find this question the most challenging 'cause remember, I used to be an equity research analyst. I worked for Frank Quattrone, we were one of the top tech shops in the banking industry, although this is 20 years ago. But the M&A team was the top team in the industry and everyone wanted them on their side. And I remember going to meetings with these CEOs, where Frank and the bankers would say, "You want us for your M&A work because we can do better." And they really could do better. But in software, it's not like with EMC in hardware because with hardware, it's easier to connect different boxes. With software, the whole point of a software company is to integrate and architect the components so they fit together and reinforce each other, and that makes M&A harder. You can do it, but it takes a long time to fit the pieces together. Let me give you examples. If they put a graph query engine, let's say something like TinkerPop, on top of, I don't even know if it's possible, but let's say they put it on top of Delta Lake, then you have this graph query engine talking to their storage layer, Delta Lake. But if you want to do analysis, you got to put the data in Photon, which is not really ideal for highly connected data. If you license a graph database, then most of your data is in the Delta Lake and how do you sync it with the graph database? If you do sync it, you've got data in two places, which kind of defeats the purpose of having a unified repository. I find this semantic layer option in number three actually more promising, because that's something that you can layer on top of the storage layer that you have already. You just have to figure out then how to have your query engines talk to that. What I'm trying to highlight is, it's easy as an analyst to say, "You can buy this company or license that technology." But the really hard work is making it all work together and that is where the challenge is. >> Yeah, and well look, I thank you for laying that out. We've seen it, certainly Microsoft and Oracle. I guess you might argue that well, Microsoft had a monopoly in its desktop software and was able to throw off cash for a decade plus while it's stock was going sideways. Oracle had won the database wars and had amazing margins and cash flow to be able to do that. Databricks isn't even gone public yet, but I want to close with some of the players to watch. Alex, if you'd bring that back up, number four here. AWS, we talked about some of their options with S3 and it's not just AWS, it's blob storage, object storage. Microsoft, as you sort of alluded to, was an early go-to market channel for Databricks. We didn't address that really. So maybe in the closing comments we can. Google obviously, Snowflake of course, we're going to dissect their options in future Breaking Analysis. Dbt labs, where do they fit? Bob Muglia's company, Relational.ai, why are these players to watch George, in your opinion? >> So everyone is trying to assemble and integrate the pieces that would make building data applications, data products easy. And the critical part isn't just assembling a bunch of pieces, which is traditionally what AWS did. It's a Unix ethos, which is we give you the tools, you put 'em together, 'cause you then have the maximum choice and maximum power. So what the hyperscalers are doing is they're taking their key value stores, in the case of ASW it's DynamoDB, in the case of Azure it's Cosmos DB, and each are putting a graph query engine on top of those. So they have a unified storage and graph database engine, like all the data would be collected in the key value store. Then you have a graph database, that's how they're going to be presenting a foundation for building these data apps. Dbt labs is putting a semantic layer on top of data lakes and data warehouses and as we'll talk about, I'm sure in the future, that makes it easier to swap out the underlying data platform or swap in new ones for specialized use cases. Snowflake, what they're doing, they're so strong in data management and with their transactional tables, what they're trying to do is take in the operational data that used to be in the province of many state stores like MongoDB and say, "If you manage that data with us, it'll be connected to your analytic data without having to send it through a pipeline." And that's hugely valuable. Relational.ai is the wildcard, 'cause what they're trying to do, it's almost like a holy grail where you're trying to take the expressiveness of connecting all your data in a graph but making it as easy to query as you've always had it in a SQL database or I should say, in a relational database. And if they do that, it's sort of like, it'll be as easy to program these data apps as a spreadsheet was compared to procedural languages, like BASIC or Pascal. That's the implications of Relational.ai. >> Yeah, and again, we talked before, why can't you just throw this all in memory? We're talking in that example of really getting down to differences in how you lay the data out on disk in really, new database architecture, correct? >> Yes. And that's why it's not clear that you could take a data lake or even a Snowflake and why you can't put a relational knowledge graph on those. You could potentially put a graph database, but it'll be compromised because to really do what Relational.ai has done, which is the ease of Relational on top of the power of graph, you actually need to change how you're storing your data on disk or even in memory. So you can't, in other words, it's not like, oh we can add graph support to Snowflake, 'cause if you did that, you'd have to change, or in your data lake, you'd have to change how the data is physically laid out. And then that would break all the tools that talk to that currently. >> What in your estimation, is the timeframe where this becomes critical for a Databricks and potentially Snowflake and others? I mentioned earlier midterm, are we talking three to five years here? Are we talking end of decade? What's your radar say? >> I think something surprising is going on that's going to sort of come up the tailpipe and take everyone by storm. All the hype around business intelligence metrics, which is what we used to put in our dashboards where bookings, billings, revenue, customer, those things, those were the key artifacts that used to live in definitions in your BI tools, and DBT has basically created a standard for defining those so they live in your data pipeline or they're defined in their data pipeline and executed in the data warehouse or data lake in a shared way, so that all tools can use them. This sounds like a digression, it's not. All this stuff about data mesh, data fabric, all that's going on is we need a semantic layer and the business intelligence metrics are defining common semantics for your data. And I think we're going to find by the end of this year, that metrics are how we annotate all our analytic data to start adding common semantics to it. And we're going to find this semantic layer, it's not three to five years off, it's going to be staring us in the face by the end of this year. >> Interesting. And of course SVB today was shut down. We're seeing serious tech headwinds, and oftentimes in these sort of downturns or flat turns, which feels like this could be going on for a while, we emerge with a lot of new players and a lot of new technology. George, we got to leave it there. Thank you to George Gilbert for excellent insights and input for today's episode. I want to thank Alex Myerson who's on production and manages the podcast, of course Ken Schiffman as well. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our EIC over at Siliconangle.com, he does some great editing. Remember all these episodes, they're available as podcasts. Wherever you listen, all you got to do is search Breaking Analysis Podcast, we publish each week on wikibon.com and siliconangle.com, or you can email me at David.Vellante@siliconangle.com, or DM me @DVellante. Comment on our LinkedIn post, and please do check out ETR.ai, great survey data, enterprise tech focus, phenomenal. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, and we'll see you next time on Breaking Analysis.

>>From the Cube Studios in Palo Alto in Boston, bringing you data-driven insights from the cube and E T R. This is breaking analysis with Dave Valante. >>Making technology predictions in 2022 was tricky business, especially if you were projecting the performance of markets or identifying I P O prospects and making binary forecast on data AI and the macro spending climate and other related topics in enterprise tech 2022, of course was characterized by a seesaw economy where central banks were restructuring their balance sheets. The war on Ukraine fueled inflation supply chains were a mess. And the unintended consequences of of forced march to digital and the acceleration still being sorted out. Hello and welcome to this week's weekly on Cube Insights powered by E T R. In this breaking analysis, we continue our annual tradition of transparently grading last year's enterprise tech predictions. And you may or may not agree with our self grading system, but look, we're gonna give you the data and you can draw your own conclusions and tell you what, tell us what you think. >>All right, let's get right to it. So our first prediction was tech spending increases by 8% in 2022. And as we exited 2021 CIOs, they were optimistic about their digital transformation plans. You know, they rushed to make changes to their business and were eager to sharpen their focus and continue to iterate on their digital business models and plug the holes that they, the, in the learnings that they had. And so we predicted that 8% rise in enterprise tech spending, which looked pretty good until Ukraine and the Fed decided that, you know, had to rush and make up for lost time. We kind of nailed the momentum in the energy sector, but we can't give ourselves too much credit for that layup. And as of October, Gartner had it spending growing at just over 5%. I think it was 5.1%. So we're gonna take a C plus on this one and, and move on. >>Our next prediction was basically kind of a slow ground ball. The second base, if I have to be honest, but we felt it was important to highlight that security would remain front and center as the number one priority for organizations in 2022. As is our tradition, you know, we try to up the degree of difficulty by specifically identifying companies that are gonna benefit from these trends. So we highlighted some possible I P O candidates, which of course didn't pan out. S NQ was on our radar. The company had just had to do another raise and they recently took a valuation hit and it was a down round. They raised 196 million. So good chunk of cash, but, but not the i p O that we had predicted Aqua Securities focus on containers and cloud native. That was a trendy call and we thought maybe an M SS P or multiple managed security service providers like Arctic Wolf would I p o, but no way that was happening in the crummy market. >>Nonetheless, we think these types of companies, they're still faring well as the talent shortage in security remains really acute, particularly in the sort of mid-size and small businesses that often don't have a sock Lacework laid off 20% of its workforce in 2022. And CO C e o Dave Hatfield left the company. So that I p o didn't, didn't happen. It was probably too early for Lacework. Anyway, meanwhile you got Netscope, which we've cited as strong in the E T R data as particularly in the emerging technology survey. And then, you know, I lumia holding its own, you know, we never liked that 7 billion price tag that Okta paid for auth zero, but we loved the TAM expansion strategy to target developers beyond sort of Okta's enterprise strength. But we gotta take some points off of the failure thus far of, of Okta to really nail the integration and the go to market model with azero and build, you know, bring that into the, the, the core Okta. >>So the focus on endpoint security that was a winner in 2022 is CrowdStrike led that charge with others holding their own, not the least of which was Palo Alto Networks as it continued to expand beyond its core network security and firewall business, you know, through acquisition. So overall we're gonna give ourselves an A minus for this relatively easy call, but again, we had some specifics associated with it to make it a little tougher. And of course we're watching ve very closely this this coming year in 2023. The vendor consolidation trend. You know, according to a recent Palo Alto network survey with 1300 SecOps pros on average organizations have more than 30 tools to manage security tools. So this is a logical way to optimize cost consolidating vendors and consolidating redundant vendors. The E T R data shows that's clearly a trend that's on the upswing. >>Now moving on, a big theme of 2020 and 2021 of course was remote work and hybrid work and new ways to work and return to work. So we predicted in 2022 that hybrid work models would become the dominant protocol, which clearly is the case. We predicted that about 33% of the workforce would come back to the office in 2022 in September. The E T R data showed that figure was at 29%, but organizations expected that 32% would be in the office, you know, pretty much full-time by year end. That hasn't quite happened, but we were pretty close with the projection, so we're gonna take an A minus on this one. Now, supply chain disruption was another big theme that we felt would carry through 2022. And sure that sounds like another easy one, but as is our tradition, again we try to put some binary metrics around our predictions to put some meat in the bone, so to speak, and and allow us than you to say, okay, did it come true or not? >>So we had some data that we presented last year and supply chain issues impacting hardware spend. We said at the time, you can see this on the left hand side of this chart, the PC laptop demand would remain above pre covid levels, which would reverse a decade of year on year declines, which I think started in around 2011, 2012. Now, while demand is down this year pretty substantially relative to 2021, I D C has worldwide unit shipments for PCs at just over 300 million for 22. If you go back to 2019 and you're looking at around let's say 260 million units shipped globally, you know, roughly, so, you know, pretty good call there. Definitely much higher than pre covid levels. But so what you might be asking why the B, well, we projected that 30% of customers would replace security appliances with cloud-based services and that more than a third would replace their internal data center server and storage hardware with cloud services like 30 and 40% respectively. >>And we don't have explicit survey data on exactly these metrics, but anecdotally we see this happening in earnest. And we do have some data that we're showing here on cloud adoption from ET R'S October survey where the midpoint of workloads running in the cloud is around 34% and forecast, as you can see, to grow steadily over the next three years. So this, well look, this is not, we understand it's not a one-to-one correlation with our prediction, but it's a pretty good bet that we were right, but we gotta take some points off, we think for the lack of unequivocal proof. Cause again, we always strive to make our predictions in ways that can be measured as accurate or not. Is it binary? Did it happen, did it not? Kind of like an O K R and you know, we strive to provide data as proof and in this case it's a bit fuzzy. >>We have to admit that although we're pretty comfortable that the prediction was accurate. And look, when you make an hard forecast, sometimes you gotta pay the price. All right, next, we said in 2022 that the big four cloud players would generate 167 billion in IS and PaaS revenue combining for 38% market growth. And our current forecasts are shown here with a comparison to our January, 2022 figures. So coming into this year now where we are today, so currently we expect 162 billion in total revenue and a 33% growth rate. Still very healthy, but not on our mark. So we think a w s is gonna miss our predictions by about a billion dollars, not, you know, not bad for an 80 billion company. So they're not gonna hit that expectation though of getting really close to a hundred billion run rate. We thought they'd exit the year, you know, closer to, you know, 25 billion a quarter and we don't think they're gonna get there. >>Look, we pretty much nailed Azure even though our prediction W was was correct about g Google Cloud platform surpassing Alibaba, Alibaba, we way overestimated the performance of both of those companies. So we're gonna give ourselves a C plus here and we think, yeah, you might think it's a little bit harsh, we could argue for a B minus to the professor, but the misses on GCP and Alibaba we think warrant a a self penalty on this one. All right, let's move on to our prediction about Supercloud. We said it becomes a thing in 2022 and we think by many accounts it has, despite the naysayers, we're seeing clear evidence that the concept of a layer of value add that sits above and across clouds is taking shape. And on this slide we showed just some of the pickup in the industry. I mean one of the most interesting is CloudFlare, the biggest supercloud antagonist. >>Charles Fitzgerald even predicted that no vendor would ever use the term in their marketing. And that would be proof if that happened that Supercloud was a thing and he said it would never happen. Well CloudFlare has, and they launched their version of Supercloud at their developer week. Chris Miller of the register put out a Supercloud block diagram, something else that Charles Fitzgerald was, it was was pushing us for, which is rightly so, it was a good call on his part. And Chris Miller actually came up with one that's pretty good at David Linthicum also has produced a a a A block diagram, kind of similar, David uses the term metacloud and he uses the term supercloud kind of interchangeably to describe that trend. And so we we're aligned on that front. Brian Gracely has covered the concept on the popular cloud podcast. Berkeley launched the Sky computing initiative. >>You read through that white paper and many of the concepts highlighted in the Supercloud 3.0 community developed definition align with that. Walmart launched a platform with many of the supercloud salient attributes. So did Goldman Sachs, so did Capital One, so did nasdaq. So you know, sorry you can hate the term, but very clearly the evidence is gathering for the super cloud storm. We're gonna take an a plus on this one. Sorry, haters. Alright, let's talk about data mesh in our 21 predictions posts. We said that in the 2020s, 75% of large organizations are gonna re-architect their big data platforms. So kind of a decade long prediction. We don't like to do that always, but sometimes it's warranted. And because it was a longer term prediction, we, at the time in, in coming into 22 when we were evaluating our 21 predictions, we took a grade of incomplete because the sort of decade long or majority of the decade better part of the decade prediction. >>So last year, earlier this year, we said our number seven prediction was data mesh gains momentum in 22. But it's largely confined and narrow data problems with limited scope as you can see here with some of the key bullets. So there's a lot of discussion in the data community about data mesh and while there are an increasing number of examples, JP Morgan Chase, Intuit, H S P C, HelloFresh, and others that are completely rearchitecting parts of their data platform completely rearchitecting entire data platforms is non-trivial. There are organizational challenges, there're data, data ownership, debates, technical considerations, and in particular two of the four fundamental data mesh principles that the, the need for a self-service infrastructure and federated computational governance are challenging. Look, democratizing data and facilitating data sharing creates conflicts with regulatory requirements around data privacy. As such many organizations are being really selective with their data mesh implementations and hence our prediction of narrowing the scope of data mesh initiatives. >>I think that was right on J P M C is a good example of this, where you got a single group within a, within a division narrowly implementing the data mesh architecture. They're using a w s, they're using data lakes, they're using Amazon Glue, creating a catalog and a variety of other techniques to meet their objectives. They kind of automating data quality and it was pretty well thought out and interesting approach and I think it's gonna be made easier by some of the announcements that Amazon made at the recent, you know, reinvent, particularly trying to eliminate ET t l, better connections between Aurora and Redshift and, and, and better data sharing the data clean room. So a lot of that is gonna help. Of course, snowflake has been on this for a while now. Many other companies are facing, you know, limitations as we said here and this slide with their Hadoop data platforms. They need to do new, some new thinking around that to scale. HelloFresh is a really good example of this. Look, the bottom line is that organizations want to get more value from data and having a centralized, highly specialized teams that own the data problem, it's been a barrier and a blocker to success. The data mesh starts with organizational considerations as described in great detail by Ash Nair of Warner Brothers. So take a listen to this clip. >>Yeah, so when people think of Warner Brothers, you always think of like the movie studio, but we're more than that, right? I mean, you think of H B O, you think of t n t, you think of C N N. We have 30 plus brands in our portfolio and each have their own needs. So the, the idea of a data mesh really helps us because what we can do is we can federate access across the company so that, you know, CNN can work at their own pace. You know, when there's election season, they can ingest their own data and they don't have to, you know, bump up against, as an example, HBO if Game of Thrones is going on. >>So it's often the case that data mesh is in the eyes of the implementer. And while a company's implementation may not strictly adhere to Jamma Dani's vision of data mesh, and that's okay, the goal is to use data more effectively. And despite Gartner's attempts to deposition data mesh in favor of the somewhat confusing or frankly far more confusing data fabric concept that they stole from NetApp data mesh is taking hold in organizations globally today. So we're gonna take a B on this one. The prediction is shaping up the way we envision, but as we previously reported, it's gonna take some time. The better part of a decade in our view, new standards have to emerge to make this vision become reality and they'll come in the form of both open and de facto approaches. Okay, our eighth prediction last year focused on the face off between Snowflake and Databricks. >>And we realized this popular topic, and maybe one that's getting a little overplayed, but these are two companies that initially, you know, looked like they were shaping up as partners and they, by the way, they are still partnering in the field. But you go back a couple years ago, the idea of using an AW w s infrastructure, Databricks machine intelligence and applying that on top of Snowflake as a facile data warehouse, still very viable. But both of these companies, they have much larger ambitions. They got big total available markets to chase and large valuations that they have to justify. So what's happening is, as we've previously reported, each of these companies is moving toward the other firm's core domain and they're building out an ecosystem that'll be critical for their future. So as part of that effort, we said each is gonna become aggressive investors and maybe start doing some m and a and they have in various companies. >>And on this chart that we produced last year, we studied some of the companies that were targets and we've added some recent investments of both Snowflake and Databricks. As you can see, they've both, for example, invested in elation snowflake's, put money into Lacework, the Secur security firm, ThoughtSpot, which is trying to democratize data with ai. Collibra is a governance platform and you can see Databricks investments in data transformation with D B T labs, Matillion doing simplified business intelligence hunters. So that's, you know, they're security investment and so forth. So other than our thought that we'd see Databricks I p o last year, this prediction been pretty spot on. So we'll give ourselves an A on that one. Now observability has been a hot topic and we've been covering it for a while with our friends at E T R, particularly Eric Bradley. Our number nine prediction last year was basically that if you're not cloud native and observability, you are gonna be in big trouble. >>So everything guys gotta go cloud native. And that's clearly been the case. Splunk, the big player in the space has been transitioning to the cloud, hasn't always been pretty, as we reported, Datadog real momentum, the elk stack, that's open source model. You got new entrants that we've cited before, like observe, honeycomb, chaos search and others that we've, we've reported on, they're all born in the cloud. So we're gonna take another a on this one, admittedly, yeah, it's a re reasonably easy call, but you gotta have a few of those in the mix. Okay, our last prediction, our number 10 was around events. Something the cube knows a little bit about. We said that a new category of events would emerge as hybrid and that for the most part is happened. So that's gonna be the mainstay is what we said. That pure play virtual events are gonna give way to hi hybrid. >>And the narrative is that virtual only events are, you know, they're good for quick hits, but lousy replacements for in-person events. And you know that said, organizations of all shapes and sizes, they learn how to create better virtual content and support remote audiences during the pandemic. So when we set at pure play is gonna give way to hybrid, we said we, we i we implied or specific or specified that the physical event that v i p experience is going defined. That overall experience and those v i p events would create a little fomo, fear of, of missing out in a virtual component would overlay that serves an audience 10 x the size of the physical. We saw that really two really good examples. Red Hat Summit in Boston, small event, couple thousand people served tens of thousands, you know, online. Second was Google Cloud next v i p event in, in New York City. >>Everything else was, was, was, was virtual. You know, even examples of our prediction of metaverse like immersion have popped up and, and and, and you know, other companies are doing roadshow as we predicted like a lot of companies are doing it. You're seeing that as a major trend where organizations are going with their sales teams out into the regions and doing a little belly to belly action as opposed to the big giant event. That's a definitely a, a trend that we're seeing. So in reviewing this prediction, the grade we gave ourselves is, you know, maybe a bit unfair, it should be, you could argue for a higher grade, but the, but the organization still haven't figured it out. They have hybrid experiences but they generally do a really poor job of leveraging the afterglow and of event of an event. It still tends to be one and done, let's move on to the next event or the next city. >>Let the sales team pick up the pieces if they were paying attention. So because of that, we're only taking a B plus on this one. Okay, so that's the review of last year's predictions. You know, overall if you average out our grade on the 10 predictions that come out to a b plus, I dunno why we can't seem to get that elusive a, but we're gonna keep trying our friends at E T R and we are starting to look at the data for 2023 from the surveys and all the work that we've done on the cube and our, our analysis and we're gonna put together our predictions. We've had literally hundreds of inbounds from PR pros pitching us. We've got this huge thick folder that we've started to review with our yellow highlighter. And our plan is to review it this month, take a look at all the data, get some ideas from the inbounds and then the e t R of January surveys in the field. >>It's probably got a little over a thousand responses right now. You know, they'll get up to, you know, 1400 or so. And once we've digested all that, we're gonna go back and publish our predictions for 2023 sometime in January. So stay tuned for that. All right, we're gonna leave it there for today. You wanna thank Alex Myerson who's on production and he manages the podcast, Ken Schiffman as well out of our, our Boston studio. I gotta really heartfelt thank you to Kristen Martin and Cheryl Knight and their team. They helped get the word out on social and in our newsletters. Rob Ho is our editor in chief over at Silicon Angle who does some great editing for us. Thank you all. Remember all these podcasts are available or all these episodes are available is podcasts. Wherever you listen, just all you do Search Breaking analysis podcast, really getting some great traction there. Appreciate you guys subscribing. I published each week on wikibon.com, silicon angle.com or you can email me directly at david dot valante silicon angle.com or dm me Dante, or you can comment on my LinkedIn post. And please check out ETR AI for the very best survey data in the enterprise tech business. Some awesome stuff in there. This is Dante for the Cube Insights powered by etr. Thanks for watching and we'll see you next time on breaking analysis.

