>>When Oracle acquired my SQL via the Sun acquisition, nobody really thought the company would put much effort into the platform preferring to focus all the wood behind its leading Oracle database, Arrow pun intended. But two years ago, Oracle surprised many folks by announcing my SQL Heatwave a new database as a service with a massively parallel hybrid Columbia in Mary Mary architecture that brings together transactional and analytic data in a single platform. Welcome to our latest database, power panel on the cube. My name is Dave Ante, and today we're gonna discuss Oracle's MySQL Heat Wave with a who's who of cloud database industry analysts. Holgar Mueller is with Constellation Research. Mark Stammer is the Dragon Slayer and Wikibon contributor. And Ron Westfall is with Fu Chim Research. Gentlemen, welcome back to the Cube. Always a pleasure to have you on. Thanks for having us. Great to be here. >>So we've had a number of of deep dive interviews on the Cube with Nip and Aggarwal. You guys know him? He's a senior vice president of MySQL, Heatwave Development at Oracle. I think you just saw him at Oracle Cloud World and he's come on to describe this is gonna, I'll call it a shock and awe feature additions to to heatwave. You know, the company's clearly putting r and d into the platform and I think at at cloud world we saw like the fifth major release since 2020 when they first announced MySQL heat wave. So just listing a few, they, they got, they taken, brought in analytics machine learning, they got autopilot for machine learning, which is automation onto the basic o l TP functionality of the database. And it's been interesting to watch Oracle's converge database strategy. We've contrasted that amongst ourselves. Love to get your thoughts on Amazon's get the right tool for the right job approach. >>Are they gonna have to change that? You know, Amazon's got the specialized databases, it's just, you know, the both companies are doing well. It just shows there are a lot of ways to, to skin a cat cuz you see some traction in the market in, in both approaches. So today we're gonna focus on the latest heat wave announcements and we're gonna talk about multi-cloud with a native MySQL heat wave implementation, which is available on aws MySQL heat wave for Azure via the Oracle Microsoft interconnect. This kind of cool hybrid action that they got going. Sometimes we call it super cloud. And then we're gonna dive into my SQL Heatwave Lake house, which allows users to process and query data across MyQ databases as heatwave databases, as well as object stores. So, and then we've got, heatwave has been announced on AWS and, and, and Azure, they're available now and Lake House I believe is in beta and I think it's coming out the second half of next year. So again, all of our guests are fresh off of Oracle Cloud world in Las Vegas. So they got the latest scoop. Guys, I'm done talking. Let's get into it. Mark, maybe you could start us off, what's your opinion of my SQL Heatwaves competitive position? When you think about what AWS is doing, you know, Google is, you know, we heard Google Cloud next recently, we heard about all their data innovations. You got, obviously Azure's got a big portfolio, snowflakes doing well in the market. What's your take? >>Well, first let's look at it from the point of view that AWS is the market leader in cloud and cloud services. They own somewhere between 30 to 50% depending on who you read of the market. And then you have Azure as number two and after that it falls off. There's gcp, Google Cloud platform, which is further way down the list and then Oracle and IBM and Alibaba. So when you look at AWS and you and Azure saying, hey, these are the market leaders in the cloud, then you start looking at it and saying, if I am going to provide a service that competes with the service they have, if I can make it available in their cloud, it means that I can be more competitive. And if I'm compelling and compelling means at least twice the performance or functionality or both at half the price, I should be able to gain market share. >>And that's what Oracle's done. They've taken a superior product in my SQL heat wave, which is faster, lower cost does more for a lot less at the end of the day and they make it available to the users of those clouds. You avoid this little thing called egress fees, you avoid the issue of having to migrate from one cloud to another and suddenly you have a very compelling offer. So I look at what Oracle's doing with MyQ and it feels like, I'm gonna use a word term, a flanking maneuver to their competition. They're offering a better service on their platforms. >>All right, so thank you for that. Holger, we've seen this sort of cadence, I sort of referenced it up front a little bit and they sat on MySQL for a decade, then all of a sudden we see this rush of announcements. Why did it take so long? And and more importantly is Oracle, are they developing the right features that cloud database customers are looking for in your view? >>Yeah, great question, but first of all, in your interview you said it's the edit analytics, right? Analytics is kind of like a marketing buzzword. Reports can be analytics, right? The interesting thing, which they did, the first thing they, they, they crossed the chasm between OTP and all up, right? In the same database, right? So major engineering feed very much what customers want and it's all about creating Bellevue for customers, which, which I think is the part why they go into the multi-cloud and why they add these capabilities. And they certainly with the AI capabilities, it's kind of like getting it into an autonomous field, self-driving field now with the lake cost capabilities and meeting customers where they are, like Mark has talked about the e risk costs in the cloud. So that that's a significant advantage, creating value for customers and that's what at the end of the day matters. >>And I believe strongly that long term it's gonna be ones who create better value for customers who will get more of their money From that perspective, why then take them so long? I think it's a great question. I think largely he mentioned the gentleman Nial, it's largely to who leads a product. I used to build products too, so maybe I'm a little fooling myself here, but that made the difference in my view, right? So since he's been charged, he's been building things faster than the rest of the competition, than my SQL space, which in hindsight we thought was a hot and smoking innovation phase. It kind of like was a little self complacent when it comes to the traditional borders of where, where people think, where things are separated between OTP and ola or as an example of adjacent support, right? Structured documents, whereas unstructured documents or databases and all of that has been collapsed and brought together for building a more powerful database for customers. >>So I mean it's certainly, you know, when, when Oracle talks about the competitors, you know, the competitors are in the, I always say they're, if the Oracle talks about you and knows you're doing well, so they talk a lot about aws, talk a little bit about Snowflake, you know, sort of Google, they have partnerships with Azure, but, but in, so I'm presuming that the response in MySQL heatwave was really in, in response to what they were seeing from those big competitors. But then you had Maria DB coming out, you know, the day that that Oracle acquired Sun and, and launching and going after the MySQL base. So it's, I'm, I'm interested and we'll talk about this later and what you guys think AWS and Google and Azure and Snowflake and how they're gonna respond. But, but before I do that, Ron, I want to ask you, you, you, you can get, you know, pretty technical and you've probably seen the benchmarks. >>I know you have Oracle makes a big deal out of it, publishes its benchmarks, makes some transparent on on GI GitHub. Larry Ellison talked about this in his keynote at Cloud World. What are the benchmarks show in general? I mean, when you, when you're new to the market, you gotta have a story like Mark was saying, you gotta be two x you know, the performance at half the cost or you better be or you're not gonna get any market share. So, and, and you know, oftentimes companies don't publish market benchmarks when they're leading. They do it when they, they need to gain share. So what do you make of the benchmarks? Have their, any results that were surprising to you? Have, you know, they been challenged by the competitors. Is it just a bunch of kind of desperate bench marketing to make some noise in the market or you know, are they real? What's your view? >>Well, from my perspective, I think they have the validity. And to your point, I believe that when it comes to competitor responses, that has not really happened. Nobody has like pulled down the information that's on GitHub and said, Oh, here are our price performance results. And they counter oracles. In fact, I think part of the reason why that hasn't happened is that there's the risk if Oracle's coming out and saying, Hey, we can deliver 17 times better query performance using our capabilities versus say, Snowflake when it comes to, you know, the Lakehouse platform and Snowflake turns around and says it's actually only 15 times better during performance, that's not exactly an effective maneuver. And so I think this is really to oracle's credit and I think it's refreshing because these differentiators are significant. We're not talking, you know, like 1.2% differences. We're talking 17 fold differences, we're talking six fold differences depending on, you know, where the spotlight is being shined and so forth. >>And so I think this is actually something that is actually too good to believe initially at first blush. If I'm a cloud database decision maker, I really have to prioritize this. I really would know, pay a lot more attention to this. And that's why I posed the question to Oracle and others like, okay, if these differentiators are so significant, why isn't the needle moving a bit more? And it's for, you know, some of the usual reasons. One is really deep discounting coming from, you know, the other players that's really kind of, you know, marketing 1 0 1, this is something you need to do when there's a real competitive threat to keep, you know, a customer in your own customer base. Plus there is the usual fear and uncertainty about moving from one platform to another. But I think, you know, the traction, the momentum is, is shifting an Oracle's favor. I think we saw that in the Q1 efforts, for example, where Oracle cloud grew 44% and that it generated, you know, 4.8 billion and revenue if I recall correctly. And so, so all these are demonstrating that's Oracle is making, I think many of the right moves, publishing these figures for anybody to look at from their own perspective is something that is, I think, good for the market and I think it's just gonna continue to pay dividends for Oracle down the horizon as you know, competition intens plots. So if I were in, >>Dave, can I, Dave, can I interject something and, and what Ron just said there? Yeah, please go ahead. A couple things here, one discounting, which is a common practice when you have a real threat, as Ron pointed out, isn't going to help much in this situation simply because you can't discount to the point where you improve your performance and the performance is a huge differentiator. You may be able to get your price down, but the problem that most of them have is they don't have an integrated product service. They don't have an integrated O L T P O L A P M L N data lake. Even if you cut out two of them, they don't have any of them integrated. They have multiple services that are required separate integration and that can't be overcome with discounting. And the, they, you have to pay for each one of these. And oh, by the way, as you grow, the discounts go away. So that's a, it's a minor important detail. >>So, so that's a TCO question mark, right? And I know you look at this a lot, if I had that kind of price performance advantage, I would be pounding tco, especially if I need two separate databases to do the job. That one can do, that's gonna be, the TCO numbers are gonna be off the chart or maybe down the chart, which you want. Have you looked at this and how does it compare with, you know, the big cloud guys, for example, >>I've looked at it in depth, in fact, I'm working on another TCO on this arena, but you can find it on Wiki bod in which I compared TCO for MySEQ Heat wave versus Aurora plus Redshift plus ML plus Blue. I've compared it against gcps services, Azure services, Snowflake with other services. And there's just no comparison. The, the TCO differences are huge. More importantly, thefor, the, the TCO per performance is huge. We're talking in some cases multiple orders of magnitude, but at least an order of magnitude difference. So discounting isn't gonna help you much at the end of the day, it's only going to lower your cost a little, but it doesn't improve the automation, it doesn't improve the performance, it doesn't improve the time to insight, it doesn't improve all those things that you want out of a database or multiple databases because you >>Can't discount yourself to a higher value proposition. >>So what about, I wonder ho if you could chime in on the developer angle. You, you followed that, that market. How do these innovations from heatwave, I think you used the term developer velocity. I've heard you used that before. Yeah, I mean, look, Oracle owns Java, okay, so it, it's, you know, most popular, you know, programming language in the world, blah, blah blah. But it does it have the, the minds and hearts of, of developers and does, where does heatwave fit into that equation? >>I think heatwave is gaining quickly mindshare on the developer side, right? It's not the traditional no sequel database which grew up, there's a traditional mistrust of oracles to developers to what was happening to open source when gets acquired. Like in the case of Oracle versus Java and where my sql, right? And, but we know it's not a good competitive strategy to, to bank on Oracle screwing up because it hasn't worked not on Java known my sequel, right? And for developers, it's, once you get to know a technology product and you can do more, it becomes kind of like a Swiss army knife and you can build more use case, you can build more powerful applications. That's super, super important because you don't have to get certified in multiple databases. You, you are fast at getting things done, you achieve fire, develop velocity, and the managers are happy because they don't have to license more things, send you to more trainings, have more risk of something not being delivered, right? >>So it's really the, we see the suite where this best of breed play happening here, which in general was happening before already with Oracle's flagship database. Whereas those Amazon as an example, right? And now the interesting thing is every step away Oracle was always a one database company that can be only one and they're now generally talking about heat web and that two database company with different market spaces, but same value proposition of integrating more things very, very quickly to have a universal database that I call, they call the converge database for all the needs of an enterprise to run certain application use cases. And that's what's attractive to developers. >>It's, it's ironic isn't it? I mean I, you know, the rumor was the TK Thomas Curian left Oracle cuz he wanted to put Oracle database on other clouds and other places. And maybe that was the rift. Maybe there was, I'm sure there was other things, but, but Oracle clearly is now trying to expand its Tam Ron with, with heatwave into aws, into Azure. How do you think Oracle's gonna do, you were at a cloud world, what was the sentiment from customers and the independent analyst? Is this just Oracle trying to screw with the competition, create a little diversion? Or is this, you know, serious business for Oracle? What do you think? >>No, I think it has lakes. I think it's definitely, again, attriting to Oracle's overall ability to differentiate not only my SQL heat wave, but its overall portfolio. And I think the fact that they do have the alliance with the Azure in place, that this is definitely demonstrating their commitment to meeting the multi-cloud needs of its customers as well as what we pointed to in terms of the fact that they're now offering, you know, MySQL capabilities within AWS natively and that it can now perform AWS's own offering. And I think this is all demonstrating that Oracle is, you know, not letting up, they're not resting on its laurels. That's clearly we are living in a multi-cloud world, so why not just make it more easy for customers to be able to use cloud databases according to their own specific, specific needs. And I think, you know, to holder's point, I think that definitely lines with being able to bring on more application developers to leverage these capabilities. >>I think one important announcement that's related to all this was the JSON relational duality capabilities where now it's a lot easier for application developers to use a language that they're very familiar with a JS O and not have to worry about going into relational databases to store their J S O N application coding. So this is, I think an example of the innovation that's enhancing the overall Oracle portfolio and certainly all the work with machine learning is definitely paying dividends as well. And as a result, I see Oracle continue to make these inroads that we pointed to. But I agree with Mark, you know, the short term discounting is just a stall tag. This is not denying the fact that Oracle is being able to not only deliver price performance differentiators that are dramatic, but also meeting a wide range of needs for customers out there that aren't just limited device performance consideration. >>Being able to support multi-cloud according to customer needs. Being able to reach out to the application developer community and address a very specific challenge that has plagued them for many years now. So bring it all together. Yeah, I see this as just enabling Oracles who ring true with customers. That the customers that were there were basically all of them, even though not all of them are going to be saying the same things, they're all basically saying positive feedback. And likewise, I think the analyst community is seeing this. It's always refreshing to be able to talk to customers directly and at Oracle cloud there was a litany of them and so this is just a difference maker as well as being able to talk to strategic partners. The nvidia, I think partnerships also testament to Oracle's ongoing ability to, you know, make the ecosystem more user friendly for the customers out there. >>Yeah, it's interesting when you get these all in one tools, you know, the Swiss Army knife, you expect that it's not able to be best of breed. That's the kind of surprising thing that I'm hearing about, about heatwave. I want to, I want to talk about Lake House because when I think of Lake House, I think data bricks, and to my knowledge data bricks hasn't been in the sites of Oracle yet. Maybe they're next, but, but Oracle claims that MySQL, heatwave, Lakehouse is a breakthrough in terms of capacity and performance. Mark, what are your thoughts on that? Can you double click on, on Lakehouse Oracle's claims for things like query performance and data loading? What does it mean for the market? Is Oracle really leading in, in the lake house competitive landscape? What are your thoughts? >>Well, but name in the game is what are the problems you're solving for the customer? More importantly, are those problems urgent or important? If they're urgent, customers wanna solve 'em. Now if they're important, they might get around to them. So you look at what they're doing with Lake House or previous to that machine learning or previous to that automation or previous to that O L A with O ltp and they're merging all this capability together. If you look at Snowflake or data bricks, they're tacking one problem. You look at MyQ heat wave, they're tacking multiple problems. So when you say, yeah, their queries are much better against the lake house in combination with other analytics in combination with O ltp and the fact that there are no ETLs. So you're getting all this done in real time. So it's, it's doing the query cross, cross everything in real time. >>You're solving multiple user and developer problems, you're increasing their ability to get insight faster, you're having shorter response times. So yeah, they really are solving urgent problems for customers. And by putting it where the customer lives, this is the brilliance of actually being multicloud. And I know I'm backing up here a second, but by making it work in AWS and Azure where people already live, where they already have applications, what they're saying is, we're bringing it to you. You don't have to come to us to get these, these benefits, this value overall, I think it's a brilliant strategy. I give Nip and Argo wallet a huge, huge kudos for what he's doing there. So yes, what they're doing with the lake house is going to put notice on data bricks and Snowflake and everyone else for that matter. Well >>Those are guys that whole ago you, you and I have talked about this. Those are, those are the guys that are doing sort of the best of breed. You know, they're really focused and they, you know, tend to do well at least out of the gate. Now you got Oracle's converged philosophy, obviously with Oracle database. We've seen that now it's kicking in gear with, with heatwave, you know, this whole thing of sweets versus best of breed. I mean the long term, you know, customers tend to migrate towards suite, but the new shiny toy tends to get the growth. How do you think this is gonna play out in cloud database? >>Well, it's the forever never ending story, right? And in software right suite, whereas best of breed and so far in the long run suites have always won, right? So, and sometimes they struggle again because the inherent problem of sweets is you build something larger, it has more complexity and that means your cycles to get everything working together to integrate the test that roll it out, certify whatever it is, takes you longer, right? And that's not the case. It's a fascinating part of what the effort around my SQL heat wave is that the team is out executing the previous best of breed data, bringing us something together. Now if they can maintain that pace, that's something to to, to be seen. But it, the strategy, like what Mark was saying, bring the software to the data is of course interesting and unique and totally an Oracle issue in the past, right? >>Yeah. But it had to be in your database on oci. And but at, that's an interesting part. The interesting thing on the Lake health side is, right, there's three key benefits of a lakehouse. The first one is better reporting analytics, bring more rich information together, like make the, the, the case for silicon angle, right? We want to see engagements for this video, we want to know what's happening. That's a mixed transactional video media use case, right? Typical Lakehouse use case. The next one is to build more rich applications, transactional applications which have video and these elements in there, which are the engaging one. And the third one, and that's where I'm a little critical and concerned, is it's really the base platform for artificial intelligence, right? To run deep learning to run things automatically because they have all the data in one place can create in one way. >>And that's where Oracle, I know that Ron talked about Invidia for a moment, but that's where Oracle doesn't have the strongest best story. Nonetheless, the two other main use cases of the lake house are very strong, very well only concern is four 50 terabyte sounds long. It's an arbitrary limitation. Yeah, sounds as big. So for the start, and it's the first word, they can make that bigger. You don't want your lake house to be limited and the terabyte sizes or any even petabyte size because you want to have the certainty. I can put everything in there that I think it might be relevant without knowing what questions to ask and query those questions. >>Yeah. And you know, in the early days of no schema on right, it just became a mess. But now technology has evolved to allow us to actually get more value out of that data. Data lake. Data swamp is, you know, not much more, more, more, more logical. But, and I want to get in, in a moment, I want to come back to how you think the competitors are gonna respond. Are they gonna have to sort of do a more of a converged approach? AWS in particular? But before I do, Ron, I want to ask you a question about autopilot because I heard Larry Ellison's keynote and he was talking about how, you know, most security issues are human errors with autonomy and autonomous database and things like autopilot. We take care of that. It's like autonomous vehicles, they're gonna be safer. And I went, well maybe, maybe someday. So Oracle really tries to emphasize this, that every time you see an announcement from Oracle, they talk about new, you know, autonomous capabilities. It, how legit is it? Do people care? What about, you know, what's new for heatwave Lakehouse? How much of a differentiator, Ron, do you really think autopilot is in this cloud database space? >>Yeah, I think it will definitely enhance the overall proposition. I don't think people are gonna buy, you know, lake house exclusively cause of autopilot capabilities, but when they look at the overall picture, I think it will be an added capability bonus to Oracle's benefit. And yeah, I think it's kind of one of these age old questions, how much do you automate and what is the bounce to strike? And I think we all understand with the automatic car, autonomous car analogy that there are limitations to being able to use that. However, I think it's a tool that basically every organization out there needs to at least have or at least evaluate because it goes to the point of it helps with ease of use, it helps make automation more balanced in terms of, you know, being able to test, all right, let's automate this process and see if it works well, then we can go on and switch on on autopilot for other processes. >>And then, you know, that allows, for example, the specialists to spend more time on business use cases versus, you know, manual maintenance of, of the cloud database and so forth. So I think that actually is a, a legitimate value proposition. I think it's just gonna be a case by case basis. Some organizations are gonna be more aggressive with putting automation throughout their processes throughout their organization. Others are gonna be more cautious. But it's gonna be, again, something that will help the overall Oracle proposition. And something that I think will be used with caution by many organizations, but other organizations are gonna like, hey, great, this is something that is really answering a real problem. And that is just easing the use of these databases, but also being able to better handle the automation capabilities and benefits that come with it without having, you know, a major screwup happened and the process of transitioning to more automated capabilities. >>Now, I didn't attend cloud world, it's just too many red eyes, you know, recently, so I passed. But one of the things I like to do at those events is talk to customers, you know, in the spirit of the truth, you know, they, you know, you'd have the hallway, you know, track and to talk to customers and they say, Hey, you know, here's the good, the bad and the ugly. So did you guys, did you talk to any customers my SQL Heatwave customers at, at cloud world? And and what did you learn? I don't know, Mark, did you, did you have any luck and, and having some, some private conversations? >>Yeah, I had quite a few private conversations. The one thing before I get to that, I want disagree with one point Ron made, I do believe there are customers out there buying the heat wave service, the MySEQ heat wave server service because of autopilot. Because autopilot is really revolutionary in many ways in the sense for the MySEQ developer in that it, it auto provisions, it auto parallel loads, IT auto data places it auto shape predictions. It can tell you what machine learning models are going to tell you, gonna give you your best results. And, and candidly, I've yet to meet a DBA who didn't wanna give up pedantic tasks that are pain in the kahoo, which they'd rather not do and if it's long as it was done right for them. So yes, I do think people are buying it because of autopilot and that's based on some of the conversations I had with customers at Oracle Cloud World. >>In fact, it was like, yeah, that's great, yeah, we get fantastic performance, but this really makes my life easier and I've yet to meet a DBA who didn't want to make their life easier. And it does. So yeah, I've talked to a few of them. They were excited. I asked them if they ran into any bugs, were there any difficulties in moving to it? And the answer was no. In both cases, it's interesting to note, my sequel is the most popular database on the planet. Well, some will argue that it's neck and neck with SQL Server, but if you add in Mariah DB and ProCon db, which are forks of MySQL, then yeah, by far and away it's the most popular. And as a result of that, everybody for the most part has typically a my sequel database somewhere in their organization. So this is a brilliant situation for anybody going after MyQ, but especially for heat wave. And the customers I talk to love it. I didn't find anybody complaining about it. And >>What about the migration? We talked about TCO earlier. Did your t does your TCO analysis include the migration cost or do you kind of conveniently leave that out or what? >>Well, when you look at migration costs, there are different kinds of migration costs. By the way, the worst job in the data center is the data migration manager. Forget it, no other job is as bad as that one. You get no attaboys for doing it. Right? And then when you screw up, oh boy. So in real terms, anything that can limit data migration is a good thing. And when you look at Data Lake, that limits data migration. So if you're already a MySEQ user, this is a pure MySQL as far as you're concerned. It's just a, a simple transition from one to the other. You may wanna make sure nothing broke and every you, all your tables are correct and your schema's, okay, but it's all the same. So it's a simple migration. So it's pretty much a non-event, right? When you migrate data from an O LTP to an O L A P, that's an ETL and that's gonna take time. >>But you don't have to do that with my SQL heat wave. So that's gone when you start talking about machine learning, again, you may have an etl, you may not, depending on the circumstances, but again, with my SQL heat wave, you don't, and you don't have duplicate storage, you don't have to copy it from one storage container to another to be able to be used in a different database, which by the way, ultimately adds much more cost than just the other service. So yeah, I looked at the migration and again, the users I talked to said it was a non-event. It was literally moving from one physical machine to another. If they had a new version of MySEQ running on something else and just wanted to migrate it over or just hook it up or just connect it to the data, it worked just fine. >>Okay, so every day it sounds like you guys feel, and we've certainly heard this, my colleague David Foyer, the semi-retired David Foyer was always very high on heatwave. So I think you knows got some real legitimacy here coming from a standing start, but I wanna talk about the competition, how they're likely to respond. I mean, if your AWS and you got heatwave is now in your cloud, so there's some good aspects of that. The database guys might not like that, but the infrastructure guys probably love it. Hey, more ways to sell, you know, EC two and graviton, but you're gonna, the database guys in AWS are gonna respond. They're gonna say, Hey, we got Redshift, we got aqua. What's your thoughts on, on not only how that's gonna resonate with customers, but I'm interested in what you guys think will a, I never say never about aws, you know, and are they gonna try to build, in your view a converged Oola and o LTP database? You know, Snowflake is taking an ecosystem approach. They've added in transactional capabilities to the portfolio so they're not standing still. What do you guys see in the competitive landscape in that regard going forward? Maybe Holger, you could start us off and anybody else who wants to can chime in, >>Happy to, you mentioned Snowflake last, we'll start there. I think Snowflake is imitating that strategy, right? That building out original data warehouse and the clouds tasking project to really proposition to have other data available there because AI is relevant for everybody. Ultimately people keep data in the cloud for ultimately running ai. So you see the same suite kind of like level strategy, it's gonna be a little harder because of the original positioning. How much would people know that you're doing other stuff? And I just, as a former developer manager of developers, I just don't see the speed at the moment happening at Snowflake to become really competitive to Oracle. On the flip side, putting my Oracle hat on for a moment back to you, Mark and Iran, right? What could Oracle still add? Because the, the big big things, right? The traditional chasms in the database world, they have built everything, right? >>So I, I really scratched my hat and gave Nipon a hard time at Cloud world say like, what could you be building? Destiny was very conservative. Let's get the Lakehouse thing done, it's gonna spring next year, right? And the AWS is really hard because AWS value proposition is these small innovation teams, right? That they build two pizza teams, which can be fit by two pizzas, not large teams, right? And you need suites to large teams to build these suites with lots of functionalities to make sure they work together. They're consistent, they have the same UX on the administration side, they can consume the same way, they have the same API registry, can't even stop going where the synergy comes to play over suite. So, so it's gonna be really, really hard for them to change that. But AWS super pragmatic. They're always by themselves that they'll listen to customers if they learn from customers suite as a proposition. I would not be surprised if AWS trying to bring things closer together, being morely together. >>Yeah. Well how about, can we talk about multicloud if, if, again, Oracle is very on on Oracle as you said before, but let's look forward, you know, half a year or a year. What do you think about Oracle's moves in, in multicloud in terms of what kind of penetration they're gonna have in the marketplace? You saw a lot of presentations at at cloud world, you know, we've looked pretty closely at the, the Microsoft Azure deal. I think that's really interesting. I've, I've called it a little bit of early days of a super cloud. What impact do you think this is gonna have on, on the marketplace? But, but both. And think about it within Oracle's customer base, I have no doubt they'll do great there. But what about beyond its existing install base? What do you guys think? >>Ryan, do you wanna jump on that? Go ahead. Go ahead Ryan. No, no, no, >>That's an excellent point. I think it aligns with what we've been talking about in terms of Lakehouse. I think Lake House will enable Oracle to pull more customers, more bicycle customers onto the Oracle platforms. And I think we're seeing all the signs pointing toward Oracle being able to make more inroads into the overall market. And that includes garnishing customers from the leaders in, in other words, because they are, you know, coming in as a innovator, a an alternative to, you know, the AWS proposition, the Google cloud proposition that they have less to lose and there's a result they can really drive the multi-cloud messaging to resonate with not only their existing customers, but also to be able to, to that question, Dave's posing actually garnish customers onto their platform. And, and that includes naturally my sequel but also OCI and so forth. So that's how I'm seeing this playing out. I think, you know, again, Oracle's reporting is indicating that, and I think what we saw, Oracle Cloud world is definitely validating the idea that Oracle can make more waves in the overall market in this regard. >>You know, I, I've floated this idea of Super cloud, it's kind of tongue in cheek, but, but there, I think there is some merit to it in terms of building on top of hyperscale infrastructure and abstracting some of the, that complexity. And one of the things that I'm most interested in is industry clouds and an Oracle acquisition of Cerner. I was struck by Larry Ellison's keynote, it was like, I don't know, an hour and a half and an hour and 15 minutes was focused on healthcare transformation. Well, >>So vertical, >>Right? And so, yeah, so you got Oracle's, you know, got some industry chops and you, and then you think about what they're building with, with not only oci, but then you got, you know, MyQ, you can now run in dedicated regions. You got ADB on on Exadata cloud to customer, you can put that OnPrem in in your data center and you look at what the other hyperscalers are, are doing. I I say other hyperscalers, I've always said Oracle's not really a hyperscaler, but they got a cloud so they're in the game. But you can't get, you know, big query OnPrem, you look at outposts, it's very limited in terms of, you know, the database support and again, that that will will evolve. But now you got Oracle's got, they announced Alloy, we can white label their cloud. So I'm interested in what you guys think about these moves, especially the industry cloud. We see, you know, Walmart is doing sort of their own cloud. You got Goldman Sachs doing a cloud. Do you, you guys, what do you think about that and what role does Oracle play? Any thoughts? >>Yeah, let me lemme jump on that for a moment. Now, especially with the MyQ, by making that available in multiple clouds, what they're doing is this follows the philosophy they've had the past with doing cloud, a customer taking the application and the data and putting it where the customer lives. If it's on premise, it's on premise. If it's in the cloud, it's in the cloud. By making the mice equal heat wave, essentially a plug compatible with any other mice equal as far as your, your database is concern and then giving you that integration with O L A P and ML and Data Lake and everything else, then what you've got is a compelling offering. You're making it easier for the customer to use. So I look the difference between MyQ and the Oracle database, MyQ is going to capture market more market share for them. >>You're not gonna find a lot of new users for the Oracle debate database. Yeah, there are always gonna be new users, don't get me wrong, but it's not gonna be a huge growth. Whereas my SQL heatwave is probably gonna be a major growth engine for Oracle going forward. Not just in their own cloud, but in AWS and in Azure and on premise over time that eventually it'll get there. It's not there now, but it will, they're doing the right thing on that basis. They're taking the services and when you talk about multicloud and making them available where the customer wants them, not forcing them to go where you want them, if that makes sense. And as far as where they're going in the future, I think they're gonna take a page outta what they've done with the Oracle database. They'll add things like JSON and XML and time series and spatial over time they'll make it a, a complete converged database like they did with the Oracle database. The difference being Oracle database will scale bigger and will have more transactions and be somewhat faster. And my SQL will be, for anyone who's not on the Oracle database, they're, they're not stupid, that's for sure. >>They've done Jason already. Right. But I give you that they could add graph and time series, right. Since eat with, Right, Right. Yeah, that's something absolutely right. That's, that's >>A sort of a logical move, right? >>Right. But that's, that's some kid ourselves, right? I mean has worked in Oracle's favor, right? 10 x 20 x, the amount of r and d, which is in the MyQ space, has been poured at trying to snatch workloads away from Oracle by starting with IBM 30 years ago, 20 years ago, Microsoft and, and, and, and didn't work, right? Database applications are extremely sticky when they run, you don't want to touch SIM and grow them, right? So that doesn't mean that heat phase is not an attractive offering, but it will be net new things, right? And what works in my SQL heat wave heat phases favor a little bit is it's not the massive enterprise applications which have like we the nails like, like you might be only running 30% or Oracle, but the connections and the interfaces into that is, is like 70, 80% of your enterprise. >>You take it out and it's like the spaghetti ball where you say, ah, no I really don't, don't want to do all that. Right? You don't, don't have that massive part with the equals heat phase sequel kind of like database which are more smaller tactical in comparison, but still I, I don't see them taking so much share. They will be growing because of a attractive value proposition quickly on the, the multi-cloud, right? I think it's not really multi-cloud. If you give people the chance to run your offering on different clouds, right? You can run it there. The multi-cloud advantages when the Uber offering comes out, which allows you to do things across those installations, right? I can migrate data, I can create data across something like Google has done with B query Omni, I can run predictive models or even make iron models in different place and distribute them, right? And Oracle is paving the road for that, but being available on these clouds. But the multi-cloud capability of database which knows I'm running on different clouds that is still yet to be built there. >>Yeah. And >>That the problem with >>That, that's the super cloud concept that I flowed and I I've always said kinda snowflake with a single global instance is sort of, you know, headed in that direction and maybe has a league. What's the issue with that mark? >>Yeah, the problem with the, with that version, the multi-cloud is clouds to charge egress fees. As long as they charge egress fees to move data between clouds, it's gonna make it very difficult to do a real multi-cloud implementation. Even Snowflake, which runs multi-cloud, has to pass out on the egress fees of their customer when data moves between clouds. And that's really expensive. I mean there, there is one customer I talked to who is beta testing for them, the MySQL heatwave and aws. The only reason they didn't want to do that until it was running on AWS is the egress fees were so great to move it to OCI that they couldn't afford it. Yeah. Egress fees are the big issue but, >>But Mark the, the point might be you might wanna root query and only get the results set back, right was much more tinier, which been the answer before for low latency between the class A problem, which we sometimes still have but mostly don't have. Right? And I think in general this with fees coming down based on the Oracle general E with fee move and it's very hard to justify those, right? But, but it's, it's not about moving data as a multi-cloud high value use case. It's about doing intelligent things with that data, right? Putting into other places, replicating it, what I'm saying the same thing what you said before, running remote queries on that, analyzing it, running AI on it, running AI models on that. That's the interesting thing. Cross administered in the same way. Taking things out, making sure compliance happens. Making sure when Ron says I don't want to be American anymore, I want to be in the European cloud that is gets migrated, right? So tho those are the interesting value use case which are really, really hard for enterprise to program hand by hand by developers and they would love to have out of the box and that's yet the innovation to come to, we have to come to see. But the first step to get there is that your software runs in multiple clouds and that's what Oracle's doing so well with my SQL >>Guys. Amazing. >>Go ahead. Yeah. >>Yeah. >>For example, >>Amazing amount of data knowledge and, and brain power in this market. Guys, I really want to thank you for coming on to the cube. Ron Holger. Mark, always a pleasure to have you on. Really appreciate your time. >>Well all the last names we're very happy for Romanic last and moderator. Thanks Dave for moderating us. All right, >>We'll see. We'll see you guys around. Safe travels to all and thank you for watching this power panel, The Truth About My SQL Heat Wave on the cube. Your leader in enterprise and emerging tech coverage.

