>> 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.

>> Welcome back to SuperCloud2. This is Dave Vellante, and we're here with Jack Greenfield. He's the Vice President of Enterprise Architecture and the Chief Architect for the global technology platform at Walmart. Jack, I want to thank you for coming on the program. Really appreciate your time. >> Glad to be here, Dave. Thanks for inviting me and appreciate the opportunity to chat with you. >> Yeah, it's our pleasure. Now we call what you've built a SuperCloud. That's our term, not yours, but how would you describe the Walmart Cloud Native Platform? >> So WCNP, as the acronym goes, is essentially an implementation of Kubernetes for the Walmart ecosystem. And what that means is that we've taken Kubernetes off the shelf as open source, and we have integrated it with a number of foundational services that provide other aspects of our computational environment. So Kubernetes off the shelf doesn't do everything. It does a lot. In particular the orchestration of containers, but it delegates through API a lot of key functions. So for example, secret management, traffic management, there's a need for telemetry and observability at a scale beyond what you get from raw Kubernetes. That is to say, harvesting the metrics that are coming out of Kubernetes and processing them, storing them in time series databases, dashboarding them, and so on. There's also an angle to Kubernetes that gets a lot of attention in the daily DevOps routine, that's not really part of the open source deliverable itself, and that is the DevOps sort of CICD pipeline-oriented lifecycle. And that is something else that we've added and integrated nicely. And then one more piece of this picture is that within a Kubernetes cluster, there's a function that is critical to allowing services to discover each other and integrate with each other securely and with proper configuration provided by the concept of a service mesh. So Istio, Linkerd, these are examples of service mesh technologies. And we have gone ahead and integrated actually those two. There's more than those two, but we've integrated those two with Kubernetes. So the net effect is that when a developer within Walmart is going to build an application, they don't have to think about all those other capabilities where they come from or how they're provided. Those are already present, and the way the CICD pipelines are set up, it's already sort of in the picture, and there are configuration points that they can take advantage of in the primary YAML and a couple of other pieces of config that we supply where they can tune it. But at the end of the day, it offloads an awful lot of work for them, having to stand up and operate those services, fail them over properly, and make them robust. All of that's provided for. >> Yeah, you know, developers often complain they spend too much time wrangling and doing things that aren't productive. So I wonder if you could talk about the high level business goals of the initiative in terms of the hardcore benefits. Was the real impetus to tap into best of breed cloud services? Were you trying to cut costs? Maybe gain negotiating leverage with the cloud guys? Resiliency, you know, I know was a major theme. Maybe you could give us a sense of kind of the anatomy of the decision making process that went in. >> Sure, and in the course of answering your question, I think I'm going to introduce the concept of our triplet architecture which we haven't yet touched on in the interview here. First off, just to sort of wrap up the motivation for WCNP itself which is kind of orthogonal to the triplet architecture. It can exist with or without it. Currently does exist with it, which is key, and I'll get to that in a moment. The key drivers, business drivers for WCNP were developer productivity by offloading the kinds of concerns that we've just discussed. Number two, improving resiliency, that is to say reducing opportunity for human error. One of the challenges you tend to run into in a large enterprise is what we call snowflakes, lots of gratuitously different workloads, projects, configurations to the extent that by developing and using WCNP and continuing to evolve it as we have, we end up with cookie cutter like consistency across our workloads which is super valuable when it comes to building tools or building services to automate operations that would otherwise be manual. When everything is pretty much done the same way, that becomes much simpler. Another key motivation for WCNP was the ability to abstract from the underlying cloud provider. And this is going to lead to a discussion of our triplet architecture. At the end of the day, when one works directly with an underlying cloud provider, one ends up taking a lot of dependencies on that particular cloud provider. Those dependencies can be valuable. For example, there are best of breed services like say Cloud Spanner offered by Google or say Cosmos DB offered by Microsoft that one wants to use and one is willing to take the dependency on the cloud provider to get that functionality because it's unique and valuable. On the other hand, one doesn't want to take dependencies on a cloud provider that don't add a lot of value. And with Kubernetes, we have the opportunity, and this is a large part of how Kubernetes was designed and why it is the way it is, we have the opportunity to sort of abstract from the underlying cloud provider for stateless workloads on compute. And so what this lets us do is build container-based applications that can run without change on different cloud provider infrastructure. So the same applications can run on WCNP over Azure, WCNP over GCP, or WCNP over the Walmart private cloud. And we have a private cloud. Our private cloud is OpenStack based and it gives us some significant cost advantages as well as control advantages. So to your point, in terms of business motivation, there's a key cost driver here, which is that we can use our own private cloud when it's advantageous and then use the public cloud provider capabilities when we need to. A key place with this comes into play is with elasticity. So while the private cloud is much more cost effective for us to run and use, it isn't as elastic as what the cloud providers offer, right? We don't have essentially unlimited scale. We have large scale, but the public cloud providers are elastic in the extreme which is a very powerful capability. So what we're able to do is burst, and we use this term bursting workloads into the public cloud from the private cloud to take advantage of the elasticity they offer and then fall back into the private cloud when the traffic load diminishes to the point where we don't need that elastic capability, elastic capacity at low cost. And this is a very important paradigm that I think is going to be very commonplace ultimately as the industry evolves. Private cloud is easier to operate and less expensive, and yet the public cloud provider capabilities are difficult to match. >> And the triplet, the tri is your on-prem private cloud and the two public clouds that you mentioned, is that right? >> That is correct. And we actually have an architecture in which we operate all three of those cloud platforms in close proximity with one another in three different major regions in the US. So we have east, west, and central. And in each of those regions, we have all three cloud providers. And the way it's configured, those data centers are within 10 milliseconds of each other, meaning that it's of negligible cost to interact between them. And this allows us to be fairly agnostic to where a particular workload is running. >> Does a human make that decision, Jack or is there some intelligence in the system that determines that? >> That's a really great question, Dave. And it's a great question because we're at the cusp of that transition. So currently humans make that decision. Humans choose to deploy workloads into a particular region and a particular provider within that region. That said, we're actively developing patterns and practices that will allow us to automate the placement of the workloads for a variety of criteria. For example, if in a particular region, a particular provider is heavily overloaded and is unable to provide the level of service that's expected through our SLAs, we could choose to fail workloads over from that cloud provider to a different one within the same region. But that's manual today. We do that, but people do it. Okay, we'd like to get to where that happens automatically. In the same way, we'd like to be able to automate the failovers, both for high availability and sort of the heavier disaster recovery model between, within a region between providers and even within a provider between the availability zones that are there, but also between regions for the sort of heavier disaster recovery or maintenance driven realignment of workload placement. Today, that's all manual. So we have people moving workloads from region A to region B or data center A to data center B. It's clean because of the abstraction. The workloads don't have to know or care, but there are latency considerations that come into play, and the humans have to be cognizant of those. And automating that can help ensure that we get the best performance and the best reliability. >> But you're developing the dataset to actually, I would imagine, be able to make those decisions in an automated fashion over time anyway. Is that a fair assumption? >> It is, and that's what we're actively developing right now. So if you were to look at us today, we have these nice abstractions and APIs in place, but people run that machine, if you will, moving toward a world where that machine is fully automated. >> What exactly are you abstracting? Is it sort of the deployment model or, you know, are you able to abstract, I'm just making this up like Azure functions and GCP functions so that you can sort of run them, you know, with a consistent experience. What exactly are you abstracting and how difficult was it to achieve that objective technically? >> that's a good question. What we're abstracting is the Kubernetes node construct. That is to say a cluster of Kubernetes nodes which are typically VMs, although they can run bare metal in certain contexts, is something that typically to stand up requires knowledge of the underlying cloud provider. So for example, with GCP, you would use GKE to set up a Kubernetes cluster, and in Azure, you'd use AKS. We are actually abstracting that aspect of things so that the developers standing up applications don't have to know what the underlying cluster management provider is. They don't have to know if it's GCP, AKS or our own Walmart private cloud. Now, in terms of functions like Azure functions that you've mentioned there, we haven't done that yet. That's another piece that we have sort of on our radar screen that, we'd like to get to is serverless approach, and the Knative work from Google and the Azure functions, those are things that we see good opportunity to use for a whole variety of use cases. But right now we're not doing much with that. We're strictly container based right now, and we do have some VMs that are running in sort of more of a traditional model. So our stateful workloads are primarily VM based, but for serverless, that's an opportunity for us to take some of these stateless workloads and turn them into cloud functions. >> Well, and that's another cost lever that you can pull down the road that's going to drop right to the bottom line. Do you see a day or maybe you're doing it today, but I'd be surprised, but where you build applications that actually span multiple clouds or is there, in your view, always going to be a direct one-to-one mapping between where an application runs and the specific cloud platform? >> That's a really great question. Well, yes and no. So today, application development teams choose a cloud provider to deploy to and a location to deploy to, and they have to get involved in moving an application like we talked about today. That said, the bursting capability that I mentioned previously is something that is a step in the direction of automatic migration. That is to say we're migrating workload to different locations automatically. Currently, the prototypes we've been developing and that we think are going to eventually make their way into production are leveraging Istio to assess the load incoming on a particular cluster and start shedding that load into a different location. Right now, the configuration of that is still manual, but there's another opportunity for automation there. And I think a key piece of this is that down the road, well, that's a, sort of a small step in the direction of an application being multi provider. We expect to see really an abstraction of the fact that there is a triplet even. So the workloads are moving around according to whatever the control plane decides is necessary based on a whole variety of inputs. And at that point, you will have true multi-cloud applications, applications that are distributed across the different providers and in a way that application developers don't have to think about. >> So Walmart's been a leader, Jack, in using data for competitive advantages for decades. It's kind of been a poster child for that. You've got a mountain of IP in the form of data, tools, applications best practices that until the cloud came out was all On Prem. But I'm really interested in this idea of building a Walmart ecosystem, which obviously you have. Do you see a day or maybe you're even doing it today where you take what we call the Walmart SuperCloud, WCNP in your words, and point or turn that toward an external world or your ecosystem, you know, supporting those partners or customers that could drive new revenue streams, you know directly from the platform? >> Great questions, Dave. So there's really two things to say here. The first is that with respect to data, our data workloads are primarily VM basis. I've mentioned before some VMware, some straight open stack. But the key here is that WCNP and Kubernetes are very powerful for stateless workloads, but for stateful workloads tend to be still climbing a bit of a growth curve in the industry. So our data workloads are not primarily based on WCNP. They're VM based. Now that said, there is opportunity to make some progress there, and we are looking at ways to move things into containers that are currently running in VMs which are stateful. The other question you asked is related to how we expose data to third parties and also functionality. Right now we do have in-house, for our own use, a very robust data architecture, and we have followed the sort of domain-oriented data architecture guidance from Martin Fowler. And we have data lakes in which we collect data from all the transactional systems and which we can then use and do use to build models which are then used in our applications. But right now we're not exposing the data directly to customers as a product. That's an interesting direction that's been talked about and may happen at some point, but right now that's internal. What we are exposing to customers is applications. So we're offering our global integrated fulfillment capabilities, our order picking and curbside pickup capabilities, and our cloud powered checkout capabilities to third parties. And this means we're standing up our own internal applications as externally facing SaaS applications which can serve our partners' customers. >> Yeah, of course, Martin Fowler really first introduced to the world Zhamak Dehghani's data mesh concept and this whole idea of data products and domain oriented thinking. Zhamak Dehghani, by the way, is a speaker at our event as well. Last question I had is edge, and how you think about the edge? You know, the stores are an edge. Are you putting resources there that sort of mirror this this triplet model? Or is it better to consolidate things in the cloud? I know there are trade-offs in terms of latency. How are you thinking about that? >> All really good questions. It's a challenging area as you can imagine because edges are subject to disconnection, right? Or reduced connection. So we do place the same architecture at the edge. So WCNP runs at the edge, and an application that's designed to run at WCNP can run at the edge. That said, there are a number of very specific considerations that come up when running at the edge, such as the possibility of disconnection or degraded connectivity. And so one of the challenges we have faced and have grappled with and done a good job of I think is dealing with the fact that applications go offline and come back online and have to reconnect and resynchronize, the sort of online offline capability is something that can be quite challenging. And we have a couple of application architectures that sort of form the two core sets of patterns that we use. One is an offline/online synchronization architecture where we discover that we've come back online, and we understand the differences between the online dataset and the offline dataset and how they have to be reconciled. The other is a message-based architecture. And here in our health and wellness domain, we've developed applications that are queue based. So they're essentially business processes that consist of multiple steps where each step has its own queue. And what that allows us to do is devote whatever bandwidth we do have to those pieces of the process that are most latency sensitive and allow the queue lengths to increase in parts of the process that are not latency sensitive, knowing that they will eventually catch up when the bandwidth is restored. And to put that in a little bit of context, we have fiber lengths to all of our locations, and we have I'll just use a round number, 10-ish thousand locations. It's larger than that, but that's the ballpark, and we have fiber to all of them, but when the fiber is disconnected, When the disconnection happens, we're able to fall back to 5G and to Starlink. Starlink is preferred. It's a higher bandwidth. 5G if that fails. But in each of those cases, the bandwidth drops significantly. And so the applications have to be intelligent about throttling back the traffic that isn't essential, so that it can push the essential traffic in those lower bandwidth scenarios. >> So much technology to support this amazing business which started in the early 1960s. Jack, unfortunately, we're out of time. I would love to have you back or some members of your team and drill into how you're using open source, but really thank you so much for explaining the approach that you've taken and participating in SuperCloud2. >> You're very welcome, Dave, and we're happy to come back and talk about other aspects of what we do. For example, we could talk more about the data lakes and the data mesh that we have in place. We could talk more about the directions we might go with serverless. So please look us up again. Happy to chat. >> I'm going to take you up on that, Jack. All right. This is Dave Vellante for John Furrier and the Cube community. Keep it right there for more action from SuperCloud2. (upbeat music)