(upbeat music) >> Hello and welcome to theCUBE's presentation of the AWS Startup Showcase. This is season two, episode four of the ongoing series covering the exciting hot startups from the AWS ecosystem. Here we're talking about cybersecurity in this episode. I'm your host, John Furrier here we're excited to have CUBE alumni who's back Snehal Antani who's the CEO and co-founder of Horizon3.ai talking about exploitable weaknesses and vulnerabilities with autonomous pen testing. Snehal, it's great to see you. Thanks for coming back. >> Likewise, John. I think it's been about five years since you and I were on the stage together. And I've missed it, but I'm glad to see you again. >> Well, before we get into the showcase about your new startup, that's extremely successful, amazing margins, great product. You have a unique journey. We talked about this prior to you doing the journey, but you have a great story. You left the startup world to go into the startup, like world of self defense, public defense, NSA. What group did you go to in the public sector became a private partner. >> My background, I'm a software engineer by education and trade. I started my career at IBM. I was a CIO at GE Capital, and I think we met once when I was there and I became the CTO of Splunk. And we spent a lot of time together when I was at Splunk. And at the end of 2017, I decided to take a break from industry and really kind of solve problems that I cared deeply about and solve problems that mattered. So I left industry and joined the US Special Operations Community and spent about four years in US Special Operations, where I grew more personally and professionally than in anything I'd ever done in my career. And exited that time, met my co-founder in special ops. And then as he retired from the air force, we started Horizon3. >> So there's really, I want to bring that up one, 'cause it's fascinating that not a lot of people in Silicon Valley and tech would do that. So thanks for the service. And I know everyone who's out there in the public sector knows that this is a really important time for the tactical edge in our military, a lot of things going on around the world. So thanks for the service and a great journey. But there's a storyline with the company you're running now that you started. I know you get the jacket on there. I noticed get a little military vibe to it. Cybersecurity, I mean, every company's on their own now. They have to build their own militia. There is no government supporting companies anymore. There's no militia. No one's on the shores of our country defending the citizens and the companies, they got to offend for themselves. So every company has to have their own military. >> In many ways, you don't see anti-aircraft rocket launchers on top of the JP Morgan building in New York City because they rely on the government for air defense. But in cyber it's very different. Every company is on their own to defend for themselves. And what's interesting is this blend. If you look at the Ukraine, Russia war, as an example, a thousand companies have decided to withdraw from the Russian economy and those thousand companies we should expect to be in the ire of the Russian government and their proxies at some point. And so it's not just those companies, but their suppliers, their distributors. And it's no longer about cyber attack for extortion through ransomware, but rather cyber attack for punishment and retaliation for leaving. Those companies are on their own to defend themselves. There's no government that is dedicated to supporting them. So yeah, the reality is that cybersecurity, it's the burden of the organization. And also your attack surface has expanded to not just be your footprint, but if an adversary wants to punish you for leaving their economy, they can get, if you're in agriculture, they could disrupt your ability to farm or they could get all your fruit to spoil at the border 'cause they disrupted your distributors and so on. So I think the entire world is going to change over the next 18 to 24 months. And I think this idea of cybersecurity is going to become truly a national problem and a problem that breaks down any corporate barriers that we see in previously. >> What are some of the things that inspired you to start this company? And I loved your approach of thinking about the customer, your customer, as defending themselves in context to threats, really leaning into it, being ready and able to defend. Horizon3 has a lot of that kind of military thinking for the good of the company. What's the motivation? Why this company? Why now? What's the value proposition? >> So there's two parts to why the company and why now. The first part was what my observation, when I left industry realm or my military background is watching "Jack Ryan" and "Tropic Thunder" and I didn't come from the military world. And so when I entered the special operations community, step one was to keep my mouth shut, learn, listen, and really observe and understand what made that community so impressive. And obviously the people and it's not about them being fast runners or great shooters or awesome swimmers, but rather there are learn-it-alls that can solve any problem as a team under pressure, which is the exact culture you want to have in any startup, early stage companies are learn-it-alls that can solve any problem under pressure as a team. So I had this immediate advantage when we started Horizon3, where a third of Horizon3 employees came from that special operations community. So one is this awesome talent. But the second part that, I remember this quote from a special operations commander that said we use live rounds in training because if we used fake rounds or rubber bullets, everyone would act like metal of honor winners. And the whole idea there is you train like you fight, you build that muscle memory for crisis and response and so on upfront. So when you're in the thick of it, you already know how to react. And this aligns to a pain I had in industry. I had no idea I was secure until the bad guy showed up. I had no idea if I was fixing the right vulnerabilities, logging the right data in Splunk, or if my CrowdStrike EDR platform was configured correctly, I had to wait for the bad guys to show up. I didn't know if my people knew how to respond to an incident. So what I wanted to do was proactively verify my security posture, proactively harden my systems. I needed to do that by continuously pen testing myself or continuously testing my security posture. And there just wasn't any way to do that where an IT admin or a network engineer could in three clicks have the power of a 20 year pen testing expert. And that was really what we set out to do, not build a autonomous pen testing platform for security people, build it so that anybody can quickly test their security posture and then use the output to fix problems that truly matter. >> So the value preposition, if I get this right is, there's a lot of companies out there doing pen tests. And I know I hate pen tests. They're like, cause you do DevOps, it changes you got to do another pen test. So it makes sense to do autonomous pen testing. So congratulations on seeing that that's obvious to that, but a lot of other have consulting tied to it. Which seems like you need to train someone and you guys taking a different approach. >> Yeah, we actually, as a company have zero consulting, zero professional services. And the whole idea is that build a true software as a service offering where an intern, in fact, we've got a video of a nine year old that in three clicks can run pen tests against themselves. And because of that, you can wire pen tests into your DevOps tool chain. You can run multiple pen tests today. In fact, I've got customers running 40, 50 pen tests a month against their organization. And that what that does is completely lowers the barrier of entry for being able to verify your posture. If you have consulting on average, when I was a CIO, it was at least a three month lead time to schedule consultants to show up and then they'd show up, they'd embarrass the security team, they'd make everyone look bad, 'cause they're going to get in, leave behind a report. And that report was almost identical to what they found last year because the older that report, the one the date itself gets stale, the context changes and so on. And then eventually you just don't even bother fixing it. Or if you fix a problem, you don't have the skills to verify that has been fixed. So I think that consulting led model was acceptable when you viewed security as a compliance checkbox, where once a year was sufficient to meet your like PCI requirements. But if you're really operating with a wartime mindset and you actually need to harden and secure your environment, you've got to be running pen test regularly against your organization from different perspectives, inside, outside, from the cloud, from work, from home environments and everything in between. >> So for the CISOs out there, for the CSOs and the CXOs, what's the pitch to them because I see your jacket that says Horizon3 AI, trust but verify. But this trust is, but is canceled out, just as verify. What's the product that you guys are offering the service. Describe what it is and why they should look at it. >> Yeah, sure. So one, when I back when I was the CIO, don't tell me we're secure in PowerPoint. Show me we're secure right now. Show me we're secure again tomorrow. And then show me we're secure again next week because my environment is constantly changing and the adversary always has a vote and they're always evolving. And this whole idea of show me we're secure. Don't trust that your security tools are working, verify that they can detect and respond and stifle an attack and then verify tomorrow, verify next week. That's the big mind shift. Now what we do is-- >> John: How do they respond to that by the way? Like they don't believe you at first or what's the story. >> I think, there's actually a very bifurcated response. There are still a decent chunk of CIOs and CSOs that have a security is a compliance checkbox mindset. So my attitude with them is I'm not going to convince you. You believe it's a checkbox. I'll just wait for you to get breached and sell to your replacement, 'cause you'll get fired. And in the meantime, I spend all my energy with those that actually care about proactively securing and hardening their environments. >> That's true. People do get fired. Can you give an example of what you're saying about this environment being ready, proving that you're secure today, tomorrow and a few weeks out. Give me an example. >> Of, yeah, I'll give you actually a customer example. There was a healthcare organization and they had about 5,000 hosts in their environment and they did everything right. They had Fortinet as their EDR platform. They had user behavior analytics in place that they had purchased and tuned. And when they ran a pen test self-service, our product node zero immediately started to discover every host on the network. It then fingerprinted all those hosts and found it was able to get code execution on three machines. So it got code execution, dumped credentials, laterally maneuvered, and became a domain administrator, which in IT, if an attacker becomes a domain admin, they've got keys to the kingdom. So at first the question was, how did the node zero pen test become domain admin? How'd they get code execution, Fortinet should have detected and stopped it. Well, it turned out Fortinet was misconfigured on three boxes out of 5,000. And these guys had no idea and it's just automation that went wrong and so on. And now they would've only known they had misconfigured their EDR platform on three hosts if the attacker had showed up. The second question though was, why didn't they catch the lateral movement? Which all their marketing brochures say they're supposed to catch. And it turned out that that customer purchased the wrong Fortinet modules. One again, they had no idea. They thought they were doing the right thing. So don't trust just installing your tools is good enough. You've got to exercise and verify them. We've got tons of stories from patches that didn't actually apply to being able to find the AWS admin credentials on a local file system. And then using that to log in and take over the cloud. In fact, I gave this talk at Black Hat on war stories from running 10,000 pen tests. And that's just the reality is, you don't know that these tools and processes are working for you until the bad guys have shown. >> The velocities there. You can accelerate through logs, you know from the days you've been there. This is now the threat. Being, I won't say lazy, but just not careful or just not thinking. >> Well, I'll do an example. We have a lot of customers that are Horizon3 customers and Splunk customers. And what you'll see their behavior is, is they'll have Horizon3 up on one screen. And every single attacker command executed with its timestamp is up on that screen. And then look at Splunk and say, hey, we were able to dump vCenter credentials from VMware products at this time on this host, what did Splunk see or what didn't they see? Why were no logs generated? And it turns out that they had some logging blind spots. So what they'll actually do is run us to almost like stimulate the defensive tools and then see what did the tools catch? What did they miss? What are those blind spots and how do they fix it. >> So your price called node zero. You mentioned that. Is that specifically a suite, a tool, a platform. How do people consume and engage with you guys? >> So the way that we work, the whole product is designed to be self-service. So once again, while we have a sales team, the whole intent is you don't need to have to talk to a sales rep to start using the product, you can log in right now, go to Horizon3.ai, you can run a trial log in with your Google ID, your LinkedIn ID, start running pen test against your home or against your network against this organization right now, without talking to anybody. The whole idea is self-service, run a pen test in three clicks and give you the power of that 20 year pen testing expert. And then what'll happen is node zero will execute and then it'll provide to you a full report of here are all of the different paths or attack paths or sequences where we are able to become an admin in your environment. And then for every attack path, here is the path or the kill chain, the proof of exploitation for every step along the way. Here's exactly what you've got to do to fix it. And then once you've fixed it, here's how you verify that you've truly fixed the problem. And this whole aha moment is run us to find problems. You fix them, rerun us to verify that the problem has been fixed. >> Talk about the company, how many people do you have and get some stats? >> Yeah, so we started writing code in January of 2020, right before the pandemic hit. And then about 10 months later at the end of 2020, we launched the first version of the product. We've been in the market for now about two and a half years total from start of the company till present. We've got 130 employees. We've got more customers than we do employees, which is really cool. And instead our customers shift from running one pen test a year to 40, 50 pen test. >> John: And it's full SaaS. >> The whole product is full SaaS. So no consulting, no pro serve. You run as often as you-- >> Who's downloading, who's buying the product. >> What's amazing is, we have customers in almost every section or sector now. So we're not overly rotated towards like healthcare or financial services. We've got state and local education or K through 12 education, state and local government, a number of healthcare companies, financial services, manufacturing. We've got organizations that large enterprises. >> John: Security's diverse. >> It's very diverse. >> I mean, ransomware must be a big driver. I mean, is that something that you're seeing a lot. >> It is. And the thing about ransomware is, if you peel back the outcome of ransomware, which is extortion, at the end of the day, what ransomware organizations or criminals or APTs will do is they'll find out who all your employees are online. They will then figure out if you've got 7,000 employees, all it takes is one of them to have a bad password. And then attackers are going to credential spray to find that one person with a bad password or whose Netflix password that's on the dark web is also their same password to log in here, 'cause most people reuse. And then from there they're going to most likely in your organization, the domain user, when you log in, like you probably have local admin on your laptop. If you're a windows machine and I've got local admin on your laptop, I'm going to be able to dump credentials, get the admin credentials and then start to laterally maneuver. Attackers don't have to hack in using zero days like you see in the movies, often they're logging in with valid user IDs and passwords that they've found and collected from somewhere else. And then they make that, they maneuver by making a low plus a low equal a high. And the other thing in financial services, we spend all of our time fixing critical vulnerabilities, attackers know that. So they've adapted to finding ways to chain together, low priority vulnerabilities and misconfigurations and dangerous defaults to become admin. So while we've over rotated towards just fixing the highs and the criticals attackers have adapted. And once again they have a vote, they're always evolving their tactics. >> And how do you prevent that from happening? >> So we actually apply those same tactics. Rarely do we actually need a CVE to compromise your environment. We will harvest credentials, just like an attacker. We will find misconfigurations and dangerous defaults, just like an attacker. We will combine those together. We'll make use of exploitable vulnerabilities as appropriate and use that to compromise your environment. So the tactics that, in many ways we've built a digital weapon and the tactics we apply are the exact same tactics that are applied by the adversary. >> So you guys basically simulate hacking. >> We actually do the hacking. Simulate means there's a fakeness to it. >> So you guys do hack. >> We actually compromise. >> Like sneakers the movie, those sneakers movie for the old folks like me. >> And in fact that was my inspiration. I've had this idea for over a decade now, which is I want to be able to look at anything that laptop, this Wi-Fi network, gear in hospital or a truck driving by and know, I can figure out how to gain initial access, rip that environment apart and be able to opponent. >> Okay, Chuck, he's not allowed in the studio anymore. (laughs) No, seriously. Some people are exposed. I mean, some companies don't have anything. But there's always passwords or so most people have that argument. Well, there's nothing to protect here. Not a lot of sensitive data. How do you respond to that? Do you see that being kind of putting the head in the sand or? >> Yeah, it's actually, it's less, there's not sensitive data, but more we've installed or applied multifactor authentication, attackers can't get in now. Well MFA only applies or does not apply to lower level protocols. So I can find a user ID password, log in through SMB, which isn't protected by multifactor authentication and still upon your environment. So unfortunately I think as a security industry, we've become very good at giving a false sense of security to organizations. >> John: Compliance drives that behavior. >> Compliance drives that. And what we need. Back to don't tell me we're secure, show me, we've got to, I think, change that to a trust but verify, but get rid of the trust piece of it, just to verify. >> Okay, we got a lot of CISOs and CSOs watching this showcase, looking at the hot startups, what's the message to the executives there. Do they want to become more leaning in more hawkish if you will, to use the military term on security? I mean, I heard one CISO say, security first then compliance 'cause compliance can make you complacent and then you're unsecure at that point. >> I actually say that. I agree. One definitely security is different and more important than being compliant. I think there's another emerging concept, which is I'd rather be defensible than secure. What I mean by that is security is a point in time state. I am secure right now. I may not be secure tomorrow 'cause something's changed. But if I'm defensible, then what I have is that muscle memory to detect, respondent and stifle an attack. And that's what's more important. Can I detect you? How long did it take me to detect you? Can I stifle you from achieving your objective? How long did it take me to stifle you? What did you use to get in to gain access? How long did that sit in my environment? How long did it take me to fix it? So on and so forth. But I think it's being defensible and being able to rapidly adapt to changing tactics by the adversary is more important. >> This is the evolution of how the red line never moved. You got the adversaries in our networks and our banks. Now they hang out and they wait. So everyone thinks they're secure. But when they start getting hacked, they're not really in a position to defend, the alarms go off. Where's the playbook. Team springs into action. I mean, you kind of get the visual there, but this is really the issue being defensible means having your own essentially military for your company. >> Being defensible, I think has two pieces. One is you've got to have this culture and process in place of training like you fight because you want to build that incident response muscle memory ahead of time. You don't want to have to learn how to respond to an incident in the middle of the incident. So that is that proactively verifying your posture and continuous pen testing is critical there. The second part is the actual fundamentals in place so you can detect and stifle as appropriate. And also being able to do that. When you are continuously verifying your posture, you need to verify your entire posture, not just your test systems, which is what most people do. But you have to be able to safely pen test your production systems, your cloud environments, your perimeter. You've got to assume that the bad guys are going to get in, once they're in, what can they do? So don't just say that my perimeter's secure and I'm good to go. It's the soft squishy center that attackers are going to get into. And from there, can you detect them and can you stop them? >> Snehal, take me through the use. You got to be sold on this, I love this topic. Alright, pen test. Is it, what am I buying? Just pen test as a service. You mentioned dark web. Are you actually buying credentials online on behalf of the customer? What is the product? What am I buying if I'm the CISO from Horizon3? What's the service? What's the product, be specific. >> So very specifically and one just principles. The first principle is when I was a buyer, I hated being nickled and dimed buyer vendors, which was, I had to buy 15 different modules in order to achieve an objective. Just give me one line item, make it super easy to buy and don't nickel and dime me. Because I've spent time as a buyer that very much has permeated throughout the company. So there is a single skew from Horizon3. It is an annual subscription based on how big your environment is. And it is inclusive of on-prem internal pen tests, external pen tests, cloud attacks, work from home attacks, our ability to harvest credentials from the dark web and from open source sources. Being able to crack those credentials, compromise. All of that is included as a singles skew. All you get as a CISO is a singles skew, annual subscription, and you can run as many pen tests as you want. Some customers still stick to, maybe one pen test a quarter, but most customers shift when they realize there's no limit, we don't nickel and dime. They can run 10, 20, 30, 40 a month. >> Well, it's not nickel and dime in the sense that, it's more like dollars and hundreds because they know what to expect if it's classic cloud consumption. They kind of know what their environment, can people try it. Let's just say I have a huge environment, I have a cloud, I have an on-premise private cloud. Can I dabble and set parameters around pricing? >> Yes you can. So one is you can dabble and set perimeter around scope, which is like manufacturing does this, do not touch the production line that's on at the moment. We've got a hospital that says every time they run a pen test, any machine that's actually connected to a patient must be excluded. So you can actually set the parameters for what's in scope and what's out of scope up front, most again we're designed to be safe to run against production so you can set the parameters for scope. You can set the parameters for cost if you want. But our recommendation is I'd rather figure out what you can afford and let you test everything in your environment than try to squeeze every penny from you by only making you buy what can afford as a smaller-- >> So the variable ratio, if you will is, how much they spend is the size of their environment and usage. >> Just size of the environment. >> So it could be a big ticket item for a CISO then. >> It could, if you're really large, but for the most part-- >> What's large? >> I mean, if you were Walmart, well, let me back up. What I heard is global 10 companies spend anywhere from 50 to a hundred million dollars a year on security testing. So they're already spending a ton of money, but they're spending it on consultants that show up maybe a couple of times a year. They don't have, humans can't scale to test a million hosts in your environment. And so you're already spending that money, spend a fraction of that and use us and run as much as you want. And that's really what it comes down to. >> John: All right. So what's the response from customers? >> What's really interesting is there are three use cases. The first is that SOC manager that is using us to verify that their security tools are actually working. So their Splunk environment is logging the right data. It's integrating properly with CrowdStrike, it's integrating properly with their active directory services and their password policies. So the SOC manager is using us to verify the effectiveness of their security controls. The second use case is the IT director that is using us to proactively harden their systems. Did they install VMware correctly? Did they install their Cisco gear correctly? Are they patching right? And then the third are for the companies that are lucky to have their own internal pen test and red teams where they use us like a force multiplier. So if you've got 10 people on your red team and you still have a million IPs or hosts in your environment, you still don't have enough people for that coverage. So they'll use us to do recon at scale and attack at scale and let the humans focus on the really juicy hard stuff that humans are successful at. >> Love the product. Again, I'm trying to think about how I engage on the test. Is there pilots? Is there a demo version? >> There's a free trials. So we do 30 day free trials. The output can actually be used to meet your SOC 2 requirements. So in many ways you can just use us to get a free SOC 2 pen test report right now, if you want. Go to the website, log in for a free trial, you can log into your Google ID or your LinkedIn ID, run a pen test against your organization and use that to answer your PCI segmentation test requirements, your SOC 2 requirements, but you will be hooked. You will want to run us more often. And you'll get a Horizon3 tattoo. >> The first hits free as they say in the drug business. >> Yeah. >> I mean, so you're seeing that kind of response then, trial converts. >> It's exactly. In fact, we have a very well defined aha moment, which is you run us to find, you fix, you run us to verify, we have 100% technical win rate when our customers hit a find, fix, verify cycle, then it's about budget and urgency. But 100% technical win rate because of that aha moment, 'cause people realize, holy crap, I don't have to wait six months to verify that my problems have actually been fixed. I can just come in, click, verify, rerun the entire pen test or rerun a very specific part of it on what I just patched my environment. >> Congratulations, great stuff. You're here part of the AWS Startup Showcase. So I have to ask, what's the relationship with AWS, you're on their cloud. What kind of actions going on there? Is there secret sauce on there? What's going on? >> So one is we are AWS customers ourselves, our brains command and control infrastructure. All of our analytics are all running on AWS. It's amazing, when we run a pen test, we are able to use AWS and we'll spin up a virtual private cloud just for that pen test. It's completely ephemeral, it's all Lambda functions and graph analytics and other techniques. When the pen test ends, you can delete, there's a single use Docker container that gets deleted from your environment so you have nothing on-prem to deal with and the entire virtual private cloud tears itself down. So at any given moment, if we're running 50 pen tests or a hundred pen tests, self-service, there's a hundred virtual private clouds being managed in AWS that are spinning up, running and tearing down. It's an absolutely amazing underlying platform for us to make use of. Two is that many customers that have hybrid environments. So they've got a cloud infrastructure, an Office 365 infrastructure and an on-prem infrastructure. We are a single attack platform that can test all of that together. No one else can do it. And so the AWS customers that are especially AWS hybrid customers are the ones that we do really well targeting. >> Got it. And that's awesome. And that's the benefit of cloud? >> Absolutely. And the AWS marketplace. What's absolutely amazing is the competitive advantage being part of the marketplace has for us, because the simple thing is my customers, if they already have dedicated cloud spend, they can use their approved cloud spend to pay for Horizon3 through the marketplace. So you don't have to, if you already have that budget dedicated, you can use that through the marketplace. The other is you've already got the vendor processes in place, you can purchase through your existing AWS account. So what I love about the AWS company is one, the infrastructure we use for our own pen test, two, the marketplace, and then three, the customers that span that hybrid cloud environment. That's right in our strike zone. >> Awesome. Well, congratulations. And thanks for being part of the showcase and I'm sure your product is going to do very, very well. It's very built for what people want. Self-service get in, get the value quickly. >> No agents to install, no consultants to hire. safe to run against production. It's what I wanted. >> Great to see you and congratulations and what a great story. And we're going to keep following you. Thanks for coming on. >> Snehal: Phenomenal. Thank you, John. >> This is the AWS Startup Showcase. I'm John John Furrier, your host. This is season two, episode four on cybersecurity. Thanks for watching. (upbeat music)

