>>Good morning fellow nerds and welcome back to AWS Reinvent. We are live from the show floor here in Las Vegas, Nevada. My name is Savannah Peterson, joined with my fabulous co-host John Furrier. Day two keynotes are rolling. >>Yeah. What do you thinking this? This is the day where everything comes, so the core gets popped off the bottle, all the announcements start flowing out tomorrow. You hear machine learning from swee lot more in depth around AI probably. And then developers with Verner Vos, the CTO who wrote the seminal paper in in early two thousands around web service that becames. So again, just another great year of next level cloud. Big discussion of data in the keynote bulk of the time was talking about data and business intelligence, business transformation easier. Is that what people want? They want the easy button and we're gonna talk a lot about that in this segment. I'm really looking forward to this interview. >>Easy button. We all want the >>Easy, we want the easy button. >>I love that you brought up champagne. It really feels like a champagne moment for the AWS community as a whole. Being here on the floor feels a bit like the before times. I don't want to jinx it. Our next guest, Scott Castle, from Si Sense. Thank you so much for joining us. How are you feeling? How's the show for you going so far? Oh, >>This is exciting. It's really great to see the changes that are coming in aws. It's great to see the, the excitement and the activity around how we can do so much more with data, with compute, with visualization, with reporting. It's fun. >>It is very fun. I just got a note. I think you have the coolest last name of anyone we've had on the show so far, castle. Oh, thank you. I'm here for it. I'm sure no one's ever said that before, but I'm so just in case our audience isn't familiar, tell us about >>Soy Sense is an embedded analytics platform. So we're used to take the queries and the analysis that you can power off of Aurora and Redshift and everything else and bring it to the end user in the applications they already know how to use. So it's all about embedding insights into tools. >>Embedded has been a, a real theme. Nobody wants to, it's I, I keep using the analogy of multiple tabs. Nobody wants to have to leave where they are. They want it all to come in there. Yep. Now this space is older than I think everyone at this table bis been around since 1958. Yep. How do you see Siente playing a role in the evolution there of we're in a different generation of analytics? >>Yeah, I mean, BI started, as you said, 58 with Peter Lu's paper that he wrote for IBM kind of get became popular in the late eighties and early nineties. And that was Gen one bi, that was Cognos and Business Objects and Lotus 1 23 think like green and black screen days. And the way things worked back then is if you ran a business and you wanted to get insights about that business, you went to it with a big check in your hand and said, Hey, can I have a report? And they'd come back and here's a report. And it wasn't quite right. You'd go back and cycle, cycle, cycle and eventually you'd get something. And it wasn't great. It wasn't all that accurate, but it's what we had. And then that whole thing changed in about two, 2004 when self-service BI became a thing. And the whole idea was instead of going to it with a big check in your hand, how about you make your own charts? >>And that was totally transformative. Everybody started doing this and it was great. And it was all built on semantic modeling and having very fast databases and data warehouses. Here's the problem, the tools to get to those insights needed to serve both business users like you and me and also power users who could do a lot more complex analysis and transformation. And as the tools got more complicated, the barrier to entry for everyday users got higher and higher and higher to the point where now you look, look at Gartner and Forester and IDC this year. They're all reporting in the same statistic. Between 10 and 20% of knowledge workers have learned business intelligence and everybody else is just waiting in line for a data analyst or a BI analyst to get a report for them. And that's why the focus on embedded is suddenly showing up so strong because little startups have been putting analytics into their products. People are seeing, oh my, this doesn't have to be hard. It can be easy, it can be intuitive, it can be native. Well why don't I have that for my whole business? So suddenly there's a lot of focus on how do we embed analytics seamlessly? How do we embed the investments people make in machine learning in data science? How do we bring those back to the users who can actually operationalize that? Yeah. And that's what Tysons does. Yeah. >>Yeah. It's interesting. Savannah, you know, data processing used to be what the IT department used to be called back in the day data processing. Now data processing is what everyone wants to do. There's a ton of data we got, we saw the keynote this morning at Adam Lesky. There was almost a standing of vision, big applause for his announcement around ML powered forecasting with Quick Site Cube. My point is people want automation. They want to have this embedded semantic layer in where they are not having all the process of ETL or all the muck that goes on with aligning the data. All this like a lot of stuff that goes on. How do you make it easier? >>Well, to be honest, I, I would argue that they don't want that. I think they, they think they want that, cuz that feels easier. But what users actually want is they want the insight, right? When they are about to make a decision. If you have a, you have an ML powered forecast, Andy Sense has had that built in for years, now you have an ML powered forecast. You don't need it two weeks before or a week after in a report somewhere. You need it when you're about to decide do I hire more salespeople or do I put a hundred grand into a marketing program? It's putting that insight at the point of decision that's important. And you don't wanna be waiting to dig through a lot of infrastructure to find it. You just want it when you need it. What's >>The alternative from a time standpoint? So real time insight, which is what you're saying. Yep. What's the alternative? If they don't have that, what's >>The alternative? Is what we are currently seeing in the market. You hire a bunch of BI analysts and data analysts to do the work for you and you hire enough that your business users can ask questions and get answers in a timely fashion. And by the way, if you're paying attention, there's not enough data analysts in the whole world to do that. Good luck. I am >>Time to get it. I really empathize with when I, I used to work for a 3D printing startup and I can, I have just, I mean, I would call it PTSD flashbacks of standing behind our BI guy with my list of queries and things that I wanted to learn more about our e-commerce platform in our, in our marketplace and community. And it would take weeks and I mean this was only in 2012. We're not talking 1958 here. We're talking, we're talking, well, a decade in, in startup years is, is a hundred years in the rest of the world life. But I think it's really interesting. So talk to us a little bit about infused and composable analytics. Sure. And how does this relate to embedded? Yeah. >>So embedded analytics for a long time was I want to take a dashboard I built in a BI environment. I wanna lift it and shift it into some other application so it's close to the user and that is the right direction to go. But going back to that statistic about how, hey, 10 to 20% of users know how to do something with that dashboard. Well how do you reach the rest of users? Yeah. When you think about breaking that up and making it more personalized so that instead of getting a dashboard embedded in a tool, you get individual insights, you get data visualizations, you get controls, maybe it's not even actually a visualization at all. Maybe it's just a query result that influences the ordering of a list. So like if you're a csm, you have a list of accounts in your book of business, you wanna rank those by who's priorities the most likely to churn. >>Yeah. You get that. How do you get that most likely to churn? You get it from your BI system. So how, but then the question is, how do I insert that back into the application that CSM is using? So that's what we talk about when we talk about Infusion. And SI started the infusion term about two years ago and now it's being used everywhere. We see it in marketing from Click and Tableau and from Looker just recently did a whole launch on infusion. The idea is you break this up into very small digestible pieces. You put those pieces into user experiences where they're relevant and when you need them. And to do that, you need a set of APIs, SDKs, to program it. But you also need a lot of very solid building blocks so that you're not building this from scratch, you're, you're assembling it from big pieces. >>And so what we do aty sense is we've got machine learning built in. We have an LQ built in. We have a whole bunch of AI powered features, including a knowledge graph that helps users find what else they need to know. And we, we provide those to our customers as building blocks so that they can put those into their own products, make them look and feel native and get that experience. In fact, one of the things that was most interesting this last couple of couple of quarters is that we built a technology demo. We integrated SI sensee with Office 365 with Google apps for business with Slack and MS teams. We literally just threw an Nlq box into Excel and now users can go in and say, Hey, which of my sales people in the northwest region are on track to meet their quota? And they just get the table back in Excel. They can build charts of it and PowerPoint. And then when they go to their q do their QBR next week or week after that, they just hit refresh to get live data. It makes it so much more digestible. And that's the whole point of infusion. It's bigger than just, yeah. The iframe based embedding or the JavaScript embedding we used to talk about four or five years >>Ago. APIs are very key. You brought that up. That's gonna be more of the integration piece. How does embedable and composable work as more people start getting on board? It's kind of like a Yeah. A flywheel. Yes. What, how do you guys see that progression? Cause everyone's copying you. We see that, but this is a, this means it's standard. People want this. Yeah. What's next? What's the, what's that next flywheel benefit that you guys coming out with >>Composability, fundamentally, if you read the Gartner analysis, right, they, when they talk about composable, they're talking about building pre-built analytics pieces in different business units for, for different purposes. And being able to plug those together. Think of like containers and services that can, that can talk to each other. You have a composition platform that can pull it into a presentation layer. Well, the presentation layer is where I focus. And so the, so for us, composable means I'm gonna have formulas and queries and widgets and charts and everything else that my, that my end users are gonna wanna say almost minority report style. If I'm not dating myself with that, I can put this card here, I can put that chart here. I can set these filters here and I get my own personalized view. But based on all the investments my organization's made in data and governance and quality so that all that infrastructure is supporting me without me worrying much about it. >>Well that's productivity on the user side. Talk about the software angle development. Yeah. Is your low code, no code? Is there coding involved? APIs are certainly the connective tissue. What's the impact to Yeah, the >>Developer. Oh. So if you were working on a traditional legacy BI platform, it's virtually impossible because this is an architectural thing that you have to be able to do. Every single tool that can make a chart has an API to embed that chart somewhere. But that's not the point. You need the life cycle automation to create models, to modify models, to create new dashboards and charts and queries on the fly. And be able to manage the whole life cycle of that. So that in your composable application, when you say, well I want chart and I want it to go here and I want it to do this and I want it to be filtered this way you can interact with the underlying platform. And most importantly, when you want to use big pieces like, Hey, I wanna forecast revenue for the next six months. You don't want it popping down into Python and writing that yourself. >>You wanna be able to say, okay, here's my forecasting algorithm. Here are the inputs, here's the dimensions, and then go and just put it somewhere for me. And so that's what you get withy sense. And there aren't any other analytics platforms that were built to do that. We were built that way because of our architecture. We're an API first product. But more importantly, most of the legacy BI tools are legacy. They're coming from that desktop single user, self-service, BI environment. And it's a small use case for them to go embedding. And so composable is kind of out of reach without a complete rebuild. Right? But with SI senses, because our bread and butter has always been embedding, it's all architected to be API first. It's integrated for software developers with gi, but it also has all those low code and no code capabilities for business users to do the minority report style thing. And it's assemble endless components into a workable digital workspace application. >>Talk about the strategy with aws. You're here at the ecosystem, you're in the ecosystem, you're leading product and they have a strategy. We know their strategy, they have some stuff, but then the ecosystem goes faster and ends up making a better product in most of the cases. If you compare, I know they'll take me to school on that, but I, that's pretty much what we report on. Mongo's doing a great job. They have databases. So you kind of see this balance. How are you guys playing in the ecosystem? What's the, what's the feedback? What's it like? What's going on? >>AWS is actually really our best partner. And the reason why is because AWS has been clear for many, many years. They build componentry, they build services, they build infrastructure, they build Redshift, they build all these different things, but they need, they need vendors to pull it all together into something usable. And fundamentally, that's what Cient does. I mean, we didn't invent sequel, right? We didn't invent jackal or dle. These are not, these are underlying analytics technologies, but we're taking the bricks out of the briefcase. We're assembling it into something that users can actually deploy for their use cases. And so for us, AWS is perfect because they focus on the hard bits. The the underlying technologies we assemble those make them usable for customers. And we get the distribution. And of course AWS loves that. Cause it drives more compute and it drives more, more consumption. >>How much do they pay you to say that >>Keynote, >>That was a wonderful pitch. That's >>Absolutely, we always say, hey, they got a lot of, they got a lot of great goodness in the cloud, but they're not always the best at the solutions and that they're trying to bring out, and you guys are making these solutions for customers. Yeah. That resonates with what they got with Amazon. For >>Example, we, last year we did a, a technology demo with Comprehend where we put comprehend inside of a semantic model and we would compile it and then send it back to Redshift. And it takes comprehend, which is a very cool service, but you kind of gotta be a coder to use it. >>I've been hear a lot of hype about the semantic layer. What is, what is going on with that >>Semantec layer is what connects the actual data, the tables in your database with how they're connected and what they mean so that a user like you or me who's saying I wanna bar chart with revenue over time can just work with revenue and time. And the semantic layer translates between what we did and what the database knows >>About. So it speaks English and then they converts it to data language. It's >>Exactly >>Right. >>Yeah. It's facilitating the exchange of information. And, and I love this. So I like that you actually talked about it in the beginning, the knowledge map and helping people figure out what they might not know. Yeah. I, I am not a bi analyst by trade and I, I don't always know what's possible to know. Yeah. And I think it's really great that you're doing that education piece. I'm sure, especially working with AWS companies, depending on their scale, that's gotta be a big part of it. How much is the community play a role in your product development? >>It's huge because I'll tell you, one of the challenges in embedding is someone who sees an amazing experience in outreach or in seismic. And to say, I want that. And I want it to be exactly the way my product is built, but I don't wanna learn a lot. And so you, what you want do is you want to have a community of people who have already built things who can help lead the way. And our community, we launched a new version of the SES community in early 2022 and we've seen a 450% growth in the c in that community. And we've gone from an average of one response, >>450%. I just wanna put a little exclamation point on that. Yeah, yeah. That's awesome. We, >>We've tripled our organic activity. So now if you post this Tysons community, it used to be, you'd get one response maybe from us, maybe from from a customer. Now it's up to three. And it's continuing to trend up. So we're, it's >>Amazing how much people are willing to help each other. If you just get in the platform, >>Do it. It's great. I mean, business is so >>Competitive. I think it's time for the, it's time. I think it's time. Instagram challenge. The reels on John. So we have a new thing. We're gonna run by you. Okay. We just call it the bumper sticker for reinvent. Instead of calling it the Instagram reels. If we're gonna do an Instagram reel for 30 seconds, what would be your take on what's going on this year at Reinvent? What you guys are doing? What's the most important story that you would share with folks on Instagram? >>You know, I think it's really what, what's been interesting to me is the, the story with Redshift composable, sorry. No, composable, Redshift Serverless. Yeah. One of the things I've been >>Seeing, we know you're thinking about composable a lot. Yes. Right? It's, it's just, it's in there, it's in your mouth. Yeah. >>So the fact that Redshift Serverless is now kind becoming the defacto standard, it changes something for, for my customers. Cuz one of the challenges with Redshift that I've seen in, in production is if as people use it more, you gotta get more boxes. You have to manage that. The fact that serverless is now available, it's, it's the default means it now people are just seeing Redshift as a very fast, very responsive repository. And that plays right into the story I'm telling cuz I'm telling them it's not that hard to put some analysis on top of things. So for me it's, it's a, maybe it's a narrow Instagram reel, but it's an >>Important one. Yeah. And that makes it better for you because you get to embed that. Yeah. And you get access to better data. Faster data. Yeah. Higher quality, relevant, updated. >>Yep. Awesome. As it goes into that 80% of knowledge workers, they have a consumer great expectation of experience. They're expecting that five ms response time. They're not waiting 2, 3, 4, 5, 10 seconds. They're not trained on theola expectations. And so it's, it matters a lot. >>Final question for you. Five years out from now, if things progress the way they're going with more innovation around data, this front end being very usable, semantic layer kicks in, you got the Lambda and you got serverless kind of coming in, helping out along the way. What's the experience gonna look like for a user? What's it in your mind's eye? What's that user look like? What's their experience? >>I, I think it shifts almost every role in a business towards being a quantitative one. Talking about, Hey, this is what I saw. This is my hypothesis and this is what came out of it. So here's what we should do next. I, I'm really excited to see that sort of scientific method move into more functions in the business. Cuz for decades it's been the domain of a few people like me doing strategy, but now I'm seeing it in CSMs, in support people and sales engineers and line engineers. That's gonna be a big shift. Awesome. >>Thank >>You Scott. Thank you so much. This has been a fantastic session. We wish you the best at si sense. John, always pleasure to share the, the stage with you. Thank you to everybody who's attuning in, tell us your thoughts. We're always eager to hear what, what features have got you most excited. And as you know, we will be live here from Las Vegas at reinvent from the show floor 10 to six all week except for Friday. We'll give you Friday off with John Furrier. My name's Savannah Peterson. We're the cube, the the, the leader in high tech coverage.