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

(upbeat music) >> When Oracle acquired Sun in 2009, it paid $5.6 billion net of Sun's cash and debt. Now I argued at the time that Oracle got one of the best deals in the history of enterprise tech, and I got a lot of grief for saying that because Sun had a declining business, it was losing money, and its revenue was under serious pressure as it tried to hang on for dear life. But Safra Catz understood that Oracle could pay Sun's lower profit and lagging businesses, like its low index 86 product lines, and even if Sun's revenue was cut in half, because Oracle has such a high revenue multiple as a software company, it could almost instantly generate $25 to $30 billion in shareholder value on paper. In addition, it was a catalyst for Oracle to initiate its highly differentiated engineering systems business, and was actually the precursor to Oracle's Cloud. Oracle saw that it could capture high margin dollars that used to go to partners like HP, it's original exit data partner, and get paid for the full stack across infrastructure, middleware, database, and application software, when eventually got really serious about cloud. Now there was also a major technology angle to this story. Remember Sun's tagline, "the network is the computer"? Well, they should have just called it cloud. Through the Sun acquisition. Oracle also got a couple of key technologies, Java, the number one programming language in the world, and MySQL, a key ingredient of the LAMP stack, that's Linux, Apache, MySQL and PHP, Perl or Python, on which the internet is basically built, and is used by many cloud services like Facebook, Twitter, WordPress, Flicker, Amazon, Aurora, and many other examples, including, by the way, Maria DB, which is a fork of MySQL created by MySQL's creator, basically in protest to Oracle's acquisition; the drama is Oscar worthy. It gets even better. In 2020, Oracle began introducing a new version of MySQL called MySQL HeatWave, and since late 2020 it's been in sort of a super cycle rolling, out three new releases in less than a year and a half in an attempt to expand its Tam and compete in new markets. Now we covered the release of MySQL Autopilot, which uses machine learning to automate management functions. And we also covered the bench marketing that Oracle produced against Snowflake, AWS, Azure, and Google. And Oracle's at it again with HeatWave, adding machine learning into its database capabilities, along with previously available integrations of OLAP and OLTP. This, of course, is in line with Oracle's converged database philosophy, which, as we've reported, is different from other cloud database providers, most notably Amazon, which takes the right tool for the right job approach and chooses database specialization over a one size fits all strategy. Now we've asked Oracle to come on theCUBE and explain these moves, and I'm pleased to welcome back Nipun Agarwal, who's the senior vice president for MySQL Database and HeatWave at Oracle. And today, in this video exclusive, we'll discuss machine learning, other new capabilities around elasticity and compression, and then any benchmark data that Nipun wants to share. Nipun's been a leading advocate of the HeatWave program. He's led engineering in that team for over 10 years, and he has over 185 patents in database technologies. Welcome back to the show Nipun. Great to see you again. Thanks for coming on. >> Thank you, Dave. Very happy to be back. >> Yeah, now for those who may not have kept up with the news, maybe to kick things off you could give us an overview of what MySQL HeatWave actually is so that we're all on the same page. >> Sure, Dave, MySQL HeatWave is a fully managed MySQL database service from Oracle, and it has a builtin query accelerator called HeatWave, and that's the part which is unique. So with MySQL HeatWave, customers of MySQL get a single database which they can use for transactional processing, for analytics, and for mixed workloads because traditionally MySQL has been designed and optimized for transaction processing. So in the past, when customers had to run analytics with the MySQL based service, they would need to move the data out of MySQL into some other database for running analytics. So they would end up with two different databases and it would take some time to move the data out of MySQL into this other system. With MySQL HeatWave, we have solved this problem and customers now have a single MySQL database for all their applications, and they can get the good performance of analytics without any changes to their MySQL application. >> Now it's no secret that a lot of times, you know, queries are not, you know, most efficiently written, and critics of MySQL HeatWave will claim that this product is very memory and cluster intensive, it has a heavy footprint that adds to cost. How do you answer that, Nipun? >> Right, so for offering any database service in the cloud there are two dimensions, performance and cost, and we have been very cognizant of both of them. So it is indeed the case that HeatWave is a, in-memory query accelerator, which is why we get very good performance, but it is also the case that we have optimized HeatWave for commodity cloud services. So for instance, we use the least expensive compute. We use the least expensive storage. So what I would suggest is for the customers who kind of would like to know what is the price performance advantage of HeatWave compared to any database we have benchmark against, Redshift, Snowflake, Google BigQuery, Azure Synapse, HeatWave is significantly faster and significantly lower price on a multitude of workloads. So not only is it in-memory database and optimized for that, but we have also optimized it for commodity cloud services, which makes it much lower price than the competition. >> Well, at the end of the day, it's customers that sort of decide what the truth is. So to date, what's been the customer reaction? Are they moving from other clouds from on-prem environments? Both why, you know, what are you seeing? >> Right, so we are definitely a whole bunch of migrations of customers who are running MySQL on-premise to the cloud, to MySQL HeatWave. That's definitely happening. What is also very interesting is we are seeing that a very large percentage of customers, more than half the customers who are coming to MySQL HeatWave, are migrating from other clouds. We have a lot of migrations coming from AWS Aurora, migrations from RedShift, migrations from RDS MySQL, TerriData, SAP HANA, right. So we are seeing migrations from a whole bunch of other databases and other cloud services to MySQL HeatWave. And the main reason we are told why customers are migrating from other databases to MySQL HeatWave are lower cost, better performance, and no change to their application because many of these services, like AWS Aurora are ETL compatible with MySQL. So when customers try MySQL HeatWave, not only do they get better performance at a lower cost, but they find that they can migrate their application without any changes, and that's a big incentive for them. >> Great, thank you, Nipun. So can you give us some names? Are there some real world examples of these customers that have migrated to MySQL HeatWave that you can share? >> Oh, absolutely, I'll give you a few names. Stutor.com, this is an educational SaaS provider raised out of Brazil. They were using Google BigQuery, and when they migrated to MySQL HeatWave, they found a 300X, right, 300 times improvement in performance, and it lowered their cost by 85 (audio cut out). Another example is Neovera. They offer cybersecurity solutions and they were running their application on an on-premise version of MySQL when they migrated to MySQL HeatWave, their application improved in performance by 300 times and their cost reduced by 80%, right. So by going from on-premise to MySQL HeatWave, they reduced the cost by 80%, improved performance by 300 times. We are Glass, another customer based out of Brazil. They were running on AWS EC2, and when they migrated, within hours they found that there was a significant improvement, like, you know, over 5X improvement in database performance, and they were able to accommodate a very large virtual event, which had more than a million visitors. Another example, Genius Senority. They are a game designer in Japan, and when they moved to MySQL HeatWave, they found a 90 times percent improvement in performance. And there many, many more like a lot of migrations, again, from like, you know, Aurora, RedShift and many other databases as well. And consistently what we hear is (audio cut out) getting much better performance at a much lower cost without any change to their application. >> Great, thank you. You know, when I ask that question, a lot of times I get, "Well, I can't name the customer name," but I got to give Oracle credit, a lot of times you guys have at your fingertips. So you're not the only one, but it's somewhat rare in this industry. So, okay, so you got some good feedback from those customers that did migrate to MySQL HeatWave. What else did they tell you that they wanted? Did they, you know, kind of share a wishlist and some of the white space that you guys should be working on? What'd they tell you? >> Right, so as customers are moving more data into MySQL HeatWave, as they're consolidating more data into MySQL HeatWave, customers want to run other kinds of processing with this data. A very popular one is (audio cut out) So we have had multiple customers who told us that they wanted to run machine learning with data which is stored in MySQL HeatWave, and for that they have to extract the data out of MySQL (audio cut out). So that was the first feedback we got. Second thing is MySQL HeatWave is a highly scalable system. What that means is that as you add more nodes to a HeatWave cluster, the performance of the system improves almost linearly. But currently customers need to perform some manual steps to add most to a cluster or to reduce the cluster size. So that was other feedback we got that people wanted this thing to be automated. Third thing is that we have shown in the previous results, that HeatWave is significantly faster and significantly lower price compared to competitive services. So we got feedback from customers that can we trade off some performance to get even lower cost, and that's what we have looked at. And then finally, like we have some results on various data sizes with TPC-H. Customers wanted to see if we can offer some more data points as to how does HeatWave perform on other kinds of workloads. And that's what we've been working on for the several months. >> Okay, Nipun, we're going to get into some of that, but, so how did you go about addressing these requirements? >> Right, so the first thing is we are announcing support for in-database machine learning, meaning that customers who have their data inside MySQL HeatWave can now run training, inference, and prediction all inside the database without the data or the model ever having to leave the database. So that's how we address the first one. Second thing is we are offering support for real time elasticity, meaning that customers can scale up or scale down to any number of nodes. This requires no manual intervention on part of the user, and for the entire duration of the resize operation, the system is fully available. The third, in terms of the costs, we have double the amount of data that can be processed per node. So if you look at a HeatWave cluster, the size of the cluster determines the cost. So by doubling the amount of data that can be processed per node, we have effectively reduced the cluster size which is required for planning a given workload to have, which means it reduces the cost to the customer by half. And finally, we have also run the TPC-DS workload on HeatWave and compared it with other vendors. So now customers can have another data point in terms of the performance and the cost comparison of HeatWave with other services. >> All right, and I promise, I'm going to ask you about the benchmarks, but I want to come back and drill into these a bit. How is HeatWave ML different from competitive offerings? Take for instance, Redshift ML, for example. >> Sure, okay, so this is a good comparison. Let's start with, let's say RedShift ML, like there are some systems like, you know, Snowflake, which don't even offer any, like, processing of machine learning inside the database, and they expect customers to write a whole bunch of code, in say Python or Java, to do machine learning. RedShift ML does have integration with SQL. That's a good start. However, when customers of Redshift need to run machine learning, and they invoke Redshift ML, it makes a call to another service, SageMaker, right, where so the data needs to be exported to a different service. The model is generated, and the model is also outside RedShift. With HeatWave ML, the data resides always inside the MySQL database service. We are able to generate models. We are able to train the models, run inference, run explanations, all inside the MySQL HeatWave service. So the data, or the model, never have to leave the database, which means that both the data and the models can now be secured by the same access control mechanisms as the rest of the data. So that's the first part, that there is no need for any ETL. The second aspect is the automation. Training is a very important part of machine learning, right, and it impacts the quality of the predictions and such. So traditionally, customers would employ data scientists to influence the training process so that it's done right. And even in the case of Redshift ML, the users are expected to provide a lot of parameters to the training process. So the second thing which we have worked on with HeatWave ML is that it is fully automated. There is absolutely no user intervention required for training. Third is in terms of performance. So one of the things we are very, very sensitive to is performance because performance determines the eventual cost to the customer. So again, in some benchmarks, which we have published, and these are all available on GitHub, we are showing how HeatWave ML is 25 times faster than Redshift ML, and here's the kicker, at 1% of the cost. So four benefits, the data all remain secure inside the database service, it's fully automated, much faster, much lower cost than the competition. >> All right, thank you Nipun. Now, so there's a lot of talk these days about explainability and AI. You know, the system can very accurately tell you that it's a cat, you know, or for you Silicon Valley fans, it's a hot dog or not a hot dog, but they can't tell you how the system got there. So what is explainability, and why should people care about it? >> Right, so when we were talking to customers about what they would like from a machine learning based solution, one of the feedbacks we got is that enterprise is a little slow or averse to uptaking machine learning, because it seems to be, you know, like magic, right? And enterprises have the obligation to be able to explain, or to provide a answer to their customers as to why did the database make a certain choice. With a rule based solution it's simple, it's a rule based thing, and you know what the logic was. So the reason explanations are important is because customers want to know why did the system make a certain prediction? One of the important characteristics of HeatWave ML is that any model which is generated by HeatWave ML can be explained, and we can do both global explanations or model explanations as well as we can also do local explanations. So when the system makes a specific prediction using HeatWave ML, the user can find out why did the system make such a prediction? So for instance, if someone is being denied a loan, the user can figure out what were the attribute, what were the features which led to that decision? So this ensures, like, you know, fairness, and many of the times there is also like a need for regulatory compliance where users have a right to know. So we feel that explanations are very important for enterprise workload, and that's why every model which is generated by HeatWave ML can be explained. >> Now I got to give Snowflakes some props, you know, this whole idea of separating compute from storage, but also bringing the database to the cloud and driving elasticity. So that's been a key enabler and has solved a lot of problems, in particular the snake swallowing the basketball problem, as I often say. But what about elasticity and elasticity in real time? How is your version, and there's a lot of companies chasing this, how is your approach to an elastic cloud database service different from what others are promoting these days? >> Right, so a couple of characteristics. One is that we have now fully automated the process of elasticity, meaning that if a user wants to scale up or scale down, the only thing they need to specify is the eventual size of the cluster and the system completely takes care of it transparently. But then there are a few characteristics which are very unique. So for instance, we can scale up or scale down to any number of nodes. Whereas in the case of Snowflake, the number of nodes someone can scale up or scale down to are the powers of two. So if a user needs 70 CPUs, well, their choice is either 64 or 128. So by providing this flexibly with MySQL HeatWave, customers get a custom fit. So they can get a cluster which is optimized for their specific portal. So that's the first thing, flexibility of scaling up or down to any number of nodes. The second thing is that after the operation is completed, the system is fully balanced, meaning the data across the various nodes is fully balanced. That is not the case with many solutions. So for instance, in the case of Redshift, after the resize operation is done, the user is expected to manually balance the data, which can be very cumbersome. And the third aspect is that while the resize operation is going on, the HeatWave cluster is completely available for queries, for DMLS, for loading more data. That is, again, not the case with Redshift. Redshift, suppose the operation takes 10 to 15 minutes, during that window of time, the system is not available for writes, and for a big part of that chunk of time, the system is not even available for queries, which is very limiting. So the advantages we have are fully flexible, the system is in a balanced state, and the system is completely available for the entire duration operation. >> Yeah, I guess you got that hypergranularity, which, you know, sometimes they say, "Well, t-shirt sizes are good enough," but then I think of myself, some t-shirts fit me better than others, so. Okay, I saw on the announcement that you have this lower price point for customers. How did you actually achieve this? Could you give us some details around that please? >> Sure, so there are two things for announcing this service, which lower the cost for the customers. The first thing is that we have doubled the amount of data that can be processed by a HeatWave node. So if we have doubled the amount of data, which can be a process by a node, the cluster size which is required by customers reduces to half, and that's why the cost drops to half. The way we have managed to do this is by two things. One is support for Bloom filters, which reduces the amount of intermediate memory. And second is we compress the base data. So these are the two techniques we have used to process more data per node. The second way by which we are lowering the cost for the customers is by supporting pause and resume of HeatWave. And many times you find customers of like HeatWave and other services that they want to run some other queries or some other workloads for some duration of time, but then they don't need the cluster for a few hours. Now with the support for pause and resume, customers can pause the cluster and the HeatWave cluster instantaneously stops. And when they resume, not only do we fetch the data, in a very, like, you know, a quick pace from the object store, but we also preserve all the statistics, which are used by Autopilot. So both the data and the metadata are fetched, extremely fast from the object store. So with these two capabilities we feel that it'll drive down the cost to our customers even more. >> Got it, thank you. Okay, I promised I was going to get to the benchmarks. Let's have it. How do you compare with others but specifically cloud databases? I mean, and how do we know these benchmarks are real? My friends at EMC, they were back in the day, they were brilliant at doing benchmarks. They would produce these beautiful PowerPoints charts, but it was kind of opaque, but what do you say to that? >> Right, so there are multiple things I would say. The first thing is that this time we have published two benchmarks, one is for machine learning and other is for SQL analytics. All the benchmarks, including the scripts which we have used are available on GitHub. So we have full transparency, and we invite and encourage customers or other service providers to download the scripts, to download the benchmarks and see if they get any different results, right. So what we are seeing, we have published it for other people to try and validate. That's the first part. Now for machine learning, there hasn't been a precedence for enterprise benchmarks so we talk about aiding open data sets and we have published benchmarks for those, right? So both for classification, as well as for aggression, we have run the training times, and that's where we find that HeatWave MLS is 25 times faster than RedShift ML at one percent of the cost. So fully transparent, available. For SQL analytics, in the past we have shown comparisons with TPC-H. So we would show TPC-H across various databases, across various data sizes. This time we decided to use TPC-DS. the advantage of TPC-DS over TPC-H is that it has more number of queries, the queries are more complex, the schema is more complex, and there is a lot more data skew. So it represents a different class of workloads, and which is very interesting. So these are queries derived from the TPC-DS benchmark. So the numbers we have are published this time are for 10 terabyte TPC-DS, and we are comparing with all the four majors services, Redshift, Snowflake, Google BigQuery, Azure Synapse. And in all the cases, HeatWave is significantly faster and significantly lower priced. Now one of the things I want to point out is that when we are doing the cost comparison with other vendors, we are being overly fair. For instance, the cost of HeatWave includes the cost of both the MySQL node as well as the HeatWave node, and with this setup, customers can run transaction processing analytics as well as machine learning. So the price captures all of it. Whereas with the other vendors, the comparison is only for the analytic queries, right? So if customers wanted to run RDP, you would need to add the cost of that database. Or if customers wanted to run machine learning, you would need to add the cost of that service. Furthermore, with the case of HeatWave, we are quoting pay as you go price, whereas for other vendors like, you know, RedShift, and like, you know, where applicable, we are quoting one year, fully paid upfront cost rate. So it's like, you know, very fair comparison. So in terms of the numbers though, price performance for TPC-DS, we are about 4.8 times better price performance compared to RedShift We are 14.4 times better price performance compared to Snowflake, 13 times better than Google BigQuery, and 15 times better than Synapse. So across the board, we are significantly faster and significantly lower price. And as I said, all of these scripts are available in GitHub for people to drive for themselves. >> Okay, all right, I get it. So I think what you're saying is, you could have said this is what it's going to cost for you to do both analytics and transaction processing on a competitive platform versus what it takes to do that on Oracle MySQL HeatWave, but you're not doing that. You're saying, let's take them head on in their sweet spot of analytics, or OLTP separately and you're saying you still beat them. Okay, so you got this one database service in your cloud that supports transactions and analytics and machine learning. How much do you estimate your saving companies with this integrated approach versus the alternative of kind of what I called upfront, the right tool for the right job, and admittedly having to ETL tools. How can you quantify that? >> Right, so, okay. The numbers I call it, right, at the end of the day in a cloud service price performance is the metric which gives a sense as to how much the customers are going to save. So for instance, for like a TPC-DS workload, if we are 14 times better price performance than Snowflake, it means that our cost is going to be 1/14th for what customers would pay for Snowflake. Now, in addition, in other costs, in terms of migrating the data, having to manage two different databases, having to pay for other service for like, you know, machine learning, that's all extra and that depends upon what tools customers are using or what other services they're using for transaction processing or for machine learning. But these numbers themselves, right, like they're very, very compelling. If we are 1/5th the cost of Redshift, right, or 1/14th of Snowflake, these numbers, like, themselves are very, very compelling. And that's the reason we are seeing so many of these migrations from these databases to MySQL HeatWave. >> Okay, great, thank you. Our last question, in the Q3 earnings call for fiscal 22, Larry Ellison said that "MySQL HeatWave is coming soon on AWS," and that caught a lot of people's attention. That's not like Oracle. I mean, people might say maybe that's an indication that you're not having success moving customers to OCI. So you got to go to other clouds, which by the way I applaud, but any comments on that? >> Yep, this is very much like Oracle. So if you look at one of the big reasons for success of the Oracle database and why Oracle database is the most popular database is because Oracle database runs on all the platforms, and that has been the case from day one. So very akin to that, the idea is that there's a lot of value in MySQL HeatWave, and we want to make sure that we can offer same value to the customers of MySQL running on any cloud, whether it's OCI, whether it's the AWS, or any other cloud. So this shows how confident we are in our offering, and we believe that in other clouds as well, customers will find significant advantage by having a single database, which is much faster and much lower price then what alternatives they currently have. So this shows how confident we are about our products and services. >> Well, that's great, I mean, obviously for you, you're in MySQL group. You love that, right? The more places you can run, the better it is for you, of course, and your customers. Okay, Nipun, we got to leave it there. As always it's great to have you on theCUBE, really appreciate your time. Thanks for coming on and sharing the new innovations. Congratulations on all the progress you're making here. You're doing a great job. >> Thank you, Dave, and thank you for the opportunity. >> All right, and thank you for watching this CUBE conversation with Dave Vellante for theCUBE, your leader in enterprise tech coverage. We'll see you next time. (upbeat music)