(upbeat music) >> Welcome back to Supercloud 22. I'm John Furrier, your host of "The Cube." We're here for a live performance in studio bringing all the thought leaders around this concept of Supercloud, which is a consortium of the smartest people in the industry, the the Cloudaratti some say, or just people in the field building out next generation Cloud technologies for businesses, for the industry, you know, software meets infrastructure at scale and platforms. All great stuff. We have an expert here, Cube alumni and friend of ours, Howie Xu, VP of machine learning and AI at Zscaler, hugely successful company, platform, whatever you want to call it. They're definitely super clouding in their own. Howie, great to see you. Thanks for spending time with us to unpack and grock the direction of the industry that we see. We call it Supercloud. >> Hey, John, great to be back. I'm expecting a nice very educational and interesting conversation here again. >> Yeah, well, you know, one of the things I love talking with you about is you're deep on the technology side, as well as you got the historian view like we do. We've seen the movies before, we've seen the patterns, and now we're seeing structural change that has happened, that's cloud. Thank you very much, AWS. And as your GCP and others. Now we're seeing structural change happening in real time and we want to talk about it as it's happening. This is the purpose of this event. And that is that Cloud is one. Okay, great Cloud operations, on premises and Edge are emerging. Software is open source. It's the perfect storm for innovation and new things are emerging. You're seeing companies like Snowflake, and Databricks, and Zscaler all building great products. But now it's not one thing anymore. It's a lot of things going on. So what is your take on Supercloud? How do you see this evolving? What is some of the structural change that's happening in your mind? >> Yeah, so when you first reached out a few weeks ago about this event, I was like, "Hey, what is Supercloud." I know you tweeted a little bit here and there, but I never really, you know, double clicked, right. So I actually listened to some of your episodes you know, the previous conversations. You know, I would say the way you define Supercloud is it's not just the multi-cloud. The multi-Cloud is probably one aspect of it, right. You know, it's actually more beyond that, right. You know, a little bit, you know, towards past, a little bit more towards the flexibility, and then, you know, including, and also you want to include the on-prem, the edge, not just the Big 3 cloud, right. So there is a lot of the, let's say hybrid, more inclusive, right. So, the way I look at it is it's now very different from my imagination of where the Cloud would be, should be 10, 12 years ago. Because, you know, at that time it was, you know, on-prem dominant and then we say, hey, let's go cloud. I never for a second thought, you know, we would've ditch the on-prem completely, right. You know, on-prem has its own value. It's own kind of characteristics we wanted to keep, right. But the way we went for the last 10 years is, hey, Cloud, Cloud everywhere. We embrace Cloud. You know, the way I look at architecture is always very much like a pendulum, right? We swung from decentralized in the mainframe days, you know back in the days, to more distributed, right, PC, kind of a architecture, you know, servers in your own data center. And then to the now, the Cloud, the Big 3 Cloud in particular, right. I think in the next 10, 15, 20 years, it will swing back to more decentralized, more distributed architecture again. Every time you have a swing, because there is some fundamental reason behind that, we all knew the reason behind the current swing to the Cloud. It's because hey, the on-prem data center was too complex, right. You know, too expensive, right. You know, it would've take at least the six months to get any business application going, right. So compared to Cloud, a swipe a credit card, frictionless, you know, pay as you go, it's so great. But I think we are going to see more and more reason for people to say, "Hey, I need a architecture the other way around because of the decentralized the use case," right. Web3 is one example. Even though Web3 is still, you know, emerging right, very, very early days. But that could be one reason, right? You mentioned the Zscaler is kind of a Supercloud of its own, right? We always embrace public Cloud, but a lot of the workloads is actually on our own, you know, within our own data center. We take advantage of the elasticity of the public Cloud, right. But we also get a value, get a performance of our private Cloud. So I want to say a company like Zscaler taking advantage of the Supercloud already, but there will be more and more use cases taking advantage. >> And the use cases are key. Let me just go back and share something we had on the panel earlier in the day, the Cloudaratti Panel. Back in 2008, a bunch of us were getting together and we kind of were riffing, oh yeah, the future's going to be web services and Clouds will talk to each other, workloads can work across this (indistinct) abstraction layer, APIs is going to be talking to each other. A little bit early but we tried to think about it in terms of the preferred architecture. Okay, way too early. Yeah. AWS was just getting going, really kind of pumping on all cylinders there, getting that trajectory up. But it was use case driven. The nirvana never happened. I mean, we were talking Supercloud back then with the Cloudaratti group and we were thinking, okay, hey this is cool. But it was just an evolutionary thing. So I want to get your reaction. Today, the use cases are different. It's not just developers deploying on public Cloud to get all those greatness and goodness of the Cloud, to your point about Zscaler and others, there's on premises use cases and edge use cases emerging. 5g is right there. That's going to explode. So, the use cases now are all Cloud based. Again, this is an input into what we're seeing around Supercloud. How do you see that? What's your reaction to that? And how do you see that evolving so that the methodologies and all the taxonomies are in place for the right solution? >> Right, I mean, you know, some of the use cases are already here, you know, have been here for the last few years. And again, I mentioned a Zscaler, right. The reason that a Zscaler needs the on-prem version of it is because it's impossible to route all the traffic to the Big 3 Cloud, because they're still far away. Sometimes you need the presence much closer to you in order for you to get the level of the performance latency you want, right. So that's why Zscaler has, you know, so many data center of our own instead of leveraging the public Cloud, you know, for most part. However, public Cloud is still super important for Zscaler. I can tell you a story, right. You know, two years ago, you know, at the beginning of the pandemics, everyone started working from home suddenly, right. You are talking about Fortune 500 companies with 200,000 employees, suddenly having 200,000 employees working from home. Their VPN architecture is not going to support that kind of the workload, right? Even Zscaler's own architecture or the presence is not enough. So overnight we just, having so many new workloads, to support this work from home, the zero trust network for our customers, literally overnight. So it wouldn't have happened without public Cloud. So we took advantage of the public Cloud. Yet at the same time, for many, many use cases that Zscaler is paying attention to in terms of the zero trust architecture, the latency, the latency guarantee aspect, the cost is so important. So we kind of take it advantage of both. >> Yeah, definitely. >> Today you may say, hey, you know, Zscaler is one of the, not a majority of the companies in terms of the Cloud adoption or public Cloud adoption, right. But I can say that, yeah, that's because it's more infrastructure, security infrastructure. It's a little bit different for some of the communication applications, right. Why not just support everything on the public Cloud? That's doable today. However, moving forward next to 5, 10, 15 years, we expect to see Web3 kind of the use cases to grow more and more. In those kind of decentralized use cases, I can totally see that we, you know, the on-prim presence is very important. >> Yeah. One of the things we're seeing with Supercloud that we're kind of seeing clarity on is that there's a lot of seamless execution around, less friction around areas that require a PhD or hard work. And you're seeing specialty Superclouds, apps, identity data security. You're also seeing vertical clouds, Goldman Sachs doing financial applications. I'm sure there'll be some insurance. People in these verse. Building on top of the CapEx on one Cloud really fast and moving to others. So that's clearly a trend. The interesting thing I want to get your thoughts on, Howie, on an architectural basis is in Cloud, public Cloud generally, SaaS depends on IAS. So there's an interplay between SaaS and the infrastructures of service and pass as well. But SaaS and IAS, they solve a lot of the problems. You mentioned latency. How do you see the interplay of these Superclouds that utilize the SaaS IS relationship to solve technical problems? So in architecturally, that's been a tight integration on these Clouds, but now as you get more complexity with Supercloud, how do you see SaaS applications changing? >> Yeah, I view the Supercloud is actually reduced the complexity. The reason I'm saying that is, think about it in the world where you have predominantly public Cloud kind of the architecture, right? 10 years ago, AWS has probably 20 services. Now they probably have, you know, more than 1,000 services. Same thing with Azure, same thing with GCP. I mean, who can make sense out of it, right. You know, if you just consume the eyes or the Big 3 Cloud service as is. You know, you need a PhD these days to make sense all of them. So the way I think about Supercloud or where, you know it is going, is it has to provide more simplicity, a better way for people to make sense out of it, right. Cause if I'm an architecture and I have to think, hey, this is a public Cloud, this is a multi-Cloud, and by the way, certain things need to be run on the on-prem. And how do I deal with the uniform nature of it? My mind would blow up. So I need a higher level abstraction. That higher level abstraction will hide the complexity of the where it is, which vendor. It will only tell me the service level, right. You know, we always say, you know, the Cloud is like electricity. I only wanted to know is that like 110 volt or 220, 240, whatever that is. I don't really want to know more than that, right. So I want to say a key requirement for the Supercloud is it's reduced the complexity, higher level abstraction. It has to be like that. >> And operational consistencies at the bottom. Howie, we have one minute left. I want to get your thoughts. I'd like you to share what you're working on that you're excited about. It doesn't have to be with Zscaler. As you see the Supercloud trend emerging, this is the next generation Cloud, Cloud 2.0, whatever we want to call it, it's happening. It's changing. It's getting better. What are you excited about? What do you see as really key inflection point variables in this big wave? >> Yeah. One of the things I really like, what I heard from you in the past about Supercloud is a Supercloud is not just a one Cloud or one vendor. It's almost like every company should have its own Supercloud, right. You're talking about JP Morgan, Goldman Sachs of the world, that they need to have their own Supercloud. Zscaler and their security vendors, they may have their own Cloud. So I think every Fortune 500, Fortune 2,000 companies will have its own Supercloud. So I'm excited about that. So why that's important? We also say that, you know, in the next 10, 20 years, AI machine learning is going to help us a lot, right. So without Supercloud, it's very hard to do AI machine learning. 'Cause if you don't have a place that you know where the data is, and then it's pretty hard. And in the context of Supercloud, I totally foresee that the AI model will follow the data. If the data is in the cloud, it will go there. If the data is on-prem, it will go there. And then the Supercloud will hide the complexity of it. So if you ask me, my passion is leveraging AI machine learning to change the world, but Supercloud will make that easier, right. If you think about why Google, Facebook of the world, are able to leverage AI better than 99% of the rest of the world, because they figure out the Supercloud for themselves, right. And I think now it's the time for the rest of the Fortune 500, of Fortune 2,000 company to figure out its own Supercloud strategy. What is my Supercloud? I need to have my own Supercloud. Each company needs to have its own Supercloud. That's how I see it. >> Howie, always great to have you on. Thanks so much for spending the time and weighing in on this really important topic. We're going to be opening this up. It's not over. We're going to continue to watch the change as it unfolds and get an open community perspective. Thank you so much for being a great expert in our network and community. We really appreciate your time. >> Thank you for having me. >> Okay. Okay, that's it. We'll be up with more coverage here, Supercloud event, after this short break. I'm John Furrier, host of "The Cube." Thanks for watching. (upbeat music)

>> From theCUBE Studios in Palo Alto and Boston bringing you data driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> After a two year hiatus, AWS re:Inforce is back on as an in-person event in Boston next week. Like the All-Star break in baseball, re:Inforce gives us an opportunity to evaluate the cyber security market overall, the state of cloud security and cross cloud security and more specifically what AWS is up to in the sector. Welcome to this week's Wikibon cube insights powered by ETR. In this Breaking Analysis we'll share our view of what's changed since our last cyber update in May. We'll look at the macro environment, how it's impacting cyber security plays in the market, what the ETR data tells us and what to expect at next week's AWS re:Inforce. We start this week with a checkpoint from Breaking Analysis contributor and stock trader Chip Simonton. We asked for his assessment of the market generally in cyber stocks specifically. So we'll summarize right here. We've kind of moved on from a narrative of the sky is falling to one where the glass is half empty you know, and before today's big selloff it was looking more and more like glass half full. The SNAP miss has dragged down many of the big names that comprise the major indices. You know, earning season as always brings heightened interest and this time we're seeing many cross currents. It starts as usual with the banks and the money centers. With the exception of JP Morgan the numbers were pretty good according to Simonton. Investment banks were not so great with Morgan and Goldman missing estimates but in general, pretty positive outlooks. But the market also shrugged off IBM's growth. And of course, social media because of SNAP is getting hammered today. The question is no longer recession or not but rather how deep the recession will be. And today's PMI data was the weakest since the start of the pandemic. Bond yields continue to weaken and there's a growing consensus that Fed tightening may be over after September as commodity prices weaken. Now gas prices of course are still high but they've come down. Tesla, Nokia and AT&T all indicated that supply issues were getting better which is also going to help with inflation. So it's no shock that the NASDAQ has done pretty well as beaten down as tech stocks started to look oversold you know, despite today's sell off. But AT&T and Verizon, they blamed their misses in part on people not paying their bills on time. SNAP's huge miss even after guiding lower and then refusing to offer future guidance took that stock down nearly 40% today and other social media stocks are off on sympathy. Meta and Google were off, you know, over 7% at midday. I think at one point hit 14% down and Google, Meta and Twitter have all said they're freezing new hires. So we're starting to see according to Simonton for the first time in a long time, the lower income, younger generation really feeling the pinch of inflation. Along of course with struggling families that have to choose food and shelter over discretionary spend. Now back to the NASDAQ for a moment. As we've been reporting back in mid-June and NASDAQ was off nearly 33% year to date and has since rallied. It's now down about 25% year to date as of midday today. But as I say, it had been, you know much deeper back in early June. But it's broken that downward trend that we talked about where the highs are actually lower and the lows are lower. That's started to change for now anyway. We'll see if it holds. But chip stocks, software stocks, and of course the cyber names have broken those down trends and have been trading above their 50 day moving averages for the first time in around four months. And again, according to Simonton, we'll see if that holds. If it does, that's a positive sign. Now remember on June 24th, we recorded a Breaking Analysis and talked about Qualcomm trading at a 12 X multiple with an implied 15% growth rate. On that day the stock was 124 and it surpassed 155 earlier this month. That was a really good call by Simonton. So looking at some of the cyber players here SailPoint is of course the anomaly with the Thoma Bravo 7 billion acquisition of the company holding that stock up. But the Bug ETF of basket of cyber stocks has definitely improved. When we last reported on cyber in May, CrowdStrike was off 23% year to date. It's now off 4%. Palo Alto has held steadily. Okta is still underperforming its peers as it works through the fallout from the breach and the ingestion of its Auth0 acquisition. Meanwhile, Zscaler and SentinelOne, those high flyers are still well off year to date, with Ping Identity and CyberArk not getting hit as hard as their valuations hadn't run up as much. But virtually all these tech stocks generally in cyber issues specifically, they've been breaking their down trend. So it will now come down to earnings guidance in the coming months. But the SNAP reaction is quite stunning. I mean, the environment is slowing, we know that. Ad spending gets cut in that type of market, we know that too. So it shouldn't be a huge surprise to anyone but as Chip Simonton says, this shows that sellers are still in control here. So it's going to take a little while to work through that despite the positive signs that we're seeing. Okay. We also turned to our friend Eric Bradley from ETR who follows these markets quite closely. He frequently interviews CISOs on his program, on his round tables. So we asked to get his take and here's what ETR is saying. Again, as we've reported while CIOs and IT buyers have tempered spending expectations since December and early January when they called for an 8% plus spending growth, they're still expecting a six to seven percent uptick in spend this year. So that's pretty good. Security remains the number one priority and also is the highest ranked sector in the ETR data set when you measure in terms of pervasiveness in the study. Within security endpoint detection and extended detection and response along with identity and privileged account management are the sub-sectors with the most spending velocity. And when you exclude Microsoft which is just dominant across the board in so many sectors, CrowdStrike has taken over the number one spot in terms of spending momentum in ETR surveys with CyberArk and Tanium showing very strong as well. Okta has seen a big dropoff in net score from 54% last survey to 45% in July as customers maybe put a pause on new Okta adoptions. That clearly shows in the survey. We'll talk about that in a moment. Look Okta still elevated in terms of spending momentum, but it doesn't have the dominant leadership position it once held in spend velocity. Year on year, according to ETR, Tenable and Elastic are seeing the biggest jumps in spending momentum, with SailPoint, Tanium, Veronis, CrowdStrike and Zscaler seeing the biggest jump in new adoptions since the last survey. Now on the downside, SonicWall, Symantec, Trellic which is McAfee, Barracuda and TrendMicro are seeing the highest percentage of defections and replacements. Let's take a deeper look at what the ETR data tells us about the cybersecurity space. This is a popular view that we like to share with net score or spending momentum on the Y axis and overlap or pervasiveness in the data on the X axis. It's a measure of presence in the data set we used to call it market share. With the data, the dot positions, you see that little inserted table, that's how the dots are plotted. And it's important to note that this data is filtered for firms with at least 100 Ns in the survey. That's why some of the other ones that we mentioned might have dropped off. The red dotted line at 40% that indicates highly elevated spending momentum and there are several firms above that mark including of course, Microsoft, which is literally off the charts in both dimensions in the upper right. It's quite incredible actually. But for the rest of the pack, CrowdStrike has now taken back its number one net score position in the ETR survey. And CyberArk and Okta and Zscaler, CloudFlare and Auth0 now Okta through the acquisition, are all above the 40% mark. You can stare at the data at your leisure but I'll just point out, make three quick points. First Palo Alto continues to impress and as steady as she goes. Two, it's a very crowded market still and it's complicated space. And three there's lots of spending in different pockets. This market has too many tools and will continue to consolidate. Now I'd like to drill into a couple of firms net scores and pick out some of the pure plays that are leading the way. This series of charts shows the net score or spending velocity or granularity for Okta, CrowdStrike, Zscaler and CyberArk. Four of the top pure plays in the ETR survey that also have over a hundred responses. Now the colors represent the following. Bright red is defections. We're leaving the platform. The pink is we're spending less, meaning we're spending 6% or worse. The gray is flat spend plus or minus 5%. The forest green is spending more, i.e, 6% or more and the lime green is we're adding the platform new. That red dotted line at the 40% net score mark is the same elevated level that we like to talk about. All four are above that target. Now that blue line you see there is net score. The yellow line is pervasiveness in the data. The data shown in each bar goes back 10 surveys all the way back to January 2020. First I want to call out that all four again are seeing down trends in spending momentum with the whole market. That's that blue line. They're seeing that this quarter, again, the market is off overall. Everybody is kind of seeing that down trend for the most part. Very few exceptions. Okta is being hurt by fewer new additions which is why we highlighted in red, that red dotted area, that square that we put there in the upper right of that Okta bar. That lime green, new ads are off as well. And the gray for Okta, flat spending is noticeably up. So it feels like people are pausing a bit and taking a breather for Okta. And as we said earlier, perhaps with the breach earlier this year and the ingestion of Auth0 acquisition the company is seeing some friction in its business. Now, having said that, you can see Okta's yellow line or presence in the data set, continues to grow. So it's a good proxy from market presence. So Okta remains a leader in identity. So again, I'll let you stare at the data if you want at your leisure, but despite some concerns on declining momentum, notice this very little red at these companies when it comes to the ETR survey data. Now one more data slide which brings us to our four star cyber firms. We started a tradition a few years ago where we sorted the ETR data by net score. That's the left hand side of this graphic. And we sorted by shared end or presence in the data set. That's the right hand side. And again, we filtered by companies with at least 100 N and oh, by the way we've excluded Microsoft just to level the playing field. The red dotted line signifies the top 10. If a company cracks the top 10 in both spending momentum and presence, we give them four stars. So Palo Alto, CrowdStrike, Okta, Fortinet and Zscaler all made the cut this time. Now, as we pointed out in May if you combined Auth0 with Okta, they jumped to the number two on the right hand chart in terms of presence. And they would lead the pure plays there although it would bring down Okta's net score somewhat, as you can see, Auth0's net score is lower than Okta's. So when you combine them it would drag that down a little bit but it would give them bigger presence in the data set. Now, the other point we'll make is that Proofpoint and Splunk both dropped off the four star list this time as they both saw marked declines in net score or spending velocity. They both got four stars last quarter. Okay. We're going to close on what to expect at re:Inforce this coming week. Re:Inforce, if you don't know, is AWS's security event. They first held it in Boston back in 2019. It's dedicated to cloud security. The past two years has been virtual and they announced that reinvent that it would take place in Houston in June, which everybody said, that's crazy. Who wants to go to Houston in June and turns out nobody did so they postponed the event, thankfully. And so now they're back in Boston, starting on Monday. Not that it's going to be much cooler in Boston. Anyway, Steven Schmidt had been the face of AWS security at all these previous events as the Chief Information Security Officer. Now he's dropped the I from his title and is now the Chief Security Officer at Amazon. So he went with Jesse to the mothership. Presumably he dropped the I because he deals with physical security now too, like at the warehouses. Not that he didn't have to worry about physical security at the AWS data centers. I don't know. Anyway, he and CJ Moses who is now the new CISO at AWS will be keynoting along with some others including MongoDB's Chief Information Security Officer. So that should be interesting. Now, if you've been following AWS you'll know they like to break things down into, you know, a couple of security categories. Identity, detection and response, data protection slash privacy slash GRC which is governance, risk and compliance, and we would expect a lot more talk this year on container security. So you're going to hear also product updates and they like to talk about how they're adding value to services and try to help, they try to help customers understand how to apply services. Things like GuardDuty, which is their threat detection that has machine learning in it. They'll talk about Security Hub, which centralizes views and alerts and automates security checks. They have a service called Detective which does root cause analysis, and they have tools to mitigate denial of service attacks. And they'll talk about security in Nitro which isolates a lot of the hardware resources. This whole idea of, you know, confidential computing which is, you know, AWS will point out it's kind of become a buzzword. They take it really seriously. I think others do as well, like Arm. We've talked about that on previous Breaking Analysis. And again, you're going to hear something on container security because it's the hottest thing going right now and because AWS really still serves developers and really that's what they're trying to do. They're trying to enable developers to design security in but you're also going to hear a lot of best practice advice from AWS i.e, they'll share the AWS dogfooding playbooks with you for their own security practices. AWS like all good security practitioners, understand that the keys to a successful security strategy and implementation don't start with the technology, rather they're about the methods and practices that you apply to solve security threats and a top to bottom cultural approach to security awareness, designing security into systems, that's really where the developers come in, and training for continuous improvements. So you're going to get heavy doses of really strong best practices and guidance and you know, some good preaching. You're also going to hear and see a lot of partners. They'll be very visible at re:Inforce. AWS is all about ecosystem enablement and AWS is going to host close to a hundred security partners at the event. This is key because AWS doesn't do it all. Interestingly, they don't even show up in the ETR security taxonomy, right? They just sort of imply that it's built in there even though they have a lot of security tooling. So they have to apply the shared responsibility model not only with customers but partners as well. They need an ecosystem to fill gaps and provide deeper problem solving with more mature and deeper security tooling. And you're going to hear a lot of positivity around how great cloud security is and how it can be done well. But the truth is this stuff is still incredibly complicated and challenging for CISOs and practitioners who are understaffed when it comes to top talent. Now, finally, theCUBE will be at re:Inforce in force. John Furry and I will be hosting two days of broadcast so please do stop by if you're in Boston and say hello. We'll have a little chat, we'll share some data and we'll share our overall impressions of the event, the market, what we're seeing, what we're learning, what we're worried about in this dynamic space. Okay. That's it for today. Thanks for watching. Thanks to Alex Myerson, who is on production and manages the podcast. Kristin Martin and Cheryl Knight, they helped get the word out on social and in our newsletters and Rob Hoff is our Editor in Chief over at siliconangle.com. You did some great editing. Thank you all. Remember all these episodes they're available, this podcast. Wherever you listen, all you do is search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com. You can get in touch with me by emailing avid.vellante@siliconangle.com or DM me @dvellante, or comment on my LinkedIn post and please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching and we'll see you in Boston next week if you're there or next time on Breaking Analysis (soft music)