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> This past week we saw two of the Big 3 cloud providers present the latest update on their respective cloud visions, their business progress, their announcements and innovations. The content at these events had many overlapping themes, including modern cloud infrastructure at global scale, applying advanced machine intelligence, AKA AI, end-to-end data platforms, collaboration software. They talked a lot about the future of work automation. And they gave us a little taste, each company of the Metaverse Web 3.0 and much more. Despite these striking similarities, the differences between these two cloud platforms and that of AWS remains significant. With Microsoft leveraging its massive application software footprint to dominate virtually all markets and Google doing everything in its power to keep up with the frenetic pace of today's cloud innovation, which was set into motion a decade and a half ago by AWS. Hello and welcome to this week's Wikibon CUBE Insights, powered by ETR. In this Breaking Analysis, we unpack the immense amount of content presented by the CEOs of Microsoft and Google Cloud at Microsoft Ignite and Google Cloud Next. We'll also quantify with ETR survey data the relative position of these two cloud giants in four key sectors: cloud IaaS, BI analytics, data platforms and collaboration software. Now one thing was clear this past week, hybrid events are the thing. Google Cloud Next took place live over a 24-hour period in six cities around the world, with the main gathering in New York City. Microsoft Ignite, which normally is attended by 30,000 people, had a smaller event in Seattle, in person with a virtual audience around the world. AWS re:Invent, of course, is much different. Yes, there's a virtual component at re:Invent, but it's all about a big live audience gathering the week after Thanksgiving, in the first week of December in Las Vegas. Regardless, Satya Nadella keynote address was prerecorded. It was highly produced and substantive. It was visionary, energetic with a strong message that Azure was a platform to allow customers to build their digital businesses. Doing more with less, which was a key theme of his. Nadella covered a lot of ground, starting with infrastructure from the compute, highlighting a collaboration with Arm-based, Ampere processors. New block storage, 60 regions, 175,000 miles of fiber cables around the world. He presented a meaningful multi-cloud message with Azure Arc to support on-prem and edge workloads, as well as of course the public cloud. And talked about confidential computing at the infrastructure level, a theme we hear from all cloud vendors. He then went deeper into the end-to-end data platform that Microsoft is building from the core data stores to analytics, to governance and the myriad tooling Microsoft offers. AI was next with a big focus on automation, AI, training models. He showed demos of machines coding and fixing code and machines automatically creating designs for creative workers and how Power Automate, Microsoft's RPA tooling, would combine with Microsoft Syntex to understand documents and provide standard ways for organizations to communicate with those documents. There was of course a big focus on Azure as developer cloud platform with GitHub Copilot as a linchpin using AI to assist coders in low-code and no-code innovations that are coming down the pipe. And another giant theme was a workforce transformation and how Microsoft is using its heritage and collaboration and productivity software to move beyond what Nadella called productivity paranoia, i.e., are remote workers doing their jobs? In a world where collaboration is built into intelligent workflows, and he even showed a glimpse of the future with AI-powered avatars and partnerships with Meta and Cisco with Teams of all firms. And finally, security with a bevy of tools from identity, endpoint, governance, et cetera, stressing a suite of tools from a single provider, i.e., Microsoft. So a couple points here. One, Microsoft is following in the footsteps of AWS with silicon advancements and didn't really emphasize that trend much except for the Ampere announcement. But it's building out cloud infrastructure at a massive scale, there is no debate about that. Its plan on data is to try and provide a somewhat more abstracted and simplified solutions, which differs a little bit from AWS's approach of the right database tool, for example, for the right job. Microsoft's automation play appears to provide simple individual productivity tools, kind of a ground up approach and make it really easy for users to drive these bottoms up initiatives. We heard from UiPath that forward five last month, a little bit of a different approach of horizontal automation, end-to-end across platforms. So quite a different play there. Microsoft's angle on workforce transformation is visionary and will continue to solidify in our view its dominant position with Teams and Microsoft 365, and it will drive cloud infrastructure consumption by default. On security as well as a cloud player, it has to have world-class security, and Azure does. There's not a lot of debate about that, but the knock on Microsoft is Patch Tuesday becomes Hack Wednesday because Microsoft releases so many patches, it's got so much Swiss cheese in its legacy estate and patching frequently, it becomes a roadmap and a trigger for hackers. Hey, patch Tuesday, these are all the exploits that you can go after so you can act before the patches are implemented. And so it's really become a problem for users. As well Microsoft is competing with many of the best-of-breed platforms like CrowdStrike and Okta, which have market momentum and appear to be more attractive horizontal plays for customers outside of just the Microsoft cloud. But again, it's Microsoft. They make it easy and very inexpensive to adopt. Now, despite the outstanding presentation by Satya Nadella, there are a couple of statements that should raise eyebrows. Here are two of them. First, as he said, Azure is the only cloud that supports all organizations and all workloads from enterprises to startups, to highly regulated industries. I had a conversation with Sarbjeet Johal about this, to make sure I wasn't just missing something and we were both surprised, somewhat, by this claim. I mean most certainly AWS supports more certifications for example, and we would think it has a reasonable case to dispute that claim. And the other statement, Nadella made, Azure is the only cloud provider enabling highly regulated industries to bring their most sensitive applications to the cloud. Now, reasonable people can debate whether AWS is there yet, but very clearly Oracle and IBM would have something to say about that statement. Now maybe it's not just, would say, "Oh, they're not real clouds, you know, they're just going to hosting in the cloud if you will." But still, when it comes to mission-critical applications, you would think Oracle is really the the leader there. Oh, and Satya also mentioned the claim that the Edge browser, the Microsoft Edge browser, no questions asked, he said, is the best browser for business. And we could see some people having some questions about that. Like isn't Edge based on Chrome? Anyway, so we just had to question these statements and challenge Microsoft to defend them because to us it's a little bit of BS and makes one wonder what else in such as awesome keynote and it was awesome, it was hyperbole. Okay, moving on to Google Cloud Next. The keynote started with Sundar Pichai doing a virtual session, he was remote, stressing the importance of Google Cloud. He mentioned that Google Cloud from its Q2 earnings was on a $25-billion annual run rate. What he didn't mention is that it's also on a 3.6 billion annual operating loss run rate based on its first half performance. Just saying. And we'll dig into that issue a little bit more later in this episode. He also stressed that the investments that Google has made to support its core business and search, like its global network of 22 subsea cables to support things like, YouTube video, great performance obviously that we all rely on, those innovations there. Innovations in BigQuery to support its search business and its threat analysis that it's always had and its AI, it's always been an AI-first company, he's stressed, that they're all leveraged by the Google Cloud Platform, GCP. This is all true by the way. Google has absolutely awesome tech and the talk, as well as his talk, Pichai, but also Kurian's was forward thinking and laid out a vision of the future. But it didn't address in our view, and I talked to Sarbjeet Johal about this as well, today's challenges to the degree that Microsoft did and we expect AWS will at re:Invent this year, it was more out there, more forward thinking, what's possible in the future, somewhat less about today's problem, so I think it's resonates less with today's enterprise players. Thomas Kurian then took over from Sundar Pichai and did a really good job of highlighting customers, and I think he has to, right? He has to say, "Look, we are in this game. We have customers, 9 out of the top 10 media firms use Google Cloud. 8 out of the top 10 manufacturers. 9 out of the top 10 retailers. Same for telecom, same for healthcare. 8 out of the top 10 retail banks." He and Sundar specifically referenced a number of companies, customers, including Avery Dennison, Groupe Renault, H&M, John Hopkins, Prudential, Minna Bank out of Japan, ANZ bank and many, many others during the session. So you know, they had some proof points and you got to give 'em props for that. Now like Microsoft, Google talked about infrastructure, they referenced training processors and regions and compute optionality and storage and how new workloads were emerging, particularly data-driven workloads in AI that required new infrastructure. He explicitly highlighted partnerships within Nvidia and Intel. I didn't see anything on Arm, which somewhat surprised me 'cause I believe Google's working on that or at least has come following in AWS's suit if you will, but maybe that's why they're not mentioning it or maybe I got to do more research there, but let's park that for a minute. But again, as we've extensively discussed in Breaking Analysis in our view when it comes to compute, AWS via its Annapurna acquisition is well ahead of the pack in this area. Arm is making its way into the enterprise, but all three companies are heavily investing in infrastructure, which is great news for customers and the ecosystem. We'll come back to that. Data and AI go hand in hand, and there was no shortage of data talk. Google didn't mention Snowflake or Databricks specifically, but it did mention, by the way, it mentioned Mongo a couple of times, but it did mention Google's, quote, Open Data cloud. Now maybe Google has used that term before, but Snowflake has been marketing the data cloud concept for a couple of years now. So that struck as a shot across the bow to one of its partners and obviously competitor, Snowflake. At BigQuery is a main centerpiece of Google's data strategy. Kurian talked about how they can take any data from any source in any format from any cloud provider with BigQuery Omni and aggregate and understand it. And with the support of Apache Iceberg and Delta and Hudi coming in the future and its open Data Cloud Alliance, they talked a lot about that. So without specifically mentioning Snowflake or Databricks, Kurian co-opted a lot of messaging from these two players, such as life and tech. Kurian also talked about Google Workspace and how it's now at 8 million users up from 6 million just two years ago. There's a lot of discussion on developer optionality and several details on tools supported and the open mantra of Google. And finally on security, Google brought out Kevin Mandian, he's a CUBE alum, extremely impressive individual who's CEO of Mandiant, a leading security service provider and consultancy that Google recently acquired for around 5.3 billion. They talked about moving from a shared responsibility model to a shared fate model, which is again, it's kind of a shot across AWS's bow, kind of shared responsibility model. It's unclear that Google will pay the same penalty if a customer doesn't live up to its portion of the shared responsibility, but we can probably assume that the customer is still going to bear the brunt of the pain, nonetheless. Mandiant is really interesting because it's a services play and Google has stated that it is not a services company, it's going to give partners in the channel plenty of room to play. So we'll see what it does with Mandiant. But Mandiant is a very strong enterprise capability and in the single most important area security. So interesting acquisition by Google. Now as well, unlike Microsoft, Google is not competing with security leaders like Okta and CrowdStrike. Rather, it's partnering aggressively with those firms and prominently putting them forth. All right. Let's get into the ETR survey data and see how Microsoft and Google are positioned in four key markets that we've mentioned before, IaaS, BI analytics, database data platforms and collaboration software. First, let's look at the IaaS cloud. ETR is just about to release its October survey, so I cannot share the that data yet. I can only show July data, but we're going to give you some directional hints throughout this conversation. This chart shows net score or spending momentum on the vertical axis and overlap or presence in the data, i.e., how pervasive the platform is. That's on the horizontal axis. And we've inserted the Wikibon estimates of IaaS revenue for the companies, the Big 3. Actually the Big 4, we included Alibaba. So a couple of points in this somewhat busy data chart. First, Microsoft and AWS as always are dominant on both axes. The red dotted line there at 40% on the vertical axis. That represents a highly elevated spending velocity and all of the Big 3 are above the line. Now at the same time, GCP is well behind the two leaders on the horizontal axis and you can see that in the table insert as well in our revenue estimates. Now why is Azure bigger in the ETR survey when AWS is larger according to the Wikibon revenue estimates? And the answer is because Microsoft with products like 365 and Teams will often be considered by respondents in the survey as cloud by customers, so they fit into that ETR category. But in the insert data we're stripping out applications and SaaS from Microsoft and Google and we're only isolating on IaaS. The other point is when you take a look at the early October returns, you see downward pressure as signified by those dotted arrows on every name. The only exception was Dell, or Dell and IBM, which showing slightly improved momentum. So the survey data generally confirms what we know that AWS and Azure have a massive lead and strong momentum in the marketplace. But the real story is below the line. Unlike Google Cloud, which is on pace to lose well over 3 billion on an operating basis this year, AWS's operating profit is around $20 billion annually. Microsoft's Intelligent Cloud generated more than $30 billion in operating income last fiscal year. Let that sink in for a moment. Now again, that's not to say Google doesn't have traction, it does and Kurian gave some nice proof points and customer examples in his keynote presentation, but the data underscores the lead that Microsoft and AWS have on Google in cloud. And here's a breakdown of ETR's proprietary net score methodology, that vertical axis that we showed you in the previous chart. It asks customers, are you adopting the platform new? That's that lime green. Are you spending 6% or more? That's the forest green. Is you're spending flat? That's the gray. Is you're spending down 6% or worse? That's the pinkest color. Or are you replacing the platform, defecting? That's the bright red. You subtract the reds from the greens and you get a net score. Now one caveat here, which actually is really favorable from Microsoft, the Microsoft data that we're showing here is across the entire Microsoft portfolio. The other point is, this is July data, we'll have an update for you once ETR releases its October results. But we're talking about meaningful samples here, the ends. 620 for AWS over a thousand from Microsoft in more than 450 respondents in the survey for Google. So the real tell is replacements, that bright red. There is virtually no churn for AWS and Microsoft, but Google's churn is 5x, those two in the survey. Now 5% churn is not high, but you'd like to see three things for Google given it's smaller size. One is less churn, two is much, much higher adoption rates in the lime green. Three is a higher percentage of those spending more, the forest green. And four is a lower percentage of those spending less. And none of these conditions really applies here for Google. GCP is still not growing fast enough in our opinion, and doesn't have nearly the traction of the two leaders and that shows up in the survey data. All right, let's look at the next sector, BI analytics. Here we have that same XY dimension. Again, Microsoft dominating the picture. AWS very strong also in both axes. Tableau, very popular and respectable of course acquired by Salesforce on the vertical axis, still looking pretty good there. And again on the horizontal axis, big presence there for Tableau. And Google with Looker and its other platforms is also respectable, but it again, has some work to do. Now notice Streamlit, that's a recent Snowflake acquisition. It's strong in the vertical axis and because of Snowflake's go-to-market (indistinct), it's likely going to move to the right overtime. Grafana is also prominent in the Y axis, but a glimpse at the most recent survey data shows them slightly declining while Looker actually improves a bit. As does Cloudera, which we'll move up slightly. Again, Microsoft just blows you away, doesn't it? All right, now let's get into database and data platform. Same X Y dimensions, but now database and data warehouse. Snowflake as usual takes the top spot on the vertical axis and it is actually keeps moving to the right as well with again, Microsoft and AWS is dominant in the market, as is Oracle on the X axis, albeit it's got less spending velocity, but of course it's the database king. Google is well behind on the X axis but solidly above the 40% line on the vertical axis. Note that virtually all platforms will see pressure in the next survey due to the macro environment. Microsoft might even dip below the 40% line for the first time in a while. Lastly, let's look at the collaboration and productivity software market. This is such an important area for both Microsoft and Google. And just look at Microsoft with 365 and Teams up into the right. I mean just so impressive in ubiquitous. And we've highlighted Google. It's in the pack. It certainly is a nice base with 174 N, which I can tell you that N will rise in the next survey, which is an indication that more people are adopting. But given the investment and the tech behind it and all the AI and Google's resources, you'd really like to see Google in this space above the 40% line, given the importance of this market, of this collaboration area to Google's success and the degree to which they emphasize it in their pitch. And look, this brings up something that we've talked about before on Breaking Analysis. Google doesn't have a tech problem. This is a go-to-market and marketing challenge that Google faces and it's up against two go-to-market champs and Microsoft and AWS. And Google doesn't have the enterprise sales culture. It's trying, it's making progress, but it's like that racehorse that has all the potential in the world, but it's just missing some kind of key ingredient to put it over at the top. It's always coming in third, (chuckles) but we're watching and Google's obviously, making some investments as we shared with earlier. All right. Some final thoughts on what we learned this week and in this research: customers and partners should be thrilled that both Microsoft and Google along with AWS are spending so much money on innovation and building out global platforms. This is a gift to the industry and we should be thankful frankly because it's good for business, it's good for competitiveness and future innovation as a platform that can be built upon. Now we didn't talk much about multi-cloud, we haven't even mentioned supercloud, but both Microsoft and Google have a story that resonates with customers in cross cloud capabilities, unlike AWS at this time. But we never say never when it comes to AWS. They sometimes and oftentimes surprise you. One of the other things that Sarbjeet Johal and John Furrier and I have discussed is that each of the Big 3 is positioning to their respective strengths. AWS is the best IaaS. Microsoft is building out the kind of, quote, we-make-it-easy-for-you cloud, and Google is trying to be the open data cloud with its open-source chops and excellent tech. And that puts added pressure on Snowflake, doesn't it? You know, Thomas Kurian made some comments according to CRN, something to the effect that, we are the only company that can do the data cloud thing across clouds, which again, if I'm being honest is not really accurate. Now I haven't clarified these statements with Google and often things get misquoted, but there's little question that, as AWS has done in the past with Redshift, Google is taking a page out of Snowflake, Databricks as well. A big difference in the Big 3 is that AWS doesn't have this big emphasis on the up-the-stack collaboration software that both Microsoft and Google have, and that for Microsoft and Google will drive captive IaaS consumption. AWS obviously does some of that in database, a lot of that in database, but ISVs that compete with Microsoft and Google should have a greater affinity, one would think, to AWS for competitive reasons. and the same thing could be said in security, we would think because, as I mentioned before, Microsoft competes very directly with CrowdStrike and Okta and others. One of the big thing that Sarbjeet mentioned that I want to call out here, I'd love to have your opinion. AWS specifically, but also Microsoft with Azure have successfully created what Sarbjeet calls brand distance. AWS from the Amazon Retail, and even though AWS all the time talks about Amazon X and Amazon Y is in their product portfolio, but you don't really consider it part of the retail organization 'cause it's not. Azure, same thing, has created its own identity. And it seems that Google still struggles to do that. It's still very highly linked to the sort of core of Google. Now, maybe that's by design, but for enterprise customers, there's still some potential confusion with Google, what's its intentions? How long will they continue to lose money and invest? Are they going to pull the plug like they do on so many other tools? So you know, maybe some rethinking of the marketing there and the positioning. Now we didn't talk much about ecosystem, but it's vital for any cloud player, and Google again has some work to do relative to the leaders. Which brings us to supercloud. The ecosystem and end customers are now in a position this decade to digitally transform. And we're talking here about building out their own clouds, not by putting in and building data centers and installing racks of servers and storage devices, no. Rather to build value on top of the hyperscaler gift that has been presented. And that is a mega trend that we're watching closely in theCUBE community. While there's debate about the supercloud name and so forth, there little question in our minds that the next decade of cloud will not be like the last. All right, we're going to leave it there today. Many thanks to Sarbjeet Johal, and my business partner, John Furrier, for their input to today's episode. Thanks to Alex Myerson who's on production and manages the podcast and Ken Schiffman as well. Kristen Martin and Cheryl Knight helped get the word out on social media and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE, who does some wonderful editing. And check out SiliconANGLE, a lot of coverage on Google Cloud Next and Microsoft Ignite. Remember, all these episodes are available as podcast wherever you listen. Just search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com. And you can always get in touch with me via email, david.vellante@siliconangle.com or you can DM me at dvellante or comment on my LinkedIn posts. And please do check out etr.ai, the best survey data in the enterprise tech business. This is Dave Vellante for the CUBE Insights, powered by ETR. Thanks for watching and we'll see you next time on Breaking Analysis. (gentle music)

[Thomas Hazel] - Hello, this is Thomas Hazel, founder CTO here at ChaosSearch. And tonight I'm going to demonstrate a new feature we are offering this quarter called JSON Flex. If you're familiar with JSON datasets, they're wonderful ways to represent information. You know, they're multidimensional, they have ability to set up arrays as attributes but those arrays are really problematic when you need to expand them or flatten them to do any type of elastic search or relational access, particularly when you're trying to do aggregations. And so the common process is to exclude those arrays or pick and choose that information. But with this new Chaos Flex capability, our system uniquely can index that data horizontally in a very small and efficient representation. And then with our Chaos Refinery, expand each attribute as you wish vertically, so you can do all the basic and natural constructs you would have done if you had, you know, a more straightforward, two dimensional, three dimensional type representation. So without further ado, I'mma get into this presentation of JSON Flex. Now, in this case, I've already set up the service to point to a particular S3 account that has CloudTrail data, one that is pretty problematic when it comes down to flattening data. And again, if you know CloudTrail, one row can become 10,000 as data gets flattened. So without further ado, let me jump right in. When you first log into the ChaosSearch service, you'll see a tab called 'Storage'. This is the S3 account, and I have variety of buckets. I have the refinery, it's a data refinery. This is where we create views or lenses into these index streams that you can do analysis that publishes it in elastic API as an index pattern or relational table in SQL Now a particular bucket I have here is a whole bunch of demonstration datasets that we have to show off our capabilities and our offering. In this bucket, I have CloudTrail data and I'm going to create what we call a 'object group'. An object group is a entry point, a filter of which files I want to index that data. Now, it can be statically there or a live streaming. These object groups had the ability to say, what type of data do you want to index on? Now through our wizard, you can type in, you know, prefix in this case, I want to type in CloudTrail, and you see here, I have a whole bunch of CloudTrail. I'mma choose one file to make it quick and easy. But this particular CloudTrail data will expand and we can show the capability of this horizontal to vertical expansion. So I walked through the wizard, as you can see here, we discovered JSON, it's a gzip file. Leave flattening unlimited 'cause we want to be able to expand infinitely. But this case, instead of doing default virtual, I'm going to horizontally represent this information. And this uniquely compresses the data in a way that can be stored efficiently on disc but then expanded in our data refinery on Pond Query or search requests. So I'mma create this object group. Now I'm going to call this, you know, 'JSON Flex test' and I could set up live indexing, SQS pops up but I'mma skip that and skip Retention and just create it. Once this object group is created, you kind of think of it as a virtual bucket, 'cause it does filter the data as you can see here. When I look at the view, I just see CloudTrail, but within the console, I can say start indexing. Now this is static data there could be a live stream and we set up workers to index this data. Whether it's one file, a million files or one terabyte, or one petabyte, we index the data. We discover all the schema, and as you see here, we discovered 104 columns. Now what's interesting is that we represent this expansion in a horizontal way. You know, if you know CloudTrail records zero, record one, record two. This can expand pretty dramatically if you fully flatten it but this case we horizontally representing it as the index. So when I go into the data refinery, I can create a view. Now, if you know the data refinery of ChaosSearch, you can bring multiple data streams together. You can do transformations virtually, you can do correlations, but in this case, I'm just going to take this one particular index stream, we call 'JSON Flex' and walk through a wizard, we try to simplify everything and select a particular attribute to expand. Now, again, we represent this in one row but if you had arrays and do all the permutations, it could go one to 100 to 10,000. We had one JSON audit that went from one row to 1 million rows. Now, clearly you don't want to create all those permutations, when you're tryna put into a database. With our unique index technology, you can do it virtually and sort horizontally. So let me just select 'Virtual' and walk through the wizard. Now, as I mentioned, we do all these different transformations changed schema, we're going to skip all that and select the order time, records event and say, 'create this'. I'm going to say, you know, 'JSON Flex View', I can set up caching, do a variety of things, I'm going to skip that. And once I create this, it's now available in the elastic API as an index pattern, as well as SQL via our Presto API dialect. And you can use Looker, Tableau, et cetera. But in this case, we go to this 'Analytics tab' and we built in the Kibana, open search tooling that is Apache Tonetto. And I click on discovery here and I'm going to select that particular view. Again, it looks like, oops, it looks like an index pattern, and I'mma choose, let's see here, let's choose 15 years from past and present and make sure I find where actually was timed. And what you'll see here is, you know, sure. It's just one particular data set has a variety of columns, but you see here is unlike that record zero, records one, now it's expanded. And so it has been expanded like a vertical flattening that you would traditionally do if you wanted to do anything that was an elastic or a relational construct, you know, that fit into a table format. Now the 'vantage of JSON Flex, you don't have that stored as a blob and use these proprietary JSON API's. You can use your native elastic API or your native SQL tooling to get access to it naturally without that expense of that explosion or without the complexity of ETLing it, and picking and choosing before you actually put into the database. That completes the demonstration of ChaosSearch new JSON Flex capability. If you're interested, come to ChaosSearch.io and set up a free trial. Thank you.