(bright music) >> Surprising many people, including myself, Oracle last year began investing pretty heavily in the MySQL space. Now those investments continue today. Let me give you a brief history. Last December, Oracle made its first HeatWave announcement. Where converged OLTP and OLAP together in a single MySQL database. Now, what wasn't surprising was the approach Oracle took. They leveraged hardware to improve the performance and lower the cost. You see when Oracle acquired Sun more than a decade ago, rather than rely on loosely coupled partnerships with hardware vendors to speed up its databases. Oracle set out on a path to tightly integrate hardware and software innovations using its own in-house engineering. So with his first, MySQL HeatWave announcement, Oracle leaned heavily on developing software on top of an in-memory database technology to create an embedded OLAP capability that eliminates the need for ETL and data from a transaction system into a separate analytics database. Now in doing so, Oracle is taking a similar approach with its MySQL today, as it does for its, or back then, whereas it does for its mainstream Oracle database. And today extends that. And what I mean by that is it's converging capabilities in a single platform. So the argument is this simplifies and accelerates analytics that lowers the costs and allows analytics, things like analytics to be run on data that is more fresh. Now, as many of you know, this is a different strategy than how, for example, an AWS approaches database where it creates purpose-built database services, targeted at specific workloads. These are philosophical design decisions made for a variety of reasons, but it's very clear which direction Oracle is headed in. Today, Oracle continues its HeatWave announcement cadence with a focus on increased automation as well. The company is continuing the trend of using clustering technology to scale out for both performance and capacity. And again, that theme of marrying hardware with software Oracle is also making announcements that focus on security. Hello everyone and welcome to this video exclusive. This is Dave Vellante. We're going to dig into these capabilities, Nipun Agarwal here. He's VP of MySQL HeatWave and advanced development in Oracle. Nipun has been leading the MySQL and HeatWave development effort for nearly a decade. He's got 180 patents to his name about half of which are associated with HeatWave. Nipun, welcome back to the show. Great to have you. >> Thank you, Dave. >> So before we get into the new news, if we could, maybe you could give us all a quick overview of HeatWave again, and what problems you originally set out to solve with it? >> Sure. So HeatWave is a in-memory query accelerator for MySQL. Now, as most people are aware, MySQL was originally designed and optimized for transactional processing. So when customers had the need to run analytics, they would need to extract data from the, MySQL database into another database and run analytics. With MySQL HeatWave, customers get a single database, which can be used both for transactional processing and for analytics. There's no need to move the data from one database to another database and all existing tools and applications, which are compatible with MySQL, continue to work as is. So in-memory query accelerator for MySQL and this is significantly faster than any version of MySQL database. And also it's much faster than specialized databases for analytics. >> Yeah, we're going to talk about that. And so obviously when you made the announcement last December, you had, I'm sure, a core group of, of early customers and beta customers, but then you opened it up to the world. So what was the reaction once you expose that to customers? >> The reaction has been very positive, Dave. So initially we're thinking that they're going to be a lot of customers who are on premise users of MySQL, who are going to migrate to the service. And surely that was the case. But the part which was very interesting and surprising is that we see many customers who are migrating from other cloud vendors or migrating from other cloud services to MySQL HeatWave. And most notably the biggest number of migrations we are seeing are from AWS Aurora and AWS RDS. >> Interesting. Okay. I wonder if you've got other feedback you're obviously responding in a pretty, pretty fast cadence here, you know, seven, eight month cadence. What are the feedback that you get, were there gaps that customers wanted you to to close? >> Sure. Yes. So as customers starting moving in to HeatWave they found that HeatWave is much faster, much cheaper. And when it's so much faster, they told us that there are some classes of queries, which could just not run earlier, which they can now with HeatWave. So it makes the applications richer because they can write new classes of queries with which they could not in the past. But in terms of the feedback or enhancement requests we got, I would say they will categorize the number one was automation. There've been customers move their database from on-premise to the cloud. They expect more automation. So that was the number one thing. The second thing was people wanted the ability to run analytics on larger sizes of data with MySQL HeatWave because they like what they saw and they wanted us to increase the data size limit, which can be processed by HeatWave. Third one was they wanted more classes of queries to be accessed with HeatWave. Initially, when we went out, HeatWave was designed to be an accelerator for analytic queries but more and more customers started seeing the benefit of beyond just analytics. More towards mixed workloads. So that was a third request. And then finally they wanted us to scale to a larger cluster size. And that's what we have done over the last several months that incorporating this feedback, which you've gotten from customers. >> So you're addressing those, those, those gaps. And thank you for sharing that with us. I got the press release here. I wonder if we could kind of go through these. Let's start with AutoPilot, you know, what's, what's that all about? What's different about AutoPilot? >> That's right. So MySQL AutoPilot provides machine learning based automation. So the first difference is that not only is it automating things, where and as a cloud provider as a service provider, we feel there are a lot of opportunities for us to automate, but the big difference about the approach we've taken with MySQL AutoPilot is that it's all driven based on the data and the queries. It's machine learning based automation. That's the first aspect. The second thing is this is all done natively in the server, right? So we are enhancing the, MySQL engine. We're enhancing the HeatWave engine and that's where all the logic and all the processing resides. In order to do this, we have had to collect new kinds of data. So for instance, in the past, people would collect statistics, which are based on just the data. Now we also collect statistics based on queries, for instance, what is the compilation time? What is the execution time? And we have augmented this with new machine learning models. And finally we have made a lot of innovations, a lot of inventions in the process where we collect data in a smart way. We process data in a smart way and the machine learning models we are talking about, also have a lot of innovation. And that's what gives us an edge over what other vendors may try to do. >> Yeah. I mean, I'm just, again, I'm looking at this meat, this pretty meaty preference, press release. Auto-provisioning, auto parallel load, auto data placement, auto encoding, auto error, auto recovery, auto scheduling, and you know, using a lot of, you know, computer science techniques that are well-known, first in first out, auto change propagation. So really focusing on, on driving that automation for customers. The other piece of it that struck me, and I said this in my intro is, you know, using clustering technology, clustering technology has been around for a long time as, as in-memory database, but applying it and integrating it. My sense is that's really about scale and performance and taking advantage of course, cloud being able to drive that scale instantaneously, but talk about scale a little bit in your philosophy there and why so much emphasis on scalability? >> Right. So what we want to do is to provide the fastest engine for running analytics. And that's why we do the processing in memory. Now, one of the issues with in process, in-memory processing is that the amount of data which you're processing has to reside in memory. So when we went out in the version one, given the footprint of the MySQL customers we spoke to, we thought 12 terabytes of processing at any given point in time, would be adequate. In the very first month, we got feedback that customers wanted us to process larger amounts of data with HeatWave, because they really like what they saw and they wanted us to increase. So if we have increased deployment from 12 terabytes to 32 terabytes and in order to do so, we now have a HeatWave cluster, which can be up to 64 nodes. That's one aspect on the query processing side. Now to answer the question as to why so much of an emphasis it's because this is something which is extremely difficult to do in query processing that as you scale the size of the cluster, the kind of algorithms, the kind of techniques you have to use so that you achieve a very high efficiency with a very large cluster. These are things which are easy to do, because what we want to make sure is that as customers have the need for like, like a processing larger amount of data, one of the big benefits customers get by using a cloud as opposed to on-premise is that they don't need to worry about provisioning gear ahead of time. So if they have more data with the cloud, they should be able to like process pool data easily. But when they process more data, they should expect the same kind of performance. So same kind of efficiency on a larger data size, similar to a smaller data size. And this is something traditionally other database vendors have struggled to provide. So this is a important problem. This is a tough engineering problem. And that's why a lot of emphasis on this to make sure that we provide our customers with very high efficiency of processing as they increase the size of the data. >> You're saying, traditionally, you'll get diminishing returns as you scale. So sort of as, as the volume grows, you're not able to take as much advantage or you're less efficient. And you're saying you've, you've largely solved that problem you're able to use. I mean, people always talk about scaling linearly and I'm always skeptical, but, but you're saying, especially in database, that's been a challenge, but you're, you're saying you've solved that problem largely. >> Right. What I would say is that we have a system which is very efficient, more efficient than like, you know, any of the database we are aware of. So as you said, perfect scaling is hard with you, right? I mean, that's a critical limit of scale factor one. That's very hard to achieve. We are now close to 90% efficiency for n2n queries. This is not for primitives. This is for n2n queries, both on industry benchmarks, as well as real world customer workloads. So this 90% efficiency we believe is very good and higher than what many of the vendors provide. >> Yeah. Right. So you're not, not just primitives the whole end to end cycle. I think 0.89, I think was the number that I, that I saw just to be technically correct there, but that's pretty, pretty good. Now let's talk about the benchmarks. It wouldn't be an Oracle announcement with some, some benchmarks. So you laid out today in your announcement, some, some pretty outstanding performance and price performance numbers, particularly you called out it's, it's. I feel like it's a badge of honor. If, if Oracle calls me out, I feel like I'm doing well. You called out Snowflake and Amazons. So maybe you could go over those benchmark results that we could peel the onion on that a little bit. >> Right. So the first thing to realize is that we want to have benchmarks, which are credible, right? So it's not the case that we have taken some specific unique workloads where HeatWave shines. That's not the case. What we did was we took a industry standard benchmark, which is like, you know, TPC-H. And furthermore, we had a third party, independent firm do this comparison. So let's first compare with Snowflake. On a 10 terabyte TPC-H benchmark HeatWave is seven times faster and one fifth the cost. So with this, it is 35 times better price performance compared to Snowflake, right? So seven times faster than Snowflake and one fifth of the cost. So HeatWave is 35 times better price performance compared to Snowflake. Not just that, Snowflake only does analytics, whereas MySQL HeatWave does both transactional processing and analytics. It's not a specialized database, MySQL HeatWave is a general purpose database, which can do both OLTP analytics whereas Snowflake can only do analytics. So to be 35 times more efficient than a database service, which is specialized only for one case, which is analytics, we think it's pretty good. So that's a comparison with Snowflake. >> So that's, that's you're using, I presume you got to be using list prices for that, obviously. >> That is correct. >> So there's discounts, let's put that into context of maybe 35 X better. You're not going to get that kind of discount. I wouldn't think. >> That is correct. >> Okay. What about Redshift? Aqua for Redshift has gained a lot of momentum in the marketplace. How do you compare against that? >> Right. So we did a comparison with Redshift, Aqua, same benchmark, 10 terabytes, TPC-H. And again, this was done by a third party. Here, HeatWave is six and a half times faster at half the cost. So HeatWave is 13 times better price performance compared to Redshift Aqua. And the same thing for Redshift. It's a specialized database only for analytics. So customers need to have two databases, one for transaction processing, one for analytics, with Redshift. Whereas with MySQL HeatWave, it's a single database for both. And it is so much faster than Redshift. That again, we feel is a pretty remarkable. >> Now, you mentioned earlier, but you're not, you're obviously I presume not, you're not cheating here. You're not including the cost of the transaction processing data store. Right? We're, we're, we're ignoring that for a minute. Ignoring that you got to, you know, move data, ETL, we're just talking about like the like, is that correct? >> Right. This is extremely fair and extremely generous comparison. Not only are we not including the cost of the source OLTP database, the cost in the case of the Redshift I'm talking about is the cost for one year paid full upfront. So this is a best pricing. A customer can get for one year subscription with Redshift. Whereas when I'm talking about HeatWave, this is the pay as you go price. And the third aspect is, this is Redshift when it is completely fully optimized. I don't think anyone else can get much better numbers on Redshift than we have. Right? So fully optimized configuration of Redshift looking at the one year pre-pay cost of Redshift and not including the source database. >> Okay. And then speaking of transaction processing database, what about Aurora? You mentioned earlier that that you're seeing a lot of migration from Aurora. Can you add some color to that? >> Right. And this is a very interesting question in a, it was a very interesting observation for us when we did the launch back in December, we had numbers on four terabytes, TPC-H with Aurora. So if you look at the same benchmark, four terabytes TPC-H HeatWave is 1,400 times faster than Aurora at half the cost, which makes it 2,800 times better price performance compared to Aurora. So very good number. What we have found is that many customers who are running on Aurora started migrating to HeatWave, and these customers had a mix of transaction processing and analytics, and the data sizes are much smaller. Even those customers found that there was a significant improvement in performance and reduction in costs when they migrated to HeatWave. In the announcement today, many of the references are those class of customers. So for that, we decided to choose another benchmark, which is called CH-benchmark on a much smaller data size. And even for that, even for mixed workloads, we find that HeatWave is 18 times faster, provides over a hundred times higher throughput than Aurora at 42% of the cost. So in terms of price performance gain, it is much, much better than Aurora, even for mixed workloads. And then if you consider a pure OLTP assume you have an application, which has only OLTP, which by the way is like, you know, a very uncommon scenario, but even if that were be the case, in that case for pure OLTP only, MySQL HeatWave is at par with Aurora, with respect to performance, but MySQL HeatWave costs 42% of Aurora. So the point is that in the whole spectrum, pure OLTP, mixed workloads or analytics, MySQL HeatWave is going to be fraction of the cost of a Aurora. And depending upon your query workload, your acceleration can be anywhere from 14,000 times to 18 times faster. >> That's interesting. I mean, you've been at this for the better part of a decade, because my sense is that HeatWave is all about OLAP. And that's really where you've put the majority, if not all of the innovation. But you're saying just coming into December's announcement, you were at par with a, in a, in a, in a, in a rare, but, but hypothetical OLTP workload. >> That is correct. >> Yeah. >> Well, you know, I got to push you still on this because a lot of times these benchmarks are a function of the skills of the individuals performing these tests, right? So can I, if I want to run them myself, you know, if you publish these benchmarks, what if a customer wants to replicate these tests and try to see if they can tune up, you know, Redshift better than you guys did? >> Sure. So I'll say a couple of things. One is all the numbers which I'm talking about both for Redshift and Snowflake were done by a third party firm, but all the numbers we is talking about, TPC-H, as well has CH-benchmark. All the scripts are published on GitHub. So anyone is very welcome. In fact, we encourage customers to go and try it for themselves, and they will find that the numbers are absolutely as advertised. In fact, we had couple of companies like in the last several months who went to GitHub, they downloaded our TPCH scripts and they reported that the performance numbers they were seeing with HeatWave were actually better than we had published back in December. And the reason was that since December we had new code, which was running. So our numbers were actually better than advertised. So all the benchmarks are published. They are all available on GitHub. You can go to the HeatWave website on oracle.com and get the link for it. And we welcome anyone to come and try these numbers for themselves. >> All right. Good. Great. Thank you for that. Now you mentioned earlier that you were somewhat surprised, not surprised that you got customers migrating from on-prem databases, but you also saw migration from other clouds. How do you expect the trend with regard to this new announcement? Do you have any sense as to how that's going to go? >> Right. So one of the big changes from December to now is that we have now focused quite a bit on mixed workloads. So in the past, in December, when we first went out, HeatWave was designed primarily for analytics. Now, what we have found is that there's a very large class of customers who have mixed workloads and who also have smaller data sizes. We now have introduced a lot of technology, including things like auto scheduling, definitely improvement in performance, where MySQL HeatWave is a very superior solution compared to Aurora or other databases out there, both in terms of performance as well as price for these mixed workloads and better latency, better throughput, lower costs. So we expect this trend of migration to MySQL HeatWave, to accelerate. So we are seeing customers migrate from Azure. We are seeing customers migrate from GCP and by far the number one migrations we are seeing are from AWS. So I think based on the new features and technologies, we have announced today, this migration is going to accelerate. >> All right, last question. So I said earlier, it's, it's, it seems like you're applying what are generally well understood and proven technologies, like in-memory, you like clustering to solve these problems. And I think about, you know, the, the things that you're doing, and I wonder, you know, I mean, these things have been around for awhile and why has this type of approach not been introduced by others previously? >> Right. Well, so the main thing is it takes time, right? That we designed HeatWave from the ground up for the cloud. And as a part of that, we had to invent new algorithms for distributed query processing for the cloud. We put in the hooks for machine learning processes. We're sealing processing right from the ground up. So this has taken us close to a decade. It's been hundreds of person-years of investment, dozens of patents which have gone in. Another aspect is it takes talent from different areas. So we have like, you know, people working in distributed query processing, we have people who have a lot of like background in machine learning. And then given that we are like the custodians of the MySQL database, we have a very rich set of customers we can reach out to, to get feedback from them as to what are the pinpoints. So culmination of these trends, which we have this talent, the customer base and the time, so we spent almost close to a decade to make this thing work. So that's what it takes. It takes time, patience, patience, and talent. >> A lot of software innovation bringing together, as I said, that hardware and software strategy. Very interesting. Nipun, thanks so much. I appreciate your, your insights and coming on this video exclusive. >> Thank you, Dave. Thank you for the opportunity. >> My pleasure. And thank you for watching everybody. This is Dave Vellante for theCUBE. We'll see you next time. (bright music)

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

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

(upbeat music) >> Welcome back to Supercloud two where we're gathering a number of industry luminaries to discuss the future of cloud services. And we'll be focusing on various real world practitioners today, their challenges, their opportunities with an emphasis on data, self-service infrastructure and how organizations are evolving their data and cloud strategies to prepare for that next era of digital innovation. And we really believe that support for multiple cloud estates is a first step of any Supercloud. And in that regard Oracle surprise some folks with its Azure collaboration the Oracle database and exit database services. And to discuss the challenges of developing a mission critical Supercloud we welcome Juan Loaiza, who's the executive vice president of Mission Critical Database Technologies at Oracle. Juan, you're many time CUBE alums so welcome back to the show. Great to see you. >> Great to see you, and happy to be here with you. >> Yeah, thank you. So a lot of people felt that Oracle was resistant to multicloud strategies and preferred to really have everything run just on the Oracle cloud infrastructure, OCI and maybe that was a misperception maybe you guys were misunderstood or maybe you had to change your heart. Take us through the decision to support multiple cloud platforms >> Now we've supported multiple cloud platforms for many years, so I think that was probably a misperception. Oracle database, we partnered up with Amazon very early on in their cloud when they had kind of the the first cloud out there. And we had Oracle database running on their cloud. We have backup, we have a lot of stuff running. So, yeah, part of the philosophy of Oracle has always been we partner with every platform. We're very open we started with SQL and APIs. As we develop new technologies we push them into the SQL standard. So that's always been part of the ecosystem at Oracle. That's how we think we get an advantage by being more open. I think if we try to create this isolated little world it actually hurts us and hurts customers. So for us it's a win-win to be open across the clouds. >> So Supercloud is this concept that we put forth to describe a platform or some people think it's an architecture if you have an opinion, and I'd love to hear it but it provides a programmatically consistent set of services that hosted on heterogeneous cloud providers. And so we look at the Oracle database service for Azure as fitting within this definition. In your view, is this accurate? >> Yeah, I would broaden it. I'd see a little bit more than that. We just think that services should be available from everywhere, right? So, I mean, it's a little bit like if you go back to the pre-internet world, there was things like AOL and CompuServe and those were kind of islands. And if you were on AOL, you really didn't have access to anything on CompuServe and vice versa. And the cloud world has evolved a little bit like that. And we just think that's the wrong model. They shouldn't these clouds are part of the world and they need to be interconnected like all the rest of the world. It's been a long time with telephones internet, everything, everything's interconnected. Everything should work seamlessly together. So that's how we believe if you're running in one cloud and you're running let's say an application, one cloud you want to use a service from another cloud should be completely simple to do that. It shouldn't be, I can only use what's in AOL or CompuServe or whatever else. It should not be isolated. >> Well, we got a long way to go before that Nirvana exists but one example is the Oracle database service with Azure. So what exactly does that service provide? I'm interested in how consistent the service experience is across clouds. Did you create a purpose-built PaaS layer to achieve this common experience? Or is it off the shelf Terraform? Is there unique value in the PaaS layer? Let's dig into some of those questions. I know I just threw six at you. >> Yeah, I mean, so what this is, is what we're trying to do is very simple. Which is, for example, starting with the Oracle database we want to make that seamless to use from anywhere you're running. Whether it's on-prem, on some other cloud, anywhere else you should be able to seamlessly use the Oracle database and it should look like the internet. There's no friction. There's not a lot of hoops you got to jump just because you're trying to use a database that isn't local to you. So it's pretty straightforward. And in terms of things like Azure, it's not easy to do because all these clouds have a lot of kind of very unique technologies. So what we've done is at Oracle is we've said, "Okay we're going to make Oracle database look exactly like if it was running on Azure." That means we'll use the Azure security systems, the identity management systems, the networking, there's things like monitoring and management. So we'll push all these technologies. For example, when we have monitoring event or we have alerts we'll push those into the Azure console. So as a user, it looks to you exactly as if that Oracle database was running inside Azure. Also, the networking is a big challenge across these clouds. So we've basically made that whole thing seamless. So we create the super high bandwidth network between Azure and Oracle. We make sure that's extremely low latency, under two milliseconds round trip. It's all within the local metro region. So it's very fast, very high bandwidth, very low latency. And we take care establishing the links and making sure that it's secure and all that kind of stuff. So at a high level, it looks to you like the database is--even the look and feel of the screens. It's the Azure colors, it's the Azure buttons it's the Azure layout of the screens so it looks like you're running there and we take care of all the technical details underlying that which there's a lot which has taken a lot of work to make it work seamlessly. >> In the magic of that abstraction. Juan, does it happen at the PaaS layer? Could you take us inside that a little bit? Is there intelligence in there that helps you deal with latency or are there any kind of purpose-built functions for this service? >> You could think of it as... I mean it happens at a lot of different layers. It happens at the identity management layer, it happens at the networking layer, it happens at the database layer, it happens at the monitoring layer, at the management layer. So all those things have been integrated. So it's not one thing that you just go and do. You have to integrate all these different services together. You can access files in Azure from the Oracle database. Again, that's completely seamless. You, it's just like if it was local to our cloud you get your Azure files in your kind of S3 equivalent. So yeah, the, it's not one thing. There's a whole lot of pieces to the ecosystem. And what we've done is we've worked on each piece separately to make sure that it's completely seamless and transparent so you don't have to think about it, it just works. >> So you kind of answered my next question which is one of the technical hurdles. It sounds like the technical hurdles are that integration across the entire stack. That's the sort of architecture that you've built. What was the catalyst for this service? >> Yeah, the catalyst is just fulfilling our vision of an open cloud world. It's really like I said, Oracle, from the very beginning has been believed in open standards. Customers should be able to have choice customers should be able to use whatever they want from wherever they want. And we saw that, you know in the new world of cloud that had broken down everybody had their own authentication system management system, monitoring system networking system, configuration system. And it became very difficult. There was a lot of friction to using services across cloud. So we said, "Well, okay we can fix that." It's work, it's significant amount of work but we know how to do it and let's just go do it and make it easy for customers. >> So given Oracle is really your main focus is on mission critical workloads. You talked about this low latency network, I mean but you still have physical distances, so how are you managing that latency? What's the experience been for customers across Azure and OCI? >> Yeah, so it, it's a good point. I mean, latency can be an issue. So the good thing about clouds is we have a lot of cloud data centers. We have dozens and dozens of cloud data centers around the world. And Azure has dozens and dozens of cloud data centers. And in most cases, they're in the same metro region because there's kind of natural metro regions within each country that you want to put your cloud data centers in. So most of our data centers are actually very close to the Azure data centers. There's the kind of northern Virginia, there's London, there's Tokyo I mean, there's natural places where everybody puts their data centers Seoul et cetera. And so that's the real key. So that allows us to put a very high bandwidth and low latency network. The real problems with latency come when you're trying to go along physical distance. If you're trying to connect, you know across the Pacific or you know across the country or something like that, then you can get in trouble with latency within the same metro region. It's extremely fast. It tends to be around one, you know the highest two millisecond that's roundtrip through all the routers and connections and gateways and everything else. With everything taken into consideration, what we guarantee is it's always less than two millisecond which is a very low latency time. So that tends to not be a problem because it's extremely low latency. >> I was going to ask you less than two milliseconds. So, earlier in the program we had Jack Greenfield who runs architecture for Walmart, and he was explaining what we call their Supercloud, and it's runs across Azure, GCP, and they're on-prem. They have this thing called the triplet model. So my question to you is, are you in situations where you guaranteeing that less than two milliseconds do you have situations where you're bringing, you know Exadata Cloud, a customer on-prem to achieve that? Or is this just across clouds? >> Yeah, in this case, we're talking public cloud data center to public cloud data center. >> Oh okay. >> So add your public cloud data center to Oracle Public Cloud data center. They're in the same metro region. We set up the connections, we do all the technology to make it seamless. And from a customer point of view they don't really see the network. Also, remember that SQL is actually designed to have very low bandwidth and latency requirements. So it is a language. So you don't go to the database and say do this one little thing for me. You send it a SQL statement that can actually access lots of data while in the database. So the real latency requirement of a SQL database is within the database. So I need to access all that data fast. So I need very fast access to storage very fast access across node. That's what exit data gives you. But you send one request and that request can do a huge amount of work and then return one answer. And that's kind of the design point of SQL. So SQL is inherently low bandwidth requirements, it was used back in the eighties when we used to have 10 megabit networks and the the biggest companies in the world ran back then. So right now we're talking over hundred hundreds of gigabits. So it's really not much of a challenge. When you're designed to run on 10 megabit to say, okay I'm going to give you 10,000 times what you were designed for it's really, it's a pretty low hurdle jump. >> What about the deployment models? How do you handle this? Is it a single global instance across clouds or do you sort of instantiate in each you got exudate in Azure and exudates in OCI? What's the deployment model look like? >> It's pretty straightforward. So customer decides where they want to run their application and database. So there's natural places where people go. If you're in Tokyo, you're going to choose the local Tokyo data centers for both, you know Microsoft and Oracle. If you're in London, you're going to do that. If you're in California you're going to choose maybe San Jose, something like that. So a customer just chooses. We both have data centers in that metro region. So they create their service on Azure and then they go to our console which looks just like an Azure console and say all right create me a database. And then we choose the closest Oracle data center which is generally a few miles away, and then it it all gets created. So from a customer point of view, it's very straightforward. >> I'm always in awe about how simple you make things sound. All right what about security? You talked a little bit before about identity access how you sort of abstracting the Azure capabilities away so that you've simplified it for your customers but are there any other specific security things that you need to do? How much did you have to abstract the underlying primitives of Azure or OCI to present that common experience to customers? >> Yeah, so there's really two big things. One is the identity management. Like my name is X on Azure and I have this set of privileges. Oracle has its own identity management system, right? So what we didn't want is that you have to kind of like bridge these things yourself. It's a giant pain to do that. So we actually what we call federate across these identity managements. So you put your credentials into Azure and then they automatically get to use the exact same credentials and identity in the Oracle cloud. So again, you don't have to think about it, it just works. And then the second part is that the whole bridging the network. So within a cloud you generally have virtual network that's private to your company. And so at Oracle, we bridge the private network that you created in, for example, Azure to the private network that we create for you in Oracle. So it is still a private network without you having to do a whole bunch of work. So it's just like if you were in your own data center other people can't get into your network. So it's secured at the network level, it's secured at the identity management, and encryption level. And again we did a lot of work to make that seamless for customers and they don't have to worry about it because we did the work. That's really as simple as it gets. >> That's what's Supercloud's supposed to be all about. Alright, we were talking earlier about sort of the misperception around multicloud, your view of Open I think, which is you run the Oracle database, wherever the customer wants to run it. So you got this database service across OCI and Azure customers today, they run Oracle database in AWS. You got heat wave, MySQL, heat wave that you announced on AWS, Google touts a bare metal offering where you can run Oracle on GCP. Do you see a day when you extend an OCI Azure like situation across multiple clouds? Would that bring benefits to customers or will the world of database generally remain largely fenced with maybe a few exceptions like what you're doing with OCI and Azure? I'm particularly interested in your thoughts on egress fees as maybe one of the reasons that there is a barrier to this happening and why maybe these stove pipes, exist today and in the future. What are your thoughts on that? >> Yeah, we're very open to working with everyone else out there. Like I said, we've always been, big believers in customers should have choice and you should be able to run wherever you want. So that's been kind of a founding principle of Oracle. We have the Azure, we did a partnership with them, we're open to doing other partnerships and you're going to see other things coming down the pipe on the topic of egress. Yeah, the large egress fees, it's pretty obvious what goes on with that. Various vendors like to have large egress fees because they want to keep things kind of locked into their cloud. So it's not a very customer friendly thing to do. And I think everybody recognizes that it's really trying to kind of course or put a lot of friction on moving data out of a particular cloud. And that's not what we do. We have very, very low egress fees. So we don't really do that and we don't think anybody else should do that. But I think customers at the end of the day, will win that battle. They're going to have to go back to their vendor and say, well I have choice in clouds and if you're going to impose these limits on me, maybe I'll make a different choice. So that's ultimately how these things get resolved. >> So do you think other cloud providers are going to take a page out of what you're doing with Azure and provide similar solutions? >> Yeah, well I think customers want, I mean, I've talked to a lot of customers, this is what they want, right? I mean, there's really no doubt no customer wants to be locked into a single ecosystem. There's nobody out there that wants that. And as the competition, when they start seeing an open ecosystem evolving they're going to be like, okay, I'd rather go there than the closed ecosystem, and that's going to put pressure on the closed ecosystems. So that's the nature of competition. That's what ultimately will tip the balance on these things. >> So Juan, even though you have this capability of distributing a workload across multiple clouds as in our Supercloud premise it's still something that's relatively new. It's a big decision that maybe many people might consider somewhat of a risk. So I'm curious who's driving the decisions for your initial customers? What do they want to get out of it? What's the decision point there? >> Yeah, I mean, this is generally driven by customers that want a specific technology in a cloud. I think the risk, I haven't seen a lot of people worry too much about the risk. Everybody involved in this is a very well known, very reputable firm. I mean, Oracle's been around for 40 years. We run most of the world's largest companies. I think customers understand we're not going to build a solution that's going to put their technology and their business at risk. And the same thing with Azure and others. So I don't see customers too worried about this is a risky move because it's really not. And you know, everybody understands networking at the end the day networking works. I mean, how does the internet work? It's a known quantity. It's not like it's some brand new invention. What we're really doing is breaking down the barriers to interconnecting things. Automating 'em, making 'em easy. So there's not a whole lot of risk here for customers. And like I said, every single customer in the world loves an open ecosystem. It's just not a question. If you go to a customer would you rather put your technology or your business to run on a closed ecosystem or an open system? It's kind of not even worth asking a question. It's a no-brainer. >> All right, so we got to go. My last question. What do you think of the term "Supercloud"? You think it'll stick? >> We'll see. There's a lot of terms out there and it's always fun to see which terms stick. It's a cool term. I like it, but the decision makers are actually the public, what sticks and what doesn't. It's very hard to predict. >> Yeah well, it's been a lot of fun having you on, Juan. Really appreciate your time and always good to see you. >> All right, Dave, thanks a lot. It's always fun to talk to you. >> You bet. All right, keep it right there. More Supercloud two content from theCUBE Community Dave Vellante for John Furrier. We'll be right back. (upbeat music)