(calm electronic music) >> Okay, welcome back everyone to theCUBE's coverage here live in New York City for AWS Summit 2022. I'm John Furrier, host of theCUBE, but a great conversation here as the day winds down. First of all, 10,000 plus people, this is a big event, just New York City. So sign of the times that some headwinds are happening? I don't think so, not in the cloud enterprise innovation game. Lot going on, this innovation conversation we're going to have now is about the confluence of cloud scale integration data and the future of how FinTech and other markets are going to change with technology. We got Chris Thomas, the CTO of Slalom, and Rob Krugman, chief digital officer at Broadridge. Gentlemen, thanks for coming on theCUBE. >> Thanks for having us. >> So we had a talk before we came on camera about your firm, what you guys do, take a quick minute to just give the scope and size of your firm and what you guys work on. >> Yeah, so Broadridge is a global financial FinTech company. We work on, part of our business is capital markets and wealth, and that's about a third of our business, about $7 trillion a day clearing through our platforms. And then the other side of our business is communications where we help all different types of organizations communicate with their shareholders, communicate with their customers across a variety of different digital channels and capabilities. >> Yeah, and Slalom, give a quick one minute on Slalom. I know you guys, but for the folks that don't know you. >> Yeah, no problem. So Slalom is a modern consulting firm focused on strategy, technology, and business transformation. And me personally, I'm part of the element lab, which is focused on forward thinking technology and disruptive technology in the next five to 10 years. >> Awesome, and that's the scope of this conversation. The next five to 10 years, you guys are working on a project together, you're kind of customer partners. You're building something. What are you guys working on? I can't wait to jump into it, explain. >> Sure, so similar to Chris, at Broadridge, we've created innovation capability, innovation incubation capability, and one of the first areas we're experimenting in is digital assets. So what we're looking to do is we're looking at a variety of different areas where we think consolidation network effects that we could bring can add a significant amount of value. And so the area we're working on is this concept of a wallet of wallets. How do we actually consolidate assets that are held across a variety of different wallets, maybe traditional locations- >> Digital wallets. >> Digital wallets, but maybe even traditional accounts, bring that together and then give control back to the consumer of who they want to share that information with, how they want their transactions to be able to control. So the idea of, people talk about Web 3 being the internet of value. I often think about it as the internet of control. How do you return control back to the individual so that they can make decisions about how and who has access to their information and assets? >> It's interesting, I totally like the value angle, but your point is what's the chicken and the egg here, the cart before the horse, you can look at it both ways and say, okay, control is going to drive the value. This is an interesting nuance, right? >> Yes, absolutely. >> So in this architectural world, they thought about the data plane and the control plane. Everyone's trying to go old school, middleware thinking. Let's own the data plane, we'll win everything. Not going to happen if it goes decentralized, right, Chris? >> Yeah, yeah. I mean, we're building a decentralized application, but it really is built on top of AWS. We have a serverless architecture that scales as our business scales built on top of things like S3, Lambda, DynamoDB, and of course using those security principles like Cognito and AWS Gateway, API Gateway. So we're really building an architecture of Web 3 on top of the Web 2 basics in the cloud. >> I mean, all evolutions are abstractions on top of each other, IG, DNS, Key, it goes the whole nine yards. In digital, at least, that's the way. Question about serverless real quick. I saw that Redshift just launched general availability of serverless in Redshift? >> Yes. >> You're starting to see the serverless now part of almost all the services in AWS. Is that enabling that abstraction, because most people don't see it that way. They go, oh, well, Amazon's not Web 3. They got databases, you could use that stuff. So how do you connect the dots and cross the bridge to the future with the idea that I might not think Web 2 or cloud is Web 3? >> I'll jump in quick. I mean, I think it's the decentralize. If you think about decentralization. serverless and decentralization, you could argue are the same way of, they're saying the same thing in different ways. One is thinking about it from a technology perspective. One is thinking about it from an ecosystem perspective and how things come together. You need serverless components that can talk to each other and communicate with each other to actually really reach the promise of what Web 3 is supposed to be. >> So digital bits or digital assets, I call it digital bits, 'cause I think zero ones. If you digitize everything and everything has value or now control drives the value. I could be a soccer team. I have apparel, I have value in my logos, I have photos, I have CUBE videos. I mean some say that this should be an NFT. Yeah, right, maybe, but digital assets have to be protected, but owned. So ownership drives it too, right? >> Absolutely. >> So how does that fit in, how do you explain that? 'Cause I'm trying to tie the dots here, connect the dots and tie it together. What do I get if I go down this road that you guys are building? >> So I think one of the challenges of digital assets right now is that it's a closed community. And I think the people that play in it, they're really into it. And so you look at things like NFTs and you look at some of the other activities that are happening and there are certain naysayers that look at it and say, this stuff is not based upon value. It's a bunch of artwork, it can't be worth this. Well, how about we do a time out there and we actually look at the underlying technology that's supporting this, the blockchain, and the potential ramifications of that across the entire financial ecosystem, and frankly, all different types of ecosystems of having this immutable record, where information gets stored and gets sent and the ability to go back to it at all times, that's where the real power is. So I think we're starting to see. We've hit a bit of a hiccup, if you will, in the cryptocurrencies. They're going to continue to be there. They won't all be there. A lot of them will probably disappear, but they'll be a finite number. >> What percentage of stuff do you think is vapor BS? If you had to pick an order of magnitude number. >> (laughs) I would say at least 75% of it. (John laughs) >> I mean, there's quite a few projects that are failing right now, but it's interesting in that in the crypto markets, they're failing gracefully. Because it's on the blockchain and it's all very transparent. Things are checked, you know immediately which companies are insolvent and which opportunities are still working. So it's very, very interesting in my opinion. >> Well, and I think the ones that don't have valid premises are the ones that are failing. Like Terra and some of these other ones, if you actually really looked at it, the entire industry knew these things were no good. But then you look at stable coins. And you look at what's going on with CBDCs. These are backed by real underlying assets that people can be comfortable with. And there's not a question of, is this going to happen? The question is, how quickly is it going to happen and how quickly are we going to be using digital currencies? >> It's interesting, we always talk about software, software as money now, money is software and gold and oil's moving over to that crypto. How do you guys see software? 'Cause we were just arguing in the queue, Dave Vellante and I, before you guys came on that the software industry pretty much does not exist anymore, it's open source. So everything's open source as an industry, but the value is integration, innovation. So it's not just software, it's the free. So you got to, it's integration. So how do you guys see this software driving crypto? Because it is software defined money at the end of the day. It's a token. >> No, I think that's absolutely one of the strengths of the crypto markets and the Web 3 market is it's governed by software. And because of that, you can build a trust framework. Everybody knows it's on the public blockchain. Everybody's aware of the software that's driving the rules and the rules of engagement in this blockchain. And it creates that trust network that says, hey, I can transact with you even though I don't know anything about you and I don't need a middleman to tell me I can trust you. Because this software drives that trust framework. >> Lot of disruption, lot of companies go out of business as a middleman in these markets. >> Listen, the intermediaries either have to disrupt themselves or they will be disrupted. I think that's what we're going to learn here. And it's going to start in financial services, but it's going to go to a lot of different places. I think the interesting thing that's happening now is for the first time, you're starting to see the regulators start to get involved. Which is actually a really good thing for the market. Because to Chris's point, transparency is here, how do you actually present that transparency and that trust back to consumers so they feel comfortable once that problem is solved. And I think everyone in the industry welcomes it. All of a sudden you have this ecosystem that people can play in, they can build and they can start to actually create real value. >> Every structural change that I've been involved in my 30 plus year career has been around inflection points. There was always some sort of underbelly. So I'm not going to judge crypto. It's been in the market for a while, but it's a good sign there's innovation happening. So as now, clarity comes into what's real. I think you guys are talking a conversation I think is refreshing because you're saying, okay, cloud is real, Lambda, serverless, all these tools. So Web 3 is certainly real because it's a future architecture, but it's attracting the young, it's a cultural shift. And it's also cooler than boring Web 2 and cloud. So I think the cultural shift, the fact that it's got data involved, there's some disruption around middleman and intermediaries, makes it very attractive to tech geeks. You look at, I read a stat, I heard a stat from a friend in the Bay Area that 30% of Cal computer science students are dropping out and jumping into crypto. So it's attracting the technical nerds, alpha geeks. It's a cultural revolution and there's some cool stuff going on from a business model standpoint. >> There's one thing missing. The thing that's missing, it's what we're trying to work on, I think is experience. I think if you're being honest about the entire marketplace, what you would agree is that this stuff is not easy to use today, and that's got to be satisfied. You need to do something that if it's the 85 year old grandma that wants to actually participate in these markets that not only can they feel comfortable, but they actually know how to do it. You can't use these crazy tools where you use these terms. And I think the industry, as it grows up, will satisfy a lot of those issues. >> And I think this is why I want to tie back and get your reaction to this. I think that's why you guys talking about building on top of AWS is refreshing, 'cause it's not dogmatic. Well, we can't use Amazon, it's not really Web 3. Well, a database could be used when you need it. You don't need to write everything through the blockchain. Databases are a very valuable capability, you get serverless. So all these things now can work together. So what do you guys see for companies that want to be Web 3 for all the good reasons and how do they leverage cloud specifically to get there? What are some things that you guys have learned that you can point to and share, you want to start? >> Well, I think not everything has to be open and public to everybody. You're going to want to have some things that are secret. You're going to want to encrypt some things. You're going to want to put some things within your own walls. And that's where AWS really excels. I think you can have the best of both worlds. So that's my perspective on it. >> The only thing I would add to it, so my view is it's 2022. I actually was joking earlier. I think I was at the first re:Invent. And I remember walking in and this was a new industry. >> It was tiny. >> This is foundational. Like cloud is not a, I don't view like, we shouldn't be having that conversation anymore. Of course you should build this stuff on top of the cloud. Of course you should build it on top of AWS. It just makes sense. And we should, instead of worrying about those challenges, what we should be worrying about are how do we make these applications easier to use? How do we actually- >> Energy efficient. >> How do we enable the promise of what these things are going to bring, and actually make it real, because if it happens, think about traditional assets. There's projects going on globally that are looking at how do you take equity securities and actually move them to the blockchain. When that stuff happens, boom. >> And I like what you guys are doing, I saw the news out through this crypto winter, some major wallet exchanges that have been advertising are hurting. Take me through what you guys are thinking, what the vision is around the wallet of wallets. Is it to provide an experience for the user or the market industry itself? What's the target, is it both? Share the design goals for the wallet of wallets. >> My favorite thing about innovation and innovation labs is that we can experiment. So I'll go in saying we don't know what the final answer is going to be, but this is the premise that we have. In this disparate decentralized ecosystem, you need some mechanism to be able to control what's actually happening at the consumer level. So I think the key target is how do you create an experience where the consumer feels like they're in control of that value? How do they actually control the underlying assets? And then how does it actually get delivered to them? Is it something that comes from their bank, from their broker? Is it coming from an independent organization? How do they manage all of that information? And I think the last part of it are the assets. It's easy to think about cryptos and NFTs, but thinking about traditional assets, thinking about identity information and healthcare records, all of that stuff is going to become part of this ecosystem. And imagine being able to go someplace and saying, oh, you need my information. Well, I'm going to give it to you off my phone and I'm going to give it to you for the next 24 hours so you can use it, but after that you have no access to it. Or you're my financial advisor, here's a view of what I actually have, my underlying assets. What do you recommend I do? So I think we're going to see an evolution in the market. >> Like a data clean room. >> Yeah, but that you control. >> Yes! (laughs) >> Yes! >> I think about it very similarly as well. As my journey into the crypto market has gone through different pathways, different avenues. And I've come to a place where I'm really managing eight different wallets and it's difficult to figure exactly where all my assets are and having a tool like this will allow me to visualize and aggregate those assets and maybe even recombine them in unique ways, I think is hugely valuable. >> My biggest fear is losing my key. >> Well, and that's an experience problem that has to be solved, but let me give you, my favorite use case in this space is, 'cause NFTs, right? People are like, what does NFTs really mean? Title insurance, right? Anyone buy a house or refinance your mortgage? You go through this crazy process that costs seven or eight thousand dollars every single time you close on something to get title insurance so they could validate it. What if that title was actually sitting on the chain, you got an NFT that you put in your wallet and when it goes time to sell your house or to refinance, everything's there. Okay, I'm the owner of the house. I don't know, JP Morgan Chase has the actual mortgage. There's another lien, there's some taxes. >> It's like a link tree in the wallet. (laughs) >> Yeah, think about it, you got a smart contract. Boom, closing happens immediately. >> I think that's one of the most important things. I think people look at NFTs and they think, oh, this is art. And that's sort of how it started in the art and collectable space, but it's actually quickly moving towards utilities and tokenization and passes. And that's where I think the value is. >> And ownership and the token. >> Identity and ownership, especially. >> And the digital rights ownership and the economics behind it really have a lot of scale 'cause I appreciate the FinTech angle you are coming from because I can now see what's going on here with you. It's like, okay, we got to start somewhere. Let's start with the experience. The wallet's a tough nut to crack, 'cause that requires defacto participation in the industry as a defacto standard. So how are you guys doing there? Can you give an update and then how can people get, what's the project called and how do people get involved? >> Yeah, so we're still in the innovation, incubation stages. So we're not launching it yet. But what I will tell you is what a lot of our focus is, how do we make these transactional things that you do? How do we make it easy to pull all your assets together? How do we make it easy to move things from one location to the other location in ways that you're not using a weird cryptographic numeric value for your wallet, but you actually can use real nomenclature that you can renumber and it's easy to understand. Our expectation is that sometime in the fall, we'll actually be in a position to launch this. What we're going to do over the summer is we're going to start allowing people to play with it, get their feedback, and we're going to iterate. >> So sandbox in when, November? >> I think launch in the fall, sometime in the fall. >> Oh, this fall. >> But over the summer, what we're expecting is some type of friends and family type release where we can start to realize what people are doing and then fix the challenges, see if we're on the right track and make the appropriate corrections. >> So right now you guys are just together on this? >> Yep. >> The opening up friends and family or community is going to be controlled. >> It is, yeah. >> Yeah, as a group, I think one thing that's really important to highlight is that we're an innovation lab. We're working with Broadridge's innovation lab, that partnership across innovation labs has allowed us to move very, very quickly to build this. Actually, if you think about it, we were talking about this not too long ago and we're almost close to having an internal launch. So I think it's very rapid development. We follow a lot of the- >> There's buy-in across the board. >> Exactly, exactly, and we saw lot of very- >> So who's going to run this? A Dow, or your companies, is it going to be a separate company? >> So to be honest, we're not entirely sure yet. It's a new product that we're going to be creating. What we actually do with it. Our thought is within an innovation environment, there's three things you could do with something. You can make it a product within the existing infrastructure, you can create a new business unit or you can spin it off as something new. I do think this becomes a product within the organization based upon it's so aligned to what we do today, but we'll see. >> But you guys are financing it? >> Yes. >> As collective companies? >> Yeah, right. >> Got it, okay, cool. Well, let us know how we can help. If you guys want to do a remote in to theCUBE. I would love the mission you guys are on. I think this is the kind of work that every company should be doing in the new R and D. You got to jump in the deep end and swim as fast as possible. But I think you can do it. I think that is refreshing and that's smart. >> And you have to do it quick because this market, I think the one thing we would probably agree on is that it's moving faster than we could, every week there's something else that happens. >> Okay, so now you guys were at Consensus down in Austin when the winter hit and you've been in the business for a long time, you got to know the industries. You see where it's going. What was the big thing you guys learned, any scar tissue from the early data coming in from the collaboration? Was there some aha moments, was there some oh shoot moments? Oh, wow, I didn't think that was going to happen. Share some anecdotal stories from the experience. Good, bad, and if you want to be bold say ugly, too. >> Well, I think the first thing I want to say about the timing, it is the crypto winter, but I actually think now's a really great time to build something because everybody's continuing to build. Folks are focused on the future and that's what we are as well. In terms of some of the challenges, well, the Web 3 space is so new. And there's not a way to just go online and copy somebody else's work and rinse and repeat. We had to figure a lot of things on our own. We had to try different technologies, see which worked better and make sure that it was functioning the way we wanted it to function. Really, so it was not easy. >> They oversold that product out, that's good, like this team. >> But think about it, so the joke is that when winter is when real work happens. If you look at the companies that have not been affected by this it's the infrastructure companies and what it reminds me of, it's a little bit different, but 2001, we had the dot com bust. The entire industry blew up, but what came out of that? >> Everything that exists. >> Amazon, lots of companies grew up out of that environment. >> Everything that was promoted actually happened. >> Yes, but you know what didn't happen- >> Food delivery. >> But you know what's interesting that didn't happen- >> (laughs) Pet food, the soccer never happened. >> The whole Super Bowl, yes. (John laughs) In financial services we built on top of legacy. I think what Web 3 is doing, it's getting rid of that legacy infrastructure. And the banks are going to be involved. There's going to be new players and stuff. But what I'm seeing now is a doubling down of the infrastructure investment of saying okay, how do we actually make this stuff real so we can actually show the promise? >> One of the things I just shared, Rob, you'd appreciate this, is that the digital advertising market's changing because now banner ads and the old techniques are based on Web 2 infrastructure, basically DNS as we know it. And token problems are everywhere. Sites and silos are built because LinkedIn doesn't share information. And the sites want first party data. It's a hoarding exercise, so those practices are going to get decimated. So in comes token economics, that's going to get decimated. So you're already seeing the decline of media. And advertising, cookies are going away. >> I think it's going to change, it's going to be a flip, because I think right now you're not in control. Other people are in control. And I think with tokenomics and some of the other things that are going to happen, it gives back control to the individual. Think about it, right now you get advertising. Now you didn't say I wanted this advertising. Imagine the value of advertising when you say, you know what, I am interested in getting information about this particular type of product. The lead generation, the value of that advertising is significantly higher. >> Organic notifications. >> Yeah. >> Well, gentlemen, I'd love to follow up with you. I'm definitely going to ping in. Now I'm going to put CUBE coin back on the table. For our audience CUBE coin's coming. Really appreciate it, thanks for sharing your insights. Great conversation. >> Excellent, thank you for having us. >> Excellent, thank you so much. >> theCUBE's coverage here from New York City. I'm John Furrier, we'll be back with more live coverage to close out the day. Stay with us, we'll be right back. >> Excellent. (calm electronic music)

(Upbeat music) >> Hey everyone. Welcome back to theCUBE's continuing coverage day two Snowflake Summit '22 live from Caesar's forum in Las Vegas. Lisa Martin here with Dave Vellante, bringing you wall to wall coverage yesterday, today, and tomorrow. We're excited to welcome Matthew Carroll to the program. The CEO of Immuta, we're going to be talking about removing barriers to secure data access security. Matthew, welcome. >> Thank you for having me, appreciate it. >> Talk to the audience a little bit about Immuta you're a Snowflake premier technology partner, but give him an overview of Immuta what you guys do, your vision, all that good stuff. >> Yeah, absolutely, thanks. Yeah, if you think about what Immunta at it's core is, we're a data security platform for the modern data stack, right? So what does that mean? It means that we embed natively into a Snowflake and we enforce policies on data, right? So, the rules to be able to use it, to accelerate data access, right? So, that means connecting to the data very easily controlling it with any regulatory or security policy on it as well as contractual policies, and then being able to audit it. So, that way, any corporation of any size can leverage their data and share that data without risking leaking it or potentially violating a regulation. >> What are some of the key as we look at industry by industry challenges that Immuta is helping those customers address and obviously quickly since everything is accelerating. >> Yeah. And it's, you're seeing it 'cause the big guys like Snowflake are verticalizing, right? You're seeing a lot of industry specific, you know, concepts. With us, if you think of, like, where we live obviously policies on data regulated, right? So healthcare, how do we automate HIPAA compliance? How do we redesign clinical trial management post COVID, right? If you're going to have billions of users and you're collecting that data, pharmaceutical companies can't wait to collect that data. They need to remove those barriers. So, they need to be able to collect it, secure it, and be able to share it. Right? So, double and triple blinded studies being redesigned in the cloud. Government organizations, how do we share security information globally with different countries instantaneously? Right? So these are some of the examples where we're helping organizations transform and be able to kind of accelerate their adoption of data. >> Matt, I don't know if you remember, I mean, I know you remember coming to our office. But we had an interesting conversation and I was telling Lisa. Years ago I wrote a piece of you know, how to build on top of, AWS. You know, there's so much opportunity. And we had a conversation, at our office, theCUBE studios in Marlborough, Massachusetts. And we both, sort of, agreed that there was this new workload emerging. We said, okay, there's AWS, there's Snowflake at the time, we were thinking, and you bring machine learning, at time where we were using data bricks, >> Yeah. >> As the example, of course now it's been a little bit- >> Yeah. Careful. >> More of a battle, right, with those guys. But, and so, you see them going in their different directions, but the premise stands is that there's an ecosystem developing, new workloads developing, on top of the hyper scale infrastructure. And you guys play a part in that. So, describe what you're seeing there 'cause you were right on in that conversation. >> Yeah. Yeah. >> It's nice to be, right. >> Yeah. So when you think of this design pattern, right, is you have a data lake, you have a warehouse, and you have an exchange, right? And this architecture is what you're seeing around you now, is this is every single organization in the world is adopting this design pattern. The challenge that where we fit into kind of a sliver of this is, the way we used to do before is application design, right? And we would build lots of applications, and we would build all of our business logic to enforce security controls and policies inside each app. And you'd go through security and get it approved. In this paradigm, any user could potentially access any data. There's just too many data sources, too many users, and too many things that can go wrong. And to scale that is really hard. So, like, with Immuta, what we've done, versus what everyone else has done is we natively embedded into every single one of those compute partners. So ,Snowflake, data breaks, big query, Redshift, synapse on and on. Natively underneath the covers, so that was BI tools, those data science tools hit Snowflake. They don't have to rewrite any of their code, but we automatically enforce policy without them having to do anything. And then we consistently audit that. I call that the