(upbeat music) >> Hey, everyone. Welcome back to New York City. Lisa Martin and John Furrier with theCUBE here live covering the AWS Summit NYC 2022. There's about 15 different summits going on this year, John, globally. We're here with about 10,000 attendees. Just finished the keynote and two guests from SoftwareONE. Please welcome David Torres, the director of cloud services and Asim Khan, a North American AWS services delivery lead at SoftwareONE. Welcome, guys. >> Thank you for having us. >> Thank you for having us. >> Talk to us, David, kick us off. Give the audience an overview of SoftwareONE. What do you guys do? And then tell us a little bit about the AWS partnership. >> Sure, so SoftwareONE, we are one of Microsoft and VMware's largest resellers. We help customers with our IT asset management services, managing their on-premises license real estate, but we're definitely a company that's undergoing a transformation. And when I say that, essentially we're focused on three key pillars with our go to market, supporting the hyperscalers. So we do support AWS, Azure, GCP at modernization because we do see this with a lot of our customers, you know, they're moving from on premises to AWS. They have a lot of technical debt and they're looking at options to modernize that, and mission critical workloads like SAP, Windows, Oracle, and we offer, you know, a suite of professional services, managed services, migrations, quite quite a bit of services. >> Asim, can you kind of double click on the services that SoftwareONE delivers to customers? Maybe some key use cases? >> Yeah, sure. I think in the Amazon space, I would say we're currently focusing in the area of funding programs that Amazon currently has, for example, the Migration Acceleration Program, which is a map with supporting customers basically with the entire cloud journey that they might have, or helping them define that cloud journey. And then we can help the customer in any phase of that journey as well to basically take them a step step above. So that's what our area of focus is right now to basically help enable customers. >> So on the Microsoft, AWS, you mentioned Microsoft, I mean, they've had the enterprise business for years and, you know, developers was their, you know, ecosystem. Back in the day, "Developers, developers, developers" as Steve Ballmer once said, and that was their crown jewel. But then, you know, .NET now has Linux. They got a lot more open source. So those enterprises, their customers are changing. A lot of them are on AWS. So talk about that dynamic of the shift to AWS. And now that Azure's out there, what's the relationship of those hyperscale? How do you guys navigate those waters? >> Sure, I mean, it's always the concept of work backwards from the customer, right? What are the business outcomes they're trying to drive and, you know, define a strategy from that. And it's still a function of change management for a lot of customers, people, process and tools. So, you know, in a lot of cases, our customers are evaluating what's a skillset of our people, do we need to upskill them, the tools that we're using, how do we use those on the multiple clouds, right? And then the processes. So for us, you know, we have some customers that prefer one cloud over another. We have customers that run cross multiple clouds. They deploy different workloads. And then we have some customers that transformation and modernization are really big top of stack for them. So in some cases, those customers are going to AWS and, you know, we're helping them kind of with that journey. It's interesting, Amazon literally won the developer cloud market early on, going back 15 years. >> Absolutely. >> But not all developers, enterprise developers who, you know, in the enterprises, they're stuck in their ways, but are changing. This is a digital transformation moment 'cause cloud native applications, the modernization piece, is developer centric. >> Absolutely. >> That's key, the developers. So I'm interested in your perspective and reaction to what's going on in that developer market right now with DevOps exploding in a great way, the goodness of the cloud coming more and more to the table. >> Sure, no, absolutely, great question. So I think with enterprise developers, you know, we see just the businesses driving a lot of the outcomes, right? So the modernization aspect of needing to get to market faster, needing to deploy applications faster, having a more efficient operating model, more automation. And for your point on the .NET modernization, you know, we work with customers too as well. We made an acquisition a couple years ago, a company, InterGrupo. They actually specialize in this in .NET modernization. So we know we're seeing some customers that are moving to Linux, right? And they want to go .NET Core and, you know, they're kind of standardizing on Linux. So we kind of see a, you know, wide spectrum, but yeah, maybe. >> Where are your customer conversations as things have changed so much in accelerated dramatically in the last couple of years? >> Sure. >> Obviously we've talked about the developers, but talk to me about, you know, business imperatives for businesses in every industry to digitally transform, number one, to survive the last couple of years, but, two, to be at a competitive advantage. >> Sure, no, so I think with businesses, you know, obviously, 2017, innovation, 2022, it's a little bit different, right? There's obviously macro conditions, you have COVID. So, you know, we're seeing where customers are essentially really doing their due diligence, right, when they make their choices more than ever before. And they're trying to maximize, right, their spend and their ROI when they move to cloud and that involves, you know, the licensing advisory, what they can move, what they can modernize, migrations, and just the roadmap and what strategy. But what I see is, it's the business outcomes, what they're trying to drive, and, you know, we're seeing some trends too with maybe a more conservative segments like healthcare, public sector, right, utilities that they are really investing and moving towards the cloud. >> Asim, I got a question from Twitter, a DM, I want to ask. You guys are on the front line. So you see the customers, which is really great 'cause it's primary data. You guys are right there. And you're not biased. You work with whatever hyperscaler. So it's really good. So the question that came up was, "Can you ask them the following, 'What's going on in the data warehouse front, cloud warehouse front, you got Redshift competing with Synapse, Azure Synapse, Google BigQuery, and then you got Snowflake and Databricks out there?'" So you got this new data provider, but it's not a data warehouse. And you got data refactoring on AWS, for instance as well. So, you know, this whole new level of data analytics with how you're doing cloud data. And you call it a data warehouse, I guess for categorically, but it's really not a warehouse. It's a data lake and you got lake front foundation. What are you guys seeing on the front lines with customers as they try to squint through how to deal with the data and which cloud to work with? >> That is a good question. I mean, I've been in the industry a long time. I've worked for some major financial institutions as well and data or big data was big for that industry. (John chuckles) So I've seen how the trends have changed, but from our perspective, because we are an agnostic services company, as you mentioned, we basically can work with any hyperscaler, we initially see what the business needs are for the customer. If the customer is already, for example, using Amazon, we initially want to have the customer use native tooling available within that hyperscaler space. If the customer is open for us to give them any recommendations, of course, we look at the business needs. We look at what type of data is going to be stored. What the industry is. Based on all of those inputs is when we basically give the right recommendation, it could be a third party data warehousing solution. It could be an area one. It all depends on what the business needs of the customer are. >> So for example, and most companies do this they build on say AWS, who is one of the first big clouds. And then they go, "Hey, we got customers over there at Azure, that's Microsoft they got thousands and thousands of customers. Snowflake's done, and they have marketplaces as well." So you guys are kind of agnostic it sounds like. Whatever the architecture is on the stack that they choose. >> Correct, so that's what makes us special. I think we are one of those services companies which is quite unique in the industry. And I don't say that just because I work for SoftwareONE, (John chuckle) that is a fact that gives us a very unique perspective of giving the customer the right piece of advice because we've seen it all and we've done it all. So that's, I think what puts us unique and regarding technology, all the different hyperscalers, they might have a very similar backend technology stack, but what the front end services each hyperscaler is building are very unique. Amazon being the leader in this space, they've been ahead of the curb by a few years, they will always have certain solutions which are above the rest. So I mean, I've always been an Amazon person, so I'm slightly biased, but, hey, I mean, I'm not complaining about that. >> The good news is the customer has choices. >> Right, absolutely. And we do see customers that want to be agnostic, right, >> Yeah. >> With their technology choices. Actually, that's a good segue about our partnership with AWS. We recently signed a strategic collaboration agreement between both parties. So there's going to continue to be big investment from us, scaling out our professional services, our practice areas, and then also key focus area for a fin ops. >> Is that your number one area? >> It's one of the areas, yeah. >> Okay, what your top three practice areas? >> Top three, mission critical workloads. So enterprise workloads like SAP, Microsoft, Oracle, two, app modernization, and, three, definitely fin ops and the hyperscalers, right? Because we see a lot of customers that have already heavily adopted cloud, they're struggling with that cloud financial management aspect. >> So if they're struggling, what are some of the key business outcomes that they come to you, to SoftwareONE, and say, "Help us figure this out. We have to achieve A, B, C." >> Sure, so depending on the maturity of the customer and where they are in the journey, if they're already very heavily adopting cloud, you know, AWS or Azure, we see in a lot of cases that the customers are unsure if they're getting the most out of their cloud spend, and they're looking at their operations, and their governance, and, you know, they're coming to us and basically asking us, "Hey, we feel like our cloud spend is a little bit out of control. Can you help us?" And that's where we can come in, you know, provide the advice, the guidance, the advisory but also give them the tooling, right, to have visibility into their cloud spend and make those conditions. And we also offer a managed fin op service that will end to end do this for the customers to help to manage their resale, their invoicing, their marketplace buy, as well as their cloud spend. >> So obviously the engagement varies customer to customer. What's a typical timeframe? Like how long does it take you to really get in there with a customer, understand the direction they need to go, and create the right plan? >> Sure, again, comes back to the cloud journey. You know, if the customer is still, you know, very much on prem and maybe more, you know, conservative, it may start with licensing assessments just to give them an idea of what it would cost to move those workloads, right? Then it turns into migration modernization, you know, it can be an anywhere from one to six months, you know, of just consulting, right, to get the customer ready. And then we help 'em, you know, obviously with their migration plan. But if they're already heavily adopting cloud, you know, we do remediation work, we do optimization. Obviously, SAP, that's a longer cycle, so. (chuckles) >> So I got to ask you guys, what is the PyraCloud? SoftwareONE as a platform PyraCloud. What is that? >> I might want to answer that. >> Sure. (chuckles) >> It's pronounced PyraCloud. >> How do you pronounce it? >> PyraCloud. >> PyraCloud, okay. I like PyraCloud better. (chuckles) >> With the Y in there. It's basically our spend insight platform. It gives customers an a truly agnostic single pane of glass view into their entire cloud enterprise spend. What I mean by that is with a single login, the customer has access to looking at their enterprise spend on AWS, on Azure, as well as GCP. And in the future, of course, we're going to add other hyperscalers in there as well. Because of the single pin of glass view, the customer has a true or the customer leadership, or, for example, the CTO has a single pane of glass view into the entire spend. We allow the customer to basically have an enterprise level tagging strategy, which is across all the hyperscalers as well as then allowing a certain amount of automated cost management as well, which is again agnostic and enterprisewide. >> Can you share an example of a customer for whom you've given them this single pane of glass through PyraCloud, and by how much they've been able to reduce costs or optimize costs? >> Yes, mostly the customers who would be a very good fit for PyraCloud would be a slightly more mature customer who already has a large amount of spend, or who is already very mature in their different hyperscalers. And usually what we've seen once a customer is mature in the cloud over a certain period of time, controlling costs does become difficult, even though you might have automation in place, but to get to that automation, you have to go through a certain amount of time of basically things breaking and you fixing them. So this is where per cloud becomes very helpful to help control that. And building a strategy, which once in place is repetitive and helps you manage costs and spend in the cloud year after year then. >> One of the things I want to get your guys reaction before we wrap up is this show here has got 10,000 people which is a big number, post COVID, events are coming back but in the past five years, or six years, or seven years, since like 2015, a lot's changed. What's changed the most? Shared to the audience what you think is the biggest step function change that's happening right now? Is it that data's now prime time? Everyone's got a lot of data, hasn't figured out the consequences with it. Is it scale? Is it super cloud? Is it the ecosystem because this is not stopping ,the growth in the enterprise on the digital transformation is expanding, even though GDPs down, and gas prices are high, and inflation, this isn't stopping. Now, some of the unicorns might be impacted by the headwinds, the big overfunded valuations but not the ecosystem. What's changed? What's the big change? >> Well, I think what I see is this cloud is becoming the defacto operating model and customers are working backwards from that as their primary goal, right, to operate in the cloud. And as I mentioned before, they really are doing due diligence, right, to really understand the best approach for seeing kind of maybe some of the challenges other customers have had when they first moved to AWS, so. And I'm, you know, seeing industries that maybe five years ago, you know, were not about moving to cloud, like healthcare. I can tell you a lot of our healthcare customers, they're trying to get to cloud as fast as possible. >> It's a wake up call. >> It's a wake up call. >> Absolutely. Absolutely. >> Asim, what's your reaction? >> In my point of view with what's happened these last few years with a lot of companies having their employees work from home and being remotely, I think end user compute was one of the big booms which happened about two years ago. We support a lot of customer in that space as well. And then overall, I think we actually saw that there was much more business focus with employees working for home for some reason. And we saw that internally in our own organization as well. And with that focus, the whole area of being more lean and agile in the cloud space, I think became much more prevalent for all the enterprises. Everybody wanted to be spend conscious, availing the different tools available in the cloud arena, like autoscaling like using, for example, containerization, using such solutions to basically be more resilient and more lean to basic control costs. >> So necessity is the mother of all inventions >> It is. >> That got forced. So you got wake up call and then a forcing function to like, okay, but exposes the consequences of a modern application, modern environment because they didn't, they're out of business. So then it's like, okay, this is actually working, (chuckles) why don't we like kill that project that we've doubled down on, move it over here." So I see that same pattern. What do you guys see? >> Yeah, no, I mean, we see that pattern as well. Just modernization, efficiency. You could just move faster, more elasticity, you know, and, again, the wake up call, you know, for organizations that people couldn't go to data centers, right? (chuckles) >> Yeah. (chuckles) >> We actually have a customer, that was literally the reason they made the move, right, to AWS. >> And I would add one more thing to that particular point. With the time available, I think customers were able to actually now re-architect their applications slightly better to be able to avail, for example, no server type of solutions or using certain design principles which were much more cost lean in the cloud. That's what we saw. I think customers spent that time available over the past couple of years to be much more cloud centric, I would say. >> Yeah, the forced March was really an accelerant and a catalyst in a lot of ways for good, and there's definitely some silver linings there. Guys, we're out of time. But thank you so much for joining John >> Oh, awesome. >> And me talking about SoftwareONE, what you guys are doing, helping customers, what you're doing with AWS and the hyperscalers. We appreciate your time and your insights. >> Thank you. >> Awesome. Thank you for having us. >> Thanks for having us. >> Really appreciate it. >> All right, for our guests and John Furrier, I'm Lisa Martin. You're watching theCUBE live from New York City at AWS Summit at NYC. Stick around, John and I will be right back with our next guest. (upbeat music) (upbeat music continues)