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

foreign welcome back to re invent 2022 we're wrapping up four days well one evening and three solid days wall-to-wall of cube coverage I'm Dave vellante John furrier's birthday is today he's on a plane to London to go see his nephew get married his his great Sister Janet awesome family the furriers uh spanning the globe and uh and John I know you wanted to be here you're watching in Newark or you were waiting to uh to get in the plane so all the best to you happy birthday one year the Amazon PR people brought a cake out to celebrate John's birthday because he's always here at AWS re invented his birthday so I'm really pleased to have two really special guests uh former Cube host Cube Alum great wikibon contributor Stu miniman now with red hat still good to see you again great to be here Dave yeah I was here for that cake uh the twitterverse uh was uh really helping to celebrate John's birthday today and uh you know always great to be here with you and then with this you know Awesome event this week and friend of the cube of many time Cube often Cube contributor as here's a cube analyst this week as his own consultancy sarbj johal great to see you thanks for coming on good to see you Dave uh great to see you stu I'm always happy to participate in these discussions and um I enjoy the discussion every time so this is kind of cool because you know usually the last day is a getaway day and this is a getaway day but this place is still packed I mean it's I mean yeah it's definitely lighter you can at least walk and not get slammed but I subjit I'm going to start with you I I wanted to have you as the the tail end here because cause you participated in the analyst sessions you've been watching this event from from the first moment and now you've got four days of the Kool-Aid injection but you're also talking to customers developers Partners the ecosystem where do you want to go what's your big takeaways I think big takeaways that Amazon sort of innovation machine is chugging along they are I was listening to some of the accessions and when I was back to my room at nine so they're filling the holes in some areas but in some areas they're moving forward there's a lot to fix still it doesn't seem like that it seems like we are done with the cloud or The Innovation is done now we are building at the millisecond level so where do you go next there's a lot of room to grow on the storage side on the network side uh the improvements we need and and also making sure that the software which is you know which fits the hardware like there's a specialized software um sorry specialized hardware for certain software you know so there was a lot of talk around that and I attended some of those sessions where I asked the questions around like we have a specialized database for each kind of workload specialized processes processors for each kind of workload yeah the graviton section and actually the the one interesting before I forget that the arbitration was I asked that like why there are so many so many databases and IRS for the egress costs and all that stuff can you are you guys thinking about reducing that you know um the answer was no egress cost is not a big big sort of uh um show stopper for many of the customers but but the from all that sort of little discussion with with the folks sitting who build these products over there was that the plethora of choice is given to the customers to to make them feel that there's no vendor lock-in so if you are using some open source you know um soft software it can be on the you know platform side or can be database side you have database site you have that option at AWS so this is a lot there because I always thought that that AWS is the mother of all lock-ins but it's got an ecosystem and we're going to talk about exactly we'll talk about Stu what's working within AWS when you talk to customers and where are the challenges yeah I I got a comment on open source Dave of course there because I mean look we criticized to Amazon for years about their lack of contribution they've gotten better they're doing more in open source but is Amazon the mother of all lock-ins many times absolutely there's certain people inside Amazon I'm saying you know many of us talk Cloud native they're like well let's do Amazon native which means you're like full stack is things from Amazon and do things the way that we want to do things and you know I talk to a lot of customers they use more than one Cloud Dave and therefore certain things absolutely I want to Leverage The Innovation that Amazon has brought I do think we're past building all the main building blocks in many ways we are like in day two yes Amazon is fanatically customer focused and will always stay that way but you know there wasn't anything that jumped out at me last year or this year that was like Wow new category whole new way of thinking about something we're in a vocals last year Dave said you know we have over 200 services and if we listen to you the customer we'd have over two thousand his session this week actually got some great buzz from my friends in the serverless ecosystem they love some of the things tying together we're using data the next flywheel that we're going to see for the next 10 years Amazon's at the center of the cloud ecosystem in the IT world so you know there's a lot of good things here and to your point Dave the ecosystem one of the things I always look at is you know was there a booth that they're all going to be crying in their beer after Amazon made an announcement there was not a tech vendor that I saw this week that was like oh gosh there was an announcement and all of a sudden our business is gone where I did hear some rumbling is Amazon might be the next GSI to really move forward and we've seen all the gsis pushing really deep into supporting Cloud bringing workloads to the cloud and there's a little bit of rumbling as to that balance between what Amazon will do and their uh their go to market so a couple things so I think I think we all agree that a lot of the the announcements here today were taping seams right I call it and as it relates to the mother of all lock-in the reason why I say that it's it's obviously very much a pejorative compare Oracle company you know really well with Amazon's lock-in for Amazon's lock-in is about bringing this ecosystem together so that you actually have Choice Within the the house so you don't have to leave you know there's a there's a lot to eat at the table yeah you look at oracle's ecosystem it's like yeah you know oracle is oracle's ecosystem so so that is how I think they do lock in customers by incenting them not to leave because there's so much Choice Dave I agree with you a thousand I mean I'm here I'm a I'm a good partner of AWS and all of the partners here want to be successful with Amazon and Amazon is open to that it's not our way or get out which Oracle tries how much do you extract from the overall I.T budget you know are you a YouTube where you give the people that help you create a large sum of the money YouTube hasn't been all that profitable Amazon I think is doing a good balance of the ecosystem makes money you know we used to talk Dave about you know how much dollars does VMware make versus there um I think you know Amazon is a much bigger you know VMware 2.0 we used to think talk about all the time that VMware for every dollar spent on VMware licenses 15 or or 12 or 20 were spent in the ecosystem I would think the ratio is even higher here sarbji and an Oracle I would say it's I don't know yeah actually 1 to 0.5 maybe I don't know but I want to pick on your discussion about the the ecosystem the the partner ecosystem is so it's it's robust strong because it's wider I was I was not saying that there's no lock-in with with Amazon right AWS there's lock-in there's lock-in with everything there's lock-in with open source as well but but the point is that they're they're the the circle is so big you don't feel like locked in but they're playing smart as well they're bringing in the software the the platforms from the open source they're picking up those packages and saying we'll bring it in and cater that to you through AWS make it better perform better and also throw in their custom chips on top of that hey this MySQL runs better here so like what do you do I said oh Oracle because it's oracle's product if you will right so they are I think think they're filing or not slenders from their go to market strategy from their engineering and they listen to they're listening to customers like very closely and that has sort of side effects as well listening to customers creates a sprawl of services they have so many services and I criticized them last year for calling everything a new service I said don't call it a new service it's a feature of a existing service sure a lot of features a lot of features this is egress our egress costs a real problem or is it just the the on-prem guys picking at the the scab I mean what do you hear from customers so I mean Dave you know I I look at what Corey Quinn talks about all the time and Amazon charges on that are more expensive than any other Cloud the cloud providers and partly because Amazon is you know probably not a word they'd use they are dominant when it comes to the infrastructure space and therefore they do want to make it a little bit harder to do that they can get away with it um because um yeah you know we've seen some of the cloud providers have special Partnerships where you can actually you know leave and you're not going to be charged and Amazon they've been a little bit more flexible but absolutely I've heard customers say that they wish some good tunning and tongue-in-cheek stuff what else you got we lay it on us so do our players okay this year I think the focus was on the upside it's shifting gradually this was more focused on offside there were less talk of of developers from the main stage from from all sort of quadrants if you will from all Keynotes right so even Werner this morning he had a little bit for he was talking about he he was talking he he's job is to Rally up the builders right yeah so he talks about the go build right AWS pipes I thought was kind of cool then I said like I'm making glue easier I thought that was good you know I know some folks don't use that I I couldn't attend the whole session but but I heard in between right so it is really adopt or die you know I am Cloud Pro for last you know 10 years and I think it's the best model for a technology consumption right um because of economies of scale but more importantly because of division of labor because of specialization because you can't afford to hire the best security people the best you know the arm chip designers uh you can't you know there's one actually I came up with a bumper sticker you guys talked about bumper sticker I came up with that like last couple of weeks The Innovation favorite scale they have scale they have Innovation so that's where the Innovation is and it's it's not there again they actually say the market sets the price Market you as a customer don't set the price the vendor doesn't set the price Market sets the price so if somebody's complaining about their margins or egress and all that I think that's BS um yeah I I have a few more notes on the the partner if you you concur yeah Dave you know with just coming back to some of this commentary about like can Amazon actually enable something we used to call like Community clouds uh your companies like you know Goldman and NASDAQ and the like where Industries will actually be able to share data uh and you know expand the usage and you know Amazon's going to help drive that API economy forward some so it's good to see those things because you know we all know you know all of us are smarter than just any uh single company together so again some of that's open source but some of that is you know I think Amazon is is you know allowing Innovation to thrive I think the word you're looking for is super cloud there well yeah I mean it it's uh Dave if you want to go there with the super cloud because you know there's a metaphor for exactly what you described NASDAQ Goldman Sachs we you know and and you know a number of other companies that are few weeks at the Berkeley Sky Computing paper yeah you know that's a former supercloud Dave Linthicum calls it metacloud I'm not really careful I mean you know I go back to the the challenge we've been you know working at for a decade is the distributed architecture you know if you talk about AI architectures you know what lives in the cloud what lives at the edge where do we train things where do we do inferences um locations should matter a lot less Amazon you know I I didn't hear a lot about it this show but when they came out with like local zones and oh my gosh out you know all the things that Amazon is building to push out to the edge and also enabling that technology and software and the partner ecosystem helps expand that and Pull It in it's no longer you know Dave it was Hotel California all of the data eventually is going to end up in the public cloud and lock it in it's like I don't think that's going to be the case we know that there will be so much data out at the edge Amazon absolutely is super important um there some of those examples we're giving it's not necessarily multi-cloud but there's collaboration happening like in the healthcare world you know universities and hospitals can all share what they're doing uh regardless of you know where they live well Stephen Armstrong in the analyst session did say that you know we're going to talk about multi-cloud we're not going to lead with it necessarily but we are going to actually talk about it and that's different to your points too than in the fullness of time all the data will be in the cloud that's a new narrative but go ahead yeah actually Amazon is a leader in the cloud so if they push the cloud even if they don't say AWS or Amazon with it they benefit from it right and and the narrative is that way there's the proof is there right so again Innovation favorite scale there are chips which are being made for high scale their software being tweaked for high scale you as a Bank of America or for the Chrysler as a typical Enterprise you cannot afford to do those things in-house what cloud providers can I'm not saying just AWS Google cloud is there Azure guys are there and few others who are behind them and and you guys are there as well so IBM has IBM by the way congratulations to your red hat I know but IBM won the award um right you know very good partner and yeah but yeah people are dragging their feet people usually do on the change and they are in denial denial they they drag their feet and they came in IBM director feed the cave Den Dell drag their feed the cave in yeah you mean by Dragon vs cloud deniers cloud deniers right so server Huggers I call them but they they actually are sitting in Amazon Cloud Marketplace everybody is buying stuff from there the marketplace is the new model OKAY Amazon created the marketplace for b2c they are leading the marketplace of B2B as well on the technology side and other people are copying it so there are multiple marketplaces now so now actually it's like if you're in in a mobile app development there are two main platforms Android and Apple you first write the application for Apple right then for Android hex same here as a technology provider as and I I and and I actually you put your stuff to AWS first then you go anywhere else yeah they are later yeah the Enterprise app store is what we've wanted for a long time the question is is Amazon alone the Enterprise app store or are they partner of a of a larger portfolio because there's a lot of SAS companies out there uh that that play into yeah what we need well and this is what you're talking about the future but I just want to make a point about the past you talking about dragging their feet because the Cube's been following this and Stu you remember this in 2013 IBM actually you know got in a big fight with with Amazon over the CIA deal you know and it all became public judge wheeler eviscerated you know IBM and it ended up IBM ended up buying you know soft layer and then we know what happened there and it Joe Tucci thought the cloud was Mosey right so it's just amazing to see we have booksellers you know VMware called them books I wasn't not all of them are like talking about how great Partnerships they are it's amazing like you said sub GC and IBM uh with the the GSI you know Partnership of the year but what you guys were just talking about was the future and that's what I wanted to get to is because you know Amazon's been leading the way I I was listening to Werner this morning and that just reminded me of back in the days when we used to listen to IBM educate us give us a master class on system design and decoupled systems and and IO and everything else now Amazon is you know the master educator and it got me thinking how long will that last you know will they go the way of you know the other you know incumbents will they be disrupted or will they you know keep innovating maybe it's going to take 10 or 20 years I don't know yeah I mean Dave you actually you did some research I believe it was a year or so ago yeah but what will stop Amazon and the one thing that worries me a little bit um is the two Pizza teams when you have over 202 Pizza teams the amount of things that each one of those groups needs to take care of was more than any human could take care of people burn out they run out of people how many amazonians only last two or three years and then leave because it is tough I bumped into plenty of friends of mine that have been you know six ten years at Amazon and love it but it is a tough culture and they are driving werner's keynote I thought did look to from a product standpoint you could say tape over some of the seams some of those solutions to bring Beyond just a single product and bring them together and leverage data so there are some signs that they might be able to get past some of those limitations but I still worry structurally culturally there could be some challenges for Amazon to keep the momentum going especially with the global economic impact that we are likely to see in the next year bring us home I think the future side like we could talk about the vendors all day right to serve the community out there I think we should talk about how what's the future of technology consumption from the consumer side so from the supplier side just a quick note I think the only danger AWS has has that that you know Fred's going after them you know too big you know like we will break you up and that can cause some disruption there other than that I think they they have some more steam to go for a few more years at least before we start thinking about like oh this thing is falling apart or anything like that so they have a lot more they have momentum and it's continuing so okay from the I think game is on retail by the way is going to get disrupted before AWS yeah go ahead from the buyer's side I think um the the future of the sort of Technology consumption is based on the paper uh use and they actually are turning all their services to uh they are sort of becoming serverless behind the scenes right all analytics service they had one service left they they did that this year so every service is serverless so that means you pay exactly for the amount you use the compute the iops the the storage so all these three layers of course Network we talked about the egress stuff and that's a problem there because of the network design mainly because Google has a flatter design and they have lower cost so so they are actually squeezing the their their designing this their services in a way that you don't waste any resources as a buyer so for example very simple example when early earlier In This Cloud you will get a VM right in Cloud that's how we started so and you can get 20 use 20 percent of the VM 80 is getting wasted that's not happening now that that has been reduced to the most extent so now your VM grows as you grow the usage and if you go higher than the tier you picked they will charge you otherwise they will not charge you extra so that's why there's still a lot of instances like many different types you have to pick one I think the future is that those instances will go away the the instance will be formed for you on the fly so that is the future serverless all right give us bumper sticker Stu and then Serb G I'll give you my quick one and then we'll wrap yeah so just Dave to play off of sharp G and to wrap it up you actually wrote about it on your preview post for here uh serverless we're talking about how developers think about things um and you know Amazon in many ways you know is the new default server uh you know for the cloud um and containerization fits into the whole serverless Paradigm uh it's the space that I live in uh you know every day here and you know I was happy to see the last few years serverless and containers there's a blurring a line and you know subject we're still going to see VMS for a long time yeah yeah we will see that so give us give us your book Instagram my number six is innovation favorite scale that's my bumper sticker and and Amazon has that but also I I want everybody else to like the viewers to take a look at the the Google Cloud as well as well as IBM with others like maybe you have a better price to Performance there for certain workloads and by the way one vendor cannot do it alone we know that for sure the market is so big there's a lot of room for uh Red Hats of the world and and and Microsoft's the world to innovate so keep an eye on them they we need the competition actually and that's why competition Will Keep Us to a place where Market sets the price one vendor doesn't so the only only danger is if if AWS is a monopoly then I will be worried I think ecosystems are the Hallmark of a great Cloud company and Amazon's got the the biggest and baddest ecosystem and I think the other thing to watch for is Industries building on top of the cloud you mentioned the Goldman Sachs NASDAQ Capital One and Warner media these all these industries are building their own clouds and that's where the real money is going to be made in the latter half of the 2020s all right we're a wrap this is Dave Valente I want to first of all thank thanks to our great sponsors AWS for for having us here this is our 10th year at the cube AMD you know sponsoring as well the the the cube here Accenture sponsor to third set upstairs upstairs on the fifth floor all the ecosystem partners that came on the cube this week and supported our mission for free content our content is always free we try to give more to the community and we we take back so go to thecube.net and you'll see all these videos go to siliconangle com for all the news wikibon.com I publish weekly a breaking analysis series I want to thank our amazing crew here you guys we have probably 30 35 people unbelievable our awesome last session John Walls uh Paul Gillen Lisa Martin Savannah Peterson John Furrier who's on a plane we appreciate Andrew and Leonard in our ear and all of our our crew Palo Alto Boston and across the country thank you so much really appreciate it all right we are a wrap AWS re invent 2022 we'll see you in two weeks we'll see you two weeks at Palo Alto ignite back here in Vegas thanks for watching thecube the leader in Enterprise and emerging Tech coverage [Music]