separation of policy from platform. So, just like in the world in big data, when we had to separate compute from storage, in this world, because we're global, right? So we're, we have a distributed workforce and our data needs to abide by all these new security rules and regulations. We provide a flexible framework for them to be able to operate at that scale. And we're the only ones in the world doing it. >> Dave Vellante: See the key there is, I mean, Snowflake is obviously building out its data cloud and the functions that it's building in are quite impressive. >> Yeah. >> Dave Vellante: But you know at some point a customer's going to say, look I have other stuff, whether it's in an Oracle database, or data lake or wherever, and that should just be a node on this global, whatever you want to call it, mesh or fabric. And then if I'm hearing you right, you participate in all of that. >> Correct? Yeah We kind of, we were able to just natively inject into each, and then be able to enforce that policy consistently, right? So, hey, can you access HIPAA data? Who are you? Are you authorized to use this? What's the purpose you want to query this data? Is it for fraud? Is it for marketing? So, what we're trying to do as part of this new design paradigm is ensure that we can automate nearly the entire data access process, but with the confidence and de-risk it, that's kind of the key thing. But the one thing I will mention is I think we talk a lot about the core compute, but I think, especially at this summit, data sharing is everything. Right? And this concept of no copy data sharing, because the data is too big and there's too many sets to share, that's the keys to the kingdom. You got to get your lake and your warehouse set with good policy, so you can effectively share it. >> Yeah, so, I wanted to just to follow up, if I may. So, you'd mentioned separating compute from storage and a lot of VC money poured into that. A lot of VC money poured into cloud database. How do you see, do you see Snowflake differentiating substantially from all the other cloud databases? And how so? >> I think it's the ease of use, right? Apple produces a phone that isn't much different than other competitors. Right? But what they do is, end to end, they provide an experience that's very simple. Right? And so yes. Are there other warehouses? Are there other ways to, you know you heard about their analytic workloads now, you know through unistore, where they're going to be able to process analytical workloads as well as their ad hoc queries. I think other vendors are obviously going to have the same capabilities, but I think the user experience of Snowflake right now is top tier. Right? Is I can, whether I'm a small business, I can load my debt in there and build an app really quickly. Or if I'm a JP Morgan or, you know, a West Farmer's I can move legacy, you know monolithic architectures in there in months. I mean, these are six months transitions. When think about 20 years of work is now being transitioned to the cloud in six months. That's the difference. >> So measuring ease of views and time to value, time to market. >> Yeah. That's it's everything is time to value. No one wants to manage the infrastructure. In the Hudup world, no one wants to have expensive customized engineers that are, you know, keeping up your Hudup infrastructure any longer. Those days are completely over. >> Can you share an example of a joint customer, where really the joint value proposition that Immuta and Snowflake bring, are delivering some pretty substantial outcomes? >> Yeah. I, what we're seeing is and we're obviously highly incentivized to get them in there because it's easier on us, right? Because we can leverage their row and com level security. We can leverage their features that they've built in to provide a better experience to our customers. And so when we talk about large banks, they're trying to move Terra data workloads into Snowflake. When we talk about clinical trial management, they're trying to get away from physical copies of data, and leverage the exchanges of mechanism, so you can manage data contracts, right? So like, you know, when we think of even like a company like Latch, right? Like Latch uses us to be able to oversee all of the consumer data they have. Without like a Snowflake, what ends up happening is they end up having to double down and invest on their own people building out all their own infrastructure. And they don't have the capital to invest in third party tools like us that keep them safe, prevent data leaks, allow them to do more and get more value out of their data, which is what they're good at. >> So TCO reduction I'm hearing. >> Matthew Carroll: Yes, exactly. >> Matt, where are you as a company, you've obviously made a lot of progress since we last talked. Maybe give us the update on you know, the headcount, and fundraising, and- >> Yeah, we're just at about 250 people, which scares me every day, but it's awesome. But yeah, we've just raised 100 million dollars- >> Lisa Martin: Saw that, congratulations. >> Series E, thank you, with night dragon leading it. And night dragon was very tactical as well. We are moving, we found that data governance, I think what you're seeing in the market now is the catalog players are really maturing, and they're starting to add a suite of features around governance, right? So quality control, observability, and just traditional asset management around their data. What we are finding is is that there's a new gap in this space, right? So if you think about legacy it's we had infrastructure security we had the four walls and we protect our four walls. Then we moved to network security. We said, oh, the adversary is inside zero trust. So, let's protect all of our endpoints, right? But now we're seeing is data is the security flaw data could be, anyone could potentially access it in this organization. So how do we protect data? And so what we have matured into is a data security company. What we have found is, there's this next generation of data security products that are missing. And it's this blend between authentication like an, an Okta or an AuthO and auth- I'm sorry, authorization. Like Immuta, where we're authorizing certain access. And we have to pair together, with the modern observability, like a data dog, to provide an a layer above this modern data stack, to protect the data to analyze the users, to look for threats. And so Immuta has transformed with this capital. And we brought Dave DeWalt onto our board because he's a cybersecurity expert, he gives us that understanding of what is it like to sell into this modern cyber environment. So now, we have this platform where we can discover data, analyze it, tag it, understand its risk, secure it to author and enforce policies. And then monitor, the key thing is monitoring. Who is using the data? Why are they using the data? What are the risks to that? In order to enforce the security. So, we are a data security platform now with this raise. >> Okay. That, well, that's a new, you know, vector for you guys. I always saw you as an adjacency, but you're saying smack dab in the heart >> Matthew Carroll: Yes. Yeah. We're jumping right in. What we've seen is there is a massive global gap. Data is no longer just in one country. So it is, how do we automate policy enforcement of regulatory oversight, like GDPR or CCPA, which I think got this whole category going. But then we quickly realized is, well we have data jurisdiction. So, where does that data have to live? Where can I send it to? Because from Europe to us, what's the export treaty? We don't have defined laws anymore. So we needed a flexible framework to handle that. And now what we're seeing is data leaks, upon data leaks, and you know, the Snowflakes and the other cloud compute vendors, the last thing they ever want is a data leak out of their ecosystem. So, the security aspects are now becoming more and more important. It's going to be an insider threat. It's someone that already has access to that and has the rights to it. That's going to be the risk. And there is no pattern for a data scientist. There's no zero trust model for data. So we have to create that. >> How are you, last question, how are you going to be using a 100 million raised in series E funding, which you mentioned, how are you going to be leveraging that investment to turn the volume up on data security? >> Well, and we still have also another 80 million still in the bank from our last raise, so 180 million now, and potentially more soon, we'll kind of throw that out there. But, the first thing is M and A I believe in a recessing market, we're going to see these platforms consolidate. Larger customer of ours are driving us to say, Hey, we need less tools. We need to make this easier. So we can go faster. They're, even in a recessing market, these customers are not going to go slower. They're moving in the cloud as fast as possible, but it needs to be easier, right? It's going back to the mid nineties kind of Lego blocks, right? Like the IBM, the SAP, the Informatica, right? So that's number one. Number two is investing globally. Customer success, engineering, support, 24 by seven support globally. Global infrastructure on cloud, moving to true SaaS everywhere in the world. That's where we're going. So sales, engineering, and customer success globally. And the third is, is doubling down on R and D. That monitor capability, we're going to be building software around. How do we monitor and understand risk of users, third parties. So how do you handle data contracts? How do you handle data use agreements? So those are three areas we're focused on. >> Dave Vellante: How are you scaling go to market at this point? I mean, I presume you are. >> Yeah, well, I think as we're leveraging these types of engagements, so like our partners are the big cloud compute vendors, right? Those data clouds. We're injecting as much as we can into them and helping them get more workloads onto their infrastructure because it benefits us. And then obviously we're working with GSIs and then RSIs to kind of help with this transformation, but we're all in, we're actually deprecating support of legacy connectors. And we're all in on cloud compute. >> How did the pivot to all in on security, how did it affect your product portfolio? I mean, is that more positioning or was there other product extensions that where you had to test product market fit? >> Yeah. This comes out of customer drive. So we've been holding customer advisory boards across Europe, Asia and U.S. And what we just saw was a pattern of some of these largest banks and pharmaceutical companies and insurance companies in the world was, hey we need to understand who is actually on our data. We have a better understanding of our data now, but we don't actually understand why they're using our data. Why are they running these types of queries? Is this machine, you know logic, that we're running on this now, we invested all this money in AI. What's the risk? They just don't know. And so, yeah, it's going to change our product portfolio. We modularized our platform to the street components over the past year, specifically now, so we can start building custom applications on top of it, for specific users like the CSO, like, you know, the legal department, and like third party regulators to come in, as well as as going back to data sharing, to build data use agreements between one or many entities, right? So an SMP global can expose their data to third parties and have one consistent digital contract, no more long memo that you have to read the contract, like, Immuta can automate those data contracts between one or many entities. >> Dave Vellante: And make it a checkbox item. >> It's just a checkbox, but then you can audit it all, right? >> The key thing is this, I always tell people, there's negligence and gross negligence. Negligence, you can go back and fix something, gross negligence you don't have anything to put into controls. Regulators want you to be at least negligent, grossly negligent. They get upset. (laughs) >> Matthew, it sounds like great stuff is going on at Immuta, lots of money in the bank. And it sounds like a very clear and strategic vision and direction. We thank you so much for joining us on theCUBE this morning. >> Thank you so much >> For our guest and Dave Vellante, I'm Lisa Martin, you're watching theCUBE's coverage of day two, Snowflake Summit '22, coming at ya live, from the show floor in Las Vegas. Be right back with our next guest. (Soft music)

(bright upbeat music) >> Hey everyone, welcome to theCUBE's AWS startup showcase. This is season two of the startup showcase, episode one. I'm your host, Lisa Martin. Pleased to be welcoming back one of our alumni, Alexis Richardson, the founder >> Hey. >> and CEO of Weaveworks. Alexis, welcome back to the program. >> Thank you so much, Lisa, I'm really happy to be here. Good to see you again. >> Likewise. So it's been a while since we've had Weaveworks on the program. Give the audience an overview of Weaveworks. You were founded in 2014, pioneering getopts, automating Kubernetes across all industries, but help us understand, unpack that a bit. >> Well, so my previous role was at Pivotal, where I was head of application platform and I was responsible for Spring and Vfabric, and some pieces of Cloud Foundry. And you may remember back in those days, everybody wanted to build like a Heroku, but for the enterprise. And so they were asking, how can we build more cloud services? And my team was involved in building out cloud services, but we were running into trouble with the technology that we had. And then when containers appeared, we thought this is the technology for us to roll out cloud services. So with some of my team, we decided to start a new company, Weaveworks, really intending to focus on developers. Because these new containers were pretty cool, but they were really complex operational centric tools, and enterprise developers need simplicity. That's what we'd learned from things like Spring. They want simplicity, productivity, velocity, all of that stuff, they don't want operational complexity. So Weaveworks' mission is to make applications easy for developers with containers. >> Talk to me about how you've accomplished that over the last seven years, and some of the things that you're doing to facilitate a DevOps practice within organizations across any industry? >> Yeah, well, our story is pretty interesting because of course in 2014, all of this was incredibly new. You couldn't even take two containers and put them together into a single application. So forget about enterprise. What we did was we built a network, which gave the company its name, Weave. But then we spent several years building out more and more pieces of the stack. We decided that we should go to market commercially because we're an open source company with a commercial SaaS. And we thought we would be like new Relic, that there'll be lots of customers in the cloud. And, therefore, they would need monitoring and management. And Weave started writing a SaaS based on Kubernetes, which was what we chose as our platform, back in the day, very, very, very early. We were one of the very first companies to start running Kubernetes in production other than Google. And so what we learned was customers didn't want to have management and monitoring for applications in the cloud, based on Kubernetes. Because they were all still struggling to get Docker working, to get basic Kubernetes clusters set up. And they kept saying to us "this is great, we love your tool, but we really need simpler things right now." So what we had done was we'd learned how to operate Kubernetes. And we discovered that we were doing it in this specific way, a way that meant that we could be reliable, we could set things up remotely, we could move things between zones. And so we called this approach getopts. So we've named the practice of getopts, which is really DevOps for Kubernetes. We decided that it was exciting after we had an outage and made a very quick recovery. Told people about it and they said, "well, we can't even Kubernetes started, let alone recover it from a crash." So we started evangelizing getopts and saying to people that we knew how to set up and run Kubernetes as operators for developers of apps, based on this experience. And people said, "well, why don't you help us do that?" So we pivoted the company away from a SaaS business, doing management, and straight back into enterprise software, providing a solution for people to run Kubernetes stacks, deploy applications, detect drifts, and operate them at scale. And we've never looked back. And since then we've built, very successfully, a big business out of telco customers, banks, car companies, really global two thousands. Starting from that open source base, continuing to respect that, but always keeping in mind helping developers build applications at scale. >> So in terms of that pivot that you've made, it sounds like you made that in conjunction with developers across industries to really understand what the right direction is here. What's the approach, what's their appetite? Talk to me about a customer example or two that really you think articulate the value and the right decision that that pivot was and how you're helping customers to really further their DevOps practice. >> Well, one of our first customers was actually Fidelity in this new world. Fidelity has a very advanced technology organization, a very forward thinking CTO, who I seem to recall is, or CEO, who I think is female. Really is into technology as a source of, you know, velocity and business strength. And we were brought to Fidelity by our partner, Amazon. And they said, "look, Fidelity have been using your open source tools, they want to run on Kubernetes, the early EKS service on AWS, but they need help, because what they want is a shared application platform that people can use across Fidelity to deploy and manage apps." So the idea Fidelity had was they're going to split their IT into a platform team, that was going to provide this platform, and a bunch of app teams that were going to write business apps like risk management, other financial processing. Paths, basically. And we came in to help Fidelity. And what we did was help Fidelity rollout, using getopts, a Amazon wide application platform. We also helped them to build, this was very early days for us post pivot, we really helped them to build an add on layer. So you could take any Kubernetes cluster and add other components to it, and then you'd have your platform right there. And the whole stack would be managed by getopts, which nobody had done before. Nobody who'd come up with a way of managing the whole stack, so you could start and stop stacks wherever you wanted, at will, correctly. I mean, if you talk to people about what's hard in IT, they'll tell you shutting down Kubernetes is hard, 'cause I know I'm never going to know how to start it again. So being able to start and stop things, move them around is really crucial. What Fidelity also wanted, which made I think the whole thing even more exciting, was to duplicate this environment on Azure and actually also on-premise later on. So where Fidelity are today is the whole Fidelity platform runs on Microsoft and on Amazon and on-premise, using three different implementations of Kubernetes. But using this platform technology and getopts that we helped Fidelity rollout. And if you want to know a bit about the story, type FIDEKS, F I D E K S into Google and you'll find a video of me three or four years ago on stage at Cube Con talking with a Fidelity chief architect about this story. It's pretty exciting and these are early days for these new Kubernetes platforms. >> Early days, but so transformative. And I can't imagine the events of the last few years without having this capability and this technology to facilitate such pivots and transformation where we would all be. I want to kind of dig into some use cases, 'cause one of the things that you just mentioned with the Fidelity example got me thinking use case of hybrid, multi-cloud, but also continuous app development. Talk to me about some of the key use cases that you work with customers on. >> Well you just named two. So hybrid and multi-cloud is absolutely critical, and also sovereign, which is when you're actually offline and you only update your cloud periodically. That's one of the major use cases for us. And what customers want there is they want consistency. They want a single operating model, across all of these different locations, so that all of their teams can get trained on one set of technologies and then move from place to place. They're not looking for magic, where apps move with the sun or any of that stuff. They just want to know they can base everything on a single, homogeneous skillset and have scale across their teams. Maybe tens of thousands of developers, all who know how to do the same thing. That's a really important use case. You also mentioned continuous delivery. That's probably the second really critical use case for us. People say, "I've got Kubernetes set up now, and I have Jenkins." At JP Morgan once told me they had 40,000 Jenkins servers, or something like that, you know, Jenkins at scale. And they're like, "okay, how do I push changes from Jenkins into the cloud?" So getopts provides a bridge between the world of CI and the runtime of Kubernetes. So one group of our customers is help me to put that middle piece of CD that gets you CI, CD, to Kubernetes, that's a classic. And then what they're looking for is an increase in velocity. And what we typically see is people go from deploying once every six months to deploying once a week, to deploying once a day, to deploying several times a day. And then they split things up into teams and suddenly, wow, that vision of microservices has come and everybody's excited 'cause IT velocity has gone up by two X. Another really >> So, >> Sorry, carry on. >> Go ahead, I was just going to say in terms of IT velocity it sounds like that's a major business outcome that you're enabling for, whether it's teleco, financial services, or whatnot. That velocity is, as you just described, is rapidly accelerating. >> Yeah, if you go to our website, you'll find a bunch of these use cases. And one that I really like is NatWest mettle, which is another financial example. They're not all financial by the way. But there's some metrics in there. We're getting people up to two X productivity, which at scale is huge, really makes a difference. Also, meantime to recovery. If you know the metric space, you'll know these are all DORA metrics. And DORA, which was acquired by Google a couple of years ago, is a really fantastic analyst in the space that came up with a bunch of ways of thinking about how to measure your performance as a business and IT organization. Recovery time and things like this that you really need to focus on if you're in this world. >> Well, from an IT velocity perspective, if I translate that to business outcomes, especially given the dynamics in the market over the last two years, this is transformative and probably helped a lot of organizations to pivot multiple times during the last couple of years. To get to that survival mode and into that thriving mode, enabling organizations to meet customer demand that was changing faster, et cetera. That's a really big imperative that this technology can deliver to the business. >> Yeah, I mean, that's been huge for us. So when the pandemic first began, obviously, we had some road bumps and there were some challenges, but what we found out very quickly was that people were moving into digital much faster. And we've been mostly enabling them, not just in finance, as I said, but also, car companies, utilities, et cetera. The other one, of course, is modern operations. So, everyone's excited about the potential for automation. If I have thousands and thousands of developers and thousands of applications, do I need thousands of operations staff? And the answer is, with Kubernetes in this new era, you can reduce your operational loads. So that actually very few people are needed to keep systems up, to do basic monitoring, to do redeployments and so on, which are all boring infrastructure tasks that no developer wants to do. If we can automate all of that, we can modernize the whole IT space. And that's what I think the promise of Kubernetes that we're also seeing as well. So applications speed first and then operational competence second. >> So you guys had a launch, here we are in early calendar year 2022, you guys had a launch just about six or eight weeks ago in November of 2021, where you were launching announcing the GA of Weave getopts enterprise, which is a licensed product building on the free open source Weave getopts core. Talk to me about that and what the significance of that is. >> Well, this is an enterprise solution that helps customers build these critical use cases, like shared service platform or secure DevOps or multi-cloud, using getopts, which gives them higher security, lower costs of management, and better operations, and higher velocity. And all of it is taking all the best practices that we've learned starting from those days of running our own Kubernetes stack and then through those early customers like Fidelity into the modern era where we have an at-scale platform for these people. And the crucial properties are it provides you with a platform, it provides you with trusted delivery, and it provides you with what we call release orchestration, which is when you deploy things at scale into production, using tools like canaries and other modern practices. So, all of it is enabling what we call the cloud native enterprise, application delivery, modern operations. >> So what's the upgrade path for customers that are using the free open-source tier to the enterprise package, what does that look like? >> The good news is it's an add on. So, I have been in the industry a while and I strongly believe it's really important that if you have an open source product, you shouldn't ask people to delete it or uninstall it to install your enterprise product, unless you really, really, really have to. And I'm not trying to be picky here. Maybe there are cases where it's important, but actually in our case, it's very simple. If you're already using one of our upstream tools, like Flux, for example, then going from Flux to Weave getopts enterprise is an add-on installation. So you don't have to change or take out what you're doing. You might be using Flux without knowing it. You may not be aware of this, but it's also insight as your AKS and ARC, it's inside the Amazon EKS anywhere bundle. It's available on Alibaba, VMware have used it in cartographer and Tanzu application platform. And even Red Hat use it too in some cases. So you may be using it already, from one of the big vendors who are partners of ours, as a precursor to buying Weave getopts enterprise. So, you know, don't be scared. Get in touch is what I would say to people. >> Get in touch. And of course, folks can go to weave.works to learn more about that. And, also we want to watch the Weave.works space, 'cause you have some news coming out relatively soon that sounds pretty exciting, Alexis. >> Well, I mentioned trusted delivery. And I think one of the things with that is no CIO wants to go faster, unless they also have the safety wheels on, let's face it. And the big question we get asked is "I love this getopts stuff, but how can I bring my team with me? How can I introduce change?" I have all of these approvals mechanisms in place, can I move into the world of getopts? And the answer is yes, yes you can because we now support policy engines as baked into our enterprise product. Now, if you don't know what policy is, it's really a way of applying rules to what you're seeing in IT. And you can detect whether something passes or fails conditions, which means that we can detect if something bad is about to happen in a deployment and stop it from happening, this is really critical. It also goes hand in hand with things like supply chain and security, which I'm sure we read about in the news far too much. >> Yeah, pretty much daily supply chain and security >> Pretty much daily. >> is one of those things that we're all in every generation concerned about. Well, Alexis, it's been a pleasure having you back on the program, talking to us about what's new at Weaveworks, the direction that you're going, how you're helping organizations across industries really advance their DevOps practice. And we will check weave.works in the next couple of weeks for more on that news that you started to break a little bit with us today. We appreciate your time, Alexis. >> Thank you very much, indeed, take care. >> Likewise. For Alexis Richardson, I'm Lisa Martin. Keep it right here on theCUBE, your leader in hybrid tech event coverage. (bright music) (music fades)