(bright upbeat music) >> Hello everyone, and welcome to the special exclusive CUBE Conversation, where we continue our coverage of the trends of the database market. With me is Nipun Agarwal, who's the vice president, MySQL HeatWave in advanced development at Oracle. Nipun, welcome. >> Thank you Dave. >> I love to have technical people on the Cube to educate, debate, inform, and we've extensively covered this market. We were all over the Snowflake IPO and at that time I remember, I challenged organizations bring your best people. Because I want to better understand what's happening at Database. After Oracle kind of won the Database wars 20 years ago, Database kind of got boring. And then it got really exciting with the big data movement, and all the, not only SQL stuff coming out, and Hadoop and blah, blah, blah. And now it's just exploding. You're seeing huge investments from many of your competitors, VCs are trying to get into the action. Meanwhile, as I've said many, many times, your chairman and head of technology, CTO, Larry Ellison, continues to invest to keep Oracle relevant. So it's really been fun to watch and I really appreciate you coming on. >> Sure thing. >> We have written extensively, we talked to a lot of Oracle customers. You get the leading mission critical database in the world. Everybody from Fortune 100, we evaluated what Gardner said about the operational databases. I think there's not a lot of question there. And we've written about that on WikiBound about you're converged databases, and the strategy there, and we're going to get into that. We've covered Autonomous Data Warehouse Exadata Cloud at Customer, and then we just want to really try to get into your area, which has been, kind of caught our attention recently. And I'm talking about the MySQL Database Service with HeatWave. I love the name, I laugh. It was an unveiled, I don't know, a few months ago. So Nipun, let's start the discussion today. Maybe you can update our viewers on what is HeatWave? What's the overall focus with Oracle? And how does it fit into the Cloud Database Service? >> Sure Dave. So HeatWave is a in-memory query accelerator for the MySQL Database Service for speeding up analytic queries as well as long running complex OLTP queries. And this is all done in the context of a single database which is the MySQL Database Service. Also, all existing MySQL applications or MySQL compatible tools and applications continue to work as is. So there is no change. And with this HeatWave, Oracle is delivering the only MySQL service which provides customers with a single unified platform for both analytic as well as transaction processing workloads. >> Okay, so, we've seen open source databases in the cloud growing very rapidly. I mentioned Snowflake, I think Google's BigQuery, get some mention, we'll talk, we'll maybe talk more about Redshift later on, but what I'm wondering, well let's talk about now, how does MySQL HeatWave service, how does that compare to MySQL-based services from other cloud vendors? I can get MySQL from others. In fact, I think we do. I think we run WikiBound on the LAMP stack. I think it's running on Amazon, but so how does your service compare? >> No other vendor, like, no other vendor offers this differentiated solution with an open source database namely, having a single database, which is optimized both for transactional processing and analytics, right? So the example is like MySQL. A lot of other cloud vendors provide MySQL service but MySQL has been optimized for transaction processing so when customs need to run analytics they need to move the data out of MySQL into some other database for any analytics, right? So we are the only vendor which is now offering this unified solution for both transactional processing analytics. That's the first point. Second thing is, most of the vendors out there have taken open source databases and they're basically hosting it in the cloud. Whereas HeatWave, has been designed from the ground up for the cloud, and it is a 100% compatible with MySQL applications. And the fact that we have designed it from the ground up for the cloud, maybe I'll spend 100s of person years of research and engineering means that we have a solution, which is very, very scalable, it's very optimized in terms of performance, and it is very inexpensive in terms of the cost. >> Are you saying, well, wait, are you saying that you essentially rewrote MySQL to create HeatWave but at the same time maintained compatibility with existing applications? >> Right. So we enhanced MySQL significantly and we wrote a whole bunch of new code which is brand new code optimized for the cloud in such a manner that yes, it is 100% compatible with all existing MySQL applications. >> What does it mean? And if I'm to optimize for the cloud, I mean, I hear that and I say, okay, it's taking advantage of cloud-native. I hear kind of the buzzwords, cloud-first, cloud-native. What does it specifically mean from a technical standpoint? >> Right. So first, let's talk about performance. What we have done is that we have looked at two aspects. We have worked with shapes like for instance, like, the compute shapes which provide the best performance for dollar, per dollar. So I'll give you a couple of examples. We have optimized for certain shifts. So, HeatWave is in-memory query accelerator. So the cost of the system is dominated by the cost. So we are working with chips which provide the cheapest cost per terabyte of memory. Secondly, we are using commodity cloud services in such a manner that it's in-optimized for both performance as well as performance per dollar. So, example is, we are not using any locally-attached SSDs. We use ObjectStore because it's very inexpensive. And then I guess at some point I will get into the details of the architecture. The system has been really, really designed for massive scalability. So as you add more compute, as you add more service, the system continues to scale almost perfectly linearly. So this is what I mean in terms of being optimized for the cloud. >> All right, great. >> And furthermore, (indistinct). >> Thank you. No, carry on. >> Over the next few months, you will see a bunch of other announcements where we're adding a whole bunch of machine learning and data driven-based automation which we believe is critical for the cloud. So optimized for performance, optimized for the cloud, and machine learning-based automation which we believe is critical for any good cloud-based service. >> All right, I want to come back and ask you more about the architecture, but you mentioned some of the others taking open source databases and shoving them into the cloud. Let's take the example of AWS. They have a series of specialized data stores and, for different workloads, Aurora is for OLTP I actually think it's based on MySQL Redshift which is based on ParAccel. And so, and I've asked Amazon about this, and their response is, actually kind of made sense to me. Look, we want the right tool for the right job, we want access to the primitives because when the market changes we can change faster as opposed to, if we put, if we start building bigger and bigger databases with more functionality, it's, we're not as agile. So that kind of made sense to me. I know we, again, we use a lot, we use, I think I said MySQL in Amazon we're using DynamoDB, works, that's cool. We're not huge. And I, we fully admit and we've researched this, when you start to get big that starts to get maybe expensive. But what do you think about that approach and why is your approach better? >> Right, we believe that there are multiple drawbacks of having different databases or different services, one, optimized for transactional processing and one for analytics and having to ETL between these different services. First of all, it's expensive because you have to manage different databases. Secondly, it's complex. From an application standpoint, applications need, now need to understand the semantics of two different databases. It's inefficient because you have to transfer data at some PRPC from one database to the other one. It's not secure because there is security aspects involved when your transferring data and also the identity of users in the two different databases is different. So it's, the approach which has been taken by Amazons and such, we believe, is more costly, complex, inefficient and not secure. Whereas with HeatWave, all the data resides in one database which is MySQL and it can run both transaction processing and analytics. So in addition to all the benefits I talked about, customers can also make their decisions in real time because there is no need to move the data. All the data resides in a single database. So as soon as you make any changes, those changes are visible to customers for queries right away, which is not the case when you have different siloed specialized databases. >> Okay, that, a lot of ways to skin a cat and that what you just said makes sense. By the way, we were saying before, companies have taken off the shelf or open source database has shoved them in the cloud. I have to give Amazon some props. They actually have done engineering to Aurora and Redshift. And they've got the engineering capabilities to do that. But you can see, for example, in Redshift the way they handle separating compute from storage it's maybe not as elegant as some of the other players like a Snowflake, for example, but they get there and they, maybe it's a little bit more brute force but so I don't want to just make it sound like they're just hosting off the shelf in the cloud. But is it fair to say that there's like a crossover point? So in other words, if I'm smaller and I'm not, like doing a bunch of big, like us, I mean, it's fine. It's easy, I spin it up. It's cheaper than having to host my own servers. So there's, presumably there's a sweet spot for that approach and a sweet spot for your approach. Is that fair or do you feel like you can cover a wider spectrum? >> We feel we can cover the entire spectrum, not wider, the entire spectrum. And we have benchmarks published which are actually available on GitHub for anyone to try. You will see that this approach you have taken with the MySQL Database Service in HeatWave, we are faster, we are cheaper without having to move the data. And the mileage or the amount of improvement you will get, surely vary. So if you have less data the amount of improvement you will get, maybe like say 100 times, right, or 500 times, but smaller data sizes. If you get to lots of data sizes this improvement amplifies to 1000 times or 10,000 times. And similarly for the cost, if the data size is smaller, the cost advantage you will have is less, maybe MySQL HeatWave is one third the cost. If the data size is larger, the cost advantage amplifies. So to your point, MySQL Database Service in HeatWave is going to be better for all sizes but the amount of mileage or the amount of benefit you will get increases as the size of the data increases. >> Okay, so you're saying you got better performance, better cost, better price performance. Let me just push back a little bit on this because I, having been around for awhile, I often see these performance and price comparisons. And what often happens is a vendor will take the latest and greatest, the one they just announced and they'll compare it to an N-1 or an N-2, running on old hardware. So, is, you're normalizing for that, is that the game you're playing here? I mean, how can you, give us confidence that this is easier kind of legitimate benchmarks in your GitHub repo. >> Absolutely. I'll give you a bunch of like, information. But let me preface this by saying that all of our scripts are available in the open source in the GitHub repo for anyone to try and we would welcome feedback otherwise. So we have taken, yes, the latest version of MySQL Database Service in HeatWave, we have optimized it, and we have run multiple benchmarks. For instance, TBC-H, TPC-DS, right? Because the amount of improvement a query will get depends upon the specific query, depends upon the predicates, it depends on the selectivity so we just wanted to use standard benchmarks. So it's not the case that if you're using certain classes of query, excuse me, benefit, get them more. So, standard benchmarks. Similarly, for the other vendors or other services like Redshift, we have run benchmarks on the latest shapes of Redshift the most optimized configuration which they recommend, running their scripts. So this is not something that, hey, we're just running out of the box. We have optimized Aurora, we have optimized (indistinct) to the best and possible extent we can based on their guidelines, based on their latest release, and that's what you're talking about in terms of the numbers. >> All right. Please continue. >> Now, for some other vendors, if we get to the benchmark section, we'll talk about, we are comparing with other services, let's say Snowflake. Well there, there are issues in terms of you can't legally run Snowflake numbers, right? So there, we have looked at some reports published by Gigaom report. and we are taking the numbers published by the Gigaom report for Snowflake, Google BigQuery and as you'll see maps numbers, right? So those, we have not won ourselves. But for AWS Redshift, as well as AWS Aurora, we have run the numbers and I believe these are the best numbers anyone can get. >> I saw that Gigaom report and I got to say, Gigaom, sometimes I'm like, eh, but I got to say that, I forget the guy's name, he knew what he was talking about. He did a good job, I thought. I was curious as to the workload. I always say, well, what's the workload. And, but I thought that report was pretty detailed. And Snowflake did not look great in that report. Oftentimes, and they've been marketing the heck out of it. I forget who sponsored it. It is, it was sponsored content. But, I did, I remember seeing that and thinking, hmm. So, I think maybe for Snowflake that sweet spot is not, maybe not that performance, maybe it's the simplicity and I think that's where they're making their mark. And most of their databases are small and a lot of read-only stuff. And so they've found a market there. But I want to come back to the architecture and really sort of understand how you've able, you've been able to get this range of both performance and cost you talked about. I thought I heard that you're optimizing the chips, you're using ObjectStore. You're, you've got an architecture that's not using SSD, it's using ObjectStore. So this, is their cashing there? I wonder if you could just give us some details of the architecture and tell us how you got to where you are. >> Right, so let me start off saying like, what are the kind of numbers we are talking about just to kind of be clear, like what the improvements are. So if you take the MySQL Database Service in HeatWave in Oracle Cloud and compare it with MySQL service in any other cloud, and if you look at smaller data sizes, say data sizes which are about half a terabyte or so, HeatWave is 400 times faster, 400 times faster. And as you get to... >> Sorry. Sorry to interrupt. What are you measuring there? Faster in terms of what? >> Latency. So we take TCP-H 22 queries, we run them on HeatWave, and we run the same queries on MySQL service on any other cloud, half a terabyte and the performance in terms of latency is 400 times faster in HeatWave. >> Thank you. Okay. >> If you go to a lot of other data sites, then the other data point of view, we're looking at say something like, 4 TB, there, we did two comparisons. One is with AWS Aurora, which is, as you said, they have taken MySQL. They have done a bunch of innovations over there and we are offering it as a premier service. So on 4 TB TPC-H, MySQL Database Service with HeatWave is 1100 times faster than Aurora. It is three times faster than the fastest shape of Redshift. So Redshift comes in different flavors some talking about dense compute too, right? And again, looking at the most recommended configuration from Redshift. So 1100 times faster that Aurora, three times faster than Redshift and at one third, the cost. So this where I just really want to point out that it is much faster and much cheaper. One third the cost. And then going back to the Gigaom report, there was a comparison done with Snowflake, Google BigQuery, Redshift, Azure Synapse. I wouldn't go into the numbers here but HeatWave was faster on both TPC-H as well as TPC-DS across all these products and cheaper compared to any of these products. So faster, cheaper on both the benchmarks across all these products. Now let's come to, like, what is the technology underneath? >> Great. >> So, basically there are three parts which you're going to see. One is, improve performance, very good scale, and improve a lower cost. So the first thing is that HeatWave has been optimized and, for the cloud. And when I say that, we talked about this a bit earlier. One is we are using the cheapest shapes which are available. We're using the cheapest services which are available without having to compromise the performance and then there is this machine learning-based automation. Now, underneath, in terms of the architecture of HeatWave there are basically, I would say, four key things. First is, HeatWave is an in-memory engine that a presentation which we have in memory is a hybrid columnar representation which is optimized for vector process. That's the first thing. And that's pretty table stakes these days for anyone who wants to do in-memory analytics except that it's hybrid columnar which is optimized for vector processing. So that's the first thing. The second thing which starts getting