>>Good afternoon everybody. I'm John Walls and welcome back to our coverage here on the cube of AWS Reinvent 22. We are bringing you another segment with the Global Startup Program, which is part of the AWS Start Showcase, and it's a pleasure to welcome two new guests here to the showcase. First, immediately to my right Han w lre. Good to see you Han. Good to see you. The leader of the Enterprise Solutions Architecture at aws. And on the far right, Rolin Lee, who is the co-founder and CEO of Heim Doll Data. Roland, good to see you. Great >>To be here. >>All right, good. Thanks for joining us. Well first off, for those at home, I may not be familiar with Heim Doll. What do you do? Why are you here? But I'll let you take it from there. >>Well, we're one of the sponsors here at AWS and great to be here. We offer a data access layer in the form of a proxy, and what it does is it provides complete visibility and the capability to enhance the interaction between the application and one's current database. And as a result, you'll, the customer will improve database scale, database security and availability. And all these features don't require any application changes. So that's sort of our marketing pitch, if you will, all these types of features to improve the experience of managing a database without any application >>Changes. And, and where's the cloud come into play then, for you then, where, where did it come into play for you? >>So we started out actually helping out customers on premise, and a lot of enterprise customers are moving over to the cloud, and it was just a natural progression to do that. And so aws, which is a key part of ours, partners with us to help solve customer problems, especially on the database side, as the application being application performance tends to have issues between the interaction between the application database and we're solving that issue. >>Right. Sohan, I mean, Roan just touched on it about OnPrem, right? There's still some kickers and screamers out there that, that don't, haven't bought in or, or they're about to, but you're about to get 'em. I, I'm sure. But talk about that, that conversion or that transition, if you would, from going OnPrem into a hybrid environment or to into the, the bigger cloud environment and and how difficult that is sometimes. Yes. Maybe to get people to, to make that kind of a leap. >>Well, I would say that a lot of customers are wanting to focus more on product innovation experimentation, and also in terms of having to manage servers and patching, you know, it's to take away from that initiative that they're trying to do. So with aws, we provide undifferentiated heavy lifting so that they can focus on product innovation. And one of the areas talking about Heim is that from the database side, we do provide Amazon rds, which is database and also Aurora, to give them that lift so they don't have to worry about patching servers and setting up provisioning servers as well. >>Right. So Roland, can you get the idea across to people very simply, let us take care of the, the hard stuff and, and that will free you up to do your product innovations, to do your experimentations to, to really free up your team, basically to do the fun stuff and, and let us sweat over the, the, the details basically. Right? >>Exactly. Our, our motto is not only why build when, when you can buy. So a lot of it has to do with offering the, the value in terms of price and the features such as it's gonna benefit a team. Large companies like amazon.com, Google, they have huge teams that can build data access layers and proxies. And what we're trying to do here is commercialize those cuz those are built in house and it's not readily available for customers to use. And you'd need some type of interface between the application and the database. And we provide that sort of why build when you can buy. >>Well, I was gonna say why h right? I mean what's your special sauce? Because everybody's got something, obviously a market differentiator that you're bringing into place here. So you started to touch on a little bit there for me, but, but dive a little deeper there. I mean, what, what is it that, that you're bringing to the table with AWS that you think puts you above the crowd? >>Well, lemme give you a use case here. In typical events like let's say Black Friday where there's a surge traffic that can overwhelm the database, the Heim doll data access layer database proxy provides an auto scaling distributed architecture such that it can absorb those surges and traffic and help scale the database while keeping the data fresh and up to date. And so basically traffic based on season time of day, we can, we can adjust automatically and all these types of features that we offer, most notably automated query caching, ReadWrite split for asset compliance don't require any code changes, which typically requires the application developer to make those changes. So we're saving months, maybe years of development and maintenance. >>Yeah, a lot of gray hairs too, right? Yeah, you're, you're solving a lot of problems there. What about database trends in just in general Hunt, if you will. I mean, this is your space, right? I mean, what we're hearing about from Heindel, you know, in terms of solutions they're providing, but what are you seeing just from the macro level in terms of what people are doing and thinking about the database and how it relates to the cloud? Right. >>And some of the things that we're seeing is that we're seeing an explosion of data, relevant data that customers need to be able to consume and also process as well. So with the explosion of data, there's also, we see customers trying to modernize their application as well through microservices, which does change the design patterns of like the applications we call the access data patterns as well. So again, going back to that, a differentiated heavy lifting, we do have something called purpose built databases, right? It's the right tool for the right purpose. And so it depends on what their like rpo, rto their access to data pattern. Is it a base, is it an acid? So we want to be able to provide them the options to build and also innovate. So with that, that's why we have the Amazon rds, the also the, we also have Redshift, we also have Aurora and et cetera. The Rediff is more of the BI side, but usually when you ingest the data, you have some level of processing to get more insight. So with that, that's why customers are moving more of towards the managed service so that they can give that lift and then focusing on that product and innovation. Yeah. >>Have we kind of caught up or are we catching up to this just the tsunami of data to begin with, right? Because I mean, that was it, you know, what, seven, eight years ago when, when that data became kind of, or becoming king and, and reams and reams and reams and all, you know, can't handle it, right? And, and are we now able to manage that process and manage that flow and get the right data into the right hands at the right time? We're doing better with that. >>I would say that it, it definitely has grown in size of the amount of data that we're ingesting. And so with the scalability and agility of the cloud, we're able to, I would say, adapt to the rapid changes and ingestions of the data. So, so that's why we have things like Aurora servers to have that or auto scale so they can do like MySQL or Postgres and then they can still, like what you know, I'm trying to do is basically don't have to co do like any code changes. It would be a data migration. They still use the same underlying database on also mechanisms, but here we're providing them at scale on the cloud. >>Yeah. Our proxies, they must have for all databases. I mean, is that, is that essential these days? >>Well, good question John. I would say yes. And this is often built in house, as I mentioned, for large companies, they do build some type of data access layer or proxy and, or some utilize some orm, some object relational map to do it. And what again, what we're trying to do is offer this, put this out into the market commercially speaking, such that it can be readily used for, for all the customers to use rather than building it from scratch all the time. >>You know what I didn't ask you was Roy, how does AWS come into play for you then? And, and as in the startup mode, the focus that they've had in startups in general, but in you in particular, I mean, talk about that partnership or that relationship and the value that you're extracting from that. >>The ad AWS partnership has been absolutely wonderful. The collaboration, they have one of the best managed service databases. The value that it that adds in terms of the durability, the manageability, what the Heim doll data does is it compliments Amazon rds, Amazon Redshift very well in the sense that we're not replacing the database. What we're doing is we are allowing the customer to get the most out of the managed service database, whether it be Redshift or Aurora Serverless, rds, all without code changes. And or the analogy that I would give John is a car, a race car may be very fast, but it takes a driver to get to those fast speeds. We're the driver, the Hyundai proxy provides that intelligence so that you can get the most out of that database engine. >>And, and Hfi would then touch on, first off AWS and the emphasis that you have put on startups and are obviously, you know, kind of putting your money where your mouth is, right? With, with the way you've encouraged and nurtured that environment. And they would be about Heim doll in general about where you see this going or what you would like to have, where you want to take this in the next say 12 months, 18 months. >>I think it's more of a better together story of how we can basically coil with our partners, right? And, and basically focusing on helping our customers drive that innovation and be collaboration. So as Heim, as a independent service vendor isv, most customers can leverage that through a marketplace where basically it integrates very nicely with aws. So that gives 'em that lift and it goes back to the undifferentiated heavy lifting on the Hein proxy side, if you will, because then you have this proxy in the middle where then it helps them with their SQL performance. And I've seen use cases where customers were, have some legacy system that they may not have time to modernize the application. So they use this as a lift to keep, keep going as they try to modernize. But also I've seen customers who use are trying to use it as a, a way to give that performance lift because they may have a third party software that they cannot change the code by putting this in there that helps optimize their lines of business or whatever that is, and maybe can be online store or whatever. So I would say it was a better together type of story. >>Yeah. Which is, there's gotta be a song in there somewhere. So peek around the corner and if you wanna be headlights here right now in terms of 12, 18 months, I mean, what, you know, what what next to solve, right? You've already taken, you've slayed a few dragons along the way, but there are others I'm sure is it always happens in innovation in this space. Just when you solve a problem you've just dealt or you have to deal with others that pop up as maybe unintended consequences or at least a new challenge. So what would that be in your world right now? What, what do you see, you know, occupying your sleepless nights here for the next year or so? >>Well, for, for HOMEDALE data, it's all about improving database performance and scale. And those workloads change. We have O ltp, we have OLA with artificial intelligence ml. There's different type of traffic profiles and we're focused on improving those data profiles. It could be unstructured structured. Right now we're focused on structured data, which is relational databases, but there's a lot of opportunity to improve the performance of data. >>Well, you're driving the car, you got a good navigator. I think the GPS is working. So keep up the good work and thank you for sharing the time today. Thank you. Thank you, joy. Do appreciate it. All right, you are watching the cube. We continue our coverage here from AWS Reinvent 22, the Cube, of course, the leader in high tech coverage.

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

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 the ascendancy of AWS under the leadership of Andy jassy was marked by a tsunami of data and corresponding cloud services to leverage that data now those Services they mainly came in the form of Primitives I.E basic building blocks that were used by developers to create more sophisticated capabilities AWS in the 2020s being led by CEO Adam solipski will be marked by four high-level Trends in our opinion one A Rush of data that will dwarf anything we've previously seen two a doubling or even tripling down on the basic elements of cloud compute storage database security Etc three a greater emphasis on end-to-end integration of AWS services to simplify and accelerate customer adoption of cloud and four significantly deeper business integration of cloud Beyond it as an underlying element of organizational operations hello and welcome to this week's wikibon Cube insights powered by ETR in this breaking analysis we extract and analyze nuggets from John furrier's annual sit-down with the CEO of AWS we'll share data from ETR and other sources to set the context for the market and competition in cloud and we'll give you our glimpse of what to expect at re invent in 2022. now before we get into the core of our analysis Alibaba has announced earnings they always announced after the big three you know a month later and we've updated our Q3 slash November hyperscale Computing forecast for the year as seen here and we're going to spend a lot of time on this as most of you have seen the bulk of it already but suffice to say alibaba's cloud business is hitting that same macro Trend that we're seeing across the board but a more substantial slowdown than we expected and more substantial than its peers they're facing China headwinds they've been restructuring its Cloud business and it's led to significantly slower growth uh in in the you know low double digits as opposed to where we had it at 15 this puts our year-end estimates for 2022 Revenue at 161 billion still a healthy 34 growth with AWS surpassing 80 billion in 2022 Revenue now on a related note one of the big themes in Cloud that we've been reporting on is how customers are optimizing their Cloud spend it's a technique that they use and when the economy looks a little shaky and here's a graphic that we pulled from aws's website which shows the various pricing plans at a high level as you know they're much more granular than that and more sophisticated but Simplicity we'll just keep it here basically there are four levels first one here is on demand I.E pay by the drink now we're going to jump down to what we've labeled as number two spot instances that's like the right place at the right time I can use that extra capacity in the moment the third is reserved instances or RIS where I pay up front to get a discount and the fourth is sort of optimized savings plans where customers commit to a one or three year term and for a better price now you'll notice we labeled the choices in a different order than AWS presented them on its website and that's because we believe that the order that we chose is the natural progression for customers this started on demand they maybe experiment with spot instances they move to reserve instances when the cloud bill becomes too onerous and if you're large enough you lock in for one or three years okay the interesting thing is the order in which AWS presents them we believe that on-demand accounts for the majority of AWS customer spending now if you think about it those on-demand customers they're also at risk customers yeah sure there's some switching costs like egress and learning curve but many customers they have multiple clouds and they've got experience and so they're kind of already up to a learning curve and if you're not married to AWS with a longer term commitment there's less friction to switch now AWS here presents the most attractive plan from a financial perspective second after on demand and it's also the plan that makes the greatest commitment from a lock-in standpoint now In fairness to AWS it's also true that there is a trend towards subscription-based pricing and we have some data on that this chart is from an ETR drill down survey the end is 300. pay attention to the bars on the right the left side is sort of busy but the pink is subscription and you can see the trend upward the light blue is consumption based or on demand based pricing and you can see there's a steady Trend toward subscription now we'll dig into this in a later episode of Breaking analysis but we'll share with you a little some tidbits with the data that ETR provides you can select which segment is and pass or you can go up the stack Etc but so when you choose is and paths 44 of customers either prefer or are required to use on-demand pricing whereas around 40 percent of customers say they either prefer or are required to use subscription pricing again that's for is so now the further mu you move up the stack the more prominent subscription pricing becomes often with sixty percent or more for the software-based offerings that require or prefer subscription and interestingly cyber security tracks along with software at around 60 percent that that prefer subscription it's likely because as with software you're not shutting down your cyber protection on demand all right let's get into the expectations for reinvent and we're going to start with an observation in data in this 2018 book seeing digital author David michella made the point that whereas most companies apply data on the periphery of their business kind of as an add-on function successful data companies like Google and Amazon and Facebook have placed data at the core of their operations they've operationalized data and they apply machine intelligence to that foundational element why is this the fact is it's not easy to do what the internet Giants have done very very sophisticated engineering and and and cultural discipline and this brings us to reinvent 2022 in the future of cloud machine learning and AI will increasingly be infused into applications we believe the data stack and the application stack are coming together as organizations build data apps and data products data expertise is moving from the domain of Highly specialized individuals to Everyday business people and we are just at the cusp of this trend this will in our view be a massive theme of not only re invent 22 but of cloud in the 2020s the vision of data mesh We Believe jamachtagani's principles will be realized in this decade now what we'd like to do now is share with you a glimpse of the thinking of Adam solipsky from his sit down with John Furrier each year John has a one-on-one conversation with the CEO of AWS AWS he's been doing this for years and the outcome is a better understanding of the directional thinking of the leader of the number one Cloud platform so we're now going to share some direct quotes I'm going to run through them with some commentary and then bring in some ETR data to analyze the market implications here we go this is from solipsky quote I.T in general and data are moving from departments into becoming intrinsic parts of how businesses function okay we're talking here about deeper business integration let's go on to the next one quote in time we'll stop talking about people who have the word analyst we inserted data he meant data data analyst in their title rather will have hundreds of millions of people who analyze data as part of their day-to-day job most of whom will not have the word analyst anywhere in their title we're talking about graphic designers and pizza shop owners and product managers and data scientists as well he threw that in I'm going to come back to that very interesting so he's talking about here about democratizing data operationalizing data next quote customers need to be able to take an end-to-end integrated view of their entire data Journey from ingestion to storage to harmonizing the data to being able to query it doing business Intelligence and human-based Analysis and being able to collaborate and share data and we've been putting together we being Amazon together a broad Suite of tools from database to analytics to business intelligence to help customers with that and this last statement it's true Amazon has a lot of tools and you know they're beginning to become more and more integrated but again under jassy there was not a lot of emphasis on that end-to-end integrated view we believe it's clear from these statements that solipsky's customer interactions are leading him to underscore that the time has come for this capability okay continuing quote if you have data in one place you shouldn't have to move it every time you want to analyze that data couldn't agree more it would be much better if you could leave that data in place avoid all the ETL which has become a nasty three-letter word more and more we're building capabilities where you can query that data in place end quote okay this we see a lot in the marketplace Oracle with mySQL Heatwave the entire Trend toward converge database snowflake [ __ ] extending their platforms into transaction and analytics respectively and so forth a lot of the partners are are doing things as well in that vein let's go into the next quote the other phenomenon is infusing machine learning into all those capabilities yes the comments from the michelleographic come into play here infusing Ai and machine intelligence everywhere next one quote it's not a data Cloud it's not a separate Cloud it's a series of broad but integrated capabilities to help you manage the end-to-end life cycle of your data there you go we AWS are the cloud we're going to come back to that in a moment as well next set of comments around data very interesting here quote data governance is a huge issue really what customers need is to find the right balance of their organization between access to data and control and if you provide too much access then you're nervous that your data is going to end up in places that it shouldn't shouldn't be viewed by people who shouldn't be viewing it and you feel like you lack security around that data and by the way what happens then is people overreact and they lock it down so that almost nobody can see it it's those handcuffs there's data and asset are reliability we've talked about that for years okay very well put by solipsky but this is a gap in our in our view within AWS today and we're we're hoping that they close it at reinvent it's not easy to share data in a safe way within AWS today outside of your organization so we're going to look for that at re invent 2022. now all this leads to the following statement by solipsky quote data clean room is a really interesting area and I think there's a lot of different Industries in which clean rooms are applicable I think that clean rooms are an interesting way of enabling multiple parties to share and collaborate on the data while completely respecting each party's rights and their privacy mandate okay again this is a gap currently within AWS today in our view and we know snowflake is well down this path and databricks with Delta sharing is also on this curve so AWS has to address this and demonstrate this end-to-end data integration and the ability to safely share data in our view now let's bring in some ETR spending data to put some context around these comments with reference points in the form of AWS itself and its competitors and partners here's a chart from ETR that shows Net score or spending momentum on the x-axis an overlap or pervasiveness in the survey um sorry let me go back up the net scores on the y-axis and overlap or pervasiveness in the survey is on the x-axis so spending momentum by pervasiveness okay or should have share within the data set the table that's inserted there with the Reds and the greens that informs us to how the dots are positioned so it's Net score and then the shared ends are how the plots are determined now we've filtered the data on the three big data segments analytics database and machine learning slash Ai and we've only selected one company with fewer than 100 ends in the survey and that's databricks you'll see why in a moment the red dotted line indicates highly elevated customer spend at 40 percent now as usual snowflake outperforms all players on the y-axis with a Net score of 63 percent off the charts all three big U.S cloud players are above that line with Microsoft and AWS dominating the x-axis so very impressive that they have such spending momentum and they're so large and you see a number of other emerging data players like rafana and datadog mongodbs there in the mix and then more established players data players like Splunk and Tableau now you got Cisco who's gonna you know it's a it's a it's a adjacent to their core networking business but they're definitely into you know the analytics business then the really established players in data like Informatica IBM and Oracle all with strong presence but you'll notice in the red from the momentum standpoint now what you're going to see in a moment is we put red highlights around databricks Snowflake and AWS why let's bring that back up and we'll explain so there's no way let's bring that back up Alex if you would there's no way AWS is going to hit the brakes on innovating at the base service level what we call Primitives earlier solipsky told Furrier as much in their sit down that AWS will serve the technical user and data science Community the traditional domain of data bricks and at the same time address the end-to-end integration data sharing and business line requirements that snowflake is positioned to serve now people often ask Snowflake and databricks how will you compete with the likes of AWS and we know the answer focus on data exclusively they have their multi-cloud plays perhaps the more interesting question is how will AWS compete with the likes of Specialists like Snowflake and data bricks and the answer is depicted here in this chart AWS is going to serve both the technical and developer communities and the data science audience and through end-to-end Integrations and future services that simplify the data Journey they're going to serve the business lines as well but the Nuance is in all the other dots in the hundreds or hundreds of thousands that are not shown here and that's the AWS ecosystem you can see AWS has earned the status of the number one Cloud platform that everyone wants to partner with as they say it has over a hundred thousand partners and that ecosystem combined with these capabilities that we're discussing well perhaps behind in areas like data sharing and integrated governance can wildly succeed by offering the capabilities and leveraging its ecosystem now for their part the snowflakes of the world have to stay focused on the mission build the best products possible and develop their own ecosystems to compete and attract the Mind share of both developers and business users and that's why it's so interesting to hear solipski basically say it's not a separate Cloud it's a set of integrated Services well snowflake is in our view building a super cloud on top of AWS Azure and Google when great products meet great sales and marketing good things can happen so this will be really fun to watch what AWS announces in this area at re invent all right one other topic that solipsky talked about was the correlation between serverless and container adoption and you know I don't know if this gets into there certainly their hybrid place maybe it starts to get into their multi-cloud we'll see but we have some data on this so again we're talking about the correlation between serverless and container adoption but before we get into that let's go back to 2017 and listen to what Andy jassy said on the cube about serverless play the clip very very earliest days of AWS Jeff used to say a lot if I were starting Amazon today I'd have built it on top of AWS we didn't have all the capability and all the functionality at that very moment but he knew what was coming and he saw what people were still able to accomplish even with where the services were at that point I think the same thing is true here with Lambda which is I think if Amazon were starting today it's a given they would build it on the cloud and I think we with a lot of the applications that comprise Amazon's consumer business we would build those on on our serverless capabilities now we still have plenty of capabilities and features and functionality we need to add to to Lambda and our various serverless services so that may not be true from the get-go right now but I think if you look at the hundreds of thousands of customers who are building on top of Lambda and lots of real applications you know finra has built a good chunk of their market watch application on top of Lambda and Thompson Reuters has built you know one of their key analytics apps like people are building real serious things on top of Lambda and the pace of iteration you'll see there will increase as well and I really believe that to be true over the next year or two so years ago when Jesse gave a road map that serverless was going to be a key developer platform going forward and so lipsky referenced the correlation between serverless and containers in the Furrier sit down so we wanted to test that within the ETR data set now here's a screen grab of The View across 1300 respondents from the October ETR survey and what we've done here is we've isolated on the cloud computing segment okay so you can see right there cloud computing segment now we've taken the functions from Google AWS Lambda and Microsoft Azure functions all the serverless offerings and we've got Net score on the vertical axis we've got presence in the data set oh by the way 440 by the way is highly elevated remember that and then we've got on the horizontal axis we have the presence in the data center overlap okay that's relative to each other so remember 40 all these guys are above that 40 mark okay so you see that now what we're going to do this is just for serverless and what we're going to do is we're going to turn on containers to see the correlation and see what happens so watch what happens when we click on container boom everything moves to the right you can see all three move to the right Google drops a little bit but all the others now the the filtered end drops as well so you don't have as many people that are aggressively leaning into both but all three move to the right so watch again containers off and then containers on containers off containers on so you can see a really major correlation between containers and serverless okay so to get a better understanding of what that means I call my friend and former Cube co-host Stu miniman what he said was people generally used to think of VMS containers and serverless as distinctly different architectures but the lines are beginning to blur serverless makes things simpler for developers who don't want to worry about underlying infrastructure as solipsky and the data from ETR indicate serverless and containers are coming together but as Stu and I discussed there's a spectrum where on the left you have kind of native Cloud VMS in the middle you got AWS fargate and in the rightmost anchor is Lambda AWS Lambda now traditionally in the cloud if you wanted to use containers developers would have to build a container image they have to select and deploy the ec2 images that they or instances that they wanted to use they have to allocate a certain amount of memory and then fence off the apps in a virtual machine and then run the ec2 instances against the apps and then pay for all those ec2 resources now with AWS fargate you can run containerized apps with less infrastructure management but you still have some you know things that you can you can you can do with the with the infrastructure so with fargate what you do is you'd build the container images then you'd allocate your memory and compute resources then run the app and pay for the resources only when they're used so fargate lets you control the runtime environment while at the same time simplifying the infrastructure management you gotta you don't have to worry about isolating the app and other stuff like choosing server types and patching AWS does all that for you then there's Lambda with Lambda you don't have to worry about any of the underlying server infrastructure you're just running code AS functions so the developer spends their time worrying about the applications and the functions that you're calling the point is there's a movement and we saw in the data towards simplifying the development environment and allowing the cloud vendor AWS in this case to do more of the underlying management now some folks will still want to turn knobs and dials but increasingly we're going to see more higher level service adoption now re invent is always a fire hose of content so let's do a rapid rundown of what to expect we talked about operate optimizing data and the organization we talked about Cloud optimization there'll be a lot of talk on the show floor about best practices and customer sharing data solipsky is leading AWS into the next phase of growth and that means moving beyond I.T transformation into deeper business integration and organizational transformation not just digital transformation organizational transformation so he's leading a multi-vector strategy serving the traditional peeps who want fine-grained access to core services so we'll see continued Innovation compute storage AI Etc and simplification through integration and horizontal apps further up to stack Amazon connect is an example that's often cited now as we've reported many times databricks is moving from its stronghold realm of data science into business intelligence and analytics where snowflake is coming from its data analytics stronghold and moving into the world of data science AWS is going down a path of snowflake meet data bricks with an underlying cloud is and pass layer that puts these three companies on a very interesting trajectory and you can expect AWS to go right after the data sharing opportunity and in doing so it will have to address data governance they go hand in hand okay price performance that is a topic that will never go away and it's something that we haven't mentioned today silicon it's a it's an area we've covered extensively on breaking analysis from Nitro to graviton to the AWS acquisition of Annapurna its secret weapon new special specialized capabilities like inferential and trainium we'd expect something more at re invent maybe new graviton instances David floyer our colleague said he's expecting at some point a complete system on a chip SOC from AWS and maybe an arm-based server to eventually include high-speed cxl connections to devices and memories all to address next-gen applications data intensive applications with low power requirements and lower cost overall now of course every year Swami gives his usual update on machine learning and AI building on Amazon's years of sagemaker innovation perhaps a focus on conversational AI or a better support for vision and maybe better integration across Amazon's portfolio of you know large language models uh neural networks generative AI really infusing AI everywhere of course security always high on the list that reinvent and and Amazon even has reinforce a conference dedicated to it uh to security now here we'd like to see more on supply chain security and perhaps how AWS can help there as well as tooling to make the cio's life easier but the key so far is AWS is much more partner friendly in the security space than say for instance Microsoft traditionally so firms like OCTA and crowdstrike in Palo Alto have plenty of room to play in the AWS ecosystem we'd expect of course to hear something about ESG it's an important topic and hopefully how not only AWS is helping the environment that's important but also how they help customers save money and drive inclusion and diversity again very important topics and finally come back to it reinvent is an ecosystem event it's the Super Bowl of tech events and the ecosystem will be out in full force every tech company on the planet will have a presence and the cube will be featuring many of the partners from the serial floor as well as AWS execs and of course our own independent analysis so you'll definitely want to tune into thecube.net and check out our re invent coverage we start Monday evening and then we go wall to wall through Thursday hopefully my voice will come back we have three sets at the show and our entire team will be there so please reach out or stop by and say hello all right we're going to leave it there for today many thanks to Stu miniman and David floyer for the input to today's episode of course John Furrier for extracting the signal from the noise and a sit down with Adam solipski thanks to Alex Meyerson who was on production and manages the podcast Ken schiffman as well Kristen Martin and Cheryl Knight helped get the word out on social and of course in our newsletters Rob hoef is our editor-in-chief over at siliconangle does some great editing thank thanks to all of you remember all these episodes are available as podcasts wherever you listen you can pop in the headphones go for a walk just search breaking analysis podcast I published each week on wikibon.com at siliconangle.com or you can email me at david.valante at siliconangle.com or DM me at di vallante or please comment on our LinkedIn posts and do check out etr.ai for the best survey data in the Enterprise Tech business this is Dave vellante for the cube insights powered by ETR thanks for watching we'll see it reinvent or we'll see you next time on breaking analysis [Music]