(bright music) >> Well, hi everybody. John Walls here on theCUBE, continuing our coverage of AnsibleFest 2021 with Tom Anderson, the Vice President of Product Management at Red Hat. And Tom, you've been the answer, man, for theCUBE here over the last a week, 10 days or so. Third cube appearance, I hope we haven't worn you out. >> No, you haven't John, I love it, I love doing it. So that's great to have you have you at the event. >> Thank you for letting us be a part of that. It's been a lot of fun. Let's let's go and look at the event now. As far as big picture here, major takeaways that you think that have been talked about, that you think you'd like people, customers to go home with. If you will, though, a lot of this has been virtual obviously, but when I say go home, I made that figuratively, but what, what do you want people to remember and then apply to their businesses? >> Right. So being a product guy, I want to talk about products usually, right? So the big kind of product announcements from this year's event have been the rollout, and really, the next generation of the Ansible automation platform, which is really a rearchitecture turning it into a cloud native application an automation application itself that scales to our customer needs. So a lot of big announcements around that. And so what does that do for customers? That's really bringing them the automation platform that they can scale from the data center, to the cloud, to the Edge and everywhere in between, across a single platform with a single easy to use automation language. And then secondly, on that, as automation starts to shift left, we always talk about technology shifting left towards the developer, as automation is also shifting left towards the developer and other personas in an organization we're really happy about the developer tools and the tooling that we're providing to the customers with the new automation platform too, that brings development of content automation content. So the creation, the testing, the deployment and the management of that content across an enterprise far easier than it's ever been. So it's really kind of, it's a little bit about the democratization of automation. We see that shifting left, if you will. And I know I've said that already, but we see that shifting left of automation into other parts of the organization, beyond the domain experts, the network engineers or the storage experts, et cetera, pushing that automation out into the hands of other personas in the organization has been a big trend that we've seen and a lot of product announcements around that. So really excited about the product announcements in particular, but also the involvement and the engagement of our ecosystem, our upstream community. So important to our product and our success, our ecosystem partners, and obviously last but not least our customers and our users. >> So you hit a lot of big topics there. So let's talk about the Edge. You know, that seems to be a, you know, a fairly significant trend at this point, right? 'Cause trying to get the automation out there where the data besides, and that's where the apps are. Right? So where the data is, that's where things are happening out there on the Edge. So maybe just dive into that a little bit and about how you're trying to facilitate that need. >> Yeah. So a couple of trends around the Edge, obviously it's the architecture itself with lower capacity or lower capability devices and compute infrastructure at the Edge. And whether that's at the far edge with very low capacity devices, or even at near edge scenarios where you don't have, you know, data center, IT people out there to support those environments. So being able to get at those low capability, low capacity environments remotely Ansible is a really good fit for that because of our agentless architecture, the agentless architecture of Ansible itself allows you to drive automation out into the devices and into the environments where there isn't a high capacity infrastructure. And the other thing that the other theme that we've seen is one of the commonalities that no matter where the compute is taking place and the users are, there always has to be network. So we see a lot of network automation use cases out at the Edge and Ansible is, you know, the defacto network automation solution in the market. So we see a lot of our customers driving Ansible use cases out into their Edge devices. >> You know, you talk about development too, and just kind of this changing relationship between Ansible and DevOps and how that has certainly been maturing and seems to be really taking off right now. >> Yeah. So for, you know, what we've seen a lot of, as you know, is becoming frictionless, right? How do we take the friction out of the system that frees developers up to be more productive for organizations to be more agile, to roll out applications faster? How do we do that? We need to get access to the infrastructure and the resources that developers need. We need to get that access into their hands when they need it. And in our frictionless sort of way, right? So, you know, all of the old school, traditional ways of developers having to get infrastructure by opening a help desk ticket to get servers built for them and waiting for IT ops to build the servers and to deploy them and to send them back a message, all that is gone now. These, you know, subsystem owners, whether that's compute or cloud or network or storage, their ability to use Ansible to expose their resources for consumption by other personas, developers in this case, makes developers happy and more efficient because they can just use those automation playbooks, those Ansible playbooks to deploy the infrastructure that they need to develop, test and deploy their applications on. And the actual subsystem owners themselves can be assured that the usage of those environments is compliant with their standards because they've built and shared the automation with those developers to be able to consume when they want. So we're making both sides happy, agile, efficient developers and happy infrastructure owners, because they know that the governance and compliance around that system usage is on point with what they need and what they want. >> Yeah. It's a big win-win and a very good point. I always like it when we kind of get down to the nitty-gritty and talk about what a customer is really doing. Yeah. And because if we could talk about hypotheticals and trends and developing and maturity rates and all those kinds of things, but in terms of actual customers, you know, what people really are doing, what do you think have been a few of the plums that you'd like to make sure people were paying attention to? >> Yeah. I think from this year's event, I was really taken by the JP Morgan Chase presentation. And it really kind of fits into my idea of shifting left in the democratization of automation. They talked about, I think the number was around 7,000 people, associates inside that organization that are across 22 countries. So kind of global consumption of this. Building automation playbooks and sharing those across the organization. I mean, so gone are the days of, you know, very small teams of people doing, just automating the things that they do and it's grown so big. And, so pervasive now, I think JP Morgan Chase really kind of brings that out, tease that out, that kind of cultural impacts that's had on their organization, the efficiencies that have been able to draw off from that their ability to bring the developers and their operations teams together to be working as one. I think their story is really fantastic. And I think this is the second year. I think this is the second year that JP Morgan Chase has been presenting at Fest and this years session was fantastic. I really, really enjoyed that. So I would encourage, I would encourage anybody to go back and look at the recording of that session and there's game six groups, total other end of the spectrum, right? Financial services, JP Morgan Chase, global company to Gamesis, right? These people who are rolling out new games and need to be able to manage capacity really well. When a new game hits, right? Think about a new game hits and the type of demand and consumption there is for that game. And then the underlying infrastructure to support it. And Gamesis did a really great presentation around being able to scale out automation to scale up and down automation, to be able to spin up clusters and deploy infrastructure, to run their games on an as-needed basis. So kind of that business agility and how automation is driving that, or business agility is driving the need for automation in these organizations. So that that's just a couple of examples, but there was a good ones from another financial services that talked about the cultural impacts of automation, their idea of extreme automation. In fact, one of the sessions I interviewed Joe Mills, a gentlemen from this card services, financial services company, and he talked about extreme automation there and how they're using automation guilds in communities of practice in their organization to get over the cultural hurdles of adopting automation and sharing automation across an organization. >> Hm. So a wide array obviously of customer uses and all very effective, I guess, and, you know, and telling their own story. Somewhat related to that, and you, as you put it out there too, if you want to go back and look, these are really great case studies to take a look at. For those who, again, who maybe couldn't attend, or haven't had a chance to look at any of the sessions yet, what are some of the kinds of things that were discussed in terms of sessions to give somebody a flavor of what was discussed and maybe to tease them a little bit for next year, right? And just in case that you weren't able to participate and can't right now, there's always next year. So maybe if you could give us a little bit of flavor of that, too. >> Yeah. So we kind of break down the sessions a little bit into the more kind of technical sessions and then the sort of less technical sessions, let's put it that way. And on the technical session front, certainly a couple of sessions were really about getting started. Those are always popular with people new to Ansible. So there's the session that aired on the 29th, which has been recorded and you can rewatch it. That's getting started Q and A with the technical Ansible experts. That's a really, really great session 'cause you see that the types of questions that are being asked. So you know, you're not alone. If you're new to Ansible, the types of questions are probably the questions that you have as well. And then the, obviously the value of the tech Ansible experts who are answering this question. So that was a great session. And then for a lot of folks who may want to get involved in the community, the upstream community, there's a great session that was also on the 29th. And it was recorded for rewatching, around getting started with participation in the Ansible community and a live Q and A there. So the Ansible community, for those who don't know is a large, robust, vibrant, upstream community of users, of software companies, of all manners of people that are contributing and contributing upstream to the code and making Ansible a better solution for them and for everybody. So that's a great session. And then last but not least, almost always the most popular session is the roadmap sessions and Massimo Ferrari, gentleman on my team did a great session on the Ansible roadmap. So I do a search on roadmap in the session catalog, and you can see the recording of that. So that's always a big deal. >> Yeah, roadmaps were great, right? Because especially for newcomers, they want to know how I'm down here at 0.0. And, I've got a destination in mind, I want to go way out there. So how do I get there? So, to that point for somebody who is beginning their journey, and maybe they have, you know, they're automated with the ability to manually intervene, right? And now you've got to take the hands off the wheel and you're going to allow for full automation. So how, what's the message you want to get across to those people who maybe are going to lose that security blanket they've been hanging on to, you know, for a long time and you take the wheels off and go. >> No John, that's a great question. And that's usually a big apprehension of kind of full automation, which is, you know, that kind of turning over the reins, if you will, right to somebody else. If I'm the person who's responsible for this storage system, if I'm the person responsible for this network elements, these routers, these firewalls, whatever it might be, I'm really kind of freaked out about giving controls or access to those things, from a configuration standpoint, to people outside of my organization, who don't have the same level of expertise that I do, but here's the deal that in a well implemented well architected Ansible automation platform environment, you can control the type of automation that people do. Who does that against what managing that automation as code. So checking in, checking out, version control, deployment access. So there's a lot of controls that can be put in place. So it isn't just a free-for-all automated. Everybody automating everything. Organizations can roll out automation and have access to different kinds of automation, can control and manage what their organizations can use and see and do with Ansible. So there's lots of controls built-in for organizations to put in place and to make those subsystem owners give them confidence that how people are accessing their subsystems using Ansible automation can be controlled in a way that makes them comfortable and assures compliance and governance around those resources. >> Well, Tom, we appreciate the time. Once again, I know you've been a regular here on theCUBE over the course of the event. We'll give you a little bit of time off and let you get back to your day job, but we do appreciate that and I wish you success down the road. >> Thank you very much. And we'll see you again next year. >> You bet. Thank you, Tom Anderson, joining us Vice President of Product Management at Red Hat, talking about AnsibleFest, 2021. I'm John Walls, and you're watching theCUBE. (lively instrumental music)

(smooth upbeat music) >> Hello, everybody, John Walls here. Welcome to "theCUBE," in our continuing coverage of Ansible Fest 2021. We now welcome onto "theCUBE," three representatives from Red Hat. Joining us is Ashesh Badani. Who's the Senior Vice President of Products at Red Hat. Ashesh, thank you for joining us today. >> Thanks for having me, John. >> You bet. Also with us Stefanie Chiras, who is the Senior Vice President of the Platforms Business Group also at Red Hat. And Stefanie, how are you doing? >> Good, thanks, it's great to be here with you, John. >> Excellent, thanks for joining us. And last, but certainly not least, Joe Fitzgerald, who is the Vice President and General Manager of the Ansible Business Unit at Red Hat. Joe, good to see you today, thanks for being with us. >> Good to see you again John, thanks for having us. >> It's like, like the big three at Red Hat. I'm looking forward to this. Stefanie, let's just jump in with you and let's talk about what's going on in terms of automation in the hybrid cloud environment these days. A lot of people making that push, making their way in that direction. Everybody trying to drive more value out of the hybrid cloud environment. How is automation making that happen? How's it making it work? >> We have been focused at Red Hat for a number of years now on the value of open hybrid cloud. We really believe in the value of being able to give your applications flexibility, to use the best technology, where you want it, how you need it, and pulling all of that together. But core to that value proposition is making sure that it is consistent, it is secure and it is able to scale. And that's really where automation has become a core space. So as we continue to work our portfolio and our ecosystems and our partnerships to make sure that that open hybrid cloud has accessibility to everything that's new and relevant in this changing market we're in, the automation space that Ansible drives is really about making sure that it can be done in a way that is predictable. And that is really essential as you start to move your workloads around and start to leverage the diversity that an open hybrid cloud can deliver. >> When you're bringing this to a client, and Joe, perhaps you can weigh in on this as well. I would assume that as you're talking about automation, there's probably a lot of, successful head-nodding this way, but also some kind of this way too. There's a little bit of fear, right? And maybe just, they have these legacy systems, there's maybe a little distrust, I don't want to give away control, all these things. So how do you all answer those kinds of concerns when you're talking to the client about this great value that you can drive, but you got to get them there, right? You have to bring them along a bit. >> It's a great question, John, and look, everybody wants to get the hybrid cloud, as Stefanie mentioned. That journey is a little complicated. And if you had silos and challenges before you went to a hybrid cloud, you're going to have more when you got there. We work with a lot of customers, and what we see is this sort of shift from, I would call it low-level task automation to much more of a strategic focus on automation, but there's also the psychology of automation. One of the analysts recently did some research on that. And imagine just getting in your car and letting the car drive you down the street to work. People are still not quite comfortable with that level of automation, they sort of want to be able to trust, but verify, and maybe have their hands near the wheel. You couldn't take the wheel away from them. We see the same thing with automation. They need automation and a lot automation, or they need to be able to verify what it is doing, what they do, what it's going to do. And once they build that confidence, then they tend to do it at scale. And we're working with a lot of customers in that area. >> Joe, you're talking about a self-driving car, that'll never work, right? (laughs) You us bring an interesting point though. Again, I get that kind of surrendering control a little bit and Ashesh, I would assume in the product development world, that's very much your focus, right? You're looking for products that people, not only can use, but they're also comfortable with. That they can accept and they can integrate, and there's buy-in, not only on the engineering level, but also on the executive level. So maybe walk us through that product development, staging or phases, however you want to put it, that you go through in terms of developing products that you think people, not only need, but they'll also accept. >> I think that's absolutely right. You know, I think both Stefanie and Joe, led us off here. I talked about hybrid cloud and Joe, started talking about moving automation forward and getting people comfortable. I think a lot of this is, meeting customers where they are and then helping them get on the journey, right? So we're seeing that today, right? So traditional configuration management on premise, but at the same time, starting to think about, how do we take them out into the cloud, bringing greater automation to bear there. But so that's true for us across our existing customer base, as well as the new customers that we see out there. So doing that in a way that Joe talked about, right? Ensuring the trust, but verify is in play, is critical. And then there's another area which I'm sure we'll talk a little bit more about, right? Is ensuring that security implications are taken into account as we go through it. >> Well, let's just jump into security, that's one of the many considerations these days. About ensuring that you have the secure operation, you're doing some very complex tasks here, right? And you're blending multi-vendor environments and multi-domain environments. I mean you've got a lot, you're juggling a lot. So I guess to that extent, how much of a consideration is security and those multiple factors today, for you. And again, I don't know which one of the three of you might want to jump on this, but I would assume, this is a high priority, if not the highest priority, because of the headlines that security and those challenges are garnering these days. >> Well, there's the general security question and answer, right? So this is the whole, shift-left DevSecOps, sort of security concerns, but I think specific to this audience, perhaps I can turn over to Joe to talk a little bit about how Ansible has been playing in the security domain. >> Now, it's a great way to start, Ashesh. People are trying to shift left, which means move, sort of security earlier on in the process where people are thinking about it and development process, right? So we've worked with a lot of customers who were trying to do DevSecOps, right? And to provide security, automation capabilities during application build and deployment. Then on the operational side, you have this ongoing issue of some vulnerability gets identified, how fast can I secure my environment, right? There's a whole new area of security, orchestration, automation, or remediation that's involved, and the challenge people have is just like with networking or other areas, they've got dozens in some cases, hundreds of different systems across their enterprise that they have to integrate with, in order to be able to close a vulnerability, whether it's deploying a patch or closing a port, or changing firewall configuration, this is really complicated and they're being measured by, okay, there's this vulnerability, how fast can we get secure? And that comes down to automation, it has to. >> Now, Joe, you mentioned customers, if you would maybe elaborate a little bit about the customers that we've been hearing from on the stage, the virtual stage, if you will, at Ansible Fest this year and maybe summarize for our audience, what you're hearing from those customers, and some of those stories when we're talking about the actual use of the platform. >> Yeah, so Ansible Fest is our annual, automation event, right? For Ansible users. And I think it's really important to hear from the customers. We're vendors, we can tell you anything you want and try and get you to believe it. Customers they're actually doing stuff, right? And so, at Ansible Fest, we've got a great mix of customers that are really pushing the envelope. I'll give you one example, JP Morgan Chase. They're talking about how in their environment with focus over the past couple of years, they've now gotten to a level of maturity with automation, where they have over 50,000 people that are using Ansible automation. They've got a community of practice where they've got people in over twenty-two countries, right? That are sharing over 10,000 playbooks, right? I mean, they've taken automation strategically and embraced it and scaled it out at a level that most other organizations are envious of, right? Another one, and I'm not going to go through the list, but another one I'll mention is Discover, which sort of stepped back and looked at automation strategically and said, we need to elevate this to a strategic area for the company. And they started looking at across all different areas, not just IT automation, business process automation, on their other practices internally. And they're doing a presentation on how to basically analyze where you are today and how to take your automation initiatives forward in a strategic way. Those are usually important to other organizations that maybe aren't as far along or aren't on a scale of that motivation. >> Yeah, so Stefanie, I see you nodding your head and you're talking about, when Joe was just talking about assessment, right? You have to kind of see where are we, how mature are we on our journey right now? So maybe if you could elaborate on that a little bit, and some of the key considerations that you're seeing from businesses, from clients and potential clients, in terms of the kind of thought process they're going through on their journey, on their evolution. >> I think there's a lot of sort of values that customers are looking for when they're on their automation journey. I think efficiency is clearly one. I think one that ties back to the security discussion that we talked about. And I use the term consistency, but it's really about predictability. And I think I have a lot of conversations with customers that if they know that it's consistently deployed, particularly as we move out and are working with customers at the edge, how do they know that it's done the same way every time and that it's predictable? There's a ton of security and confidence built into that. And I think coming back to Joe's point, it is a journey providing transparency and visibility is step one, then taking action on that is then step two. And I think as we look at the customers who are on this automation journey, it's them understanding what's the value they're looking for? Are they looking for consistency in the deployments? Are they looking for efficiency across their deployments? Are they looking for ways to quickly migrate between areas in the open hybrid cloud? What is the value they're looking for? And then they look at how do they start to build in confidence in how they deliver that. And I think it starts with transparency. The next step is starting to move into taking action, and this is a space where Joe and the whole team, along with the community have really focused on pulling together things like collections, right? Playbooks that folks can count on and deploy. We've looked within the portfolio, we're leveraging the capabilities of this type of automation into our products itself with Red Hat enterprise Linux, we've introduced systems roles. And we're seeing a lot of by pulling in that Ansible capability directly into the product, it provides consistency of how it gets deployed and that delivers a ton of confidence to customers. >> So, Ashesh I mean, Stefanie was talking about, the customers and obviously developing, I guess, cultural acceptance and political acceptance, within the ranks there. Where are we headed here, past what know now in terms of the traditional applications and traditional automations and whatever. Kind of where is this going, if you would give me your crystal ball a bit about automation and what's going to happen here in the next 12-18 months. >> So what I'm going to do, John, is try to marry two ideas. So we talked about hybrid cloud, right? Stefanie started talking about joining a hybrid cloud. I'm going to marry automation with containers, right? On this journey of hybrid cloud, right? And give you two examples, both some successful progress we've been making on that front, right? Number one, especially for the group here, right? Check out the Ansible collection for Kubernetes, it's been updated for Python Three, of course, with the end-of-life for Python Two, but more important, right? It's the focus on improving performance for large automation tasks, right? Huge area where Ansible shines, then taking advantage of turbo mode, where instead of the default being a single connection to a Culebra API, for every request that's out there with turbo mode turned on, the API connection gets reused significantly and obviously improving performance. Huge other set of enhancements as well, right? So I think that's an interesting area for the Ansible community to leverage and obviously to grow. And the second one that I wanted to call out was just kind of the, again, back to this sort of your notion of the marriage of automation with containers, right? Is the work that's going on, on the front of the integration, the tight integration between Ansible as well as Red Hat's, advanced cluster management, right? Which is helping to manage Kubernetes clusters at scale. So now Red Hat's ACM technology can help our monthly trigger Ansible playbooks, upon key lifecycle actions that have happened. And so taking advantage of technologies like operators, again, core Kubernetes construct for the hybrid cloud environment. This integration between advanced cluster management and Ansible, allows for much more efficient execution of tasks, right? So I think that's really powerful. So wrapping that up, right? This world of hybrid cloud really can be brought together by just a tighter integration between working Ansible as well as the work that's going on on the container plant. >> Great, well, thank you. Ashesh, Stefanie, Joe, thank you all for sharing the time here. Part of our Ansible Fest coverage here, enjoy the conversation and continuous success at Red Hat. Thank you for the time today. >> Thank you so much John. >> Thank you. >> You bet. I'm joined here by three executives at Red Hat, talking about our Ansible Fest 2021 coverage. I'm John Walls, and you're watching "theCUBE." (bright music)