(upbeat music beginning) >> From the Cube Studios in Palo Alto and Boston, bringing you data-driven insights from the Cube and ETR, this is "Breaking Analysis" with Dave Vellante. >> Making predictions about the future of enterprise tech is more challenging if you strive to lay down forecasts that are measurable. In other words, if you make a prediction, you should be able to look back a year later and say, with some degree of certainty, whether the prediction came true or not, with evidence to back that up. Hello and welcome to this week's Wikibon Cube Insights, powered by ETR. In this breaking analysis, we aim to do just that, with predictions about the macro IT spending environment, cost optimization, security, lots to talk about there, generative AI, cloud, and of course supercloud, blockchain adoption, data platforms, including commentary on Databricks, snowflake, and other key players, automation, events, and we may even have some bonus predictions around quantum computing, and perhaps some other areas. To make all this happen, we welcome back, for the third year in a row, my colleague and friend Eric Bradley from ETR. Eric, thanks for all you do for the community, and thanks for being part of this program. Again. >> I wouldn't miss it for the world. I always enjoy this one. Dave, good to see you. >> Yeah, so let me bring up this next slide and show you, actually come back to me if you would. I got to show the audience this. These are the inbounds that we got from PR firms starting in October around predictions. They know we do prediction posts. And so they'll send literally thousands and thousands of predictions from hundreds of experts in the industry, technologists, consultants, et cetera. And if you bring up the slide I can show you sort of the pattern that developed here. 40% of these thousands of predictions were from cyber. You had AI and data. If you combine those, it's still not close to cyber. Cost optimization was a big thing. Of course, cloud, some on DevOps, and software. Digital... Digital transformation got, you know, some lip service and SaaS. And then there was other, it's kind of around 2%. So quite remarkable, when you think about the focus on cyber, Eric. >> Yeah, there's two reasons why I think it makes sense, though. One, the cybersecurity companies have a lot of cash, so therefore the PR firms might be working a little bit harder for them than some of their other clients. (laughs) And then secondly, as you know, for multiple years now, when we do our macro survey, we ask, "What's your number one spending priority?" And again, it's security. It just isn't going anywhere. It just stays at the top. So I'm actually not that surprised by that little pie chart there, but I was shocked that SaaS was only 5%. You know, going back 10 years ago, that would've been the only thing anyone was talking about. >> Yeah. So true. All right, let's get into it. First prediction, we always start with kind of tech spending. Number one is tech spending increases between four and 5%. ETR has currently got it at 4.6% coming into 2023. This has been a consistently downward trend all year. We started, you know, much, much higher as we've been reporting. Bottom line is the fed is still in control. They're going to ease up on tightening, is the expectation, they're going to shoot for a soft landing. But you know, my feeling is this slingshot economy is going to continue, and it's going to continue to confound, whether it's supply chains or spending. The, the interesting thing about the ETR data, Eric, and I want you to comment on this, the largest companies are the most aggressive to cut. They're laying off, smaller firms are spending faster. They're actually growing at a much larger, faster rate as are companies in EMEA. And that's a surprise. That's outpacing the US and APAC. Chime in on this, Eric. >> Yeah, I was surprised on all of that. First on the higher level spending, we are definitely seeing it coming down, but the interesting thing here is headlines are making it worse. The huge research shop recently said 0% growth. We're coming in at 4.6%. And just so everyone knows, this is not us guessing, we asked 1,525 IT decision-makers what their budget growth will be, and they came in at 4.6%. Now there's a huge disparity, as you mentioned. The Fortune 500, global 2000, barely at 2% growth, but small, it's at 7%. So we're at a situation right now where the smaller companies are still playing a little bit of catch up on digital transformation, and they're spending money. The largest companies that have the most to lose from a recession are being more trepidatious, obviously. So they're playing a "Wait and see." And I hope we don't talk ourselves into a recession. Certainly the headlines and some of their research shops are helping it along. But another interesting comment here is, you know, energy and utilities used to be called an orphan and widow stock group, right? They are spending more than anyone, more than financials insurance, more than retail consumer. So right now it's being driven by mid, small, and energy and utilities. They're all spending like gangbusters, like nothing's happening. And it's the rest of everyone else that's being very cautious. >> Yeah, so very unpredictable right now. All right, let's go to number two. Cost optimization remains a major theme in 2023. We've been reporting on this. You've, we've shown a chart here. What's the primary method that your organization plans to use? You asked this question of those individuals that cited that they were going to reduce their spend and- >> Mhm. >> consolidating redundant vendors, you know, still leads the way, you know, far behind, cloud optimization is second, but it, but cloud continues to outpace legacy on-prem spending, no doubt. Somebody, it was, the guy's name was Alexander Feiglstorfer from Storyblok, sent in a prediction, said "All in one becomes extinct." Now, generally I would say I disagree with that because, you know, as we know over the years, suites tend to win out over, you know, individual, you know, point products. But I think what's going to happen is all in one is going to remain the norm for these larger companies that are cutting back. They want to consolidate redundant vendors, and the smaller companies are going to stick with that best of breed and be more aggressive and try to compete more effectively. What's your take on that? >> Yeah, I'm seeing much more consolidation in vendors, but also consolidation in functionality. We're seeing people building out new functionality, whether it's, we're going to talk about this later, so I don't want to steal too much of our thunder right now, but data and security also, we're seeing a functionality creep. So I think there's further consolidation happening here. I think niche solutions are going to be less likely, and platform solutions are going to be more likely in a spending environment where you want to reduce your vendors. You want to have one bill to pay, not 10. Another thing on this slide, real quick if I can before I move on, is we had a bunch of people write in and some of the answer options that aren't on this graph but did get cited a lot, unfortunately, is the obvious reduction in staff, hiring freezes, and delaying hardware, were three of the top write-ins. And another one was offshore outsourcing. So in addition to what we're seeing here, there were a lot of write-in options, and I just thought it would be important to state that, but essentially the cost optimization is by and far the highest one, and it's growing. So it's actually increased in our citations over the last year. >> And yeah, specifically consolidating redundant vendors. And so I actually thank you for bringing that other up, 'cause I had asked you, Eric, is there any evidence that repatriation is going on and we don't see it in the numbers, we don't see it even in the other, there was, I think very little or no mention of cloud repatriation, even though it might be happening in this in a smattering. >> Not a single mention, not one single mention. I went through it for you. Yep. Not one write-in. >> All right, let's move on. Number three, security leads M&A in 2023. Now you might say, "Oh, well that's a layup," but let me set this up Eric, because I didn't really do a great job with the slide. I hid the, what you've done, because you basically took, this is from the emerging technology survey with 1,181 responses from November. And what we did is we took Palo Alto and looked at the overlap in Palo Alto Networks accounts with these vendors that were showing on this chart. And Eric, I'm going to ask you to explain why we put a circle around OneTrust, but let me just set it up, and then have you comment on the slide and take, give us more detail. We're seeing private company valuations are off, you know, 10 to 40%. We saw a sneak, do a down round, but pretty good actually only down 12%. We've seen much higher down rounds. Palo Alto Networks we think is going to get busy. Again, they're an inquisitive company, they've been sort of quiet lately, and we think CrowdStrike, Cisco, Microsoft, Zscaler, we're predicting all of those will make some acquisitions and we're thinking that the targets are somewhere in this mess of security taxonomy. Other thing we're predicting AI meets cyber big time in 2023, we're going to probably going to see some acquisitions of those companies that are leaning into AI. We've seen some of that with Palo Alto. And then, you know, your comment to me, Eric, was "The RSA conference is going to be insane, hopping mad, "crazy this April," (Eric laughing) but give us your take on this data, and why the red circle around OneTrust? Take us back to that slide if you would, Alex. >> Sure. There's a few things here. First, let me explain what we're looking at. So because we separate the public companies and the private companies into two separate surveys, this allows us the ability to cross-reference that data. So what we're doing here is in our public survey, the tesis, everyone who cited some spending with Palo Alto, meaning they're a Palo Alto customer, we then cross-reference that with the private tech companies. Who also are they spending with? So what you're seeing here is an overlap. These companies that we have circled are doing the best in Palo Alto's accounts. Now, Palo Alto went and bought Twistlock a few years ago, which this data slide predicted, to be quite honest. And so I don't know if they necessarily are going to go after Snyk. Snyk, sorry. They already have something in that space. What they do need, however, is more on the authentication space. So I'm looking at OneTrust, with a 45% overlap in their overall net sentiment. That is a company that's already existing in their accounts and could be very synergistic to them. BeyondTrust as well, authentication identity. This is something that Palo needs to do to move more down that zero trust path. Now why did I pick Palo first? Because usually they're very inquisitive. They've been a little quiet lately. Secondly, if you look at the backdrop in the markets, the IPO freeze isn't going to last forever. Sooner or later, the IPO markets are going to open up, and some of these private companies are going to tap into public equity. In the meantime, however, cash funding on the private side is drying up. If they need another round, they're not going to get it, and they're certainly not going to get it at the valuations they were getting. So we're seeing valuations maybe come down where they're a touch more attractive, and Palo knows this isn't going to last forever. Cisco knows that, CrowdStrike, Zscaler, all these companies that are trying to make a push to become that vendor that you're consolidating in, around, they have a chance now, they have a window where they need to go make some acquisitions. And that's why I believe leading up to RSA, we're going to see some movement. I think it's going to pretty, a really exciting time in security right now. >> Awesome. Thank you. Great explanation. All right, let's go on the next one. Number four is, it relates to security. Let's stay there. Zero trust moves from hype to reality in 2023. Now again, you might say, "Oh yeah, that's a layup." A lot of these inbounds that we got are very, you know, kind of self-serving, but we always try to put some meat in the bone. So first thing we do is we pull out some commentary from, Eric, your roundtable, your insights roundtable. And we have a CISO from a global hospitality firm says, "For me that's the highest priority." He's talking about zero trust because it's the best ROI, it's the most forward-looking, and it enables a lot of the business transformation activities that we want to do. CISOs tell me that they actually can drive forward transformation projects that have zero trust, and because they can accelerate them, because they don't have to go through the hurdle of, you know, getting, making sure that it's secure. Second comment, zero trust closes that last mile where once you're authenticated, they open up the resource to you in a zero trust way. That's a CISO of a, and a managing director of a cyber risk services enterprise. Your thoughts on this? >> I can be here all day, so I'm going to try to be quick on this one. This is not a fluff piece on this one. There's a couple of other reasons this is happening. One, the board finally gets it. Zero trust at first was just a marketing hype term. Now the board understands it, and that's why CISOs are able to push through it. And what they finally did was redefine what it means. Zero trust simply means moving away from hardware security, moving towards software-defined security, with authentication as its base. The board finally gets that, and now they understand that this is necessary and it's being moved forward. The other reason it's happening now is hybrid work is here to stay. We weren't really sure at first, large companies were still trying to push people back to the office, and it's going to happen. The pendulum will swing back, but hybrid work's not going anywhere. By basically on our own data, we're seeing that 69% of companies expect remote and hybrid to be permanent, with only 30% permanent in office. Zero trust works for a hybrid environment. So all of that is the reason why this is happening right now. And going back to our previous prediction, this is why we're picking Palo, this is why we're picking Zscaler to make these acquisitions. Palo Alto needs to be better on the authentication side, and so does Zscaler. They're both fantastic on zero trust network access, but they need the authentication software defined aspect, and that's why we think this is going to happen. One last thing, in that CISO round table, I also had somebody say, "Listen, Zscaler is incredible. "They're doing incredibly well pervading the enterprise, "but their pricing's getting a little high," and they actually think Palo Alto is well-suited to start taking some of that share, if Palo can make one move. >> Yeah, Palo Alto's consolidation story is very strong. Here's my question and challenge. Do you and me, so I'm always hardcore about, okay, you've got to have evidence. I want to look back at these things a year from now and say, "Did we get it right? Yes or no?" If we got it wrong, we'll tell you we got it wrong. So how are we going to measure this? I'd say a couple things, and you can chime in. One is just the number of vendors talking about it. That's, but the marketing always leads the reality. So the second part of that is we got to get evidence from the buying community. Can you help us with that? >> (laughs) Luckily, that's what I do. I have a data company that asks thousands of IT decision-makers what they're adopting and what they're increasing spend on, as well as what they're decreasing spend on and what they're replacing. So I have snapshots in time over the last 11 years where I can go ahead and compare and contrast whether this adoption is happening or not. So come back to me in 12 months and I'll let you know. >> Now, you know, I will. Okay, let's bring up the next one. Number five, generative AI hits where the Metaverse missed. Of course everybody's talking about ChatGPT, we just wrote last week in a breaking analysis with John Furrier and Sarjeet Joha our take on that. We think 2023 does mark a pivot point as natural language processing really infiltrates enterprise tech just as Amazon turned the data center into an API. We think going forward, you're going to be interacting with technology through natural language, through English commands or other, you know, foreign language commands, and investors are lining up, all the VCs are getting excited about creating something competitive to ChatGPT, according to (indistinct) a hundred million dollars gets you a seat at the table, gets you into the game. (laughing) That's before you have to start doing promotion. But he thinks that's what it takes to actually create a clone or something equivalent. We've seen stuff from, you know, the head of Facebook's, you know, AI saying, "Oh, it's really not that sophisticated, ChatGPT, "it's kind of like IBM Watson, it's great engineering, "but you know, we've got more advanced technology." We know Google's working on some really interesting stuff. But here's the thing. ETR just launched this survey for the February survey. It's in the field now. We circle open AI in this category. They weren't even in the survey, Eric, last quarter. So 52% of the ETR survey respondents indicated a positive sentiment toward open AI. I added up all the sort of different bars, we could double click on that. And then I got this inbound from Scott Stevenson of Deep Graham. He said "AI is recession-proof." I don't know if that's the case, but it's a good quote. So bring this back up and take us through this. Explain this chart for us, if you would. >> First of all, I like Scott's quote better than the Facebook one. I think that's some sour grapes. Meta just spent an insane amount of money on the Metaverse and that's a dud. Microsoft just spent money on open AI and it is hot, undoubtedly hot. We've only been in the field with our current ETS survey for a week. So my caveat is it's preliminary data, but I don't care if it's preliminary data. (laughing) We're getting a sneak peek here at what is the number one net sentiment and mindshare leader in the entire machine-learning AI sector within a week. It's beating Data- >> 600. 600 in. >> It's beating Databricks. And we all know Databricks is a huge established enterprise company, not only in machine-learning AI, but it's in the top 10 in the entire survey. We have over 400 vendors in this survey. It's number eight overall, already. In a week. This is not hype. This is real. And I could go on the NLP stuff for a while. Not only here are we seeing it in open AI and machine-learning and AI, but we're seeing NLP in security. It's huge in email security. It's completely transforming that area. It's one of the reasons I thought Palo might take Abnormal out. They're doing such a great job with NLP in this email side, and also in the data prep tools. NLP is going to take out data prep tools. If we have time, I'll discuss that later. But yeah, this is, to me this is a no-brainer, and we're already seeing it in the data. >> Yeah, John Furrier called, you know, the ChatGPT introduction. He said it reminded him of the Netscape moment, when we all first saw Netscape Navigator and went, "Wow, it really could be transformative." All right, number six, the cloud expands to supercloud as edge computing accelerates and CloudFlare is a big winner in 2023. We've reported obviously on cloud, multi-cloud, supercloud and CloudFlare, basically saying what multi-cloud should have been. We pulled this quote from Atif Kahn, who is the founder and CTO of Alkira, thanks, one of the inbounds, thank you. "In 2023, highly distributed IT environments "will become more the norm "as organizations increasingly deploy hybrid cloud, "multi-cloud and edge settings..." Eric, from one of your round tables, "If my sources from edge computing are coming "from the cloud, that means I have my workloads "running in the cloud. "There is no one better than CloudFlare," That's a senior director of IT architecture at a huge financial firm. And then your analysis shows CloudFlare really growing in pervasion, that sort of market presence in the dataset, dramatically, to near 20%, leading, I think you had told me that they're even ahead of Google Cloud in terms of momentum right now. >> That was probably the biggest shock to me in our January 2023 tesis, which covers the public companies in the cloud computing sector. CloudFlare has now overtaken GCP in overall spending, and I was shocked by that. It's already extremely pervasive in networking, of course, for the edge networking side, and also in security. This is the number one leader in SaaSi, web access firewall, DDoS, bot protection, by your definition of supercloud, which we just did a couple of weeks ago, and I really enjoyed that by the way Dave, I think CloudFlare is the one that fits your definition best, because it's bringing all of these aspects together, and most importantly, it's cloud agnostic. It does not need to rely on Azure or AWS to do this. It has its own cloud. So I just think it's, when we look at your definition of supercloud, CloudFlare is the poster child. >> You know, what's interesting about that too, is a lot of people are poo-pooing CloudFlare, "Ah, it's, you know, really kind of not that sophisticated." "You don't have as many tools," but to your point, you're can have those tools in the cloud, Cloudflare's doing serverless on steroids, trying to keep things really simple, doing a phenomenal job at, you know, various locations around the world. And they're definitely one to watch. Somebody put them on my radar (laughing) a while ago and said, "Dave, you got to do a breaking analysis on CloudFlare." And so I want to thank that person. I can't really name them, 'cause they work inside of a giant hyperscaler. But- (Eric laughing) (Dave chuckling) >> Real quickly, if I can from a competitive perspective too, who else is there? They've already taken share from Akamai, and Fastly is their really only other direct comp, and they're not there. And these guys are in poll position and they're the only game in town right now. I just, I don't see it slowing down. >> I thought one of your comments from your roundtable I was reading, one of the folks said, you know, CloudFlare, if my workloads are in the cloud, they are, you know, dominant, they said not as strong with on-prem. And so Akamai is doing better there. I'm like, "Okay, where would you want to be?" (laughing) >> Yeah, which one of those two would you rather be? >> Right? Anyway, all right, let's move on. Number seven, blockchain continues to look for a home in the enterprise, but devs will slowly begin to adopt in 2023. You know, blockchains have got a lot of buzz, obviously crypto is, you know, the killer app for blockchain. Senior IT architect in financial services from your, one of your insight roundtables said quote, "For enterprises to adopt a new technology, "there have to be proven turnkey solutions. "My experience in talking with my peers are, "blockchain is still an open-source component "where you have to build around it." Now I want to thank Ravi Mayuram, who's the CTO of Couchbase sent in, you know, one of the predictions, he said, "DevOps will adopt blockchain, specifically Ethereum." And he referenced actually in his email to me, Solidity, which is the programming language for Ethereum, "will be in every DevOps pro's playbook, "mirroring the boom in machine-learning. "Newer programming languages like Solidity "will enter the toolkits of devs." His point there, you know, Solidity for those of you don't know, you know, Bitcoin is not programmable. Solidity, you know, came out and that was their whole shtick, and they've been improving that, and so forth. But it, Eric, it's true, it really hasn't found its home despite, you know, the potential for smart contracts. IBM's pushing it, VMware has had announcements, and others, really hasn't found its way in the enterprise yet. >> Yeah, and I got to be honest, I don't think it's going to, either. So when we did our top trends series, this was basically chosen as an anti-prediction, I would guess, that it just continues to not gain hold. And the reason why was that first comment, right? It's very much a niche solution that requires a ton of custom work around it. You can't just plug and play it. And at the end of the day, let's be very real what this technology is, it's a database ledger, and we already have database ledgers in the enterprise. So why is this a priority to move to a different database ledger? It's going to be very niche cases. I like the CTO comment from Couchbase about it being adopted by DevOps. I agree with that, but it has to be a DevOps in a very specific use case, and a very sophisticated use case in financial services, most likely. And that's not across the entire enterprise. So I just think it's still going to struggle to get its foothold for a little bit longer, if ever. >> Great, thanks. Okay, let's move on. Number eight, AWS Databricks, Google Snowflake lead the data charge with Microsoft. Keeping it simple. So let's unpack this a little bit. This is the shared accounts peer position for, I pulled data platforms in for analytics, machine-learning and AI and database. So I could grab all these accounts or these vendors and see how they compare in those three sectors. Analytics, machine-learning and database. Snowflake and Databricks, you know, they're on a crash course, as you and I have talked about. They're battling to be the single source of truth in analytics. They're, there's going to be a big focus. They're already started. It's going to be accelerated in 2023 on open formats. Iceberg, Python, you know, they're all the rage. We heard about Iceberg at Snowflake Summit, last summer or last June. Not a lot of people had heard of it, but of course the Databricks crowd, who knows it well. A lot of other open source tooling. There's a company called DBT Labs, which you're going to talk about in a minute. George Gilbert put them on our radar. We just had Tristan Handy, the CEO of DBT labs, on at supercloud last week. They are a new disruptor in data that's, they're essentially making, they're API-ifying, if you will, KPIs inside the data warehouse and dramatically simplifying that whole data pipeline. So really, you know, the ETL guys should be shaking in their boots with them. Coming back to the slide. Google really remains focused on BigQuery adoption. Customers have complained to me that they would like to use Snowflake with Google's AI tools, but they're being forced to go to BigQuery. I got to ask Google about that. AWS continues to stitch together its bespoke data stores, that's gone down that "Right tool for the right job" path. David Foyer two years ago said, "AWS absolutely is going to have to solve that problem." We saw them start to do it in, at Reinvent, bringing together NoETL between Aurora and Redshift, and really trying to simplify those worlds. There's going to be more of that. And then Microsoft, they're just making it cheap and easy to use their stuff, you know, despite some of the complaints that we hear in the community, you know, about things like Cosmos, but Eric, your take? >> Yeah, my concern here is that Snowflake and Databricks are fighting each other, and it's allowing AWS and Microsoft to kind of catch up against them, and I don't know if that's the right move for either of those two companies individually, Azure and AWS are building out functionality. Are they as good? No they're not. The other thing to remember too is that AWS and Azure get paid anyway, because both Databricks and Snowflake run on top of 'em. So (laughing) they're basically collecting their toll, while these two fight it out with each other, and they build out functionality. I think they need to stop focusing on each other, a little bit, and think about the overall strategy. Now for Databricks, we know they came out first as a machine-learning AI tool. They were known better for that spot, and now they're really trying to play catch-up on that data storage compute spot, and inversely for Snowflake, they were killing it with the compute separation from storage, and now they're trying to get into the MLAI spot. I actually wouldn't be surprised to see them make some sort of acquisition. Frank Slootman has been a little bit quiet, in my opinion there. The other thing to mention is your comment about DBT Labs. If we look at our emerging technology survey, last survey when this came out, DBT labs, number one leader in that data integration space, I'm going to just pull it up real quickly. It looks like they had a 33% overall net sentiment to lead data analytics integration. So they are clearly growing, it's fourth straight survey consecutively that they've grown. The other name we're seeing there a little bit is Cribl, but DBT labs is by far the number one player in this space. >> All right. Okay, cool. Moving on, let's go to number nine. With Automation mixer resurgence in 2023, we're showing again data. The x axis is overlap or presence in the dataset, and the vertical axis is shared net score. Net score is a measure of spending momentum. As always, you've seen UI path and Microsoft Power Automate up until the right, that red line, that 40% line is generally considered elevated. UI path is really separating, creating some distance from Automation Anywhere, they, you know, previous quarters they were much closer. Microsoft Power Automate came on the scene in a big way, they loom large with this "Good enough" approach. I will say this, I, somebody sent me a results of a (indistinct) survey, which showed UiPath actually had more mentions than Power Automate, which was surprising, but I think that's not been the case in the ETR data set. We're definitely seeing a shift from back office to front soft office kind of workloads. Having said that, software testing is emerging as a mainstream use case, we're seeing ML and AI become embedded in end-to-end automations, and low-code is serving the line of business. And so this, we think, is going to increasingly have appeal to organizations in the coming year, who want to automate as much as possible and not necessarily, we've seen a lot of layoffs in tech, and people... You're going to have to fill the gaps with automation. That's a trend that's going to continue. >> Yep, agreed. At first that comment about Microsoft Power Automate having less citations than UiPath, that's shocking to me. I'm looking at my chart right here where Microsoft Power Automate was cited by over 60% of our entire survey takers, and UiPath at around 38%. Now don't get me wrong, 38% pervasion's fantastic, but you know you're not going to beat an entrenched Microsoft. So I don't really know where that comment came from. So UiPath, looking at it alone, it's doing incredibly well. It had a huge rebound in its net score this last survey. It had dropped going through the back half of 2022, but we saw a big spike in the last one. So it's got a net score of over 55%. A lot of people citing adoption and increasing. So that's really what you want to see for a name like this. The problem is that just Microsoft is doing its playbook. At the end of the day, I'm going to do a POC, why am I going to pay more for UiPath, or even take on another separate bill, when we know everyone's consolidating vendors, if my license already includes Microsoft Power Automate? It might not be perfect, it might not be as good, but what I'm hearing all the time is it's good enough, and I really don't want another invoice. >> Right. So how does UiPath, you know, and Automation Anywhere, how do they compete with that? Well, the way they compete with it is they got to have a better product. They got a product that's 10 times better. You know, they- >> Right. >> they're not going to compete based on where the lowest cost, Microsoft's got that locked up, or where the easiest to, you know, Microsoft basically give it away for free, and that's their playbook. So that's, you know, up to UiPath. UiPath brought on Rob Ensslin, I've interviewed him. Very, very capable individual, is now Co-CEO. So he's kind of bringing that adult supervision in, and really tightening up the go to market. So, you know, we know this company has been a rocket ship, and so getting some control on that and really getting focused like a laser, you know, could be good things ahead there for that company. Okay. >> One of the problems, if I could real quick Dave, is what the use cases are. When we first came out with RPA, everyone was super excited about like, "No, UiPath is going to be great for super powerful "projects, use cases." That's not what RPA is being used for. As you mentioned, it's being used for mundane tasks, so it's not automating complex things, which I think UiPath was built for. So if you were going to get UiPath, and choose that over Microsoft, it's going to be 'cause you're doing it for more powerful use case, where it is better. But the problem is that's not where the enterprise is using it. The enterprise are using this for base rote tasks, and simply, Microsoft Power Automate can do that. >> Yeah, it's interesting. I've had people on theCube that are both Microsoft Power Automate customers and UiPath customers, and I've asked them, "Well you know, "how do you differentiate between the two?" And they've said to me, "Look, our users and personal productivity users, "they like Power Automate, "they can use it themselves, and you know, "it doesn't take a lot of, you know, support on our end." The flip side is you could do that with UiPath, but like you said, there's more of a focus now on end-to-end enterprise automation and building out those capabilities. So it's increasingly a value play, and that's going to be obviously the challenge going forward. Okay, my last one, and then I think you've got some bonus ones. Number 10, hybrid events are the new category. Look it, if I can get a thousand inbounds that are largely self-serving, I can do my own here, 'cause we're in the events business. (Eric chuckling) Here's the prediction though, and this is a trend we're seeing, the number of physical events is going to dramatically increase. That might surprise people, but most of the big giant events are going to get smaller. The exception is AWS with Reinvent, I think Snowflake's going to continue to grow. So there are examples of physical events that are growing, but generally, most of the big ones are getting smaller, and there's going to be many more smaller intimate regional events and road shows. These micro-events, they're going to be stitched together. Digital is becoming a first class citizen, so people really got to get their digital acts together, and brands are prioritizing earned media, and they're beginning to build their own news networks, going direct to their customers. And so that's a trend we see, and I, you know, we're right in the middle of it, Eric, so you know we're going to, you mentioned RSA, I think that's perhaps going to be one of those crazy ones that continues to grow. It's shrunk, and then it, you know, 'cause last year- >> Yeah, it did shrink. >> right, it was the last one before the pandemic, and then they sort of made another run at it last year. It was smaller but it was very vibrant, and I think this year's going to be huge. Global World Congress is another one, we're going to be there end of Feb. That's obviously a big big show, but in general, the brands and the technology vendors, even Oracle is going to scale down. I don't know about Salesforce. We'll see. You had a couple of bonus predictions. Quantum and maybe some others? Bring us home. >> Yeah, sure. I got a few more. I think we touched upon one, but I definitely think the data prep tools are facing extinction, unfortunately, you know, the Talons Informatica is some of those names. The problem there is that the BI tools are kind of including data prep into it already. You know, an example of that is Tableau Prep Builder, and then in addition, Advanced NLP is being worked in as well. ThoughtSpot, Intelius, both often say that as their selling point, Tableau has Ask Data, Click has Insight Bot, so you don't have to really be intelligent on data prep anymore. A regular business user can just self-query, using either the search bar, or even just speaking into what it needs, and these tools are kind of doing the data prep for it. I don't think that's a, you know, an out in left field type of prediction, but it's the time is nigh. The other one I would also state is that I think knowledge graphs are going to break through this year. Neo4j in our survey is growing in pervasion in Mindshare. So more and more people are citing it, AWS Neptune's getting its act together, and we're seeing that spending intentions are growing there. Tiger Graph is also growing in our survey sample. I just think that the time is now for knowledge graphs to break through, and if I had to do one more, I'd say real-time streaming analytics moves from the very, very rich big enterprises to downstream, to more people are actually going to be moving towards real-time streaming, again, because the data prep tools and the data pipelines have gotten easier to use, and I think the ROI on real-time streaming is obviously there. So those are three that didn't make the cut, but I thought deserved an honorable mention. >> Yeah, I'm glad you did. Several weeks ago, we did an analyst prediction roundtable, if you will, a cube session power panel with a number of data analysts and that, you know, streaming, real-time streaming was top of mind. So glad you brought that up. Eric, as always, thank you very much. I appreciate the time you put in beforehand. I know it's been crazy, because you guys are wrapping up, you know, the last quarter survey in- >> Been a nuts three weeks for us. (laughing) >> job. I love the fact that you're doing, you know, the ETS survey now, I think it's quarterly now, right? Is that right? >> Yep. >> Yep. So that's phenomenal. >> Four times a year. I'll be happy to jump on with you when we get that done. I know you were really impressed with that last time. >> It's unbelievable. This is so much data at ETR. Okay. Hey, that's a wrap. Thanks again. >> Take care Dave. Good seeing you. >> All right, many thanks to our team here, Alex Myerson as production, he manages the podcast force. Ken Schiffman as well is a critical component of our East Coast studio. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hoof is our editor-in-chief. He's at siliconangle.com. He's just a great editing for us. Thank you all. Remember all these episodes that are available as podcasts, wherever you listen, podcast is doing great. Just search "Breaking analysis podcast." Really appreciate you guys listening. I publish each week on wikibon.com and siliconangle.com, or you can email me directly if you want to get in touch, david.vellante@siliconangle.com. That's how I got all these. I really appreciate it. I went through every single one with a yellow highlighter. It took some time, (laughing) but I appreciate it. You could DM me at dvellante, or comment on our LinkedIn post and please check out etr.ai. Its data is amazing. 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 next time on "Breaking Analysis." (upbeat music beginning) (upbeat music ending)

>> Welcome back to SuperCloud2. This is Dave Vellante, and we're here with Jack Greenfield. He's the Vice President of Enterprise Architecture and the Chief Architect for the global technology platform at Walmart. Jack, I want to thank you for coming on the program. Really appreciate your time. >> Glad to be here, Dave. Thanks for inviting me and appreciate the opportunity to chat with you. >> Yeah, it's our pleasure. Now we call what you've built a SuperCloud. That's our term, not yours, but how would you describe the Walmart Cloud Native Platform? >> So WCNP, as the acronym goes, is essentially an implementation of Kubernetes for the Walmart ecosystem. And what that means is that we've taken Kubernetes off the shelf as open source, and we have integrated it with a number of foundational services that provide other aspects of our computational environment. So Kubernetes off the shelf doesn't do everything. It does a lot. In particular the orchestration of containers, but it delegates through API a lot of key functions. So for example, secret management, traffic management, there's a need for telemetry and observability at a scale beyond what you get from raw Kubernetes. That is to say, harvesting the metrics that are coming out of Kubernetes and processing them, storing them in time series databases, dashboarding them, and so on. There's also an angle to Kubernetes that gets a lot of attention in the daily DevOps routine, that's not really part of the open source deliverable itself, and that is the DevOps sort of CICD pipeline-oriented lifecycle. And that is something else that we've added and integrated nicely. And then one more piece of this picture is that within a Kubernetes cluster, there's a function that is critical to allowing services to discover each other and integrate with each other securely and with proper configuration provided by the concept of a service mesh. So Istio, Linkerd, these are examples of service mesh technologies. And we have gone ahead and integrated actually those two. There's more than those two, but we've integrated those two with Kubernetes. So the net effect is that when a developer within Walmart is going to build an application, they don't have to think about all those other capabilities where they come from or how they're provided. Those are already present, and the way the CICD pipelines are set up, it's already sort of in the picture, and there are configuration points that they can take advantage of in the primary YAML and a couple of other pieces of config that we supply where they can tune it. But at the end of the day, it offloads an awful lot of work for them, having to stand up and operate those services, fail them over properly, and make them robust. All of that's provided for. >> Yeah, you know, developers often complain they spend too much time wrangling and doing things that aren't productive. So I wonder if you could talk about the high level business goals of the initiative in terms of the hardcore benefits. Was the real impetus to tap into best of breed cloud services? Were you trying to cut costs? Maybe gain negotiating leverage with the cloud guys? Resiliency, you know, I know was a major theme. Maybe you could give us a sense of kind of the anatomy of the decision making process that went in. >> Sure, and in the course of answering your question, I think I'm going to introduce the concept of our triplet architecture which we haven't yet touched on in the interview here. First off, just to sort of wrap up the motivation for WCNP itself which is kind of orthogonal to the triplet architecture. It can exist with or without it. Currently does exist with it, which is key, and I'll get to that in a moment. The key drivers, business drivers for WCNP were developer productivity by offloading the kinds of concerns that we've just discussed. Number two, improving resiliency, that is to say reducing opportunity for human error. One of the challenges you tend to run into in a large enterprise is what we call snowflakes, lots of gratuitously different workloads, projects, configurations to the extent that by developing and using WCNP and continuing to evolve it as we have, we end up with cookie cutter like consistency across our workloads which is super valuable when it comes to building tools or building services to automate operations that would otherwise be manual. When everything is pretty much done the same way, that becomes much simpler. Another key motivation for WCNP was the ability to abstract from the underlying cloud provider. And this is going to lead to a discussion of our triplet architecture. At the end of the day, when one works directly with an underlying cloud provider, one ends up taking a lot of dependencies on that particular cloud provider. Those dependencies can be valuable. For example, there are best of breed services like say Cloud Spanner offered by Google or say Cosmos DB offered by Microsoft that one wants to use and one is willing to take the dependency on the cloud provider to get that functionality because it's unique and valuable. On the other hand, one doesn't want to take dependencies on a cloud provider that don't add a lot of value. And with Kubernetes, we have the opportunity, and this is a large part of how Kubernetes was designed and why it is the way it is, we have the opportunity to sort of abstract from the underlying cloud provider for stateless workloads on compute. And so what this lets us do is build container-based applications that can run without change on different cloud provider infrastructure. So the same applications can run on WCNP over Azure, WCNP over GCP, or WCNP over the Walmart private cloud. And we have a private cloud. Our private cloud is OpenStack based and it gives us some significant cost advantages as well as control advantages. So to your point, in terms of business motivation, there's a key cost driver here, which is that we can use our own private cloud when it's advantageous and then use the public cloud provider capabilities when we need to. A key place with this comes into play is with elasticity. So while the private cloud is much more cost effective for us to run and use, it isn't as elastic as what the cloud providers offer, right? We don't have essentially unlimited scale. We have large scale, but the public cloud providers are elastic in the extreme which is a very powerful capability. So what we're able to do is burst, and we use this term bursting workloads into the public cloud from the private cloud to take advantage of the elasticity they offer and then fall back into the private cloud when the traffic load diminishes to the point where we don't need that elastic capability, elastic capacity at low cost. And this is a very important paradigm that I think is going to be very commonplace ultimately as the industry evolves. Private cloud is easier to operate and less expensive, and yet the public cloud provider capabilities are difficult to match. >> And the triplet, the tri is your on-prem private cloud and the two public clouds that you mentioned, is that right? >> That is correct. And we actually have an architecture in which we operate all three of those cloud platforms in close proximity with one another in three different major regions in the US. So we have east, west, and central. And in each of those regions, we have all three cloud providers. And the way it's configured, those data centers are within 10 milliseconds of each other, meaning that it's of negligible cost to interact between them. And this allows us to be fairly agnostic to where a particular workload is running. >> Does a human make that decision, Jack or is there some intelligence in the system that determines that? >> That's a really great question, Dave. And it's a great question because we're at the cusp of that transition. So currently humans make that decision. Humans choose to deploy workloads into a particular region and a particular provider within that region. That said, we're actively developing patterns and practices that will allow us to automate the placement of the workloads for a variety of criteria. For example, if in a particular region, a particular provider is heavily overloaded and is unable to provide the level of service that's expected through our SLAs, we could choose to fail workloads over from that cloud provider to a different one within the same region. But that's manual today. We do that, but people do it. Okay, we'd like to get to where that happens automatically. In the same way, we'd like to be able to automate the failovers, both for high availability and sort of the heavier disaster recovery model between, within a region between providers and even within a provider between the availability zones that are there, but also between regions for the sort of heavier disaster recovery or maintenance driven realignment of workload placement. Today, that's all manual. So we have people moving workloads from region A to region B or data center A to data center B. It's clean because of the abstraction. The workloads don't have to know or care, but there are latency considerations that come into play, and the humans have to be cognizant of those. And automating that can help ensure that we get the best performance and the best reliability. >> But you're developing the dataset to actually, I would imagine, be able to make those decisions in an automated fashion over time anyway. Is that a fair assumption? >> It is, and that's what we're actively developing right now. So if you were to look at us today, we have these nice abstractions and APIs in place, but people run that machine, if you will, moving toward a world where that machine is fully automated. >> What exactly are you abstracting? Is it sort of the deployment model or, you know, are you able to abstract, I'm just making this up like Azure functions and GCP functions so that you can sort of run them, you know, with a consistent experience. What exactly are you abstracting and how difficult was it to achieve that objective technically? >> that's a good question. What we're abstracting is the Kubernetes node construct. That is to say a cluster of Kubernetes nodes which are typically VMs, although they can run bare metal in certain contexts, is something that typically to stand up requires knowledge of the underlying cloud provider. So for example, with GCP, you would use GKE to set up a Kubernetes cluster, and in Azure, you'd use AKS. We are actually abstracting that aspect of things so that the developers standing up applications don't have to know what the underlying cluster management provider is. They don't have to know if it's GCP, AKS or our own Walmart private cloud. Now, in terms of functions like Azure functions that you've mentioned there, we haven't done that yet. That's another piece that we have sort of on our radar screen that, we'd like to get to is serverless approach, and the Knative work from Google and the Azure functions, those are things that we see good opportunity to use for a whole variety of use cases. But right now we're not doing much with that. We're strictly container based right now, and we do have some VMs that are running in sort of more of a traditional model. So our stateful workloads are primarily VM based, but for serverless, that's an opportunity for us to take some of these stateless workloads and turn them into cloud functions. >> Well, and that's another cost lever that you can pull down the road that's going to drop right to the bottom line. Do you see a day or maybe you're doing it today, but I'd be surprised, but where you build applications that actually span multiple clouds or is there, in your view, always going to be a direct one-to-one mapping between where an application runs and the specific cloud platform? >> That's a really great question. Well, yes and no. So today, application development teams choose a cloud provider to deploy to and a location to deploy to, and they have to get involved in moving an application like we talked about today. That said, the bursting capability that I mentioned previously is something that is a step in the direction of automatic migration. That is to say we're migrating workload to different locations automatically. Currently, the prototypes we've been developing and that we think are going to eventually make their way into production are leveraging Istio to assess the load incoming on a particular cluster and start shedding that load into a different location. Right now, the configuration of that is still manual, but there's another opportunity for automation there. And I think a key piece of this is that down the road, well, that's a, sort of a small step in the direction of an application being multi provider. We expect to see really an abstraction of the fact that there is a triplet even. So the workloads are moving around according to whatever the control plane decides is necessary based on a whole variety of inputs. And at that point, you will have true multi-cloud applications, applications that are distributed across the different providers and in a way that application developers don't have to think about. >> So Walmart's been a leader, Jack, in using data for competitive advantages for decades. It's kind of been a poster child for that. You've got a mountain of IP in the form of data, tools, applications best practices that until the cloud came out was all On Prem. But I'm really interested in this idea of building a Walmart ecosystem, which obviously you have. Do you see a day or maybe you're even doing it today where you take what we call the Walmart SuperCloud, WCNP in your words, and point or turn that toward an external world or your ecosystem, you know, supporting those partners or customers that could drive new revenue streams, you know directly from the platform? >> Great question, Steve. So there's really two things to say here. The first is that with respect to data, our data workloads are primarily VM basis. I've mentioned before some VMware, some straight open stack. But the key here is that WCNP and Kubernetes are very powerful for stateless workloads, but for stateful workloads tend to be still climbing a bit of a growth curve in the industry. So our data workloads are not primarily based on WCNP. They're VM based. Now that said, there is opportunity to make some progress there, and we are looking at ways to move things into containers that are currently running in VMs which are stateful. The other question you asked is related to how we expose data to third parties and also functionality. Right now we do have in-house, for our own use, a very robust data architecture, and we have followed the sort of domain-oriented data architecture guidance from Martin Fowler. And we have data lakes in which we collect data from all the transactional systems and which we can then use and do use to build models which are then used in our applications. But right now we're not exposing the data directly to customers as a product. That's an interesting direction that's been talked about and may happen at some point, but right now that's internal. What we are exposing to customers is applications. So we're offering our global integrated fulfillment capabilities, our order picking and curbside pickup capabilities, and our cloud powered checkout capabilities to third parties. And this means we're standing up our own internal applications as externally facing SaaS applications which can serve our partners' customers. >> Yeah, of course, Martin Fowler really first introduced to the world Zhamak Dehghani's data mesh concept and this whole idea of data products and domain oriented thinking. Zhamak Dehghani, by the way, is a speaker at our event as well. Last question I had is edge, and how you think about the edge? You know, the stores are an edge. Are you putting resources there that sort of mirror this this triplet model? Or is it better to consolidate things in the cloud? I know there are trade-offs in terms of latency. How are you thinking about that? >> All really good questions. It's a challenging area as you can imagine because edges are subject to disconnection, right? Or reduced connection. So we do place the same architecture at the edge. So WCNP runs at the edge, and an application that's designed to run at WCNP can run at the edge. That said, there are a number of very specific considerations that come up when running at the edge, such as the possibility of disconnection or degraded connectivity. And so one of the challenges we have faced and have grappled with and done a good job of I think is dealing with the fact that applications go offline and come back online and have to reconnect and resynchronize, the sort of online offline capability is something that can be quite challenging. And we have a couple of application architectures that sort of form the two core sets of patterns that we use. One is an offline/online synchronization architecture where we discover that we've come back online, and we understand the differences between the online dataset and the offline dataset and how they have to be reconciled. The other is a message-based architecture. And here in our health and wellness domain, we've developed applications that are queue based. So they're essentially business processes that consist of multiple steps where each step has its own queue. And what that allows us to do is devote whatever bandwidth we do have to those pieces of the process that are most latency sensitive and allow the queue lengths to increase in parts of the process that are not latency sensitive, knowing that they will eventually catch up when the bandwidth is restored. And to put that in a little bit of context, we have fiber lengths to all of our locations, and we have I'll just use a round number, 10-ish thousand locations. It's larger than that, but that's the ballpark, and we have fiber to all of them, but when the fiber is disconnected, and it does get disconnected on a regular basis. In fact, I forget the exact number, but some several dozen locations get disconnected daily just by virtue of the fact that there's construction going on and things are happening in the real world. When the disconnection happens, we're able to fall back to 5G and to Starlink. Starlink is preferred. It's a higher bandwidth. 5G if that fails. But in each of those cases, the bandwidth drops significantly. And so the applications have to be intelligent about throttling back the traffic that isn't essential, so that it can push the essential traffic in those lower bandwidth scenarios. >> So much technology to support this amazing business which started in the early 1960s. Jack, unfortunately, we're out of time. I would love to have you back or some members of your team and drill into how you're using open source, but really thank you so much for explaining the approach that you've taken and participating in SuperCloud2. >> You're very welcome, Dave, and we're happy to come back and talk about other aspects of what we do. For example, we could talk more about the data lakes and the data mesh that we have in place. We could talk more about the directions we might go with serverless. So please look us up again. Happy to chat. >> I'm going to take you up on that, Jack. All right. This is Dave Vellante for John Furrier and the Cube community. Keep it right there for more action from SuperCloud2. (upbeat music)