>> Welcome to theCUBE, I am Dave Vellante. And today we're going to explore the ebb and flow of data as it travels into the cloud and the data lake. The concept of data lakes was alluring when it was first coined last decade by CTO James Dixon. Rather than be limited to highly structured and curated data that lives in a relational database in the form of an expensive and rigid data warehouse or a data mart. A data lake is formed by flowing data from a variety of sources into a scalable repository, like, say an S3 bucket that anyone can access, dive into, they can extract water, A.K.A data, from that lake and analyze data that's much more fine-grained and less expensive to store at scale. The problem became that organizations started to dump everything into their data lakes with no schema on our right, no metadata, no context, just shoving it into the data lake and figure out what's valuable at some point down the road. Kind of reminds you of your attic, right? Except this is an attic in the cloud. So it's too big to clean out over a weekend. Well look, it's 2021 and we should be solving this problem by now. A lot of folks are working on this, but often the solutions add other complexities for technology pros. So to understand this better, we're going to enlist the help of ChaosSearch CEO Ed Walsh, and Thomas Hazel, the CTO and Founder of ChaosSearch. We're also going to speak with Kevin Miller who's the Vice President and General Manager of S3 at Amazon web services. And of course they manage the largest and deepest data lakes on the planet. And we'll hear from a customer to get their perspective on this problem and how to go about solving it, but let's get started. Ed, Thomas, great to see you. Thanks for coming on theCUBE. >> Likewise. >> Face to face, it's really good to be here. >> It is nice face to face. >> It's great. >> So, Ed, let me start with you. We've been talking about data lakes in the cloud forever. Why is it still so difficult to extract value from those data lakes? >> Good question. I mean, data analytics at scale has always been a challenge, right? So, we're making some incremental changes. As you mentioned that we need to see some step function changes. But in fact, it's the reason ChaosSearch was really founded. But if you look at it, the same challenge around data warehouse or a data lake. Really it's not just to flowing the data in, it's how to get insights out. So it kind of falls into a couple of areas, but the business side will always complain and it's kind of uniform across everything in data lakes, everything in data warehousing. They'll say, "Hey, listen, I typically have to deal with a centralized team to do that data prep because it's data scientists and DBAs". Most of the time, they're a centralized group. Sometimes they're are business units, but most of the time, because they're scarce resources together. And then it takes a lot of time. It's arduous, it's complicated, it's a rigid process of the deal of the team, hard to add new data, but also it's hard to, it's very hard to share data and there's no way to governance without locking it down. And of course they would be more self-serve. So there's, you hear from the business side constantly now underneath is like, there's some real technology issues that we haven't really changed the way we're doing data prep since the two thousands, right? So if you look at it, it's, it falls two big areas. It's one, how to do data prep. How do you take, a request comes in from a business unit. I want to do X, Y, Z with this data. I want to use this type of tool sets to do the following. Someone has to be smart, how to put that data in the right schema, you mentioned. You have to put it in the right format, that the tool sets can analyze that data before you do anything. And then second thing, I'll come back to that 'cause that's the biggest challenge. But the second challenge is how these different data lakes and data warehouses are now persisting data and the complexity of managing that data and also the cost of computing it. And I'll go through that. But basically the biggest thing is actually getting it from raw data so the rigidness and complexity that the business sides are using it is literally someone has to do this ETL process, extract, transform, load. They're actually taking data, a request comes in, I need so much data in this type of way to put together. They're literally physically duplicating data and putting it together on a schema. They're stitching together almost a data puddle for all these different requests. And what happens is anytime they have to do that, someone has to do it. And it's, very skilled resources are scanned in the enterprise, right? So it's a DBS and data scientists. And then when they want new data, you give them a set of data set. They're always saying, what can I add to this data? Now that I've seen the reports. I want to add this data more fresh. And the same process has to happen. This takes about 60% to 80% of the data scientists in DPA's to do this work. It's kind of well-documented. And this is what actually stops the process. That's what is rigid. They have to be rigid because there's a process around that. That's the biggest challenge of doing this. And it takes an enterprise, weeks or months. I always say three weeks or three months. And no one challenges beyond that. It also takes the same skill set of people that you want to drive digital transformation, data warehousing initiatives, motorization, being data driven or all these data scientists and DBS they don't have enough of. So this is not only hurting you getting insights out of your day like in the warehouses. It's also, this resource constraint is hurting you actually getting. >> So that smallest atomic unit is that team, that's super specialized team, right? >> Right. >> Yeah. Okay. So you guys talk about activating the data lake. >> Yep. >> For analytics. What's unique about that? What problems are you all solving? You know, when you guys crew created this magic sauce. >> No, and basically, there's a lot of things. I highlighted the biggest one is how to do the data prep, but also you're persisting and using the data. But in the end, it's like, there's a lot of challenges at how to get analytics at scale. And this is really where Thomas and I founded the team to go after this, but I'll try to say it simply. What we're doing, I'll try to compare and contrast what we do compared to what you do with maybe an elastic cluster or a BI cluster. And if you look at it, what we do is we simply put your data in S3, don't move it, don't transform it. In fact, we're against data movement. What we do is we literally point and set that data and we index that data and make it available in a data representation that you can give virtual views to end-users. And those virtual views are available immediately over petabytes of data. And it actually gets presented to the end-user as an open API. So if you're elastic search user, you can use all your elastic search tools on this view. If you're a SQL user, Tableau, Looker, all the different tools, same thing with machine learning next year. So what we do is we take it, make it very simple. Simply put it there. It's already there already. Point us at it. We do the hard of indexing and making available. And then you publish in the open API as your users can use exactly what they do today. So that's, dramatically I'll give you a before and after. So let's say you're doing elastic search. You're doing logging analytics at scale, they're lending their data in S3. And then they're ETL physically duplicating and moving data. And typically deleting a lot of data to get in a format that elastic search can use. They're persisting it up in a data layer called leucine. It's physically sitting in memories, CPU, SSDs, and it's not one of them, it's a bunch of those. They in the cloud, you have to set them up because they're persisting ECC. They stand up same by 24, not a very cost-effective way to the cloud computing. What we do in comparison to that is literally pointing it at the same S3. In fact, you can run a complete parallel, the data necessary it's being ETL out. When just one more use case read only, or allow you to get that data and make this virtual views. So we run a complete parallel, but what happens is we just give a virtual view to the end users. We don't need this persistence layer, this extra cost layer, this extra time, cost and complexity of doing that. So what happens is when you look at what happens in elastic, they have a constraint, a trade-off of how much you can keep and how much you can afford to keep. And also it becomes unstable at time because you have to build out a schema. It's on a server, the more the schema scales out, guess what? you have to add more servers, very expensive. They're up seven by 24. And also they become brutalized. You lose one node, the whole thing has to be put together. We have none of that cost and complexity. We literally go from to keep whatever you want, whatever you want to keep an S3 is single persistence, very cost effective. And what we are able to do is, costs, we save 50 to 80%. Why? We don't go with the old paradigm of sit it up on servers, spin them up for persistence and keep them up 7 by 24. We're literally asking their cluster, what do you want to cut? We bring up the right compute resources. And then we release those sources after the query done. So we can do some queries that they can't imagine at scale, but we're able to do the exact same query at 50 to 80% savings. And they don't have to do any tutorial of moving that data or managing that layer of persistence, which is not only expensive, it becomes brittle. And then it becomes, I'll be quick. Once you go to BI, it's the same challenge, but the BI systems, the requests are constant coming at from a business unit down to the centralized data team. Give me this flavor of data. I want to use this piece of, you know, this analytic tool in that desk set. So they have to do all this pipeline. They're constantly saying, okay, I'll give you this data, this data, I'm duplicating that data, I'm moving it and stitching it together. And then the minute you want more data, they do the same process all over. We completely eliminate that. >> And those requests are queue up. Thomas, it had me, you don't have to move the data. That's kind of the exciting piece here, isn't it? >> Absolutely no. I think, you know, the data lake philosophy has always been solid, right? The problem is we had that Hadoop hang over, right? Where let's say we were using that platform, little too many variety of ways. And so, I always believed in data lake philosophy when James came and coined that I'm like, that's it. However, HTFS, that wasn't really a service. Cloud object storage is a service that the elasticity, the security, the durability, all that benefits are really why we founded on-cloud storage as a first move. >> So it was talking Thomas about, you know, being able to shut off essentially the compute so you don't have to keep paying for it, but there's other vendors out there and stuff like that. Something similar as separating, compute from storage that they're famous for that. And you have Databricks out there doing their lake house thing. Do you compete with those? How do you participate and how do you differentiate? >> Well, you know you've heard this term data lakes, warehouse, now lake house. And so what everybody wants is simple in, easy in, however, the problem with data lakes was complexity of out. Driving value. And I said, what if, what if you have the easy in and the value out? So if you look at, say snowflake as a warehousing solution, you have to all that prep and data movement to get into that system. And that it's rigid static. Now, Databricks, now that lake house has exact same thing. Now, should they have a data lake philosophy, but their data ingestion is not data lake philosophy. So I said, what if we had that simple in with a unique architecture and indexed technology, make it virtually accessible, publishable dynamically at petabyte scale. And so our service connects to the customer's cloud storage. Data stream the data in, set up what we call a live indexing stream, and then go to our data refinery and publish views that can be consumed the elastic API, use cabana Grafana, or say SQL tables look or say Tableau. And so we're getting the benefits of both sides, use scheme on read-write performance with scheme write-read performance. And if you can do that, that's the true promise of a data lake, you know, again, nothing against Hadoop, but scheme on read with all that complexity of software was a little data swamping. >> Well, you've got to start it, okay. So we got to give them a good prompt, but everybody I talked to has got this big bunch of spark clusters, now saying, all right, this doesn't scale, we're stuck. And so, you know, I'm a big fan of Jamag Dagani and our concept of the data lake and it's early days. But if you fast forward to the end of the decade, you know, what do you see as being the sort of critical components of this notion of, people call it data mesh, but to get the analytics stack, you're a visionary Thomas, how do you see this thing playing out over the next decade? >> I love her thought leadership, to be honest, our core principles were her core principles now, 5, 6, 7 years ago. And so this idea of, decentralize that data as a product, self-serve and, and federated computer governance, I mean, all that was our core principle. The trick is how do you enable that mesh philosophy? I can say we're a mesh ready, meaning that, we can participate in a way that very few products can participate. If there's gates data into your system, the CTL, the schema management, my argument with the data meshes like producers and consumers have the same rights. I want the consumer, people that choose how they want to consume that data. As well as the producer, publishing it. I can say our data refinery is that answer. You know, shoot, I'd love to open up a standard, right? Where we can really talk about the producers and consumers and the rights each others have. But I think she's right on the philosophy. I think as products mature in this cloud, in this data lake capabilities, the trick is those gates. If you have to structure up front, if you set those pipelines, the chance of you getting your data into a mesh is the weeks and months that Ed was mentioning. >> Well, I think you're right. I think the problem with data mesh today is the lack of standards you've got. You know, when you draw the conceptual diagrams, you've got a lot of lollipops, which are APIs, but they're all unique primitives. So there aren't standards, by which to your point, the consumer can take the data the way he or she wants it and build their own data products without having to tap people on the shoulder to say, how can I use this?, where does the data live? And being able to add their own data. >> You're exactly right. So I'm an organization, I'm generating data, when the courageously stream it into a lake. And then the service, a ChaosSearch service, is the data is discoverable and configurable by the consumer. Let's say you want to go to the corner store. I want to make a certain meal tonight. I want to pick and choose what I want, how I want it. Imagine if the data mesh truly can have that producer of information, you know, all the things you can buy a grocery store and what you want to make for dinner. And if you'd static, if you call up your producer to do the change, was it really a data mesh enabled service? I would argue not. >> Ed, bring us home. >> Well, maybe one more thing with this. >> Please, yeah. 'Cause some of this is we're talking 2031, but largely these principles are what we have in production today, right? So even the self service where you can actually have a business context on top of a data lake, we do that today, we talked about, we get rid of the physical ETL, which is 80% of the work, but the last 20% it's done by this refinery where you can do virtual views, the right or back and do all the transformation need and make it available. But also that's available to, you can actually give that as a role-based access service to your end-users, actually analysts. And you don't want to be a data scientist or DBA. In the hands of a data scientist the DBA is powerful, but the fact of matter, you don't have to affect all of our employees, regardless of seniority, if they're in finance or in sales, they actually go through and learn how to do this. So you don't have to be it. So part of that, and they can come up with their own view, which that's one of the things about data lakes. The business unit wants to do themselves, but more importantly, because they have that context of what they're trying to do instead of queuing up the very specific request that takes weeks, they're able to do it themselves. >> And if I have to put it on different data stores and ETL that I can do things in real time or near real time. And that's game changing and something we haven't been able to do ever. >> And then maybe just to wrap it up, listen, you know 8 years ago, Thomas and his group of founders, came up with the concept. How do you actually get after analytics at scale and solve the real problems? And it's not one thing, it's not just getting S3. It's all these different things. And what we have in market today is the ability to literally just simply stream it to S3, by the way, simply do, what we do is automate the process of getting the data in a representation that you can now share an augment. And then we publish open API. So can actually use a tool as you want, first use case log analytics, hey, it's easy to just stream your logs in. And we give you elastic search type of services. Same thing that with CQL, you'll see mainstream machine learning next year. So listen, I think we have the data lake, you know, 3.0 now, and we're just stretching our legs right now to have fun. >> Well, and you have to say it log analytics. But if I really do believe in this concept of building data products and data services, because I want to sell them, I want to monetize them and being able to do that quickly and easily, so I can consume them as the future. So guys, thanks so much for coming on the program. Really appreciate it.

>>Hi, Brian, this is Dave Volante. Welcome to this cube conversation with Thomas Hazel was the founder and CTO of chaos surgeon. I'm also joined by ed Walsh. Who's the CEO Thomas. Good to see you. >>Great to be here. >>Explain Jason. First of all, what >>Jason, Jason has a powerful data representation, a data source. Uh, but let's just say that we try to drive value out of it. It gets complicated. Uh, I can search. We activate customers, data lakes. So, you know, customers stream their Jason data to this, uh, cloud stores that we activate. Now, the trick is the complexity of a Jason data structure. You can do all these complexity of representation. Now here's the problem putting that representation into a elastic search database or relational databases, very problematic. So what people choose to do is they pick and choose what they want and or they just stored as a blob. And so I said, what if, what if we create a new index technology that could store it as a full representation, but dynamically in a, we call our data refinery published access to all the permutations that you may want, where if you do a full on flatten, your flattening of its Jason, one row theoretically could be put into a million rows and relational data sort of explode, >>But then it gets really expensive. But so, but everybody says they have Jason support, every database vendor that I talked to, it's a big announcement. We now support Jason. What's the deal. >>Exactly. So you take your relational database with all those relational constructs and you have a proprietary Jason API to pick and choose. So instead of picking, choosing upfront, now you're picking, choosing in the backend where you really want us the power of the relational analysis of that Jaison data. And that's where chaos comes in, where we expand those data streams we do in a relational way. So all that tooling you've been built to know and love. Now you can access to it. So if you're doing proprietary APIs or Jason data, you're not using Looker, you're not using Tableau. You're doing some type of proprietary, probably emailing now on the backend. >>Okay. So you say all the tools that you've trained, everybody on you can't really use them. You got to build some custom stuff and okay, so, so, so maybe bring that home then in terms of what what's the money, why do the suits care about this stuff? >>The reason this is so important is think about anything, cloud native Kubernetes, your different applications. What you're doing in Mongo is all Jason is it's very powerful but painful, but if you're not keeping the data, what people are doing a data scientist is, or they're just doing leveling, they're saying I'm going to only keep the first four things. So think about it's Kubernetes, it's your app logs. They're trying to figure out for black Friday, what happens? It's Lilly saying, Hey, every minute they'll cut a new log. You're able to say, listen, these are the users that were in that system for an hour. And here's a different things. They do. The fact of the matter is if you cut it off, you lose all that fidelity, all that data. So it's really important that to have. So if you're trying to figure out either what happened for security, what happened for on a performance, or if you're trying to figure out, Hey, I'm VP of product or growth, how do I cross sell things? >>You need to know what everyone's doing. If you're not handling Jason natively, like we're doing either your, it keeps on expanding on black Friday. All of a sudden the logs get huge. And the next day it's not, but it's really powerful data that you need to harness for business values. It's, what's going to drive growth. It's what's going to do the digital transformation. So without the technology, you're kind of blind. And to be honest, you don't know. Cause a data scientist is kind of deleted the data on you. So this is big for the business and digital transformation, but also it was such a pain. The data scientists in DBS were forced to just basically make it simple. So it didn't blow up their system. We allow them to keep it simple, but yes, >>Both power. It reminds me if you like, go on vacation, you got your video camera. Somebody breaks into your house. You go back to Lucas and see who and that the data's gone. The video's gone because it didn't, you didn't, you weren't able to save it cause it's too >>Expensive. Well, it's funny. This is the first day source. That's driving the design of the database because of all the value we should be designed the database around the information. It stores not the structure and how it's been organized. And so our viewpoint is you get to choose your structure yet contain all that content. So if a vendor >>It says to kind of, I'm a customer then says, Hey, we got Jason support. What questions should I ask to really peel the onion? >>Well, particularly relational. Is it a relational access to that data? Now you could say, oh, I've ETL does Jason into it. But chances are the explosion of Jason permutations of one row to a million. They're probably not doing the full representation. So from our viewpoint is either you're doing a blob type access to proprietary Jason APIs or you're picking and choosing those, the choices say that is the market thought. However, what if you could take all the vegetation and design your schema based on how you want to consume it versus how you could store it. And that's a big difference with, >>So I should be asking how, how do I consume this data? Are you ETL? Bring it in how much data explosion is going to occur. Once I do this, and you're saying for chaos, search the answer to those questions. >>The answer is, again, our philosophy simply stream your data into your cloud object, storage, your data lake and with our index technology and our data refinery. You get to create views, dynamic the incident, whether it's a terabyte or petabyte, and describe how you want your data because consumed in a relational way or an elastic search way, both are consumable through our data refinery, which is >>For us. The refinery gives you the view. So what happens if someone wants a different view, I want to actually unpack different columns or different matrices. You able to do that in a virtual view, it's available immediately over petabytes of data. You don't have that episode where you come back, look at the video camera. There's no data there left. So that's, >>We do appreciate the time and the explanation on really understanding Jason. Thank you. All right. And thank you for watching this cube conversation. This is Dave Volante. We'll see you next time.