(upbeat intro music) >> Good afternoon, fellow tech nerds. My name is Savannah Peterson, coming to you from theCube's Remote Studio here in Motown, Detroit, Michigan where we are at KubeCon. John, this is our 12th interview of the day. How are you feeling? >> I'm feeling fresh as the first interview. (Savannah laughs) As always. >> That delivery really implied a level of freshness. >> Let's go! No, this is only Day 1. In three days, reinvent. We go hardcore. These are great events. We get so much great content. The conversations are amazing. The guests are awesome. They're technical, they're smart, and they're making the difference in the future. So, this next segment about Scale MySQL should be awesome. >> I am very excited to introduce our next guest who actually has a Twitter handle that I think most people, at least of my gender in this industry would love to have. She is @ATechGirl. So you can go ahead and tweet her and tell her how great this interview is while we're live. Please welcome Deepthi Sigireddi. Thank you so much for being here with us. >> Thank you for having me. >> You're feeding us in. You've got two talks you're giving while we're here. >> Yes, yes. So tomorrow we will be talking about VTR, myself and one of the other maintainers of Vitess and on Friday we have the Vitess Maintainer Talk. All graduated projects get a maintainer talk. >> Wow, so you are like KubeCon VIP celebrity. >> Well, I hope so. >> Well, you're a maintainer and technical lead, also software engineer at the PlanetScale. But talk about the graduation process where that means to the project and the people involved. >> So Vitess graduated in 2019 and there are strict criteria for graduation and you don't just have to meet the minimum, you sort of have to over perform on the graduation criteria. Some of which are like there must be at least two large production deploys and people from those companies have to go in front of the CNCF committee that approves these things and say that, "Yes, this project is critical to our business." >> A lot of peer review, a lot of deployment success. >> Yes. >> Good consistency in the code. >> Deepthi: Community diversity. >> All that. >> All those things. >> Talk about the importance of this project. What is the top story that people should know about around the project? Why it exists, why it's important, why it's relevant, why it's cool. How would you answer that? >> So MySQL is now 30 years old and yet they are still- >> Makes me feel a little sidebar. (Deepthi laughs) Yeah. >> And yet even though there are many other newer databases, it continues to be used at many of the largest internet scale companies. And some of them, for example, Slack, GitHub, Square, they have grown to a level where they could not have if they had tried to do it with Vanilla MySQL that they started with, and the only reason they are where they are is Vitess. So that is I think the number one thing people should know about Vitess. >> And the origination story on notes say "Came from YouTube." >> Yes. So the way Vitess started was that YouTube was having problems with their MySQL deployment and they got tired of dealing with the site being down. So the founders of Vitess decided that they had to do something about it and they started building Vitess which started as a pretty small, relatively code-based with limited features, and over time they built charting and all of the other things that we have today. >> Well, this is exciting Savannah because we've seen this industry. Like with Facebook, when they started, everyone built their own stuff. MySQL was a great- >> Oh gosh, and everyone wanted to build it their way, reinventing the wheel. >> And MySQL was great. And then as it kind of broke when it grew, it got retrofitted. So, it was constantly being scaled up to the point where now you guys, if I get this right, said, "Hey, we're going to work on this. We're going to make it next-gen." So it's kind of like next-gen MySQL. Almost. >> Yes, yes. I would say that's pretty accurate, yeah. So there are still large companies which run their own MySQL and they have scaled it in their own way, but Vitess happens to be an open source way of scaling MySQL that people can adopt without having to build all of their own tooling around it. >> Speaking of that and growing, you just announced a new version today. >> Yes, yes. >> Tell us about that. >> The focus in this version was to make Vitess easier to use and to deploy. So in the past, there was one glaring gap in Vitess which was that Vitess did not automatically detect and repair MySQL level failures. With this release, we've actually closed that gap. And what that means for people using Vitess is that they will actually spend less time dealing with outages manually, or less human intervention, More automated recovery is what it means. The other thing we've released today is a new web UI. Vitess had a very old web UI, ugly, hard to maintain. Nobody liked it. But it was functional, except we couldn't add anything new to it because it was so old. So, the backend functionality kept advancing but the front end was kind of frozen. Now we have a next generation UI to which in upcoming releases we can add more and more functionality. >> So, it's extensible. They add things in. >> Deepthi: Oh yes, of course. Yeah. >> Awesome. What's the biggest thing that you like about the new situation? Is it more contributors are on board the UI? What's the fresh new impact that's happening in the community? What's getting you excited about with the current project? And the UI's great 'cause usability is important. >> Deepthi: Right. >> Scalability is important. >> I think Vitess solved the scalability problem way early and only now we are really grappling with the usability problem. So the hope and the desire is to make Vitess autopilot so that you reduce human intervention to a minimum once you deploy it. Obviously, you have to go through the process of deploying it. But once you've deployed it, it should just run itself. >> Runs at scale. So, the scale's huge? >> Deepthi: Yes. >> How many contributors are involved in the project? Can you give some numbers? Do you have any handy that you can speak to? >> Right. So, CNCF actually tracks these statistics for all the projects and we consolidated some numbers for the last two full calendar years, 2020 and 2021. We had over 400 contributors and 200 plus of them contributed code and the others contributed documentation issues, website changes, and things like that. So that gives- >> How about downloads? Download's good? >> Oh, okay. So we started publishing the current official Vitess Docker Image in 2018. And by October of 2020, we had about 3.8 million downloads. And by August of 2021, we had 5.2 million. And today, we have had over 10 million downloads- >> Wow! >> Of the main image. >> Starting to see a minute of that hockey stick that we all like to see. Seems like you're very clearly a community-first leader and it seems like that's in the PlanetScale and the test's DNA. Is that how the whole company culture views it? Would you say it's community-first business? >> PlanetScale is very much committed to Vitess as an open source project and to serving the Vitess community. So as part of my role at PlanetScale, some of the things I do are helping new contributors whether they are from PlanetScale or from outside PlanetScale. A number of PlanetScale engineers who don't work full-time on Vitess still contribute bug fixes and features to Vitess. We spend a significant amount of our energy helping users in our community Slack. The releases we do are mainly for the benefit of the community and PlanetScale is making those releases because for Planet Scale... Within PlanetScale, we actually do separate releases versus the public ones. >> One of the things that's coming up here at the show is deploying on Kubernetes. How does that look like? Everyone wants ease of use. Are you guys easy to use? >> Yes, yes. So PlanetScale also open sourced a Kubernetes operator for Vitess that people outside PlanetScale are using to run their production deployments of Vitess. Prior to that, there were Vitess users who actually built their own Kubernetes deployments of Vitess and they are still running those, but new users and new adopters of Vitess tend to use the Kubernetes operator that we are publishing. >> And you guys are the managed service for Vitess for the people that that's the business model for PlanetScale. >> Correct. So PlanetScale has a serverless database on demand which is built on Vitess. So if someone's starting something new and they just need a database, you sign up. It takes 30 seconds to get a database. Connect to it and start doing things with it. Versus if you are a large enterprise and you have a huge database deployment, you can migrate to PlanetScale, import all of your existing data, cut over with minimal downtime and then go, and then PlanetScale manages that. >> And why would they do that? What's the use case for that? Save time new development team or refactoring? >> Save time not being able to hire people with the skills to run it in-house. Not wanting to invest engineering resources in what businesses think is not their core competency. They want to focus on their business value. >> So, this database is a service in their whatever they're doing without adding more costs. >> Right. >> And speed. Okay, cool. How's that going? >> It's going well. >> Any feedback from customers in terms of why that there are any benefit statements you seek popping out? What are the big... What's the big aha when they... When people realize what they have here, what's the aha moment for them? Do they go, "Wow, this is awesome. It's so easy. Push a button. Migrate." Or is it... >> All of those. And people have actually seen cost savings when they've migrated from Amazon RDS to PlanetScale and we have testimonials from people who've said that, "It was so easy to use PlanetScale. Why would we try to do it ourselves?" >> It's the best thing a customer could say, right? We're all about being painkillers and solving some sort of problem. I think that that's a great opportunity to let you show off some of your customers. So, who is receiving this benefit? 'Cause I know PlanetScale specifically is for a certain style of business. >> Hmm. We have a list of customers on the website. >> Savannah: I was going to say you have a really- >> John: She's a software engineer. She's not marketing. >> You did sexy. >> You're doing a great job as much as marketing. >> So the reason I am bringing this up is because it's clear this is a solution for companies like Square, SoundCloud, Etsy, Jordan, and other exciting brands. So when you're talking about companies at scale, these companies are very much at scale, which is awesome. >> Yeah. >> What's next? What do you guys see the future for the project? >> I think we talked about that a little bit already. So, usability is a big thing. We did the new UI. It's not complete, right? Because over the last four years we've built more features into the backend which you can't yet access from the UI. So we want to be able for people to use things like online schema changes which is a big feature of Vitess. Doing schema changes without downtime from the UI. So, schema management from the UI. Vitess has something called VReplication which is the core technology that enables charting. And right now you can from the UI monitor your charting status, but you can't actually start charting from the UI. So more of the administrative functions we want to enable from the UI. >> John: Awesome. >> Last question. What are you personally most excited about this week being here with our wonderful community? >> I always enjoy being at KubeCon. This is my fifth or sixth in-person and I've done a couple of virtual ones. >> Savannah: Awesome. >> Because of the energy, because you get to meet people in person whom previously you've only met in Slack or maybe in a monthly community Zoom calls. We always have people come to our project booth. We have a project booth here for Vitess. People come to the company booth. PlanetScale has a booth. People come to our talks, ask questions. We end up having design discussions, architecture discussions. We get feedback on what is important to the people who show up here. That always informs what we do with the project in future releases. >> Perfect answer. I already mentioned that you can get a hold and in touch with Deepthi through her wonderful Twitter handle. Is there any other website or anything you want to shout out here before I do our close? >> vitess.io. V-I-T-E-S-S dot I-O is the Vitess website and planetscale.com is the PlanetScale website. >> Deepthi Sigireddi, thank you so much for being on the show with us today. John, thanks for keeping me company as always. >> You're welcome. >> And thank all of you for tuning into theCUBE. We will be here in Detroit, Michigan all week live from KubeCon and we hope to see you there. (gentle upbeat music)

(upbeat music) >> Hello everyone. This is Dave Vellante. We're diving into the deep end with AMD and Oracle on the topic of mySQL HeatWave performance. And we want to explore the important issues around machine learning. As applications become more data intensive and machine intelligence continues to evolve, workloads increasingly are seeing a major shift where data and AI are being infused into applications. And having a database that simplifies the convergence of transaction and analytics data without the need to context, switch and move data out of and into different data stores. And eliminating the need to perform extensive ETL operations is becoming an industry trend that customers are demanding. At the same time, workloads are becoming more automated and intelligent. And to explore these issues further, we're happy to have back in theCUBE Nipun Agarwal, who's the Senior Vice President of mySQL HeatWave and Kumaran Siva, who's the Corporate Vice President Strategic Business Development at AMD. Gents, hello again. Welcome back. >> Hello. Hi Dave. >> Thank you, Dave. >> Okay. Nipun, obviously machine learning has become a must have for analytics offerings. It's integrated into mySQL HeatWave. Why did you take this approach and not the specialized database approach as many competitors do right tool for the right job? >> Right? So, there are a lot of customers of mySQL who have the need to run machine learning on the data which is store in mySQL database. So in the past, customers would need to extract the data out of mySQL and they would take it to a specialized service for running machine learning. Now, the reason we decided to incorporate machine learning inside the database, there are multiple reasons. One, customers don't need to move the data. And if they don't need to move the data, it is more secure because it's protected by the same access controlled mechanisms as rest of the data There is no need for customers to manage multiple services. But in addition to that, when we run the machine learning inside the database customers are able to leverage the same service the same hardware, which has been provisioned for OTP analytics and use machine learning capabilities at no additional charge. So from a customer's perspective, they get the benefits that it is a single database. They don't need to manage multiple services. And it is offered at no additional charge. And then as another aspect, which is kind of hard to learn which is based on the IP, the work we have done it is also significantly faster than what customers would get by having a separate service. >> Just to follow up on that. How are you seeing customers use HeatWaves machine learning capabilities today? How is that evolving? >> Right. So one of the things which, you know customers very often want to do is to train their models based on the data. Now, one of the things is that data in a database or in a transaction database changes quite rapidly. So we have introduced support for auto machine learning as a part of HeatWave ML. And what it does is that it fully automates the process of training. And this is something which is very important to database users, very important to mySQL users that they don't really want to hire or data scientists or specialists for doing training. So that's the first part that training in HeatWave ML is fully automated. Doesn't require the user to provide any like specific parameters, just the source data and the task which they want to train. The second aspect is the training is really fast. So the training is really fast. The benefit is that customers can retrain quite often. They can make sure that the model is up to date with any changes which have been made to their transaction database. And as a result of the models being up to date, the accuracy of the prediction is high. Right? So that's the first aspect, which is training. The second aspect is inference, which customers run once they have the models trained. And the third thing, which is perhaps been the most sought after request from the mySQL customers is the ability to provide explanations. So, HeatWave ML provides explanations for any model which has been generated or trained by HeatWave ML. So these are the three capabilities- training, inference and explanations. And this whole process is completely automated, doesn't require a specialist or a data scientist. >> Yeah, that's nice. I mean, training obviously very popular today. I've said inference I think is going to explode in the coming decade. And then of course, AI explainable AI is a very important issue. Kumaran, what are the relevant capabilities of the AMD chips that are used in OCI to support HeatWave ML? Are they different from say the specs for HeatWave in general? >> So, actually they aren't. And this is one of the key features of this architecture or this implementation that is really exciting. Um, there with HeatWave ML, you're using the same CPU. And by the way, it's not a GPU, it's a CPU for both for all three of the functions that Nipun just talked about- inference, training and explanation all done on CPU. You know, bigger picture with the capabilities we bring here we're really providing a balance, you know between the CPU cores, memory and the networking. And what that allows you to do here is be able to feed the CPU cores appropriately. And within the cores, we have these AVX instruc... extensions in with the Zen 2 and Zen 3 cores. We had AVX 2, and then with the Zen 4 core coming out we're going to have AVX 512. But we were able to with that balance of being able to bring in the data and utilize the high memory bandwidth and then use the computation to its maximum we're able to provide, you know, build pride enough AI processing that we are able to get the job done. And then we're built to build a fit into that larger pipeline that that we build out here with the HeatWave. >> Got it. Nipun you know, you and I every time we have a conversation we've got to talk benchmarks. So you've done machine learning benchmarks with HeatWave. You might even be the first in the industry to publish you know, transparent, open ML benchmarks on GitHub. I mean, I, I wouldn't know for sure but I've not seen that as common. Can you describe the benchmarks and the data sets that you used here? >> Sure. So what we did was we took a bunch of open data sets for two categories of tasks- classification and regression. So we took about a dozen data sets for classification and about six for regression. So to give an example, the kind of data sets we used for classifications like the airlines data set, hex sensors bank, right? So these are open data sets. And what we did was for on these data sets we did a comparison of what would it take to train using HeatWave ML? And then the other service we compared with is that RedShift ML. So, there were two observations. One is that with HeatWave ML, the user does not need to provide any tuning parameters, right? The HeatWave ML using RML fully generates a train model, figures out what are the right algorithms? What are the right features? What are the right hyper parameters and sets, right? So no need for any manual intervention not so the case with Redshift ML. The second thing is the performance, right? So the performance of HeatWave ML aggregate on these 12 data sets for classification and the six data sets on regression. On an average, it is 25 times faster than Redshift ML. And note that Redshift ML in turn involves SageMaker, right? So on an average, HeatWave ML provides 25 times better performance for training. And the other point to note is that there is no need for any human intervention. That's fully automated. But in the case of Redshift ML, many of these data sets did not even complete in the set duration. If you look at price performance, one of the things again I want to highlight is because of the fact that AMD does pretty well in all kinds of workloads. We are able to use the same cluster users and use the same cluster for analytics, for OTP or for machine learning. So there is no additional cost for customers to run HeatWave ML if they have provision HeatWave. But assuming a user is provisioning a HeatWave cluster only to run HeatWave ML, right? That's the case, even in that case the price performance advantage of HeatWave ML over Redshift ML is 97 times, right? So 25 times faster at 1% of the cost compared to Redshift ML And all these scripts and all this information is available on GitHub for customers to try to modify and like, see, like what are the advantages they would get on their workloads? >> Every time I hear these numbers, I shake my head. I mean, they're just so overwhelming. Um, and so we'll see how the competition responds when, and if they respond. So, but thank you for sharing those results. Kumaran, can you elaborate on how the specs that you talked about earlier contribute to HeatWave ML's you know, benchmark results. I'm particularly interested in scalability, you know Typically things degrade as you push the system harder. What are you seeing? >> No, I think, I think it's good. Look, yeah. That's by those numbers, just blow me, blow my head too. That's crazy good performance. So look from, from an AMD perspective, we have really built an architecture. Like if you think about the chiplet architecture to begin with, it is fundamentally, you know, it's kind of scaling by design, right? And, and one of the things that we've done here is been able to work with, with the HeatWave team and heat well ML team, and then been able to, to within within the CPU package itself, be able to scale up to take very efficient use of all of the course. And then of course, work with them on how you go between nodes. So you can have these very large systems that can run ML very, very efficiently. So it's really, you know, building on the building blocks of the chiplet architecture and how scaling happens there. >> Yeah. So it's you're saying it's near linear scaling or essentially. >> So, let Nipun comment on that. >> Yeah. >> Is it... So, how about as cluster sizes grow, Nipun? >> Right. >> What happens there? >> So one of the design points for HeatWave is scale out architecture, right? So as you said, that as we add more data set or increase the size of the data, or we add the number of nodes to the cluster, we want the performance to scale. So we show that we have near linear scale factor, or nearly near scale scalability for SQL workloads in the case of HeatWave ML, as well. As users add more nodes to the cluster so the size of the cluster the performance of HeatWave ML improves. So I was giving you this example that HeatWave ML is 25 times faster compared to Redshift ML. Well, that was on a cluster size of two. If you increase the cluster size of HeatWave ML to a larger number. But I think the number is 16. The performance advantage over Redshift ML increases from 25 times faster to 45 times faster. So what that means is that on a cluster size of 16 nodes HeatWave ML is 45 times faster for training these again, dozen data sets. So this shows that HeatWave ML skills better than the computation. >> So you're saying adding nodes offsets any management complexity that you would think of as getting in the way. Is that right? >> Right. So one is the management complexity and which is why by features like last customers can scale up or scale down, you know, very easily. The second aspect is, okay What gives us this advantage, right, of scalability? Or how are we able to scale? Now, the techniques which we use for HeatWave ML scalability are a bit different from what we use for SQL processing. So in the case of HeatWave ML, they really like, you know, three, two trade offs which we have to be careful about. One is the accuracy. Because we want to provide better performance for machine learning without compromising on the accuracy. So accuracy would require like more synchronization if you have multiple threads. But if you have too much of synchronization that can slow down the degree of patterns that we get. Right? So we have to strike a fine balance. So what we do is that in HeatWave ML, there are different phases of training, like algorithm selection, feature selection, hyper probability training. Each of these phases is analyzed. And for instance, one of the ways techniques we use is that if you're trying to figure out what's the optimal hyper parameter to be used? We start up with the search space. And then each of the VMs gets a part of the search space. And then we synchronize only when needed, right? So these are some of the techniques which we have developed over the years. And there are actually paper's filed, research publications filed on this. And this is what we do to achieve good scalability. And what that results to the customer is that if they have some amount of training time and they want to make it better they can just provision a larger cluster and they will get better performance. >> Got it. Thank you. Kumaran, when I think of machine learning, machine intelligence, AI, I think GPU but you're not using GPU. So how are you able to get this type of performance or price performance without using GPU's? >> Yeah, definitely. So yeah, that's a good point. And you think about what is going on here and you consider the whole pipeline that Nipun has just described in terms of how you get you know, your training, your algorithms And using the mySQL pieces of it to get to the point where the AI can be effective. In that process what happens is you have to have a lot of memory to transactions. A lot of memory bandwidth comes into play. And then bringing all that data together, feeding the actual complex that does the AI calculations that in itself could be the bottleneck, right? And you can have multiple bottlenecks along the way. And I think what you see in the AMD architecture for epic for this use case is the balance. And the fact that you are able to do the pre-processing, the AI, and then the post-processing all kind of seamlessly together, that has a huge value. And that goes back to what Nipun was saying about using the same infrastructure, gets you the better TCO but it also gets you gets you better performance. And that's because of the fact that you're bringing the data to the computation. So the computation in this case is not strictly the bottleneck. It's really about how you pull together what you need and to do the AI computation. And that is, that's probably a more, you know, it's a common case. And so, you know, you're going to start I think the least start to see this especially for inference applications. But in this case we're doing both inference explanation and training. All using the the CPU in the same OCI infrastructure. >> Interesting. Now Nipun, is the secret sauce for HeatWave ML performance different than what we've discussed before you and I with with HeatWave generally? Is there some, you know, additive engine additive that you're putting in? >> Right? Yes. The secret sauce is indeed different, right? Just the way I was saying that for SQL processing. The reason we get very good performance and price performance is because we have come up with new algorithms which help the SQL process can scale out. Similarly for HeatWave ML, we have come up with new IP, new like algorithms. One example is that we use meta-learn proxy models, right? That's the technique we use for automating the training process, right? So think of this meta-learn proxy models to be like, you know using machine learning for machine learning training. And this is an IP which we developed. And again, we have published the results and the techniques. But having such kind of like techniques is what gives us a better performance. Similarly, another thing which we use is adaptive sampling that you can have a large data set. But we intelligently sample to figure out that how can we train on a small subset without compromising on the accuracy? So, yes, there are many techniques that you have developed specifically for machine learning which is what gives us the better performance, better price performance, and also better scalability. >> What about mySQL autopilot? Is there anything that differs from HeatWave ML that is relevant? >> Okay. Interesting you should ask. So mySQL Autopilot is think of it to be an application using machine learning. So mySQL Autopilot uses machine learning to automate various aspects of the database service. So for instance, if you want to figure out that what's the right partitioning scheme to partition the data in memory? We use machine learning techniques to figure out that what's the right, the best column based on the user's workload to partition the data in memory Or given a workload, if you want to figure out what is the right cluster size to provision? That's something we use mySQL autopilot for. And I want to highlight that we don't aware of any other database service which provides this level of machine learning based automation which customers get with mySQL Autopilot. >> Hmm. Interesting. Okay. Last question for both of you. What are you guys working on next? What can customers expect from this collaboration specifically in this space? Maybe Nipun, you can start and then Kamaran can bring us home. >> Sure. So there are two things we are working on. One is based on the feedback we have gotten from customers, we are going to keep making the machine learning capabilities richer in HeatWave ML. That's one dimension. And the second thing is which Kamaran was alluding to earlier, We are looking at the next generation of like processes coming from AMD. And we will be seeing as to how we can more benefit from these processes whether it's the size of the L3 cache, the memory bandwidth, the network bandwidth, and such or the newer effects. And make sure that we leverage the all the greatness which the new generation of processes will offer. >> It's like an engineering playground. Kumaran, let's give you the final word. >> No, that's great. Now look with the Zen 4 CPU cores, we're also bringing in AVX 512 instruction capability. Now our implementation is a little different. It was in, in Rome and Milan, too where we use a double pump implementation. What that means is, you know, we take two cycles to do these instructions. But the key thing there is we don't lower our speed of the CPU. So there's no noisy neighbor effects. And it's something that OCI and the HeatWave has taken full advantage of. And so like, as we go out in time and we see the Zen 4 core, we can... we see up to 96 CPUs that that's going to work really well. So we're collaborating closely with, with OCI and with the HeatWave team here to make sure that we can take advantage of that. And we're also going to upgrade the memory subsystem to get to 12 channels of DDR 5. So it should be, you know there should be a fairly significant boost in absolute performance. But more important or just as importantly in TCO value for the customers, the end customers who are going to adopt this great service. >> I love their relentless innovation guys. Thanks so much for your time. We're going to have to leave it there. Appreciate it. >> Thank you, David. >> Thank you, David. >> Okay. Thank you for watching this special presentation on theCUBE. Your leader in enterprise and emerging tech coverage.