(upbeat music) >> Hello, everybody. This is Dave Vellante. Welcome back to Supercloud2, where we're exploring the intersection of data analytics and the future of cloud. In this segment, we're going to look at how the Supercloud will support a new class of applications, not just work that runs on multiple clouds, but rather a new breed of apps that can orchestrate things in the real world. Think Uber for many types of businesses. These applications, they're not about codifying forms or business processes. They're about orchestrating people, places, and things in a business ecosystem. And I'm pleased to welcome my colleague and friend, George Gilbert, former Gartner Analyst, Wiki Bond market analyst, former equities analyst as my co-host. And we're thrilled to have Tristan Handy, who's the founder and CEO of DBT Labs and Bob Muglia, who's the former President of Microsoft's Enterprise business and former CEO of Snowflake. Welcome all, gentlemen. Thank you for coming on the program. >> Good to be here. >> Thanks for having us. >> Hey, look, I'm going to start actually with the SuperCloud because both Tristan and Bob, you've read the definition. Thank you for doing that. And Bob, you have some really good input, some thoughts on maybe some of the drawbacks and how we can advance this. So what are your thoughts in reading that definition around SuperCloud? >> Well, I thought first of all that you did a very good job of laying out all of the characteristics of it and helping to define it overall. But I do think it can be tightened a bit, and I think it's helpful to do it in as short a way as possible. And so in the last day I've spent a little time thinking about how to take it and write a crisp definition. And here's my go at it. This is one day old, so gimme a break if it's going to change. And of course we have to follow the industry, and so that, and whatever the industry decides, but let's give this a try. So in the way I think you're defining it, what I would say is a SuperCloud is a platform that provides programmatically consistent services hosted on heterogeneous cloud providers. >> Boom. Nice. Okay, great. I'm going to go back and read the script on that one and tighten that up a bit. Thank you for spending the time thinking about that. Tristan, would you add anything to that or what are your thoughts on the whole SuperCloud concept? >> So as I read through this, I fully realize that we need a word for this thing because I have experienced the inability to talk about it as well. But for many of us who have been living in the Confluence, Snowflake, you know, this world of like new infrastructure, this seems fairly uncontroversial. Like I read through this, and I'm just like, yeah, this is like the world I've been living in for years now. And I noticed that you called out Snowflake for being an example of this, but I think that there are like many folks, myself included, for whom this world like fully exists today. >> Yeah, I think that's a fair, I dunno if it's criticism, but people observe, well, what's the big deal here? It's just kind of what we're living in today. It reminds me of, you know, Tim Burns Lee saying, well, this is what the internet was supposed to be. It was supposed to be Web 2.0, so maybe this is what multi-cloud was supposed to be. Let's turn our attention to apps. Bob first and then go to Tristan. Bob, what are data apps to you? When people talk about data products, is that what they mean? Are we talking about something more, different? What are data apps to you? >> Well, to understand data apps, it's useful to contrast them to something, and I just use the simple term people apps. I know that's a little bit awkward, but it's clear. And almost everything we work with, almost every application that we're familiar with, be it email or Salesforce or any consumer app, those are applications that are targeted at responding to people. You know, in contrast, a data application reacts to changes in data and uses some set of analytic services to autonomously take action. So where applications that we're familiar with respond to people, data apps respond to changes in data. And they both do something, but they do it for different reasons. >> Got it. You know, George, you and I were talking about, you know, it comes back to SuperCloud, broad definition, narrow definition. Tristan, how do you see it? Do you see it the same way? Do you have a different take on data apps? >> Oh, geez. This is like a conversation that I don't know has an end. It's like been, I write a substack, and there's like this little community of people who all write substack. We argue with each other about these kinds of things. Like, you know, as many different takes on this question as you can find, but the way that I think about it is that data products are atomic units of functionality that are fundamentally data driven in nature. So a data product can be as simple as an interactive dashboard that is like actually had design thinking put into it and serves a particular user group and has like actually gone through kind of a product development life cycle. And then a data app or data application is a kind of cohesive end-to-end experience that often encompasses like many different data products. So from my perspective there, this is very, very related to the way that these things are produced, the kinds of experiences that they're provided, that like data innovates every product that we've been building in, you know, software engineering for, you know, as long as there have been computers. >> You know, Jamak Dagani oftentimes uses the, you know, she doesn't name Spotify, but I think it's Spotify as that kind of example she uses. But I wonder if we can maybe try to take some examples. If you take, like George, if you take a CRM system today, you're inputting leads, you got opportunities, it's driven by humans, they're really inputting the data, and then you got this system that kind of orchestrates the business process, like runs a forecast. But in this data driven future, are we talking about the app itself pulling data in and automatically looking at data from the transaction systems, the call center, the supply chain and then actually building a plan? George, is that how you see it? >> I go back to the example of Uber, may not be the most sophisticated data app that we build now, but it was like one of the first where you do have users interacting with their devices as riders trying to call a car or driver. But the app then looks at the location of all the drivers in proximity, and it matches a driver to a rider. It calculates an ETA to the rider. It calculates an ETA then to the destination, and it calculates a price. Those are all activities that are done sort of autonomously that don't require a human to type something into a form. The application is using changes in data to calculate an analytic product and then to operationalize that, to assign the driver to, you know, calculate a price. Those are, that's an example of what I would think of as a data app. And my question then I guess for Tristan is if we don't have all the pieces in place for sort of mainstream companies to build those sorts of apps easily yet, like how would we get started? What's the role of a semantic layer in making that easier for mainstream companies to build? And how do we get started, you know, say with metrics? How does that, how does that take us down that path? >> So what we've seen in the past, I dunno, decade or so, is that one of the most successful business models in infrastructure is taking hard things and rolling 'em up behind APIs. You take messaging, you take payments, and you all of a sudden increase the capability of kind of your median application developer. And you say, you know, previously you were spending all your time being focused on how do you accept credit cards, how do you send SMS payments, and now you can focus on your business logic, and just create the thing. One of, interestingly, one of the things that we still don't know how to API-ify is concepts that live inside of your data warehouse, inside of your data lake. These are core concepts that, you know, you would imagine that the business would be able to create applications around very easily, but in fact that's not the case. It's actually quite challenging to, and involves a lot of data engineering pipeline and all this work to make these available. And so if you really want to make it very easy to create some of these data experiences for users, you need to have an ability to describe these metrics and then to turn them into APIs to make them accessible to application developers who have literally no idea how they're calculated behind the scenes, and they don't need to. >> So how rich can that API layer grow if you start with metric definitions that you've defined? And DBT has, you know, the metric, the dimensions, the time grain, things like that, that's a well scoped sort of API that people can work within. How much can you extend that to say non-calculated business rules or governance information like data reliability rules, things like that, or even, you know, features for an AIML feature store. In other words, it starts, you started pragmatically, but how far can you grow? >> Bob is waiting with bated breath to answer this question. I'm, just really quickly, I think that we as a company and DBT as a product tend to be very pragmatic. We try to release the simplest possible version of a thing, get it out there, and see if people use it. But the idea that, the concept of a metric is really just a first landing pad. The really, there is a physical manifestation of the data and then there's a logical manifestation of the data. And what we're trying to do here is make it very easy to access the logical manifestation of the data, and metric is a way to look at that. Maybe an entity, a customer, a user is another way to look at that. And I'm sure that there will be more kind of logical structures as well. >> So, Bob, chime in on this. You know, what's your thoughts on the right architecture behind this, and how do we get there? >> Yeah, well first of all, I think one of the ways we get there is by what companies like DBT Labs and Tristan is doing, which is incrementally taking and building on the modern data stack and extending that to add a semantic layer that describes the data. Now the way I tend to think about this is a fairly major shift in the way we think about writing applications, which is today a code first approach to moving to a world that is model driven. And I think that's what the big change will be is that where today we think about data, we think about writing code, and we use that to produce APIs as Tristan said, which encapsulates those things together in some form of services that are useful for organizations. And that idea of that encapsulation is never going to go away. It's very, that concept of an API is incredibly useful and will exist well into the future. But what I think will happen is that in the next 10 years, we're going to move to a world where organizations are defining models first of their data, but then ultimately of their business process, their entire business process. Now the concept of a model driven world is a very old concept. I mean, I first started thinking about this and playing around with some early model driven tools, probably before Tristan was born in the early 1980s. And those tools didn't work because the semantics associated with executing the model were too complex to be written in anything other than a procedural language. We're now reaching a time where that is changing, and you see it everywhere. You see it first of all in the world of machine learning and machine learning models, which are taking over more and more of what applications are doing. And I think that's an incredibly important step. And learned models are an important part of what people will do. But if you look at the world today, I will claim that we've always been modeling. Modeling has existed in computers since there have been integrated circuits and any form of computers. But what we do is what I would call implicit modeling, which means that it's the model is written on a whiteboard. It's in a bunch of Slack messages. It's on a set of napkins in conversations that happen and during Zoom. That's where the model gets defined today. It's implicit. There is one in the system. It is hard coded inside application logic that exists across many applications with humans being the glue that connects those models together. And really there is no central place you can go to understand the full attributes of the business, all of the business rules, all of the business logic, the business data. That's going to change in the next 10 years. And we'll start to have a world where we can define models about what we're doing. Now in the short run, the most important models to build are data models and to describe all of the attributes of the data and their relationships. And that's work that DBT Labs is doing. A number of other companies are doing that. We're taking steps along that way with catalogs. People are trying to build more complete ontologies associated with that. The underlying infrastructure is still super, super nascent. But what I think we'll see is this infrastructure that exists today that's building learned models in the form of machine learning programs. You know, some of these incredible machine learning programs in foundation models like GPT and DALL-E and all of the things that are happening in these global scale models, but also all of that needs to get applied to the domains that are appropriate for a business. And I think we'll see the infrastructure developing for that, that can take this concept of learned models and put it together with more explicitly defined models. And this is where the concept of knowledge graphs come in and then the technology that underlies that to actually implement and execute that, which I believe are relational knowledge graphs. >> Oh, oh wow. There's a lot to unpack there. So let me ask the Colombo question, Tristan, we've been making fun of your youth. We're just, we're just jealous. Colombo, I'll explain it offline maybe. >> I watch Colombo. >> Okay. All right, good. So but today if you think about the application stack and the data stack, which is largely an analytics pipeline. They're separate. Do they, those worlds, do they have to come together in order to achieve Bob's vision? When I talk to practitioners about that, they're like, well, I don't want to complexify the application stack cause the data stack today is so, you know, hard to manage. But but do those worlds have to come together? And you know, through that model, I guess abstraction or translation that Bob was just describing, how do you guys think about that? Who wants to take that? >> I think it's inevitable that data and AI are going to become closer together? I think that the infrastructure there has been moving in that direction for a long time. Whether you want to use the Lakehouse portmanteau or not. There's also, there's a next generation of data tech that is still in the like early stage of being developed. There's a company that I love that is essentially Cross Cloud Lambda, and it's just a wonderful abstraction for computing. So I think that, you know, people have been predicting that these worlds are going to come together for awhile. A16Z wrote a great post on this back in I think 2020, predicting this, and I've been predicting this since since 2020. But what's not clear is the timeline, but I think that this is still just as inevitable as it's been. >> Who's that that does Cross Cloud? >> Let me follow up on. >> Who's that, Tristan, that does Cross Cloud Lambda? Can you name names? >> Oh, they're called Modal Labs. >> Modal Labs, yeah, of course. All right, go ahead, George. >> Let me ask about this vision of trying to put the semantics or the code that represents the business with the data. It gets us to a world that's sort of more data centric, where data's not locked inside or behind the APIs of different applications so that we don't have silos. But at the same time, Bob, I've heard you talk about building the semantics gradually on top of, into a knowledge graph that maybe grows out of a data catalog. And the vision of getting to that point, essentially the enterprise's metadata and then the semantics you're going to add onto it are really stored in something that's separate from the underlying operational and analytic data. So at the same time then why couldn't we gradually build semantics beyond the metric definitions that DBT has today? In other words, you build more and more of the semantics in some layer that DBT defines and that sits above the data management layer, but any requests for data have to go through the DBT layer. Is that a workable alternative? Or where, what type of limitations would you face? >> Well, I think that it is the way the world will evolve is to start with the modern data stack and, you know, which is operational applications going through a data pipeline into some form of data lake, data warehouse, the Lakehouse, whatever you want to call it. And then, you know, this wide variety of analytics services that are built together. To the point that Tristan made about machine learning and data coming together, you see that in every major data cloud provider. Snowflake certainly now supports Python and Java. Databricks is of course building their data warehouse. Certainly Google, Microsoft and Amazon are doing very, very similar things in terms of building complete solutions that bring together an analytics stack that typically supports languages like Python together with the data stack and the data warehouse. I mean, all of those things are going to evolve, and they're not going to go away because that infrastructure is relatively new. It's just being deployed by companies, and it solves the problem of working with petabytes of data if you need to work with petabytes of data, and nothing will do that for a long time. What's missing is a layer that understands and can model the semantics of all of this. And if you need to, if you want to model all, if you want to talk about all the semantics of even data, you need to think about all of the relationships. You need to think about how these things connect together. And unfortunately, there really is no platform today. None of our existing platforms are ultimately sufficient for this. It was interesting, I was just talking to a customer yesterday, you know, a large financial organization that is building out these semantic layers. They're further along than many companies are. And you know, I asked what they're building it on, and you know, it's not surprising they're using a, they're using combinations of some form of search together with, you know, textual based search together with a document oriented database. In this case it was Cosmos. And that really is kind of the state of the art right now. And yet those products were not built for this. They don't really, they can't manage the complicated relationships that are required. They can't issue the queries that are required. And so a new generation of database needs to be developed. And fortunately, you know, that is happening. The world is developing a new set of relational algorithms that will be able to work with hundreds of different relations. If you look at a SQL database like Snowflake or a big query, you know, you get tens of different joins coming together, and that query is going to take a really long time. Well, fortunately, technology is evolving, and it's possible with new join algorithms, worst case, optimal join algorithms they're called, where you can join hundreds of different relations together and run semantic queries that you simply couldn't run. Now that technology is nascent, but it's really important, and I think that will be a requirement to have this semantically reach its full potential. In the meantime, Tristan can do a lot of great things by building up on what he's got today and solve some problems that are very real. But in the long run I think we'll see a new set of databases to support these models. >> So Tristan, you got to respond to that, right? You got to, so take the example of Snowflake. We know it doesn't deal well with complex joins, but they're, they've got big aspirations. They're building an ecosystem to really solve some of these problems. Tristan, you guys are part of that ecosystem, and others, but please, your thoughts on what Bob just shared. >> Bob, I'm curious if, I would have no idea what you were talking about except that you introduced me to somebody who gave me a demo of a thing and do you not want to go there right now? >> No, I can talk about it. I mean, we can talk about it. Look, the company I've been working with is Relational AI, and they're doing this work to actually first of all work across the industry with academics and research, you know, across many, many different, over 20 different research institutions across the world to develop this new set of algorithms. They're all fully published, just like SQL, the underlying algorithms that are used by SQL databases are. If you look today, every single SQL database uses a similar set of relational algorithms underneath that. And those algorithms actually go back to system R and what IBM developed in the 1970s. We're just, there's an opportunity for us to build something new that allows you to take, for example, instead of taking data and grouping it together in tables, treat all data as individual relations, you know, a key and a set of values and then be able to perform purely relational operations on it. If you go back to what, to Codd, and what he wrote, he defined two things. He defined a relational calculus and relational algebra. And essentially SQL is a query language that is translated by the query processor into relational algebra. But however, the calculus of SQL is not even close to the full semantics of the relational mathematics. And it's possible to have systems that can do everything and that can store all of the attributes of the data model or ultimately the business model in a form that is much more natural to work with. >> So here's like my short answer to this. I think that we're dealing in different time scales. I think that there is actually a tremendous amount of work to do in the semantic layer using the kind of technology that we have on the ground today. And I think that there's, I don't know, let's say five years of like really solid work that there is to do for the entire industry, if not more. But the wonderful thing about DBT is that it's independent of what the compute substrate is beneath it. And so if we develop new platforms, new capabilities to describe semantic models in more fine grain detail, more procedural, then we're going to support that too. And so I'm excited about all of it. >> Yeah, so interpreting that short answer, you're basically saying, cause Bob was just kind of pointing to you as incremental, but you're saying, yeah, okay, we're applying it for incremental use cases today, but we can accommodate a much broader set of examples in the future. Is that correct, Tristan? >> I think you're using the word incremental as if it's not good, but I think that incremental is great. We have always been about applying incremental improvement on top of what exists today, but allowing practitioners to like use different workflows to actually make use of that technology. So yeah, yeah, we are a very incremental company. We're going to continue being that way. >> Well, I think Bob was using incremental as a pejorative. I mean, I, but to your point, a lot. >> No, I don't think so. I want to stop that. No, I don't think it's pejorative at all. I think incremental, incremental is usually the most successful path. >> Yes, of course. >> In my experience. >> We agree, we agree on that. >> Having tried many, many moonshot things in my Microsoft days, I can tell you that being incremental is a good thing. And I'm a very big believer that that's the way the world's going to go. I just think that there is a need for us to build something new and that ultimately that will be the solution. Now you can argue whether it's two years, three years, five years, or 10 years, but I'd be shocked if it didn't happen in 10 years. >> Yeah, so we all agree that incremental is less disruptive. Boom, but Tristan, you're, I think I'm inferring that you believe you have the architecture to accommodate Bob's vision, and then Bob, and I'm inferring from Bob's comments that maybe you don't think that's the case, but please. >> No, no, no. I think that, so Bob, let me put words into your mouth and you tell me if you disagree, DBT is completely useless in a world where a large scale cloud data warehouse doesn't exist. We were not able to bring the power of Python to our users until these platforms started supporting Python. Like DBT is a layer on top of large scale computing platforms. And to the extent that those platforms extend their functionality to bring more capabilities, we will also service those capabilities. >> Let me try and bridge the two. >> Yeah, yeah, so Bob, Bob, Bob, do you concur with what Tristan just said? >> Absolutely, I mean there's nothing to argue with in what Tristan just said. >> I wanted. >> And it's what he's doing. It'll continue to, I believe he'll continue to do it, and I think it's a very good thing for the industry. You know, I'm just simply saying that on top of that, I would like to provide Tristan and all of those who are following similar paths to him with a new type of database that can actually solve these problems in a much more architected way. And when I talk about Cosmos with something like Mongo or Cosmos together with Elastic, you're using Elastic as the join engine, okay. That's the purpose of it. It becomes a poor man's join engine. And I kind of go, I know there's a better answer than that. I know there is, but that's kind of where we are state of the art right now. >> George, we got to wrap it. So give us the last word here. Go ahead, George. >> Okay, I just, I think there's a way to tie together what Tristan and Bob are both talking about, and I want them to validate it, which is for five years we're going to be adding or some number of years more and more semantics to the operational and analytic data that we have, starting with metric definitions. My question is for Bob, as DBT accumulates more and more of those semantics for different enterprises, can that layer not run on top of a relational knowledge graph? And what would we lose by not having, by having the knowledge graph store sort of the joins, all the complex relationships among the data, but having the semantics in the DBT layer? >> Well, I think this, okay, I think first of all that DBT will be an environment where many of these semantics are defined. The question we're asking is how are they stored and how are they processed? And what I predict will happen is that over time, as companies like DBT begin to build more and more richness into their semantic layer, they will begin to experience challenges that customers want to run queries, they want to ask questions, they want to use this for things where the underlying infrastructure becomes an obstacle. I mean, this has happened in always in the history, right? I mean, you see major advances in computer science when the data model changes. And I think we're on the verge of a very significant change in the way data is stored and structured, or at least metadata is stored and structured. Again, I'm not saying that anytime in the next 10 years, SQL is going to go away. In fact, more SQL will be written in the future than has been written in the past. And those platforms will mature to become the engines, the slicer dicers of data. I mean that's what they are today. They're incredibly powerful at working with large amounts of data, and that infrastructure is maturing very rapidly. What is not maturing is the infrastructure to handle all of the metadata and the semantics that that requires. And that's where I say knowledge graphs are what I believe will be the solution to that. >> But Tristan, bring us home here. It sounds like, let me put pause at this, is that whatever happens in the future, we're going to leverage the vast system that has become cloud that we're talking about a supercloud, sort of where data lives irrespective of physical location. We're going to have to tap that data. It's not necessarily going to be in one place, but give us your final thoughts, please. >> 100% agree. I think that the data is going to live everywhere. It is the responsibility for both the metadata systems and the data processing engines themselves to make sure that we can join data across cloud providers, that we can join data across different physical regions and that we as practitioners are going to kind of start forgetting about details like that. And we're going to start thinking more about how we want to arrange our teams, how does the tooling that we use support our team structures? And that's when data mesh I think really starts to get very, very critical as a concept. >> Guys, great conversation. It was really awesome to have you. I can't thank you enough for spending time with us. Really appreciate it. >> Thanks a lot. >> All right. This is Dave Vellante for George Gilbert, John Furrier, and the entire Cube community. Keep it right there for more content. You're watching SuperCloud2. (upbeat music)