>>Welcome to the cube. I'm Dave Volante. Today, we're going to explore the ebb and flow of data as it travels into the cloud. In the data lake, the concept of data lakes was a Loring when it was first coined last decade by CTO James Dickson, rather than be limited to highly structured and curated data that lives in a relational database in the form of an expensive and rigid data warehouse or a data Mart, a data lake is formed by flowing data from a variety of sources into a scalable repository, like say an S3 bucket that anyone can access, dive into. They can extract water. It can a data from that lake and analyze data. That's much more fine-grained and less expensive to store at scale. The problem became that organizations started to dump everything into their data lakes with no schema on it, right? No metadata, no context to shove it into the data lake and figure out what's valuable. >>At some point down the road kind of reminds you of your attic, right? Except this is an attic in the cloud. So it's too big to clean out over a weekend. We'll look it's 2021 and we should be solving this problem by now, a lot of folks are working on this, but often the solutions at other complexities for technology pros. So to understand this better, we're going to enlist the help of chaos search CEO and Walsh and Thomas Hazel, the CTO and founder of chaos search. We're also going to speak with Kevin Miller. Who's the vice president and general manager of S3 at Amazon web services. And of course they manage the largest and deepest data lakes on the planet. And we'll hear from a customer to get their perspective on this problem and how to go about solving it, but let's get started. Ed Thomas. Great to see you. Thanks for coming on the cube. Likewise face. It's really good to be in this nice face. Great. So let me start with you. We've been talking about data lakes in the cloud forever. Why is it still so difficult to extract value from those data? >>Good question. I mean, a data analytics at scale is always been a challenge, right? So, and it's, uh, we're making some incremental changes. As you mentioned that we need to seem some step function changes, but, uh, in fact, it's the reason, uh, search was really founded. But if you look at it the same challenge around data warehouse or a data lake, really, it's not just a flowing the data in is how to get insights out. So it kind of falls into a couple of areas, but the business side will always complain and it's kind of uniform across everything in data lakes, everything that we're offering, they'll say, Hey, listen, I typically have to deal with a centralized team to do that data prep because it's data scientist and DBS. Most of the time they're a centralized group, sometimes are business units, but most of the time, because they're scarce resources together. >>And then it takes a lot of time. It's arduous, it's complicated. It's a rigid process of the deal of the team, hard to add new data. But also it's hard to, you know, it's very hard to share data and there's no way to governance without locking it down. And of course they would be more self-service. So there's you hear from the business side constantly now underneath is like, there's some real technology issues that we haven't really changed the way we're doing data prep since the two thousands. Right? So if you look at it, it's, it falls, uh, two big areas. It's one. How do data prep, how do you take a request comes in from a business unit. I want to do X, Y, Z with this data. I want to use this type of tool sets to do the following. Someone has to be smart, how to put that data in the right schema. >>You mentioned you have to put it in the right format, that the tool sets can analyze that data before you do anything. And then secondly, I'll come back to that because that's a biggest challenge. But the second challenge is how these different data lakes and data we're also going to persisting data and the complexity of managing that data and also the cost of computing. And I'll go through that. But basically the biggest thing is actually getting it from raw data so that the rigidness and complexity that the business sides are using it is literally someone has to do this ETL process extract, transform load. They're actually taking data request comes in. I need so much data in this type of way to put together their Lilly, physically duplicating data and putting it together and schema they're stitching together almost a data puddle for all these different requests. >>And what happens is anytime they have to do that, someone has to do it. And it's very skilled. Resources are scant in the enterprise, right? So it's a DBS and data scientists. And then when they want new data, you give them a set of data set. They're always saying, what can I add this data? Now that I've seen the reports, I want to add this data more fresh. And the same process has to happen. This takes about 60 to 80% of the data scientists in DPA's to do this work. It's kind of well-documented. Uh, and this is what actually stops the process. That's what is rigid. They have to be rigid because there's a process around that. Uh, that's the biggest challenge to doing this. And it takes in the enterprise, uh, weeks or months. I always say three weeks to three months. And no one challenges beyond that. It also takes the same skill set of people that you want to drive. Digital transformation, data, warehousing initiatives, uh, monitorization being, data driven, or all these data scientists and DBS. They don't have enough of, so this is not only hurting you getting insights out of your dead like that, or else it's also this resource constraints hurting you actually getting smaller. >>The Tomic unit is that team that's super specialized team. Right. Right. Yeah. Okay. So you guys talk about activating the data lake. Yep, sure. Analytics, what what's unique about that? What problems are you all solving? You know, when you guys crew created this, this, this magic sauce. >>No, and it basically, there's a lot of things I highlighted the biggest one is how to do the data prep, but also you're persisting and using the data. But in the end, it's like, there's a lot of challenges that how to get analytics at scale. And this is really where Thomas founded the team to go after this. But, um, I'll try to say it simply, what are we doing? I'll try to compare and stress what we do compared to what you do with maybe an elastic cluster or a BI cluster. Um, and if you look at it, what we do is we simply put your data in S3, don't move it, don't transform it. In fact, we're not we're against data movement. What we do is we literally pointed at that data and we index that data and make it available in a data representation that you can give virtual views to end users. >>And those virtual views are available immediately over petabytes of data. And it re it actually gets presented to the end user as an open API. So if you're elastic search user, you can use all your lesser search tools on this view. If you're a SQL user, Tableau, Looker, all the different tools, same thing with machine learning next year. So what we do is we take it, make it very simple. Simply put it there. It's already there already. Point is at it. We do the hard of indexing and making available. And then you publish in the open API as your users can use exactly what they do today. So that's dramatically. I'll give you a before and after. So let's say you're doing elastic search. You're doing logging analytics at scale, they're lending their data in S3. And then they're,, they're physically duplicating a moving data and typically deleting a lot of data to get in a format that elastic search can use. >>They're persisting it up in a data layer called leucine. It's physically sitting in memories, CPU, uh, uh, SSDs. And it's not one of them. It's a bunch of those. They in the cloud, you have to set them up because they're persisting ECC. They stand up semi by 24, not a very cost-effective way to the cloud, uh, cloud computing. What we do in comparison to that is literally pointing it at the same S3. In fact, you can run a complete parallel, the data necessary. It's being ETL. That we're just one more use case read only, or allow you to get that data and make this virtual views. So we run a complete parallel, but what happens is we just give a virtual view to the end users. We don't need this persistence layer, this extra cost layer, this extra, um, uh, time cost and complexity of doing that. >>So what happens is when you look at what happens in elastic, they have a constraint, a trade-off of how much you can keep and how much you can afford to keep. And also it becomes unstable at time because you have to build out a schema. It's on a server, the more the schema scales out, guess what you have to add more servers, very expensive. They're up seven by 24. And also they become brittle. As you lose one node. The whole thing has to be put together. We have none of that cost and complexity. We literally go from to keep whatever you want, whatever you want to keep an S3, a single persistence, very cost effective. And what we do is, um, costs. We save 50 to 80% why we don't go with the old paradigm of sit it up on servers, spin them up for persistence and keep them up. >>Somebody 24, we're literally asking her cluster, what do you want to cut? We bring up the right compute resources. And then we release those sources after the query done. So we can do some queries that they can't imagine at scale, but we're able to do the exact same query at 50 to 80% savings. And they don't have to do any of the toil of moving that data or managing that layer of persistence, which is not only expensive. It becomes brittle. And then it becomes an I'll be quick. Once you go to BI, it's the same challenge, but the BI systems, the requests are constant coming at from a business unit down to the centralized data team. Give me this flavor of debt. I want to use this piece of, you know, this analytic tool in that desk set. So they have to do all this pipeline. They're constantly saying, okay, I'll give you this data, this data I'm duplicating that data. I'm moving in stitching together. And then the minute you want more data, they do the same process all over. We completely eliminate that. >>The questions queue up, Thomas, it had me, you don't have to move the data. That's, that's kind of the >>Writing piece here. Isn't it? I absolutely, no. I think, you know, the daylight philosophy has always been solid, right? The problem is we had that who do hang over, right? Where let's say we were using that platform, little, too many variety of ways. And so I always believed in daily philosophy when James came and coined that I'm like, that's it. However, HTFS that wasn't really a service cloud. Oddish storage is a service that the, the last society, the security and the durability, all that benefits are really why we founded, uh, Oncotype storage as a first move. >>So it was talking Thomas about, you know, being able to shut off essentially the compute and you have to keep paying for it, but there's other vendors out there and stuff like that. Something similar as separating, compute from storage that they're famous for that. And, and, and yet Databricks out there doing their lake house thing. Do you compete with those? How do you participate and how do you differentiate? >>I know you've heard this term data lakes, warehouse now, lake house. And so what everybody wants is simple in easy N however, the problem with data lakes was complexity of out driving value. And I said, what if, what if you have the easy end and the value out? So if you look at, uh, say snowflake as a, as a warehousing solution, you have to all that prep and data movement to get into that system. And that it's rigid static. Now, Databricks, now that lake house has exact same thing. Now, should they have a data lake philosophy, but their data ingestion is not daily philosophy. So I said, what if we had that simple in with a unique architecture, indexed technology, make it virtually accessible publishable dynamically at petabyte scale. And so our service connects to the customer's cloud storage data, stream the data in set up what we call a live indexing stream, and then go to our data refinery and publish views that can be consumed the lasted API, use cabana Grafana, or say SQL tables look or say Tableau. And so we're getting the benefits of both sides, you know, schema on read, write performance with scheme on, right. Reperformance. And if you can do that, that's the true promise of a data lake, you know, again, nothing against Hadoop, but a schema on read with all that complexity of, uh, software was, uh, what was a little data, swamp >>Got to start it. Okay. So we got to give a good prompt, but everybody I talked to has got this big bunch of spark clusters now saying, all right, this, this doesn't scale we're stuck. And so, you know, I'm a big fan of and our concept of the data lake and it's it's early days. But if you fast forward to the end of the decade, you know, what do you see as being the sort of critical components of this notion of, you know, people call it data mesh, but you've got the analytics stack. Uh, you, you, you're a visionary Thomas, how do you see this thing playing out over the next? >>I love for thought leadership, to be honest, our core principles were her core principles now, you know, 5, 6, 7 years ago. And so this idea of, you know, de centralize that data as a product, you know, self-serve and, and federated, computer, uh, governance, I mean, all that, it was our core principle. The trick is how do you enable that mesh philosophy? We, I could say we're a mesh ready, meaning that, you know, we can participate in a way that very few products can participate. If there's gates data into your system, the CTLA, the schema management, my argument with the data meshes like producers and consumers have the same rights. I want the consumer people that choose how they want to consume that data, as well as the producer publishing it. I can say our data refinery is that answer. You know, shoot, I love to open up a standard, right, where we can really talk about the producers and consumers and the rights each others have. But I think she's right on the philosophy. I think as products mature in this cloud, in this data lake capabilities, the trick is those gates. If you have the structure up front, it gets at those pipelines. You know, the chance of you getting your data into a mesh is the weeks and months that it was mentioning. >>Well, I think you're right. I think the problem with, with data mesh today is the lack of standards you've got. You know, when you draw the conceptual diagrams, you've got a lot of lollipops, which are API APIs, but they're all, you know, unique primitives. So there aren't standards by which to your point, the consumer can take the data the way he or she wants it and build their own data products without having to tap people on the shoulder to say, how can I use this? Where's the data live and, and, and, and, and being able to add their own >>You're exactly right. So I'm an organization I'm generally data will be courageous, a stream it to a lake. And then the service, uh, Ks search service is the data's con uh, discoverable and configurable by the consumer. Let's say you want to go to the corner store? You know, I want to make a certain meal tonight. I want to pick and choose what I want, how I want it. Imagine if the data mesh truly can have that producer of information, you, all the things you can buy a grocery store and what you want to make for dinner. And if you'd static, if you call up your producer to do the change, was it really a data mesh enabled service? I would argue not that >>Bring us home >>Well. Uh, and, um, maybe one more thing with this, cause some of this is we talking 20, 31, but largely these principles are what we have in production today, right? So even the self service where you can actually have business context on top of a debt, like we do that today, we talked about, we get rid of the physical ETL, which is 80% of the work, but the last 20% it's done by this refinery where you can do virtual views, the right our back and do all the transformation need and make it available. But also that's available to, you can actually give that as a role-based access service to your end users actually analysts, and you don't want to be a data scientist or DBA in the hands of a data science. The DBA is powerful, but the fact of matter, you don't have to affect all of our employees, regardless of seniority. If they're in finance or in sales, they actually go through and learn how to do this. So you don't have to be it. So part of that, and they can come up with their own view, which that's one of the things about debt lakes, the business unit wants to do themselves, but more importantly, because they have that context of what they're trying to do instead of queuing up the very specific request that takes weeks, they're able to do it themselves and to find out that >>Different data stores and ETL that I can do things in real time or near real time. And that's that's game changing and something we haven't been able to do, um, ever. Hmm. >>And then maybe just to wrap it up, listen, um, you know, eight years ago is a group of founders came up with the concept. How do you actually get after analytics at scale and solve the real problems? And it's not one thing it's not just getting S3, it's all these different things. And what we have in market today is the ability to literally just simply stream it to S3 by the way, simply do what we do is automate the process of getting the data in a representation that you can now share an augment. And then we publish open API. So can actually use a tool as you want first use case log analytics, Hey, it's easy to just stream your logs in and we give you elastic search puppet services, same thing that with CQL, you'll see mainstream machine learning next year. So listen, I think we have the data lake, you know, 3.0 now, and we're just stretching our legs run off >>Well, and you have to say it log analytics. But if I really do believe in this concept of building data products and data services, because I want to sell them, I want to monetize them and being able to do that quickly and easily, so that can consume them as the future. So guys, thanks so much for coming on the program. Really appreciate it. All right. In a moment, Kevin Miller of Amazon web services joins me. You're watching the cube, your leader in high tech coverage.

(gentle music) >> Welcome to the new abnormal. Yes, you know, the pandemic, it did accelerate the shift to digital, but it's also created disorder in our world. I mean, every day it seems that companies are resetting their office reopening playbooks. They're rethinking policies on large gatherings and vaccination mandates. There's an acute labor shortage in many industries, and we're seeing an inventory glutton in certain goods, like bleach and hand sanitizer. Airline schedules and pricing algorithms, they're all unsettled. Is inflation transitory? Is that a real threat to the economy? GDP forecasts are seesawing. In short, the world is out of whack and the need for fast access to quality, trusted and governed data has never been greater. Can coherent data strategies help solve these problems, or will we have to wait for the world to reach some type of natural equilibrium? And how are companies, like Google, helping customers solve these problems in critical industries, like financial services, retail, manufacturing, and other sectors? And with me to share his perspectives on data is a long-time CUBE alum, Bruno Aziza. He's the head of data analytics at Google. Bruno, my friend, great to see you again, welcome. >> Great to see you, thanks for having me, Dave. >> So you heard my little narrative upfront, how do you see this crazy world of data today? >> I think you're right. I think there's a lot going on in the world of data analytics today. I mean, certainly over the last 30 years, we've all tried to just make the life of people better and give them access more readily to the information that they need. But certainly over the last year and half, two years, we've seen an amazing acceleration in digital transformation. And what I think we're seeing is that even after three decades of investment in the data analytics world, you know, the opportunity is still really out wide and is still available for organizations to get value out of their data. I was looking at some of the latest research in the market, and, you know, only 32% of companies are actually able to say that they get tangible, valuable insights out of their data. So after all these years, we still have a lot of opportunity ahead of us, of course, with the democratization of access to data, but also the advent in machine learning and AI, so that people can make better decisions faster than their competitors. >> So do you think that the pandemic has heightened that sort of awareness as they were sort of forced to pivot to digital, that they're maybe not getting enough out of their data strategies? That maybe their whatever, their organization, their technology, their way they were thinking about data was not adequate and didn't allow them to be agile enough? Why do you think that only 32% are getting that type of value? >> I think it's true. I think, one, digital transformation has been accelerated over the last two years. I think, you know, if you look at research the last two years, I've seen almost a decade of digital acceleration, you know, happening. But I also think that we're hitting a particular time where employees are expecting more from their employers in terms of the type of insights that can get. Consumers are now evolving, right? So they want more information. And I think now technology has evolved to a point where it's a lot easier to provision a data cloud environment so you can get more data out to your constituents. So I think the connection of these three things, expectation of employees, expectation of customers to better customer experiences, and, of course, the global environment, has accelerated quite a bit, you know, where the space can go. And for people like me, you know, 20 years ago, nobody really cared about databases and so forth. And now I feel like, you know, everybody's, you know, understands the value that we can get out of it. And we're kind of getting, you know, in the sexy territory, finally, data now is sexy for everyone and there's a lot of interest in the space. >> You and I met, of course, in the early days of Hadoop. And there were many things about Hadoop that were profound and, of course, many things that, you know, just were overly complex, et cetera. And one of the things we saw was this sort of decentralization. We thought that Hadoop was going to send five megabytes of code to petabytes of data. And what happened is everything, you know, came into this centralized repository and that centralized thinking, the data pipeline organization was very centralized. Are you seeing companies rethink that? I mean, has the cloud changed their thinking? You know, especially as the cloud expands to the edge, on-prem, everywhere. How are you seeing organizations rethink their regimes for data? >> Yeah, I think, you know, we've seen over the last three decades kind of the pendulum, right, from really centralizing everything and making the IT organization kind of the center of excellence for data analytics, all the way to now, you know, providing data as a self-service, you know, application for end-users. And I think what we're seeing now is there's a few forces happening. The first one is, of course, multicloud, right? So the world today is clearly multicloud and it's going to be multicloud for many, many years. So I think not only are now people considering their on-prem information, but they're also looking at data across multiple clouds. And so I think that is a huge force for chief data officers to consider is that, you know, you're not going to have data centralized in one place, nicely organized, because sometimes it's going to be a factor of where you want to be as an organization. Maybe you're going to be partnering with other organizations that have data in other clouds. And so you want to have an architecture that is modern and that accommodates this idea of an open cloud. The second problem that we see is this idea around data governance, intelligent data governance, right? So the world of managing data is becoming more complex because, of course, you're now dealing with many different speeds, you're dealing with many different types of data. And so you want to be able to empower people to get access to the information, without necessarily having to move this data, so they can make quick decisions on the data. So this idea of a data fabric is becoming really important. And then the third trend that we see, of course, is this idea around data sharing, right? People are now looking to use their own data to create a data economy around their business. And so the ability to augment their existing data with external data and create data products around it is becoming more and more important to the chief data officers. So it's really interesting we're seeing a switch from, you know, this chief data officer really only worried about governance, to this we're now worried about innovation, while making sure that security and governance is taken care of. You know, we call this freedom within the framework, which is a great challenge, but a great opportunity for many of these data leaders. >> You mentioned several things there. Self-service, multicloud, the governance key, especially if we can federate that governance in a decentralized world. Data fabric is interesting. I was talking to Zhamak Dehghani this weekend on email. She coined the term data mesh. And there seems to be some confusion, data mesh, data fabric. I think Gartner's using the term fabric. I know like NetApp, I think coined that term, which to me is like an infrastructure layer, you know. But what do you mean by data fabric? >> Well, the first thing that I would say is that it's not up to the vendors to define what it is. It really is up to the customer. The problem that we're seeing these customers trying to fix is you have a diversity of data, right? So you have data stored in the data mart, in a data lake, in a data warehouse, and they all have their specific, you know, reasons for being there. And so this idea of a data fabric is that without moving the data, can you, one, govern it intelligently? And, two, can you provide landing zones for people to actually do their work without having to go through the pain of setting up new infrastructure, or moving information left and right, and creating new applications? So it's this idea of basically taking advantage of your existing environment, but also governing it centrally, and also now providing self-service capabilities so people can do their job easily. So, you know, you might call it a data mesh, you might call it a data fabric. You know, the terminology to me, you know, doesn't seem to be the barrier. The issue today is how do we enable, you know, this freedom for customers? Because, you know, I think what we've seen with vendors out there is they're trying to just take the customer down to their paradigms. So if they believe in all the answers need to be in a data warehouse, they're going to guide the customer there. If they believe that, you know, everything needs to be in a data lake, they're going to guide the customer there. What we believe in is this idea of choice. You should be able to do every single use case. And we should be able to enable you to manage it intelligently, both from an access standpoint, as well as a governance standpoint. >> So when you think about those different, and I like that, you're making it somewhat technology agnostic, so whether it's a data warehouse, or a data lake, or a data hub, a data mart, those are nodes within the mesh or the fabric, right? That are discoverable, accessible, I guess, governed. I think that there's got to be some kind of centralized governance edict, but in a federated governance model so you don't have to move the data around. Is that how you're thinking about it? >> Absolutely, you know, in our recent event, in the Data Cloud Summit, we had Equifax. So the gentleman there was the VP of data governance and data fabric. So you can start seeing now these roles, you know, created around this problem. And really when you listen to what they're trying to do, they're trying to provide as much value as they can without changing the habits of their users. I think that's what's key here, is that the minute you start changing habits, force people into paradigms that maybe, you know, are useful for you as a vendor, but not so useful to the customer, you get into the danger zone. So the idea here is how can you provide a broad enough platform, a platform that is deep enough, so the data can be intelligently managed and also distributed and activated at the point of interaction for the end-user, so they can do their job a lot easier? And that's really what we're about, is how do you make data simpler? How do you make, you know, the process of getting to insight a lot more fluid without changing habits necessarily, both on the IT side and the business side? >> I want to get to specifics on what Google is doing, but the last sort of uber-trends I want to ask you about 'cause, again, we've known each other for a long time. We've seen this data world grow up. And you're right, 20, 30 years ago, nobody cared about database. Well, maybe 30 years ago. But 20 years ago, it was a boring market, right now it's like the hottest thing going. But we saw, you know, bromide like data is the new oil. Well, we found out, well, actually data is more valuable than oil 'cause you can use, you know, data in a lot of different places, oil you can use once. And then the term like data as an asset, and you said data sharing. And it brings up the notion that, you know, you don't want to share your assets, but you do want to share your data as long as it can be governed. So we're starting to change the language that we use to describe data and our thinking is changing. And so it says to me that the next 10 years, aren't going to be like the last 10 years. What are your thoughts on that? >> I think you're absolutely right. I think if you look at how companies are maturing their use of data, obviously the first barrier is, "How do I, as a company, make sure that I take advantage of my data as an asset? How do I turn, you know, all this information into a sustainable, competitive advantage, really top of mind for organizations?" The