to be novel is that HeatWave has a massively parallel architecture which is enabled by a massively partitioned architecture. So we take the data, we read the data from MySQL into the memory of the HeatWave and we massively partition this data. So as we're reading the data, we're partitioning the data based on the workload, the sizes of these partitions is such that it fits in the cache of the underlying processor and then we're able to consume these partitions really, really fast. So that's the second bit which is like, massively parallel architecture enabled by massively partitioned architecture. Then the third thing is, that we have developed new state-of-art algorithms for distributed query processing. So for many of the workloads, we find that joints are the long pole in terms of the amount of time it takes. So we at Oracle have developed new algorithms for distributed joint processing and similarly for many other operators. And this is how we're being able to consume this data or process this data, which is in-memory really, really fast. And finally, and what we have, is that we have an eye for scalability and we have designed algorithms such that there's a lot of overlap between compute and communication, which means that as you're sending data across various nodes and there could be like, dozens of of nodes or 100s of nodes that they're able to overlap the computation time with the communication time and this is what gives us massive scalability in the cloud. >> Yeah, so, some hard core database techniques that you've brought to HeatWave, that's impressive. Thank you for that description. Let me ask you, just to go to quicker side. So, MySQL is open source, HeatWave is what? Is it like, open core? Is it open source? >> No, so, HeatWave is something which has been designed and optimized for the cloud. So it can't be open source. So any, it's not open service. >> It is a service. >> It is a service. That's correct. >> So it's a managed service that I pay Oracle to host for me. Okay. Got it. >> That's right. >> Okay, I wonder if you could talk about some of the use cases that you're seeing for HeatWave, any patterns that you're seeing with customers? >> Sure, so we've had the service, we had the HeatWave service in limited availability for almost 15 months and it's been about five months since we have gone G. And there's a very interesting trend of our customers we're seeing. The first one is, we are seeing many migrations from AWS specifically from Aurora. Similarly, we are seeing many migrations from Azure MySQL we're migrations from Google. And the number one reason customers are coming is because of ease of use. Because they have their databases currently siloed. As you were talking about some for optimized for transactional processing, some for analytics. Here, what customers find is that in a single database, they're able to get very good performance, they don't need to move the data around, they don't need to manage multiple databaes. So we are seeing many migrations from these services. And the number one reason is reduce complexity of ease of use. And the second one is, much better performance and reduced costs, right? So that's the first thing. We are very excited and delighted to see the number of migrations we're getting. The second thing which we're seeing is, initially, when we had the service announced, we were like, targeting really towards analytics. But now what are finding is, many of these customers, for instance, who have be running on Aurora, when they are moving from MySQL in HeatWave, they are finding that many of the OLTP queries as well, are seeing significant acceleration with the HeatWave. So now customers are moving their entire applications or, to HeatWave. So that's the second trend we're seeing. The third thing, and I think I kind of missed mentioning this earlier, one of the very key and unique value propositions we provide with the MySQL Database Service in HeatWave, is that we provide a mechanism where if customers have their data stored on premise they can still leverage the HeatWave service by enabling MySQL replication. So they can have their data on premise, they can replicate this data in the Oracle Cloud and then they can run analytics. So this deployment which we are calling the hybrid deployment is turning out to be very, very popular because there are customers, there are some customers who for various reasons, compliance or regulatory reasons cannot move the entire data to the cloud or migrate the data to the cloud completely. So this provides them a very good setup where they can continue to run their existing database and when it comes to getting benefits of HeatWave for query acceleration, they can set up this replication. >> And I can run that on anyone, any available server capacity or is there an appliance to facilitate that? >> No, this is just standard MySQL replication. So if a customer is running MySQL on premise they can just turn off this application. We have obviously enhanced it to support this inbound replication between on-premise and Oracle Cloud with something which can be enabled as long as the source and destination are both MySQL. >> Okay, so I want to come back to this sort of idea of the architecture a little bit. I mean, it's hard for me to go toe to toe with the, I'm not an engineer, but I'm going to try anyway. So you've talked about OLTP queries. I thought, I always thought HeatWave was optimized for analytics. But so, I want to push on this notion because people think of this the converged database, and what you're talking about here with HeatWave is sort of the Swiss army knife which is great 'cause you got a screwdriver and you got Phillips and a flathead and some scissors, maybe they're not as good. They're not as good necessarily as the purpose-built tool. But you're arguing that this is best of breed for OLTP and best of breed for analytics, both in terms of performance and cost. Am I getting that right or is this really a Swiss army knife where that flathead is really not as good as the big, long screwdriver that I have in my bag? >> Yes, so, you're getting it right but I did want to make a clarification. That HeatWave is definitely the accelerator for all your queries, all analytic queries and also for the long running complex transaction processing inquiries. So yes, HeatWave the uber query accelerator engine. However, when it comes to transaction processing in terms of your insert statements, delete statements, those are still all done and served by the MySQL database. So all, the transactions are still sent to the MySQL database and they're persistent there, it's the queries for which HeatWave is the accelerator. So what you said is correct. For all query acceleration, HeatWave is the engine. >> Makes sense. Okay, so if I'm a MySQL customer and I want to use HeatWave, what do I have to do? Do I have to make changes to my existing applications? You applied earlier that, no, it's just sort of plugs right in. But can you clarify that. >> Yes, there are absolutely no changes, which any MySQL or MySQL compatible application needs to make to take advantage of HeatWave. HeatWave is an in-memory accelerator and it's completely transparent to the application. So we have like, dozens and dozens of like, applications which have migrated to HeatWave, and they are seeing the same thing, similarly tools. So if you look at various tools which work for analytics like, Tableau, Looker, Oracle Analytics Cloud, all of them will work just seamlessly. And this is one of the reasons we had to do a lot of heavy lifting in the MySQL database itself. So the MySQL database engineering team was, has been very actively working on this. And one of the reasons is because we did the heavy lifting and we meet enhancements to the MySQL optimizer in the MySQL storage layer to do the integration of HeatWave in such a seamless manner. So there is absolutely no change which an application needs to make in order to leverage or benefit from HeatWave. >> You said earlier, Nipun, that you're seeing migrations from, I think you said Aurora and Google BigQuery, you might've said Redshift as well. Do you, what kind of tooling do you have to facilitate migrations? >> Right, now, there are multiple ways in which customers may want to do this, right? So the first tooling which we have is that customers, as I was talking about the replication or the inbound replication mechanism, customers can set up heat HeatWave in the Oracle Cloud and they can send the data, they can set up replication within their instances in their cloud and HeatWave. Second thing is we have various kinds of tools to like, facilitate the data migration in terms of like, fast ingestion sites. So there are a lot of such customers we are seeing who are kind of migrating and we have a plethora of like, tools and applications, in addition to like, setting up this inbound application, which is the most seamless way of getting customers started with HeatWave. >> So, I think you mentioned before, I have my notes, machine intelligence and machine learning. We've seen that with autonomous database it's a big, big deal obviously. How does HeatWave take advantage of machine intelligence and machine learning? >> Yeah, and I'm probably going to be talking more about this in the future, but what we have already is that HeatWave uses machine learning to intelligently automate many operations. So we know that when there's a service being offered in the cloud, our customers expect automation. And there're a lot of vendors and a lot of services which do a good job in automation. One of the places where we're going to be very unique is that HeatWave uses machine learning to automate many of these operations. And I'll give you one such example which is provisioning. Right now with HeatWave, when a customer wants to determine how many nodes are needed for running their workload, they don't need to make a guess. They invoke a provisioning advisor and this advisor uses machine learning to sample a very small percentage of the data. We're talking about, like, 0.1% sampling and it's able to predict the amount of memory with 95% accuracy, which this data is going to take. And based on that, it's able to make a prediction of how many servers are needed. So just a simple operation, the first step of provisioning, this is something which is done manually across, on any of the service, whereas at HeatWave, we have machine learning-based advisor. So this is an example of what we're doing. And in the future, we'll be offering many such innovations as a part of the MySQL Database and the HeatWave service. >> Well, I've got to say I was skeptic but I really appreciate it, you're, answering my questions. And, a lot of people when you made the acquisition and inherited MySQL, thought you were going to kill it because they thought it would be competitive to Oracle Database. I'm happy to see that you've invested and figured out a way to, hey, we can serve our community and continue to be the steward of MySQL. So Nipun, thanks very much for coming to the CUBE. Appreciate your time. >> Sure. Thank you so much for the time, Dave. I appreciate it. >> And thank you for watching everybody. This is Dave Vellante with another CUBE Conversation. We'll see you next time. (bright upbeat music)

>> From theCUBE's studios in Palo Alto, in Boston, connecting with all leaders all around the world. This is a CUBE Conversation. >> Hello, everyone, this is Dave Vellante and welcome. We're going to do a little preview of ThoughtSpot Beyond, and we're going to look at the intersection of cloud, data, search and analytics. For a decade, we've been collecting all this information and tapping data sources for many, many different places. Now we're at the point where we can very cost-effectively and quickly put data into the hands of many orders of magnitude, more users so the data can inform opinions and ultimately actions. With me is Sudheesh Nair, who's the CEO of ThoughtSpot. Sudheesh, it's always a pleasure to have you on theCUBE. Thanks for coming on. >> Absolutely, my pleasure, Dave. Thanks for having me. >> You know it's ironic that we start this decade with so much disruption to our lives. It's forced us to become digital businesses really overnight. I wonder if you could talk about the role of data as it relates to our digital lives? >> I think the idea that data somehow directly impacts our lives sometimes can be farfetched. That is because we don't really talk about it in the right way. Data can be this archaic mountain of things that people don't really connect with. What we should really be talking about is what data does, the byproduct, the end product of data, which is the signal that we get out of the mountain of data, the insight that we derive from it and the action, the bespoke actions that makes our lives possible in this new world that we are all living in. If you really do a good job of talking about what data does for you or the by-product of what the data does for you, I think people will understand that we are incredibly connected, incredibly dependent on the signals that we derive from the data that we are giving out to the world that we are operating in today. >> We had a fire ready and aim because the speed at which we've had to adapt as we've never seen this before. I'm wondering if you could share with us what you're seeing. What kind of challenges this creates for organizations, specifically in terms of being able to leverage their data assets? >> See, I think if you think of the last eight, nine months, sometimes in our industry, it is easy to sort of look at this as an opportunity, more of an opportunistic way of looking at how can I sell more data driven things when the world is sort of falling apart. You walk on a downtown, you see all these restaurants closed, parking lots empty. My sort of less than in the last eight, nine months is to be more outside-in as opposed to inside-out. That is, why are we doing this, is now more important than what we are doing. In that context, my biggest lesson that I've learned is that the thing that stand in the way of delivering value for customers almost always is not technology, not product and not even quality of data. A lot of data people will say it is the data quality that is holding me back from doing. It is lack of courage, lack of vision, lack of ability to sort of empathize with your customers and truly see what can we do to make their lives better, where data driven insights might be a part of it. I really believe that organizations that are differentiating by providing better services where they use data to do that are clearly coming out ahead as we are looking at the end of this global pandemic. >> It's interesting what you're saying about data quality, because I agree with you. I actually think it's access to data because as a business user, I can look at data, ask a couple of questions and say, I can get pretty close to the truth. If you think about organizations generally, but specifically business users, they've been clamoring for more fast style access to data and really the time is now for them to realize this vision. I wonder if you could share with us what's happening in ThoughtSpot business in the past month, 'cause that's what you're all about, is that easy, fast access to data. >> I always talk about the decision making pipeline. I know one end, you have the data that customers are happy to give. However, it's a two way street. They are saying, look, I'll give you my data, in return I want you to do two things. Number one, make sure it is safe and protected. Number two, you are using that data to deliver a bespoke experiences for me, bespoke services for me. That is I'm giving you the data so you will get to know me and treat me as an individual, as a person with the likes and dislikes that are different from someone else's. If you don't do that, you're breaking that contract. When I think of this continuum of data to insight to knowledge to action, action is where the users benefit. I sort of sometimes worry that the chasm that exists between the people who can speak the data, the SQL, the data, warehouse people who have usually the answers and not necessarily have the questions because questions are usually coming from the business users. Our sort of purpose in life as a company in the world has been simple. That is let us break that barrier. Let's move that silos and then unify so that people with questions can get answers. People who know the business can get the answer from the data without any tax on their curiosity. It is easier said than done, but it is a journey. I strongly believe that pushing the ability to inquire and get insights from the data all the way to the front line, where business users interact with their customers, the businesses customers, the consumers, the clients, if you don't do that properly, there is no way to keep up with the velocity of change that the world is throwing at your business. >> So speaking of the data sources, one of the data sources I sometimes look at it, you look at the stock market, it is funny. The last month Pfizer announces they got a very highly successful trial and the stock market goes up 800 points. You sort of look at that and say, that's a data point. I recently released a number of pieces on cloud and its impact. After that you saw up on a cloud stocks, everybody panicked, sell tech. Even though written cloud's not immune to COVID, it's clear from our data that cloud migration has been very much accelerated since the pandemic hit and I don't really see that changing. I wonder if you could talk about the ways in which you see cloud changing, how organizations operate and really what's missing when it comes to getting the most out of their cloud investments, specifically around analytics. >> It is like any other function. Data analytics is not different in what the cloud does for the customers. I used to always talk about the world of computing, the world of technology as a race against commoditization. Imagine that it's a ocean that is warming and there's an iceberg that is floating on it. As the ocean warms the iceberg is melting and if you want to survive, you've got to