(upbeat music) >> Oracle continues to pursue a multi-mode converged database strategy. The premise of this all in one approach is to make life easier for practitioners and developers. And the most recent example is the Oracle database API for MongoDB, which was announced today. Now, Oracle, they're not the first to come out with a MongoDB compatible API, but Oracle hopes to use its autonomous database as a differentiator and further build a moat around OCI, Oracle Cloud Infrastructure. And with us to talk about Oracle's MongoDB compatible API is Gerald Venzl, who's a distinguished Product Manager at Oracle. Gerald was a guest along with Maria Colgan on the CUBE a while back, and we talked about Oracle's converge database and the kind of Swiss army knife strategy, I called it, of databases. This is dramatically different. It's an approach that we see at the opposite end of the the spectrum, for instance, from AWS, who, for example, goes after the world of developers with a different database for every use case. So, kind of picking up from there, Gerald, I wonder if you could talk about how this new MongoDB API adds to your converged model and the whole strategy there. Where does it fit? >> Yeah, thank you very much, Dave and, by the way, thanks for having me on the CUBE again. A pleasure to be here. So, essentially the MongoDB API to build the compatibility that we used with this API is a continuation of the converge database story, as you said before. Which is essentially bringing the many features of the many single purpose databases that people often like and use, together into one technology so that everybody can benefit from it. So as such, this is just a continuation that we have from so many other APIs or standards that we support. Since a long time, we already, of course to SQL because we are relational database from the get go. Also other standard like GraphQL, Sparkle, et cetera that we have. And the MongoDB API, is now essentially just the next step forward to give the developers this API that they've gotten to love and use. >> I wonder if you could talk about from the developer angle, what do they get out of it? Obviously you're appealing to the Mongo developers out there, but you've got this Mongo compatible API you're pouting the autonomous database on OCI. Why aren't they just going to use MongoDB Atlas on whatever cloud, Azure or AWS or Google Cloud platform? >> That's a very good question. We believe that the majority of developers want to just worry about their application, writing the application, and not so much about the database backend that they're using. And especially in cloud with cloud services, the reason why developers choose these services is so that they don't have to manage them. Now, autonomous database brings many topnotch advanced capabilities to database cloud services. We firmly believe that autonomous database is essentially the next generation of cloud services with all the self-driving features built in, and MongoDB developers writing applications against the MongoDB API, should not have to hold out on these capabilities either. It's like no developer likes to tune the database. No developer likes to take a downtime when they have to rescale their database to accommodate a bigger workload. And this is really where we see the benefit here, so for the developer, ideally nothing will change. You have MongoDB compatible API so they can keep on using their tools. They can build the applications the way that they do, but the benefit from the best cloud database service out there not having to worry about any of these package things anymore, that even MongoDB Atlas has a lot of shortcomings still today, as we find. >> Of cos, this is always a moving target The technology business, that's why we love it. So everybody's moving fast and investing and shaking and jiving. But, I want to ask you about, well, by the way, that's so you're hiding the underlying complexity, That's really the big takeaway there. So that's you huge for developers. But take, I was talking before about, the Amazon's approach, right tool for the right job. You got document DB, you got Microsoft with Cosmos, they compete with Mongo and they've been doing so for some time. How does Oracle's API for Mongo different from those offerings and how you going to attract their users to your JSON offering. >> So, you know, for first of all we have to kind of separate slightly document DB and AWS and Cosmos DB in Azure, they have slightly different approaches there. Document DB essentially is, a document store owned by and built by AWS, nothing different to Mongo DB, it's a head to head comparison. It's like use my document store versus the other document store. So you don't get any of the benefits of a converge database. If you ever want to do a different data model, run analytics over, etc. You still have to use the many other services that AWS provides you to. You cannot all do it into one database. Now Cosmos DB it's more in interesting because they claim to be a multi-model database. And I say claim because what we understand as multi-model database is different to what they understand as multimodel database. And also one of the reasons why we start differentiating with converge database. So what we mean is you should be able to regardless what data format you want to store in the database leverage all the functionality of the database over that data format, with no trade offs. Cosmos DB when you look at it, it essentially gives you mode of operation. When you connect as the application or the user, you have to decide at connection time, how you want, how this database should be treated. Should it be a document store? Should it be a graph store? Should it be a relational store? Once you make that choice, you are locked into that. As long as you establish that connection. So it's like, if you say, I want a document store, all you get is a document store. There's no way for you to crossly analyze with the relational data sitting in the same service. There's no for you to break these boundaries. If you ever want to add some graph data and graph analytics, you essentially have to disconnect and now treat it as a graph store. So you get multiple data models in it, but really you still get, one trick pony the moment you connect to it that you have to choose to. And that is where we see a huge differentiation again with our converge database, because we essentially say, look, one database cloud service on Oracle cloud, where it allows you to do anything, if you wish to do so. You can start as a document store if you wish to do so. If you want to write some SQL queries on top, you can do so. If you want to add some graph data, you can do so. But there's no way for you to have to rewrite your application, use different libraries and frameworks now to connect et cetera, et cetera. >> Got it. Thank you for that. Do you have any data when you talk to customers? Like I'm interested in the diversity of deployments, like for instance, how many customers are using more than one data model? Do for instance, do JSON users need support for other data types or are they happy to stay kind of in their own little sandbox? Do you have any data on that? >> So what we see from the majority of our customers, there is no such thing as one data model fits everything. So, and it's like, there again we have to differentiate the developer that builds a certain microservice, that makes happy to stay in the JSON world or relational world, or the company that's trying to derive value from the data. So it's like the relational model has not gone away since 40 years of it existence. It's still kicking strong. It's still really good at what it does. The JSON data model is really good in what it does. The graph model is really good at what it does. But all these models have been built for different purposes. Try to do graph analytics on relational or JSON data. It's like, it's really tricky, but that's why you use a graph model to begin with. Try to shield yourself from the organization of the data, how it's structured, that's really easy in the relational world, not so much when you get into a document store world. And so what we see about our customers is like as they accumulate more data, is they have many different applications to run their enterprises. The question always comes back, as we have predicted since about six, seven years now, where they say, hey, we have all this different data and different data formats. We want to bring it all together, analyze it together, get value out of the data together. We have seen a whole trend of big data emerge and disappear to answer the question and didn't quite do the trick. And we are basically now back to where we were in the early 2000's when XML databases have faded away, because everybody just allowed you to store XML in the database. >> Got it. So let's make this real for people. So maybe you could give us some examples. You got this new API from Mongo, you have your multi model database. How, take a, paint a picture of how customers are going to benefit in real world use cases. How does it kind of change the customer's world before and after if you will? >> Yeah, absolutely. So, you know the API essentially we are going to use it to accept before, you know, make the lives of the developers easier, but also of course to assist our customers with migrations from Mongo DB over to Oracle Autonomous Database. One customer that we have, for example, that would've benefited of the API several a couple of years ago, two, three years ago, it's one of the largest logistics company on the planet. They track every package that is being sent in JSON documents. So every track package is entries resembled in a JSON document, and they very early on came in with the next question of like, hey, we track all these packages and document in JSON documents. It will be really nice to know actually which packages are stuck, or anywhere where we have to intervene. It's like, can we do this? Can we analyze just how many packages get stuck, didn't get delivered on, the end of a day or whatever. And they found this struggle with this question a lot, they found this was really tricky to do back then, in that case in MongoDB. So they actually approached Oracle, they came over, they migrated over and they rewrote their applications to accommodate that. And there are happy JSON users in Oracle database, but if we were having this API already for them then they wouldn't have had to rewrite their applications or would we often see like worry about the rewriting the application later on. Usually migration use cases, we want to get kind of the migration done, get the data over be running, and then worry about everything else. So this would be one where they would've greatly benefited to shorten this migration time window. If we had already demo the Mongo API back then or this compatibility layer. >> That's a good use case. I mean, it's, one of the most prominent and painful, so anything you could do to help that is key. I remember like the early days of big data, NoSQL, of course was the big thing. There was a lot of confusion. No, people thought was none or not only SQL, which is kind of the more widely accepted interpretation today. But really, it's talking about data that's stored in a non-relational format. So, some people, again they thought that SQL was going to fade away, some people probably still believe that. And, we saw the rise of NoSQL and document databases, but if I understand it correctly, a premise for your Mongo DB API is you really see SQL as a main contributor over Mongo DB's document collections for analytics for example. Can you make, add some color here? Are you seeing, what are you seeing in terms of resurgence of SQL or the momentum in SQL? Has it ever really waned? What's your take? >> Yeah, no, it's a very good point. So I think there as well, we see to some extent history repeating itself from, this all has been tried beforehand with object databases, XML database, et cetera. But if we stay with the NoSQL databases, I think it speaks at length that every NoSQL database that as you write for the sensor you started with NoSQL, and then while actually we always meant, not only SQL, everybody has introduced a SQL like engine or interface. The last two actually join this family is MongoDB. Now they have just recently introduced a SQL compatibility for the aggregation pipelines, something where you can put in a SQL statement and that essentially will then work with aggregation pipeline. So they all acknowledge that SQL is powerful, for us this was always clear. SQL is a declarative language. Some argue it's the only true 4GL language out there. You don't have to code how to get the data, but you just ask the question and the rest is done for you. And, we think that as we, basically, has SQL ever diminished as you said before, if you look out there? SQL has always been a demand. Look at the various developer surveys, etc. The various top skills that are asked for SQL has never gone away. Everybody loves and likes and you wants to use SQL. And so, yeah, we don't think this has ever been, going away. It has maybe just been, put in the shadow by some hypes. But again, we had the same discussion in the 2000's with XML databases, with the same discussions in the 90's with object databases. And we have just frankly, all forgotten about it. >> I love when you guys come on and and let me do my thing and I can pretty much ask any question I want, because, I got to say, when Oracle starts talking about another company I know that company's doing well. So I like, I see Mongo in the marketplace and I love that you guys are calling it out and making some moves there. So here's the thing, you guys have a large install base and that can be an advantage, but it can also be a weight in your shoulder. These specialized cloud databases they don't have that legacy. So they can just kind of move freely about, less friction. Now, all the cloud database services they're going to have more and more automation. I mean, I think that's pretty clear and inevitable. And most if not all of the database vendors they're going to provide support for these kind of converged data models. However they choose to do that. They might do it through the ecosystem, like what Snowflake's trying to do, or bring it in the house themselves, like a watch maker that brings an in-house movement, if you will. But it's like death and taxes, you can't avoid it. It's got to happen. That's what customers want. So with all that being said, how do you see the capabilities that you have today with automation and converge capabilities, How do you see that, that playing out? What's, do you think it gives you enough of an advantage? And obviously it's an advantage, but is it enough of an advantage over the specialized cloud database vendors, where there's clearly a lot of momentum today? >> I mean, honestly yes, absolutely. I mean, we are with some of these databases 20 years ahead. And I give you concrete examples. It's like Oracle had transaction support asset transactions since forever. NoSQL players all said, oh, we don't need assets transactions, base transactions is fine. Yada, yada, yada. Mongo DB started introducing some transaction support. It comes with some limits, cannot be longer than 60 seconds, cannot touch more than a thousand documents as well, et cetera. They still will have to do some catching up there. I mean, it took us a while to get there, let's be honest. Glad We have been around for a long time. Same thing, now that happened with version five, is like we started some simple version of multi version concurrency control that comes along with asset transactions. The interesting part here is like, we've introduced this also an Oracle five, which was somewhere in the 80's before I even started using Oracle Database. So there's a lot of catching up to do. And then you look at the cloud services as well, there's actually certain, a lot of things that we kind of gotten take, we've kind of, we Oracle people have taken for granted and we kind of keep forgetting. For example, our elastic scale, you want to add one CPU, you add one CPU. Should you take downtime for that? Absolutely not. It's like, this is ridiculous. Why would you, you cannot take it downtime in a 24/7 backend system that runs the world. Take any of our customers. If you look at most of these cloud services or you want to reshape, you want to scale your cloud service, that's fine. It's just the VM under the covers, we just shut everything down, give you a VM with more CPU, and you boot it up again, downtown right there. So it's like, there's a lot of these things where we go like, well, we solved this frankly decades ago, that these cloud vendors will run into. And just to add one more point here, so it's like one thing that we see with all these migrations happening is exactly in that field. It's like people essentially started building on whether it's Mongo DB or other of these NoSQL databases or cloud databases. And eventually as these systems grow, as they ask more difficult questions, their use cases expand, they find shortcomings. Whether it's the scalability, whether it's the security aspects, the functionalities that we have, and this is essentially what drives them back to Oracle. And this is why we see essentially this popularity now of pendulum swimming towards our direction again, where people actually happily come over back and they come over to us, to get their workloads enterprise grade if you like. >> Well, It's true. I mean, I just reported on this recently, the momentum that you guys have in cloud because it is, 'cause you got the best mission critical database. You're all about maps. I got to tell you a quick story. I was at a vertical conference one time, I was on stage with Kurt Monash. I don't know if you know Kurt, but he knows this space really well. He's probably forgot and more about database than I'll ever know. But, and I was kind of busting his chops. He was talking about asset transactions. I'm like, well with NoSQL, who needs asset transactions, just to poke him. And he was like, "Are you out of your mind?" And, and he said, look it's everybody is going to head in this direction. It turned out, it's true. So I got to give him props for that. And so, my last question, if you had a message for, let's say there's a skeptical developer out there that's using Mongo DB and Atlas, what would you say to them? >> I would say go try it for yourself. If you don't believe us, we have an always free cloud tier out there. You just go to oracle.com/cloud/free. You sign up for an always free tier, spin up an autonomous database, go try it for yourself. See what's actually possible today. Don't just follow your trends on Hackernews and use a set study here or there. Go try it for yourself and see what's capable of >> All right, Gerald. Hey, thanks for coming into my firing line today. I really appreciate your time. >> Thank you for having me again. >> Good luck with the announcement. You're very welcome, and thank you for watching this CUBE conversation. This is Dave Vellante, We'll see you next time. (gentle music)

from the cube studios in palo alto in boston bringing you data-driven insights from the cube and etr this is breaking analysis with dave vellante despite all the chatter about cloud repatriation and the exorbitant cost of cloud computing customer spending momentum continues to accelerate in the post-isolation economy if the pandemic was good for the cloud it seems that the benefits of cloud migration remain lasting in the late stages of covid and beyond and we believe this stickiness is going to continue for quite some time we expect i asked revenue for the big four hyperscalers to surpass 115 billion dollars in 2021 moreover the strength of aws specifically as well as microsoft azure remain notable such large organizations showing elevated spending momentum as shown in the etr survey results is perhaps unprecedented in the technology sector hello everyone and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll share some some fresh july survey data that indicates accelerating momentum for the largest cloud computing firms importantly not only is the momentum broad-based but it's also notable in key strategic sectors namely ai and database there seems to be no stopping the cloud momentum there's certainly plenty of buzz about the cloud tax so-called cloud tax but other than wildly assumptive valuation models and some pockets of anecdotal evidence you don't really see the supposed backlash impacting cloud momentum our forecast calls for the big four hyperscalers aws azure alibaba and gcp to surpass 115 billion as we said in is revenue this year the latest etr survey results show that aws lambda has retaken the lead among all major cloud services tracked in the data set as measured in spending momentum this is the service with the most elevated scores azure overall azure functions vmware cloud on aws and aws overall also demonstrate very highly elevated performance all above that of gcp now impressively aws momentum in the all-important fortune 500 where it has always showed strength is also accelerating one concern in the most recent survey data is that the on-prem clouds and so-called hybrid platforms which we had previously reported as showing an upward spending trajectory seem to have cooled off a bit but the data is mixed and it's a little bit too early to draw firm conclusions nonetheless while hyperscalers are holding steady the spending data appears to be somewhat tepid for the on-prem players you know particularly for their cloud we'll study that further after etr drops its full results on july 23rd now turning our attention back to aws the aws cloud is showing strength across its entire portfolio and we're going to show you that shortly in particular we see notable strength relative to others in analytics ai and the all-important database category aurora and redshift are particularly strong but several other aws database services are showing elevated spending velocity which we'll quantify in a moment all that said snowflake continues to lead all database suppliers in spending momentum by a wide margin which again will quantify in this episode but before we dig into the survey let's take a look at our latest projections for the big four hyperscalers in is as you know we track quarterly revenues for the hyperscalers remember aws and alibaba ias data is pretty clean and reported in their respective earnings reports azure and gcp we have to extrapolate and strip out all a lot of the the apps and other certain revenue to make an apples-to-apples comparison with aws and alibaba and as you can see we have the 2021 market exceeding 115 billion dollars worldwide that's a torrid 35 growth rate on top of 41 in 2020 relative to 2019. aggressive yes but the data continues to point us in this direction until we see some clearer headwinds for the cloud players this is the call we're making aws is perhaps losing a sharepoint or so but it's also is so large that its annual incremental revenue is comparable to alibaba's and google's respective cloud business in total is business in total the big three u.s cloud companies all report at the end of july while alibaba is mid mid-august so we'll update these figures at that time okay let's move on and dig into the survey data we don't have the data yet on alibaba and we're limited as to what we can share until etr drops its research update on on the 23rd but here's a look at the net score timeline in the fortune 500 specifically so we filter the fortune 500 for cloud computing you got azure and the yellow aws and the black and gcp in blue so two points here stand out first is that aws and microsoft are converging and remember the customers who respond to the survey they probably include a fair amount of application software spending in their cloud answers so it favors microsoft in that respect and gcp second point is showing notable deceleration relative to the two leaders and the green call out is because this cut is from an aws point of view so in other words gcp declines are a positive for aws so that's how it should be interpreted now let's take a moment to better understand the idea of net score this is one of the fundamental metrics of the etr methodology here's the data for aws so we use that as a as a reference point net score is calculated by asking customers if they're adding a platform new that's the lime green bar that you see here in the current survey they're asking are you spending six percent or more in the second half relative to the first half of the year that's the forest green they're also asking is spending flat that's the gray or are you spending less that's the pink or are you replacing the platform i.e repatriating so not much spending going on in replacements now in fairness one percent of aws is half a billion dollars so i can see where some folks would get excited about that but in the grand scheme of things it's a sliver so again we don't see repatriation in the numbers okay back to net score subtract the reds from the greens and you get net score which in the case of aws is 61 now just for reference my personal subjective elevated net score level is 40 so anything above that is really impressive based on my experience and to have a company of this size be so elevated is meaningful same for microsoft by the way which is consistently well above the 50 mark in net score in the etr surveys so that's you can think about it that's even more impressive perhaps than aws because it's triple the revenue okay let's stay with aws and take a look at the portfolio and the strength across the board this chart shows net score for the past three surveys serverless is on fire by the way not just aws but azure and gcp functions as well but look at the aws portfolio every category is well above the 40 percent elevated red line the only exception is chime and even chime is showing an uptick and chime is meh if you've ever used chime every other category is well above 50 percent next net score very very strong for aws now as we've frequently reported ai is one of the four biggest focus areas from a spending standpoint along with cloud containers and rpa so it stands to reason that the company with the best ai and ml and the greatest momentum in that space has an advantage because ai is being embedded into apps data processes machines everywhere this chart compares the ai players on two dimensions net score on the vertical axis and market share or presence in the data set on the horizontal axis for companies with more than 15 citations in the survey aws has the highest net score and what's notable is the presence on the horizontal axis databricks is a company where high on also shows elevated scores above both google and microsoft who are showing strength in their own right and then you can see data iq data robot anaconda and salesforce with einstein all above that 40 percent mark and then below you can see the position of sap with leonardo ibm watson and oracle which is well below the 40 line all right let's look at at the all-important database category for a moment and we'll first take a look at the aws database portfolio this chart shows the database services in aws's arsenal and breaks down the net score components with the total net score superimposed on top of the bars point one is aurora is highly elevated with a net score above 70 percent that's due to heavy new adoptions redshift is also very strong as are virtually all aws database offerings with the exception of neptune which is the graph database rds dynamodb elastic document db time stream and quantum ledger database all show momentum above that all important 40 line so while a lot of people criticize the fragmentation of the aws data portfolio and their right tool for the right job approach the spending spending metrics tell a story and that that the strategy is working now let's take a look at the microsoft database portfolio there's a story here similar similar to that of aws azure sql and cosmos db microsoft's nosql distributed database are both very highly elevated as are azure database for mysql and mariadb azure cash for redis and azure for cassandra also microsoft is giving look at microsoft's giving customers a lot of options which is kind of interesting you know we've often said that oracle's strategy because we think about oracle they're building the oracle database cloud we've said oracle strategy should be to not just be the cloud for oracle databases but be the cloud for all databases i mean oracle's got a lot of specialty capability there but it looks like microsoft is beating oracle to that punch not that oracle is necessarily going there but we think it should to expand the appeal of its cloud okay last data chart that we'll show and then and then this one looks at database disruption the chart shows how the cloud database companies are doing in ibm oracle teradata in cloudera accounts the bars show the net score granularity as we described earlier and the etr callouts are interesting so first remember this is an aws this is in an aws context so with 47 responses etr rightly indicates that aws is very well positioned in these accounts with the 68 net score but look at snowflake it has an 81 percent net score which is just incredible and you can see google database is also very strong and the high 50 percent range while microsoft even though it's above the 40 percent mark is noticeably lower than the others as is mongodb with presumably atlas which is surprisingly low frankly but back to snowflake so the etr callout stresses that snowflake doesn't have a strong as strong a presence in the legacy database vendor accounts yet now i'm not sure i would put cloudair in the legacy database category but okay whatever cloudera they're positioning cdp is a hybrid platform as are all the on-prem players with their respective products and platforms but it's going to be interesting to see because snowflake has flat out said it's not straddling the cloud and on-prem rather it's all in on cloud but there is a big opportunity to connect on-prem to the cloud and across clouds which snowflake is pursuing that that ladder the cross-cloud the multi-cloud and snowflake is betting on incremental use cases that involve data sharing and federated governance while traditional players they're protecting their turf at the same time trying to compete in cloud native and of course across cloud i think there's room for both but clearly as we've shown cloud has the spending velocity and a tailwind at its back and aws along with microsoft seem to be getting stronger especially in the all-important categories related to machine intelligence ai and database now to be an essential infrastructure technology player in the data era it would seem obvious that you have to have database and or data management intellectual property in your portfolio or you're going to be less valuable to customers and investors okay we're going to leave it there for today remember these episodes they're all available as podcasts wherever you listen all you do is search breaking analysis podcast and please subscribe to the series check out etr's website at etr dot plus plus etr plus we also publish a full report every week on wikibon.com and siliconangle.com you can get in touch with me david.velante at siliconangle.com you can dm me at d vallante or you can hit hit me up on our linkedin post this is dave vellante for the cube insights powered by etr have a great week stay safe be well and we'll see you next time you