second piece around it is, "How do I create now this innovation flywheel so that I can create value for my customers, and my employees, and my partners?" And then, finally, "How do I use data as the center of a product that I can then further monetize and create further value into my ecosystem?" I think the piece that's been happening that people have not talked a lot about I think, with the cloud, what's come is it's given us the opportunity to think about data as an ecosystem. Now you and I are partnering on insights. You and I are creating assets that might be the combination of your data and my data. Maybe it's an intelligent application on top of that data that now has become an intelligent, rich experience, if you will, that we can either both monetize or that we can drive value from. And so I think, you know, it's just scratching the surface on that. But I think that's where the next 10 years, to your point, are going to be, is that the companies that win with data are going to create products, intelligent products, out of that data. And they're just going to take us to places that, you know, we are not even thinking about right now. >> Yeah, and I think you're right on. That is going to be one of the big differences in the coming years is data as product. And that brings up sort of the line of business, right? I mean the lines of business heads historically have been kind of removed from the data group, that's why I was asking you about the organization before. But let's get into Google. How do you describe Google's strategy, its approach, and why it's unique? >> You know, I think one of the reasons, so I just, you know, started about a year ago, and one of the reasons for why I found, you know, the Google mission interesting, is that it's really rooted at who we are and what we do. If you think about it, we make data simple. That's really what we're about. And we live that value. If you go to google.com today, what's happening? Right, as an end-user, you don't need any training. You're going to type in whatever it is that you're looking for, and then we're going to return to you highly personalized, highly actionable insights to you as a consumer of insights, if you will. And I think that's where the market is going to. Now, you know, making data simple doesn't mean that you have to have simple infrastructure. In fact, you need to be able to handle sophistication at scale. And so simply our differentiation here is how do we go from highly sophisticated world of the internet, disconnected data, changing all the time, vast volume, and a lot of different types of data, to a simple answer that's actionable to the end-user? It's intelligence. And so our differentiation is around that. Our mission is to make data simple and we use intelligence to take the sophistication and provide to you an answer that's highly actionable, highly relevant, highly personalized for you, so you can go on and do your job, 'cause ultimately the majority of people are not in the data business. And so they need to get the information just like you said, as a business user, that's relevant, actionable, timely, so they can go off and, you know, create value for their organization. >> So I don't think anybody would argue that Google, obviously, are data experts, arguably the best in the world. But it's interesting, some of the uniqueness here that I'm hearing in your language. You used the word multicloud, Amazon doesn't, you know, use that term. So that's a differentiation. And you sell a cloud, right? You sell cloud services, but you're talking about multicloud. You sell databases, but, of course, you host other databases, like Snowflake. So where do you fit in all this? Do you see your role, as the head of data analytics, is to sort of be the chef that helps combine all these different capabilities? Or are you sort of trying to help people adopt Google products and services? How should we think about that? >> Yeah, the best way to think about, you know, I spend 60 to 70% of my time with customers. And the best way I can think about our role is to be your innovation partner as an organization. And, you know, whichever is the scenario that you're going to be using, I think you talked about open cloud, I think another uniqueness of Google is that we have a very partner friendly, you know, approach to the business. Because we realized that when you walk into an enterprise or a digital native, and so forth, they already have a lot of assets that they have accumulated over the years. And it might be technology assets, but also might be knowledge, and know-how, right? So we want to be able to be the innovation vendor that enables you to take these assets, put them together, and create simplicity towards the data. You know, ultimately, you can have all types of complexity in the backend. But what we can do the best for you is make that really simple, really integrated, really unified, so you, as a business user, you don't have to worry about, "Where is my data? Do I need to think about moving data from here to there? Are there things that I can do only if the data is formatted that way and this way?" We want to remove all that complexity, just like we do it on google.com, so you can do your job. And so that's our job, and that's the reason for why people come to us, is because they see that we can be their best innovation partner, regardless where the data is and regardless, you know, what part of the stack they're using. >> Well, I want to take an example, because my example, I mean, I don't know Google's portfolio like you do, obviously, but one of the things I hear from customers is, "We're trying to inject as much machine intelligence into our data as possible. We see opportunities to automate." So I look at something like BigQuery, which has a strong affinity in embedded machine learning and machine intelligence, as an example, maybe of that simplification. But maybe you could pick up on that and give us some other concrete examples. >> Yeah, specifically on products, I mean, there are a lot products we can talk about, and certainly BigQuery has tremendous market momentum. You know, and it's really anchored on this idea that, you know, the idea behind BigQuery is that just add data and we'll do the rest, right? So that's kind of the idea where you can start small and you can scale at incredible, you know, volumes without really having to think about tuning it, about creating indexes, and so forth. Also, we think about BigQuery as the place that people start in order to build their ecosystem. That's why we've invested a lot in machine learning. Just a few years ago, we introduced this functionality called BigQuery Machine Learning, or BigQuery ML, if you're familiar with it. And you notice out of the top 100 customers we have, 80% of these customers are using machine learning right out of, you know, BigQuery. So now why is that? Why is it that it's so easy to use machine learning using BigQuery is because it's built in. It was built from the ground up. Instead of thinking about machine learning as an afterthought, or maybe something that only data scientists have access to that you're going to license just for narrow scenarios, we think about you have your data in a warehouse that can scale, that is equally awesome at small volume as very large volume, and we build on top of that. You know, similarly, we just announced our analytics exchange, which is basically the place where you can now build these data analytics assets that we discussed, so you can now build an ecosystem that creates value for end-users. And so BigQuery is really at the center of a lot of that strategy, but it's not unlike any of the other products that we have. We want to make it simple for people to onboard, simple to scale, to really accomplish, you know, whatever success is ahead of them. >> Well, I think ecosystems is another one of those big differences in the coming decade, because you're able to build ecosystems around data, especially if you can share that data, you know, and do so in a governed and secure way. But it leads to my question on industries, and I'm wondering if you see any patterns emerging in industries? And each industry seems to have its own unique disruption scenario. You know, retail obviously has been, you know, disrupted with online commerce. And healthcare with, of course, the pandemic. Financial services, you wonder, "Okay, are traditional banks going to lose control of payment systems?" Manufacturing you see our reliance on China's supply chain in, of course, North America. Are you seeing any patterns in industry as it pertains to data? And what can you share with us in terms of insights there? >> Yeah, we are. And, I mean, you know, there's obviously the industries that are, you know, very data savvy or data hungry. You think about, you know, the telecommunication industry, you think about manufacturing, you think about financial services and retail. I mean, financial services and retailers are particularly interesting, because they're kind of both in the retail business and having to deal with this level of complexity of they have physical locations and they also have a relationship with people online, so they really want to be able to bring these two worlds together. You know, I think, you know, about those scenarios of Carrefour, for instance. It's a large retailer in Europe that has been able to not only to, you know, onboard on our platform and they're using, you know, everything from BigQuery, all the way to Looker, but also now create the data assets that enable them to differentiate within their own industry. And so we see a lot of that happening across pretty much all industries. It's difficult to think about an industry that is not really taking a hard look at their data strategy recently, especially over the last two years, and really thought about how they're creating innovation. We have actually created what we call design patterns, which are basically blueprints for organization to take on. It's free, it's free guidance, it's free datasets and code that can accelerate their building of these innovative solutions. So think about the, you know, ability to determine propensity to purchase. Or build, you know, a big trend is recommendation systems. Another one is anomaly detection, and this was great because anomaly detection is a scenario that works in telco, but also in financial services. So we certainly are seeing now companies moving up in their level of maturity, because we're making it easier and simpler for them to assemble these technologies and create, you know, what we call data-rich experiences. >> The last question is how you see the emerging edge, IoT, analytics in that space? You know, a lot of the machine learning or AI today is modeling in the cloud, as you well know. But when you think about a lot of the consumer applications, whether it's voice recognition or, you know, or fingerprinting, et cetera, you're seeing some really interesting use cases that could bleed into the enterprise. And we think about AI inferencing at the edge as really driving a lot of value. How do you see that playing out and what's Google's role there? >> So there's a lot going on in that space. I'll give you just a simple example. Maybe something that's easy for the community to understand is there's still ways that we define certain metrics that are not taking into account what actually is happening in reality. I was just talking to a company whose job is to deliver meals to people. And what they have realized is that in order for them to predict exactly the time it's going to take them from the kitchen to your desk, they have to take into account the fact that distance sometimes it's not just horizontal, it's also vertical. So if you're distributing and you're delivering meals, you know, in Singapore, for instance, high density, you have to understand maybe the data coming from the elevators. So you can determine, "Oh, if you're on the 20th floor, now my distance to you, and my ability to forecast exactly when you're going to get that meal, is going to be different than if you are on the fifth floor. And, particularly, if you're ordering at 11:32, versus if you're ordering at 11:58." And so what's happening here is that as people are developing these intelligent systems, they're now starting to input a lot of information that historically we might not have thought about, but that actually is very relevant to the end-user. And so, you know, how do you do that? Again, and you have to have a platform that enables you to have a large diversity of use cases, and that thinks ahead, if you will, of the problems you might run into. Lots and lots of innovation in this space. I mean, we work with, you know, companies like Ford to, you know, reinvent the connected, you know, cars. We work with companies like Vodafone, 700 use cases, to think about how they're going to deal with what they call their data ocean. You know, I thought you would like this term, because we've gone from data lakes to data oceans. And so there is certainly a ton of innovation and certainly, you know, the chief data officers that I have the opportunity to work with are really not short of ideas. I think what's been happening up until now, they haven't had this kind of single, unified, simple experience that they can use in order to onboard quickly and then enable their people to build great, rich-data applications. >> Yeah, we certainly had fun with that over the years, data lake or data ocean. And thank you for remembering that, Bruno. Always a pleasure seeing you. Thanks so much for your time and sharing your perspectives, and informing us about what Google's up to. Can't wait to have you back. >> Thanks for having me, Dave. >> All right, and thank you for watching, everybody. This is Dave Vellante. Appreciate you watching this CUBE Conversation, and we'll see you next time. (gentle music)

(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)

(upbeat music) >> On February 19th of this year, Barron's dropped an article declaring Oracle, a cloud giant and the article explained why the stock was a buy. Investors took notice and the stock ran up 18% over the next nine trading days and it peaked on March 9th, the day before Oracle announced its latest earnings. The company beat consensus earnings on both top-line and EPS last quarter, but investors, they did not like Oracle's tepid guidance and the stock pulled back. But it's still, as you can see, well above its pre-Barron's article price. What does all this mean? Is Oracle a cloud giant? What are its growth prospects? Now many parts of Oracle's business are growing including Fusion ERP, Fusion HCM, NetSuite, we're talking deep into the double digits, 20 plus percent growth. It's OnPrem legacy licensed business however, continues to decline and that moderates, the overall company growth because that OnPrem business is so large. So the overall Oracle's growing in the low single digits. Now what stands out about Oracle is it's recurring revenue model. That figure, the company says now it represents 73% of its revenue and that's going to continue to grow. Now two other things stood out on the earnings call to us. First, Oracle plans on increasing its CapEX by 50% in the coming quarter, that's a lot. Now it's still far less than AWS Google or Microsoft Spend on capital but it's a meaningful data point. Second Oracle's consumption revenue for Autonomous Database and Cloud Infrastructure, OCI or Oracle Cloud Infrastructure grew at 64% and 139% respectively and these two factors combined with the CapEX Spend suggest that the company has real momentum. I mean look, it's possible that the CapEx announcements maybe just optics in they're front loading, some spend to show the street that it's a player in cloud but I don't think so. Oracle's Safra Catz's usually pretty disciplined when it comes to it's spending. Now today on March 17th, Oracle announced updates towards Autonomous Data Warehouse and with me is David Floyer who has extensively researched Oracle over the years and today we're going to unpack the Oracle Autonomous Data Warehouse, ADW announcement. What it means to customers but we also want to dig into Oracle's strategy. We want to compare it to some other prominent database vendors specifically, AWS and Snowflake. David Floyer, Welcome back to The Cube, thanks for making some time for me. >> Thank you Vellante, great pleasure to be here. >> All right, I want to get into the news but I want to start with this idea of the autonomous database which Oracle's announcement today is building on. Oracle uses the analogy of a self-driving car. It's obviously powerful metaphor as they call it the self-driving database and my takeaway is that, this means that the system automatically provisions, it upgrades, it does all the patching for you, it tunes itself. Oracle claims that all reduces labor costs or admin costs by 90%. So I ask you, is this the right interpretation of what Oracle means by autonomous database? And is it real? >> Is that the right interpretation? It's a nice analogy. It's a test to that analogy, isn't it? I would put it as the first stage of the Autonomous Data Warehouse was to do the things that you talked about, which was the tuning, the provisioning, all of that sort of thing. The second stage is actually, I think more interesting in that what they're focusing on is making it easy to use for the end user. Eliminating the requirement for IT, staff to be there to help in the actual using of it and that is a very big step for them but an absolutely vital step because all of the competition focusing on ease of use, ease of use, ease of use and cheapness of being able to manage and deploy. But, so I think that is the really important area that Oracle has focused on and it seemed to have done so very well. >> So in your view, is this, I mean you don't really hear a lot of other companies talking about this analogy of the self-driving database, is this unique? Is it differentiable for Oracle? If so, why, or maybe you could help us understand that a little bit better. >> Well, the whole strategy is unique in its breadth. It has really brought together a whole number of things together and made it of its type the best. So it has a single, whole number of data sources and database types. So it's got a very broad range of different ways that you can look at the data and the second thing that is also excellent is it's a platform. It is fully self provisioned and its functionality is very, very broad indeed. The quality of the original SQL and the query languages, etc, is very, very good indeed and it's a better agent to do joints for example, is excellent. So all of the building blocks are there and together with it's sharing of the same data with OLTP and inference and in memory data paces as well. All together the breadth of what they have is unique and very, very powerful. >> I want to come back to this but let's get into the news a little bit and the announcement. I mean, it seems like what's new in the autonomous data warehouse piece for Oracle's new tooling around four areas that so Andy Mendelsohn, the head of this group instead of the guy who releases his baby, he talked about four things. My takeaway, faster simpler loads, simplified transforms, autonomous machine learning models which are facilitating, What do you call it? Citizen data science and then faster time to insights. So tooling to make those four things happen. What's your take and takeaways on the news? >> I think those are all correct. I would add the ease of use in terms of being able to drag and drop, the user interface has been dramatically improved. Again, I think those, strategically are actually more important that the others are all useful and good components of it but strategically, I think is more important. There's ease of use, the use of apex for example, are more important. And, >> Why are they more important strategically? >> Because they focus on the end users capability. For example, one of other things that they've started to introduce is Python together with their spatial databases, for example. That is really important that you reach out to the developer as they are and what tools they want to use. So those type of ease of use things, those types of things are respecting what the end users use. For example, they haven't come out with anything like click or Tableau. They've left that there for that marketplace for the end user to use what they like best. >> Do you mean, they're not trying to compete with those two tools. They indeed had a laundry list of stuff that they supported, Talend, Tableau, Looker, click, Informatica, IBM, I had IBM there. So their claim was, hey, we're open. But so that's smart. That's just, hey, they realized that people use these tools. >> I'm trying to exclude other people, be a platform and be an ecosystem for the end users. >> Okay, so Mendelsohn who made the announcement said that Oracle's the smartphone of databases and I think, I actually think Alison kind of used that or maybe that was us planing to have, I thought he did like the iPhone of when he announced the exit data way back when the integrated hardware and software but is that how you see it, is Oracle, the smartphone of databases? >> It is, I mean, they are trying to own the complete stack, the hardware with the exit data all the way up to the databases at the data warehouses and the OLTP databases, the inference databases. They're trying to own the complete stack from top to bottom and that's what makes autonomy process possible. You can make it autonomous when you control all of that. Take away all of the requirements for IT in the business itself. So it's democratizing the use of data warehouses. It is pushing it out to the lines of business and it's simplifying it and making it possible to push out so that they can own their own data. They can manage their own data and they do not need an IT person from headquarters to help them. >> Let's stay in this a little bit more and then I want to go into some of the competitive stuff because Mendelsohn mentioned AWS several times. One of the things that struck me, he said, hey, we're basically one API 'cause we're doing analytics in the cloud, we're doing data in the cloud, we're doing integration in the cloud and that's sort of a big part of the value proposition. He made some comparisons to Redshift. Of course, I would say, if you can't find a workload where you beat your big competitor then you shouldn't be in this business. So I take those things with a grain of salt but one of the other things that caught me is that migrating from OnPrem to Oracle, Oracle Cloud was very simple and I think he might've made some comparisons to other platforms. And this to me is important because he also brought in that Gartner data. We looked at that Gardner data when they came out with it in the operational database class, Oracle smoked everybody. They were like way ahead and the reason why I think that's important is because let's face it, the Mission Critical Workloads, when you look at what's moving into AWS, the Mission Critical Workloads, the high performance, high criticality OLTP stuff. That's not moving in droves and you've made the point often that companies with their own cloud particularly, Oracle you've mentioned this about IBM for certain, DB2 for instance, customers are going to, there should be a lower risk environment moving from OnPrem to their cloud, because you could do, I don't think you could get Oracle RAC on AWS. For example, I don't think EXIF data is running in AWS data centers and so that like component is going to facilitate migration. What's your take on all that spiel? >> I think that's absolutely right. You all crown Jewels, the most expensive and the most valuable applications, the mission-critical applications. The ones that have got to take a beating, keep on taking. So those types of applications are where Oracle really shines. They own a very large high percentage of those Mission Critical Workloads and you have the choice if you're going to AWS, for example of either migrating to Oracle on AWS and that is frankly not a good fit at all. There're a lot of constraints to running large systems on AWS, large mission critical systems. So that's not an option and then the option, of course, that AWS will push is move to a Roller, change your way of writing applications, make them tiny little pieces and stitch them all together with microservices and that's okay if you're a small organization but that has got a lot of problems in its own, right? Because then you, the user have to stitch all those pieces together and you're responsible for testing it and you're responsible for looking after it. And that as you grow becomes a bigger and bigger overhead. So AWS, in my opinion needs to have a move towards a tier-one database of it's own and it's not in that position at the moment. >> Interesting, okay. So, let's talk about the competitive landscape and the choices that customers have. As I said, Mendelssohn mentioned AWS many times, Larry on the calls often take shy, it's a compliment to me. When Larry Ellison calls you out, that means you've made it, you're doing well. We've seen it over the years, whether it's IBM or Workday or Salesforce, even though Salesforce's big Oracle customer 'cause AWS, as we know are Oracle customer as well, even though AWS tells us they've off called when you peel the onion >> Five years should be great, some of the workers >> Well, as I said, I believe they're still using Oracle in certain workloads. Way, way, we digress. So AWS though, they take a different approach and I want to push on this a little bit with database. It's got more than a dozen, I think purpose-built databases. They take this kind of right tool for the right job approach was Oracle there converging all this function into a single database. SQL JSON graph databases, machine learning, blockchain. I'd love to talk about more about blockchain if we have time but seems to me that the right tool for the right job purpose-built, very granular down to the primitives and APIs. That seems to me to be a pretty viable approach versus kind of a Swiss Army approach. How do you compare the two? >> Yes, and it is to many initial programmers who are very interested for example, in graph databases or in time series databases. They are looking for a cheap database that will do the job for a particular project and that makes, for the program or for that individual piece of work is making a very sensible way of doing it and they pay for ads on it's clear cloud dynamics. The challenge as you have more and more data and as you're building up your data warehouse in your data lakes is that you do not want to have to move data from one place to another place. So for example, if you've got a Roller,, you have to move the database and it's a pretty complicated thing to do it, to move it to Redshift. It's a five or six steps to do that and each of those costs money and each of those take time. More importantly, they take time. The Oracle approach is a single database in terms of all the pieces that obviously you have multiple databases you have different OLTP databases and data warehouse databases but as a single architecture and a single design which means that all of the work in terms of moving stuff from one place to another place is within Oracle itself. It's Oracle that's doing that work for you and as you grow, that becomes very, very important. To me, very, very important, cost saving. The overhead of all those different ones and the databases themselves originate with all as open source and they've done very well with it and then there's a large revenue stream behind the, >> The AWS, you mean? >> Yes, the original database is in AWS and they've done a lot of work in terms of making it set with the panels, etc. But if a larger organization, especially very large ones and certainly if they want to combine, for example data warehouse with the OLTP and the inference which is in my opinion, a very good thing that they should be trying to do then that is incredibly difficult to do with AWS and in my opinion, AWS has to invest enormously in to make the whole ecosystem much better. >> Okay, so innovation required there maybe is part of the TAM expansion strategy but just to sort of digress for a second. So it seems like, and by the way, there are others that are doing, they're taking this converged approach. It seems like that is a trend. I mean, you certainly see it with single store. I mean, the name sort of implies that formerly MemSQL I think Monte Zweben of splice machine is probably headed in a similar direction, embedding AI in Microsoft's, kind of interesting. It seems like Microsoft is willing to build this abstraction layer that hides that complexity of the different tooling. AWS thus far has not taken that approach and then sort of looking at Snowflake, Snowflake's got a completely different, I think Snowflake's trying to do something completely different. I don't think they're necessarily trying to take Oracle head-on. I mean, they're certainly trying to just, I guess, let's talk about this. Snowflake simplified EDW, that's clear. Zero to snowflake in 90 minutes. It's got this data cloud vision. So you sign on to this Snowflake, speaking of layers they're abstracting the complexity of the underlying cloud. That's what the data cloud vision is all about. They, talk about this Global Mesh but they've not done a good job of explaining what the heck it is. We've been pushing them on that, but we got, >> Aspiration of moment >> Well, I guess, yeah, it seems that way. And so, but conceptually, it's I think very powerful but in reality, what snowflake is doing with data sharing, a lot of reading it's probably mostly read-only and I say, mostly read-only, oh, there you go. You'll get better but it's mostly read and so you're able to share the data, it's governed. I mean, it's exactly, quite genius how they've implemented this with its simplicity. It is a caching architecture. We've talked about that, we can geek out about that. There's good, there's bad, there's ugly but generally speaking, I guess my premise here I would love your thoughts. Is snowflakes trying to do something different? It's trying to be not just another data warehouse. It's not just trying to compete with data lakes. It's trying to create this data cloud to facilitate data sharing, put data in the hands of business owners in terms of a product build, data product builders. That's a different vision than anything I've seen thus far, your thoughts. >> I agree and even more going further, being a place where people can sell data. Put it up and make it available to whoever needs it and making it so simple that it can