(upbeat music) >> For those of you who've been following the cloud database space, you know that MySQL HeatWave has been on a technology tear over the last 24 months with Oracle claiming record breaking benchmarks relative to other database platforms. So far, those benchmarks remain industry leading as competitors have chosen not to respond, perhaps because they don't feel the need to, or maybe they don't feel that doing so would serve their interest. Regardless, the HeatWave team at Oracle has been very aggressive about its performance claims, making lots of noise, challenging the competition to respond, publishing their scripts to GitHub. But so far, there are no takers, but customers seem to be picking up on these moves by Oracle and it's likely the performance numbers resonate with them. Now, the other area we want to explore, which we haven't thus far, is the engine behind HeatWave and that is AMD. AMD's epic processors have been the powerhouse on OCI, running MySQL HeatWave since day one. And today we're going to explore how these two technology companies are working together to deliver these performance gains and some compelling TCO metrics. In fact, a recent Wikibon analysis from senior analyst Marc Staimer made some TCO comparisons in OLAP workloads relative to AWS, Snowflake, GCP, and Azure databases, you can find that research on wikibon.com. And with that, let me introduce today's guest, Nipun Agarwal senior vice president of MySQL HeatWave and Kumaran Siva, who's the corporate vice president for strategic business development at AMD. Welcome to theCUBE gentlemen. >> Welcome. Thank you. >> Thank you, Dave. >> Hey Nipun, you and I have talked a lot about this. You've been on theCUBE a number of times talking about MySQL HeatWave. But for viewers who may not have seen those episodes maybe you could give us an overview of HeatWave and how it's different from competitive cloud database offerings. >> Sure. So MySQL HeatWave is a fully managed MySQL database service offering from Oracle. It's a single database, which can be used to run transactional processing, analytics and machine learning workloads. So, in the past, MySQL has been designed and optimized for transaction processing. So customers of MySQL when they had to run, analytics machine learning, would need to extract the data out of MySQL, into some other database or service, to run analytics or machine learning. MySQL HeatWave offers a single database for running all kinds of workloads so customers don't need to extract data into some of the database. In addition to having a single database, MySQL HeatWave is also very performant compared to one up databases and also it is very price competitive. So the advantages are; single database, very performant, and very good price performance. >> Yes. And you've published some pretty impressive price performance numbers against competitors. Maybe you could describe those benchmarks and highlight some of the results, please. >> Sure. So one thing to notice that the performance of any database is going to like vary, the performance advantage is going to vary based on, the size of the data and the specific workloads, so the mileage varies, that's the first thing to know. So what we have done is, we have published multiple benchmarks. So we have benchmarks on PPCH or PPCDS and we have benchmarks on different data sizes because based on the customer's workload, the mileage is going to vary, so we want to give customers a broad range of comparisons so that they can decide for themselves. So in a specific case, where we are running on a 30 terabyte PPCH workload, HeatWave is about 18 times better price performance compared to Redshift. 18 times better compared to Redshift, about 33 times better price performance, compared to Snowflake, and 42 times better price performance compared to Google BigQuery. So, this is on 30 Terabyte PPCH. Now, if the data size is different, or the workload is different, the characteristics may vary slightly but this is just to give a flavor of the kind of performance advantage MySQL HeatWave offers. >> And then my last question before we bring in Kumaran. We've talked about the secret sauce being the tight integration between hardware and software, but would you add anything to that? What is that secret sauce in HeatWave that enables you to achieve these performance results and what does it mean for customers? >> So there are three parts to this. One is HeatWave has been designed with a scale out architecture in mind. So we have invented and implemented new algorithms for skill out query processing for analytics. The second aspect is that HeatWave has been really optimized for cloud, commodity cloud, and that's where AMD comes in. So for instance, many of the partitioning schemes we have for processing HeatWave, we optimize them for the L3 cache of the AMD processor. The thing which is very important to our customers is not just the sheer performance but the price performance, and that's where we have had a very good partnership with AMD because not only does AMD help us provide very good performance, but the price performance, right? And that all these numbers which I was showing, big part of it is because we are running on AMD which provides very good price performance. So that's the second aspect. And the third aspect is, MySQL autopilot, which provides machine learning based automation. So it's really these three things, a combination of new algorithms, design for scale out query processing, optimized for commodity cloud hardware, specifically AMD processors, and third, MySQL auto pilot which gives us this performance advantage. >> Great, thank you. So that's a good segue for AMD and Kumaran. So Kumaran, what is AMD bringing to the table? What are the, like, for instance, relevance specs of the chips that are used in Oracle cloud infrastructure and what makes them unique? >> Yeah, thanks Dave. That's a good question. So, OCI is a great customer of ours. They use what we call the top of stack devices meaning that they have the highest core count and they also are very, very fast cores. So these are currently Zen 3 cores. I think the HeatWave product is right now deployed on Zen 2 but will shortly be also on the Zen 3 core as well. But we provide in the case of OCI 64 cores. So that's the largest devices that we build. What actually happens is, because these large number of CPUs in a single package and therefore increasing the density of the node, you end up with this fantastic TCO equation and the cost per performance, the cost per for deployed services like HeatWave actually ends up being extraordinarily competitive and that's a big part of the contribution that we're bringing in here. >> So Zen 3 is the AMD micro architecture which you introduced, I think in 2017, and it's the basis for EPIC, which is sort of the enterprise grade that you really attacked the enterprise with. Maybe you could elaborate a little bit, double click on how your chips contribute specifically to HeatWave's, price performance results. >> Yeah, absolutely. So in the case of HeatWave, so as Nipun alluded to, we have very large L3 caches, right? So in our very, very top end parts just like the Milan X devices, we can go all the way up to like 768 megabytes of L3 cache. And that gives you just enormous performance and performance gains. And that's part of what we're seeing with HeatWave today and that not that they're currently on the second generation ROM based product, 'cause it's a 7,002 based product line running with the 64 cores. But as time goes on, they'll be adopting the next generation Milan as well. And the other part of it too is, as our chip led architecture has evolved, we know, so from the first generation Naples way back in 2017, we went from having multiple memory domains and a sort of NUMA architecture at the time, today we've really optimized that architecture. We use a common I/O Die that has all of the memory channels attached to it. And what that means is that, these scale out applications like HeatWave, are able to really scale very efficiently as they go from a small domain of CPUs to, for example the entire chip, all 64 cores that scaling, is been a key focus for AMD and being able to design and build architectures that can take advantage of that and then have applications like HeatWave that scale so well on it, has been, a key aim of ours. >> And Gen 3 moving up the Italian countryside. Nipun, you've taken the somewhat unusual step of posting the benchmark parameters, making them public on GitHub. Now, HeatWave is relatively new. So people felt that when Oracle gained ownership of MySQL it would let it wilt on the vine in favor of Oracle database, so you lost some ground and now, you're getting very aggressive with HeatWave. What's the reason for publishing those benchmark parameters on GitHub? >> So, the main reason for us to publish price performance numbers for HeatWave is to communicate to our customers a sense of what are the benefits they're going to get when they use HeatWave. But we want to be very transparent because as I said the performance advantages for the customers may vary, based on the data size, based on the specific workloads. So one of the reasons for us to publish, all these scripts on GitHub is for transparency. So we want customers to take a look at the scripts, know what we have done, and be confident that we stand by the numbers which we are publishing, and they're very welcome, to try these numbers themselves. In fact, we have had customers who have downloaded the scripts from GitHub and run them on our service to kind of validate. The second aspect is in some cases, they may be some deviations from what we are publishing versus what the customer would like to run in the production deployments so it provides an easy way, for customers to take the scripts, modify them in some ways which may suit their real world scenario and run to see what the performance advantages are. So that's the main reason, first, is transparency, so the customers can see what we are doing, because of the comparison, and B, if they want to modify it to suit their needs, and then see what is the performance of HeatWave, they're very welcome to do so. >> So have customers done that? Have they taken the benchmarks? And I mean, if I were a competitor, honestly, I wouldn't get into that food fight because of the impressive performance, but unless I had to, I mean, have customers picked up on that, Nipun? >> Absolutely. In fact, we have had many customers who have benchmarked the performance of MySQL HeatWave, with other services. And the fact that the scripts are available, gives them a very good starting point, and then they've also tweaked those queries in some cases, to see what the Delta would be. And in some cases, customers got back to us saying, hey the performance advantage of HeatWave is actually slightly higher than what was published and what is the reason. And the reason was, when the customers were trying, they were trying on the latest version of the service, and our benchmark results were posted let's say, two months back. So the service had improved in those two to three months and customers actually saw better performance. So yes, absolutely. We have seen customers download the scripts, try them and also modify them to some extent and then do the comparison of HeatWave with other services. >> Interesting. Maybe a question for both of you how is the competition responding to this? They haven't said, "Hey, we're going to come up "with our own benchmarks." Which is very common, you oftentimes see that. Although, for instance, Snowflake hasn't responded to data bricks, so that's not their game, but if the customers are actually, putting a lot of faith in the benchmarks and actually using that for buying decisions, then it's inevitable. But how have you seen the competition respond to the MySQL HeatWave and AMD combo? >> So maybe I can take the first track from the database service standpoint. When customers have more choice, it is invariably advantages for the customer because then the competition is going to react, right? So the way we have seen the reaction is that we do believe, that the other database services are going to take a closer eye to the price performance, right? Because if you're offering such good price performance, the vendors are already looking at it. And, you know, instances where they have offered let's say discount to the customers, to kind of at least like close the gap to some extent. And the second thing would be in terms of the capability. So like one of the things which I should have mentioned even early on, is that not only does MySQL HeatWave on AMD, provide very good price performance, say on like a small cluster, but it's all the way up to a cluster size of 64 nodes, which has about 1000 cores. So the point is, that HeatWave performs very well, both on a small system, as well as a huge scale out. And this is again, one of those things which is a differentiation compared to other services so we expect that even other database services will have to improve their offerings to provide the same good scale factor, which customers are now starting to expectancy, with MySQL HeatWave. >> Kumaran, anything you'd add to that? I mean, you guys are an arms dealer, you love all your OEMs, but at the same time, you've got chip competitors, Silicon competitors. How do you see the competitive-- >> I'd say the broader answer and the big picture for AMD, we're very maniacally focused on our customers, right? And OCI and Oracle are huge and important customers for us, and this particular use cases is extremely interesting both in that it takes advantage, very well of our architecture and it pulls out some of the value that AMD bring. I think from a big picture standpoint, our aim is to execute, to build to bring out generations of CPUs, kind of, you know, do what we say and say, sorry, say what we do and do what we say. And from that point of view, we're hitting, the schedules that we say, and being able to bring out the latest technology and bring it in a TCO value proposition that generationally keeps OCI and HeatWave ahead. That's the crux of our partnership here. >> Yeah, the execution's been obvious for the last several years. Kumaran, staying with you, how would you characterize the collaboration between, the AMD engineers and the HeatWave engineering team? How do you guys work together? >> No, I'd say we're in a very, very deep collaboration. So, there's a few aspects where, we've actually been working together very closely on the code and being able to optimize for both the large L3 cache that AMD has, and so to be able to take advantage of that. And then also, to be able to take advantage of the scaling. So going between, you know, our architecture is chip like based, so we have these, the CPU cores on, we call 'em CCDs and the inter CCD communication, there's opportunities to optimize an application level and that's something we've been engaged with. In the broader engagement, we are going back now for multiple generations with OCI, and there's a lot of input that now, kind of resonates in the product line itself. And so we value this very close collaboration with HeatWave and OCI. >> Yeah, and the cadence, Nip, and you and I have talked about this quite a bit. The cadence has been quite rapid. It's like this constant cycle every couple of months I turn around, is something new on HeatWave. But for question again, for both of you, what new things do you think that organizations, customers, are going to be able to do with MySQL HeatWave if you could look out next 12 to 18 months, is there anything you can share at this time about future collaborations? >> Right, look, 12 to 18 months is a long time. There's going to be a lot of innovation, a lot of new capabilities coming out on in MySQL HeatWave. But even based on what we are currently offering, and the trend we are seeing is that customers are bringing, more classes of workloads. So we started off with OLTP for MySQL, then it went to analytics. Then we increased it to mixed workloads, and now we offer like machine learning as alike. So one is we are seeing, more and more classes of workloads come to MySQL HeatWave. And the second is a scale, that kind of data volumes people are using HeatWave for, to process these mixed workloads, analytics machine learning OLTP, that's increasing. Now, along the way we are making it simpler to use, we are making it more cost effective use. So for instance, last time, when we talked about, we had introduced this real time elasticity and that's something which is a very, very popular feature because customers want the ability to be able to scale out, or scale down very efficiently. That's something we provided. We provided support for compression. So all of these capabilities are making it more efficient for customers to run a larger part of their workloads on MySQL HeatWave, and we will continue to make it richer in the next 12 to 18 months. >> Thank you. Kumaran, anything you'd add to that, we'll give you the last word as we got to wrap it. >> No, absolutely. So, you know, next 12 to 18 months we will have our Zen 4 CPUs out. So this could potentially go into the next generation of the OCI infrastructure. This would be with the Genoa and then Bergamo CPUs taking us to 96 and 128 cores with 12 channels at DDR five. This capability, you know, when applied to an application like HeatWave, you can see that it'll open up another order of magnitude potentially of use cases, right? And we're excited to see what customers can do do with that. It certainly will make, kind of the, this service, and the cloud in general, that this cloud migration, I think even more attractive. So we're pretty excited to see how things evolve in this period of time. >> Yeah, the innovations are coming together. Guys, thanks so much, we got to leave it there really appreciate your time. >> Thank you. >> All right, and thank you for watching this special Cube conversation, this is Dave Vellante, and we'll see you next time. (soft calm music)

>> From The Cube Studios in Palo Alto and Boston, bringing you data driven insights from The Cube at ETR. This is "Breaking Analysis" with Dave Vellante. >> The reverse momentum in tech stocks caused by rising interest rates, less attractive discounted cash flow models, and more tepid forward guidance, can be easily measured by public market valuations. And while there's lots of discussion about the impact on private companies and cash runway and 409A valuations, measuring the performance of non-public companies isn't as easy. IPOs have dried up and public statements by private companies, of course, they accentuate the good and they kind of hide the bad. Real data, unless you're an insider, is hard to find. Hello and welcome to this week's "Wikibon Cube Insights" powered by ETR. In this "Breaking Analysis", we unlock some of the secrets that non-public, emerging tech companies may or may not be sharing. And we do this by introducing you to a capability from ETR that we've not exposed you to over the past couple of years, it's called the Emerging Technologies Survey, and it is packed with sentiment data and performance data based on surveys of more than a thousand CIOs and IT buyers covering more than 400 companies. And we've invited back our colleague, Erik Bradley of ETR to help explain the survey and the data that we're going to cover today. Erik, this survey is something that I've not personally spent much time on, but I'm blown away at the data. It's really unique and detailed. First of all, welcome. Good to see you again. >> Great to see you too, Dave, and I'm really happy to be talking about the ETS or the Emerging Technology Survey. Even our own clients of constituents probably don't spend as much time in here as they should. >> Yeah, because there's so much in the mainstream, but let's pull up a slide to bring out the survey composition. Tell us about the study. How often do you run it? What's the background and the methodology? >> Yeah, you were just spot on the way you were talking about the private tech companies out there. So what we did is we decided to take all the vendors that we track that are not yet public and move 'em over to the ETS. And there isn't a lot of information out there. If you're not in Silicon (indistinct), you're not going to get this stuff. So PitchBook and Tech Crunch are two out there that gives some data on these guys. But what we really wanted to do was go out to our community. We have 6,000, ITDMs in our community. We wanted to ask them, "Are you aware of these companies? And if so, are you allocating any resources to them? Are you planning to evaluate them," and really just kind of figure out what we can do. So this particular survey, as you can see, 1000 plus responses, over 450 vendors that we track. And essentially what we're trying to do here is talk about your evaluation and awareness of these companies and also your utilization. And also if you're not utilizing 'em, then we can also figure out your sales conversion or churn. So this is interesting, not only for the ITDMs themselves to figure out what their peers are evaluating and what they should put in POCs against the big guys when contracts come up. But it's also really interesting for the tech vendors themselves to see how they're performing. >> And you can see 2/3 of the respondents are director level of above. You got 28% is C-suite. There is of course a North America bias, 70, 75% is North America. But these smaller companies, you know, that's when they start doing business. So, okay. We're going to do a couple of things here today. First, we're going to give you the big picture across the sectors that ETR covers within the ETS survey. And then we're going to look at the high and low sentiment for the larger private companies. And then we're going to do the same for the smaller private companies, the ones that don't have as much mindshare. And then I'm going to put those two groups together and we're going to look at two dimensions, actually three dimensions, which companies are being evaluated the most. Second, companies are getting the most usage and adoption of their offerings. And then third, which companies are seeing the highest churn rates, which of course is a silent killer of companies. And then finally, we're going to look at the sentiment and mindshare for two key areas that we like to cover often here on "Breaking Analysis", security and data. And data comprises database, including data warehousing, and then big data analytics is the second part of data. And then machine learning and AI is the third section within data that we're going to look at. Now, one other thing before we get into it, ETR very often will include open source offerings in the mix, even though they're not companies like TensorFlow or Kubernetes, for example. And we'll call that out during this discussion. The reason this is done is for context, because everyone is using open source. It is the heart of innovation and many business models are super glued to an open source offering, like take MariaDB, for example. There's the foundation and then there's with the open source code and then there, of course, the company that sells services around the offering. Okay, so let's first look at the highest and lowest sentiment among these private firms, the ones that have the highest mindshare. So they're naturally going to be somewhat larger. And we do this on two dimensions, sentiment on the vertical axis and mindshare on the horizontal axis and note the open source tool, see Kubernetes, Postgres, Kafka, TensorFlow, Jenkins, Grafana, et cetera. So Erik, please explain what we're looking at here, how it's derived and what the data tells us. >> Certainly, so there is a lot here, so we're going to break it down first of all by explaining just what mindshare and net sentiment is. You explain the axis. We have so many evaluation metrics, but we need to aggregate them into one so that way we can rank against each other. Net sentiment is really the aggregation of all the positive and subtracting out the negative. So the net sentiment is a very quick way of looking at where these companies stand versus their peers in their sectors and sub sectors. Mindshare is basically the awareness of them, which is good for very early stage companies. And you'll see some names on here that are obviously been around for a very long time. And they're clearly be the bigger on the axis on the outside. Kubernetes, for instance, as you mentioned, is open source. This de facto standard for all container orchestration, and it should be that far up into the right, because that's what everyone's using. In fact, the open source leaders are so prevalent in the emerging technology survey that we break them out later in our analysis, 'cause it's really not fair to include them and compare them to the actual companies that are providing the support and the security around that open source technology. But no survey, no analysis, no research would be complete without including these open source tech. So what we're looking at here, if I can just get away from the open source names, we see other things like Databricks and OneTrust . They're repeating as top net sentiment performers here. And then also the design vendors. People don't spend a lot of time on 'em, but Miro and Figma. This is their third survey in a row where they're just dominating that sentiment overall. And Adobe should probably take note of that because they're really coming after them. But Databricks, we all know probably would've been a public company by now if the market hadn't turned, but you can see just how dominant they are in a survey of nothing but private companies. And we'll see that again when we talk about the database later. >> And I'll just add, so you see automation anywhere on there, the big UiPath competitor company that was not able to get to the public markets. They've been trying. Snyk, Peter McKay's company, they've raised a bunch of money, big security player. They're doing some really interesting things in developer security, helping developers secure the data flow, H2O.ai, Dataiku AI company. We saw them at the Snowflake Summit. Redis Labs, Netskope and security. So a lot of names that we know that ultimately we think are probably going to be hitting the public market. Okay, here's the same view for private companies with less mindshare, Erik. Take us through this one. >> On the previous slide too real quickly, I wanted to pull that security scorecard and we'll get back into it. But this is a newcomer, that I couldn't believe how strong their data was, but we'll bring that up in a second. Now, when we go to the ones of lower mindshare, it's interesting to talk about open source, right? Kubernetes was all the way on the top right. Everyone uses containers. Here we see Istio up there. Not everyone is using service mesh as much. And that's why Istio is in the smaller breakout. But still when you talk about net sentiment, it's about the leader, it's the highest one there is. So really interesting to point out. Then we see other names like Collibra in the data side really performing well. And again, as always security, very well represented here. We have Aqua, Wiz, Armis, which is a standout in this survey this time around. They do IoT security. I hadn't even heard of them until I started digging into the data here. And I couldn't believe how well they were doing. And then of course you have AnyScale, which is doing a second best in this and the best name in the survey Hugging Face, which is a machine learning AI tool. Also doing really well on a net sentiment, but they're not as far along on that access of mindshare just yet. So these are again, emerging companies that might not be as well represented in the enterprise as they will be in a couple of years. >> Hugging Face sounds like something you do with your two year old. Like you said, you see high performers, AnyScale do machine learning and you mentioned them. They came out of Berkeley. Collibra Governance, InfluxData is on there. InfluxDB's a time series database. And yeah, of course, Alex, if you bring that back up, you get a big group of red dots, right? That's the bad zone, I guess, which Sisense does vis, Yellowbrick Data is a NPP database. How should we interpret the red dots, Erik? I mean, is it necessarily a bad thing? Could it be misinterpreted? What's your take on that? >> Sure, well, let me just explain the definition of it first from a data science perspective, right? We're a data company first. So the gray dots that you're seeing that aren't named, that's the mean that's the average. So in order for you to be on this chart, you have to be at least one standard deviation above or below that average. So that gray is where we're saying, "Hey, this is where the lump of average comes in. This is where everyone normally stands." So you either have to be an outperformer or an underperformer to even show up in this analysis. So by definition, yes, the red dots are bad. You're at least one standard deviation below the average of your peers. It's not where you want to be. And if you're on the lower left, not only are you not performing well from a utilization or an actual usage rate, but people don't even know who you are. So that's a problem, obviously. And the VCs and the PEs out there that are backing these companies, they're the ones who mostly are interested in this data. >> Yeah. Oh, that's great explanation. Thank you for that. No, nice benchmarking there and yeah, you don't want to be in the red. All right, let's get into the next segment here. Here going to look at evaluation rates, adoption and the all important churn. First new evaluations. Let's bring up that slide. And Erik, take us through this. >> So essentially I just want to explain what evaluation means is that people will cite that they either plan to evaluate the company or they're currently evaluating. So that means we're aware of 'em and we are choosing to do a POC of them. And then we'll see later how that turns into utilization, which is what a company wants to see, awareness, evaluation, and then actually utilizing them. That's sort of the life cycle for these emerging companies. So what we're seeing here, again, with very high evaluation rates. H2O, we mentioned. SecurityScorecard jumped up again. Chargebee, Snyk, Salt Security, Armis. A lot of security names are up here, Aqua, Netskope, which God has been around forever. I still can't believe it's in an Emerging Technology Survey But so many of these names fall in data and security again, which is why we decided to pick those out Dave. And on the lower side, Vena, Acton, those unfortunately took the dubious award of the lowest evaluations in our survey, but I prefer to focus on the positive. So SecurityScorecard, again, real standout in this one, they're in a security assessment space, basically. They'll come in and assess for you how your security hygiene is. And it's an area of a real interest right now amongst our ITDM community. >> Yeah, I mean, I think those, and then Arctic Wolf is up there too. They're doing managed services. You had mentioned Netskope. Yeah, okay. All right, let's look at now adoption. These are the companies whose offerings are being used the most and are above that standard deviation in the green. Take us through this, Erik. >> Sure, yet again, what we're looking at is, okay, we went from awareness, we went to evaluation. Now it's about utilization, which means a survey respondent's going to state "Yes, we evaluated and we plan to utilize it" or "It's already in our enterprise and we're actually allocating further resources to it." Not surprising, again, a lot of open source, the reason why, it's free. So it's really easy to grow your utilization on something that's free. But as you and I both know, as Red Hat proved, there's a lot of money to be made once the open source is adopted, right? You need the governance, you need the security, you need the support wrapped around it. So here we're seeing Kubernetes, Postgres, Apache Kafka, Jenkins, Grafana. These are all open source based names. But if we're looking at names that are non open source, we're going to see Databricks, Automation Anywhere, Rubrik all have the highest mindshare. So these are the names, not surprisingly, all names that probably should have been public by now. Everyone's expecting an IPO imminently. These are the names that have the highest mindshare. If we talk about the highest utilization rates, again, Miro and Figma pop up, and I know they're not household names, but they are just dominant in this survey. These are applications that are meant for design software and, again, they're going after an Autodesk or a CAD or Adobe type of thing. It is just dominant how high the utilization rates are here, which again is something Adobe should be paying attention to. And then you'll see a little bit lower, but also interesting, we see Collibra again, we see Hugging Face again. And these are names that are obviously in the data governance, ML, AI side. So we're seeing a ton of data, a ton of security and Rubrik was interesting in this one, too, high utilization and high mindshare. We know how pervasive they are in the enterprise already. >> Erik, Alex, keep that up for a second, if you would. So yeah, you mentioned Rubrik. Cohesity's not on there. They're sort of the big one. We're going to talk about them in a moment. Puppet is interesting to me because you remember the early days of that sort of space, you had Puppet and Chef and then you had Ansible. Red Hat bought Ansible and then Ansible really took off. So it's interesting to see Puppet on there as well. Okay. So now let's look at the churn because this one is where you don't want to be. It's, of course, all red 'cause churn is bad. Take us through this, Erik. >> Yeah, definitely don't want to be here and I don't love to dwell on the negative. So we won't spend as much time. But to your point, there's one thing I want to point out that think it's important. So you see Rubrik in the same spot, but Rubrik has so many citations in our survey that it actually would make sense that they're both being high utilization and churn just because they're so well represented. They have such a high overall representation in our survey. And the reason I call that out is Cohesity. Cohesity has an extremely high churn rate here about 17% and unlike Rubrik, they were not on the utilization side. So Rubrik is seeing both, Cohesity is not. It's not being utilized, but it's seeing a high churn. So that's the way you can look at this data and say, "Hm." Same thing with Puppet. You noticed that it was on the other slide. It's also on this one. So basically what it means is a lot of people are giving Puppet a shot, but it's starting to churn, which means it's not as sticky as we would like. One that was surprising on here for me was Tanium. It's kind of jumbled in there. It's hard to see in the middle, but Tanium, I was very surprised to see as high of a churn because what I do hear from our end user community is that people that use it, like it. It really kind of spreads into not only vulnerability management, but also that endpoint detection and response side. So I was surprised by that one, mostly to see Tanium in here. Mural, again, was another one of those application design softwares that's seeing a very high churn as well. >> So you're saying if you're in both... Alex, bring that back up if you would. So if you're in both like MariaDB is for example, I think, yeah, they're in both. They're both green in the previous one and red here, that's not as bad. You mentioned Rubrik is going to be in both. Cohesity is a bit of a concern. Cohesity just brought on Sanjay Poonen. So this could be a go to market issue, right? I mean, 'cause Cohesity has got a great product and they got really happy customers. So they're just maybe having to figure out, okay, what's the right ideal customer profile and Sanjay Poonen, I guarantee, is going to have that company cranking. I mean they had been doing very well on the surveys and had fallen off of a bit. The other interesting things wondering the previous survey I saw Cvent, which is an event platform. My only reason I pay attention to that is 'cause we actually have an event platform. We don't sell it separately. We bundle it as part of our offerings. And you see Hopin on here. Hopin raised a billion dollars during the pandemic. And we were like, "Wow, that's going to blow up." And so you see Hopin on the churn and you didn't see 'em in the previous chart, but that's sort of interesting. Like you said, let's not kind of dwell on the negative, but you really don't. You know, churn is a real big concern. Okay, now we're going to drill down into two sectors, security and data. Where data comprises three areas, database and data warehousing, machine learning and AI and big data analytics. So first let's take a look at the security sector. Now this is interesting because not only is it a sector drill down, but also gives an indicator of how much money the firm has raised, which is the size of that bubble. And to tell us if a company is punching above its weight and efficiently using its venture capital. Erik, take us through this slide. Explain the dots, the size of the dots. Set this up please. >> Yeah. So again, the axis is still the same, net sentiment and mindshare, but what we've done this time is we've taken publicly available information on how much capital company is raised and that'll be the size of the circle you see around the name. And then whether it's green or red is basically saying relative to the amount of money they've raised, how are they doing in our data? So when you see a Netskope, which has been around forever, raised a lot of money, that's why you're going to see them more leading towards red, 'cause it's just been around forever and kind of would expect it. Versus a name like SecurityScorecard, which is only raised a little bit of money and it's actually performing just as well, if not better than a name, like a Netskope. OneTrust doing absolutely incredible right now. BeyondTrust. We've seen the issues with Okta, right. So those are two names that play in that space that obviously are probably getting some looks about what's going on right now. Wiz, we've all heard about right? So raised a ton of money. It's doing well on net sentiment, but the mindshare isn't as well as you'd want, which is why you're going to see a little bit of that red versus a name like Aqua, which is doing container and application security. And hasn't raised as much money, but is really neck and neck with a name like Wiz. So that is why on a relative basis, you'll see that more green. As we all know, information security is never going away. But as we'll get to later in the program, Dave, I'm not sure in this current market environment, if people are as willing to do POCs and switch away from their security provider, right. There's a little bit of tepidness out there, a little trepidation. So right now we're seeing overall a slight pause, a slight cooling in overall evaluations on the security side versus historical levels a year ago. >> Now let's stay on here for a second. So a couple things I want to point out. So it's interesting. Now Snyk has raised over, I think $800 million but you can see them, they're high on the vertical and the horizontal, but now compare that to Lacework. It's hard to see, but they're kind of buried in the middle there. That's the biggest dot in this whole thing. I think I'm interpreting this correctly. They've raised over a billion dollars. It's a Mike Speiser company. He was the founding investor in Snowflake. So people watch that very closely, but that's an example of where they're not punching above their weight. They recently had a layoff and they got to fine tune things, but I'm still confident they they're going to do well. 'Cause they're approaching security as a data problem, which is probably people having trouble getting their arms around that. And then again, I see Arctic Wolf. They're not red, they're not green, but they've raised fair amount of money, but it's showing up to the right and decent level there. And a couple of the other ones that you mentioned, Netskope. Yeah, they've raised a lot of money, but they're actually performing where you want. What you don't want is where Lacework is, right. They've got some work to do to really take advantage of the money that they raised last November and prior to that. >> Yeah, if you're seeing that more neutral color, like you're calling out with an Arctic Wolf, like that means relative to their peers, this is where they should be. It's when you're seeing that red on a Lacework where we all know, wow, you raised a ton of money and your mindshare isn't where it should be. Your net sentiment is not where it should be comparatively. And then you see these great standouts, like Salt Security and SecurityScorecard and Abnormal. You know they haven't raised that much money yet, but their net sentiment's higher and their mindshare's doing well. So those basically in a nutshell, if you're a PE or a VC and you see a small green circle, then you're doing well, then it means you made a good investment. >> Some of these guys, I don't know, but you see these small green circles. Those are the ones you want to start digging into and maybe help them catch a wave. Okay, let's get into the data discussion. And again, three areas, database slash data warehousing, big data analytics and ML AI. First, we're going to look at the database sector. So Alex, thank you for bringing that up. Alright, take us through this, Erik. Actually, let me just say Postgres SQL. I got to ask you about this. It shows some funding, but that actually could be a mix of EDB, the company that commercializes Postgres and Postgres the open source database, which is a transaction system and kind of an open source Oracle. You see MariaDB is a database, but open source database. But the companies they've raised over $200 million and they filed an S-4. So Erik looks like this might be a little bit of mashup of companies and open source products. Help us understand this. >> Yeah, it's tough when you start dealing with the open source side and I'll be honest with you, there is a little bit of a mashup here. There are certain names here that are a hundred percent for profit companies. And then there are others that are obviously open source based like Redis is open source, but Redis Labs is the one trying to monetize the support around it. So you're a hundred percent accurate on this slide. I think one of the things here that's important to note though, is just how important open source is to data. If you're going to be going to any of these areas, it's going to be open source based to begin with. And Neo4j is one I want to call out here. It's not one everyone's familiar with, but it's basically geographical charting database, which is a name that we're seeing on a net sentiment side actually really, really high. When you think about it's the third overall net sentiment for a niche database play. It's not as big on the mindshare 'cause it's use cases aren't as often, but third biggest play on net sentiment. I found really interesting on this slide. >> And again, so MariaDB, as I said, they filed an S-4 I think $50 million in revenue, that might even be ARR. So they're not huge, but they're getting there. And by the way, MariaDB, if you don't know, was the company that was formed the day that Oracle bought Sun in which they got MySQL and MariaDB has done a really good job of replacing a lot of MySQL instances. Oracle has responded with MySQL HeatWave, which was kind of the Oracle version of MySQL. So there's some interesting battles going on there. If you think about the LAMP stack, the M in the LAMP stack was MySQL. And so now it's all MariaDB replacing that MySQL for a large part. And then you see again, the red, you know, you got to have some concerns about there. Aerospike's been around for a long time. SingleStore changed their name a couple years ago, last year. Yellowbrick Data, Fire Bolt was kind of going after Snowflake for a while, but yeah, you want to get out of that red zone. So they got some work to do. >> And Dave, real quick for the people that aren't aware, I just want to let them know that we can cut this data with the public company data as well. So we can cross over this with that because some of these names are competing with the larger public company names as well. So we can go ahead and cross reference like a MariaDB with a Mongo, for instance, or of something of that nature. So it's not in this slide, but at another point we can certainly explain on a relative basis how these private names are doing compared to the other ones as well. >> All right, let's take a quick look at analytics. Alex, bring that up if you would. Go ahead, Erik. >> Yeah, I mean, essentially here, I can't see it on my screen, my apologies. I just kind of went to blank on that. So gimme one second to catch up. >> So I could set it up while you're doing that. You got Grafana up and to the right. I mean, this is huge right. >> Got it thank you. I lost my screen there for a second. Yep. Again, open source name Grafana, absolutely up and to the right. But as we know, Grafana Labs is actually picking up a lot of speed based on Grafana, of course. And I think we might actually hear some noise from them coming this year. The names that are actually a little bit more disappointing than I want to call out are names like ThoughtSpot. It's been around forever. Their mindshare of course is second best here but based on the amount of time they've been around and the amount of money they've raised, it's not actually outperforming the way it should be. We're seeing Moogsoft obviously make some waves. That's very high net sentiment for that company. It's, you know, what, third, fourth position overall in this entire area, Another name like Fivetran, Matillion is doing well. Fivetran, even though it's got a high net sentiment, again, it's raised so much money that we would've expected a little bit more at this point. I know you know this space extremely well, but basically what we're looking at here and to the bottom left, you're going to see some names with a lot of red, large circles that really just aren't performing that well. InfluxData, however, second highest net sentiment. And it's really pretty early on in this stage and the feedback we're getting on this name is the use cases are great, the efficacy's great. And I think it's one to watch out for. >> InfluxData, time series database. The other interesting things I just noticed here, you got Tamer on here, which is that little small green. Those are the ones we were saying before, look for those guys. They might be some of the interesting companies out there and then observe Jeremy Burton's company. They do observability on top of Snowflake, not green, but kind of in that gray. So that's kind of cool. Monte Carlo is another one, they're sort of slightly green. They are doing some really interesting things in data and data mesh. So yeah, okay. So I can spend all day on this stuff, Erik, phenomenal data. I got to get back and really dig in. Let's end with machine learning and AI. Now this chart it's similar in its dimensions, of course, except for the money raised. We're not showing that size of the bubble, but AI is so hot. We wanted to cover that here, Erik, explain this please. Why TensorFlow is highlighted and walk us through this chart. >> Yeah, it's funny yet again, right? Another open source name, TensorFlow being up there. And I just want to explain, we do break out machine learning, AI is its own sector. A lot of this of course really is intertwined with the data side, but it is on its own area. And one of the things I think that's most important here to break out is Databricks. We started to cover Databricks in machine learning, AI. That company has grown into much, much more than that. So I do want to state to you Dave, and also the audience out there that moving forward, we're going to be moving Databricks out of only the MA/AI into other sectors. So we can kind of value them against their peers a little bit better. But in this instance, you could just see how dominant they are in this area. And one thing that's not here, but I do want to point out is that we have the ability to break this down by industry vertical, organization size. And when I break this down into Fortune 500 and Fortune 1000, both Databricks and Tensorflow are even better than you see here. So it's quite interesting to see that the names that are succeeding are also succeeding with the largest organizations in the world. And as we know, large organizations means large budgets. So this is one area that I just thought was really interesting to point out that as we break it down, the data by vertical, these two names still are the outstanding players. >> I just also want to call it H2O.ai. They're getting a lot of buzz in the marketplace and I'm seeing them a lot more. Anaconda, another one. Dataiku consistently popping up. DataRobot is also interesting because all the kerfuffle that's going on there. The Cube guy, Cube alum, Chris Lynch stepped down as executive chairman. All this stuff came out about how the executives were taking money off the table and didn't allow the employees to participate in that money raising deal. So that's pissed a lot of people off. And so they're now going through some kind of uncomfortable things, which is unfortunate because DataRobot, I noticed, we haven't covered them that much in "Breaking Analysis", but I've noticed them oftentimes, Erik, in the surveys doing really well. So you would think that company has a lot of potential. But yeah, it's an important space that we're going to continue to watch. Let me ask you Erik, can you contextualize this from a time series standpoint? I mean, how is this changed over time? >> Yeah, again, not show here, but in the data. I'm sorry, go ahead. >> No, I'm sorry. What I meant, I should have interjected. In other words, you would think in a downturn that these emerging companies would be less interesting to buyers 'cause they're more risky. What have you seen? >> Yeah, and it was interesting before we went live, you and I were having this conversation about "Is the downturn stopping people from evaluating these private companies or not," right. In a larger sense, that's really what we're doing here. How are these private companies doing when it comes down to the actual practitioners? The people with the budget, the people with the decision making. And so what I did is, we have historical data as you know, I went back to the Emerging Technology Survey we did in November of 21, right at the crest right before the market started to really fall and everything kind of started to fall apart there. And what I noticed is on the security side, very much so, we're seeing less evaluations than we were in November 21. So I broke it down. On cloud security, net sentiment went from 21% to 16% from November '21. That's a pretty big drop. And again, that sentiment is our one aggregate metric for overall positivity, meaning utilization and actual evaluation of the name. Again in database, we saw it drop a little bit from 19% to 13%. However, in analytics we actually saw it stay steady. So it's pretty interesting that yes, cloud security and security in general is always going to be important. But right now we're seeing less overall net sentiment in that space. But within analytics, we're seeing steady with growing mindshare. And also to your point earlier in machine learning, AI, we're seeing steady net sentiment and mindshare has grown a whopping 25% to 30%. So despite the downturn, we're seeing more awareness of these companies in analytics and machine learning and a steady, actual utilization of them. I can't say the same in security and database. They're actually shrinking a little bit since the end of last year. >> You know it's interesting, we were on a round table, Erik does these round tables with CISOs and CIOs, and I remember one time you had asked the question, "How do you think about some of these emerging tech companies?" And one of the executives said, "I always include somebody in the bottom left of the Gartner Magic Quadrant in my RFPs. I think he said, "That's how I found," I don't know, it was Zscaler or something like that years before anybody ever knew of them "Because they're going to help me get to the next level." So it's interesting to see Erik in these sectors, how they're holding up in many cases. >> Yeah. It's a very important part for the actual IT practitioners themselves. There's always contracts coming up and you always have to worry about your next round of negotiations. And that's one of the roles these guys play. You have to do a POC when contracts come up, but it's also their job to stay on top of the new technology. You can't fall behind. Like everyone's a software company. Now everyone's a tech company, no matter what you're doing. So these guys have to stay in on top of it. And that's what this ETS can do. You can go in here and look and say, "All right, I'm going to evaluate their technology," and it could be twofold. It might be that you're ready to upgrade your technology and they're actually pushing the envelope or it simply might be I'm using them as a negotiation ploy. So when I go back to the big guy who I have full intentions of writing that contract to, at least I have some negotiation leverage. >> Erik, we got to leave it there. I could spend all day. I'm going to definitely dig into this on my own time. Thank you for introducing this, really appreciate your time today. >> I always enjoy it, Dave and I hope everyone out there has a great holiday weekend. Enjoy the rest of the summer. And, you know, I love to talk data. So anytime you want, just point the camera on me and I'll start talking data. >> You got it. I also want to thank the team at ETR, not only Erik, but Darren Bramen who's a data scientist, really helped prepare this data, the entire team over at ETR. I cannot tell you how much additional data there is. We are just scratching the surface in this "Breaking Analysis". So great job guys. I want to thank Alex Myerson. Who's on production and he manages the podcast. Ken Shifman as well, who's just coming back from VMware Explore. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE. Does some great editing for us. Thank you. All of you guys. Remember these episodes, they're all available as podcast, wherever you listen. All you got to do is just search "Breaking Analysis" podcast. I publish each week on wikibon.com and siliconangle.com. Or you can email me to get in touch david.vellante@siliconangle.com. You can DM me at dvellante or comment on my LinkedIn posts and please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for Erik Bradley and The Cube Insights powered by ETR. Thanks for watching. Be well. And we'll see you next time on "Breaking Analysis". (upbeat music)