>> Narrator: From theCUBE Studios in Palo Alto, in Boston bringing you data-driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> The next 10 years of cloud, they're going to differ dramatically from the past decade. The early days of cloud, deployed virtualization of standard off-the-shelf components, X86 microprocessors, disk drives et cetera, to then scale out and build a large distributed system. The coming decade is going to see a much more data-centric, real-time, intelligent, call it even hyper-decentralized cloud that will comprise on-prem, hybrid, cross-cloud and edge workloads with a services layer that will obstruct the underlying complexity of the infrastructure which will also comprise much more custom and varied components. This was a key takeaway of the guests from theCUBE on Cloud, an event hosted by SiliconANGLE on theCUBE. Welcome to this week's Wikibon CUBE Insights Powered by ETR. In this episode, we'll summarize the findings of our recent event and extract the signal from our great guests with a couple of series and comments and clips from the show. CUBE on Cloud is our very first virtual editorial event. It was designed to bring together our community in an open forum. We ran the day on our 365 software platform and had a great lineup of CEOs, CIOs, data practitioners technologists. We had cloud experts, analysts and many opinion leaders all brought together in a day long series of sessions that we developed in order to unpack the future of cloud computing in the coming decade. Let me briefly frame up the conversation and then turn it over to some of our guests. First, we put forth our view of how modern cloud has evolved and where it's headed. This graphic that we're showing here, talks about the progression of cloud innovation over time. A cloud like many innovations, it started as a novelty. When AWS announced S3 in March of 2006, nobody in the vendor or user communities really even in the trade press really paid too much attention to it. Then later that year, Amazon announced EC2 and people started to think about a new model of computing. But it was largely tire kickers, bleeding-edge developers that took notice and really leaned in. Now the financial crisis of 2007 to 2009, really created what we call a cloud awakening and it put cloud on the radar of many CFOs. Shadow IT emerged within departments that wanted to take IT in bite-sized chunks and along with the CFO wanted to take it as OPEX versus CAPEX. And then I teach transformation that really took hold. We came out of the financial crisis and we've been on an 11-year cloud boom. And it doesn't look like it's going to stop anytime soon, cloud has really disrupted the on-prem model as we've reported and completely transformed IT. Ironically, the pandemic hit at the beginning of this decade, and created a mandate to go digital. And so it accelerated the industry transformation that we're highlighting here, which probably would have taken several more years to mature but overnight the forced March to digital happened. And it looks like it's here to stay. Now the next wave, we think we'll be much more about business or industry transformation. We're seeing the first glimpses of that. Holger Mueller of Constellation Research summed it up at our event very well I thought, he basically said the cloud is the big winner of COVID. Of course we know that now normally we talk about seven-year economic cycles. He said he was talking about for planning and investment cycles. Now we operate in seven-day cycles. The examples he gave where do we open or close the store? How do we pivot to support remote workers without the burden of CAPEX? And we think that the things listed on this chart are going to be front and center in the coming years, data AI, a fully digitized and intelligence stack that will support next gen disruptions in autos, manufacturing, finance, farming and virtually every industry where the system will expand to the edge. And the underlying infrastructure across physical locations will be hidden. Many issues remain, not the least of which is latency which we talked about at the event in quite some detail. So let's talk about how the Big 3 cloud players are going to participate in this next era. Well, in short, the consensus from the event was that the rich get richer. Let's take a look at some data. This chart shows our most recent estimates of IaaS and PaaS spending for the Big 3. And we're going to update this after earning season but there's a couple of points stand out. First, we want to make the point that combined the Big 3 now account for almost $80 billion of infrastructure spend last year. That $80 billion, was not all incremental (laughs) No it's caused consolidation and disruption in the on-prem data center business and within IT shops companies like Dell, HPE, IBM, Oracle many others have felt the heat and have had to respond with hybrid and cross cloud strategies. Second while it's true that Azure and GCP they appear to be growing faster than AWS. We don't know really the exact numbers, of course because only AWS provides a clean view of IaaS and passwords, Microsoft and Google. They kind of hide them all ball on their numbers which by the way, I don't blame them but they do leave breadcrumbs and clues on growth rates. And we have other means of estimating through surveys and the like, but it's undeniable Azure is closing the revenue gap on AWS. The third is that I like the fact that Azure and Google are growing faster than AWS. AWS is the only company by our estimates to grow its business sequentially last quarter. And in and of itself, that's not really enough important. What is significant is that because AWS is so large now at 45 billion, even at their slower growth rates it grows much more in absolute terms than its competitors. So we think AWS is going to keep its lead for some time. We think Microsoft and AWS will continue to lead the pack. You know, they might converge maybe it will be a 200 just race in terms of who's first who's second in terms of cloud revenue and how it's counted depending on what they count in their numbers. And Google look with its balance sheet and global network. It's going to play the long game and virtually everyone else with the exception of perhaps Alibaba is going to be secondary players on these platforms. Now this next graphic underscores that reality and kind of lays out the competitive landscape. What we're showing here is survey data from ETR of more than 1400 CIOs and IT buyers and on the vertical axis is Net Score which measures spending momentum on the horizontal axis is so-called Market Share which is a measure of pervasiveness in the data set. The key points are AWS and Microsoft look at it. They stand alone so far ahead of the pack. I mean, they really literally, it would have to fall down to lose their lead high spending velocity and large share of the market or the hallmarks of these two companies. And we don't think that's going to change anytime soon. Now, Google, even though it's far behind they have the financial strength to continue to position themselves as an alternative to AWS. And of course, an analytics specialist. So it will continue to grow, but it will be challenged. We think to catch up to the leaders. Now take a look at the hybrid zone where the field is playing. These are companies that have a large on-prem presence and have been forced to initiate a coherent cloud strategy. And of course, including multicloud. And we include Google in this so pack because they're behind and they have to take a differentiated approach relative to AWS, and maybe cozy up to some of these traditional enterprise vendors to help Google get to the enterprise. And you can see from the on-prem crowd, VMware Cloud on AWS is stands out as having some, some momentum as does Red Hat OpenShift, which is it's cloudy, but it's really sort of an ingredient it's not really broad IaaS specifically but it's a component of cloud VMware cloud which includes VCF or VMware Cloud Foundation. And even Dell's cloud. We would expect HPE with its GreenLake strategy. Its financials is shoring up, should be picking up momentum in the future in terms of what the customers of this survey consider cloud. And then of course you could see IBM and Oracle you're in the game, but they don't have the spending momentum and they don't have the CAPEX chops to compete with the hyperscalers IBM's cloud revenue actually dropped 7% last quarter. So that highlights the challenges that that company facing Oracle's cloud business is growing in the single digits. It's kind of up and down, but again underscores these two companies are really about migrating their software install basis to their captive clouds and as well for IBM, for example it's launched a financial cloud as a way to differentiate and not take AWS head-on an infrastructure as a service. The bottom line is that other than the Big 3 in Alibaba the rest of the pack will be plugging into hybridizing and cross-clouding those platforms. And there are definitely opportunities there specifically related to creating that abstraction layer that we talked about earlier and hiding that underlying complexity and importantly creating incremental value good examples, snowfallLike what snowflake is doing with its data cloud, what the data protection guys are doing. A company like Loomio is headed in that direction as are others. So, you keep an eye on that and think about where the white space is and where the value can be across-clouds. That's where the opportunity is. So let's see, what is this all going to look like? How does the cube community think it's going to unfold? Let's hear from theCUBE Guests and theCUBE on Cloud speakers and some of those highlights. Now, unfortunately we don't have time to show you clips from every speaker. We are like 10-plus hours of video content but we've tried to pull together some comments that summarize the sentiment from the community. So I'm going to have John Furrier briefly explain what theCUBE on Cloud is all about and then let the guests speak for themselves. After John, Pradeep Sindhu is going to give a nice technical overview of how the cloud was built out and what's changing in the future. I'll give you a hint it has to do with data. And then speaking of data, Mai-Lan Bukovec, who heads up AWS is storage portfolio. She'll explain how she views the coming changes in cloud and how they look at storage. Again, no surprise, it's all about data. Now, one of the themes that you'll hear from guests is the notion of a distributed cloud model. And Zhamak Deghani, he was a data architect. She'll explain her view of the future of data architectures. We also have thoughts from analysts like Zeus Karavalla and Maribel Lopez, and some comments from both Microsoft and Google to compliment AWS's view of the world. In fact, we asked JG Chirapurath from Microsoft to comment on the common narrative that Microsoft products are not best-to-breed. They put out a one dot O and then they get better, or sometimes people say, well, they're just good enough. So we'll see what his response is to that. And Paul Gillin asks, Amit Zavery of Google his thoughts on the cloud leaderboard and how Google thinks about their third-place position. Dheeraj Pandey gives his perspective on how technology has progressed and been miniaturized over time. And what's coming in the future. And then Simon Crosby gives us a framework to think about the edge as the most logical opportunity to process data not necessarily a physical place. And this was echoed by John Roese, and Chris Wolf to experience CTOs who went into some great depth on this topic. Unfortunately, I don't have the clips of those two but their comments can be found on the CTO power panel the technical edge it's called that's the segment at theCUBE on Cloud events site which we'll share the URL later. Now, the highlight reel ends with CEO Joni Klippert she talks about the changes in securing the cloud from a developer angle. And finally, we wrap up with a CIO perspective, Dan Sheehan. He provides some practical advice on building on his experience as a CIO, COO and CTO specifically how do you as a business technology leader deal with the rapid pace of change and still be able to drive business results? Okay, so let's now hear from the community please run the highlights. >> Well, I think one of the things we talked about COVID is the personal impact to me but other people as well one of the things that people are craving right now is information, factual information, truth, textures that we call it. But here this event for us Dave is our first inaugural editorial event. Rob, both Kristen Nicole the entire cube team, SiliconANGLE on theCUBE we're really trying to put together more of a cadence. We're going to do more of these events where we can put out and feature the best people in our community that have great fresh voices. You know, we do interview the big names Andy Jassy, Michael Dell, the billionaires of people making things happen, but it's often the people under them that are the real Newsmakers. >> If you look at the architecture of cloud data centers the single most important invention was scale-out. Scale-out of identical or near identical servers all connected to a standard IP ethernet network. That's the architecture. Now the building blocks of this architecture is ethernet switches which make up the network, IP ethernet switches. And then the server is all built using general purpose x86 CPU's with DRAM, with SSD, with hard drives all connected to inside the CPU. Now, the fact that you scale these server nodes as they're called out was very, very important in addressing the problem of how do you build very large scale infrastructure using general purpose compute but this architecture, Dave is a compute centric architecture. And the reason it's a compute centric architecture is if you open this, is server node. What you see is a connection to the network typically with a simple network interface card. And then you have CPU's which are in the middle of the action. Not only are the CPU's processing the application workload but they're processing all of the IO workload what we call data centric workload. And so when you connect SSDs and hard drives and GPU is everything to the CPU, as well as to the network you can now imagine that the CPU is doing two functions. It's running the applications but it's also playing traffic cop for the IO. So every IO has to go to the CPU and you're executing instructions typically in the operating system. And you're interrupting the CPU many many millions of times a second. Now general purpose CPU and the architecture of the CPU's was never designed to play traffic cop because the traffic cop function is a function that requires you to be interrupted very, very frequently. So it's critical that in this new architecture where does a lot of data, a lot of these stress traffic the percentage of workload, which is data centric has gone from maybe one to 2% to 30 to 40%. >> The path to innovation is paved by data. If you don't have data, you don't have machine learning you don't have the next generation of analytics applications that helps you chart a path forward into a world that seems to be changing every week. And so in order to have that insight in order to have that predictive forecasting that every company needs, regardless of what industry that you're in today, it all starts from data. And I think the key shift that I've seen is how customers are thinking about that data, about being instantly usable. Whereas in the past, it might've been a backup. Now it's part of a data Lake. And if you can bring that data into a data lake you can have not just analytics or machine learning or auditing applications it's really what does your application do for your business and how can it take advantage of that vast amount of shared data set in your business? >> We are actually moving towards decentralization if we think today, like if it let's move data aside if we said is the only way web would work the only way we get access to various applications on the web or pages to centralize it We would laugh at that idea. But for some reason we don't question that when it comes to data, right? So I think it's time to embrace the complexity that comes with the growth of number of sources, the proliferation of sources and consumptions models, embrace the distribution of sources of data that they're not just within one part of organization. They're not just within even bounds of organizations that are beyond the bounds of organization. And then look back and say, okay, if that's the trend of our industry in general, given the fabric of compensation and data that we put in, you know, globally in place then how the architecture and technology and organizational structure incentives need to move to embrace that complexity. And to me that requires a paradigm shift a full stack from how we organize our organizations how we organize our teams, how we put a technology in place to look at it from a decentralized angle. >> I actually think we're in the midst of the transition to what's called a distributed cloud, where if you look at modernized cloud apps today they're actually made up of services from different clouds. And also distributed edge locations. And that's going to have a pretty profound impact on the way we go vast. >> We wake up every day, worrying about our customer and worrying about the customer condition and to absolutely make sure we dealt with the best in the first attempt that we do. So when you take the plethora of products we've dealt with in Azure, be it Azure SQL be it Azure cosmos DB, Synapse, Azure Databricks, which we did in partnership with Databricks Azure machine learning. And recently when we sort of offered the world's first comprehensive data governance solution and Azure overview, I would, I would humbly submit to you that we are leading the way. >> How important are rankings within the Google cloud team or are you focused mainly more on growth and just consistency? >> No, I don't think again, I'm not worried about we are not focused on ranking or any of that stuff. Typically I think we are worried about making sure customers are satisfied and the adding more and more customers. So if you look at the volume of customers we are signing up a lot of the large deals we did doing. If you look at the announcement we've made over the last year has been tremendous momentum around that. >> The thing that is really interesting about where we have been versus where we're going is we spend a lot of time talking about virtualizing hardware and moving that around. And what does that look like? And creating that as more of a software paradigm. And the thing we're talking about now is what does cloud as an operating model look like? What is the manageability of that? What is the security of that? What, you know, we've talked a lot about containers and moving into different, DevSecOps and all those different trends that we've been talking about. Like now we're doing them. So we've only gotten to the first crank of that. And I think every technology vendor we talked to now has to address how are they are going to do a highly distributed management insecurity landscape? Like, what are they going to layer on top of that? Because it's not just about, oh, I've taken a rack of something, server storage, compute, and virtualized it. I know have to create a new operating model around it in a way we're almost redoing what the OSI stack looks like and what the software and solutions are for that. >> And the whole idea of we in every recession we make things smaller. You know, in 91 we said we're going to go away from mainframes into Unix servers. And we made the unit of compute smaller. Then in the year, 2000 windows the next bubble burst and the recession afterwards we moved from Unix servers to Wintel windows and Intel x86 and eventually Linux as well. Again, we made things smaller going from million dollar servers to $5,000 servers, shorter lib servers. And that's what we did in 2008, 2009. I said, look, we don't even need to buy servers. We can do things with virtual machines which are servers that are an incarnation in the digital world. There's nothing in the physical world that actually even lives but we made it even smaller. And now with cloud in the last three, four years and what will happen in this coming decade. They're going to make it even smaller not just in space, which is size, with functions and containers and virtual machines, but also in time. >> So I think the right way to think about edges where can you reasonably process the data? And it obviously makes sense to process data at the first opportunity you have but much data is encrypted between the original device say and the application. And so edge as a place doesn't make as much sense as edge as an opportunity to decrypt and analyze it in the care. >> When I think of Shift-left, I think of that Mobius that we all look at all of the time and how we deliver and like plan, write code, deliver software, and then manage it, monitor it, right like that entire DevOps workflow. And today, when we think about where security lives, it either is a blocker to deploying production or most commonly it lives long after code has been deployed to production. And there's a security team constantly playing catch up trying to ensure that the development team whose job is to deliver value to their customers quickly, right? Deploy as fast as we can as many great customer facing features. They're then looking at it months after software has been deployed and then hurrying and trying to assess where the bugs are and trying to get that information back to software developers so that they can fix those issues. Shifting left to me means software engineers are finding those bugs as they're writing code or in the CIC CD pipeline long before code has been deployed to production. >> During this for quite a while now, it still comes down to the people. I can get the technology to do what it needs to do as long as they have the right requirements. So that goes back to people making sure we have the partnership that goes back to leadership and the people and then the change management aspects right out of the gate, you should be worrying about how this change is going to be how it's going to affect, and then the adoption and an engagement, because adoption is critical because you can go create the best thing you think from a technology perspective. But if it doesn't get used correctly, it's not worth the investment. So I agree, what is a digital transformation or innovation? It still comes down to understand the business model and injecting and utilizing technology to grow our reduce costs, grow the business or reduce costs. >> Okay, so look, there's so much other content on theCUBE on Cloud events site we'll put the link in the description below. We have other CEOs like Kathy Southwick and Ellen Nance. We have the CIO of UI path. Daniel Dienes talks about automation in the cloud and Appenzell from Anaplan. And a plan is not her company. By the way, Dave Humphrey from Bain also talks about his $750 million investment in Nutanix. Interesting, Rachel Stevens from red monk talks about the future of software development in the cloud and CTO, Hillary Hunter talks about the cloud going vertical into financial services. And of course, John Furrier and I along with special guests like Sergeant Joe Hall share our take on key trends, data and perspectives. So right here, you see the coupon cloud. There's a URL, check it out again. We'll, we'll pop this URL in the description of the video. So there's some great content there. I want to thank everybody who participated and thank you for watching this special episode of theCUBE Insights Powered by ETR. This is Dave Vellante and I'd appreciate any feedback you might have on how we can deliver better event content for you in the future. We'll be doing a number of these and we look forward to your participation and feedback. Thank you, all right, take care, we'll see you next time. (upbeat music)

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle. Okay, >>we're now going to explore the vision of the future of cloud computing From the perspective of one of the leaders in the field, J G >>Share >>a pure off is the vice president of As Your Data ai and Edge at Microsoft G. Welcome to the Cuban cloud. Thanks so much for participating. >>Well, thank you, Dave, and it's a real pleasure to be here with you. And I just wanna welcome the audience as well. >>Well, jg judging from your title, we have a lot of ground to cover, and our audience is definitely interested in all the topics that are implied there. So let's get right into it. You know, we've said many times in the Cube that the new innovation cocktail comprises machine intelligence or a I applied to troves of data. With the scale of the cloud. It's it's no longer, you know, we're driven by Moore's law. It's really those three factors, and those ingredients are gonna power the next wave of value creation and the economy. So, first, do you buy into that premise? >>Yes, absolutely. we do buy into it. And I think, you know, one of the reasons why we put Data Analytics and Ai together is because all of that really begins with the collection of data and managing it and governing it, unlocking analytics in it. And we tend to see things like AI, the value creation that comes from a I as being on that continues off, having started off with really things like analytics and proceeding toe. You know, machine learning and the use of data. Interesting breaks. Yes. >>I'd like to get some more thoughts around a data and how you see the future data and the role of cloud and maybe how >>Microsoft, you >>know, strategy fits in there. I mean, you, your portfolio, you got you got sequel server, Azure, Azure sequel. You got arc, which is kinda azure everywhere for people that aren't familiar with that. You've got synapse. Which course that's all the integration a data warehouse, and get things ready for B I and consumption by the business and and the whole data pipeline and a lot of other services as your data bricks you got You got cosmos in their, uh, Blockchain. You've got open source services like Post Dress and my sequel. So lots of choices there. And I'm wondering, you know, how do you think about the future of Of of Cloud data platforms? It looks like your strategies, right tool for the right job? Is that fair? >>It is fair, but it's also just to step back and look at it. It's fundamentally what we see in this market today is that customer was the Sikh really a comprehensive proposition? And when I say a comprehensive proposition, it is sometimes not just about saying that. Hey, listen way No, you're a sequel server company. We absolutely trust that you have the best Azure sequel database in the cloud, but tell us more. We've got data that's sitting in her group systems. We've got data that's sitting in Post Press in things like mongo DB, right? So that open source proposition today and data and data management and database management has become front and center, so are really sort of push. There is when it comes to migration management, modernization of data to present the broadest possible choice to our customers so we can meet them where they are. However, when it comes to analytics. One of the things they asked for is give us a lot more convergence use. You know it, really, it isn't about having 50 different services. It's really about having that one comprehensive service that is converged. That's where things like synapse Fitzer, where in just land any kind of data in the leg and then use any compute engine on top of it to drive insights from it. So, fundamentally, you know, it is that flexibility that we really sort of focus on to meet our customers where they are and really not pushing our dogma and our beliefs on it. But to meet our customers according to the way they have deployed stuff like this. >>So that's great. I want to stick on this for a minute because, you know, I know when when I have guests on like yourself, do you never want to talk about you know, the competition? But that's all we ever talk about. That's all your customers ever talk about, because because the counter to that right tool for the right job and that I would say, is really kind of Amazon's approach is is that you got the single unified data platform, the mega database that does it all. And that's kind of Oracle's approach. It sounds like you wanna have your cake and eat it, too, so you you got the right tool for the right job approach. But you've got an integration layer that allows you to have that converge database. I wonder if you could add color to that and you confirm or deny what I just said. >>No, that's a That's a very fair observation, but I I say there's a nuance in what I sort of describe when it comes to data management. When it comes to APS, we have them customers with the broadest choice. Even in that, even in that perspective, we also offer convergence. So, case in point, when you think about Cosmos TV under that one sort of service, you get multiple engines, but with the same properties, right global distribution, the five nines availability. It gives customers the ability to basically choose when they have to build that new cloud native AB toe, adopt cosmos Davey and adopted in a way that it's and choose an engine that is most flexible. Tow them, however you know when it comes to say, you know, writing a sequel server, for example from organizing it you know you want. Sometimes you just want to lift and shift it into things. Like I asked In other cases, you want to completely rewrite it, so you need to have the flexibility of choice there that is presented by a legacy off What's its on premises? When it moved into things like analytics, we absolutely believe in convergence, right? So we don't believe that look, you need to have a relation of data warehouse that is separate from a loop system that is separate from, say, a B I system. That is just, you know, it's a bolt on for us. We love the proposition off, really building things that are so integrated that once you land data, once you prep it inside the lake, you can use it for analytics. You can use it for being. You can use it for machine learning. So I think you know, are sort of differentiated. Approach speaks for itself there. Well, >>that's that's interesting, because essentially, again, you're not saying it's an either or, and you're seeing a lot of that in the marketplace. You got some companies say no, it's the Data Lake and others saying No, no put in the data warehouse and that causes confusion and complexity around the data pipeline and a lot of calls. And I'd love to get your thoughts on this. Ah, lot of customers struggled to get value out of data and and specifically data product builders of frustrated that it takes too long to go from. You know, this idea of Hey, I have an idea for a data service and it could drive monetization, but to get there, you gotta go through this complex data lifecycle on pipeline and beg people to add new data sources. And do you do you feel like we have to rethink the way that we approach data architectures? >>Look, I think we do in the cloud, and I think what's happening today and I think the place where I see the most amount of rethink the most amount of push from our customers to really rethink is the area of analytics in a I. It's almost as if what worked in the past will not work going forward. Right? So when you think about analytics on in the Enterprise today, you have relational systems, you have produced systems. You've got data marts. You've got data warehouses. You've got enterprise data warehouses. You know, those large honking databases that you use, uh, to close your books with right? But when you start to modernize it, what deep you are saying is that we don't want to simply take all of that complexity that we've built over say, you know, 34 decades and simply migrated on mass exactly as they are into the cloud. What they really want is a completely different way of looking at things. And I think this is where services like synapse completely provide a differentiated proposition to our customers. What we say there is land the data in any way you see shape or form inside the lake. Once you landed inside the lake, you can essentially use a synapse studio toe. Prep it in the way that you like, use any compute engine of your choice and and operate on this data in any way that you see fit. So, case in point, if you want to hydrate relation all data warehouse, you can do so if you want to do ad hoc analytics using something like spark. You can do so if you want to invoke power. Bi I on that data or b i on that data you can do so if you want to bring in a machine learning model on this breath data you can do so, so inherently. So when customers buy into this proposition, what it solves for them and what it gives them is complete simplicity, right? One way to land the data, multiple ways to use it. And it's all eso. >>Should we think of synapse as an abstraction layer that abstracts away the complexity of the underlying technology? Is that a fair way toe? Think about it. >>Yeah, you can think of it that way. It abstracts away, Dave a couple of things. It takes away the type of data, you know, sort of the complexities related to the type of data. It takes away the complexity related to the size of data. It takes away the complexity related to creating pipelines around all these different types of data and fundamentally puts it in a place where it can be now consumed by any sort of entity inside the actual proposition. And by that token, even data breaks. You know, you can, in fact, use data breaks in in sort off an integrated way with a synapse, Right, >>Well, so that leads me to this notion of and then wonder if you buy into it s Oh, my inference is that a data warehouse or a data lake >>could >>just be a node in inside of a global data >>mesh on. >>Then it's synapses sort of managing, uh, that technology on top. Do you buy into that that global data mesh concept >>we do. And we actually do see our customers using synapse and the value proposition that it brings together in that way. Now it's not where they start. Often times when a customer comes and says, Look, I've got an enterprise data warehouse, I want to migrate it or I have a group system. I want to migrate it. But from there, the evolution is absolutely interesting to see. I give you an example. You know, one of the customers that we're very proud off his FedEx And what FedEx is doing is it's completely reimagining its's logistics system that basically the system that delivers What is it? The three million packages a day on in doing so in this covert times, with the view of basically delivering our covert vaccines. One of the ways they're doing it is basically using synapse. Synapse is essentially that analytic hub where they can get complete view into their logistic processes. Way things are moving, understand things like delays and really put all that together in a way that they can essentially get our packages and these vaccines delivered as quickly as possible. Another example, you know, is one of my favorite, uh, we see once customers buy into it, they essentially can do other things with it. So an example of this is, uh is really my favorite story is Peace Parks Initiative. It is the premier Air White Rhino Conservancy in the world. They essentially are using data that has landed in azure images in particular. So, basically, you know, use drones over the vast area that they patrol and use machine learning on this data to really figure out where is an issue and where there isn't an issue so that this part with about 200 rangers can scramble surgically versus having to read range across the last area that they cover. So What do you see here is you know, the importance is really getting your data in order. Landed consistently. Whatever the kind of data ideas build the right pipelines and then the possibilities of transformation are just endless. >>Yeah, that's very nice how you worked in some of the customer examples. I appreciate that. I wanna ask you, though, that that some people might say that putting in that layer while it clearly adds simplification and e think a great thing that they're begins over time to be be a gap, if you will, between the ability of that layer to integrate all the primitives and all the peace parts on that, that you lose some of that fine grain control and it slows you down. What would you say to that? >>Look, I think that's what we excel at, and that's what we completely sort of buy into on. It's our job to basically provide that level off integration that granularity in the way that so it's an art, absolutely admit it's an art. There are areas where people create simplicity and not a lot of you know, sort of knobs and dials and things like that. But there are areas where customers want flexibility, right? So I think just to give you an example of both of them in landing the data inconsistency in building pipelines, they want simplicity. They don't want complexity. They don't want 50 different places to do this. Just 100 to do it. When it comes to computing and reducing this data analyzing this data, they want flexibility. This is one of the reasons why we say, Hey, listen, you want to use data breaks? If you're you're buying into that proposition and you're absolutely happy with them, you can plug plug it into it. You want to use B I and no, essentially do a small data mart. You can use B I If you say that. Look, I've landed in the lake. I really only want to use em melt, bringing your animal models and party on. So that's where the flexibility comes in. So that's sort of really sort of think about it. Well, >>I like the strategy because, you know, my one of our guest, Jim Octagon, e E. I think one of the foremost thinkers on this notion of off the data mesh and her premises that that that data builders, data product and service builders air frustrated because the the big data system is generic to context. There's no context in there. But by having context in the big data architecture and system, you could get products to market much, much, much faster. So but that seems to be your philosophy. But I'm gonna jump ahead to do my ecosystem question. You've mentioned data breaks a couple of times. There's another partner that you have, which is snowflake. They're kind of trying to build out their own, uh, data cloud, if you will, on global mesh in and the one hand, their partner. On the other hand, there are competitors. How do you sort of balance and square that circle? >>Look, when I see snowflake, I actually see a partner. You know that when we essentially you know, we are. When you think about as you know, this is where I sort of step back and look at Azure as a whole and in azure as a whole. Companies like snowflakes are vital in our ecosystem, right? I mean, there are places we compete, but you know, effectively by helping them build the best snowflake service on Asia. We essentially are able toe, you know, differentiate and offer a differentiated value proposition compared to, say, a Google or on AWS. In fact, that's being our approach with data breaks as well, where you know they are effectively on multiple club, and our opportunity with data breaks is toe essentially integrate them in a way where we offer the best experience. The best integrations on Azure Barna That's always been a focus. >>That's hard to argue with. Strategy. Our data with our data partner eat er, shows Microsoft is both pervasive and impressively having a lot of momentum spending velocity within the budget cycles. I wanna come back thio ai a little bit. It's obviously one of the fastest growing areas in our in our survey data. As I said, clearly, Microsoft is a leader in this space. What's your what's your vision of the future of machine intelligence and how Microsoft will will participate in that opportunity? >>Yeah, so fundamentally, you know, we've built on decades of research around, you know, around, you know, essentially, you know, vision, speech and language that's being the three core building blocks and for the for a really focused period of time we focused on essentially ensuring human parody. So if you ever wondered what the keys to the kingdom are it, czar, it's the most we built in ensuring that the research posture that we've taken there, what we then done is essentially a couple of things we focused on, essentially looking at the spectrum. That is a I both from saying that, Hollis and you know it's gotta work for data. Analysts were looking toe basically use machine learning techniques, toe developers who are essentially, you know, coding and building a machine learning models from scratch. So for that select proposition manifesto us, as you know, really a. I focused on all skill levels. The other court thing we've done is that we've also said, Look, it will only work as long as people trust their data and they can trust their AI models. So there's a tremendous body of work and research we do in things like responsibility. So if you ask me where we sort of push on is fundamentally to make sure that we never lose sight of the fact that the spectrum off a I, and you can sort of come together for any skill level, and we keep that responsibly. I proposition. Absolutely strong now against that canvas, Dave. I'll also tell you that you know, as edge devices get way more capable, right where they can input on the edge, see a camera or a mike or something like that, you will see us pushing a lot more of that capability onto the edge as well. But to me, that's sort of a modality. But the core really is all skill levels and that responsible denia. >>Yeah, So that that brings me to this notion of wanna bring an edge and and hybrid cloud Understand how you're thinking about hybrid cloud multi cloud. Obviously one of your competitors, Amazon won't even say the word multi cloud you guys have, Ah, you know, different approach there. But what's the strategy with regard? Toe, toe hybrid. You know, Do you see the cloud you bringing azure to the edge? Maybe you could talk about that and talk about how you're different from the competition. >>Yeah, I think in the edge from Annette, you know, I live in I'll be the first one to say that the word nge itself is conflated. Okay, It's, uh but I will tell you, just focusing on hybrid. This is one of the places where you know I would say the 2020 if I would have looked back from a corporate perspective. In particular, it has Bean the most informative because we absolutely saw customers digitizing moving to the cloud. And we really saw hybrid in action. 2020 was the year that hybrid sort of really became really from a cloud computing perspective and an example of this is we understood it's not all or nothing. So sometimes customers want azure consistency in their data centers. This is where things like Azure stack comes in. Sometimes they basically come to us and say, We want the flexibility of adopting flexible pattern, you know, platforms like, say, containers orchestra, Cuban Pettis, so that we can essentially deployed wherever you want. And so when we design things like art, it was built for that flexibility in mind. So here is the beauty of what's something like our can do for you. If you have a kubernetes endpoint anywhere we can deploy and as your service onto it, that is the promise, which means if for some reason, the customer says that. Hey, I've got this kubernetes endpoint in AWS and I love as your sequel. You will be able to run as your sequel inside AWS. There's nothing that stops you from doing it so inherently you remember. Our first principle is always to meet our customers where they are. So from that perspective, multi cloud is here to stay. You know, we're never going to be the people that says, I'm sorry, we will never see a But it is a reality for our customers. >>So I wonder if we could close. Thank you for that by looking, looking back and then and then ahead. And I wanna e wanna put forth. Maybe it's, Ah criticism, but maybe not. Maybe it's an art of Microsoft, but But first you know, you get Microsoft an incredible job of transitioning. It's business as your nominee president Azzawi said. Our data shows that so two part question First, Microsoft got there by investing in the cloud, really changing its mind set, I think, in leveraging its huge software state and customer base to put Azure at the center of its strategy, and many have said me included that you got there by creating products that air Good enough. You know, we do a 1.0, it's not that great. And the two Dato, and maybe not the best, but acceptable for your customers. And that's allowed you to grow very rapidly expanding market. >>How >>do you respond to that? Is that is that a fair comment? Ume or than good enough? I wonder if you could share your >>thoughts, gave you? You hurt my feelings with that question. I don't hate me, g getting >>it out there. >>So there was. First of all, thank you for asking me that. You know, I am absolutely the biggest cheerleader. You'll find a Microsoft. I absolutely believe you know that I represent the work off almost 9000 engineers and we wake up every day worrying about our customer and worrying about the customer condition and toe. Absolutely. Make sure we deliver the best in the first time that we do. So when you take the platter off products we've delivered in nausea, be it as your sequel, be it as your cosmos TV synapse as your data breaks, which we did in partnership with data breaks, a za machine learning and recently when we prevail, we sort off, you know, sort of offered the world's first comprehensive data government solution in azure purview. I would humbly submit to you that we're leading the way and we're essentially showing how the future off data ai and the actual work in the cloud. >>I'd be disappointed if you if you had If you didn't, if you capitulated in any way J g So so thank you for that. And the kind of last question is, is looking forward and how you're thinking about the future of cloud last decade. A lot about your cloud migration simplifying infrastructure management, deployment SAS if eyeing my enterprise, lot of simplification and cost savings. And, of course, the redeployment of resource is toward digital transformation. Other other other valuable activities. How >>do >>you think this coming decade will will be defined? Will it be sort of more of the same? Or is there Is there something else out there? >>I think I think that the coming decade will be one where customers start one law outside value out of this. You know what happened in the last decade when people leave the foundation and people essentially looked at the world and said, Look, we've got to make the move, you know, the largely hybrid, but we're going to start making steps to basically digitize and modernize our platforms. I would tell you that with the amount of data that people are moving to the cloud just as an example, you're going to see use of analytics ai for business outcomes explode. You're also going to see a huge sort of focus on things like governance. You know, people need to know where the data is, what the data catalog continues, how to govern it, how to trust this data and given all other privacy and compliance regulations out there. Essentially, they're complying this posture. So I think the unlocking of outcomes versus simply Hey, I've saved money Second, really putting this comprehensive sort off, you know, governance, regime in place. And then, finally, security and trust. It's going to be more paramount than ever before. Yeah, >>nobody's gonna use the data if they don't trust it. I'm glad you brought up your security. It's It's a topic that hits number one on the CEO list. J G. Great conversation. Obviously the strategy is working, and thanks so much for participating in Cuba on cloud. >>Thank you. Thank you, David. I appreciate it and thank you to. Everybody was tuning in today. >>All right? And keep it right there. I'll be back with our next guest right after this short break.