be shared across the country and across the world. I think it's a very powerful vision indeed. The challenge they have is that the pieces at the moment are very, very easy to use but the quality in terms of the, for example, joints, I mentioned, the joints were very powerful in Oracle. They don't try and do joints. They, they say >> They being Snowflake, snowflake. Yeah, they don't even write it. They would say use another Postgres >> Yeah. >> Database to do that. >> Yeah, so then they have a long way to go. >> Complex joints anyway, maybe simple joints, yeah. >> Complex joints, so they have a long way to go in terms of the functionality of their product and also in my opinion, they sure be going to have more types of databases inside it, including OLTP and they can do that. They have obviously got a great market gap and they can do that by acquisition as well as they can >> They've started. I think, I think they support JSON, right. >> Do they support JSON? And graph, I think there's a graph database that's either coming or it's there, I can't keep all that stuff in my head but there's no reason they can't go in that direction. I mean, in speaking to the founders in Snowflake they were like, look, we're kind of new. We would focus on simple. A lot of them came from Oracle so they know all database and they know how hard it is to do things like facilitate complex joints and do complex workload management and so they said, let's just simplify, we'll put it in the cloud and it will spin up a separate data warehouse. It's a virtual data warehouse every time you want one to. So that's how they handle those things. So different philosophy but again, coming back to some of the mission critical work and some of the larger Oracle customers, they said they have a thousand autonomous database customers. I think it was autonomous database, not ADW but anyway, a few stood out AON, lift, I think Deloitte stood out and as obviously, hundreds more. So we have people who misunderstand Oracle, I think. They got a big install base. They invest in R and D and they talk about lock-in sure but the CIO that I talked to and you talked to David, they're looking for business value. I would say that 75 to 80% of them will gravitate toward business value over the fear of lock-in and I think at the end of the day, they feel like, you know what? If our business is performing, it's a better business decision, it's a better business case. >> I fully agree, they've been very difficult to do business with in the past. Everybody's in dread of the >> The audit. >> The knock on the door from the auditor. >> Right. >> And that from a purchasing point of view has been really bad experience for many, many customers. The users of the database itself are very happy indeed. I mean, you talk to them and they understand why, what they're paying for. They understand the value and in terms of availability and all of the tools for complex multi-dimensional types of applications. It's pretty well, the only game in town. It's only DB2 and SQL that had any hope of doing >> Doing Microsoft, Microsoft SQL, right. >> Okay, SQL >> Which, okay, yeah, definitely competitive for sure. DB2, no IBM look, IBM lost its dominant position in database. They kind of seeded that. Oracle had to fight hard to win it. It wasn't obvious in the 80s who was going to be the database King and all had to fight. And to me, I always tell people the difference is that the chairman of Oracle is also the CTO. They spend money on R and D and they throw off a ton of cash. I want to say something about, >> I was just going to make one extra point. The simplicity and the capability of their cloud versions of all of this is incredibly good. They are better in terms of spending what you need or what you use much better than AWS, for example or anybody else. So they have really come full circle in terms of attractiveness in a cloud environment. >> You mean charging you for what you consume. Yeah, Mendelsohn talked about that. He made a big point about the granularity, you pay for only what you need. If you need 33 CPUs or the other databases you've got to shape, if you need 33, you've got to go to 64. I know that's true for everyone. I'm not sure if that's true too for snowflake. It may be, I got to dig into that a little bit, but maybe >> Yes, Snowflake has got a front end to hiding behind. >> Right, but I didn't want to push it that a little bit because I want to go look at their pricing strategies because I still think they make you buy, I may be wrong. I thought they make you still do a one-year or two-year or three-year term. I don't know if you can just turn it off at any time. They might allow, I should hold off. I'll do some more research on that but I wanted to make a point about the audits, you mentioned audits before. A big mistake that a lot of Oracle customers have made many times and we've written about this, negotiating with Oracle, you've got to bring your best and your brightest when you negotiate with Oracle. Some of the things that people didn't pay attention to and I think they've sort of caught onto this is that Oracle's SOW is adjudicate over the MSA, a lot of legal departments and procurement department. Oh, do we have an MSA? With all, Yes, you do, okay, great and because they think the MSA, they then can run. If they have an MSA, they can rubber stamp it but the SOW really dictateS and Oracle's gotcha there and they're really smart about that. So you got to bring your best and the brightest and you've got to really negotiate hard with Oracle, you get trouble. >> Sure. >> So it is what it is but coming back to Oracle, let's sort of wrap on this. Dominant position in mission critical, we saw that from the Gartner research, especially for operational, giant customer base, there's cloud-first notion, there's investing in R and D, open, we'll put a question Mark around that but hey, they're doing some cool stuff with Michael stuff. >> Ecosystem, I put that, ecosystem they're promoting their ecosystem. >> Yeah, and look, I mean, for a lot of their customers, we've talked to many, they say, look, there's actually, a tail at the tail way, this saves us money and we don't have to migrate. >> Yeah. So interesting, so I'll give you the last word. We started sort of focusing on the announcement. So what do you want to leave us with? >> My last word is that there are platforms with a certain key application or key parts of the infrastructure, which I think can differentiate themselves from the Azures or the AWS. and Oracle owns one of those, SAP might be another one but there are certain platforms which are big enough and important enough that they will, in my opinion will succeed in that cloud strategy for this. >> Great, David, thanks so much, appreciate your insights. >> Good to be here. Thank you for watching everybody, this is Dave Vellante for The Cube. We'll see you next time. (upbeat music)

>> Narrator: From around the globe it's theCUBE, with digital coverage of AWS re:Invent 2020. Special coverage sponsored by AWS Global Partner Network. >> Hello and welcome to theCUBE Virtual and our coverage of AWS re:Invent 2020 with special coverage of APN partner experience. We are theCUBE Virtual and I'm your host, Justin Warren. And today I'm joined by Ed Walsh, CEO of ChaosSearch. Ed, welcome to theCUBE. >> Well thank you for having me, I really appreciate it. >> Now, this is not your first time here on theCUBE. You're a regular here and I've loved it to have you back. >> I love the platform you guys are great. >> So let's start off by just reminding people about what ChaosSearch is and what do you do there? >> Sure, the best way to say is so ChaosSearch helps our clients know better. We don't do that by a special wizard or a widget that you give to your, you know, SecOp teams. What we do is a hard work to give you a data platform to get insights at scale. And we do that also by achieving the promise of data lakes. So what we have is a Chaos data platform, connects and indexes data in a customer's S3 or glacier accounts. So inside your data lake, not our data lake but renders that data fully searchable and available for analysis using your existing tools today 'cause what we do is index it and publish open API, it's like API like Elasticsearch API, and soon SQL. So give you an example. So based upon those capabilities were an ideal replacement for a commonly deployed, either Elasticsearch or ELK Stack deployments, if you're hitting scale issues. So we talk about scalable log analytics, and more and more people are hitting these scale issues. So let's say if you're using Elasticsearch ELK or Amazon Elasticsearch, and you're hitting scale issues, what I mean by that is like, you can't keep enough retention. You want longer retention, or it's getting very expensive to keep that retention, or because the scale you hit where you have availability, where the cluster is hard to keep up running or is crashing. That's what we mean by the issues at scale. And what we do is simply we allow you, because we're publishing the open API of Elasticsearch use all your tools, but we save you about 80% off your monthly bill. We also give you an, and it's an and statement and give you unlimited retention. And as much as you want to keep on S3 or into Glacier but we also take care of all the hassles and management and the time to manage these clusters, which ends up being on a database server called leucine. And we take care of that as a managed service. And probably the biggest thing is all of this without changing anything your end users are using. So we include Kibana, but imagine it's an Elastic API. So if you're using API or Kibana, it's just easy to use the exact same tools used today, but you get the benefits of a true data lake. In fact, we're running now Elasticsearch on top of S3 natively. If that makes it sense. >> Right and natively is pretty cool. And look, 80% savings, is a dramatic number, particularly this year. I think there's a lot of people who are looking to save a few quid. So it'd be very nice to be able to save up to 80%. I am curious as to how you're able to achieve that kind of saving though. >> Yeah, you won't be the first person to ask me that. So listen, Elastic came around, it was, you know we had Splunk and we also have a lot of Splunk clients, but Elastic was a more cost effective solution open source to go after it. But what happens is, especially at scale, if it's fall it's actually very cost-effective. But underneath last six tech ELK Stack is a leucine database, it's a database technology. And that sits on our servers that are heavy memory count CPU count in and SSDs. So you can do on-prem or even in the clouds, so if you do an Amazon, basically you're spinning up a server and it stays up, it doesn't spin up, spin down. So those clusters are not one server, it's a cluster of those servers. And typically if you have any scale you're actually having multiple clusters because you don't dare put it on one, for different use cases. So our savings are actually you no longer need those servers to spin up and you don't need to pay for those seen underneath. You can still use Kibana under API but literally it's $80 off your bill that you're paying for your service now, and it's hard dollars. So it's not... And we typically see clients between 70 and 80%. It's up to 80, but it's literally right within a 10% margin that you're saving a lot of money, but more importantly, saving money is a great thing. But now you have one unified data lake that you can have. You used to go across some of the data or all the data through the role-based access. You can give different people. Like we've seen people who say, hey give that, help that person 40 days of this data. But the SecOp up team gets to see across all the different law. You know, all the machine generated data they have. And we can give you a couple of examples of that and walk you through how people deploy if you want. >> I'm always keen to hear specific examples of how customers are doing things. And it's nice that you've thought of drawn that comparison there around what what cloud is good for and what it isn't is. I'll often like to say that AWS is cheap to fail in, but expensive to succeed. So when people are actually succeeding with this and using this, this broad amount of data so what you're saying there with that savings I've actually got access to a lot more data that I can do things with. So yeah, if you could walk through a couple of examples of what people are doing with this increased amount of data that they have access to in EKL Search, what are some of the things that people are now able to unlock with that data? >> Well, literally it's always good for a customer size so we can go through and we go through it however it might want, Kleiner, Blackboard, Alert Logic, Armor Security, HubSpot. Maybe I'll start with HubSpot. One of our good clients, they were doing some Cloud Flare data that was one of their clusters they were using a lot to search for. But they were looking at to look at a denial service. And they were, we find everyone kind of at scale, they get limited. So they were down to five days retention. Why? Well, it's not that they meant to but basically they couldn't cost-effectively handle that in the scale. And also they're having scale issues with the environment, how they set the cluster and sharding. And when they also denial service tech, what happened that's when the influx of data that is one thing about scale is how fast it comes out, yet another one is how much data you have. But this is as the data was coming after them at denial service, that's when the cluster would actually go down believe it or not, you know right. When you need your log analysis tools. So what we did is because they're just using Kibana, it was easy swap. They ran in parallel because we published the open API but we took them from five days to nine days. They could keep as much as they want but nine days for denial services is what they wanted. And then we did save them in over $4 million a year in hard dollars, What they're paying in their environment from really is the savings on the server farm and a little bit on the Elasticsearch Stack. But more importantly, they had no outages since. Now here's the thing. Are you talking about the use case? They also had other clusters and you find everyone does it. They don't dare put it on one cluster, even though these are not one server, they're multiple servers. So the next use case for CloudFlare was one, the next QS and it was a 10 terabyte a day influx kept it for 90 days. So it's about a petabyte. They brought another use case on which was NetMon, again, Network Monitoring. And again, I'm having the same scale issue, retention area. And what they're able to do is easily roll that on. So that's one data platform. Now they're adding the next one. They have about four different use cases and it's just different clusters able to bring together. But now what they're able to do give you use cases either they getting more cost effective, more stability and freedom. We say saves you a lot of time, cost and complexity. Just the time they manage that get the data in the complexities around it. And then the cost is easy to kind of quantify but they've got better but more importantly now for particular teams they only need their access to one data but the SecOP team wants to see across all the data. And it's very easy for them to see across all the data where before it was impossible to do. So now they have multiple large use cases streaming at them. And what I love about that particular case is at one point they were just trying to test our scale. So they started tossing more things at it, right. To see if they could kind of break us. So they spiked us up to 30 terabytes a day which is for Elastic would even 10 terabytes a day makes things fall over. Now, if you think of what they just did, what were doing is literally three steps, put your data in S3 and as fast as you can, don't modify, just put it there. Once it's there three steps connect to us, you give us readability access to those buckets and a place to write the indexy. All of that stuff is in your S3, it never comes out. And then basically you set up, do you want to do live or do you want to do real time analysis? Or do you want to go after old data? We do the rest, we ingest, we normalize the schema. And basically we give you our back and the refinery to give the right people access. So what they did is they basically throw a whole bunch of stuff at it. They were trying to outrun S3. So, you know, we're on shoulders of giants. You know, if you think about our platform for clients what's a better dental like than S3. You're not going to get a better cross curve, right? You're not going to get a better parallelism. And so, or security it's in your, you know a virtual environment. But if you... And also you can keep data in the right location. So Blackboard's a good example. They need to keep that in all the different regions and because it's personal data, they, you know, GDPR they got to keep data in that location. It's easy, we just put compute in each one of the different areas they are. But the net net is if you think that architecture is shoulders of giants if you think you can outrun by just sheer volume or you can put in more cost-effective place to keep long-term or you think you can out store you have so much data that S3 and glacier can't possibly do it. Then you got me at your bigger scale at me but that's the scale we'r&e talking about. So if you think about the spiked our throughput what they really did is they try to outrun S3. And we didn't pick up. Now, the next thing is they tossed a bunch of users at us which were just spinning up in our data fabric different ways to do the indexing, to keep up with it. And new use cases in case they're going after everyone gets their own worker nodes which are all expected to fail in place. So again, they did some of that but really they're like you guys handled all the influx. And if you think about it, it's the shoulders of giants being on top of an Amazon platform, which is amazing. You're not going to get a more cost effective data lake in the world, and it's continuing to fall in price. And it's a cost curve, like no other, but also all that resiliency, all that security and the parallelism you can get, out of an S3 Glacier is just a bar none is the most scalable environment, you can build an environment. And what we do is a thin layer. It's a data platform that allows you to have your data now fully searchable and queryable using your tools >> Right and you, you mentioned there that, I mean you're running in AWS, which has broad experience in doing these sorts of things at scale but on that operational management side of things. As you mentioned, you actually take that off, off the hands of customers so that you run it on their behalf. What are some of the areas that you see people making in trying to do this themselves, when you've gone into customers, and brought it into the EKL Search platform? >> Yeah, so either people are just trying their best to build out clusters of Elasticsearch or they're going to services like Logz.io, Sumo Logic or Amazon Elasticsearch services. And those are all basically on the same ELK Stack. So they have the exact same limits as the same bits. Then we see people trying to say, well I really want to go to a data lake. I want to get away from these database servers and which have their limits. I want to use a data Lake. And then we see a lot of people putting data into environments before they, instead of using Elasticsearch, they want to use SQL type tools. And what they do is they put it into a Parquet or Presto form. It's a Presto dialect, but it into Parquet and structure it. And they go a lot of other way to, Hey it's in the data lake, but they end up building these little islands inside their data lake. And it's a lot of time to transform the data, to get it in a format that you can go after our tools. And then what we do is we don't make you do that. Just literally put the data there. And then what we do is we do the index and a polish API. So right now it's Elasticsearch in a very short time we'll publish Presto or the SQL dialect. You can use the same tool. So we do see people, either brute forcing and trying their best with a bunch of physical servers. We do see another group that says, you know, I want to go use an Athena use cases, or I want to use a there's a whole bunch of different startups saying, I do data lake or data lake houses. But they are, what they really do is force you to put things in the structure before you get insight. True data lake economics is literally just put it there, and use your tools natively to go after it. And that's where we're unique compared to what we see from our competition. >> Hmm, so with people who have moved into ChaosSearch, what's, let's say pick one, if you can, the most interesting example of what people have started to do with, with their data. What's new? >> That's good. Well, I'll give you another one. And so Armor Security is a good one. So Armor Security is a security service company. You know, thousands of clients doing great I mean a beautiful platform, beautiful business. And they won Rackspace as a partner. So now imagine thousand clients, but now, you know massive scale that to keep up with. So that would be an example but another example where we were able to come in and they were facing a major upgrade of their environment just to keep up, and they expose actually to their customers is how their customers do logging analytics. What we're able to do is literally simply because they didn't go below the API they use the exact same tools that are on top and in 30 days replaced that use case, save them tremendous amount of dollars. But now they're able to go back and have unlimited retention. They used to restrict their clients to 14 days. Now they have an opportunity to do a bunch of different things, and possible revenue opportunities and other. But allow them to look at their business differently and free up their team to do other things. And now they're, they're putting billing and other things into the same environment with us because one is easy it's scale but also freed up their team. No one has enough team to do things. And then the biggest thing is what people do interesting with our product is actually in their own tools. So, you know, we talk about Kibana when we do SQL again we talk about Looker and Tableau and Power BI, you know, the really interesting thing, and we think we did the hard work on the data layer which you can say is, you know I can about all the ways you consolidate the performance. Now, what becomes really interesting is what they're doing at the visibility level, either Kibana or the API or Tableau or Looker. And the key thing for us is we just say, just use the tools you're used to. Now that might be a boring statement, but to me, a great value proposition is not changing what your end users have to use. And they're doing amazing things. They're doing the exact same things they did before. They're just doing it with more data at bigger scale. And also they're able to see across their different machine learning data compared to being limited going at one thing at a time. And that getting the correlation from a unified data lake is really what we, you know we get very excited about. What's most exciting to our clients is they don't have to tell the users they have to use a different tool, which, you know, we'll decide if that's really interesting in this conversation. But again, I always say we didn't build a new algorithm that you going to give the SecOp team or a new pipeline cool widget that going to help the machine learning team which is another API we'll publish. But basically what we do is a hard work of making the data platform scalable, but more importantly give you the APIs that you're used to. So it's the platform that you don't have to change what your end users are doing, which is a... So we're kind of invisible behind the scenes. >> Well, that's certainly a pretty strong proposition there and I'm sure that there's plenty of scope for customers to come and and talk to you because no one's creating any less data. So Ed, thanks for coming out of theCUBE. It's always great to see you here. >> Know, thank you. >> You've been watching theCUBE Virtual and our coverage of AWS re:Invent 2020 with special coverage of APN partner experience. Make sure you check out all our coverage online, either on your desktop, mobile on your phone, wherever you are. I've been your host, Justin Warren. And I look forward to seeing you again soon. (soft music)

(gentle music) >> From the cube studios in Palo Alto and Boston, connecting with thought leaders all around the world. This is a Cube conversation. >> With Snowflake's, enormously successful IPO, it's clear that data warehousing in the cloud has come of age and a few companies know more about data and analytics than Google. Hi, I'm Paul Gillen. This is a cube conversation. And today we're going to talk about data warehousing and data analytics in the cloud. Google BigQuery, of course, is a popular, fully managed server less data warehouse that enables rapid SQL queries and interactive analysis of massive data sets. This summer, Google previewed BigQuery Omni, which essentially brings the capabilities of BigQuery to additional platforms including Amazon web services and soon Microsoft Azure. It's all part of Google's multicloud strategy. No one knows more about this strategy than Debanjan Saha, General Manager and Vice President of engineering for data analytics and Google cloud. And he joins me today. Debanjan, thanks so much for joining me. >> Paul, nice to meet you and thank you for having me today. >> So it's clear the data warehousing is now part of many enterprise data strategies. How has the rise of cloud change the way organizations are using data science in your view? >> Well, I mean, you know, the cloud definitely is a big enabler of data warehousing and data science, as you mentioned. I mean, it has enabled things that people couldn't do on-prem, for example, if you think about data science, the key ingredient of data science, before you can start anything is access to data and you need massive amount of data in order to build the right model that you want to use. And this was a big problem on-prem because people are always thinking about what data to keep, what to discard. That's not an issue in cloud. You can keep as much of data as you want, and that has been a big boon for data science. And it's not only your data, you can also have access to other data your, for example, your partner's data, public data sets and many other things that people have access to right? That's number one, number two of course, it's a very compute intensive operation and you know, large enterprises of course can afford them build a large data center and bring in lots of tens of thousands of CPU codes, GPU codes, TPU codes whatever have you, but it is difficult especially for smaller enterprises to have access to that amount of computing power which is very very important for data science. Cloud makes it easy. I mean, you know, it has in many ways democratize the use of data science and not only the big enterprises everyone can take advantage of the power of the computing power that various different cloud vendors make it available on their platform. And the third, not to overlook that, cloud also makes it available to customers and users, lots of various different data science platform, for example, Google's own TensorFlow and you have many other platforms Spark being one example of that, right? Both a cloud native platform as well as open source platforms, which is very very useful for people using data science and managed to open source, Spark also makes it very very affordable. And all of these things have contributed to massive boon in data science in the cloud and from my perspective. >> Now, of course we've seen over the last seven months a rush to the cloud triggered by the COVID-19 pandemic. How has that played out in the analytics field? Do you see any longterm changes to, to the landscape? The way customers are using analytics as a result of what's happened these last seven months? >> You know, I think as you know about kind of a digitization of our business is happening over a long period of time, right? And people are using AIML analytics in increasing numbers. What I've seen because of COVID-19 that trend has accelerated both in terms of people moving to cloud, and in terms of they're using advanced analytics and AIML and they have to do that, right? Pretty much every business is kind of leaning heavily on their data infrastructure in order to gain insight of what's coming next. A lot of the models that people are used to, is no longer valid things are changing very very rapidly right? So in order to survive and thrive people have to lean on data, lean on analytics to figure out what's coming around the corner. And that trend in my view is only going to accelerate. It's not going to go the other way round. >> One of the problems with cloud databases, We often hear complaints about is that there's so many of them. Do you see any resolution to that proliferation? >> Well, you know, I do think a one size does not fit all right. So it is important to have choice. It's important to have specialization. And that's why you see a lot of cloud databases. I don't think the number of cloud databases is going to go down. What I do expect to happen. People are going to use interoperable data formats. They are going to use open API so that it's very, very portable as people want to move from one database to another. The way I think the convergence is going to come is two ways, One, you know, a lot of databases, for example, use Federation. If you look at BigQuery, for example, you can start with BigQuery, but with BigQuery, you can have also access to data in other databases, not only in GCP or Google cloud but also in AWS with BigQuery Omni, for example, right? So that provides a layer of Federation, which kind of create convergence with respect, to weighing various different data assets people may have. I have also seen with, for example, with Looker, you know creation of enterprise wide data models and data API is gives people a platform so that they can build their custom data app and data solutions on top up and even from data API. Those I believe are going to be the points of convergence. I think data is probably going to be in different databases because different databases do different things well, that does not mean people wouldn't have access to all their data through one API or one set