keep going up the mountain, the iceberg mountain. In this example, the commoditization of technology is the ocean. Anything that you think is unique, anything that you think is proprietary, it's going to get commoditized. The reason why that's happening is because people want to go up the value chain. That's the iceberg, that's the mountain. If you use that metaphor, what you will see here is that people want to go up the value that the data analytics deliver as opposed to how cool or how differentiated the process of delivering value is. Let me explain that. Imagine that you are producing a lot of content, I am pretty sure that you have ways to sort of collect the data on how it is making an impact. That is how many people watched it, how many of them were young versus old versus Salesforce engineering versus marketing versus... You can slice and dice the data. That is where today's data analytics stops. Now, imagine if you can take it to the next level, that is what impact is it having on my consumers? Are they able to get better jobs, for example, because of a technology that you talked about or theCUBE's ability to sort of democratize access, the way sometimes you take complex technology and simplify it. Is that making easier for some execs to catch up with the speed with which technology is changing? In turn, which makes their business model agile. Our thesis is that when we stop data analytics at the noise level, the data level, the insight level, we are only doing half the job. We need to go all the way through that value chain, climb all the way up in that iceberg and think for the customer. What am I doing for the customer? There are recent examples of our banks, largest of large banks, where they had inherent bias when it comes to how they were giving loans to minorities and people of color, or the people who have an accent on the phone, they're actually calling on customer support. These sort of things are not an AI problem or a BI problem, these are human problems. By breaking the barrier between business users and their consumers, where data become an inherent part of deficient making, you can make tangible difference in the world. I think that is what we are trying to do. I know it sounds somewhat naive and utopian, but I do think this is possible if you really approach it outside-in. >> And outside-in thinking is critical. I want to pick up on something you said about kind of moving up the value chain. We've watched over the last decade, sort of the SASification of many industries. You guys recently announced ThoughtSpot Cloud, which was your first SAS offering. Tell us, how's it going? What's the uptake like, the adoption? What are customers telling you about what it's doing for their business? >> Again, this is the same outside-in story. It is relatively new, it's only been a month. The interest is pretty high and we have closed a handful of customers. I don't want to claim victory yet, but the signs have been very positive and it does not surprise me because it aligns with that story that I talked about growing up the value chain. Traditionally, when we deployed ThoughtSpot, we deployed in the customer's VPC, their own cloud or in the data center. The problem is when you are doing that, they are responsible for integrating the data, connecting the data, prepping the data, managing it. There's a lot of work that goes with it. But ThoughtSpot I would ask you, is it possible for us to do as much for the customer with TS Cloud, ThoughtSpot Cloud? That is you just go to ThoughtSpot Cloud and connect to your SAS data warehouse services that you may have, but there's Snowflake or Redshift or in a DBQ, Google BigQuery, or a Microsoft synapse and then get going immediately. To give you an idea, a typical ThoughtSpot deployment used to take around four to five months, now it is taking around 35 minutes. That's what ThoughtSpot Cloud does for our customers. If it happens in 35 minutes, their business of delivering value to their clients is happening that much faster. >> Everything shifts to actually getting insights as opposed to setting stuff up. One of the other things to do that I've been reporting on. I've said in the last decade, we kind of moved from really a product centric world to one that's more platform centric, particularly with cloud and SAS. The latest research that we've been doing shows that ecosystems, we think are going to power the next wave of innovation. I wonder what your view is of that premise and how you're thinking about ecosystems as a lever of growth. >> This word platform is one of the most abused word in our industry because people like to say, don't say product, say solution, and then say, don't say solution use platform. In reality, a platform is useless if people are not standing on. If you're standing on a railway platform, nobody's there, watch the point? The same thing applies to business, our business as to when it comes to platform. A platform is only a real platform if there are other players making money of what you have built. If you build a platform, all it does is a bunch of API. Nobody's consuming, it's not useful. In that context, we have long ways to go, we have really long ways to go. I do think one of sort of... I wouldn't say mistake, but one of the oversights that our sport had was not delivering on the vision of platform. That it is easy to make for others to come together and do commerce on ThoughtSpot. Most importantly, make sure that it is not just easy but when customers come to them, that one plus one is like 10 or 11, as opposed to one plus one equal two. That is something that we have to remedy. At the Beyond Conference, next month on December 9th, you will see us make some interesting announcements around this thing. It is one of my favorite sort of projects because once we do that very well, you will see that it becomes a platform. Think of Stripe, think of Square. These are platforms because it made their customers' lives easier, but at the same time, multiple companies could come together to deliver joint solutions where the sum is much bigger than equals of the parts. That is a vision that ThoughtSpot needs to really deliver on and Beyond will be a stock. >> I mean, the power of many versus the resources of one and this is well understood over time and now we're seeing it really applied to our industry. Sudheesh, a lot of the analytics that we produce today are the result of humans clicking and typing and interacting with systems. That's obviously going to continue to grow, but you think about things like IOT, the build-out of 5G, it brings this whole new dimension of machine to machine communications and tons of new data. Much of the data out there is analog, today, it's being increasingly become digital. How are you thinking about these trends in terms of the impact on your company and your customers? >> I think if anyone asks me, what does ThoughtSpot do for the data analytics world? My answer is very simple. We have introduced a new interface to access structure data that can be used by anybody, search that is driven by AI, that's an AI driven search. That core idea is about scale, but more importantly, rate of change. That's where the new inventions around 5G where the bottlenecks are being removed at IoT and mobile. I mean, we want to put mobile as well. So you have mobile devices, IOT devices, very big pipe, and then cloud on the backend where processing and storing is cheap. Now if you think of that, it is a 12 lane super highway, all the way to the end user, all the way to the end device, to the mothership. When you have that much speed and when you remove everything, you have to think about the asset, the artifacts that you build out of that kind of a data stream. That's where the old way of looking at dashboards will die. It's not a question of if or when it is dying. What we need is now to make sure that at that speed, when the data is changing much faster than ever before, you have new way to deliver insight to the people who can act on it, which is business users. If you think of it, there used to be cases where companies used to make supply chain decisions for the year. Now, supply chain decisions are made monthly because you don't know what next month will look like with COVID. When you have annual decisions become monthly decisions, monthly decisions become weekly decisions, weekly decisions became minute by minute decisions sometimes like placing social media sentiment changes, things like that, there is no way that you can depend on a Monday morning report or a Monday morning meeting, and then send out, here is what you need to do, action items to the front end. Everyone should have the pulse on where the business is, which is where the data is going to help them. However, human experience is so critical. You don't want to remove human experience. That's why as we deliver more and more on 5G and IoT, making the data as it is changing and then delivering those signals that insights directly to business users in the frontline is going to be like the de facto way businesses will operate. I think we are just beginning that journey in terms of what is possible. >> Well, it reminds me of when we were kids, the coaches would tell us, go to where you think the ball is going to be, find opportunities for that open space, not to where it is today. That's the notion of whether it's soccer or basketball, or of course, hockey skate to the puck is obviously a famous term. So how do you stay ahead of that disruption curve in a space like analytics? What are the innovation opportunities that organizations should be tapping today and beyond? >> I was thinking about this a lot myself, which is the important thing is to be ready to unlearn. I know it is a simple thing but it was one of the most difficult things because as you grow up in the organizations, as you become an exec, as you gain more experience, we actually calcify our knowledge. That's a problem, because things are changing. There are new way to do things, new opportunities. Being open to unlearning is going to be more critical than learning new things sometimes. That will require humility. I won't say it's a go learn AI, or go learn a new language or Python or coding. Those things might be necessary, but having that mentality of willing to unlearn and then having the courage to make some difficult decisions. If you do those two things, I think this is an exciting role. And if you're not, you're going to go the wayside of a lot of industries have been going. >> That's great advice. I mean, we saw that a lot coming into the pandemic. There was a lot of complacency around digital and of course there isn't anymore. Sudheesh, thanks so much for joining me in this CUBE Conversation. It's always great to talk to you. >> Thank you for taking the time, I appreciate it. >> My pleasure. Thank you for watching, everybody. This is Dave Vellante for theCUBE, will see you next time. (bright upbeat music)

>> Announcer: From the CUBEs Studios in Palo Alto in Boston, connecting with thought leaders all around the world. This is a CUBE Conversation. >> Welcome to this CUBE Conversation, I'm Lisa Martin. Today talking with the co CEO of MemSQL, Raj Verma. Raj, eelcome back to the CUBE. >> Thank you, Lisa. Good to see you again as always. >> You as well. So we're living in a really strange time, right? There is disruption coming at us from every angle we're used to talking about disruptors and technology as technology innovations like Cloud, for example, but now we've got this other disruption, this catalyst for more disruption with COVID-19. I wanted to ask you though, as we see so much changing in the business world for long storied businesses filing for chapter 11. What, why do companies get disrupted and how can they actually become... How can a company to become a disruptor? >> I think disruption is a tale of innovation, really, innovation from the incumbent or lack there off. And the fact that, you know, incumbents become a lot more inward focused. They become more about doing more of what got them to be successful, more process focused and outcome focused. And the disruptors are essentially again, all about innovation and all about solving the customer's problems for today and for tomorrow. So I do think disruption is at its very core, two tales of innovation, one cautionary and the other somehow legendary. And we see that in the Valley all the time. You see the favorite innovators of a decade ago, just limping along now and just being completely leapfrogged by the innovators of today. And that's really what the Valley is known for. I do think that a big part of being a disruptor or being disrupted, as I said, you know, two sides of the same sort of coin or a double edged sword really, I think for a disruptor, it's all about challenging the status quo and to be effectively able to challenge the status quo, you need a team which is United in purpose and in passion about waking up every morning and trying to, you know, as I said, challenge the status quo and not accept just because things were being done the way they were being done. And that's what tomorrow should be. I think that's really important. And I think there is a total elements to being disrupted or, you know, aiding the disruptors, which is a catalyst event of any sort that might be. You know, it was the internet for some, I mean, some really called itself, the network is a computer, one of my favorite companies and, you know, Scott G. McNealy, someone that I greatly admire and I've got to know over the years and they were preaching this gospel for 15 years and then the internet hit and they just went, they became a rocket ship and you know, Cisco, the same thing happened. A lot of companies and you know, one in particular that we even worked for together, at least I got completely disrupted and blindsided by the Cloud. I do believe that one such a disruptor right now, or one such catalyst, which will disrupt business. And you alluded to that a little while ago, is COVID-19 and the data deluge or the tsunami of data that our accompanies it you know, I was just talking to a friend and he said, you know, we are now living really in 2023, COVID-19, four months of living in COVID-19 as kind of ended up propelling us three years forward in terms of the problems that we had three years to solve, we need to solve it now. And I think, yeah, the innovation, a team that challenges the status quo and a catalyst is what disrupts companies and what aids disruptors. >> You brought up a really good point though, that there's such a huge component of the team to be able to not just react quickly, but be creative enough and confident enough to challenge that status quo. There's a lot of folks who are very comfortable in their swim lanes. memSQL has been a disruptor in the database space, but I think that team that you hit on is really essential. Without that, and without the right folks really focused together, the disruptors are going to be disrupted. >> I agree with you wholeheartedly. I think, I often say it town halls or in private meetings that we are in the talent business. We are only as good as our teams. No, if ands and buts about it. If not, you know, united in 4% in mission have immense diversity of thought and be okay to change our minds when presented with empirical evidence of something different, we will never succeed we will never disrupt. But I think a majority of majority of the population wakes up and it looks for evidence that further makes them comfortable in the prejudices and the biases that they have. And now whether that's in your professional life or in your personal life, that's majority of the population. That's why, you know, majority of the population does not innovate. If you have the courage to say that I was wrong, but the status quo is just not enough, there is a better way out there it's hard, but there is a better way out there. And that is going to add phenomenal value to our customer base, to the world at large. Now that's the kind of people that we are looking for. And we are very lucky to have. And if you are one of them, and if you really do want to make a dent in the database, universe, I know of a company. So give me a call (laughs) >> Well, challenging the status quo is hard, like you said. 