>> Okay, we're now going to explore the vision of the future of cloud computing from the perspective of one of the leaders in the field, JG Chirapurath is the Vice President of Azure Data AI and Edge at Microsoft. JG, welcome to theCUBE on Cloud, thanks so much for participating. >> Well, thank you, Dave. And it's a real pleasure to be here with you and just want to welcome the audience as well. >> Well, JG, judging from your title, we have a lot of ground to cover and our audience is definitely interested in all the topics that are implied there. So let's get right into it. We've said many times in theCUBE that the new innovation cocktail comprises machine intelligence or AI applied to troves of data with the scale of the cloud. It's no longer we're driven by Moore's law. It's really those three factors and those ingredients are going to power the next wave of value creation in the economy. So first, do you buy into that premise? >> Yes, absolutely. We do buy into it and I think one of the reasons why we put data analytics and AI together, is because all of that really begins with the collection of data and managing it and governing it, unlocking analytics in it. And we tend to see things like AI, the value creation that comes from AI as being on that continuum of having started off with really things like analytics and proceeding to be machine learning and the use of data in interesting ways. >> Yes, I'd like to get some more thoughts around data and how you see the future of data and the role of cloud and maybe how Microsoft strategy fits in there. I mean, your portfolio, you've got SQL Server, Azure SQL, you got Arc which is kind of Azure everywhere for people that aren't familiar with that you got Synapse which course does all the integration, the data warehouse and it gets things ready for BI and consumption by the business and the whole data pipeline. And then all the other services, Azure Databricks, you got you got Cosmos in there, you got Blockchain, you've got Open Source services like PostgreSQL and MySQL. So lots of choices there. And I'm wondering, how do you think about the future of cloud data platforms? It looks like your strategy is right tool for the right job. Is that fair? >> It is fair, but it's also just to step back and look at it. It's fundamentally what we see in this market today, is that customers they seek really a comprehensive proposition. And when I say a comprehensive proposition it is sometimes not just about saying that, "Hey, listen "we know you're a sequence of a company, "we absolutely trust that you have the best "Azure SQL database in the cloud. "But tell us more." We've got data that is sitting in Hadoop systems. We've got data that is sitting in PostgreSQL, in things like MongoDB. So that open source proposition today in data and data management and database management has become front and center. So our real sort of push there is when it comes to migration management modernization of data to present the broadest possible choice to our customers, so we can meet them where they are. However, when it comes to analytics, one of the things they ask for is give us lot more convergence use. It really, it isn't about having 50 different services. It's really about having that one comprehensive service that is converged. That's where things like Synapse fits in where you can just land any kind of data in the lake and then use any compute engine on top of it to drive insights from it. So fundamentally, it is that flexibility that we really sort of focus on to meet our customers where they are. And really not pushing our dogma and our beliefs on it but to meet our customers according to the way they've deployed stuff like this. >> So that's great. I want to stick on this for a minute because when I have guests on like yourself they never want to talk about the competition but that's all we ever talk about. And that's all your customers ever talk about. Because the counter to that right tool for the right job and that I would say is really kind of Amazon's approach is that you got the single unified data platform, the mega database. So it does it all. And that's kind of Oracle's approach. It sounds like you want to have your cake and eat it too. So you got the right tool with the right job approach but you've got an integration layer that allows you to have that converged database. I wonder if you could add color to that and confirm or deny what I just said. >> No, that's a very fair observation but I'd say there's a nuance in what I sort of described. When it comes to data management, when it comes to apps, we have then customers with the broadest choice. Even in that perspective, we also offer convergence. So case in point, when you think about cosmos DB under that one sort of service, you get multiple engines but with the same properties. Right, global distribution, the five nines availability. It gives customers the ability to basically choose when they have to build that new cloud native app to adopt cosmos DB and adopt it in a way that is an choose an engine that is most flexible to them. However, when it comes to say, writing a SequenceServer for example, if modernizing it, you want sometimes, you just want to lift and shift it into things like IS. In other cases, you want to completely rewrite it. So you need to have the flexibility of choice there that is presented by a legacy of what sits on premises. When you move into things like analytics, we absolutely believe in convergence. So we don't believe that look, you need to have a relational data warehouse that is separate from a Hadoop system that is separate from say a BI system that is just, it's a bolt-on. For us, we love the proposition of really building things that are so integrated that once you land data, once you prep it inside the Lake you can use it for analytics, you can use it for BI, you can use it for machine learning. So I think, our sort of differentiated approach speaks for itself there. >> Well, that's interesting because essentially again you're not saying it's an either or, and you see a lot of that in the marketplace. You got some companies you say, "No, it's the data lake." And others say "No, no, put it in the data warehouse." And that causes confusion and complexity around the data pipeline and a lot of cutting. And I'd love to get your thoughts on this. A lot of customers struggle to get value out of data and specifically data product builders are frustrated that it takes them too long to go from, this idea of, hey, I have an idea for a data service and it can drive monetization, but to get there you got to go through this complex data life cycle and pipeline and beg people to add new data sources and do you feel like we have to rethink the way that we approach data architecture? >> Look, I think we do in the cloud. And I think what's happening today and I think the place where I see the most amount of rethink and the most amount of push from our customers to really rethink is the area of analytics and AI. It's almost as if what worked in the past will not work going forward. So when you think about analytics only in the enterprise today, you have relational systems, you have Hadoop systems, you've got data marts, you've got data warehouses you've got enterprise data warehouse. So those large honking databases that you use to close your books with. But when you start to modernize it, what people are saying is that we don't want to simply take all of that complexity that we've built over, say three, four decades and simply migrate it en masse exactly as they are into the cloud. What they really want is a completely different way of looking at things. And I think this is where services like Synapse completely provide a differentiated proposition to our customers. What we say there is land the data in any way you see, shape or form inside the lake. Once you landed inside the lake, you can essentially use a Synapse Studio to prep it in the way that you like. Use any compute engine of your choice and operate on this data in any way that you see fit. So case in point, if you want to hydrate a relational data warehouse, you can do so. If you want to do ad hoc analytics using something like Spark, you can do so. If you want to invoke Power BI on that data or BI on that data, you can do so. If you want to bring in a machine learning model on this prep data, you can do so. So inherently, so when customers buy into this proposition, what it solves for them and what it gives to them is complete simplicity. One way to land the data multiple ways to use it. And it's all integrated. >> So should we think of Synapse as an abstraction layer that abstracts away the complexity of the underlying technology? Is that a fair way to think about it? >> Yeah, you can think of it that way. It abstracts away Dave, a couple of things. It takes away that type of data. Sort of complexities related to the type of data. It takes away the complexity related to the size of data. It takes away the complexity related to creating pipelines around all these different types of data. And fundamentally puts it in a place where it can be now consumed by any sort of entity inside the Azure proposition. And by that token, even Databricks. You can in fact use Databricks in sort of an integrated way with the Azure Synapse >> Right, well, so that leads me to this notion of and I wonder if you buy into it. So my inference is that a data warehouse or a data lake could just be a node inside of a global data mesh. And then it's Synapse is sort of managing that technology on top. Do you buy into that? That global data mesh concept? >> We do and we actually do see our customers using Synapse and the value proposition that it brings together in that way. Now it's not where they start, oftentimes when a customer comes and says, "Look, I've got an enterprise data warehouse, "I want to migrate it." Or "I have a Hadoop system, I want to migrate it." But from there, the evolution is absolutely interesting to see. I'll give you an example. One of the customers that we're very proud of is FedEx. And what FedEx is doing is it's completely re-imagining its logistics system. That basically the system that delivers, what is it? The 3 million packages a day. And in doing so, in this COVID times, with the view of basically delivering on COVID vaccines. One of the ways they're doing it, is basically using Synapse. Synapse is essentially that analytic hub where they can get complete view into the logistic processes, way things are moving, understand things like delays and really put all of that together in a way that they can essentially get our packages and these vaccines delivered as quickly as possible. Another example, it's one of my favorite. We see once customers buy into it, they essentially can do other things with it. So an example of this is really my favorite story is Peace Parks initiative. It is the premier of white rhino conservancy in the world. They essentially are using data that has landed in Azure, images in particular to basically use drones over the vast area that they patrol and use machine learning on this data to really figure out where is an issue and where there isn't an issue. So that this part with about 200 radios can scramble surgically versus having to range across the vast area that they cover. So, what you see here is, the importance is really getting your data in order, landing consistently whatever the kind of data it is, build the right pipelines, and then the possibilities of transformation are just endless. >> Yeah, that's very nice how you worked in some of the customer examples and I appreciate that. I want to ask you though that some people might say that putting in that layer while you clearly add simplification and is I think a great thing that there begins over time to be a gap, if you will, between the ability of that layer to integrate all the primitives and all the piece parts, and that you lose some of that fine grain control and it slows you down. What would you say to that? >> Look, I think that's what we excel at and that's what we completely sort of buy into. And it's our job to basically provide that level of integration and that granularity in the way that it's an art. I absolutely admit it's an art. There are areas where people crave simplicity and not a lot of sort of knobs and dials and things like that. But there are areas where customers want flexibility. And so I think just to give you an example of both of them, in landing the data, in consistency in building pipelines, they want simplicity. They don't want complexity. They don't want 50 different places to do this. There's one way to do it. When it comes to computing and reducing this data, analyzing this data, they want flexibility. This is one of the reasons why we say, "Hey, listen you want to use Databricks. "If you're buying into that proposition. "And you're absolutely happy with them, "you can plug it into it." You want to use BI and essentially do a small data model, you can use BI. If you say that, "Look, I've landed into the lake, "I really only want to use ML." Bring in your ML models and party on. So that's where the flexibility comes in. So that's sort of that we sort of think about it. >> Well, I like the strategy because one of our guests, Jumark Dehghani is I think one of the foremost thinkers on this notion of of the data mesh And her premise is that the data builders, data product and service builders are frustrated because the big data system is generic to context. There's no context in there. But by having context in the big data architecture and system you can get products to market much, much, much faster. So, and that seems to be your philosophy but I'm going to jump ahead to my ecosystem question. You've mentioned Databricks a couple of times. There's another partner that you have, which is Snowflake. They're kind of trying to build out their own DataCloud, if you will and GlobalMesh, and the one hand they're a partner on the other hand they're a competitor. How do you sort of balance and square that circle? >> Look, when I see Snowflake, I actually see a partner. When we see essentially we are when you think about Azure now this is where I sort of step back and look at Azure as a whole. And in Azure as a whole, companies like Snowflake are vital in our ecosystem. I mean, there are places we compete, but effectively by helping them build the best Snowflake service on Azure, we essentially are able to differentiate and offer a differentiated value proposition compared to say a Google or an AWS. In fact, that's been our approach with Databricks as well. Where they are effectively on multiple clouds and our opportunity with Databricks is to essentially integrate them in a way where we offer the best experience the best integrations on Azure Berna. That's always been our focus. >> Yeah, it's hard to argue with the strategy or data with our data partner and ETR shows Microsoft is both pervasive and impressively having a lot of momentum spending velocity within the budget cycles. I want to come back to AI a little bit. It's obviously one of the fastest growing areas in our survey data. As I said, clearly Microsoft is a leader in this space. What's your vision of the future of machine intelligence and how Microsoft will participate in that opportunity? >> Yeah, so fundamentally, we've built on decades of research around essentially vision, speech and language. That's been the three core building blocks and for a really focused period of time, we focused on essentially ensuring human parity. So if you ever wonder what the keys to the kingdom are, it's the boat we built in ensuring that the research or posture that we've taken there. What we've then done is essentially a couple of things. We've focused on essentially looking at the spectrum that is AI. Both from saying that, "Hey, listen, "it's got to work for data analysts." We're looking to basically use machine learning techniques to developers who are essentially, coding and building machine learning models from scratch. So for that select proposition manifest to us as really AI focused on all skill levels. The other core thing we've done is that we've also said, "Look, it'll only work as long "as people trust their data "and they can trust their AI models." So there's a tremendous body of work and research we do and things like responsible AI. So if you asked me where we sort of push on is fundamentally to make sure that we never lose sight of the fact that the spectrum of AI can sort of come together for any skill level. And we keep that responsible AI proposition absolutely strong. Now against that canvas Dave, I'll also tell you that as Edge devices get way more capable, where they can input on the Edge, say a camera or a mic or something like that. You will see us pushing a lot more of that capability onto the edge as well. But to me, that's sort of a modality but the core really is all skill levels and that responsibility in AI. >> Yeah, so that brings me to this notion of, I want to bring an Edge and hybrid cloud, understand how you're thinking about hybrid cloud, multicloud obviously one of your competitors Amazon won't even say the word multicloud. You guys have a different approach there but what's the strategy with regard to hybrid? Do you see the cloud, you're bringing Azure to the edge maybe you could talk about that and talk about how you're different from the competition. >> Yeah, I think in the Edge from an Edge and I even I'll be the first one to say that the word Edge itself is conflated. Okay, a little bit it's but I will tell you just focusing on hybrid, this is one of the places where, I would say 2020 if I were to look back from a COVID perspective in particular, it has been the most informative. Because we absolutely saw customers digitizing, moving to the cloud. And we really saw hybrid in action. 2020 was the year that hybrid sort of really became real from a cloud computing perspective. And an example of this is we understood that it's not all or nothing. So sometimes customers want Azure consistency in their data centers. This is where things like Azure Stack comes in. Sometimes they basically come to us and say, "We want the flexibility of adopting "flexible button of platforms let's say containers, "orchestrating Kubernetes "so that we can essentially deploy it wherever you want." And so when we designed things like Arc, it was built for that flexibility in mind. So, here's the beauty of what something like Arc can do for you. If you have a Kubernetes endpoint anywhere, we can deploy an Azure service onto it. That is the promise. Which means, if for some reason the customer says that, "Hey, I've got "this Kubernetes endpoint in AWS. And I love Azure SQL. You will be able to run Azure SQL inside AWS. There's nothing that stops you from doing it. So inherently, remember our first principle is always to meet our customers where they are. So from that perspective, multicloud is here to stay. We are never going to be the people that says, "I'm sorry." We will never say (speaks indistinctly) multicloud but it is a reality for our customers. >> So I wonder if we could close, thank you for that. By looking back and then ahead and I want to put forth, maybe it's a criticism, but maybe not. Maybe it's an art of Microsoft. But first, you did Microsoft an incredible job at transitioning its business. Azure is omnipresent, as we said our data shows that. So two-part question first, Microsoft got there by investing in the cloud, really changing its mindset, I think and leveraging its huge software estate and customer base to put Azure at the center of it's strategy. And many have said, me included, that you got there by creating products that are good enough. We do a one Datto, it's still not that great, then a two Datto and maybe not the best, but acceptable for your customers. And that's allowed you to grow very rapidly expand your market. How do you respond to that? Is that a fair comment? Are you more than good enough? I wonder if you could share your thoughts. >> Dave, you hurt my feelings with that question. >> Don't hate me JG. (both laugh) We're getting it out there all right, so. >> First of all, thank you for asking me that. I am absolutely the biggest cheerleader you'll find at Microsoft. I absolutely believe that I represent the work of almost 9,000 engineers. And we wake up every day worrying about our customer and worrying about the customer condition and to absolutely make sure we deliver the best in the first attempt that we do. So when you take the plethora of products we deliver in Azure, be it Azure SQL, be it Azure Cosmos DB, Synapse, Azure Databricks, which we did in partnership with Databricks, Azure Machine Learning. And recently when we premiered, we sort of offered the world's first comprehensive data governance solution in Azure Purview. I would humbly submit it to you that we are leading the way and we're essentially showing how the future of data, AI and the Edge should work in the cloud. >> Yeah, I'd be disappointed if you capitulated in any way, JG. So, thank you for that. And that's kind of last question is looking forward and how you're thinking about the future of cloud. Last decade, a lot about cloud migration, simplifying infrastructure to management and deployment. SaaSifying My Enterprise, a lot of simplification and cost savings and of course redeployment of resources toward digital transformation, other valuable activities. How do you think this coming decade will be defined? Will it be sort of more of the same or is there something else out there? >> I think that the coming decade will be one where customers start to unlock outsize value out of this. What happened to the last decade where people laid the foundation? And people essentially looked at the world and said, "Look, we've got to make a move. "They're largely hybrid, but you're going to start making "steps to basically digitize and modernize our platforms. I will tell you that with the amount of data that people are moving to the cloud, just as an example, you're going to see use of analytics, AI or business outcomes explode. You're also going to see a huge sort of focus on things like governance. People need to know where the data is, what the data catalog continues, how to govern it, how to trust this data and given all of the privacy and compliance regulations out there essentially their compliance posture. So I think the unlocking of outcomes versus simply, Hey, I've saved money. Second, really putting this comprehensive sort of governance regime in place and then finally security and trust. It's going to be more paramount than ever before. >> Yeah, nobody's going to use the data if they don't trust it, I'm glad you brought up security. It's a topic that is at number one on the CIO list. JG, great conversation. Obviously the strategy is working and thanks so much for participating in Cube on Cloud. >> Thank you, thank you, Dave and I appreciate it and thank you to everybody who's tuning into today. >> All right then keep it right there, I'll be back with our next guest right after this short break.

>> From The Cube Studios in Palo Alto and Boston connecting with thought-leaders all around the world, this is a Cube Conversation. >> Hello, everyone, and welcome to this Cube Conversation, I'm Dave Vellante. Now, you know I love data, and today we're going to introduce you to a new data and analytical platform, and we're going to take it to the world of cloud database and data warehouses. And with me is Sagar Kadakia who's the head of Enterprise IT (indistinct) 7Park Data. Sagar, welcome back to the Cube. Good to see you. >> Thank you so much, David. I appreciate you having me back on. >> Hey, so new gig for you, how's it going? Tell us about 7Park Data. >> Yeah. Look, things are going well. It started at about two months ago, just a, you know, busy. I had a chance last, you know a few months to kind of really dig into the dataset. We have a tremendous amount of research coming out in Q4 Q1 around kind of the public cloud database market public cloud analytics market. So, you know, really looking forward to that. >> Okay, good. Well, let's bring up the first slide. Let's talk about where this data comes from. Tell us a little bit more about the platform. Where's the insight. >> Yeah, absolutely. So I'll talk a little about 7Park and then we'd kind of jump into the data a little bit. So 7Park was founded in 2012 in terms of differentiator, you know with other alternative data firms, you know we use NLP machine learning, you know AI to really kind of, you know, structure like noisy and unstructured data sets really kind of generate insight from that. And so, because a lot of that know how we ended up being acquired by Vista back in 2018. And really like for us, you know the mandate there is to really, you know look across all their different portfolio companies and try to generate insight from all the data assets you know, that these portfolio companies have. So, you know, today we're going to be talking about you know, one of the data sets from those companies it's that cloud infrastructure data set. We get it from one of the portfolio companies that you know, helps organizations kind of manage and optimize their cloud spend. It's real time data. We essentially get this aggregated daily. So this certainly different than, you know your traditional providers maybe giving you quarterly or kind of by annual data. This is incredibly granular, real time all the way down to the invoice level. So within this cloud infrastructure dataset we're tracking several billion dollars worth of spend across AWS, Azure and GCP. Something like 350 services across like 20 plus markets. So, you know, security machine learning analytics database which we're going to talk about today. And again like the granularity of the KPIs I think is kind of really what kind of you know, differentiates this dataset you know, with just within database itself, you know we're tracking over 20 services. So, you know, lots to kind of look forward to kind of into Q4 and Q1. >> So, okay. So the main spring of your data is if I'm a customer and I there's a service out there there are many services like this that can help me optimize my spend and the way they do that is I basically connect their APIs. So they have visibility on what the transactions that I'm making my usage statistics et cetera. And then you take that and then extrapolate that and report on that. Is that right? >> Exactly. Yeah. We're seeing just on this one data set that we're going to talk about today, it's something like six 700 million rows worth of data. And so kind of what we do is, you know we kind of have the insight layer on top of that or the analytics layer on top of all that unstructured data, so that we can get a feel for, you know a whole host of different kind of KPIs spend, adoption rates, market share, you know product size, retention rates, spend, you know, net price all that type of stuff. So, yeah, that's exactly what we're doing. >> Love it, there's more transparency the better. Okay. So, so right, because this whole world of market sizing has been very opaque you know, over the years, and it's like you know, backroom conversations, whether it's IDC, Gartner who's got what don't take, you know and the estimations and it's very, very, you know it's not very transparent so I'm excited to see what you guys have. Okay. So, so you have some data on the public cloud and specifically the database market that you want to share with our audience. Let's bring up the next graphic here. What are we looking at here Sagar? What are these blue lines and red lines what's this all about? >> Yeah. So and look, we can kind of start at the kind of the 10,000 foot view kind of level here. And so what we're looking at here is our estimates for the entire kind of cloud database market, including data warehousing. If you look all the way over to the right I'll kind of explain some of these bars in a minute but just high level, you know we're forecasting for this year, $11.8 billion. Now something to kind of remember about that is that's just AWS, Azure and GCP, right? So that's not the entire cloud database market. It's just specific to those three providers. What you're looking at here is the breakout and blue and purple is SQL databases and then no SQL databases. And so, you know, to no one's surprise here and you can see, you know SQL database is obviously much larger from a revenue standpoint. And so you can see just from this time last year, you know the database market has grown 40% among these three cloud providers. And, you know, though, we're not showing it here, you know from like a PI perspective, you know database is playing a larger and larger role for all three of these providers. And so obviously this is a really hot market, which is why, you know we're kind of discussing a lot of the dynamics. You don't need to Q and Q Q4 and Q1 >> So, okay. Let's get into some of the specific firm-level data. You have numbers that you want to share on Amazon Redshift and Google BigQuery, and some comments on Snowflake let's bring up the next graphic. So tell us, it says public cloud data, warehousing growth tempered by Snowflake, what's the data showing. And let's talk about some of the implications there. >> Yeah, no problem. So yeah, this is kind of one of the markets, you know that we kind of did a deep dive in tomorrow and we'll kind of get this, you know, get to this in a few minutes, we're kind of doing a big CIO panel kind of covering data, warehousing, RDBMS documents store key value, graph all these different database markets but I thought it'd be great, you know just cause obviously what's occurring here and with snowflake to kind of talk about, you know the data warehousing market, you know, look if you look here, these are some of the KPIs that we have you know, and I'll kind of start from the left. Here are some of the orange bars, the darker orange bars. Those are our estimates for AWS Redshift. And so you can see here, you know we're projecting about 667 million in revenue for Redshift. But if you look at the lighter arm bars, you can see that the service went from representing about 2% of you know, AWS revenue to about 1.5%. And we think some of that is because of Snowflake. And if we kind of, take a look at some of these KPIs you know, below those bar charts here, you know one of the things that we've been looking at is, you know how are longer-term customer spending and how are let's just say like newer customers spending, so to speak. So kind of just like organic growth or kind of net expansion analysis. And if you look at on the bottom there, you'll see, you know customers in our dataset that we looked at, you know that were there 3Q20 as well as 3Q19 their spend on AWS Redshift is 23%. Right? And then look at the bifurcation, right? When we include essentially all the new customers that onboard it, right after 3Q19, look at how much they're bringing down the spend increase. And it's because, you know a lot of spend that was perhaps meant for Redshift is now going to Snowflake. And look, you would expect longer-term customers to spend more than newer customers. But really what we're doing is here is really highlighting the stark contrast because you have kind of back to back KPIs here, you know between organic spend versus total spend and obviously the deceleration in market share kind of coming down. So, you know, something that's interesting here and we'll kind of continue tracking that. >> Okay. So let's maybe come back to this mass Colombo questions here. So the start with the orange side. So we're talking about Snowflake being 667 million. These are your estimates extrapolated based on what we talked about earlier, 1.5% of the AWS portfolio of course you see things like, they continue to grow. Amazon made a bunch of storage announcements last week at the first week of re-invent (indistinct) I mean just name all kinds of databases. And so it's competing with a lot of other services in the portfolio and then, but it's interesting to see Google BigQuery a much larger percentage of the portfolio, which again to me, makes sense people like BigQuery. They like the data science components that are built in the machine learning components that are built in. But then if you look at Snowflake's last quarter and just on a run rate basis, it's over there over $600 million. Now, if you just multiply their last quarter by four from a revenue standpoint. So they got Redshift in their sites, you know if this is, you know to the extent this is the correct number and I know it's an estimate but I haven't seen any better numbers out there. Interesting Sagar, I mean Snowflake surpassed the value of snowflakes or past service now last Friday, it's probably just in trading today you know, on Monday it's maybe Snowflake is about a billion dollars less than the in value than IBM. So you're saying snowflake in a lot of attention, post IPO the thing is even exploded more. I mean, it's crazy. And I presume that's rippled into the customer interest areas. Now the ironic thing here of course, is that that snowflake most of its revenue comes from AWS running on AWS at the same time, AWS and or Redshift and snowflake compete. So you have this interesting dynamic going on. >> Yeah. You know, we've spoken to so many CIOs about kind of the dynamics here with Redshift and BigQuery and Snowflake, you know as it kind of pertains to, you know, Redshift and Snowflake. I think, you know, what I've heard the most is, look if you're using Redshift, you're going to keep using it. But if you're new to data warehousing kind of, so to speak you're going to move to Snowflake, or you're going to start with Snowflake, you know, that and I think, you know when it comes to data warehousing, you're seeing a lot of decisions kind of coming from, you know, bottom up now. So a lot of developers and so obviously their preference is going to be Snowflake. And then when you kind of look at BigQuery here over to the right again, like look you're seeing revenue growth, but again, as a as a percentage of total, you know, GCP revenue you're seeing it come down and look, we don't show it here. But another dynamic that we're seeing amongst BigQuery is that we are seeing adoption rates fall versus this time last year. So we think, again, that could be because of Snowflake. Now, one thing to kind of highlight here with BigQuery look it's kind of the low cost alternative, you know, so to speak, you know once Redshift gets too expensive, so to speak, you know you kind of move over to, to BigQuery and we kind of put some price KPIs down here all the way at the bottom of the chart, you know kind of for both of them, you know when you kind of think about the net price per kind of TB scan, you know, Redshift does it pro rate right? It's five bucks or whatever you, you know whatever you scan in, whereas, you know GCP and get the first terabyte for free. And then everything is prorated after that. And so you can see the net price, right? So that's the price that people actually pay. You can see it's significantly lower that than Redshift. And again, you know it's a lower cost alternative. And so when you think about, you know organizations or CIO's that want to save some money certainly BigQuery, you know, is an option. But certainly I think just overall, you know, Snowflake is is certainly having, you know, an impact here and you can see it from, you know the percentage of total revenue for both these coming down. You know, if we look at other AWS database services or you mentioned a few other services, you know we're not seeing that trend, we're seeing, you know percentage of total revenue hang in or accelerate. And so that's kind of why we want to point this out as this is something unique, you know for AWS and GCP where even though you're seeing growth, it's decelerating. And then of course you can kind of see the percentage of revenue represents coming down. >> I think it's interesting to look at these two companies and then of course Snowflake. So if you think about Snowflake and BigQuery both of those started in the cloud they were true born in the cloud databases. Whereas Redshift was a deal that Amazon did, you know with parxl back in the day, one time license fee and then they re-engineered it to be kind of cloud based. And so there is some of that historical o6n-prem baggage in there. I know that AWS did a tremendous job in rearchitecting that but nonetheless, so I'll give you a couple of examples. If you go back to last year's reinvent 2019 of course Snowflake was really the first to popularize this idea of separating compute from storage and even compute from compute, which is kind of nuance. So I won't go into that, but the idea being you can dial up or dial down compute as you need it you can even turn off compute in the world of Snowflake and just, you know, you're paying an S3 for storage charges. What Amazon did last reinvent was they announced the separation of compute and storage, but what the way they did it was they did it with a tiering architecture. So you can't ever actually fully turn off the compute, but it's great. I mean, it's customers I've talked to say, yes I'm saving a lot of money, you know, with this approach. But again, there's these little nuances. So what Snowflake announced this year was their data cloud and what the data cloud is as a whole new architecture. It's based on this global mesh. It lives across both AWS and Azure and GCP. And what Snowflake has done is they've taken they've abstracted the complexity of the clouds. So you don't even necessarily have to know what you're running on. You have to worry about it any Snowflake user inside of that data cloud if given access can share data with any other user. So it's a very powerful concept that they're doing. AWS at reinvent this year announced something called AWS glue elastic views which basically allows you to take data across their entire database portfolio. And I'm going to put, share in quotes. And I put it in quotes because it's essentially doing copying from a source pushing to a target AWS database and then doing a change data management capture and pushes that over time. So it, it feels like kind of an attempt to do their own data cloud. The advantages of AWS is that they've got way more data stores than just Snowflake cause it's one data store. So was AWS says Aurora dynamo DB Redshift on and on and on streaming databases, et cetera where Snowflake is just Snowflake. And so it's going to be interesting to see, you know these two juxtaposing philosophies but I want it to sort of lay that out because this is just it's setting up as a really interesting dynamic. Then you can bring in Azure as well with Microsoft and what they're doing. And I think this is going to be really fascinating to see how this plays out over the next decade. >> Yeah. I think some of the points you brought up maybe a little bit earlier were just around like the functional limits of a Redshift. Right. And I think that's where, you know Snowflake obviously does it does very, very well you know, you kind of have these, you know kind of to come, you know, you kind of have these, you know if you kind of think about like the market drivers right? Like, let's think about even like the prior slide that we showed, where we saw overall you know, database growth, like what's driving all of that what's driving Redshift, right. Obviously proximity application, interdependencies, right. Costs. You get all the credits or people are already working with the big three providers. And so there's so many reasons to continue spending with them, obviously, you know, COVID-19 right. Obviously all these apps being developed right in the cloud versus data centers and things of that nature. So you have all of these market drivers, you know for the cloud database services for Redshift. And so from that perspective, you know you kind of think, well why are people even to go to a third party vendor? And I think, you know, at that point it has to be the functional superiority. And so again, like a lot of times it depends on, you know, where decisions are coming from you know, top down or bottom up obviously at the engineering at the developer level they're going to want better functionality. Maybe, you know, top-down sometimes, you know it's like, look, we have a lot of credits, you know we're trying to save money, you know from a security perspective it could just be easier to spin something up you know, in AWS, so to speak. So, yeah, I think these are all the dynamics that, you know organizations have to figure out every day, but at least within the data warehousing space, you are seeing spend go towards Snowflake and it's going away to an extent as we kind of see, you know growth decelerate for both of these vendors, right. It's not that revenue's not going out there is growth which is that growth is, it's just not the same as it used to be, you know, so to speak. So yeah, this is a interesting area to kind of watch and I think across all the other markets as well, you know when you think about document store, right you have AWS document DB, right. What are the impacts there with with Mongo and some of these other kind of third party data warehousing vendors, right. Having to compete with all the, you know all the different services offered by AWS Azure like the cosmos and all that stuff. So, yeah, it's definitely kind of turning into a battle Royal, you know as we kind of head into, into 2021. And so I think having all these KPIs is really helping us kind of break down and figure out, you know which areas like data warehousing are slowing down. But then what other areas in database where they're seeing a tremendous amount of acceleration, like as we said, database revenue is driving. Like it's becoming a bigger part of their overall revenue. And so they are doing well. It just, you know, there's obviously snowflake they have to compete with here. >> Well, and I think maybe to your point I infer from your point, it's not necessarily a zero sum game. And as I was discussing before, I think Snowflake's really trying to create a new market. It's not just trying to steal share from the Terra datas and the Redshifts and the PCPs of the world, big queries and and Azure SQL server and Oracle and so forth. They're trying to create a whole new concept called the data cloud, which to me is really important because my prediction is what Snowflake is doing. And they don't even really talk a ton about this but they sort of do, if you squint through the lines I think what they're doing is first of all, simplicity is there, what they're doing. And then they're putting data in the hands of business people, business line people who have domain context, that's a whole new way of thinking about a data architecture versus the prevalent way to do a data pipeline is you got data engineers and data scientists, and you ingest data. It's goes to the beginning of the pipeline and that's kind of a traditional way to do it. And kind of how I think most of the AWS customers do it. I think over time, because of the simplicity of Snowflake you're going to see people begin to look at new ways to architect data. Anyway, we're almost out of time here but I want to bring up the next slide which is a graphic, which talks about a database discussion that you guys are having on 12/8 at 2:00 PM Eastern time with Bain and Verizon who what's this all about. >> Yeah. So, you know, one of the things we wanted to do is we kind of kick off a lot of the, you know Q4 Q1 research or putting on the database spark. It is just like kind of, we did, you know we did today, which obviously, you know we're really going to expand on tomorrow at a at 2:00 PM is discuss all the different KPIs. You know, we track something like 20 plus database services. So we're going to be going through a lot more than just kind of Redshift and BigQuery. Look at all the dynamics there, look at, you know how they're very against some of the third party vendors like the Snowflake, like a Mongo DB, as an example we got some really great, you know, thought leaders you know, Michael Delzer and Praveen from verizon they're going to kind of help, or they're going to opine on all the dynamics that we're seeing. And so it's going to be a very kind of, you know structured wise, it's going to be very quantitative but then you're going to have this beautiful qualitative discussion to kind of help support a lot of the data points that we're capturing. And so, yeah, we're really excited about the panel you know, from, you know, why you should join standpoint. Look, it's just, it's great, competitive Intel. If you're a third party, you know, database, data warehousing vendor, this is the type of information that you're going to want to know, you know, adoption rates market sizing, retention rates, you know net price reservers, on demand dynamics. You know, we're going through a lot that tomorrow. So I'm really excited about that. I'm just in general, really excited about a lot of the research that we're kind of putting out. So >> That's interesting. I mean, and we were talking earlier about AWS glue elastic views. I'd love to see your view of all the database services from Amazon. Cause that's where it's really designed to do is leverage those across those. And you know, you listen to Andrew, Jesse talk they've got a completely different philosophy than say Oracle, which says, Hey we've got one database to do all things Amazon saying we need that fine granularity. So it's going to be again. And to the extent that you're providing market context they're very excited to see that data Sagar and see how that evolves over time. Really appreciate you coming back in the cube and look forward to working with you. >> Appreciate Dave. Thank you so much. >> All right. Welcome. Thank you everybody for watching. This is Dave Vellante for the cube. We'll see you next time. (upbeat music)