>> From theCUBE studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE in ETR. This is Breaking Analysis with Dave Vellante. >> Every CEO is figuring out the right balance for new hybrid business models. Now, regardless of the chosen approach, which is going to vary, technology executives, they understand they have to accelerate their digital and build resilience as well as optionality into their platforms. Now, this is driving a dramatic shift in IT investments. And at the macro level, we expect total spending to increase at as much as 8% or even more in 2021, compared to last year's contraction. Investments in cybersecurity, cloud collaboration that are enabling hybrid work as well as data, including analytics, AI, and automation are at the top of the spending priorities for CXOs. Hello everyone. And welcome to this week's Wiki Bond Cube insights, powered by ETR. In this Breaking Analysis, we're pleased to welcome back Erik Bradley, who is the chief engagement strategist at our partner, ETR. Now in this segment, we're going to share some of the latest findings from ETR's surveys and provide our commentary on what it means for the markets, for sellers, and for buyers. Erik, great to see you, my friend. Welcome back to Breaking Analysis. >> Thank you for having me, always enjoy it. We've got some fresh data to talk about on this beautiful summer Friday, so I'm ready to go. >> All right. I'm excited too. Okay, last year we saw a contraction in IT spending by at least 5%. And now we're seeing a snapback to, as I said, at least 8% growth relative to last year. You got to go back to 2007 just before the financial crisis to see this type of top line growth. The shift to hybrid work, it's exposed us to new insidious security threats. And we're going to discuss that in a lot more detail. Cloud migration of course picked up dramatically last year, and based on the recent earnings results of the big cloud players, for now we got two quarters of data, that trend continues as organizations are accelerating their digital platform build-outs, and this is bringing a lot of complexity and a greater need for so-called observability solutions, which Erik is going to talk about extensively later on in this segment. Data, we think is entering a new era of de-centralization. We see organizations not only focused on analytics and insights, but actually creating data products. Leading technology organizations like JP Morgan, they're heavily leaning into this trend toward packaging and monetizing data products. And finally, as part of the digital transformation trend, we see no slow down in spending momentum for AI and automation, generally in RPA specifically. Erik, anything you want to add to that top level narrative? >> Yeah, there's a lot to take on the macro takeaways. The first thing I want to state is that that 8, 8.5% number that started off at just 3 to 4% beginning of the year. So as the year has continued, we are just seeing this trend in budgets continue to accelerate, and we don't have any reason to believe that's going to stop. So I think we're going to just keep moving on heading into 2021. And we're going to see a banner year of spend this year and probably next as well. >> All right, now we're going to bring up a chart that shows kind of that progression here of spending momentum. So Erik, I'm going to let you comment on this chart that tracks those projections over time. >> Erik: Yeah. Great. So thank you very much for pulling this up. As you can see in the beginning part of the year, when we asked people, "What do you plan to spend throughout 2021?" They were saying it would be about a 4% increase. Which we were happy with because as you said last year, it was all negative. That continues to accelerate and is only hyper accelerating now as we head into the back half of the year. In addition, after we do this data, I always host a panel of IT end users to kind of get their feedback on what we collected, to a man, every one of them expects continued increase throughout next year. There are some concerns and uncertainty about what we're seeing right now with COVID, but even with that, they're planning their budgets now for 2022 and they're planning for even further increases going forward. >> Dave: Great, thank you. So we circled that 8%. That's really kind of where we thought it was going to land. And so we're happy with that number, but let's take a look at where the action is by technology sector. This chart that we're showing you here, it tracks spending priorities back to last September. When I believe that was the point, Erik, that cyber became the top priority in the survey, ahead of cloud collaboration, analytics, and data, and the other sectors that you see there. Now, Erik, we should explain. These areas, they're the top seven, and they outrank all the other sectors. ETR tracks many, many other sectors, but please weigh in here and share your thoughts on this data. >> Erik: Yeah. Security, security, security. It hasn't changed. It had really hasn't. The hybrid work. The fact that you're behind the firewall one day and then you're outside working from home the next, switching in and out of networks. This is just a field day for bad actors. And we have no choice right now, but to continue to spend, because as you're going to talk about in a minute, hybrid's here to stay. So we have to figure out a way to secure behind the firewall on-prem. We also have to secure our employees and our assets that are not in the office. So it is a main priority. One of the things that point out on this chart, I had a couple of ITN users talk to me about customer experience and automation really need to move from the right part of that chart to the left. So they're seeing more in what you were talking about in RPA and automation, starting to creep up heading into next year. As cloud migration matures, as you know, cybersecurity spending has been ramping up. People are going to see a little bit more on the analytics and a little bit more on the automation side going forward. >> Dave: Great. Now, this next data view- well, first of all, one of the great things about the ETR dataset is that you can ask key questions and get a time series. And I will tell you again, I go back to last March, ETR hit it. They were the first on the work from home trend. And so if you were on that trend, you were able to anticipate it. And a lot of investors I think took advantage of that. Now, but we've shown this before, but there's new data points that we want to introduce. So the data tracks how CIOs and IT buyers have responded to the pandemic since last March. Still 70% of the organizations have employees working remotely, but 39% now have employees fully returning to the office and Erik, the rest of the metrics all point toward positives for IT spending, although accelerating IT deployments there at the right peaked last year, as people realized they had to invest in the future. Your thoughts? >> Erik: Yeah, this is the slide for optimism, without a doubt. Of the entire macro survey we did, this is the most optimistic slide. It's great for overall business. It's great for business travel. This is well beyond just IT. Hiring is up. I've had some people tell me that they possibly can't hire enough people right now. They had to furlough employees, they had to stop projects, and they want to re accelerate those now. But talent is very hard to find. Another point to you about your automation and RPA, another underlying trend for there. The one thing I did want to talk about here is the hybrid workplace, but I believe there's another slide on it. So just to recap on this extremely optimistic, we're seeing a lot of hiring. We're seeing increased spending, and I do believe that that's going to continue. >> Yeah I'm glad you brought that up because a session that you and I did a while ago, we pointed out, it was earlier this year, that the skill shortage is one potential risk to our positive scenario. We'll keep an eye on that, but so I want to show another set of data that we've showed previously, but ETR again, has added some new questions in here. So note here that 60% of employees still work remotely with 33% in a hybrid model currently, and the CIO's expect that to land on about 42% hybrid workforce with around 30% working remotely, which is around, it's been consistent by the way on your surveys, but that's about double the historic norm, Eric. >> Erik: Yeah, and even further to your point Dave, recently I did a panel asking people to give me some feedback on this. And three of those four experts basically said to me, if we had greed run this survey right now, that even more people would be saying remote. That they believe that that number, that's saying they're expecting that number of people to be back in office, is actually too optimistic. They're actually saying that maybe if we had- cause as a survey launched about six, seven weeks ago before this little blip on the radar, before the little COVID hiccup we're seeing now, and they're telling me that they believe if we reran this now that it would be even more remote work, even more hybrid and less returned to the office. So that's just an update I wanted to offer on this slide. >> Dave: Yeah. Thank you for that. I mean, we're still in this kind of day to day, week to week, month to month mode, but I want to do a little double click on this. We're not going to share this data, but there was so much ETR data. We got to be selective. But if you double click on the hybrid models, you'll see that 50% of organizations plan to have time roughly equally split between onsite and remote with again around 30 or 31% mostly remote, with onsite space available if they need it. And Erik, very few don't plan to have some type of hybrid model, at least. >> Yeah, I think it was less than 10% that said it was going to be exclusively onsite. And again, that was a more optimistic scenario six, seven weeks ago than we're seeing right now throughout the country. So I agree with you, hybrid is here to stay. There really is no doubt about it. from everyone I speak to when, you know, I basically make a living talking to IT end users. Hybrid is here to stay. They're planning for it. And that's really the drive behind the spending because you have to support both. You have to give people the option. You have to, from an IT perspective, you also have to support both, right? So if somebody is in office, I need the support staff to be in office. Plus I need them to be able to remote in and fix something from home. So they're spending on both fronts right now. >> Okay. Let's get into some of the vendor performance data. And I want to start with the cloud hyperscalers. It's something that we followed pretty closely. I got some Wiki bond data, that we just had earnings released. So here's data that shows the Q2 revenue shares on the left-hand side in the pie and the growth rates for the big four cloud players on the right hand side. It goes back to Q1 2019. Now the first thing I want to say is these players generated just under $39 billion in the quarter with AWS capturing 50% of that number. I said 39, it was 29 billion, sorry, with AWS capturing 50% of that in the quarter. As you're still tracking around a third in Alibaba and GCP in the, you know, eight or 9% range. But what's most interesting to me, Erik, is that AWS, which generated almost 15 billion in the quarter, was the only player to grow its revenue, both sequentially and year over year. And Erik, I think the street is missing the real story here on Amazon. Amazon announced earnings on Thursday night. The company had a 2% miss on the top line revenues and a meaningful 22% beat on earnings per share. So the retail side of the business missed its revenue targets, so that's why everybody's freaked out. But AWS, the cloud side, saw a 4% revenue beat. So the stock was off more than 70% after hours and into Friday. Now to me, a mix shift toward AWS, that's great news for investors. Now, tepid guidance is a negative, but the shift to a more profitable cloud business is a huge positive. >> Yeah, there's a lot that goes into stock price, right? I remember I was a director of research back in the day. One of my analysts said to me, "Am I crazy for putting a $1,000 target on Amazon?" And I laughed and I said, "No, you're crazy if you don't make it $2,000." (both chuckling) So, you know, at that time it was basically the mix shift towards AWS. You're a thousand percent right. I think the tough year over year comps had something to do with that reaction. That, you know, it's just getting really hard. What's that? The law of large numbers, right? It's really hard to grow at that percentage rate when you're getting this big. But from our data perspective, we're seeing no slowdown in AWS, in cloud, none whatsoever. The only slowdown we're seeing in cloud is GCP. But to, you know, to focus on AWS, extremely strong across the board and not only just in cloud, but in all their data products as well, data and analytics. >> Yeah and I think that the AWS, don't forget folks, that funds Amazon's TAM expansion into so many different places. Okay. As we said at the top, the world of digital and hybrid work, and multi-cloud, it's more complicated than it used to be. And that means if you need to resolve issues, which everybody does, like poor application performance, et cetera, what's happening at the user level, you have to have a better way to sort of see what's going on. And that's what the emergence of the observability space is all about. So Erik, let me set this up and you have a lot of comments here because you've recently had some, and you always have had a lot of round table discussions with CXOs on this topic. So this chart plots net score or spending momentum on the vertical axis, and market share or pervasiveness in the dataset on the horizontal axis. And we inserted a table that shows the data points in detail. Now that red dotted line is just sort of Dave Vellante's subjective mark in the sand for elevated spending levels. And there are three other points here. One is Splunk as well off is two-year peak, as highlighted in the red, but Signal FX, which Splunk acquired, has made a big move northward this last quarter. As has Datadog. So Erik, what can you share with us on this hot, but increasingly crowded space? >> Yeah. I could talk about the space for a long time. As you know, I've gotten some flack over the last year and a half about, you know, kind of pointing out this trend, this negative trend in Splunk. So I do want to be the first one to say that this data set is rebounding. Splunk has been horrific in our data for going back almost two years now, straight downward trend. This is the first time we're seeing any increase, any positivity there. So I do want to be fair and state that because I've been accused of being a little too negative on Splunk in the past. But I would basically say for observability right now, it's a rising tide lifts all boats, if I can use a New England phrase. The data across the board in analytics for these observability players is up, is accelerating. None more so than Datadog. And it's exactly your point, David. The complexity, the increased cloud migration is a perfect setup for Datadog, which is a cloud native. It focuses on microservices. It focuses on cloud observability. Old Splunk was just application monitoring. Don't get me wrong, they're changing, but they were on-prem application monitoring, first and foremost. Datadog came out as cloud native. They, you know, do microservices. This is just a perfect setup for them. And not only is Datadog leading the observability, it's leading the entire analytics sector, all of it. Not just the observability niche. So without a doubt, that is the strongest that we're seeing. It's leading Dynatrace new Relic. The only one that really isn't rebounding is Cisco App Dynamics. That's getting the dreaded legacy word really attached to it. But this space is really on fire, elastic as well, really doing well in this space. New Relic has shown a little bit of improvement as well. And what I heard when I asked my panelists about this, is that because of the maturity of cloud migration, that this observability has to grow. Spending on this has to happen. So they all say the chart looks right. And it's really just about the digital transformation maturity. So that's largely what they think is happening here. And they don't really see it getting, you know, changing anytime soon. >> Yeah, and I would add, and you see that it's getting crowded. You saw a service now acquired LightStep, and they want to get into the game. You mentioned, you know, last deck of the elk stack is, you know, the open source alternative, but then we see a company who's raised a fair amount of money, startup, chaos search, coming in, going after kind of the complexity of the elk stack. You've got honeycomb, which has got a really innovative approach, Jeremy Burton's company observes. So you have venture capital coming in. So we'll see if those guys could be disruptive enough or are they, you know, candidates to get acquired? We'll see how that all- you know that well. The M and A space. You think this space is ripe for M and A? >> I think it's ripe for consolidation, M and A. Something has to shake out. There's no doubt. I do believe that all of these can be standalone. So we shall see what's happened to, you mentioned the Splunk acquisition of Signal FX, just a house cleaning point. That was really nice acceleration by Signal FX, but it was only 20 citations. We'd looked into this a little bit deeper. Our data scientists did. It appears as if the majority of people are just signaling spunk and not FX separately. So moving forward for our data set, we're going to combine those two, so we don't have those anomalies going forward. But that type of acquisition does show what we should expect to see more of in this group going forward. >> Well that's I want to mention. That's one of the challenges that any data company has, and you guys do a great job of it. You're constantly having to reevaluate. There's so much M and A going on in the industry. You've got to pick the right spots in terms of when to consolidate. There's some big, you know, Dell and EMC, for example. You know, you've beautifully worked through that transition. You're seeing, you know, open shift and red hat with IBM. You just got to be flexible. And that's where it's valuable to be able to have a pipeline to guys like Erik, to sort of squint through that. So thank you for that clarification. >> Thank you too, because having a resource like you with industry knowledge really helps us navigate some of those as well for everyone out there. So that's a lot to do with you do Dave, >> Thank you. It's going to be interesting to watch Splunk. Doug Merritt's made some, you know, management changes, not the least of which is bringing in Teresa Carlson to run go to market. So if you know, I'd be interested if they are hitting, bouncing off the bottom and rising up again. They have a great customer base. Okay. Let's look at some of the same dimensions. Go ahead. You got a comment? >> A few of ETR's clients looked at our data and then put a billion dollar investment into it too. So obviously I agree. (Dave laughing) Splunk is looking like it's set for a rebound, and it's definitely something to watch, I agree. >> Not to rat hole in this, but I got to say. When I look back, cause theCUBE gives us kind of early visibility. So companies with momentum and you talk to the customers that all these shows that we go to. I will tell you that three companies stood out last decade. It was Splunk. It was Service Now and Tableau. And you could tell just from just discussions with their customers, the enthusiasm in that customer base. And so that's a real asset, and that helps them build them a moat. So we'll see. All right, let's take a look at the same dimensions now for cyber. This is cybersecurity net score in the vertical, and market share in the horizontal. And I filtered by in greater than a hundred shared in because just gets so crowded. Erik, the only things I would point out here is CrowdStrike and Zscaler continue to shine, CyberArk also showing momentum over that 40% line. Very impressively, Palo Alto networks, which has a big presence in the market. They've bounced back. We predicted that a while back. Your round table suggested people like working with Palo Alto. They're a gold standard. You know, we had reported earlier on that divergence with four to net in terms of valuation and some of the challenges they had in cloud, clearly, you know, back with the momentum. And of course, Microsoft in the upper, right. It's just, they're literally off the charts and obviously a major player here, but your thoughts on cyber? >> Erik: Yeah. Going back to the backdrop. Security, security, security. It has been the number one priority going back to last September. No one sees it changing. It has to happen. The threat vectors are actually expanding and we have no choice but to spend here. So it is not surprising to see. You did name our three favorite names. So as you know, we look at the dataset, we see which ones have the most positive inflections, and we put outlooks on those. And you did mention Zscaler, Okta and CrowdStrike, by far the three standouts that we're seeing. I just recently did a huge panel on Okta talking about their acquisition of Auth Zero. They're pushed into Sale Point space, trying to move just from single sign on and MFA to going to really privileged account management. There is some hurdles there. Really Okta's ability to do this on-prem is something that a little bit of the IT end users are concerned about. But what we're seeing right now, both Okta and Auth Zero are two of the main adopted names in security. They look incredibly well set up. Zscaler as well. With the ZTNA push more towards zero trust, Zscaler came out so hot in their IPO. And everyone was wondering if it was going to trail off just like Snowflake. It's not trailing off. This thing just keeps going up into the right, up into the right. The data supports a lot of tremendous growth for the three names that you just mentioned. >> Yeah. Yeah. I'm glad you brought up Auth Zero. We had reported on that earlier. I just feel like that was a great acquisition. You had Okta doing the belly to belly enterprise, you know, selling. And the one thing that they really lacked was that developer momentum. And that's what Auth Zero brings. Just a smart move by Todd McKinnon and company. And I mean, so this, you know, I want to, I want to pull up another chart show a quick snapshot of some of the players in the survey who show momentum and have you comment on this. We haven't mentioned Snowflake so far, but they remain again with like this gold standard of net score, they've consistently had those high marks with regard to spending velocity. But here's some other data. Erik, how should we interpret this? >> Erik: Yeah, just to harp on Snowflake for a second. Right, I mean the rich get richer. They came out- IPO was so hyped, so it was hard for us as a research company to say, "Oh, you know, well, you know, we agree." But we did. The data is incredible. You can't beat the management team. You can't beat what they're doing. They've got so much cash. I can't wait to see what they do with it. And meanwhile, you would expect something that debuted with that high of a net score, that high of spending velocity to trail off. It would be natural. It's not Dave, it's still accelerating. It's gone even higher. It's at all time highs. And we just don't see it stopping anytime soon. It's a really interesting space right now. Maybe another name to look at on here that I think is pretty interesting, kind of a play on return to business is Kupa. It's a great project expense management tool that got hit really hard. Listen, traveling stopped, business expense stopped, and I did a panel on it. And a lot of our guys basically said, "Yeah, it was the first thing I cut." But we're seeing a huge rebound in spending there in that space. So that's a name that I think might be worth being called out on a positive side. Negative, If you look down to the bottom right of that chart, unfortunately we're seeing some issues in RingCentral and Zoom. Anything that's sort of playing in this next, you know, video conferencing, IP telephony space, they seem to be having really decelerating spending. Also now with Zoom's acquisition of five nine. I'm not really sure how RingCentral's going to compete on that. But yeah, that's one where we debuted for the first time with a negative outlook on that name. And looking and asking to some of the people in our community, a lot of them say externally, you still need IP telepany, but internally you don't. Because the You Cast communication systems are getting so sophisticated, that if I have Teams, if I have Slack, I don't need phones anymore. (chuckling) That you and I can just do a Slack call. We can do a Teams call. And many of them are saying I'm truly ripping out my IP Telepany internally as soon as possible because we just don't need it. So this whole collaboration, productivity space is here to stay. And it's got wide ranging implications to some of these more legacy type of tools. >> You know, one of the other things I'd call out on this chart is Accenture. You and I had a session earlier this year, and we had predicted that that skill shortage was going to lead to an uptick in traditional services. We've certainly seen that. I mean, IBM beat its quarter on the strength of services largely. And seeing Accenture on that is I think confirmation. >> Yeah that was our New Year prediction show, right Dave? When we made top 10 predictions? >> That's right. That was part of our predictions show. Exactly, good memory. >> The data is really showing that continue. People want the projects, they need to do the projects, but hiring is very difficult. So obviously the number one beneficiary there are going to be the Accentures of the world. >> All right. So let's do a quick wrap. I'm going to make a few comments and then have you bring us home, Erik. So we laid out our scenario for the tech spending rebound. We definitely believe last year tracked downward, along with GDP contraction. It was interesting. Gardner doesn't believe, at least factions of Gardner don't believe there's a correlation between GDP and tech spending. But, you know, I personally think there generally is some kind of relatively proportional pattern there. And I think we saw contraction last year. People are concerned about inflation. Of course, that adds some uncertainty. And as well, as you mentioned around the Delta variant. But I feel as though that the boards of directors and CEOs, they've mandated that tech execs have to build out digital platforms for the future. They're data centric. They're highly automated, to your earlier points. They're intelligent with AI infused, and that's going to take investment. I feel like the tech community has said, "Look, we know what to do here. We're dealing with hybrid work. We can't just stop doing what we're doing. Let's move forward." You know, and as you say, we're flying again and so forth. You know, getting hybrid right is a major priority that directly impacts strategies. Technology strategies, particularly around security, cloud, the productivity of remote workers with collaboration. And as we've said many times, we are entering a new era of data that's going to focus on decentralized data, building data products, and Erik let's keep an eye on this observability space. Lot of interest there, and buyers have a number of choices. You know, do they go with a specialist, as we saw recently, we've seen in the past, or did they go with the generalist like Service Now with the acquisition of LightStep? You know, it's going to be interesting. A lot of people are going to get into this space, start bundling into larger platforms. And so as you said, there's probably not enough room for all the players. We're going to see some consolidation there. But anyway, let me give you the final word here. >> Yeah, no, I completely agree with all of it. And I think your earlier points are spot on, that analytics and automation are certainly going to be moving more and more to that left of that chart we had of priorities. I think as we continue that survey heading into 2022, we'll have some fresh data for you again in a few months, that's going to start looking at 2022 priorities and overall spend. And the one other area that I keep hearing about over and over and over again is customer experience. There's a transition from good old CRM to CXM. Right now, everything is digital. It is not going away. So you need an omni-channel support to not only track your customer experience, but improve it. Make sure there's a two way communication. And it's a really interesting space. Salesforce is going to migrate into it. We've got Qualtrics out there. You've got Medallia. You've got FreshWorks, you've got Sprinkler. You got some names out there. And everyone I keep talking to on the IT end user side keeps bringing up customer experience. So let's keep an eye on that as well. >> That's a great point. And again, it brings me back to Service Now. We wrote a piece last week that's sort of, Service Now and Salesforce are on a collision course. We've said that for many, many years. And you've got this platform of platforms. They're just kind of sucking in different functions saying, "Hey, we're friends with everybody." But as you know Erik, software companies, they want to own it all. (both chuckling) All right. Hey Erik, thank you so much. I want to thank you for coming back on. It's always a pleasure to have you on Breaking Analysis. Great to see you. >> Love the partnership. Love the collaboration. Let's go enjoy this summer Friday. >> All right. Let's do. Okay, remember everybody, these episodes, they're all available as podcasts, wherever you listen. All you got to do is search Breaking Analysis Podcast, click subscribe to the series. Check out ETR's website at etr.plus. They've just launched a new website. They've got a whole new pricing model. It's great to see that innovation going on. Now remember we also publish a full report every week on WikiBond.com and SiliconAngle.com. You can always email me, appreciate the back channel comments, the metadata insights. David.Vellante@SiliconAngle.com. DM me on Twitter @DVellante or comment on the LinkedIn posts. This is Dave Vellante for Erik Bradley and theCUBE insights powered by ETR. Have a great week, a good rest of summer, be well. And we'll see you next time. (inspiring music)