'Cause getting up every day and just assuming things are going to be the same and align with your thought process, that's easy, but being willing to, as you said, be courageous is a whole other ball game. And as right now, data from yesterday is too late. You know, not only are we living in an on-demand culture, but now with the disruption, the microbial disruption data from yesterday, isn't good enough to help solve tomorrow's problems. Neither is yesterday's technology. How is memSQL helping your customers even, break the status quo? >> Lisa that's really most of the conversations I tend to have with CIO's and CEO's and given the digital work environment that we live in, there is a lot more availability because of lack of travel and other social obligations. So, you know, I have a number of these conversations with CEOs and CIOs on a weekly basis. And one of the things that most CEO's and CIO's ask for is large, how can I get the now, now? As I was saying that, you know, the COVID-19 crisis, so as to speak or event as really spurred and catalyzed, a lot of these digital innovations and something that could be for, you know, another year and another two years, maybe, or even three years needs to be done now. And the need for the now, has never been greater. Whether it be the responsiveness of your AI ML tools, or how close can we actually put a transaction? Do we, have AI ML Layer for near real time or other real, real time insights as to what's going on in the business? Because the one data point that you have, which can help you make informed decisions in this digital world is data. So how do you do it at speed? How do you do it at scale? How do you do it in a flexible environment? Is the need for the hour. Now, another aspect that they talk about is don't give me a fancier mousetrap as my CPO, the gentleman that we just hired from Google BigQuery is one of the founding members and head their engineering and product management even. And he actually put it really well. He said, you know, I, haven't come here to build a fancier mousetrap. I've come here to build in novel, new way of solving a customer's data problems and delivering the now when the customer needs us. As I said in the fastest, most economical, flexible, secure manner. That is in my opinion, the biggest need for the hour and someone who can deliver that, is going to be extremely successful in my humble opinion, because let me ask a question of any CIO or CEO or whoever is watching here. That if we could say that we would juice up your AI ML dashboard reports, you know, real time dashboards 4X in four weeks. How many of you are going to say no? How many of you are going to say that from a response time of 15 minutes, if he could give you subsecond response times like we've done for many of our vendors in the last three to four weeks, how many of the world would say, no, I stick to my slow dashboards. And that's what we are enabling Lisa, and that's why I am superbly excited about where we are and where we are headed. >> So companies that can work with innovative technology like memSQL, whether it be a retailer or a telco, for example, or healthcare organization, the companies that are going to be able to get the data, to get the now, now are those the next disruptors? >> Beyond doubt, beyond doubt. And we are already seeing like you and I were talking about defaulter show and we have you ever seen a lot of bankruptcies, amazing amount of bankruptcies for companies who did not have the infrastructure for delivering the now, now. And they essentially were feeding their own prejudices and biases by saying, oh no, I made the decision on our goal 15 years ago and I'm just going to stick by it because they're the biggest baddest database yet. But, they can solve the now problem. And guess what happened to your company? And those who were courageous enough to say, yeah, it's some of the problems of yesterday. If you had an unprecedented times and it would take a very hard and deep look and something which will shake up the status quo to be able to deliver the tomorrow for our company. For our company, to see the sunrise of tomorrow, we have to be courageous enough to question our prejudices and bias. And those are the companies which will not only survive, but they will thrive. We were talking to, you know, naturally I have a lot of conversations with investors here. You know, the SAS technology areas, is the new gold now, I mean, that's one segment of the market that's held up because that is what is enabling the courageous enterprises to deliver the tomorrow and help the employees and the customers see the sunrise of tomorrow. And those who don't, they just don't see the sunrise tomorrow. >> I know working and talking with customers is near and dear to your heart. How do you help businesses, like you mentioned a whole bunch of really big brands have filed for chapter 11 recently, brands that we've all known for decades and decades, maybe it's, you know, team, that's just not innovative enough. Like you said, Oracle, we're going to use it. How does memSQL come in? How do you, when you're talking with those customers who might be on the brink of not surviving, how do you help them from a, like a thought diversity perspective to understand what they need to do to not just survive but thrive? >> Yeah, you know, I would like to take too much of the credit here that we can be saviors of companies. The company, and the executive team needs to know their why, and we can deliver the how and we can deliver it faster, cheaper in a more secure fashion. But the courage of saying that if we don't change, we rather die and we will not see the sunrise of tomorrow has to come from the organizations. And I think that's the starting point. And we give them enough empirical evidence that there is a better way out there. And we were working with a very, very large electronic retailer. And for the retail telemetry as you pointed to, they could only get telemetry of their stores all over the world on a every day basis. I think I ran the report every 16 hours and that was just not enough of them. And they've got a very involved CEO. And they wanted sub-second response times. And the team actually taught it was not possible. And continue to say that to the executive team. Till they came across us and he showed that the 16 hour time difference was now 0.8 seconds and they jumped on it. And now their dashboards are powered by memSQL. And instead of running reports, every 16 hours, they run it every second. So you can query your retail telemetry every second. And those kinds of courageous asks and a team saying just because I made a decision two years ago now is the time actually for those teams to say, it was a different world. I made the right decision two years ago, but in the new world, there is a better way of doing things and better way of securing a future and delivering the now. And that's where we come in and we've helped a number of customers. And I actually urge a lot of the organizations who are looking for the now to have that introspective conversations internally. >> So how do you advise companies, whether it's your prospects or customers, or even memSQL to build a team that's poised for disruption? Is it generational? Is it more than that? >> I don't think it's generational at all. I don't think it's an age issue of, you know, seeing the future or having the ability to seek honors. I think it's ultimately, and I know I'm using this term a lot, it is... I've always found that very bright, intelligent self aware individuals have the ability to question their own prejudices and biases, and it requires courage and intelligence and all the rest of it. But I think that is the key that isn't that much more. And what greater impetus or reward would a company want than survival? Sometimes survival is a great impetus for innovation and we are seeing that happen a lot. And those that aren't, then don't do that. However, that said, teams which have focused from early on, on diversity of thought on, you know, different perspectives, just their DNA is more open to challenging the status quo. And that's where the organizations who've had the right cultural values of it's okay to question the status quo, it's okay to have diverse opinions, even though they drive a knife through your prejudices and biases at an organizational level and at an individual level, that DNA helps companies is coming in and paying off, you know, in spades because that cultural thought, you know, Think Tank is driving the new age of innovation and in doing so survival. So I do think that the companies that invested in the right cultural values, the right war shoes, that's being off in spades. And I think that those teams we are seeing, and those organizations with that kind of culture are jumping on the bandwagon of questioning the status quo, of using the technology of tomorrow to solve tomorrow's problems and not be steeped in heritage and even see those companies. And you can see who they will be actually I mentioned them, but they won't survive. And up here you're seeing a whole host of other companies who are so still sort of steeped in justifying that their original thought was the right thought, and I bet my bottom dollar, they don't survive. >> Next question for you, how have you been able to build your executive team at MemSQL? You've been able to build that diverse culture and how has it shaped your leadership style? >> Yeah, you know, I don't think we've... It's not as if we've gotten there, it's a constant journey and it's just something that starts off by saying, you know, we are not going to have a know-it-all culture, but we are going to have a learn-it-all culture. You know, we are going to listen and we are going to think, consider and respond. For me, diversity was a given, you know, I sort of grew up around diversity. Some of the influences of my life that have made me the person I am today came from a viewpoint of, you know, of women, you know, I had some very, very strong female influence in my life. And as I've said repeatedly, I wouldn't have been who I am or half the person I am today without that influence. So for me, it's a very natural sort of progression to have that diversity of thought and opinion as a, you know, weaved into the very fiber of any organization that I've been apart of. And we do that in a manner where we, it's not just good enough to say, we will hire the best team. I don't think that is the way that you are going to sort of address the historical imbalance, which has resulted in very single threaded thought cultures in organizations. We make it a point that at the top end of the funnel, of course, we, in our best candidate, right? However, at the top end of the funnel, we almost know legislate that there has to be X percentage of candidates who are, you know, diverse candidates. So candidates from minorities and then let the best, you know, candidates sort of get qualified. And also if there are two candidates who are equally qualified, then, you know, we encourage someone with a lot more diversity and, you know, to come onto the team. And ultimately that drives a lot of I've thought leadership in the organizations and helps us manage our blind spots a lot better. And I have so many examples of that. The amount of innovation that happens because of these working groups, which are very diverse working groups, is just, you know, unmeasurable. And we've been extremely clear about the fact of what candidates would survive, thrive, and enjoy being at memSQL. And those are the candidates who are here to build something build something for the ages, do right by each other and by the customer. You know, we don't accept the unacceptable challenge, the status quo, if you feel strongly about something stand up and your voice will be heard. You know, just because things were being done a certain way doesn't mean it has to be done the same way. And I'm very proud, very, very proud of the team that we have built and the one that we are building and, you know, it's a team that is united in purpose and very diverse in thought. And I have become a better person and a better professional with all the diversity of thought and the learnings that we have had as a consequence of that over the last a year and a half or so. And that is the cornerstone of what we are building here at memSQL and Lisa, you and I worked with one such individual, who's just made an unbelievable difference in our organization. And lastly, I think, you know, just on a personal note, the diversity angle becomes that much closer to my heart. I'm a father of two lovely girls and two lovely boys. And I just, you know, it's personal to me that if I can't leave the tech industry a better place for my daughters, then I found it, for that matter, even for my sons. But I think, you know, the daughters had their historical, you know, debts to pay. Then I don't think I would have really achieved the success that we all, as a team are hoping for. So yeah, this is extremely personal. >> And thank you for sharing all your insights. You tell a really interesting story. You know, we started talking about disruption, disruptors, how not to be disrupted, how to become a disruptor. And really some of the things that you talk about, it all really kind of comes down to the team, the DNA of the organization, and that thought diversity being courageous to break the status quo. Raj, I wish we had more time 'cause we could keep going on this, but thank you for sharing your insights. It's been really interesting conversation. >> Thank you, Lisa, it's been great to see you and stay safe and well. >> Likewise. For my guests, Raj Verma. I'm Lisa Martin, you're watching this CUBE Conversation. (soft music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR. This is breaking analysis with Dave Vellante. >> In June of this year, Snowflake filed a confidential document suggesting that it would do an IPO. Now of course, everybody knows about it, found out about it and it had a $20 billion valuation. So, many in the community and the investment community and so forth are excited about this IPO. It could be the hottest one of the year, and we're getting a number of questions from investors and practitioners and the entire Wiki bond, ETR and CUBE community. So, welcome everybody. This is Dave Vellante. This is "CUBE Insights" powered by ETR. In this breaking analysis, we're going to unpack five critical questions around Snowflake's IPO or pending IPO. And with me to discuss that is Erik Bradley. He's the Chief Engagement Strategists at ETR and he's also the Managing Director of VENN. Erik, thanks for coming on and great to see you as always. >> Great to see you too. Always enjoy being on the show. Thank you. >> Now for those of you don't know Erik, VENN is a roundtable that he hosts and he brings in CIOs, IT practitioners, CSOs, data experts and they have an open and frank conversation, but it's private to ETR clients. But they know who the individual is, what their role is, what their title is, et cetera and it's a kind of an ask me anything. And I participated in one of them this past week. Outstanding. And we're going to share with you some of that. But let's bring up the agenda slide if we can here. And these are really some of the questions that we're getting from investors and others in the community. There's really five areas that we want to address. The first is what's happening in this enterprise data warehouse marketplace? The second thing is kind of a one area. What about the legacy EDW players like Oracle and Teradata and Netezza? The third question we get a lot is can Snowflake compete with the big cloud players? Amazon, Google, Microsoft. I mean they're right there in the heart, in the thick of things there. And then what about that multi-cloud strategy? Is that viable? How much of a differentiator is that? And then we get a lot of questions on the TAM. Meaning the total available market. How big is that market? Does it justify the valuation for Snowflake? Now, Erik, you've been doing this now. You've run a couple VENNs, you've been following this, you've done some other work that you've done with Eagle Alpha. What's your, just your initial sort of takeaway from all this work that you've been doing. >> Yeah, sure. So my first take on Snowflake was about two and a half years ago. I actually hosted them for one of my VENN interviews and my initial thought was impressed. So impressed. They were talking at the time about their ability to kind of make ease of use of a multi-cloud strategy. At the time although I was impressed, I did not expect the growth and the hyper growth that we have seen now. But, looking at the company in its current iteration, I understand where the hype is coming from. I mean, it's 12 and a half billion private valuation in the last round. The least confidential IPO (laughs) anyone's ever seen (Dave laughs) with a 15 to $20 billion valuation coming out, which is more than Teradata, Margo and Cloudera combined. It's a great question. So obviously the success to this point is warranted, but we need to see what they're going to be able to do next. So I think the agenda you laid out is a great one and I'm looking forward to getting into some of those details. >> So let's start with what's happening in the marketplace and let's pull up a slide that I very much love to use. It's the classic X-Y. On the vertical axis here we show net score. And remember folks, net score is an indicator of spending momentum. ETR every quarter does like a clockwork survey where they're asking people, "Essentially are you spending more or less?" They subtract the less from the more and comes up with a net score. It's more complicated than, but like NPS, it's a very simple and reliable methodology. That's the vertical axis. And the horizontal axis is what's called market share. Market share is the pervasiveness within the data set. So it's calculated by the number of mentions of the vendor divided by the number of mentions within that sector. And what we're showing here is the EDW sector. And we've pulled out a few companies that I want to talk about. So the big three, obviously Microsoft, AWS and Google. And you can see Microsoft has a huge presence far to the right. AWS, very, very strong. A lot of Redshift in there. And then they're pretty high on the vertical axis. And then Google, not as much share, but very solid in that. Close to 60% net score. And then you can see above all of them from a vertical standpoint is Snowflake with a 77.5% net score. You can see them in the upper right there in the green. One of the highest Erik in the entire data set. So, let's start with some sort of initial comments on the big guys and Snowflakes. Your thoughts? >> Sure. Just first of all to comment on the data, what we're showing there is just the data warehousing sector, but Snowflake's actual net score is that high amongst the entire universe that we follow. Their data strength is unprecedented and we have forward-looking spending intention. So this bodes very well for them. Now, what you did say very accurately is there's a difference between their spending intentions on a net revenue level compared to AWS, Microsoft. There no one's saying that this is an apples-to-apples comparison when it comes to actual revenue. So we have to be very cognizant of that. There is domination (laughs) quite frankly from AWS and from Azure. And Snowflake is a necessary component for them not only to help facilitate a multi-cloud, but look what's happening right now in the US Congress, right? We have these tech leaders being grilled on their actual dominance. And one of the main concerns they have is the amount of data that they're collecting. So I think the environment is right to have another player like this. I think Snowflake really has a lot of longevity and our data is supporting that. And the commentary that we hear from our end users, the people that take the survey are supporting that as well. >> Okay, and then let's stay on this X-Y slide for a moment. I want to just pull out a couple of