>>live from San Francisco. It's the Q covering Micron Insight 2019. Brought to you by Micron. >>Welcome back to Pier 27 sentences. You're beautiful day here. You're watching the Cube, the leader in live tech coverage recovering micron inside 2019 hashtag micron in sight. My co host, David Floy er and I are pleased to welcome Michael Wood, Acre Cube alum and a fellow at Hewlett Packard Enterprise. Michael, good to see you again. Thanks. Coming on. >>Thanks for having me. >>So you're welcome? So you're talking about HBC on a panel today? But of course, your role inside of HP is is a wider scope. Talk about that a little bit. >>She also I'm the lead technologists in our Compute Solutions business unit that pack out Enterprise. So I've come from the group that worked on in memory computing the Superdome flex platform around things like traditional enterprise computing s it, Hannah. But I'm now responsible not only for that mission critical solutions platform, but also looking at our blades and edge line businesses. Well said broader technology. >>Okay. And then, of course, today we're talking a lot about data, the growth of data and As you say, you're sitting on a panel talking about high performance computing and the impact on science. What are you seeing? One of the big trends in terms of the intersection between data in the collision with H. P. C and science. >>So what we're seeing is just this explosion of data and this really move from traditionally science of space around how you put equations into supercomputers. Run simulations. You test your theories out, look at results. >>Come back in a couple weeks, >>exactly a potential years. Now. We're seeing a lot of work around collecting data from instruments or whether it's genomic analysis, satellite observations of the planner or of the universe. These aerial generating data in vast quantities, very high rates. And so we need to rethink how we're doing our science to gain insights from this massive data increase with seeing, >>you know, when we first started covering the 10th year, the Cuban So in 2010 if you could look at the high performance computing market as sort of an indicator of some of the things that were gonna happen in so called big data, and some of those things have played out on I think it probably still is a harbinger. I wonder, how are you seeing machine intelligence applied to all this data? And what can we learn from that? In your opinion, in terms of its commercial applications. >>So a CZ we'll know this massive data explosion is how do we gain insights from this data? And so, as I mentioned, we serve equations of things like computational fluid dynamics. But now things are progressing, so we need to use other techniques to gain understanding. And so we're using artificial intelligence and particularly today, deep learning techniques to basically gain insights from the state of Wei. Don't have equations that we can use to mind this information. So we're using these aye aye techniques to effectively generate the algorithms that can. Then you bring patterns of interest to our you know, focused of them, really understand what is the scientific phenomenon that's driving the things particular pattern we're seeing within the data? So it's just beyond the ability of the number of HPC programmers, we have the sort of traditional equation based methodologies algorithms to gain insight. We're moving into this world where way just have outstripped knowledge and capabilities to gain insight. >>So So how does that? How is that being made possible? What are the differences in the architecture that you've had to put in, for example, to make this sort of thing possible? >>Yeah, it's it's really interesting time, actually, a few years ago seemed like computing was starting to get boring because wears. Now we've got this explosion of new hardware devices being built, basically moving into the more of a hetero genius. Well, because we have this expo exponential growth of data. But traditional computing techniques are slowing down, so people are looking at exaggerate er's to close that gap and all sorts of hatred genius devices. So we've really been thinking. How do we change that? The whole computing infrastructure to move from a compute centric world to a memory centric world? And how can we use memory driven computing techniques to close that gap to gain insight, so kind of rethinking the whole architectural direction basically merge, sort of collapsing down the traditional hierarchy you have, from storage to memory to the CPU to get rid of the legacy bottlenecks in converting protocols from process of memory storage down to just a simple basically memory driven architecture where you have access to the entire data set you're looking at, which could be many terabytes to pad of eyes to exabytes that you can do simple programming. Just directly load store to that huge data set to gain insights. So that's that's really changed. >>Fascinating, isn't it? So it's the Gen Z. The hope of Gen Z is actually taking place now. >>Yes, so Gen Z is an industry led consulting around a memory fabric and the, you know, Hewlett Packard Enterprise Onda whole host of industry partners, a part of the ecosystem looking at building a memory fabric where people can bring different innovations to operate, whether it's processing types, memory types, that having that common infrastructure. I mean, there's other work to in the industry the Compute Express Link Consortium. So there's a lot of interest now in getting memory semantics out of the process, er into a common fabric for people to innovate. >>Do you have some examples of where this is making a difference now, from from the work in the H B and your commercial work? >>Certainly. Yeah, we're working with customers in areas like precision medicine, genomex basically exaggerating the ability to gain insights into you know what medical pathway to go on for a particular disease were working in cybersecurity. Looking at how you know, we're worried about security of our data and things like network intrusion. So we're looking at How can you gain insights not only into known attacking patterns on a network that the unknown patents that just appearing? So we're actually a flying machine learning techniques on sort of graft data to understand those things. So there's there's really a very broad spectrum where you can apply these techniques to Data Analytics >>are all scientists now, data scientists. And what's the relationship between sort of a classic data scientist, where you think of somebody with stats and math and maybe a little bit of voting expertise and a scientist that has much more domain expertise you're seeing? You see, data scientists sort of traversed domains. How are those two worlds coming together? >>It's funny you mentioned I had that exact conversation with one of the members of the Cosmos Group in Cambridge is the Stephen Hawking's cosmology team, and he said, actually, he realized a couple of years ago, maybe he should call himself a day two scientists not cosmologist, because it seemed like what he was doing was exactly what you said. It's all about understanding their case. They're taking their theoretical ideas about the early universe, taking the day to measurements from from surveys of the sky, the background, the cosmic background radiation and trying to pair these together. So I think your data science is tremendously important. Right now. Thio exhilarate you as they are insights into data. But it's not without you can't really do in isolation because a day two scientists in isolation is just pointing out peaks or troughs trends. But how do you relate that to the underlying scientific phenomenon? So you you need experts in whatever the area you're looking at data to work with, data scientists to really reach that gap. >>Well, with all this data and all this performance, computing capacity and almost all its members will be fascinating to see what kind of insights come out in the next 10 years. Michael, thanks so much for coming on. The Cube is great to have you. >>Thank you very much. >>You're welcome. And thank you for watching. Everybody will be right back at Micron Insight 2019 from San Francisco. You're watching the Cube

>> live from Orlando, Florida It's the que covering Accelerate nineteen. Brought to you by Ford. >> Welcome back to the Q. We air live in Orlando, Florida At Fortinet Accelerate twenty nineteen Lisa Martin with Peter Burst. Pleased to welcome back one of our alumni on ly the CEO and founder of Fortinet. Kensi. Ken, thank you so much for joining Peter and me on the Cuban. Thanks for having the Cube back at accelerate. >> Yeah, I love to be here again. Yeah, Thank you. >> So, so quick by the numbers Can Kino. This morning was awesome. Loved the music and all the lights to start four thousand attendees from forty countries. You guys now have about three hundred eighty five thousand customers globally. Your revenue and F eighteen was up twenty percent year on year. I could go on and on. Lots of partners, lots of academies, tremendous growth. Talk to us about in the evolution of security. Where are we today and why is supporting that so well positioned to help customers dramatically transform security >> First world happy to see all the partner of the cosmos were come here. And also we keep him like every year we in this program also is a great program on another side. Like I say, securities of wherever dynamic space you need to keep in landing on We see more and more people come here s o that's we'LL be happy to discuss in the new technology the new market opportunity and also the new trend on DH Also What we see is a the space is so old and I'm making Also we see a lot of people keeping come here for the training for other sins And also I love the music make make us feel young again So But I >> think one of the reasons why security is so dynamic it is you don't for example, in the server world you don't have, you know you know gangs of bad guys running around with baseball bats trying to eat your servers. In the security world, you have people trying to enable the business to be able to do more, but also people constantly trying to tear the business down. And that tension drives a lot of invention and requires a lot of innovation. How is that changing? We're driving some of the key trends and networks and network security >> Yeah, that's where like I presented this morning. Wait, You see, with more device connected, Actually motive, I Some people being connect today and eventually in few years we'LL be calm. Motive eyes on people. There also is all the five G or icy went technology you can make is connected faster, more broadly reached. And then there's a more application More data also come to the Internet. So that's all you quist tax servants. There's all additional risk We'LL have all this connection. We have all these data transfer to all these different diversity on people. So that's all security business, right? Because secure to have the address where they now walking cannot really are dresses above the connection above the speed. So we have our dressing a content layered application layer the device user layer all regionally or country lier s O. That's making the security always keeping foreign faster than the night walk in the night. He spending on the study become the biggest sector United ninety idea spending environment. That's also one time we just feel security also need a study merger convert together is not working because no longer oh now will get only kind of the speed I can activities secure, canniness and bob. They had to be working together to smart rain route. In a data, put a low risk area tow without a polluted like transfer. All this conscience on that way, see, is the two industries that emerged together. That's where Koda security driven that walk are the arson about how this kind of we see today the mobile on cloud started replacing the traditional PC, right? So about going forward, the wearable divine's all the glass and we award study replaced the mobile. You don't have the whole mobile phone the season, while they're probably in your eyes on the same piled. A smart car that's my home, the wise every single connecting way Are you walking? Like if I walking here our sins related my information on power for me so I don't have to carry innocents, so that's going for you. A few years we'LL be happy. First, security will be part of this space. How this will be going forward contrato today The mobile the cloud way also have some discussion about that one. So we need to prepare for all this because that's how fortunate being founded. That's how our culture about generation, about long career advancement. So that's where we want to make sure the technology the part already for this chance. That's what gave the use of the past benefit of leverage of connection. Same time, lower the risk >> organ has taken an approach in the marketplace of Let Me Step Back. Put it this way. We all talk about software to find everything in virtual ization, and that's clearly an important technology and important trend. Ford has taken advantage of that as well, but the stuff doesn't run. All that's offered stuff doesn't run on hamsters. It runs in hardware. Unfortunate has made taking a strategic position, and it's been a feature of your nearly twenty year history to continuously invest in hardware and open up the performance aperture. Increase the size of the bucket of that hardware. How is that? Both altered your ability to add additional functionality, get ahead of the curve relative to competition, but also enabled your ecosystem to do a lot of new and interesting things that we're not seeing on other another network security companies? >> Yeah, that's why I totally agree with you. Israeli howto unable the past ecosystem for everybody playing a space for the partners of his provider, carrier enterprise, on the photo leverage technology benefit. More broadly, Cosmo base is very important. That's where we feel like a sulfur cloud. They do study in kind of a change, a lot of sense. But you also need a balance among clothes. Suffers were important, but also the hardwork also very important. All right, so that's the hybrid. More post the power on the sulfur. Both the cloud at age both have equal equal weight. Equally important, going forward How to leverage all this post is also also kind of very important for the future growth of future trend Another So you also can see like a mission. Uh, will you have the immersive device? We'LL have some, like security applied in tow Storage in that work in small Sadie, you also need a bad lie. Security be part of it. No, just security. I don't cop as a cost of additional Whatever process are all since, But you know, once you make it secure to be part ofthe like we mentioned a security for even that Working security driven like a future like a wearable device or the other since without it will be huge ecosystem going forward. That's where is the chip technology you can. Bad. We just saw Fervor is also additional servants. We can all walk in together. So that's where we want to look at the whole spectrum. There, make sure different part all can walk in together on also different technology. No, just limiting some part of it. I make sure the faux technologists face hole. Attack service can be a poor tag. And also we can leverage for the security of the high table addition. Opinions? You know, this conducted a war. >> This is what you're calling the third generation of Security? >> Yes, there's more. You for structure security. That's the whole security compared tto first dinners and second generation is our security just secured himself right. So you don't involve with other night walking star recharge the infrastructure? No, because Because they view everything you inside the companies secure You only need a guard at the door This Hey, who has come here? Anything inside I'll find But with today all the mobile pouring on Devise all the data everywhere Go outside the company you need to make sure security for all of the data. So that's the new trend. So now the border disappeared. So it doesn't matter. You said the company or not, is no longer secure anymore because you can use the mobile, the access rights o outside. All people can also come here with data also go out. So that's where the infrastructure security neither give or imposing their work inside on points. I under the cloud of the age and all this a different device on the diversity. Why? So you're even your mobile phone? Hi! Still working together. So it's a much bigger before structure. Much bigger are traceable space. Now that's making secure, more exciting. >> Well, we have gotten used over the past twenty years of building applications that operate on somebody else's device, typically a PC or mobile phone. And we've learned how to deal with that. You're suggesting that we're actually going to be integrating our systems with somebody else's systems at their edge or our edge on a deeply intimate level and life and death level. Sometimes on that, obviously, place is a real premium on security and networking whatnot. So how does the edge and the cloud together informed changes and how we think about security, how we think about networking, >> That's where, like I think age and a cloud they each complaint. Different role, because architecture. So the cloud has a good C all the bigger picture. They're very good on the provisioning. There could archiving cloud, also relatively slow, and also you can see most of data generated and age. That's where, whether you're immersive device, all your mobile, whatever ages were we called a digital made physical, and that's all the people in Device Connect. So that's where, like a seven eighty percent data, Carrion a probably never traveled to the club. They need a processed locally. They also need have the privacy and autonomy locally and also even interactive with other eighty vice locally there. So that's what we see is very important. Both the cloud on age security can be addressed together and also celebrity of architecture, that I say the cloud is good for detection so you can see a something wrong. You can cry the information, but the age new market on the provisions, because prevention need to be really time needed back, moreover, quickly because a lot of application they cannot afford a late Nancy like where do the V I. R. Even you slow down in a microsecond. Pickle feet is the famous signals. You also see the also drive a car. If you react too slow, you may hear something right the same scene for a lot of harder. Even you. Commerce, whatever. If you not response picking out within a half second, people may drop the connection. The memos are married, so that's what the late and see the speed on DH that's making the club play there at all into all this management on their age, playing hero in a really kind on Barlow. Ladies, you're really kind reaction there. So what? That's where we see the both side need to play their role on important transposed market. You said that just a one cloud, which I feel a little bit too hard right now. Try to cool down a little bit of our same age. Also, we see a very important even going forward what I been a bad security in age >> with this massive evolution that you've witnessed for a very long time. As the head of forty nine last nearly twenty years EJ cloud. How how dramatically technology changes in such a short period of time. I'm curious. Can How has your customer conversations evolved in terms of, you know, ten years ago were you talk ng more to security professionals? And now are you talking more to the C suite? As security is fundamental? Teo Digital transformation and unlocking tremendous value in both dollars in society impact has that conversation elevated as security has changed in the threat landscape has changed. >> Yeah, they do go to the board level, the CEO level now compared to like a ten, twenty years ago. Probably gaiety people maybe see so level, because security become probably the most important part of it. Now they keep you got a high high percentage that ikey spending there because when we connect everything together, we can make all the people all this business together to be on the connection. That's where security handled up, right? So that's where we see security studying kind of more. You hope me more important now. But another side, also the space also changing over quick. So that's where we always have to learn it. Woman engaged with Cosmo partner here. That's where this event is about way keeping less into what's the issue they have, how we can help the dress. All these security really the usual. Some even be honest security. Go to like a connection you for structure, some other, like architectural design, whatever their penis model there. So that's all we're very important on. Like I said, security space we need to keep in Lenny every day. Even I spent a few hours a day to Lenny. I You don't feel ready? Can K child? Oh, they >> said, It's a very dynamic world security world. >> You have our dynamic, the knowledge base, the technology refreshed quickly. Way always had to be Len have training. That's where he also see Try to position forty Niners lending company. So that's where we all for the because training program and all the train is afraid for partner for customers. All this kind is really it's a big investment. That's where a lot of people say, Oh, how can you? You've asked more in the training. You said of all come better. You must move your marketing. I say journeys of over a long term benefit. When people get trained, they also see Hey, what's the pants technology? So that's where a lot of organization, a lot of investment, really looking for. How five years here come benefit of space can benefit. The car's my partner, so that's all we see. Training's far long time measurement see modern technology. >> So can you've talked in the keynote? You've talked in the Cube about how networking security come together on how, as they move forward, they're going in form. Or they'LL have an impact on business and have an impact and other technologies. There's a lot of technology change when you talk to network in professional or even your own employees. What technologies out there do you think are going to start impacting how security works? Micro services containers? Are there any technologies that Ford that's looking at and saying, We gotta watch that really closely and that networking professionals have to pay more attention to. I >> have to say pretty much all of them, right? So all these Michael, all this contender technology, micro segmentation, according computing, the immersion lending all this is all very important because security has deal with all this different new technology application on like it was all this a huge, competent power raised on the cost lower ball corner computer. And maybe some of the old technology may not really work any more for some additional risks. Like where the equipment can be break by cute from the computing or some moderate eventually can also kind of take over. All this country is always we tryto tryto learn, tryto tried. Okay, chop every day. Hey, that that's what I say is that's so exciting. Keep you wake up, Keep your Lenny everyday, which I enjoy. But at the same time, there's a lot of young people they probably even even better than us to catch the new technology. >> Oh, no. Oh, no, no, no. >> Yeah. Somehow, my kids can play the fool much greater than mere. That's always the way >> we want to thank you so much for joining Peter and me on the kid this afternoon for having the Cube back at forty nine. Accelerate and really kind of talking about how you guys are leading in the space and we're gonna be having more guests on from Fortinet. And your partner's talking about educate ecosystems and technology that you talked about in your keynote. So we thank you again for your time. And we look forward to a very successful day here. >> Oh, thank you. Thank you very much. You enjoy all this programme for many years. Thank you. >> Excellent. We love to hear that. We want to thank you for watching the Cube for Peter Burress. I'm Lisa Martin. You're watching the Cube. >> Thank you.