>> From theCUBE studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is braking analysis with Dave Vellante. >> A new era of data is upon us, and we're in a state of transition. You know, even our language reflects that. We rarely use the phrase big data anymore, rather we talk about digital transformation or digital business, or data-driven companies. Many have come to the realization that data is a not the new oil, because unlike oil, the same data can be used over and over for different purposes. We still use terms like data as an asset. However, that same narrative, when it's put forth by the vendor and practitioner communities, includes further discussions about democratizing and sharing data. Let me ask you this, when was the last time you wanted to share your financial assets with your coworkers or your partners or your customers? Hello everyone, and welcome to this week's Wikibon Cube Insights powered by ETR. In this breaking analysis, we want to share our assessment of the state of the data business. We'll do so by looking at the data mesh concept and how a leading financial institution, JP Morgan Chase is practically applying these relatively new ideas to transform its data architecture. Let's start by looking at what is the data mesh. As we've previously reported many times, data mesh is a concept and set of principles that was introduced in 2018 by Zhamak Deghani who's director of technology at ThoughtWorks, it's a global consultancy and software development company. And she created this movement because her clients, who were some of the leading firms in the world had invested heavily in predominantly monolithic data architectures that had failed to deliver desired outcomes in ROI. So her work went deep into trying to understand that problem. And her main conclusion that came out of this effort was the world of data is distributed and shoving all the data into a single monolithic architecture is an approach that fundamentally limits agility and scale. Now a profound concept of data mesh is the idea that data architectures should be organized around business lines with domain context. That the highly technical and hyper specialized roles of a centralized cross functional team are a key blocker to achieving our data aspirations. This is the first of four high level principles of data mesh. So first again, that the business domain should own the data end-to-end, rather than have it go through a centralized big data technical team. Second, a self-service platform is fundamental to a successful architectural approach where data is discoverable and shareable across an organization and an ecosystem. Third, product thinking is central to the idea of data mesh. In other words, data products will power the next era of data success. And fourth data products must be built with governance and compliance that is automated and federated. Now there's lot more to this concept and there are tons of resources on the web to learn more, including an entire community that is formed around data mesh. But this should give you a basic idea. Now, the other point is that, in observing Zhamak Deghani's work, she is deliberately avoided discussions around specific tooling, which I think has frustrated some folks because we all like to have references that tie to products and tools and companies. So this has been a two-edged sword in that, on the one hand it's good, because data mesh is designed to be tool agnostic and technology agnostic. On the other hand, it's led some folks to take liberties with the term data mesh and claim mission accomplished when their solution, you know, maybe more marketing than reality. So let's look at JP Morgan Chase in their data mesh journey. Is why I got really excited when I saw this past week, a team from JPMC held a meet up to discuss what they called, data lake strategy via data mesh architecture. I saw that title, I thought, well, that's a weird title. And I wondered, are they just taking their legacy data lakes and claiming they're now transformed into a data mesh? But in listening to the presentation, which was over an hour long, the answer is a definitive no, not at all in my opinion. A gentleman named Scott Hollerman organized the session that comprised these three speakers here, James Reid, who's a divisional CIO at JPMC, Arup Nanda who is a technologist and architect and Serita Bakst who is an information architect, again, all from JPMC. This was the most detailed and practical discussion that I've seen to date about implementing a data mesh. And this is JP Morgan's their approach, and we know they're extremely savvy and technically sound. And they've invested, it has to be billions in the past decade on data architecture across their massive company. And rather than dwell on the downsides of their big data past, I was really pleased to see how they're evolving their approach and embracing new thinking around data mesh. So today, we're going to share some of the slides that they use and comment on how it dovetails into the concept of data mesh that Zhamak Deghani has been promoting, and at least as we understand it. And dig a bit into some of the tooling that is being used by JP Morgan, particularly around it's AWS cloud. So the first point is it's all about business value, JPMC, they're in the money business, and in that world, business value is everything. So Jr Reid, the CIO showed this slide and talked about their overall goals, which centered on a cloud first strategy to modernize the JPMC platform. I think it's simple and sensible, but there's three factors on which he focused, cut costs always short, you got to do that. Number two was about unlocking new opportunities, or accelerating time to value. But I was really happy to see number three, data reuse. That's a fundamental value ingredient in the slide that he's presenting here. And his commentary was all about aligning with the domains and maximizing data reuse, i.e. data is not like oil and making sure there's appropriate governance around that. Now don't get caught up in the term data lake, I think it's just how JP Morgan communicates internally. It's invested in the data lake concept, so they use water analogies. They use things like data puddles, for example, which are single project data marts or data ponds, which comprise multiple data puddles. And these can feed in to data lakes. And as we'll see, JPMC doesn't strive to have a single version of the truth from a data standpoint that resides in a monolithic data lake, rather it enables the business lines to create and own their own data lakes that comprise fit for purpose data products. And they do have a single truth of metadata. Okay, we'll get to that. But generally speaking, each of the domains will own end-to-end their own data and be responsible for those data products, we'll talk about that more. Now the genesis of this was sort of a cloud first platform, JPMC is leaning into public cloud, which is ironic since the early days, in the early days of cloud, all the financial institutions were like never. Anyway, JPMC is going hard after it, they're adopting agile methods and microservices architectures, and it sees cloud as a fundamental enabler, but it recognizes that on-prem data must be part of the data mesh equation. Here's a slide that starts to get into some of that generic tooling, and then we'll go deeper. And I want to make a couple of points here that tie back to Zhamak Deghani's original concept. The first is that unlike many data architectures, this puts data as products right in the fat middle of the chart. The data products live in the business domains and are at the heart of the architecture. The databases, the Hadoop clusters, the files and APIs on the left-hand side, they serve the data product builders. The specialized roles on the right hand side, the DBA's, the data engineers, the data scientists, the data analysts, we could have put in quality engineers, et cetera, they serve the data products. Because the data products are owned by the business, they inherently have the context that is the middle of this diagram. And you can see at the bottom of the slide, the key principles include domain thinking, an end-to-end ownership of the data products. They build it, they own it, they run it, they manage it. At the same time, the goal is to democratize data with a self-service as a platform. One of the biggest points of contention of data mesh is governance. And as Serita Bakst said on the Meetup, metadata is your friend, and she kind of made a joke, she said, "This sounds kind of geeky, but it's important to have a metadata catalog to understand where data resides and the data lineage in overall change management. So to me, this really past the data mesh stink test pretty well. Let's look at data as products. CIO Reid said the most difficult thing for JPMC was getting their heads around data product, and they spent a lot of time getting this concept to work. Here's the slide they use to describe their data products as it related to their specific industry. They set a common language and taxonomy is very important, and you can imagine how difficult that was. He said, for example, it took a lot of discussion and debate to define what a transaction was. But you can see at a high level, these three product groups around wholesale, credit risk, party, and trade and position data as products, and each of these can have sub products, like, party, we'll have to know your customer, KYC for example. So a key for JPMC was to start at a high level and iterate to get more granular over time. So lots of decisions had to be made around who owns the products and the sub-products. The product owners interestingly had to defend why that product should even exist, what boundaries should be in place and what data sets do and don't belong in the various products. And this was a collaborative discussion, I'm sure there was contention around that between the lines of business. And which sub products should be part of these circles? They didn't say this, but tying it back to data mesh, each of these products, whether in a data lake or a data hub or a data pond or data warehouse, data puddle, each of these is a node in the global data mesh that is discoverable and governed. And supporting this notion, Serita said that, "This should not be infrastructure-bound, logically, any of these data products, whether on-prem or in the cloud can connect via the data mesh." So again, I felt like this really stayed true to the data mesh concept. Well, let's look at some of the key technical considerations that JPM discussed in quite some detail. This chart here shows a diagram of how JP Morgan thinks about the problem, and some of the challenges they had to consider were how to write to various data stores, can you and how can you move data from one data store to another? How can data be transformed? Where's the data located? Can the data be trusted? How can it be easily accessed? Who has the right to access that data? These are all problems that technology can help solve. And to address these issues, Arup Nanda explained that the heart of this slide is the data in ingestor instead of ETL. All data producers and contributors, they send their data to the ingestor and the ingestor then registers the data so it's in the data catalog. It does a data quality check and it tracks the lineage. Then, data is sent to the router, which persists the data in the data store based on the best destination as informed by the registration. This is designed to be a flexible system. In other words, the data store for a data product is not fixed, it's determined at the point of inventory, and that allows changes to be easily made in one place. The router simply reads that optimal location and sends it to the appropriate data store. Nowadays you see the schema infer there is used when there is no clear schema on right. In this case, the data product is not allowed to be consumed until the schema is inferred, and then the data goes into a raw area, and the inferer determines the schema and then updates the inventory system so that the data can be routed to the proper location and properly tracked. So that's some of the detail of how the sausage factory works in this particular use case, it was very interesting and informative. Now let's take a look at the specific implementation on AWS and dig into some of the tooling. As described in some detail by Arup Nanda, this diagram shows the reference architecture used by this group within JP Morgan, and it shows all the various AWS services and components that support their data mesh approach. So start with the authorization block right there underneath Kinesis. The lake formation is the single point of entitlement and has a number of buckets including, you can see there the raw area that we just talked about, a trusted bucket, a refined bucket, et cetera. Depending on the data characteristics at the data catalog registration block where you see the glue catalog, that determines in which bucket the router puts the data. And you can see the many AWS services in use here, identity, the EMR, the elastic MapReduce cluster from the legacy Hadoop work done over the years, the Redshift Spectrum and Athena, JPMC uses Athena for single threaded workloads and Redshift Spectrum for nested types so they can be queried independent of each other. Now remember very importantly, in this use case, there is not a single lake formation, rather than multiple lines of business will be authorized to create their own lakes, and that creates a challenge. So how can that be done in a flexible and automated manner? And that's where the data mesh comes into play. So JPMC came up with this federated lake formation accounts idea, and each line of business can create as many data producer or consumer accounts as they desire and roll them up into their master line of business lake formation account. And they cross-connect these data products in a federated model. And these all roll up into a master glue catalog so that any authorized user can find out where a specific data element is located. So this is like a super set catalog that comprises multiple sources and syncs up across the data mesh. So again to me, this was a very well thought out and practical application of database. Yes, it includes some notion of centralized management, but much of that responsibility has been passed down to the lines of business. It does roll up to a master catalog, but that's a metadata management effort that seems compulsory to ensure federated and automated governance. As well at JPMC, the office of the chief data officer is responsible for ensuring governance and compliance throughout the federation. All right, so let's take a look at some of the suspects in this world of data mesh and bring in the ETR data. Now, of course, ETR doesn't have a data mesh category, there's no such thing as that data mesh vendor, you build a data mesh, you don't buy it. So, what we did is we use the ETR dataset to select and filter on some of the culprits that we thought might contribute to the data mesh to see how they're performing. This chart depicts a popular view that we often like to share. It's a two dimensional graphic with net score or spending momentum on the vertical axis and market share or pervasiveness in the data set on the horizontal axis. And we filtered the data on sectors such as analytics, data warehouse, and the adjacencies to things that might fit into data mesh. And we think that these pretty well reflect participation that data mesh is certainly not all compassing. And it's a subset obviously, of all the vendors who could play in the space. Let's make a few observations. Now as is often the case, Azure and AWS, they're almost literally off the charts with very high spending velocity and large presence in the market. Oracle you can see also stands out because much of the world's data lives inside of Oracle databases. It doesn't have the spending momentum or growth, but the company remains prominent. And you can see Google Cloud doesn't have nearly the presence in the dataset, but it's momentum is highly elevated. Remember that red dotted line there, that 40% line, anything over that indicates elevated spending momentum. Let's go to Snowflake. Snowflake is consistently shown to be the gold standard in net score in the ETR dataset. It continues to maintain highly elevated spending velocity in the data. And in many ways, Snowflake with its data marketplace and its data cloud vision and data sharing approach, fit nicely into the data mesh concept. Now, a caution, Snowflake has used the term data mesh in it's marketing, but in our view, it lacks clarity, and we feel like they're still trying to figure out how to communicate what that really is. But is really, we think a lot of potential there to that vision. Databricks is also interesting because the firm has momentum and we expect further elevated levels in the vertical axis in upcoming surveys, especially as it readies for its IPO. The firm has a strong product and managed service, and is really one to watch. Now we included a number of other database companies for obvious reasons like Redis and Mongo, MariaDB, Couchbase and Terradata. SAP as well is in there, but that's not all database, but SAP is prominent so we included them. As is IBM more of a database, traditional database player also with the big presence. Cloudera includes Hortonworks and HPE Ezmeral comprises the MapR business that HPE acquired. So these guys got the big data movement started, between Cloudera, Hortonworks which is born out of Yahoo, which was the early big data, sorry early Hadoop innovator, kind of MapR when it's kind of owned course, and now that's all kind of come together in various forms. And of course, we've got Talend and Informatica are there, they are two data integration companies that are worth noting. We also included some of the AI and ML specialists and data science players in the mix like DataRobot who just did a monster $250 million round. Dataiku, H2O.ai and ThoughtSpot, which is all about democratizing data and injecting AI, and I think fits well into the data mesh concept. And you know we put VMware Cloud in there for reference because it really is the predominant on-prem infrastructure platform. All right, let's wrap with some final thoughts here, first, thanks a lot to the JP Morgan team for sharing this data. I really want to encourage practitioners and technologists, go to watch the YouTube of that meetup, we'll include it in the link of this session. And thank you to Zhamak Deghani and the entire data mesh community for the outstanding work that you're doing, challenging the established conventions of monolithic data architectures. The JPM presentation, it gives you real credibility, it takes Data Mesh well beyond concept, it demonstrates how it can be and is being done. And you know, this is not a perfect world, you're going to start somewhere and there's going to be some failures, the key is to recognize that shoving everything into a monolithic data architecture won't support massive scale and agility that you're after. It's maybe fine for smaller use cases in smaller firms, but if you're building a global platform in a data business, it's time to rethink data architecture. Now much of this is enabled by the cloud, but cloud first doesn't mean cloud only, doesn't mean you'll leave your on-prem data behind, on the contrary, you have to include non-public cloud data in your Data Mesh vision just as JPMC has done. You've got to get some quick wins, that's crucial so you can gain credibility within the organization and grow. And one of the key takeaways from the JP Morgan team is, there is a place for dogma, like organizing around data products and domains and getting that right. On the other hand, you have to remain flexible because technologies is going to come, technology is going to go, so you got to be flexible in that regard. And look, if you're going to embrace the metaphor of water like puddles and ponds and lakes, we suggest maybe a little tongue in cheek, but still we believe in this, that you expand your scope to include data ocean, something John Furry and I have talked about and laughed about extensively in theCUBE. Data oceans, it's huge. It's the new data lake, go transcend data lake, think oceans. And think about this, just as we're evolving our language, we should be evolving our metrics. Much the last the decade of big data was around just getting the stuff to work, getting it up and running, standing up infrastructure and managing massive, how much data you got? Massive amounts of data. And there were many KPIs built around, again, standing up that infrastructure, ingesting data, a lot of technical KPIs. This decade is not just about enabling better insights, it's a more than that. Data mesh points us to a new era of data value, and that requires the new metrics around monetizing data products, like how long does it take to go from data product conception to monetization? And how does that compare to what it is today? And what is the time to quality if the business owns the data, and the business has the context? the quality that comes out of them, out of the shoot should be at a basic level, pretty good, and at a higher mark than out of a big data team with no business context. Automation, AI, and very importantly, organizational restructuring of our data teams will heavily contribute to success in the coming years. So we encourage you, learn, lean in and create your data future. Okay, that's it for now, remember these episodes, they're all available as podcasts wherever you listen, all you got to do is search, breaking analysis podcast, and please subscribe. Check out ETR's website at etr.plus for all the data and all the survey information. We publish a full report every week on wikibon.com and siliconangle.com. And you can get in touch with us, email me david.vellante@siliconangle.com, you can DM me @dvellante, or you can comment on my LinkedIn posts. This is Dave Vellante for theCUBE insights powered by ETR. Have a great week everybody, stay safe, be well, and we'll see you next time. (upbeat music)

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

>> Good morning, good afternoon and good evening. I'm Jeff Corcoran, Worldwide Go To Market Program Manager for the Compute Business Group. And I'm here today to talk to you about enabling and empowering your remote workforce with virtual desktop infrastructure or VDI. The pandemic has changed the way everyone works. And we're unlikely to go back to the way things were before 2020. The entire world has seen a dramatic fore shift to remote working. As you can see on the graphic here, 75% of CEOs say the pandemic has changed and accelerated this transformation. This brings with it a whole host of challenges. There are technical challenges like security and connectivity but there are also important challenges like culture and productivity to be concerned with. Gartner found that around half of employers now see remote work as a go forward motion for them which is opposed to less than a third before the pandemic. Of course there's work that you just can't do remotely. There the question is, how do you ensure maximum employee safety for work that needs to be physically co-located? 60% of CEOs say that their top concern is keeping employees safe and productive. It's becoming quite clear that the future is one of hybrid. It means that you have the flexibility to get work done regardless of your physical location. Because it's better for business continuity, better for employee productivity and better for long-term effectiveness. And employers are demanding it. Gartner reports that around 80% of employees want to work remotely, at least some of the time as opposed to those that want to work remotely all the time which is around 56%. This is because employees report the flexibility to work from home. It's a boost to retention, productivity and work-life balance. It's no coincidence that a JP Morgan CIO Survey found that the single biggest tech spending shift has been for technologies that enable remote working. This is seeing a 15% increase while other technologies in the rest of the market is flat to declining. When we talk about remote and hybrid work, one of the key enabling technologies is VDI. VDI is a client desktop virtualization workload. That's a subset of the more expansive spectrum of end user computing or EUC for short. These are technologies that allow users to access corporate applications and data regardless of where they are. Within this EUC spectrum, there are server-based computing which is sometimes known as application virtualization. These are for users with less complex computing needs. And then you've got the aforementioned VDI which is for task or productivity users. And then we have physical hosted desktops which is for the most demanding end-users. To understand why VDI has become so popular, we need to understand the benefits that it can provide. So you've got ease of access. And again we're talking about remote work, work from home. This is a way of life. So the VDI has the ability to provide that ease of access. Flexibility, so organizations have vastly different needs predicated on their users and their computing needs. So VDI enables organizations to provision right size solutions for their workforce. Less administrative overhead, you can now manage devices in the desktop to updates from a centralized location for VDI which is a tremendous boost. Resource consolidation, for those deployments where the users don't require full capacity all the time, you can see tremendous consolidation ratios. Data security and sovereignty, this is probably the number one reason why people go with VDI. You safely keep your data where it belongs in the data center where you have the ability to build a secure perimeter around it. So in this scenario with VDI, users are accessing the data. It's not on their laptop, it's in the data center. And now what happens is when they access it, the data itself doesn't come across the line. It's just the pixels of what that data represents so that it paints it on their screen. So if somebody were to intercept that stream they wouldn't get the data itself but just the pixels so security is greatly enhanced. And this is also closely predicated to performance. Applications reside close to the data, in the data center. So they're able to operate at data center speed, so think about 10 gigabyte or higher speeds. And so for those engineering workloads, for example that have maybe large models and they have lowered huge dataset with many different parts because this is operating at wire speed in the data center it happens very quickly. And this is a boon to productivity. It's a great way to realize the benefit of VDI. The process of developing your HPE VDI solution starts with identifying the types of users you have and understanding the applications that they use to perform their duties. That way we can size the VDI deployment correctly. If they provide or perform more simple office tasks or just a single function positions, these are what we might call task workers. So they use limited office, Microsoft Office, you know, they're maybe some word processing. But think about customer service, telesales, data entry, healthcare, telemedicine is a good one here. Perhaps they need more performance and they're oriented towards analysis or content creation. These are what we call knowledge workers. And this is probably most of you in the audience. Think about heavy office 365 usage teams and zoom for collaboration, web based SaaS apps. This is office workers, sales and operations, marketing, finance legal. And then lastly for those users that are really dependent on a heavy graphical usage, think about MRIs scans for healthcare, maybe complex graphs for investment bankers, maybe simulations or modeling and engineering, these are power users. So again, you know, CAD engineering design simulation, financial traders, geo-physical analysis for the energy industry, software developers and the media and entertain industry. These are great places for power users. Whatever the right mix is for your organization, we ensure that the solution provides each and every type of worker, the performance they need to perform the tasks they need to have success. Netherlands Cancer Institute is one of the foremost cancer research centers in the world. They were looking to improve IT agility and performance to support demanding research projects and dynamic clinical services. And to do this, we worked with them and deployed HPE ProLiant DL380 Gen10 with VMware Horizon for their VDI infrastructure. And what this did was supported during the day up to 2000 VDI users. And at night, the usage went down to 400 to 600 users and the flexible design of the solution allowed them to take advantage of this infrastructure. And they could allocate capacity at night to some batch jobs that were running to improve image sharpness of imagery that's used to aid in the early research of cancer disease. And what used to take one hour to work on an image, took 10 minutes now in this new environment. So they are able to increase the agility to run diverse clinical and research workloads. They (indistinct) their IT infrastructure to handle consistently and constantly evolving business needs. And it also freed clinicians to focus more time on patient care which is really what they wanted to do. And the quote here says that by spending less time working with technology, the clinicians were able to spend more time focusing on the patients which is what they, you know, what's the most important part of this equation. With the introduction of HPE ProLiant Gen10 Plus, we see a tremendous opportunity to help our customers drive better outcomes. For VDI that means we can leverage the innovation that the 3rd Generation AMD EPYC Processor provides. Improved clock speeds and increased instructions per clock will greatly benefit VDI workloads as well increased memory, so up to four terabytes per CPU. Storage and networking are no longer going to bottlenecks either as there's 128 PCIe Gen4 lanes to support this increased IO. This is twice the bandwidth that was available with Gen3. So with this increased performance envelopes for several sub-systems, we're able to build higher performing VDI solutions that'll help our customers drive the outcomes needed to move their business forward. When we leverage HPE GreenLake for VDI, it brings the simplicity of the cloud experience to VDI. The ability to scale capacity and costs up and down is a key benefit of cloud. But most VDI implementations need to meet certain standards of security, compliance and performance that cannot readily be met with pure public cloud solutions. HPE GreenLake for VDI brings that cloud-like economics and agility together with the performance compliance and control that you expect from your on premises IT. And because it is managed for you and build, use monthly, you can focus your IT teams on other critical aspects of delivering outcomes that help you drive your business forward. We just talked about GreenLake which is a great way for us to help you accelerate your transformation. You can deploy any workload as a service with GreenLake services. You can now bring that cloud speed agility and an as a service model to where your apps and data are today. You can transform the way you do business with one experience and one operating model across your distributed clouds for depths and data at the edge in co-locations and in your data center. With over 11,000 IT projects conducted and 1.4 million customer interactions each and every year, HPE Pointnext 15,000 experts in its vast ecosystem of solution partners and channel partners are uniquely able to help you at every stage of your digital transformation. Because we address some of the biggest areas that can slow you down. We bring together technology and expertise to help you deliver your most strategic outcomes. Flexible investment capacity is a key consideration for businesses to drive digital transformation initiatives. In order to forge a path forward, you need access to flexible payment terms that allow you to match your IT costs to usage. You need help releasing capital from existing infrastructures to deferring payments and providing pre-owned technology to relieve capacity strain. HPE Financial Services or HPE FS, unlocks the value of your entire IT estate from edge to cloud to end user with multi-vendor solutions consistently and sustainably around the world. HPE FS helps you create the financial capacity to transform your work business. There is a lot of other resources that are available to help you learn about the VDI solutions that we have available to help you. So there's a few links on the screen that talk about some of our VDI solutions, our product portfolio. And there's also some social media engagements that we can do on LinkedIn, Twitter or Facebook. I'd like to thank you for taking some time out of your day to attend this session. Have a great rest of your day.