of models. >> Well, since we're on the subject of BigQuery. Now this summer, you introduced BigQuery Omni which is a database data warehouse, essentially a version of BigQuery that can query data in other cloud platforms, what, what is the strategy there? And what is the customer reaction been so far? >> Well, I mean, you know as you probably have seen talking to customers more than 80% of the customers that we talk to use multiple clouds and that trend is probably not going to change. I mean, it happens for various different reasons sometime because of compliance sometimes because they want to have different tools and different platform sometime because of M and a, we are a big believer of multi-cloud strategy and that's what we are trying to do with BigQuery Omni. We do realize people have choices. Customers will have their data in various different places and we will take our analytics wherever the data is. So customers won't have to worry about moving data from one place to another., and that's what we are trying to do with BigQuery Omni you know, going to see, you know for example, with Anthos, we have created a platform over which you can build this video as different data stacks and applications, which spans multiple clouds. I believe we are going to see more of that. And BigQuery Omni is just the beginning. >> And how have your customers reacted to that announcement. >> Oh deep! They reacted very, very positively. This is the first time they have a major cloud vendor offering a fully managed server less data warehouse platform on multiple clouds. And as I mentioned, I mean we have many customers who have some of their data assets for example, in GCP, they really love BigQuery. And they also have for example, applications running on AWS and Azure. And today the only option they have is to essentially shuttle their data between various different clouds in order to gain insight across the collective pool of data sets that they have, with BigQuery, Omni, they all tended to do that. They can keep their data wherever it is. They can still join across that data and get insights irrespective of which cloud their data is. >> You recently wrote on Forbes about the shortage of data scientists and the need to make data analytics more accessible to the average business user. What is Google doing in that respect? >> So we strongly, I mean, you know one of our goals is to make the data and insight from data available to everybody in the business right? That is the way you can democratize the use of analytics and AIML. And you know, one way to do that is to teach everybody R or Python or some specific tools but that's going to take a long time. So our approach is make the power of data analytics and AI AML available to our users, no matter what tools they're comfortable with. So for example, if you look at a B Q ML BigQuery ML, we have made it possible for our users who like SQL very much to use the power of ML without having to learn anything else or without having to move their data anywhere else. We have a lot of business users for example, who prefer X prefer spreadsheets and, you know, we've connected sheets. We have made the spreadsheet interface available on top of BigQuery, and they can use the power of BigQuery without having to learn anything else. Better yet we recently launched a BigQuery Q and A. And what Q and A allows you to do is to use natural language on top of big query data, right? So the goal, I mean, if you can do that that I think is the Nevada where people, anyone for example, somebody working in a call center talking to a customer can use a simple query to figure out what's going on with the bill, for example, right? And we believe that if we can democratize the use of data, insight and analytics that not only going to accelerate the digital transformation of the businesses, it's also going to grow consumption. And that's good for both the users, as well as business. >> Now you bought Looker last year, what would you say is different about the way Google is coming out the data analytics market from the way other cloud vendors are doing it. >> So Looker is a great addition to already strong portfolio of products that we have but you know, a lot of people think about Looker as a business intelligence platform. It's actually much more than that. What is unique about Looker is the semantic model that Looker can build on top of data assets, govern semantic model Looker can build on top of data assets, which may be in BigQuery maybe in cloud SQL maybe, you know, in other cloud for example, in Redshift or SQL data warehouse. And once you have the data model, you can create a data API and essentially an ID or integrated development environment on top of which you can build your custom workflows. You can build your custom dashboard you can build your custom data application. And that is, I think, where we are moving. I don't think people want the old dashboards anymore. They want their data experience to be immersive within the workflow and within the context in which they are using the data. And that's where I see Lot of customers are now using the power of Looker and BigQuery and other platform that we have and building this custom data apps. And what again, like BigQuery, Looker is also multi-platform it supports multiple data warehouses and databases and that kind of aligns very well with our philosophy of having an open platform that is multicloud as well as hybrid. >> Certainly, with Anthos and with BigQuery Omni, you demonstrated your commitment on P cloud, but not all cloud vendors have an interest in being multicloud. Do you see any, any change that standoff and are you really in a position to influence it? >> Absolutely. I think more than us it's a customer who is going to influence that, right? And almost every customer I talk to, they don't want to be in a walled garden. They want to be an open platform where they have the choice they have the flexibility and I believe these customers are going to push essentially the adoption of platforms, which are open and multicloud. And, you know, I believe over time the successful platforms have to be open platform. And the closed platform if you look at history has never been very successful, right? And you know, I sincerely think that we are on the right path and we are on the side of customers in this philosophy. >> Final question. What's your most important priority right now? >> You know, I wake up everyday thinking about how can you make our customer successful? And the best way to make our customer successful is to make sure that they can get business outcome out of the data that they have. And that's what we are trying to do. We want to accelerate time to value to data, you know, so that people can keep their data in a governed way. They can gain insight by using the tools that we can provide them. A lot of them, we have used internally for many years and those tools are now available to our customers. We also believe we need to democratize the use of analytics and AIML. And that's why we are trying to give customers tools where they don't have to learn a lot of new things and new skills in order to use them. And if we can do them successfully I think we are going to help our customers get more value out of their data and create businesses which can use that value. I'll give you a couple of quick examples. I mean, for example, if you look at Home Depot, they use our platform to improve the predictability of the inventory by two X. If you look at, for example HSBC, they have been able to use our platform to detect financial fraud 10 X faster. If you look at, for example Juan Perez, who's the CIO of UPS, they have used our AIML and analytics to do better logistics and route planning. And they have been able to save 10 million gallons of fuel every year which amounts to 400 million in cost savings. Those are the kind of business outcome we would like to drive with the power of our platform. >> Powerful stuff, democratize data multicloud data in any cloud who can argue with that. Debanjan Saha, General Manager and Vice President of engineering for data analytics at Google cloud. Thanks so much for joining me today. >> Paul, thank you thank you for inviting me. >> I'm Paul Gillen. This has been a cube conversation. >> Debanjan: Thank you. (soft music)

>> From the CUBE studios in Palo Alto in Boston, bringing you data-driven insights from the CUBE in ETR. This is breaking analysis with Dave Vellante. >> Earlier these week, the U S department of justice, along with attorneys general from 11 States filed a long expected antitrust lawsuit, accusing Google of being a monopoly gatekeeper for the internet. The suit draws on section two of the Sherman antitrust act, which makes it illegal to monopolize trade or commerce. Of course, Google is going to fight the lawsuit, but in our view, the company has to make bigger moves to diversify its business and the answer we think lies in the cloud and at the edge. Hello everyone. This is Dave Vellante and welcome to this week's Wiki Bond Cube insights powered by ETR. In this Breaking Analysis, we want to do two things. First we're going to review a little bit of history, according to Dave Vollante of the monopolistic power in the computer industry. And then next, we're going to look into the latest ETR data. And we're going to make the case that Google's response to the DOJ suit should be to double or triple its focus on cloud and edge computing, which we think is a multi-trillion dollar opportunity. So let's start by looking at the history of monopolies in technology. We start with IBM. In 1969 the U S government filed an antitrust lawsuit against Big Blue. At the height of its power. IBM generated about 50% of the revenue and two thirds of the profits for the entire computer industry, think about that. IBM has monopoly on a relative basis, far exceeded that of the virtual Wintel monopoly that defined the 1990s. IBM had 90% of the mainframe market and controlled the protocols to a highly vertically integrated mainframe stack, comprising semiconductors, operating systems, tools, and compatible peripherals like terminal storage and printers. Now the government's lawsuit dragged on for 13 years before it was withdrawn in 1982, IBM at one point had 200 lawyers on the case and it really took a toll on IBM and to placate the government during this time and someone after IBM made concessions such as allowing mainframe plug compatible competitors to access its code, limiting the bundling of application software in fear of more government pressure. Now the biggest mistake IBM made when it came out of antitrust was holding on to its mainframe past. And we saw this in the way it tried to recover from the mistake of handing its monopoly over to Microsoft and Intel. The virtual monopoly. What it did was you may not remember this, but it had OS/2 and Windows and it said to Microsoft, we'll keep OS/2 you take Windows. And the mistake IBM was making with sticking to the PC could be vertically integrated, like the main frame. Now let's fast forward to Microsoft. Microsoft monopoly power was earned in the 1980s and carried into the 1990s. And in 1998 the DOJ filed the lawsuit against Microsoft alleging that the company was illegally thwarting competition, which I argued at the time was the case. Now, ironically, this is the same year that Google was started in a garage. And I'll come back to that in a minute. Now, in the early days of the PC, Microsoft they were not a dominant player in desktop software, you had Lotus 1-2-3, WordPerfect. You had this company called Harvard Presentation Graphics. These were discreet products that competed very effectively in the market. Now in 1987, Microsoft paid $14 million for PowerPoint. And then in 1990 launched Office, which bundled Spreadsheets, Word Processing, and presentations into a single suite. And it was priced far more attractively than the some of the alternative point products. Now in 1995, Microsoft launched Internet Explorer, and began bundling its browser into windows for free. Windows had a 90% market share. Netscape was the browser leader and a high flying tech company at the time. And the company's management who pooed Microsoft bundling of IE saying, they really weren't concerned because they were moving up the stack into business software, now they later changed that position after realizing the damage that Microsoft bundling would do to its business, but it was too late. So in similar moves of ineptness, Lotus refuse to support Windows at its launch. And instead it wrote software to support the (indistinct). A mini computer that you probably have never even heard of. Novell was a leader in networking software at the time. Anyone remember NetWare. So they responded to Microsoft's move to bundle network services into its operating systems by going on a disastrous buying spree they acquired WordPerfect, Quattro Pro, which was a Spreadsheet and a Unix OS to try to compete with Microsoft, but Microsoft turned the volume and kill them. Now the difference between Microsoft and IBM is that Microsoft didn't build PC hardware rather it partnered with Intel to create a virtual monopoly and the similarities between IBM and Microsoft, however, were that it fought the DOJ hard, Okay, of course. But it made similar mistakes to IBM by hugging on to its PC software legacy. Until the company finally pivoted to the cloud under the leadership of Satya Nadella, that brings us to Google. Google has a 90% share of the internet search market. There's that magic number again. Now IBM couldn't argue that consumers weren't hurt by its tactics. Cause they were IBM was gouging mainframe customers because it could on pricing. Microsoft on the other hand could argue that consumers were actually benefiting from lower prices. Google attorneys are doing what often happens in these cases. First they're arguing that the government's case is deeply flawed. Second, they're saying the government's actions will cause higher prices because they'll have to raise prices on mobile software and hardware, Hmm. Sounds like a little bit of a threat. And of course, it's making the case that many of its services are free. Now what's different from Microsoft is Microsoft was bundling IE, that was a product which was largely considered to be crap, when it first came out, it was inferior. But because of the convenience, most users didn't bother switching. Google on the other hand has a far superior search engine and earned its rightful place at the top by having a far better product than Yahoo or Excite or Infoseek or even Alta Vista, they all wanted to build portals versus having a clean user experience with some non-intrusive of ads on the side. Hmm boy, is that part changed, regardless? What's similar in this case with, as in the case with Microsoft is the DOJ is arguing that Google and Apple are teaming up with each other to dominate the market and create a monopoly. Estimates are that Google pays Apple between eight and $11 billion annually to have its search engine embedded like a tick into Safari and Siri. That's about one third of Google's profits go into Apple. And it's obviously worth it because according to the government's lawsuit, Apple originated search accounts for 50% of Google search volume, that's incredible. Now, does the government have a case here? I don't know. I'm not qualified to give a firm opinion on this and I haven't done enough research yet, but I will say this, even in the case of IBM where the DOJ eventually dropped the lawsuit, if the U S government wants to get you, they usually take more than a pound of flesh, but the DOJ did not suggest any remedies. And the Sherman act is open to wide interpretation so we'll see. What I am suggesting is that Google should not hang too tightly on to it's search and advertising past. Yes, Google gives us amazing free services, but it has every incentive to appropriate our data. And there are innovators out there right now, trying to develop answers to that problem, where the use of blockchain and other technologies can give power back to us users. So if I'm arguing that Google shouldn't like the other great tech monopolies, hang its hat too tightly on the past, what should Google do? Well, the answer is obvious, isn't it? It's cloud and edge computing. Now let me first say that Google understandably promotes G Suite quite heavily as part of its cloud computing story, I get that. But it's time to move on and aggressively push into the areas that matters in cloud core infrastructure, database, machine intelligence containers and of course the edge. Not to say that Google isn't doing this, but there are areas of greatest growth potential that they should focus on. And the ETR data shows it. But let me start with one of our favorite graphics, which shows the breakdown of survey respondents used to derive net score. Net score remembers ETR's quarterly measurement of spending velocity. And here we show the breakdown for Google cloud. The lime green is new adoptions. The forest green is the percentage of customers increasing spending more than 5%. The gray is flat and the pinkish is decreased by 6% or more. And the bright red is we're replacing or swapping out the platform. You subtract the reds from the greens and you get a net score at 43%, which is not off the charts, but it's pretty good. And compares quite favorably to most companies, but not so favorite with AWS, which is at 51% and Microsoft which is at 49%, both AWS and Microsoft red scores are in the single digits. Whereas Google's is at 10%, look all three are down since January, thanks to COVID, but AWS and Microsoft are much larger than Google. And we'd like to see stronger across the board scores from Google. But there's good news in the numbers for Google. Take a look at this chart. It's a breakdown of Google's net scores over three survey snapshots. Now we skip January in this view and we do that to provide a year of a year context for October. But look at the all important database category. We've been watching this very closely, particularly with the snowflake momentum because big query generally is considered the other true cloud native database. And we have a lot of respect for what Google is doing in this area. Look at the areas of strength highlighted in the green. You've got machine intelligence where Google is a leader AI you've got containers. Kubernetes was an open source gift to the industry, and linchpin of Google's cloud and multi-cloud strategy. Google cloud is strong overall. We were surprised to see some deceleration in Google cloud functions at 51% net scores to be on honest with you, because if you look at AWS Lambda and Microsoft Azure functions, they're showing net scores in the mid to high 60s. But we're still elevated for Google. Now. I'm not that worried about steep declines, and Apogee and Looker because after an acquisitions things kind of get spread out around the ETR taxonomy so don't be too concerned about that. But as I said earlier, G Suite may just not that compelling relative to the opportunity in other areas. Now I won't show the data, but Google cloud is showing good momentum across almost all interest industries and sectors with the exception of consulting and small business, which is understandable, but notable deceleration in healthcare, which is a bit of a concern. Now I want to share some customer anecdotes about Google. These comments come from an ETR Venn round table. The first comment comes from an architect who says that "it's an advantage that Google is "not entrenched in the enterprise." Hmm. I'm not sure I agree with that, but anyway, I do take stock in what this person is saying about Microsoft trying to lure people away from AWS. And this person is right that Google essentially is exposed its internal cloud to the world and has ways to go, which is why I don't agree with the first statement. I think Google still has to figure out the enterprise. Now the second comment here underscores a point that we made earlier about big query customers really like the out of the box machine learning capabilities, it's quite compelling. Okay. Let's look at some of the data that we shared previously, we'll update this chart once the company's all report earnings, but here's our most recent take on the big three cloud vendors market performance. The key point here is that our data and the ETR data reflects Google's commentary in its earning statements. And the GCP is growing much faster than its overall cloud business, which includes things that are not apples to apples with AWS the same thing is true with Azure. Remember AWS is the only company that provides clear data on its cloud business. Whereas the others will make comments, but not share the data explicitly. So these are estimates based on those comments. And we also use, as I say, the ETR survey data and our own intelligence. Now, as one of the practitioners said, Google has a long ways to go as buddy an eighth of the size of AWS and about a fifth of the size of Azure. And although it's growing faster at this size, we feel that its growth should be even higher, but COVID is clear a factor here so we have to take that into consideration. Now I want to close by coming back to antitrust. Google spends a lot on R&D, these are quick estimates but let me give you some context. Google shells out about $26 billion annually on research and development. That's about 16% of revenue. Apple spends less about 16 billion, which is about 6% of revenue, Amazon 23 billion about 8% of the top line, Microsoft 19 billion or 13% of revenue and Facebook 14 billion or 20% of revenue, wow. So Google for sure spends on innovation. And I'm not even including CapEx in any of these numbers and the hype guys as you know, spend tons on CapEx building data centers. So I'm not saying Google cheaping out, they're not. And I got plenty of cash in there balance sheet. They got to run 120 billion. So I can't criticize they're roughly $9 billion in stock buybacks the way I often point fingers at what I consider IBM's overly wall street friendly use of cash, but I will say this and it was Jeff Hammerbacher, who I spoke with on the Cube in the early part of last decade at a dupe world, who said "the best minds of my generation are spending there time, "trying to figure out how to get people to click on ads." And frankly, that's where much of Google's R&D budget goes. And again, I'm not saying Google doesn't spend on cloud computing. It does, but I'm going to make a prediction. The post cookie apocalypse is coming soon, it may be here. iOS 14 makes you opt in to find out everything about you. This is why it's such a threat to Google. The days when Google was able to be the keeper of all of our data and to house it and to do whatever it likes with that data that ended with GDPR. And that was just the beginning of the end. This decade is going to see massive changes in public policy that will directly affect Google and other consumer facing technology companies. So my premise is that Google needs to step up its game and enterprise cloud and the edge much more than it's doing today. And I like what Thomas Kurian is doing, but Google's undervalued relative to some of the other big tech names. And I think it should tell wall street that our future is in enterprise cloud and edge computing. And we're going to take a hit to our profitability and go big in those areas. And I would suggest a few things, first ramp up R&D spending and acquisitions even more. Go on a mission to create cloud native fabric across all on-prem and the edge multicloud. Yes, I know this is your strategy, but step it up even more forget satisfying investors. You're getting dinged in the market anyway. So now's the time the moon wall street and attack the opportunity unless you don't see it, but it's staring you right in the face. Second, get way more cozy with the enterprise players that are scared to death of the cloud generally. And they're afraid of AWS in particular, spend the cash and go way, way deeper with the big tech players who have built the past IBM, Dell, HPE, Cisco, Oracle, SAP, and all the others. Those companies that have the go to market shops to help you win the day in enterprise cloud. Now, I know you partner with these companies already, but partner deeper identify game-changing innovations that you can co-create with these companies and fund it with your cash hoard. I'm essentially saying, do what you do with Apple. And instead of sucking up all our data and getting us to click on ads, solve really deep problems in the enterprise and the edge. It's all about actually building an on-prem to cloud across cloud, to the edge fabric and really making that a unified experience. And there's a data angle too, which I'll talk about now, the data collection methods that you've used on consumers, it's incredibly powerful if applied responsibly and correctly for IOT and edge computing. And I don't mean to trivialize the complexity at the edge. There really isn't one edge it's Telcos and factories and banks and cars. And I know you're in all these places Google because of Android, but there's a new wave of data coming from machines and cars. And it's going to dwarf people's clicks and believe me, Tesla wants to own its own data and Google needs to put forth a strategy that's a win-win. And so far you haven't done that because your head is an advertising. Get your heads out of your ads and cut partners in on the deal. Next, double down on your open source commitment. Kubernetes showed the power that you have in the industry. Ecosystems are going to be the linchpin of innovation over the next decade and transcend products and platforms use your money, your technology, and your position in the marketplace to create the next generation of technology leveraging the power of the ecosystem. Now I know Google is going to say, we agree, this is exactly what we're doing, but I'm skeptical. Now I think you see either the cloud is a tiny little piece of your business. You have to do with Satya Nadella did and completely pivot to the new opportunity, make cloud and the edge your mission bite the bullet with wall street and go dominate a multi-trillion dollar industry. Okay, well there you have it. Remember, all these episodes are available as podcasts, so please subscribe wherever you listen. I publish weekly on Wikibond.com and Siliconangle.com and I post on LinkedIn each week as well. So please comment or DM me @DVollante, or you can email me @David.Vollante @Siliconangle.com. And don't forget to check out etr.plus that's where all the survey action is. This is Dave Vollante for the Cube Insights powered by ETR. Thanks for watching everybody be well. And we'll see you next. (upbeat instrumental)