>> Live from Orlando, Florida, it's theCUBE, covering Microsoft Ignite. Brought to you by Cohesity and theCUBE's ecosystem partners. >> Welcome back to theCUBE'S coverage of Microsoft Ignite 2018 here in Orlando. I'm Stu Miniman, and happy to welcome back to the program John Mracek who's the CEO of a Imanis Data. It's our first time at the show, but not your first time on theCube. Thanks so much for joining us and tell us we caught up with you in New York City talking about kind of the AI, analytics, all those things there. what what what brings a Imanis to Microsoft Ignite? >> So this has been a great show for us. And what I really see happening here is there's a vibrancy that probably didn't exist in Microsoft events, maybe four or five years ago. Because Microsoft really getting their act together on the whole how you migrate and bring people to the Azure. Right, because that's their agenda. And so where we fit in there is in our data management platform. We help customers migrate to a Azure. So whether it's moving your Hadoop workloads to Azure, or one of the products that's been featured here that we've gotten a lot of Microsoft support on is our migration tool to move from MongoDB to Cosmo DB. So we play really well into the migration story and it really leverages our platform. >> Yeah, one of the questions we talk about all the time is customers trying to figure out where things live and, well, it's like your cloud strategy. Things are changing over time. Customers have really multi-cloud environments, which really means they're doing a lot of different things and a lot of times they need to move them and sort those out. So what are the challenges you're seeing? How do you help those businesses make decisions today and be able to move things as needed in the future? >> Yeah, what we see and what we're playing into is really this evolution. You know, solutions really drive technologies. So in a large enterprise, you might have a division or a particular group that says, I need this BI or analytics tool and I need a big data platform to do it. So they build this. They build on top of some either NoSQL or Hadoop and then they've got this great solution. Well, that happens four or five times across the enterprise, and at some point in the enterprise, the CIO or somebody says, "you know, "we kind of got all these distributed data systems, "and like, who's managing them? "How is that data being moved "to your point about cloud migration? "Well, these are on-prem, these are in the cloud. "We want to put them all in the cloud, how do we do that?" And so that's where we're seeing as kind of the call for our product, which is, okay, I need a central way to manage and manipulate this data, as a fundamental problem. >> Yeah, so we all know that data is fundamental to a business. It's one of the most important things. We can use all the tropes of, it's the new oil or anything like that. But when you dig down, it's a lot of complexity into how, how do I get data? How do I manage data? How do I share data? We're sitting here in Cohesity, is the where we are in the booth. Can you help us understand, what are the solutions that you complement in the data space? What are the solutions that you replace? or a modern version or compete against in this space? >> So the way to look at us, we're at our most general, we're a platform for moving data from one platform to another. Okay, and that has many different use cases. But where we're getting a lot of customer uptake is on the backup recovery. It's like, I've got it here, I want to make a backup. We also see a lot in terms of migration, whether it's the Mongo to DB or I want to move from on-prem to cloud or cloud to cloud. And where we fit is if you look there's a legacy providers who don't traditionally go after the NoSQL and Hadoop space. And so where were a perfect complement to either those companies or folks like Cohesity. We have partnerships with Cohesity, Veem and others where they get in RFP or they're talking to a customer and the customer has a specific request for data management solution for NoSQL or Hadoop platforms. And that's where we come in. Because that's what we focused on exclusively from day one. >> Yeah, well, being at a Microsoft show, I mean, applications are central to so much and Microsoft does. Everything from Office, but on the data side, we spend a lot of time this week talking about SQL. Talking about Cosmos DB and cool new things they're doing. And of course, Microsoft's playing in a lot of the modern areas. We see them, big developers base here, even more of it at the Microsoft Build show, what do you see in the Microsoft space on the application modernization? Sounds like that would tie in quite a bit to what you're helping customers with. >> Yeah, so we have customers across all the cloud providers. But what we see in the Microsoft case is really people looking for maybe global easy deployment, customer facing as typical examples. So people who are really pushing the envelope, frankly. And there's almost like a bi-modal distribution. There's kind of some folks who are still trying to retrofit the old world and then others who are really embracing some of the new platforms. >> I'm not sure if you were at the keynote on Monday, Satya Nadella unveiled the Open Data Initiative. We've got Adobe and SAP and Microsoft there. I was talking to one analyst and reading some reports, and I'm like, well, it's not a coincidence that this was launched the week of Salesforce. Salesforce has a lot of data. Maybe that's a little bit of an attack there. But data across these big providers is important. I want to be able to share and leverage my data. You're in the data business. But what viewpoint you have of some of these really big providers of the application as they're going through their digital transformation, and making how do customers get the best value out of their data? >> So, my background, most recent background, I was in an ad tech company, where we're all big data. And the whole play there, is how do you manage your audiences, right? How do you have a unifying way to look at audiences? And so this is what's playing out on a more higher level, a more general level of how do I normalize and create a unified view of the customer and consistent data so that I can then manage it. And so that's an essential requirement to get the maximum value at out of that. Once you have that and you're in your data repositories, it's incredibly critical to protect them, to be able to orchestrate and move around. Where we fit in and how we see it is, these things are data, to reuse the term is the new oil and the new gold. And companies are realizing that it's really time to protect this data. I put all this investment into getting unified view of data. Wow, what are we doing about how do we back it up, restore it and move it? >> It's interesting, I've watched the space long enough. You go back kind of BI and DW days, go through big data. Now, we talk about a lot more of the analytics in the intelligence there. Help us as to, what are we actually realizing today that we were been talking about for years, and what what are still some of the stumbling blocks as to what we need to mature as an industry to really help unlock data. >> So, I mean, there's clearly the, what's driven a lot of the machine learning AI is the availability of data. It wasn't so much algorithms change dramatically, it was, we have a, so all the machine learning applications are really benefiting from this. But what we see as you know, some of the immediate things with our customers, is they're using big data as they create their front ends, engage with their customers. So how do they have the most up to date, real-time information to whether it's present an offer to a customer, provide customer service. So a lot of the use case we see is in that really bread and butter customer-level interactions and having an appropriate database to front end that process. >> Alright, so one of the biggest challenges of our time is really talking about distributed architecture. When I talk to companies scale comes on a lot, but it means very different things to different people. Can maybe talk about what you're hearing from customers, and how your solution helps customers for a variety of implementations. >> Yeah, so, we typically are targeting and working with customers in the 10s to 100s of terabytes. Up to, and our system handles up into into the petabytes. Typically, what we see is an evolution is, as I said earlier, somebody will develop a solution in a particular division, and then realize we've got this asset to protect. And then so IT starts to get involved and basically look at it holistically. So, we had one of our prospects, we went in and pitched at an SVP level and said, "what are the problems you're facing?" and it was basically this, I have all these silos of data. To get the maximum value out of them, and have a uniform look, whether it's look at our customers, the market, I need a uniform view to do my BI and AI. And so they brought us in and said, "Okay, paint a picture of how I can continue to have "these groups run autonomously and run their solutions, "yet at the same time, give me a unified view "and make me feel comfortable "that I've been able to protect the data, "move the data, massage the data." >> Great. Talk to me, when I look at this show, I see a lot of customers are still doing things, I'm trying to think how to say it nicely. Kind of the old way, it's like, if you look at them five years ago, is like, okay, Windows 2019's there great. I'll get there in five years, you play with a lot of more modern applications. What do you hear from customers? What, what is the profile of a customer that is, taking advantage and being competitive in the world? And what do you advise companies that maybe are a little bit behind the eight ball. >> So, you're right, and there's a really big spectrum of where people are in the adoption curve. And the way we look at it, if people were waiting for it, you know, when somebody goes, "Yeah, we're looking at setting up a big data system", it's like, okay, we'll talk to you in a year once you get the basics set up. But I see kind of two types of things. There's, say, the smaller, more aggressive companies, who are willing to move forward and say, "I just got to create a product, I don't care how I do it, "I don't have legacy issues." And they've moved ahead, and they're starting to get to the point where they're like, "Okay, we're mature enough where we actually need to spend on data management." The more typical case though is, as I said earlier. It's like these these new apps, that larger companies might have bleeding edge groups. So it's not being driven centrally. And so my, you asked about advice, right? So if you're sitting in the top of large enterprise and say, "Well, how do we get there. "There's kind of the tops down, "I need somebody to help me figure out." But there's also, let 1,000 flowers and let there be some kind of anarchy, if you will. Breaking the model, breaking the mold. Let people go build stuff and then over time start to figure out how to assimilate. So that'd be the biggest single biggest advice is, Yeah, you want to do the top down, but you really want to do the bottoms up. Because those people really know how to use the technology to provide a solution. >> Yeah, absolutely. Guy Kawasaki let 1,000 flowers bloom out there and everything. All right. Help bring this in. What kind of customer conversations are you having this week? We talked to the top about, there's real good energy to this show. Definitely, I felt that. What would you share with your peers that haven't been at the show? >> So the topics here are typically around the migration. Whether it's like to like, moving an existing workload into Azure, or the transformation. We also announced the show cooperation with Microsoft on moving any of your NoSQL workloads to Cosmos DB. So most of the conversations here have been related to migration. Either of, if you will, within the same Hadoop family, or, you know, like to unlike. Going from something to Cosmos DB. And that goes back to your earlier point about people trying to figure out what to do. They know there's this imperative to move to the cloud, and they're trying to figure out how they do it in bite-sized chunks. Right and protect their business at the same time. >> Yeah, so you mentioned Cosmos DB. We had an interview earlier this week about Cosmos DB. I definitely heard some good buzz at the show, What is it about that is drawing customers to it and what's that enable for them? >> Two things that I'm aware of, that I've seen is, again, the global nature and the ability to just kind of deploy anywhere. But also, I've seen a little bit around the dynamic schemas and the ability to map between them as a very quick way to ingest data. So you can get up and running quickly, instead of doing a lot of manual work to start using it. So those are things that are going to win developers 'cause it makes their life easier. >> Alright, John I want to give you the final word. What should we look to see from Imanis over the next six to 12 months. >> So we're going to continue to push forward with our platform around data management. You've seen in some recent announcements that, where leveraging machine learning in a very concrete way to do anomaly detection around ransomware. And also for administrators to be able to basically set rules or set goals and have the software do it. And that really steams from the fact that we're using a big data platform and machine learning to solve the problem of well, if you're running a big data platform, how do you manage the data? So the whole DNA of the company is built around that, and from a go-to-market standpoint, you know, partnering with folks like Cohesity and others where you've already got people in market selling a broad solution but they're missing a piece. So the other thing you'll see from us, is more partner announcements as we go forward. Alright, well, John Mracek really appreciate all the updates on a Imanis Data. Congrats on the progress so far. And look forward to catching with you up at future show. >> Great, thank you. >> Alright, we'll be back with more coverage here. Day three of three days live coverage. Microsoft Ignite here in Orlando. I'm Stu Miniman and thanks for watching theCUBE.

>> Live from Orlando, Florida, it's theCUBE covering Microsoft Ignite. Brought to you by Cohesity and theCUBE's ecosystem partners. >> Welcome back everyone, we are wrapping up day three of Microsoft Ignite here in Orlando, Florida. CUBE's live coverage, I'm your host Rebecca Knight, along with Stu Miniman, my esteemed cohost for these past three days, it's been fun working with you, Stu. >> Rebecca, it's been a great show, real excited. Our first time at a Microsoft show and it's a big one. I mean, the crowds are phenomenal. Great energy at the show and yeah, it's been great breaking down this ecosystem with you. >> So, three days, what do we know, what did you learn, what is your big takeaway, what are you going to to go back to Boston with? >> You know, it's interesting, we've been all talking and people that I know that have been here a couple of years, I've talked to people that have been at this show for decades, this is a different show. There's actually a friend of mine said, he's like, "Well look, Windows pays the bills for a lot of companies." There's a lot of people that all the Windows components, that's their job. I mean, I think back through my career when I was on the vendor side, how many rollouts of Exchange and SharePoint and all these things we've done over the years. Office 365 been a massive wave that we watched. So Microsoft has a broad portfolio and they've got three anchor shows. I was talking with one of the partners here and he's like, "You know, there's not a lot of channel people "at this event, at VMworld there's a lot of channel people." I'm like, "Well yeah because there's a separate show "that Microsoft has for them." You and I were talking at an earlier analytics session with Patrick Moorhead and he said, "You know when I look at the buy versus build, "a lot of these people are buying and I don't "feel I have as many builders." Oh wait, what's that other show that they have in the Spring, it's called Microsoft Build. A lot of the developers have moved there so it's a big ecosystem, Microsoft has a lot of products. Everything from, my son's excited about a lot of the Xbox stuff that they have here. Heck, a bunch of our crew was pickin' up Xbox sweatshirts while they're here. But a lot has changed, as Tim Crawford said, this is a very, it feels like a different Microsoft, than it even was 12 or 24 months ago. They're innovating, so look at how fast Microsoft moves and some of these things. There's good energy, people are happy and it's still trying to, you know. It's interesting, I definitely learned a lot at this show even though it wasn't the most sparkly or shiny but that's not necessarily a bad thing. >> Right, I mean, I think as you made a great point about just how integral Microsoft is to all of our lives as consumers, as enterprise, the Xbox, the Windows, the data storage, there's just so much that Microsoft does that if we were to take away Microsoft, I can't even imagine what life would be like. What have been your favorite guests? I mean, we've had so many really, really interesting people. Customers, we've had partners, we're going to have a VC. What are some of the most exciting things you've heard? >> Yeah, it's interesting, we've had Jeffrey Snover on the program a couple of years ago and obviously a very smart person here. But at this show, in his ecosystem, I mean, he created PowerShell. And so many people is like, I built my career off of what he did and this product that he launched back in 2001. But we talked a little bit about PowerShell with him but then we were talking about Edge and the Edge Boxes and AI and all those things, it's like this is really awesome stuff. And help connecting the dots to where we hid. So obviously, big name guest star, always, and I always love talking to the customers. The thing I've been looking at the last couple of years is how all of these players fit into a multicloud world. And Microsoft, if you talk about digital transformation, and you talk about who will customers turn to to help them in this multicloud world. Well, I don't think there's any company that is closer to companies applications across the spectrum of options. Office 365 and other options in SaaS, all the private cloud things, you start with Windows Server, you've got Windows on the desktop, Windows on the server. Virtualization, they're starting to do hyperconversion everything, even deeper. As well as all the public cloud with Azure and developers. I talked to the Azure functions team while I was here. Such breadth and depth of offering that Microsoft is uniquely positioned to play in a lot of those areas even if, as I said, certain areas if the latest in data there might be some other company, Google, Amazon, well positioned there. We had a good discussion Bernard Golden, who's with Capital One, gave us some good commentary on where Alibaba fits in the global scheme. So, nice broad ecosystem, and I learned a lot and I know resonated with both of us, the "you want to be a learn it all, not a know it all." And I think people that are in that mindset, this was a great show for them. >> Well, you bring up the mindset, and that is something that Satya Nadella is really such a proponent of. He says that we need to have a growth mindset. This is off of the Carol Dweck and Angela Duckworth research that talks about how important that is, how important continual learning is for success. And that is success in life and success on the job and organization success and I think that that is something that we are also really picked up on. This is the vibe of Microsoft, this is a company, Satya Nadella's leadership, talking to so many of the employees, and these are employees who've been there for decades, these are people who are really making their career, and they said, "Yeah, I been here 20 years, if I had my way, "I'll be here another 30." But the point is that people have really recommitted to Microsoft, I feel. And that's really something interesting to see, especially in the tech industry where people, millennials especially, stay a couple years and then move on to the next shiny, new thing. >> Yeah, there was one of our first guests on for Microsoft said that, "Been there 20 years and what is different about "the Satya Nadella Microsoft to the others is "we're closer and listening even more to our customers." We talk about co-creation, talk about how do we engage. Microsoft is focusing even deeper on industries. So that's really interesting. An area that I wanted to learn a little bit more about is we've been talking about Azure Stack for a number of years, we've been talking about how people are modernizing their data center. I actually had something click with me this week because when I look at Azure Stack, it reminds me of solutions I helped build with converged infrastructure and I was a big proponent of the hyper-converged infrastructure wave. And what you heard over and over again, especially from Microsoft people, is I shouldn't think of Azure Stack in that continuum. Really, Azure Stack is not from the modernization out but really from the cloud in. This is the operating model of Azure. And of course it's in the name, it's Azure, but when I looked at it and said, "Oh, well I've got partners like "Lenovo and Dell and HPE and Sysco." Building this isn't this just the next generation of platform there? But really, it's the Azure model, it's the Azure operating stack, and that is what it has. And it's more, WSSD is their solution for the converged and then what they're doing with Windows Server 2019 is the hyper-converged. Those the models that we just simplify what was happening in the data center and it's similar but a little bit different when we go to things like Azure and Azure Stack and leads to something that I wanted to get your feedback on. You talk business productivity because when we talk to companies like Nutanix, we talk to companies like Cohesity who we really appreciate their support bringing us here, giving us this great thing right in the center of it, they talk about giving people back their nights and weekends, giving them back time, because they're an easy button for a lot of things, they help make the infrastructure invisible and allow that. Microsoft says we're going to try to give you five to ten percent back of your business productivity, going to allow you to focus on things like AI and your data rather than all the kind of underlying spaghetti underneath. What's your take on the business productivity piece of things? >> I mean, I'm in favor of it; it is a laudable goal. If I can have five to ten percent of my day back of just sort of not doing the boring admin stuff, I would love that. Is it going to work, I don't know. I mean, the fact of the matter is I really applaud what Cohesity said and the customers and the fact that people are getting, yes, time back in their day to focus on the more creative projects, the more stimulating challenges that they face, but also just time back in their lives to spend with their children and their spouse and doing whatever they want to do. So those are really critical things, and those are critical things to employee satisfaction. We know, a vast body of research shows, how much work life balance is important to employees coming to their office or working remotely and doing their best work. They need time to recharge and rest and so if Microsoft can pull that off, wow, more power to them. >> And the other thing I'll add to that is if you, say, if you want that work life balance and you want to be fulfilled in your job, a lot of times what we're getting rid of is some of those underlying, those menial tasks the stuff that you didn't love doing in the first place. And what you're going to have more time to do, and every end user that we talked to says, "By the way, I'm not getting put out of a job, "I've got plenty of other tasks I could do." And those new tasks are really tying back to what the business needs. Because business and IT, they need to tie together, they need to work together, it is a partnership there. Because if IT can't deliver what the business needs, there's other alternatives, that's what Stealth IT was and the public cloud could be. And Microsoft really positions things as we're going to help you work through that transition and get there to work on these environments. >> I want to bring up another priority of Microsoft's and that is diversity. So that is another track here, there's a lot of participants who are learning about diversity in tech. It's not a good place right now, we know that. The tech industry is way too male, way too white. And Satya Nadella, along with a lot of other tech industry leaders, has said we need more underrepresented minorities, we need more women, not only as employees but also in leadership positions. Bev Crair, who was on here yesterday, she's from Lenovo. She said that things are starting to change because women are buying a lot of the tech and so that is going to force changes. What do you think, do you buy it? >> And I do, and here's where I'd say companies like Lenovo and Microsoft, when you talk about who makes decisions and how are decisions made, these are global companies. Big difference between a multi-national company or a company that's headquartered in Silicon Valley or Seattle or anything versus a global company. You look at both of those companies, they have, they are working not just to localize but have development around the world, have their teams that are listening to requirements, understand what is needed in those environments. Going back to what we talked about before, different industries, different geographies, and different cultures, we need to be able to fit and work and have products that work in those environments, everything. I think it was Bev that talked about, even when we think about what color lights. Well, you know, oh well default will use green and red. Well, in different cultures, those have different meanings. So yeah, it is, it's something that definitely I've heard the last five to ten years of my career that people understand that, it's not just, in the United States, it can't just be the US or Silicon Valley creating great technology and delivering that device all the way around the world. It needs to be something that is globally developed, that co-creation, and more, and hopefully we're making progress on the diversity front. We definitely try to do all we can to bring in diverse voices. I was glad we had a gentleman from Italy shouting back to his daughters that were watching it. We had a number of diverse guests from a geography, from a gender, from ethnicity, on the program and always trying to give those various viewpoints on theCUBE. >> I want to ask you about the show itself: the 30,000 people from 5,000 different organizations around the globe have convened here at the Orange County Convention Center, what do you think? >> Yeah, so it was impressive. We go to a lot of shows, I've been to bigger shows. Amazon Reinvent was almost 50,000 last year. I've been to Oracle OpenWorld, it's like takes over San Francisco, 60 or 70,000. This convention center is so sprawling, it's not my favorite convention center, but at least the humidity is to make sure I don't get dried out like Las Vegas. But logistics have run really well, the food has not been a complaint, it's been good, the show floor has been bustling and sessions are going well. I was talking to a guy at breakfast this morning that was like, "Oh yeah, I'm a speaker, "I'm doing a session 12 times." I'm like, "You're not speaking on the same thing 12 times?" He's like, "No, no it's a demo and hands on lab." I'm like, "Oh, of course." So they make sure that you have lots of different times to be able to do what you want. There is so much that people want to see. The good news is that they can go watch the replays of almost all of them online. Even the demos are usually something that they're cloud enabled and they get on live. And of course we help to bring a lot of this back to them to give them a taste of what's there. All of our stuff's always available on the website of thecube.net. This one, actually, this interview goes up on a podcast we call theCUBE Insights. So please, our audience, we ask you, whether it's iTunes or your favorite podcast reader, go to Spotify, theCUBE Insights. You can get a key analysis from every show that we do, we put that up there and that's kind of a tease to let you go to thecube.net and see the hundreds and thousands of interviews that we do across all of our shows. >> Great, and I want to give a final, second shout out to Cohesity, it's been so fun having them, being in the Cohesity booth, and having a lot of great Cohesity people around. >> Yeah, absolutely, I mean, so much I wish we could spend a little more time even. AI, if we go back to the keynote analysis then, but you can watch that, I can talk about the research we've done, and said how the end user information that Microsoft can get access to to help people when you talk about what they have, the TouchPoint to Microsoft Office. And even things like Xbox, down to the consumer side, to understand, have a position in the marketplace that really is unparalleled if you look at kind of the breadth and depth that Microsoft has. So yeah, big thanks to Cohesity, our other sponsors of the program that help allow us to bring this great content out to our community, and big shout out I have to give out to the community too. First time we've done this show, I reached out to all my connections and the community reached back, helped bring us a lot of great guests. I learned a lot: Cosmos DB, all the SQL stuff, all the Office and Microsoft 365, so much. My brain's full leaving this show and it's been a real pleasure. >> Great, I agree, Stu, and thank you so much to Microsoft, thank you to the crew, this has been a really fun time. We will have more coming up from the Orange County Civic Center, Microsoft Ignite. I'm Rebecca Knight for Stu Miniman, we will see you in just a little bit. (digital music)

>> Live from Orlando, Florida. It's theCUBE. Covering Microsoft Ignite. Brought to you by Cohesity, and theCUBE's ecosystem partners. >> Welcome back everyone to theCUBE's live coverage of Microsoft Ignite here in Orlando, Florida. I'm your host, Rebecca Knight along with my cohost Stu Miniman. We're joined by Joachim Hammer, he is the Principal Product Manager at Microsoft. Thanks so much for coming on the show. >> Sure, you're welcome. Happy to be here. >> So there's been a lot of news and announcements with Azure SQL, can you sort of walk our viewers through a little bit about what's happened here at Ignite this week? >> Oh sure thing, so first of all I think it's a great time to be a customer of Azure SQL Database. We have a lot of innovations, and the latest one that we're really proud of, and we're just announced GA is SQL Managed Instance. So our family of database offers had so far a single database and then a pool of databases where you could do resource sharing. What was missing was this one ability for enterprise customers to migrate their workloads into Azure and take advantage of Azure without having to do any rewriting or refactoring and Managed Instance does exactly this. It's a way for enterprise customers to take their workloads, migrate them, it has all the features that they are used to from sequel server on-prem including all the security, which is of course as you can imagine always a concern in the cloud where you need to have the same or better security that customers are used to from on-prem, and with Managed Instance we have the security isolation, we have private IPV nets, we have all the intelligent protection that we have in Azure so it's a real package. And so this is a big deal for us, and the general purpose went GA yesterday actually, so I heard. >> Security's really interesting 'cause of course database is at the core of so many customer's businesses. You've been in this industry for a while, what do you see from customers as to the drivers and the differences of going to public cloud deployments versus really owning their database in-house and are security meeting the needs of what customers need now? >> Yeah sure, so, you're right, security is probably the most important topic or one of the most important topics that comes up when you discuss the cloud. And what customers want is they want a trust, they want this trust relationship that we do the right thing and doing the right thing means we have all the compliances, we adhere to all the privacy standards, but then we also offer them state of the art security so that they can rely on Microsoft on Azure for the next however many years they want to use the cloud to develop customer leading-edge security. And we do this for example with our encryption technology with Always Encrypted. This is one of those technologies that helps you protect your database against attacks by encrypting sensitive data and the data remains encrypted even though we process queries against it. So we protect against third-party attacks on the database, so Always Encrypted is one of those technologies that may not be for everybody today but customers get the sense that yes, Microsoft is thinking ahead, they're developing this security offering, and I can trust them that they continue to do this, keep my data safe and secure. >> Trust is so fundamental to this whole entire enterprise. How do you build trust with your customers, I mean you have the reputation, but how do you really go about getting your customers to say "Okay, I'm going to board your train?" >> That's a good question, Rebecca. I think as I said it starts with the portfolio of compliance requirements that we have and that we provide for Azure's SQL Database and all the other Azure services as well. But it also goes beyond that, it goes, for example, we have this right to audit capability in Azure where a company can come to us and says we want to look behind the scenes, we want to see what auditors see so that we can really believe that you are doing all the things you're saying. You're updating your virus protection, you're patching and you have all the right administrative workflows. So this is one way for us to say our doors are open if you want to come and see what we do, then you can come and peek behind the scenes so to speak. And then the other, the third part is by developing features like we do that help customers, first of all make it easy to secure the database, and help them understand vulnerabilities, and help them understand the configurations of their database and then implement the security strategy that they feel comfortable with and then letting them move that strategy into the cloud and implement it, and I think that's what we do in Azure, and that's why we've had so much success so far. >> Earlier this week we interviewed one of your peers, talked about Cosmos DB. >> Okay. >> There's a certain type of scale we talk about there. Scale means different things to different sized customers. What does scale mean in your space? >> Yeah so you're right, scale can mean a lot of different things, and actually thank you for bringing this up so we have another announcement that we made on namely Hyper-Scale architecture. So far in Azure SQL DB, we were pretty much constrained in terms of space by the underlying hardware, how much storage comes on these VMs, and thanks to our re-architectured hardware, sorry software, we now have the ability to scale way beyond four terabytes which is the current scale of Azure SQL DB. So we can go to 64 terabytes, 100 terabytes. And we can, not only does that free up, free us from the limitations, but it also keeps it simple for customers. So customers don't have to go and build a complicated scale out architecture to take advantage of this. They can just turn a knob in a portal, and then we give them as much horsepower as they need to include in the storage. And in order for this to happen, we had to do a lot of work. So it doesn't just mean, we didn't just re-architect storage but we also have to make fail-over's faster. We have to continue to invest in online operations like online index rebuild and create to make those resumable, pause and resumable, so that with bigger and bigger databases, you can actually do all those activities that you used to do ya know, without getting in the way of your workloads. So lot of work, but we have Hyper-Scale now in Azure SQL DB and so I think this is another sort of something that customers will be really excited about. >> Sounds like that could have been a real pain point for a lot of DBA's out there, and I'm wondering, I'm sure, as a PM, you get lots of feedback from customers. What are the biggest challenges they're facing? What are some of the things they're excited about that Microsoft's helping them with these days? >> So you're right, this was a big pain point, because if you go to a big enterprise customer and say, hey bring your workload to Azure, and then they say oh yeah great, we've got this big telemetry database, what's your size limit? And you have to say four terabytes, that doesn't go too well. So that's one thing, we've removed that blocker thankfully. Other pain points I think we have by and large, I think the large pain points are we've removed, I think we have small ones where we're still working on making our deployments less painful for some customers. There's customers who are really, really sensitive to disconnects or latent variations in latency. And sometimes when we do deployments, worldwide deployments, we are impacting somebody's customer, so this is a pain point that we're currently working on. Security, as you said, is always a pain point, so this is something that will stay with us, and we just have to make sure that we're keeping up with the security demands from customers. And then, another pain point, or has been a pain point for customers, especially customers sequel server on-prem is the performance tuning. When you have to be a really, really good DBA to tune your workloads well, and so this is something that we are working on in Azure SQL DB with our intelligence performance tuning. This is a paint point that we are removing. We've removed a lot of it already. There's still, occasionally, there's still customers who complaining about performance and that's understood. And this is something that we're also trying to help them with, make it easier, give 'em insights into what their workload is doing, where are the weights, where are the slow queries, and then help them diffuse that. >> So thinking about these announcements and the changes that you've made to improve functionality and increase, not have size limits be such a road block, when you're thinking ahead to making the database more intelligent, what are some of the things you're most excited about that are still in progress right now, still in development, that we'll be talking about at next year's Ignite? >> Yeah, so personally for me on the security side, what's really exciting to me is the, so security's a very complicated topic, and not all of our customers are fully comfortable figuring out what is my security strategy and how do I implement it, and is my data really secure. So understanding threats, understanding all this technology, so I think one of the visions that gets me excited about the potential of the cloud, is that we can make security in the future hopefully as easy as we were able to make query processing with the invention of the relational model, where we made this leap from having to write code to access your data to basically a declarative SQL type language where you say this is what I want and I don't care how to database system returns it to me. If you translate that to security, what would be ideal the sort of the North Star, is to tell it to have customers in some sort of declarative policy based manner, say I have some data that I don't trust to the cloud please find the sensitive information here, and then protect it so that I'm meeting ISO or I'm meeting HIPPA requirements or that I'm meeting my internal ya know, every company has internal policies about how data needs to be secured and handled. And so if you could translate that into a declarative policy and then upload that to us, and we figure out behind the scenes these are the things we need, you need to turn on auditing, these are where the audit events have to go, and this is where the data has to be protected. But before all that, we actually identify all the sensitive data for you, we'll tag it and so forth. That to me has been a tremendous, sort of untapped potential of the cloud. That's where I think this intelligence could go potentially. >> Yeah, great. >> Who knows, maybe. >> (laughs) Well, we shall see at next year's Ignite. >> We are making handholds there. We have a classification engine that helps customers find sensitive data. We have a vulnerability assessment, a rules engine that allows you to basically test the configuration of your database against potential vulnerabilities, and we have threat detection. So we have a lot of the pieces, and I think the next step for us is to put these all together into something that can then be much more automated so that a customer doesn't have to think technology anymore. They can they business. They can think about the kinds of compliances they have to meet. They can think about, based on these compliances, this data can go this month, this data can go maybe next year, or ya know, in that kind of terms. So I think, that to me is exciting. >> Well Joachim, thank you so much for coming on theCUBE. It was a pleasure having you here. >> It was my pleasure too. Thank you. >> I'm Rebecca Knight for Stu Miniman, we'll have more from theCUBE's live coverage of Microsoft Ignite coming up in just a little bit. (upbeat music)