(bright music) >> "Supermetafragilisticexpialadotious." What's in a name? In an homage to the inimitable Charles Fitzgerald, we've chosen this title for today's session because of all the buzz surrounding "supercloud," a term that we introduced last year to signify a major architectural trend and shift that's occurring in the technology industry. Since that time, we've published numerous videos and articles on the topic, and on August 9th, kicked off "Supercloud22," an open industry event designed to advance the supercloud conversation, gathering input from more than 30 experienced technologists and business leaders in "The Cube" and broader technology community. We're talking about individuals like Benoit Dageville, Kit Colbert, Ali Ghodsi, Mohit Aron, David McJannet, and dozens of other experts. And today, we're pleased to welcome David Linthicum, who's a Chief Strategy Officer of Cloud Services at Deloitte Consulting. David is a technology visionary, a technical CTO. He's an author and a frequently sought after keynote speaker at high profile conferences like "VMware Explore" next week. David Linthicum, welcome back to "The Cube." Good to see you again. >> Oh, it's great to be here. Thanks for the invitation. Thanks for having me. >> Yeah, you're very welcome. Okay, so this topic of supercloud, what you call metacloud, has created a lot of interest. VMware calls it cross-cloud services, Snowflake calls it their data cloud, there's a lot of different names, but recently, you published a piece in "InfoWorld" where you said the following. "I really don't care what we call it, "and I really don't care if I put "my own buzzword into the mix. "However, this does not change the fact "that metacloud is perhaps the most important "architectural evolution occurring right now, "and we need to get this right out of the gate. "If we do that, who cares what it's named?" So very cool. And you also mentioned in a recent article that you don't like to put out new terms out in the wild without defining them. So what is a metacloud, or what we call supercloud? What's your definition? >> Yeah, and again, I don't care what people call it. The reality is it's the ability to have a layer of cross-cloud services. It sits above existing public cloud providers. So the idea here is that instead of building different security systems, different governance systems, different operational systems in each specific cloud provider, using whatever native features they provide, we're trying to do that in a cross-cloud way. So in other words, we're pushing out data integration, security, all these other things that we have to take care of as part of deploying a particular cloud provider. And in a multicloud scenario, we're building those in and between the clouds. And so we've been tracking this for about five years. We understood that multicloud is not necessarily about the particular public cloud providers, it's about things that you build in and between the clouds. >> Got it, okay. So I want to come back to that, to the definition, but I want to tie us to the so-called multicloud. You guys did a survey recently. We've said that multicloud was mostly a symptom of multi-vendor, Shadow Cloud, M&A, and only recently has become a strategic imperative. Now, Deloitte published a survey recently entitled "Closing the Cloud Strategy, Technology, Innovation Gap," and I'd like to explore that a little bit. And so in that survey, you showed data. What I liked about it is you went beyond what we all know, right? The old, "Our research shows that on average, "X number of clouds are used at an individual company." I mean, you had that too, but you really went deeper. You identified why companies are using multiple clouds, and you developed different categories of practitioners across 500 survey respondents. But the reasons were very clear for "why multicloud," as this becomes more strategic. Service choice scale, negotiating leverage, improved business resiliency, minimizing lock-in, interoperability of data, et cetera. So my question to you, David, is what's the problem supercloud or metacloud solves, and what's different from multicloud? >> That's a great question. The reality is that if we're... Well, supercloud or metacloud, whatever, is really something that exists above a multicloud, but I kind of view them as the same thing. It's an architectural pattern. We can name it anything. But the reality is that if we're moving to these multicloud environments, we're doing so to leverage best of breed things. In other words, best of breed technology to provide the innovators within the company to take the business to the next level, and we determine that in the survey. And so if we're looking at what a multicloud provides, it's the ability to provide different choices of different services or piece parts that allows us to build anything that we need to do. And so what we found in the survey and what we found in just practice in dealing with our clients is that ultimately, the value of cloud computing is going to be the innovation aspects. In other words, the ability to take the company to the next level from being more innovative and more disruptive in the marketplace that they're in. And the only way to do that, instead of basically leveraging the services of a particular walled garden of a single public cloud provider, is to cast a wider net and get out and leverage all kinds of services to make these happen. So if you think about that, that's basically how multicloud has evolved. In other words, it wasn't planned. They didn't say, "We're going to go do a multicloud." It was different developers and innovators in the company that went off and leveraged these cloud services, sometimes with the consent of IT leadership, sometimes not. And now we have these multitudes of different services that we're leveraging. And so many of these enterprises are going from 1000 to, say, 3000 services under management. That creates a complexity problem. We have a problem of heterogeneity, different platforms, different tools, different services, different AI technology, database technology, things like that. So the metacloud, or the supercloud, or whatever you want to call it, is the ability to deal with that complexity on the complexity's terms. And so instead of building all these various things that we have to do individually in each of the cloud providers, we're trying to do so within a cross-cloud service layer. We're trying to create this layer of technology, which removes us from dealing with the complexity of the underlying multicloud services and makes it manageable. Because right now, I think we're getting to a point of complexity we just can't operate it at the budgetary limits that we are right now. We can't keep the number of skills around, the number of operators around, to keep these things going. We're going to have to get creative in terms of how we manage these things, how we manage a multicloud. And that's where the supercloud, metacloud, whatever they want to call it, comes that. >> Yeah, and as John Furrier likes to say, in IT, we tend to solve complexity with more complexity, and that's not what we're talking about here. We're talking about simplifying, and you talked about the abstraction layer, and then it sounds like I'm inferring more. There's value that's added on top of that. And then you also said the hyperscalers are in a walled garden. So I've been asked, why aren't the hyperscalers superclouds? And I've said, essentially, they want to put your data into their cloud and keep it there. Now, that doesn't mean they won't eventually get into that. We've seen examples a little bit, Outposts, Anthos, Azure Arc, but the hyperscalers really aren't building superclouds or metaclouds, at least today, are they? >> No, they're not. And I always have the predictions for every major cloud conference that this is the conference that the hyperscaler is going to figure out some sort of a multicloud across-cloud strategy. In other words, building services that are able to operate across clouds. That really has never happened. It has happened in dribs and drabs, and you just mentioned a few examples of that, but the ability to own the space, to understand that we're not going to be the center of the universe in how people are going to leverage it, is going to be multiple things, including legacy systems and other cloud providers, and even industry clouds that are emerging these days, and SaaS providers, and all these things. So we're going to assist you in dealing with complexity, and we're going to provide the core services of being there. That hasn't happened yet. And they may be worried about conflicting their market, and the messaging is a bit different, even actively pushing back on the concept of multicloud, but the reality is the market's going to take them there. So in other words, if enough of their customers are asking for this and asking that they take the lead in building these cross-cloud technologies, even if they're participating in the stack and not being the stack, it's too compelling of a market that it's not going to drag a lot of the existing public cloud providers there. >> Well, it's going to be interesting to see how that plays out, David, because I never say never when it comes to a company like AWS, and we've seen how fast they move. And at the same time, they don't want to be commoditized. There's the layer underneath all this infrastructure, and they got this ecosystem that's adding all this tremendous value. But I want to ask you, what are the essential elements of supercloud, coming back to the definition, if you will, and what's different about metacloud, as you call it, from plain old SaaS or PaaS? What are the key elements there? >> Well, the key elements would be holistic management of all of the IT infrastructure. So even though it's sitting above a multicloud, I view metacloud, supercloud as the ability to also manage your existing legacy systems, your existing security stack, your existing network operations, basically everything that exists under the purview of IT. If you think about it, we're moving our infrastructure into the clouds, and we're probably going to hit a saturation point of about 70%. And really, if the supercloud, metacloud, which is going to be expensive to build for most of the enterprises, it needs to support these things holistically. So it needs to have all the services, that is going to be shareable across the different providers, and also existing legacy systems, and also edge computing, and IoT, and all these very diverse systems that we're building there right now. So if complexity is a core challenge to operate these things at scale and the ability to secure these things at scale, we have to have commonality in terms of security architecture and technology, commonality in terms of our directory services, commonality in terms of network operations, commonality in term of cloud operations, commonality in terms of FinOps. All these things should exist in some holistic cross-cloud layer that sits above all this complexity. And you pointed out something very profound. In other words, that is going to mean that we're hiding a lot of the existing cloud providers in terms of their interfaces and dashboards and things like that that we're dealing with today, their APIs. But the reality is that if we're able to manage these things at scale, the public cloud providers are going to benefit greatly from that. They're going to sell more services because people are going to find they're able to leverage them easier. And so in other words, if we're removing the complexity wall, which many in the industry are calling it right now, then suddenly we're moving from, say, the 25 to 30% migrated in the cloud, which most enterprises are today, to 50, 60, 70%. And we're able to do this at scale, and we're doing it at scale because we're providing some architectural optimization through the supercloud, metacloud layer. >> Okay, thanks for that. David, I just want to tap your CTO brain for a minute. At "Supercloud22," we came up with these three deployment models. Kit Colbert put forth the idea that one model would be your control planes running in one cloud, let's say AWS, but it interacts with and can manage and deploy on other clouds, the Kubernetes Cluster Management System. The second one, Mohit Aron from Cohesity laid out, where you instantiate the stack on different clouds and different cloud regions, and then you create a layer, a common interface across those. And then Snowflake was the third deployment model where it's a single global instance, it's one instantiation, and basically building out their own cloud across these regions. Help us parse through that. Do those seem like reasonable deployment models to you? Do you have any thoughts on that? >> Yeah, I mean, that's a distributed computing trick we've been doing, which is, in essence, an agent of the supercloud that's carrying out some of the cloud native functions on that particular cloud, but is, in essence, a slave to the metacloud, or the supercloud, whatever, that's able to run across the various cloud providers. In other words, when it wants to access a service, it may not go directly to that service. It goes directly to the control plane, and that control plane is responsible... Very much like Kubernetes and Docker works, that control plane is responsible for reaching out and leveraging those native services. I think that that's thinking that's a step in the right direction. I think these things unto themselves, at least initially, are going to be a very complex array of technology. Even though we're trying to remove complexity, the supercloud unto itself, in terms of the ability to build this thing that's able to operate at scale across-cloud, is going to be a collection of many different technologies that are interfacing with the public cloud providers in different ways. And so we can start putting these meta architectures together, and I certainly have written and spoke about this for years, but initially, this is going to be something that may escape the detail or the holistic nature of these meta architectures that people are floating around right now. >> Yeah, so I want to stay on this, because anytime I get a CTO brain, I like to... I'm not an engineer, but I've been around a long time, so I know a lot of buzzwords and have absorbed a lot over the years, but so you take those, the second two models, the Mohit instantiate on each cloud and each cloud region versus the Snowflake approach. I asked Benoit Dageville, "Does that mean if I'm in "an AWS east region and I want to do a query on Azure West, "I can do that without moving data?" And he said, "Yes and no." And the answer was really, "No, we actually take a subset of that data," so there's the latency problem. From those deployment model standpoints, what are the trade-offs that you see in terms of instantiating the stack on each individual cloud versus that single instance? Is there a benefit of the single instance for governance and security and simplicity, but a trade-off on latency, or am I overthinking this? >> Yeah, you hit it on the nose. The reality is that the trade-off is going to be latency and performance. If we get wiggy with the distributed nature, like the distributed data example you just provided, we have to basically separate the queries and communicate with the databases on each instance, and then reassemble the result set that goes back to the people who are recording it. And so we can do caching systems and things like that. But the reality is, if it's distributed system, we're going to have latency and bandwidth issues that are going to be limiting us. And also security issues, because if we're removing lots of information over the open internet, or even private circuits, that those are going to be attack vectors that hackers can leverage. You have to keep that in mind. We're trying to reduce those attack vectors. So it would be, in many instances, and I think we have to think about this, that we're going to keep the data in the same physical region for just that. So in other words, it's going to provide the best performance and also the most simplistic access to dealing with security. And so we're not, in essence, thinking about where the data's going, how it's moving across things, things like that. So the challenge is going to be is when you're dealing with a supercloud or metacloud is, when do you make those decisions? And I think, in many instances, even though we're leveraging multiple databases across multiple regions and multiple public cloud providers, and that's the idea of it, we're still going to localize the data for performance reasons. I mean, I just wrote a blog in "InfoWorld" a couple of months ago and talked about, people who are trying to distribute data across different public cloud providers for different reasons, distribute an application development system, things like that, you can do it. With enough time and money, you can do anything. I think the challenge is going to be operating that thing, and also providing a viable business return based on the application. And so why it may look like a good science experiment, and it's cool unto itself as an architect, the reality is the more pragmatic approach is going to be a leavitt in a single region on a single cloud. >> Very interesting. The other reason I like to talk to companies like Deloitte and experienced people like you is 'cause I can get... You're agnostic, right? I mean, you're technology agnostic, vendor agnostic. So I want to come back with another question, which is, how do you deal with what I call the lowest common denominator problem? What I mean by that is if one cloud has, let's say, a superior service... Let's take an example of Nitro and Graviton. AWS seems to be ahead on that, but let's say some other cloud isn't quite quite there yet, and you're building a supercloud or a metacloud. How do you rationalize that? Does it have to be like a caravan in the army where you slow down so all the slowest trucks can keep up, or are the ways to adjudicate that that are advantageous to hide that deficiency? >> Yeah, and that's a great thing about leveraging a supercloud or a metacloud is we're putting that management in a single layer. So as far as a user or even a developer on those systems, they shouldn't worry about the performance that may come back, because we're dealing with the... You hit the nail on the head with that one. The slowest component is the one that dictates performance. And so we have to have some sort of a performance management layer. We're also making dynamic decisions to move data, to move processing, from one server to the other to try to minimize the amount of latency that's coming from a single component. So the great thing about that is we're putting that volatility into a single domain, and it's making architectural decisions in terms of where something will run and where it's getting its data from, things are stored, things like that, based on the performance feedback that's coming back from the various cloud services that are under management. And so if you're running across clouds, it becomes even more interesting, because ultimately, you're going to make some architectural choices on the fly in terms of where that stuff runs based on the active dynamic performance that that public cloud provider is providing. So in other words, we may find that it automatically shut down a database service, say MySQL, on one cloud instance, and moved it to a MySQL instance on another public cloud provider because there was some sort of a performance issue that it couldn't work around. And by the way, it does so dynamically. Away from you making that decision, it's making that decision on your behalf. Again, this is a matter of abstraction, removing complexity, and dealing with complexity through abstraction and automation, and this is... That would be an example of fixing something with automation, self-healing. >> When you meet with some of the public cloud providers and they talk about on-prem private cloud, the general narrative from the hyperscalers is, "Well, that's not a cloud." Should on-prem be inclusive of supercloud, metacloud? >> Absolutely, I mean, and they're selling private cloud instances with the edge cloud that they're selling. The reality is that we're going to have to keep a certain amount of our infrastructure, including private clouds, on premise. It's something that's shrinking as a market share, and it's going to be tougher and tougher to justify as the public cloud providers become better and better at what they do, but we certainly have edge clouds now, and hyperscalers have examples of that where they run a instance of their public cloud infrastructure on premise on physical hardware and software. And the reality is, too, we have data centers and we have systems that just won't go away for another 20 or 30 years. They're just too sticky. They're uneconomically viable to move into the cloud. That's the core thing. It's not that we can't do it. The fact of the matter is we shouldn't do it, because there's not going to be an economic... There's not going to be an economic incentive of making that happen. So if we're going to create this meta layer or this infrastructure which is going to run across clouds, and everybody agrees on, that's what the supercloud is, we have to include the on-premise systems, including private clouds, including legacy systems. And by the way, include the rising number of IoT systems that are out there, and edge-based systems out there. So we're managing it using the same infrastructure into cloud services. So they have metadata systems and they have specialized services, and service finance and retail and things like doing risk analytics. So it gets them further down that path, but not necessarily giving them a SaaS application where they're forced into all of the business processes. We're giving you piece parts. So we'll give you 1000 different parts that are related to the finance industry. You can assemble anything you need, but the thing is, it's not going to be like building it from scratch. We're going to give you risk analytics, we're giving you the financial analytics, all these things that you can leverage within your applications how you want to leverage them. We'll maintain them. So in other words, you don't have to maintain 'em just like a cloud service. And suddenly, we can build applications in a couple of weeks that used to take a couple of months, in some cases, a couple of years. So that seems to be a large take of it moving forward. So get it up in the supercloud. Those become just other services that are under managed... That are under management on the supercloud, the metacloud. So we're able to take those services, abstract them, assemble them, use them in different applications. And the ability to manage where those services are originated versus where they're consumed is going to be managed by the supercloud layer, which, you're dealing with the governance, the service governance, the security systems, the directory systems, identity access management, things like that. They're going to get you further along down the pike, and that comes back as real value. If I'm able to build something in two weeks that used to take me two months, and I'm able to give my creators in the organization the ability to move faster, that's a real advantage. And suddenly, we are going to be valued by our digital footprint, our ability to do things in a creative and innovative way. And so organizations are able to move that fast, leveraging cloud computing for what it should be leveraged, as a true force multiplier for the business. They're going to win the game. They're going to get the most value. They're going to be around in 20 years, the others won't. >> David Linthicum, always love talking. You have a dangerous combination of business and technology expertise. Let's tease. "VMware Explore" next week, you're giving a keynote, if they're going to be there. Which day are you? >> Tuesday. Tuesday, 11 o'clock. >> All right, that's a big day. Tuesday, 11 o'clock. And David, please do stop by "The Cube." We're in Moscone West. Love to get you on and continue this conversation. I got 100 more questions for you. Really appreciate your time. >> I always love talking to people at "The Cube." Thank you very much. >> All right, and thanks for watching our ongoing coverage of "Supercloud22" on "The Cube," your leader in enterprise tech and emerging tech coverage. (bright music)