other comments here, because one of the questions we're asking is Whither, the legacy EDW players. So we've got in here, IBM, Oracle, you can see Teradata and then Hortonworks and MapR. We're going to talk a little bit about Hortonworks 'cause it's now Cloudera. We're going to talk a little bit about Hadoop and some of the data lakes. So you can see there they don't have nearly the net score momentum. Oracle obviously has a huge install base and is investing quite frankly in R&D and do an Exadata and it has its own cloud. So, it's got a lock on it's customers and if it keeps investing and adding value, it's not going away. IBM with Netezza, there's really been some questions around their commitment to that base. And I know that a lot of the folks in the VENNs that we've talked to Erik have said, "Well, we're replacing Netezza." Frank Slootman has been very vocal about going after Teradata. And then we're going to talk a little bit about the Hadoop space. But, can you summarize for us your thoughts in your research and the commentary from your community, what's going on with the legacy guys? Are these guys cooked? Can they hang on? What's your take? >> Sure. We focus on this quite a bit actually. So, I'm going to talk about it from the data perspective first, and then we'll go into some of the commentary and the panel. You even joined one yesterday. You know that it was touched upon. But, first on the data side, what we're noticing and capturing is a widening bifurcation between these cloud native and the legacy on-prem. It is undeniable. There is nothing that you can really refute. The data is concrete and it is getting worse. That gap is getting wider and wider and wider. Now, the one thing I will say is, nobody's going to rip out their legacy applications tomorrow. It takes years and years. So when you look at Teradata, right? Their market cap's only 2 billion, 2.3 billion. How much revenue growth do they need to stay where they are? Not much, right? No one's expecting them to grow 20%, which is what you're seeing on the left side of that screen. So when you look at the legacy versus the cloud native, there is very clear direction of what's happening. The one thing I would note from the data perspective is if you switched from net score or adoptions and you went to flat spending, you suddenly see Oracle and Teradata move over to that left a little bit, because again what I'm trying to say is I don't think they're going to catch up. No, but also don't think they're going away tomorrow. That these have large install bases, they have relationships. Now to kind of get into what you were saying about each particular one, IBM, they shut down Netezza. They shut it down and then they brought it back to life. How does that make you feel if you're the head of data architecture or you're DevOps and you're trying to build an application for a large company? I'm not going back to that. There's absolutely no way. Teradata on the other hand is known to be incredibly stable. They are known to just not fail. If you need to kind of re-architect or you do a migration, they work. Teradata also has a lot of compliance built in. So if you're a financials, if you have a regulated business or industry, there's still some data sets that you're not going to move up to the cloud. Whether it's a PII compliance or financial reasons, some of that stuff is still going to live on-prem. So Teradata is still has a very good niche. And from what we're hearing from our panels, then this is a direct quote if you don't mind me looking off screen for one second. But this is a great one. Basically said, "Teradata is the only one from the legacy camp who is putting up a fight and not giving up." Basically from a CIO perspective, the rest of them aren't an option anymore. But Teradata is still fighting and that's great to hear. They have their own data as a service offering and listen, they're a small market cap compared to these other companies we're talking about. But, to summarize, the data is very clear. There is a widening bifurcation between the two camps. I do not think legacy will catch up. I think all net new workloads are moving to data as a service, moving to cloud native, moving to hosted, but there are still going to be some existing legacy on-prem applications that will be supported with these older databases. And of those, Oracle and Teradata are still viable options. >> I totally agree with you and my colleague David Floyd is actually quite high on Teradata Vantage because he really does believe that a key component, we're going to talk about the TAM in a minute, but a key component of the TAM he believes must include the on-premises workloads. And Frank Slootman has been very clear, "We're not doing on-prem, we're not doing this halfway house." And so that's an opportunity for companies like Teradata, certainly Oracle I would put it in that camp is putting up a fight. Vertica is another one. They're very small, but another one that's sort of battling it out from the old NPP world. But that's great. Let's go into some of the specifics. Let's bring up here some of the specific commentary that we've curated here from the roundtables. I'm going to go through these and then ask you to comment. The first one is just, I mean, people are obviously very excited about Snowflake. It's easy to use, the whole thing zero to Snowflake in 90 minutes, but Snowflake is synonymous with cloud-native data warehousing. There are no equals. We heard that a lot from your VENN panelist. >> We certainly did. There was even more euphoria around Snowflake than I expected when we started hosting these series of data warehousing panels. And this particular gentleman that said that happens to be the global head of data architecture for a fortune 100 financials company. And you mentioned earlier that we did a report alongside Eagle Alpha. And we noticed that among fortune 100 companies that are also using the big three public cloud companies, Snowflake is growing market share faster than anyone else. They are positioned in a way where even if you're aligned with Azure, even if you're aligned with AWS, if you're a large company, they are gaining share right now. So that particular gentleman's comments was very interesting. He also made a comment that said, "Snowflake is the person who championed the idea that data warehousing is not dead yet. Use that old monthly Python line and you're not dead yet." And back in the day where the Hadoop came along and the data lakes turned into a data swamp and everyone said, "We don't need warehousing anymore." Well, that turned out to be a head fake, right? Hadoop was an interesting technology, but it's a complex technology. And it ended up not really working the way people want it. I think Snowflake came in at that point at an opportune time and said, "No, data warehousing isn't dead. We just have to separate the compute from the storage layer and look at what I can do. That increases flexibility, security. It gives you that ability to run across multi-cloud." So honestly the commentary has been nothing but positive. We can get into some of the commentary about people thinking that there's competition catching up to what they do, but there is no doubt that right now Snowflake is the name when it comes to data as a service. >> The other thing we heard a lot was ETL is going to get completely disrupted, you sort of embedded ETL. You heard one panelist say, "Well, it's interesting to see that guys like Informatica are talking about how fast they can run inside a Snowflake." But Snowflake is making that easy. That data prep is sort of part of the package. And so that does not bode well for ETL vendors. >> It does not, right? So ETL is a legacy of on-prem databases and even when Hadoop came along, it still needed that extra layer to kind of work with the data. But this is really, really disrupting them. Now the Snowflake's credit, they partner well. All the ETL players are partnered with Snowflake, they're trying to play nice with them, but the writings on the wall as more and more of this application and workloads move to the cloud, you don't need the ETL layer. Now, obviously that's going to affect their talent and Informatica the most. We had a recent comment that said, this was a CIO who basically said, "The most telling thing about the ETL players right now is every time you speak to them, all they talk about is how they work in a Snowflake architecture." That's their only metric that they talk about right now. And he said, "That's very telling." That he basically used it as it's their existential identity to be part of Snowflake. If they're not, they don't exist anymore. So it was interesting to have sort of a philosophical comment brought up in one of my roundtables. But that's how important playing nice and finding a niche within this new data as a service is for ETL, but to be quite honest, they might be going the same way of, "Okay, let's figure out our niche on these still the on-prem workloads that are still there." I think over time we might see them maybe as an M&A possibility, whether it's Snowflake or one of these new up and comers, kind of bring them in and sort of take some of the technology that's useful and layer it in. But as a large market cap, solo existing niche, I just don't know how long ETL is for this world. >> Now, yeah. I mean, you're right that if it wasn't for the marketing, they're not fighting fashion. But >> No. >> really there're some challenges there. Now, there were some contrarians in the panel and they signaled some potential icebergs ahead. And I guarantee you're going to see this in Snowflake's Red Herring when we actually get it. Like we're going to see all the risks. One of the comments, I'll mention the two and then we can talk about it. "Their engineering advantage will fade over time." Essentially we're saying that people are going to copycat and we've seen that. And the other point is, "Hey, we might see some similar things that happened to Hadoop." The public cloud players giving away these offerings at zero cost. Essentially marginal cost of adding another service is near zero. So the cloud players will use their heft to compete. Your thoughts? >> Yeah, first of all one of the reasons I love doing panels, right? Because we had three gentlemen on this panel that all had nothing but wonderful things to say. But you always get one. And this particular person is a CTO of a well known online public travel agency. We'll put it that way. And he said, "I'm going to be the contrarian here. I have seven different technologies from private companies that do the same thing that I'm evaluating." So that's the pressure from behind, right? The technology, they're going to catch up. Right now Snowflake has the best engineering which interestingly enough they took a lot of that engineering from IBM and Teradata if you actually go back and look at it, which was brought up in our panel as well. He said, "However, the engineering will catch up. They always do." Now from the other side they're getting squeezed because the big cloud players just say, "Hey, we can do this too. I can bundle it with all the other services I'm giving you and I can squeeze your pay. Pretty much give it a waive at the cost." So I do think that there is a very valid concern. When you come out with a $20 billion IPO evaluation, you need to warrant that. And when you see competitive pressures from both sides, from private emerging technologies and from the more dominant public cloud players, you're going to get squeezed there a little bit. And if pricing gets squeezed, it's going to be very, very important for Snowflake to continue to innovate. That comment you brought up about possibly being the next Cloudera was certainly the best sound bite that I got. And I'm going to use it as Clickbait in future articles, because I think everyone who starts looking to buy a Snowflake stock and they see that, they're going to need to take a look. But I would take that with a grain of salt. I don't think that's happening anytime soon, but what that particular CTO was referring to was if you don't innovate, the technology itself will become commoditized. And he believes that this technology will become commoditized. So therefore Snowflake has to continue to innovate. They have to find other layers to bring in. Whether that's through their massive war chest of cash they're about to have and M&A, whether that's them buying analytics company, whether that's them buying an ETL layer, finding a way to provide more value as they move forward is going to be very important for them to justify this valuation going forward. >> And I want to comment on that. The Cloudera, Hortonworks, MapRs, Hadoop, et cetera. I mean, there are dramatic differences obviously. I mean, that whole space was so hard, very difficult to stand up. You needed science project guys and lab coats to do it. It was very services intensive. As well companies like Cloudera had to fund all these open source projects and it really squeezed their R&D. I think Snowflake is much more focused and you mentioned some of the background of their engineers, of course Oracle guys as well. However, you will see Amazon's going to trot out a ton of customers using their RA3 managed storage and their flash. I think it's the DC two piece. They have a ton of action in the marketplace because it's just so easy. It's interesting one of the comments, you asked this yesterday, was with regard to separating compute from storage, which of course it's Snowflakes they basically invented it, it was one of their climbs to fame. The comment was what AWS has done to separate compute from storage for Redshift is largely a bolt on. Which I thought that was an interesting comment. I've had some other comments. My friend George Gilbert said, "Hey, despite claims to the contrary, AWS still hasn't separated storage from compute. What they have is really primitive." We got to dig into that some more, but you're seeing some data points that suggest there's copycatting going on. May not be as functional, but at the same time, Erik, like I was saying good enough is maybe good enough in this space. >> Yeah, and especially with the enterprise, right? You see what Microsoft has done. Their technology is not as good as all the niche players, but it's good enough and I already have a Microsoft license. So, (laughs) you know why am I going to move off of it. But I want to get back to the comment you mentioned too about that particular gentleman who made that comment about RedShift, their separation is really more of a bolt on than a true offering. It's interesting because I know who these people are behind the scenes and he has a very strong relationship with AWS. So it was interesting to me that in the panel yesterday he said he switched from Redshift to Snowflake because of that and some other functionality issues. So there is no doubt from the end users that are buying this. And he's again a fortune 100 financial organization. Not the same one we mentioned. That's a different one. But again, a fortune 100 well known financials organization. He switched from AWS to Snowflake. So there is no doubt that right now they have the technological lead. And when you look at our ETR data platform, we have that adoption reasoning slide that you show. When you look at the number one reason that people are adopting Snowflake is their feature set of technological lead. They have that lead now. They have to maintain it. Now, another thing to bring up on this to think about is when you have large data sets like this, and as we're moving forward, you need to have machine learning capabilities layered into it, right? So they need to make sure that they're playing nicely with that. And now you could go open source with the Apache suite, but Google is doing so well with BigQuery and so well with their machine learning aspects. And although they don't speak enterprise well, they don't sell to the enterprise well, that's changing. I think they're somebody to really keep an eye on because their machine learning capabilities that are layered into the BigQuery are impressive. Now, of course, Microsoft Azure has Databricks. They're layering that in, but this is an area where I think you're going to see maybe what's next. You have to have machine learning capabilities out of the box if you're going to do data as a service. Right now Snowflake doesn't really have that. Some of the other ones do. So I had one of my guest panelist basically say to me, because of that, they ended up going with Google BigQuery because he was able to run a machine learning algorithm within hours of getting set up. Within hours. And he said that that kind of capability out of the box is what people are going to have to use going forward. So that's another thing we should dive into a little bit more. >> Let's get into that right now. Let's bring up the next slide which shows net score. Remember this is spending momentum across the major cloud players and plus Snowflake. So you've got Snowflake on the left, Google, AWS and Microsoft. And it's showing three survey timeframes last October, April 20, which is right in the middle of the pandemic. And then the most recent survey which has just taken place this month in July. And you can see Snowflake very, very high scores. Actually improving from the last October survey. Google, lower net scores, but still very strong. Want to come back to that and pick up on your comments. AWS dipping a little bit. I think what's happening here, we saw this yesterday with AWS's results. 30% growth. Awesome. Slight miss on the revenue side for AWS, but look, I mean massive. And they're so exposed to so many industries. So some of their industries have been pretty hard hit. Microsoft pretty interesting. A little softness there. But one of the things I wanted to pick up on Erik, when you're talking about Google and BigQuery and it's ML out of the box was what we heard from a lot of the VENN participants. There's no question about it that Google technically I would say is one of Snowflake's biggest competitors because it's cloud native. Remember >> Yep. >> AWS did a license one time. License deal with PowerShell and had a sort of refactor the thing to be cloud native. And of course we know what's happening with Microsoft. They basically were on-prem and then they put stuff in the cloud and then all the updates happen in the cloud. And then they pushed to on-prem. But they have that what Frank Slootman calls that halfway house, but BigQuery no question technically is very, very solid. But again, you see Snowflake right now anyway outpacing these guys in terms of momentum. >> Snowflake is out outpacing everyone (laughs) across our entire survey universe. It really is impressive to see. And one of the things that they have going for them is they can connect all three. It's that multi-cloud ability, right? That portability that they bring to you is such an important piece for today's modern CIO as data architects. They don't want vendor lock-in. They are afraid of vendor lock-in. And this ability to make their data portable and to do that with ease and the flexibility that they offer is a huge advantage right now. However, I think you're a hundred percent right. Google has been so focused on the engineering side and never really focusing on the enterprise sales side. That is why they're playing catch up. I think they can catch up. They're bringing in some really important enterprise salespeople with experience. They're starting to learn how to talk to enterprise, how to sell, how to support. And nobody can really doubt their engineering. How many open sources have they given us, right? They invented Kubernetes and the entire container space. No one's really going to compete with them on that side if they learn how to sell it and support it. Yeah, right now they're behind. They're a distant third. Don't get me wrong. From a pure hosted ability, AWS is number one. Microsoft is yours. Sometimes it looks like it's number one, but you have to recognize that a lot of that is because of simply they're hosted 365. It's a SAS app. It's not a true cloud type of infrastructure as a service. But Google is a distant third, but their technology is really, really great. And their ability to catch up is there. And like you said, in the panels we were hearing a lot about their machine learning capability is right out of the box. And that's where this is going. What's the point of having this huge data if you're not going to be supporting it on new application architecture. And all of those applications require machine learning. >> Awesome. So we're. And I totally agree with what you're saying about Google. They just don't have it figured out how to sell the enterprise yet. And a hundred percent AWS has the best cloud. I mean, hands down. But a very, very competitive market as we heard yesterday in front of Congress. Now we're on the point about, can Snowflake compete with the big cloud players? I want to show one more data point. So let's bring up, this is the same chart as we showed before, but it's new adoptions. And this is really telling. >> Yeah. >> You can see Snowflake with 34% in the yellow, new adoptions, down yes from previous surveys, but still significantly higher than the other players. Interesting to see Google showing momentum on new adoptions, AWS down on new adoptions. And again, exposed to a lot of industries that have been hard hit. And Microsoft actually quite low on new adoption. So this is very impressive for Snowflake. And I want to talk about the multi-cloud strategy now Erik. This came up a lot. The VENN participants who are sort of fans of Snowflake said three things: It was really the flexibility, the security which is really interesting to me. And a lot of that had to do with the flexibility. The ability to easily set up roles and not have to waste a lot of time wrangling. And then the third was multi-cloud. And that was really something that came through heavily in the VENN. Didn't it? >> It really did. And again, I think it just comes down to, I don't think you can ever overstate how afraid these guys are of vendor lock-in. They can't have it. They don't want it. And it's best practice to make sure your sensitive information is being kind of spread out a little bit. We all know that people don't trust Bezos. So if you're in certain industries, you're not going to use AWS at all, right? So yeah, this ability to have your data portability through multi-cloud is the number one reason I think people start looking at Snowflake. And to go to your point about the adoptions, it's very telling and it bodes well for them going forward. Most of the things that we're seeing right now are net new workloads. So let's go again back to the legacy side that we were talking about, the Teradatas, IBMs, Oracles. They still have the monolithic applications and the data that needs to support that, right? Like an old ERP type of thing. But anyone who's now building a new application, bringing something new to market, it's all net new workloads. There is no net new workload that is going to go to SAP or IBM. It's not going to happen. The net new workloads are going to the cloud. And that's why when you switch from net score to adoption, you see Snowflake really stand out because this is about new adoption for net new workloads. And that's really where they're driving everything. So I would just say that as this continues, as data as a service continues, I think Snowflake's only going to gain more and more share for all the reasons you stated. Now get back to your comment about security. I was shocked by that. I really was. I did not expect these guys to say, "Oh, no. Snowflake enterprise security not a concern." So two panels ago, a gentleman from a fortune 100 financials said, "Listen, it's very difficult to get us to sign off on something for security. Snowflake is past it, it is enterprise ready, and we are going full steam ahead." Once they got that go ahead, there was no turning back. We gave it to our DevOps guys, we gave it to everyone and said, "Run with it." So, when a company that's big, I believe their fortune rank is 28. (laughs) So when a company that big says, "Yeah, you've got the green light. That we were okay with the internal compliance aspect, we're okay with the security aspect, this gives us multi-cloud portability, this gives us flexibility, ease of use." Honestly there's a really long runway ahead for Snowflake. >> Yeah, so the big question I have around the multi-cloud piece and I totally and I've been on record saying, "Look, if you're going looking for an agnostic multi-cloud, you're probably not going to go with the cloud vendor." (laughs) But I've also said that I think multi-cloud to date anyway has largely been a symptom as opposed to a strategy, but that's changing. But to your point about lock-in and also I think people are maybe looking at doing things across clouds, but I think that certainly it expands Snowflake's TAM and we're going to talk about that because they support multiple clouds and they're going to be the best at that. That's a mandate for them. The question I have is how much of complex joining are you going to be doing across clouds? And is that something that is just going to be too latency intensive? Is that really Snowflake's expertise? You're really trying to build that data layer. You're probably going to maybe use some kind of Postgres database for that. >> Right. >> I don't know. I need to dig into that, but that would be an opportunity from a TAM standpoint. I just don't know how real that is. >> Yeah, unfortunately I'm going to just be honest with this one. I don't think I have great expertise there and I wouldn't want to lead anyone a wrong direction. But from what I've heard from some of my VENN interview subjects, this is happening. So the data portability needs to be agnostic to the cloud. I do think that when you're saying, are there going to be real complex kind of workloads and applications? Yes, the answer is yes. And I think a lot of that has to do with some of the container architecture as well, right? If I can just pull data from one spot, spin it up for as long as I need and then just get rid of that container, that ethereal layer of compute. It doesn't matter where the cloud lies. It really doesn't. I do think that multi-cloud is the way of the future. I know that the container workloads right now in the enterprise are still very small. I've heard people say like, "Yeah, I'm kicking the tires. We got 5%." That's going to grow. And if Snowflake can make themselves an integral part of that, then yes. I think that's one of those things where, I remember the guy said, "Snowflake has to continue to innovate. They have to find a way to grow this TAM." This is an area where they can do so. I think you're right about that, but as far as my expertise, on this one I'm going to be honest with you and say, I don't want to answer incorrectly. So you and I need to dig in a little bit on this one. >> Yeah, as it relates to question four, what's the viability of Snowflake's multi-cloud strategy? I'll say unquestionably supporting multiple clouds, very viable. Whether or not portability across clouds, multi-cloud joins, et cetera, TBD. So we'll keep digging into that. The last thing I want to focus on here is the last question, does Snowflake's TAM justify its $20 billion valuation? And you think about the data pipeline. You go from data acquisition to data prep. I mean, that really is where Snowflake shines. And then of course there's analysis. You've got to bring in EMI or AI and ML tools. That's not Snowflake's strength. And then you're obviously preparing that, serving that up to the business, visualization. So there's potential adjacencies that they could get into that they may or may not decide to. But so we put together this next chart which is kind of the TAM expansion opportunity. And I just want to briefly go through it. We published this stuff so you can go and look at all the fine print, but it's kind of starts with the data lake disruption. You called it data swamp before. The Hadoop no schema on, right? Basically the ROI of Hadoop became reduction of investment as my friend Abby Meadow would say. But so they're kind of disrupting that data lake which really was a failure. And then really going after that enterprise data warehouse which is kind of I have it here as a 10 billion. It's actually bigger than that. It's probably more like a $20 billion market. I'll update this slide. And then really what Snowflake is trying to do is be data as a service. A data layer across data stores, across clouds, really make it easy to ingest and prepare data and then serve the business with insights. And then ultimately this huge TAM around automated decision making, real-time analytics, automated business processes. I mean, that is potentially an enormous market. We got a couple of hundred billion. I mean, just huge. Your thoughts on their TAM? >> I agree. I'm not worried about their TAM and one of the reasons why as I mentioned before, they are coming out with a whole lot of cash. (laughs) This is going to be a red hot IPO. They are going to have a lot of money to spend. And look at their management team. Who is leading the way? A very successful, wise, intelligent, acquisitive type of CEO. I think there is going to be M&A activity, and I believe that M&A activity is going to be 100% for the mindset of growing their TAM. The entire world is moving to data as a service. So let's take as a backdrop. I'm going to go back to the panel we did yesterday. The first question we asked was, there was an understanding or a theory that when the virus pandemic hit, people wouldn't be taking on any sort of net new architecture. They're like, "Okay, I have Teradata, I have IBM. Let's just make sure the lights are on. Let's stick with it." Every single person I've asked, they're just now eight different experts, said to us, "Oh, no. Oh, no, no." There is the virus pandemic, the shift from work from home. Everything we're seeing right now has only accelerated and advanced our data as a service strategy in the cloud. We are building for scale, adopting cloud for data initiatives. So, across the board they have a great backdrop. So that's going to only continue, right? This is very new. We're in the early innings of this. So for their TAM, that's great because that's the core of what they do. Now on top of it you mentioned the type of things about, yeah, right now they don't have great machine learning. That could easily be acquired and built in. Right now they don't have an analytics layer. I for one would love to see these guys talk to Alteryx. Alteryx is red hot. We're seeing great data and great feedback on them. If they could do that business intelligence, that analytics layer on top of it, the entire suite as a service, I mean, come on. (laughs) Their TAM is expanding in my opinion. >> Yeah, your point about their leadership is right on. And I interviewed Frank Slootman right in the heart of the pandemic >> So impressed. >> and he said, "I'm investing in engineering almost sight unseen. More circumspect around sales." But I will caution people. That a lot of people I think see what Slootman did with ServiceNow. And he came into ServiceNow. I have to tell you. It was they didn't have their unit economics right, they didn't have their sales model and marketing model. He cleaned that up. Took it from 120 million to 1.2 billion and really did an amazing job. People are looking for a repeat here. This is a totally different situation. ServiceNow drove a truck through BMCs install base and with IT help desk and then created this brilliant TAM expansion. Let's learn and expand model. This is much different here. And Slootman also told me that he's a situational CEO. He doesn't have a playbook. And so that's what is most impressive and interesting about this. He's now up against the biggest competitors in the world: AWS, Google and Microsoft and dozens of other smaller startups that have raised a lot of money. Look at the company like Yellowbrick. They've raised I don't know $180 million. They've got a great team. Google, IBM, et cetera. So it's going to be really, really fun to watch. I'm super excited, Erik, but I'll tell you the data right now suggest they've got a great tailwind and if they can continue to execute, this is going to be really fun to watch. >> Yeah, certainly. I mean, when you come out and you are as impressive as Snowflake is, you get a target on your back. There's no doubt about it, right? So we said that they basically created the data as a service. That's going to invite competition. There's no doubt about it. And Yellowbrick is one that came up in the panel yesterday about one of our CIOs were doing a proof of concept with them. We had about seven others mentioned as well that are startups that are in this space. However, none of them despite their great valuation and their great funding are going to have the kind of money and the market lead that Slootman is going to have which Snowflake has as this comes out. And what we're seeing in Congress right now with some antitrust scrutiny around the large data that's being collected by AWS as your Google, I'm not going to bet against this guy either. Right now I think he's got a lot of opportunity, there's a lot of additional layers and because he can basically develop this as a suite service, I think there's a lot of great opportunity ahead for this company. >> Yeah, and I guarantee that he understands well that customer acquisition cost and the lifetime value of the customer, the retention rates. Those are all things that he and Mike Scarpelli, his CFO learned at ServiceNow. Not learned, perfected. (Erik laughs) Well Erik, really great conversation, awesome data. It's always a pleasure having you on. Thank you so much, my friend. I really appreciate it. >> I appreciate talking to you too. We'll do it again soon. And stay safe everyone out there. >> All right, and thank you for watching everybody this episode of "CUBE Insights" powered by ETR. This is Dave Vellante, and we'll see you next time. (soft music)

>> Live from San Francisco, it's theCUBE covering Google Cloud Next 2018, brought to you by Google Cloud and its ecosystem partners. (techy music) >> Hey, welcome back, everyone, this is theCUBE Live in San Francisco coverage of Google Cloud Next '18, I'm John Furrier with Jeff Frick. Day three of three days of coverage, kind of getting day three going here. Our next guest, Sudhir, as the director of product management, Google Cloud, has the luxury and great job of managing BigTable, BigQuery, I'm sorry, BigQuery, I guess BigTable, BigQuery. (laughs) Welcome back to the table, good to see you. >> Thank you. >> So, you guys had a great demo yesterday, I want to get your thoughts on that, I want to explore some of the machine learning things that you guys announced, but first I want to get perspective of the show for you guys. What's going on with you guys at the show here, what are some of the big announcements, what's happening? >> A lot of different announcements across the board, so I'm responsible for data analytics on the Google Cloud. One of our key products is Google BigQuery. Large scale, cloud scale data warehouse, a lot of customers using it for bringing all their enterprise data into the data warehouse, analyzing it at scale, you can do petabyte scale queries in seconds, so that's the kind of scale we provide. So, a lot of momentum on that, we announced a lot of things, a lot of enhancements within that. For example, one of the things we announced was we have a new experience, new UI of BigQuery, now you can literally do the query, as I was saying, of petabyte scale or something, any queries that you want, and with one click you can go into Data Studio, which is our DI tool that's available, or you can go in Sheets and then from there quickly go ahead and fire up a connector, connect to BigQuery, get the data in Sheets and do analysis. >> So, ease of use is a focus. >> Ease of use is a major focus for us. As we are growing we want to make sure everybody in the organization can get access to their data, analyze it. That was one, one of the things, which is pretty unique to BigQuery, which is there is a real time collection of information, so you can... There are customers that are actually collecting real time data from click-stream, for example, on their websites or other places, and moving it directly into BigQuery and analyzing it. Example, in-game analytics, if in-game you're actually playing games and you're going to collect those events and do real time analysis, you're going to literally put it into BigQuery at scale and do that. So, a lot of customers using BigQuery at different levels. We also announced Clustering that allows you to reduce the cost, improve efficiency, and make queries almost two X faster for us. So, a lot of announcements other than the machine learning. >> Well, the one thing I saw in the demo I thought was, I mean, it was machine learning, so that's hot topic here, obviously. >> Yes. >> Is you don't have to move the data, and this is something that we've been covering, go back to the Hadoop, back when we first started doing theCUBE, you know, data pipeline, all the complexities involved in moving the data, and at the scale and size of the data all this wrangling was going on just to get some machine learning in. >> Yep. >> So, talk about that new feature where you guys are doing it inside BigQuery. I think that's important, take a minute to explain that. >> Yeah, so when we were talking to our customers one of the biggest challenges they were facing with machine learning in general, or a couple of them were, one, every time you want to do machine learning you are to take data from your core data warehouse, like in BigQuery you have petabytes of scaled data sets, terabytes of data sets. Now, if you want to do machine learning on any portion of it you take it out of BigQuery, move it into some machine learning engine, ML engine, auto-ML, anything, then you realize, "Oh, I missed some of the data that I needed." I go back then again take the data, move it, and you have to go back and forth too much time. There are analysis I think that different organizations have done. 80% of the time the data scientists say they're spending on the moving of data-- >> Right. >> Wrangling data and all of that, so that is one big problem. The second big challenge we were hearing was skillset gap, there are just not that many PhD data scientists in the industry, how do we solve that problem? So, what we said is first problem, how do we solve it, why do people have to move data to the machine learning engines? Why can't I take the machine learning capability, move it inside where the data is, so bring the machine learning closer to data rather than data closer to machine learning. So, that's what BigQuery ML is, it's an ability to run regression-like models inside the data warehouse itself in BigQuery so that you can do that. The second we said the interface can't be complex. Our audiences already know SQL, they're already analyzing data, these folks, business analysts that are using BigQuery are the experts on the data. So, what we said is use your standard SQL, write two lines of code, create model, type of the model you want to run, give us the data, we will just run the machine learning model on the backend and you can do predictions pretty easily. So, that's what we are doing with that. >> That's awesome. >> So, Sudhir, I love to hear that you were driven by that, by your customers, because one of the things we talk about all the time is democratization. >> Yeah. >> If you want innovation you've got to democratize access to the data, and then you got to democratize access to the tools to actually do stuff with the data-- >> Yes. >> That goes way beyond just the hardcore data scientist in the organization-- >> Yeah, exactly. >> And that's really what you're trying to enable the customers to be able to do. >> Absolutely, if you look at it, if you just go on LinkedIn and search for data analyst versus data scientist there is 100 X more analysts in the industry, and our thing was how do we empower these analysts that understand the data, that are familiar with SQL, to go ahead and do data science. Now, we realize they're not going to be expert machine learning folks who understand all the intricacies of how the gradient descent works, all that, that's not their skillset, so our thing was reduce the complexity, make it very simple for them to use. The framework, like just use SQL and we take care of the internal hyper-tuning, the complexity of it, model selection. We try to do that internally within the technology, and they just get a simple interface for that. So, it's really empowering the SQL analyst with an organization to do machine learning with very little to no knowledge of machine learning. >> Right. >> Talk about the history of BigQuery, where did it come from? I mean, Google has this DNA of they do it internally for themselves-- >> Yes. >> Which is a tough customer-- >> Yes. >> In Cloud Spatter we had the product manager on for Cloud Spatter. Dip Dee, she was, like amazing, like okay, baked internally, did that have the same-- >> Yes. >> BigQuery, take a minute to talk about that, because you're now making it consumable for enterprise customers. >> Yeah. >> It's not a just, "Here's BigQuery." >> No. >> Talk about the origination, how it started, why, and how you guys use it internally. >> So, BigQuery internally is called Dremel. There's a paper on Dremel available. I think in 2012 or something we published it. Dremel has been used internally for analytics across Google. So, if you think about Spanner being used for transaction management in the company across all areas, BigQuery, or Dremel internally, is what we use for all large scale data analytics within Google. So, the whole company runs on, analyzes data with it, so our things was how do we take this capability that we are driving, and imagine like, when you have seven products that are more than a billion active users, the amount of data that gets generated, the insights we are giving in Maps and all the different places, a lot of those things are first analyzed in Dremel internally and we're making it available. So, our thing was how do we take that capability that's there internally and make it available to all enterprises. >> Right. >> As Sundhir was saying yesterday, our goal is empower all our customers to go ahead and do more. >> Right. >> And so, this is a way of taking the piece of technology that's powered Google for a while and also make it available to enterprises. >> It's tested, hardened and tested. >> Yeah, absolutely. >> It's not like it's vaporware. >> Yeah, it's not. (laughs) >> No, I mean, this is what I think is important about the show this year. If you look at it, you guys have done a really good job of taking the big guns of Google, the big stuff, and not try to just say, "We're Google and you can be like Google." You've taken it and you've kind of made it consumable. >> Yes. >> This has been a big focus, explain the mindset behind the product management. >> Absolutely, there is actually one of the key things Google is good at doing is taking what's there internally used, but also the research part of it. Actually, Corinna Cortes, who is head of our AI side who does a lot of research in SQL-based machine learning, so again, the-- >> Yeah. >> BigQuery ML is nothing new, like we internally have a research team that has been developing it for a few years. We have been using it internally for running all these models and all, and so what we were able to do it bring product management from our side, like hey, this is really a problem we are facing, moving data, skillset gap, and then we were like, research team was already enabling it and then we had an engineering team which is pretty strong. We were like, okay, let's bring all three triads together and go ahead and make sure we provide a real value to our customers with all of that we're doing, so that's how it came to light. >> So, I just want to get your take, early days like when there was the early Google search appliance, I'll just pick that up, and that was ancient, ancient ago, but one of the digs was, right, it didn't work as well in the enterprise, per se, because you just didn't have the same amount of data when you applied that type of technique to a Google flow of data and a Google flow of queries. So, how's that evolved over time, because you guys, like you said, seven applications with a billion-- >> Yep. >> Users, most enterprises don't have that, so how do they get the same type of performance if they don't have the same kind of throughput to build the models and to get that data, how's that kind of evolved? >> So, this is why I think thinking about, when we think about scale we think about scaling up and scaling down, right? We have customers who are using BigQuery with a few terabytes of data. Not every customer has petabytes scale, but what we're also noticing is these same customers, when they see value in data they collect more. I will give you a real example, Zulily, one of our customers, I used to be there before, so when they started doing real time data collection for doing real time analytics they were collecting like 50 million events a day. Within 18 months they started collecting five billion a day, 100 x improvement, and the reason is they started seeing value. They could take this real time data, analyze it, make some real time experiences possible on their website and all, with all of that they were able to go out and get real valuer for their customers, drive growth, so when customers see that kind of value they collect more data. So, what I would say is yes, a lot of customers start small, but they all have an aspiration to have lots of data, leverage that to create operational efficiency as well as growth, and so as they start doing that I think they will need infrastructure that can scale down and up all the way, and I think that's what we're focusing on, providing that. >> You guys look at the possibility, and I've seen some examples where customers are just, like, they're shell-shocked, and you're almost too good, right? I mean, it's like, "We've been doing "Dremel on a large scale, I bought this "data warehouse like 10 years ago," like what are you talking about? (laughs) I mean, there's a reality of we've been buying IT, enterprises have been buying IT and in comes Google, the gunslinger saying, "Hey, man, you can do all this stuff." There's a little bit of shell-shock factor for some IT people. Some engineering organizations get it right away. How are you guys dealing with this as you make it consumable? >> Yeah. >> There's probably a lot of education. As a product manager do you see, is that something that you think about, is that something you guys talk about? >> Yes, we do, so I think I actually see a difference in how customers, what customers need, enterprise customers versus cloud native companies. As you said, cloud native companies starting new, starting fresh, so it's a very different set of requirement. Enterprise customers, thinking about scale, thinking about security and how do you do that. So, BigQuery is a highly secure data warehouse. The other thing BigQuery has is it's a completely serverless platform, so we take care of the security. We encrypt all the data at rest and when it's moving. The key thing is when we share what is possible and how easy it is to manage and how fast people can start analyzing, you can bring the data. Like you can actually get started with BigQuery in minutes, like you just bring your data in and start analyzing it. You don't have to worry about how many machines do I need, how do I provision it, how many servers do I need. >> Yeah. >> So, enterprises, when they look at-- >> Cloud native ready. >> Yeah. >> All right, so take a minute to explain BigTable versus, I mean, BigTable versus BigQuery. >> Yes. >> What's the difference between the two, one's a data warehouse and the other one is a system for managing data? What's the difference between Big-- >> So, it's a no-SQL system, so I will... The simple example, I will give you a real example how customers use it, right. BigQuery is great for large scale analytics, people who want to take, like, petabyte scale data or terabyte scale data and analyze historical patterns, all of that, and do complex analysis. You want to do machine learning model creation, you can do that. What BigTable is great at is once you have pre-aggregated data you want to go ahead and really fast serving. If you have a website, I don't expect you to run a website and back it with BigQuery, it's not built for that. Whereas BigTable is exactly for that scenario, so for example, you have millions of people coming on the website, they want to see some key metrics that have been pre-created ready to go, you go to BigTable and that can actually do high performance, high throughput. Last statement on that, like almost 10,000-- >> Yeah. >> Requests per second per node and you can just create as many as you want, so you can really create high scale-- >> Auto-scaling, all kinds of stuff there. >> Exactly. >> And that's good for unstructured data as well-- >> Exactly. >> And managing it. >> Absolutely. >> Okay, so structured data, SQL, basically large scale-- >> Yes. >> BigTable for real time-- >> Yes. >> New kinds of datas, different data types. >> Absolutely, yes. >> What else do you have in the bag of goodies in there that you're working on? >> The one big thing that we also announced with this week was a GIS capability within BigQuery. GIS is geographical information, like everything today is location-based, latitude, longitude. Our customers were telling us really difficult to analyze it, right, like I want to know... Example would be we are here, I want to know how many food restaurants are in a two-mile radius of here, which ones are those, how many, should we create the next one here or not. Those kind of analyses are really difficult, so we partnered with Earth Engine, Earth Engine team within Google with Maps, and then what we're launching is ability to do geospatial analysis within BigQuery. Additionally along with that we also have a visualization tool that we launched this week, so folks who haven't seen that should go check that out. One great example I will give you is Geotab, their CEO is here, Neil. He was showing a demo in one of the sessions and he was talking about how he was able to transform his business. I'll give you an example, Geotab is basically into vehicle tracking, so they have these sensors that track different things with vehicles, and then with, and they store everything in BigQuery, collect all of that and all, and his thing was with BigQuery ML and a GIS capability, what he's now able to do is create models that can predict what intersections in a city when it's snowing are going to be dangerous, and for smart cities he can now recommend to cities where and how to invest in these kind of scenarios. Completely transforming his business because his business is not smart cities, his business was vehicle tracking and all, he's like, but with these capabilities they're transforming what they were doing and solving-- >> New discoveries. >> New discoveries, solving new problems, it's amazing. I wonder if you could just dig at a little bit to, you know, the fact that you've got this, these seven billion activities or apps that you can leverage, you know, specific functionality or goals or objectives or priorities in those groups, and now apply those, pull that data, pull that knowledge, pull those use cases into a completely different application on the enterprise. I mean, is that an active process-- >> I don't think that's how people. >> Do people query? >> No, no. >> But how does that happen? >> No, we don't-- >> As a customer. >> As a customer completely different, right? Our focus in Google Cloud is primarily enabling enterprises to collect their data, process their data, innovate on their data. We don't bring in, like, the Google side of it at all, like that's their completely different area that way, so we basically, enterprises, all their data stays within their environment. They basically, we don't touch it, we don't get to access it at all, and they can know it. >> Yeah, yeah, no, I didn't mean that, I meant, you know, like say Maps for instance, it's interesting to see how Maps has evolved over all these years. Every time you open it, oh, and it's directions-- >> Yep. >> Oh, now it's better directions, oh, now it's got gas stations, oh, now it's where the... And it triggered because you said the restaurants that are close by, so it's kind of adding value to the core app on that side, and as you just said, now geolocation can be used on the enterprise side-- >> Yeah, yes. >> And lots of different things, so that-- >> Exactly. >> That's where I meant that kind of connection-- >> Exactly right, so-- >> In terms of the value of what can I do with geolocation. >> Absolutely, exactly, so like, that's exactly what we did. With Earth Engine we had a lot of learnings on geospatial analysis and our thing was how do you make it easy for our enterprise customers to do that. We've partnered with them closely and we said, "Okay, here are the core pieces of things "we can add in BigQuery that will allow you "to do better geospatial analysis, visualize it." One of the big challenges is lat longs, I don't think they're that friendly with analysts, like oh, numbers and all that. So, we actually will turn a UI visualization tool that allows you to just fire a query and see visually on a map where things are, all the points look like and all. >> Awesome. >> So, just simplifying what analysts can do with all these. >> Sudhir, thanks for coming on, really appreciate it and congratulations on your success. Got a lot of great, big products there, hardened internally, now-- >> Yes. >> Making consumable, it's clear here at Google Cloud you guys are recognized that making it consumable-- >> Yep. >> Pre-existing, proven technologies, so I want to give you guys props for that, congratulations. >> Thank you, thanks a lot. >> Thanks for coming on the show. >> Thanks for coming on. >> Thank you. >> It's theCUBE coverage here, Google Cloud coverage, Google Next 2018. I'm John Furrier with Jeff Frick, stay with us, we've got all day with more coverage for day three. Stay with us after this short break. (techy music)