>> Narrator: From around the globe, it's theCUBE. With digital coverage of smart data marketplaces. Brought to you by Io-Tahoe. >> Digital transformation has really gone from a buzzword to a mandate, but digital business is a data business. And for the last several months we've been working with Io-Tahoe on an ongoing content series, focused on smart data and automation to drive better insights and outcomes, essentially putting data to work. And today we're going to do a deeper dive on automating data discovery. And one of the thought leaders in this space is Ajay Vohora, who's the CEO of Io-Tahoe. Once again, joining me, Ajay good to see you. Thanks for coming on. >> Great to be here, David, thank you. >> So let's, let's start by talking about some of the business realities and what are the economics that are driving automated data discovery? Why is that so important? >> Yeah, on this one, David it's a number of competing factors. We've got the reality of data which may be sensitive. So there's control. Three other elements wanting to drive value from that data to innovation. You can't really drive a lot of value without exchanging data. So the ability to exchange data and to manage those cost overheads and data discovery is at the root of managing that in an automated way to classify that data and set some policies to put that automation in place. >> Yeah, look, we have a picture of this. If we could bring it up guys, cause I want to, Ajay, help the audience understand kind of where data discovery fits in here. This is, as we talked about, this is a complicated situation for a lot of customers. They've got variety of different tools and you've really laid it out nicely here in this diagram. So, take us through sort of where that piece fits. >> Yeah, I mean, we're at the right hand side of this exchange, you know. We're really now in a data driven economy that is everything's connected through APIs that we consume online through mobile apps. And what's not apparent is the chain of activities and tasks that have to go into serving that data to an API at the outset. They may be many legacy systems, technologies, platforms On-premise, in cloud, hybrid, you name it and across those silos, getting to a unified view is the heavy lifting. I think we've seen some, some great impacts that BI tools, such as Power BI, Tableau, Looker, and so on, and Qlik have had, and they're in our ecosystem on visualizing Data and, you know, CEOs, managers, people that are working in companies day-to-day get a lot of value from saying, "What's the real time activity? "What was the trend over this month versus last month?" The tools to enable that, you know, we hear a lot of good things that we're doing with Snowflake, MongoDB on the public Cloud platforms, GCP Azure about enabling building those pipelines to feed into those analytics. But what often gets hidden is how do you source that data that could be locked into a mainframe, a data warehouse, IOT data, and pull over all of that together. And that is the reality of that is it's a lot of heavy lifting. It's hands on work that can be time consuming. And the issue there is that data may have value. It might have potential to have an impact on the top line for a business, on outcomes for consumers, but you're never really sure unless you've done the investigation, discovered it, unified that, and be able to serve that through to other technologies. >> Guys, if you would bring that picture back up again, because Ajay you made a point and I want to land on that for a second. There's a lot of manual curating. An example would be the data catalog. You know, data scientists complain all the time that they're manually wrangling data. And so you're trying to inject automation into the cycle. And then the other piece that I want you to address is the importance of APIs. You really can't do this without an architecture that allows you to connect things together that sort of enables some of the automation. >> Yep, I mean, I'll take that in two parts, David, the APIs, so virtual machines connected by APIs, business rules, and business logic driven by APIs, applications, so everything across the stack from infrastructure down to the network, hardware is all connected through APIs and the work of serving data through to an API, building those pipelines, is often miscalculated, just how much manual effort that takes and that manual effort, we've got a nice list here of what we automate down at the bottom, those tasks of indexing, labeling, mapping across different legacy systems, all of that takes away from the job of a data scientist or data engineer, looking to produce value, monetize data, and to help that business convey to consumers. >> Yeah, it's that top layer that the business sees, of course, there's a lot of work that has to go into achieving that. I want to talk about some of the key tech trends that you're seeing. And one of the things that we talk about a lot is metadata. The importance of metadata, you know, can't be understated. What are some of the big trends that you're seeing metadata and others? >> Yeah, I'll summarize it as five. There's a trend now look at metadata more holistically across the enterprise. And that really makes sense from trying to look across different data silos and apply a policy to manage that data. So that's the control piece. That's that lever. The other side, sometimes competing with that control around sensitive data around managing the cost of data is innovation. Innovation being able to speculate and experiment and try things out where you don't really know what the outcome is if you're a data scientist and engineer, you've got a hypothesis and therefore you've got that tension between control over data and innovation and driving value from it. So enterprise wide metadata management is really helping to unlock where might that latent value be across that sets of data. The other piece is adaptive data governance. Those controls that stick from the data policemen, data stewards, where they're trying to protect the organization, protect the brand, protect consumers data necessary, but in different use cases, you might want to nuance and apply a different policy to govern that data relevant to the context where you might have data that is less sensitive, that can be used for innovation and adapting the style of governance to fit the context is another trend that we're seeing coming up here. A few others is where we're sitting quite extensively in working with automating data discovery. We're now breaking that down into what can we direct? What do we know is a business outcome is a known upfront objective and direct that data discovery to towards that. And that means applying our algorithms around technology and our tools towards solving a known problem. The other one is autonomous data discovery. And that means, you know, trying to allow background processes to understand what changes are happening with data over time, flagging those anomalies. And the reason that's important is when you look over a length of time to see different spikes, different trends and activity, that's really giving a data ops team the ability to manage and calibrate how they're applying policies and controls the data. And the last two, David, that we're seeing is this huge drive towards self-service. So re-imagining how to apply policy data governance into the hands of a data consumer inside a business, or indeed the consumer themselves, to self-service if they're a banking customer or healthcare customer and the policies and the controls and rules, making sure that those are all in place to adaptively serve those data marketplaces that when are involved in creating. >> I want to ask you about the autonomous data discovering, the adaptive data governance, is the problem we're addressing there one of quality, in other words, machines are better than humans are at doing this? Is it one of scale? That humans just don't don't scale that well? Is it both? Can you add some color to that? >> Yeah, honestly, it's the same equation that existed 10 years ago, 20 years ago, it's being exacerbated, but it's that equation of how do I control all the things that I need to protect? How do I enable innovation where it is going to deliver business value? How do I exchange data between a customer, somebody in my supply chain safely, and do all of that whilst managing the fourth leg, which is cost overheads. There's not an open checkbook here. I've got to figure out if I'm the CIO and CDO, how I do all of this within a fixed budget. So those aspects have always been there, now with more choices, infrastructure in the Cloud, API driven applications, On-premises, and that is expanding the choices that a business has and how they put their data to work. It's also then creating a layer of management and data governance that really has to now manage those four aspects, control, innovation, exchange of data, and the cost overhead. >> That top layer of the first slide that we showed was all about the business value. So, I wonder if we could drill into the business impact a little bit. What are your customers seeing specifically in terms of the impact of all this automation on their business? >> Yeah, so we've had some great results. I think a few of the biggest have been helping customers move away from manually curating their data and their metadata. It used to be a time where if data initiatives or data governance initiatives, there'd be teams of people manually feeding a data catalog. And it's great to have that inventory of classified data to be able to understand single version of the truth, but having 10, 15 people manually process that, keep it up to date, when it's moving feet, the reality of it is what's true about data today, add another few sources and a few months time to your business, start collaborating with new partners, suddenly the landscape has changed. The amount of work has gone up, but what we're finding is through automating, creating that data discovery, feeding our data catalog, that's releasing a lot more time for our customers to spend on innovating and managing their data. A couple of others is around self service data analytics, moving the choices of what data might have business value into the hands of business users and data consumers to have faster cycle times around generating insights. And we're really helping them by automating the creation of those data sets that are needed for that. And the last piece, I'd have to say where we're seeing impacts more recently is in the exchange of data. There are a number of marketplaces out there who are now being compelled to become more digital, to rewire their business processes and everything from an RPA initiative to automation involving digital transformation is having CIOs, chief data officers and enterprise architects rethink how do they, how do they rewire the pipelines for their data to feed that digital transformation? >> Yeah, to me, it comes down to monetization. Now, of course, that's for a for-profit industry. For non-profits, for sure, the cost cutting or in the case of healthcare, which we'll talk about in a moment, I mean, it's patient outcomes, but the job of a Chief Data Officer has gone from data quality and governance and compliance to really figuring out how data can be monetized, not necessarily selling the data, but how it contributes to the monetization of the company. And then really understanding specifically for that organization, how to apply that. And that is a big challenge. We sort of chatted about 10 years ago, the early days of a dupe. And then 1% of the companies had enough engineers to figure it out, but now the tooling is available. The technology is there and the practices are there. And that really, to me is the bottom line, Ajay, is it's show me the money. >> Absolutely. It's definitely is focusing in on the single view of that customer and where we're helping there is to pull together those disparate, siloed sources of data to understand what are the needs of the patient, of the broker of the, if it's insurance? What are the needs of the supply chain manager, if it's manufacturing? And providing that 360 view of data is helping to see, helping that individual unlock the value for the business. So data's providing the lens provided, you know which data it is that can assist in doing that. >> And, you know, you mentioned RPA before, I had an RPA customer tell me she was a Six Sigma expert and she told me, "We would never try to apply Six Sigma "to a business process, "but with RPA we can do so very cheaply." Well, what that means is lower costs. It means better employee satisfaction and really importantly, better customer satisfaction and better customer outcomes. Let's talk about healthcare for a minute because it's a really important industry. It's one that is ripe for disruption and has really been, up until recently, pretty slow to adopt a lot of the major technologies that have been made available. But what are you seeing in terms of this theme we're using a putting data to work in healthcare specifically? >> Yeah, I mean, health care's has had a lot thrown at it. There's been a lot of change in terms of legislation recently, particularly in the U.S. market, in other economies, healthcare is on a path to becoming more digital. And part of that is around transparency of price. So, to be operating effectively as a healthcare marketplace, being able to have that price transparency around what an elective procedure is going to cost before taking that step forward. It's super important to have an informed decision around that. So if we look at the U.S., for example, we've seen that healthcare costs annually have risen to $4 trillion, but even with all of that cost, we have healthcare consumers who are reluctant sometimes to take up healthcare even if they have symptoms. And a lot of that is driven through not knowing what they're opening themselves up to. And, you know, I think David, if you or I were to book travel a holiday, maybe, or trip, we'd want to know what we're in for, what we're paying for upfront. But sometimes in healthcare that choice, the option might be the plan, but the cost that comes with it isn't. So recent legislation in the U.S. is certainly helpful to bring forward that price transparency. The underlying issue there though is the disparate different format types of data that are being used from payers, patients, employers, different healthcare departments to try and make that work. And where we're helping on that aspect in particular related to price transparency is to help make that data machine readable. So, sometimes with data, the beneficiary might be a person, but in a lot of cases, now we're seeing the ability to have different systems interact and exchange data in order to process the workflow to generate online lists of pricing from a provider that's been negotiated with a payer is really an enabling factor. >> So guys, I wonder if you could bring up the next slide, which is kind of the nirvana. So, if you saw the previous slide that the middle there was all different shapes and presumably to disparate data, this is the outcome that you want to get, where everything fits together nicely. And you've got this open exchange. It's not opaque as it is today. It's not bubble gum, band-aids and duct tape, but describe this sort of outcome that you're trying to achieve and maybe a little bit about what it's going to take to get there. >> Ajay: Yeah, that that's the culmination of a number of things. It's making sure that the data is machine readable, making it available to APIs, that could be RPA tools. We're working with technology companies that employ RPA for healthcare, and specifically to manage that patient and payer data to bring that together. In our data discovery, what we're able to do is to classify that data and have it made available to a downstream tool technology or person to apply that, that workflow to the data. So this looks like nirvana, it looks like utopia, but it's, you know, the end objective of a journey that we can see in different economies, that are at different stages of maturity in turning healthcare into a digital service even so that you can consume it from where you live, from home with telemedicine and tele care. >> Yeah, so, and this is not just for healthcare, but you know, you want to achieve that self-service data marketplace in virtually any industry. You're working with TCS, Tata Consulting Services to achieve this. You know, a company like Io-Tahoe has to have partnerships with organizations that have deep industry expertise. Talk about your relationship with TCS and what you guys are doing specifically in this regard. >> Yeah, we've been working with TCS now for a long while and we'll be announcing some of those initiatives here where we're now working together to reach their customers where they've got a brilliant framework of business, 4.0, where they're re-imagining with the clients, how their business can operate with AI, with automation and become more agile and digital. Our technology, now, the reams of patients that we have in our portfolio, being able to apply that at scale, on a global scale across industries, such as banking, insurance and healthcare is really allowing us to see a bigger impact on consumer outcomes, patient outcomes. And the feedback from TCS is that we're really helping in those initiatives remove that friction. They talk a lot about data friction. I think that's a polite term for the image that we just saw with the disparate technologies that the legacy that has built up. So if we want to create a transformation, having that partnership with TCS across industries is giving us that reach and that impact on many different people's day-to-day jobs and lives. >> Let's talk a little bit about the Cloud. It's a topic that we've hit on quite a bit here in this content series. But, but you know, the Cloud companies, the big hyper-scalers, they've put everything into the Cloud, right? But customers are more circumspect than that. But at the same time, machine intelligence, ML, AI, the Cloud is a place to do a lot of that. That's where a lot of the innovation occurs. And so what are your thoughts on getting to the Cloud, putting data to work, if you will, with machine learning, stuff that you're doing with AWS, what's your fit there? >> Yeah, we, David, we work with all of the Cloud platforms, Microsoft Azure, GCP, IBM, but we're expanding our partnership now with AWS. And we're really opening up the ability to work with their Greenfield accounts, where a lot of that data, that technology is in their own data centers at the customer. And that's across banking, healthcare, manufacturing, and insurance. And for good reason, a lot of companies that have taken the time to see what works well for them with the technologies that the Cloud providers are offering, and a lot of cases, testing services or analytics using the Cloud to move workloads to the Cloud to drive data analytics is a real game changer. So there's good reason to maintain a lot of systems On-premise. If that makes sense from a cost, from a liability point of view and the number of clients that we work with that do have, and will keep their mainframe systems when in Cobra is no surprise to us, but equally they want to tap into technologies that AWS has such as SageMaker. The issue is as a Chief Data Officer, I didn't have the budget to move everything to the Cloud they want, I might want to show some results first upfront to my business users and work closely with my Chief Marketing Officer to look at what's happening in terms of customer trends and customer behavior> What are the customer outcomes, patient outcomes and partner outcomes that you can achieve through analytics, data science? So, working with AWS and with clients to manage that hybrid topology of some of that data being in the Cloud, being put to work with AWS SageMaker and Io-Tahoe being used to identify where is the data that needs to be amalgamated and curated to provide the dataset for machine learning, advanced analytics to have an impact for the business. >> So what are the critical attributes of what you're looking at to help customers decide what to move and what the keep if you will? >> Well, one of the quickest outcomes that we help customers achieve is to buy that business glossary, you know, that the items of data, that means something to them across those different silos and pull all of that together into a unified view. Once they've got that data engineer working with a business manager to think through, how do we want to create this application? Now, what is the churn model, the loyalty or the propensity model that we want to put in place here? How do we use predictive analytics to understand what needs for a patient that sort of innovation is what we're unlocking, applying a tools such as SageMaker on AWS to then do the computation and to build those models to deliver that outcome is across that value chain. And it goes back to the first picture that we put up, David, you know, the outcome is that API on the back of it, you've got a machine learning model that's been developed in a tool such as Databricks or Jupiter notebook. That data has to be sourced from somewhere. Somebody has to say that, "Yep, "You've got permission to do what you're trying to do without falling foul "of any compliance around data." And it all goes back to discovering that data, classifying it, indexing it in an automated way to cut those timelines down to hours and days. >> Yeah, it's the innovation part of your data portfolio, if you will, that you're going to put into the Cloud, apply tools like SageMaker and others, your tool Azure. I mean, whatever your favorite tool is, you don't care. The customer's going to choose that. And you know, the Cloud vendors, maybe they want you to use their tool, but they're making their marketplaces available to everybody, but it's that innovation piece, the ones that you, where you want to apply that self-service data marketplace to, and really drive, as I said before, monetization, All right, give us your final thoughts. Ajay, bring us home. >> So final thoughts on this, David, is at the moment, we're seeing a lot of value in helping customers discover their data using automation, automatically curating a data catalog. And that unified view is then being put to work through our API is having an open architecture to plug in whatever tool technology our clients have decided to use. And that open architecture is really feeding into the reality of what CIOs and Chief Data Officers are managing, which is a hybrid On-premise Cloud approach to use best of breed. But business users wanting to use a particular technology to get their business outcome, having the flexibility to do that no matter where your data is sitting On-premise, on Cloud is where self-service comes in so that sales service view of what data I can plug together, jive exchange, monetizing that data is where we're starting to see some real traction with customers. Now accelerating, becoming more digital to serve their own customers. >> Yeah, we really have seen a cultural mind shift going from sort of complacency, and obviously COVID has accelerated this, but the combination of that cultural shift, the Cloud machine intelligence tools give me a lot of hope that the promises of big data will ultimately be lived up to in this next 10 years. So Ajay Vohora, thanks so much for coming back on theCUBE. You're a great guest and appreciate your insights. >> Appreciate it, David. See you next time. >> All right, keep it right there, everybody, right back after this short break. 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(upbeat music) >> Announcer: From around the globe, it's theCUBE covering Google Cloud Next OnAir '20. >> Hi everybody, welcome back. This is Dave Vellante and you're watching theCUBE's continuous coverage of Google Next OnAir, nine weeks of cloud content. There was just a buffet of content. It started out with sort of industry trends, we got into productivity, infrastructure, deep dive in security analytics, database, app modernization, cloud AI and we're wrapping up the nine weeks with Business Application Platform. And with me is Amit Zavery, who's the general manager and vice president of the Business Application Platform at Google cloud. Amit, always a pleasure. Thanks for coming on. >> Definitely, Thanks for having me Dave. You're welcome. So tell me more about this role and kind of your swim lane, if you will. >> Definitely. I think as you can imagine with especially all this digital transformation getting accelerated due to COVID, that's a huge amount of demand and interest from customers to be able to build applications, integrate them and modernize systems and automate all of them very quickly and easily in a cost effective manner. So that has been driving a lot of the thinking at Google for quite a few of years already. But I think that a little more accelerated with some of the work we've been doing previously with our stack around API management, no code app development, automation capabilities in our platform as well and we're bringing a lot of these things together in an offering so that customers can take advantage of a lot of the innovation in this space and improve the digital transformation and innovate quickly as well. So that's what we've done with Business Application Platform. We're providing capabilities for any kind of developers, be it the technical user who has a lot of programming experience as well as the other spectrum, which are the system developers who don't really have any kind of a software engineering background, but want be able to build applications and automate and there're processes very quickly and easily. So we want to provide them all the tooling and capabilities so that they can do that and be more effective than they would otherwise be. >> I want to ask you about digital transformation. I mean, obviously it's a word that's thrown around, a phrase that's thrown around a lot and there's a spectrum of what it means to people. I was talking to somebody the other day, and this obviously will resonate with you, with your background in enterprise apps but they were talking about an ERP system that was put in 15 years ago before Iphone, before cloud and it just says you know those systems are fossilized and the business has changed dramatically but the ERP system hasn't. To them, digital transformation was basically upgrading the system. And so, but obviously to Google and your role, it means something much different, doesn't it? >> I saw a lot more, right? I think no doubt having a digital application. No doubt is important, it's a good starting point. But you said some of the systems are pretty old and they're not connected together between different parts of the business. And this is huge amount of manual processes. and there's a lot of, I would say disparate pieces which never come together if you don't really put a well thought out digital transformation project or intimidation around it. So a lot of times all these businesses, when they're connecting things together, they do need a platform to kind of bring their business processes, their workflows, their applications, and the interaction between different users, be it external and internal into a more automated system. And that's really where digital transformation really shines and improves a lot of the ability for customers to compete as well as meet their customer demands and be more effective than otherwise they would be. >> And cloud is critical there but it's connecting to an ecosystem. So I want to ask you about your strategy of the Business Application Platform. And of course, Google is known for great tech. It's very open, a lot of downstream contributions, you think about Kubernetes and Anthos. So how would you describe your group strategy and how does it dovetail with Google cloud overall? >> Yeah no doubt, I think the cloud is kind of the central team underneath the covers, right? So it does run on a multicloud and hybrid mechanism. So that is available anywhere as well as you have choice of and flexibility of deployment. It's also a platform on top of Anthos so you have the advantage of multicloud as well as support for all the different systems. You might have both on-prem as well as in various other cloud providers as well. And the other things we are doing is we're taking advantage a lot of the AIML capabilities, a lot of our data analytics capabilities and bringing a lot of those underlying technologies and extracting it out to a SaaS based offering on Business Application Platform. So the customer's perspective, they want to build an application, They use, we recently acquired a company called AppSheet at the start of this year. So they can easily now use AppSheet to build those applications without writing a single line of code. And then if you create that application, it provides connectivity to also a lot of other systems out there be it applications like SAP, salesforce.com. But also a lot of legacy systems in house or custom systems you might have built and put connectors to that. And then allows you to now monetize and take systems and provide API so then you can now extend it and bring it out into the partner community, as well as customers to be able to build applications around that as well. So it connects all those things together, takes advantage of the Google cloud and the ecosystem we have built and provides customers and users a much easier way to kind of build and deliver applications and automation on it. >> Okay, so that makes sense in terms of why you acquired, made that acquisition. But I want to talk about no code development. It's something that you've been talking about quite a bit lately. Tell the audience, what is no code development? Why do we need it? >> Yeah, I think if you look at some of these report nowadays, there's a limited amount of capacity and capabilities IT can provide. And for complicated and very large systems, you of course need IT to kind of make your business efficient and implement a lot of the systems together. But there a lot of other applications which departments and line of business users want to use and build and they can't wait around for IT. And there, I think you look at some of the reports from Gartner, for example, they're going to be four times more developers outside IT than they are going to be in IT. And those folks are not going to be software engineers, they're not professional programmers but still they need efficiency and automation and application development tools. This is where no code really brings a lot of value. So tools like AppSheet, which we acquired, as market leading no code development platform makes it very easy for anybody without any experience writing any code and building applications. They can point click and start building an application and be effectively produce something which they can collaborate and use between different users inside the company or outside without spending a lot of money and time to deliver that. And that's why the no-code application platforms are becoming very popular because it does make your business more efficient, makes your business more automated, it's cost effective and it's very productive, right? So that has been the trend now more and more, and we speak a lot of, especially nowadays, if you look at telehealth, you look at say, if you want to do mortgage lending, you want to build an app easily quickly without having to wait around for it. You are interacting with a lot of people through digital mediums now and instead of people using a lot of digital tools. And that's why I think there's no-code a platforms become much more important, powerful and usable in this mechanism as well. >> Okay, I think it's important to point out. We're talking about no-code here, not low-code, no-code, there's a difference. >> There's a big difference. I think the low-code was kind of the interim stage where tools, which are coming out into the market were available to make it a little easier for development but not enough to kind of democratize it for everybody. With no-code, you are now allowing and opening it up to a lot more vaster community of users who can multiple build applications and take advantage of a lot of technology innovation happening in the platform like cloud and other things as well. Media reporting is another good example where you want to be able to build dashboards quickly and easily without again writing codes. So the no-code becomes a lot more important and usable for this kind of needs. >> So I wonder if we could stay on this for a minute. You've used the example of programming a VCR, many of us remember how difficult that was early on and now it's just you talk to it and it works. You used that as an example of what no code is like. Can you explain that a little bit more? >> I think, basically it should be natural, right? I think when we used to program a VCR, you'd read some manuals, you'd read some code, you have to kind of go through the whole process. I don't even know how many of our audience nowadays even know about that or even think about it anymore. makes us all very dated. But it was a very cumbersome process and then you would worry about whether you recorded it or not, and that you got it on the right time and did you get the right show? And then you'd up deleting the wrong things or whatever it may be the case. A Lot of those things are now getting extracted and simpler in terms of the no-code development where if you are looking for a particular application interface, if you're looking to build say a mortgage lending app, a lot of those building blocks are already available to you. You kind of making it specific to your need, but really using a lot of the building blocks and get you the final solution versus learning about wiring, everything yourself with a lot of pieces of code in there, right? So that's becoming a straightforward. We have customers like Solvay, for example, which is a large chemical automation company. And they are being able to build multiple applications with 400 plus users inside the company and deliver a lot more automation inside the organization than they would otherwise be. >> So you kind of touched on this with the different modules and capabilities and functions within an organization. But when I think about that VCR analogy, I mean, it's doing one thing and that's pretty simple. How does that apply? And again, you kind of touched on it, but it seems like IT is much or business is much more complicated but so this actually works? >> Yeah I think it's a works. We provide a lot of our kind of templates and system examples in the no-code tooling, as well as the a lot of complexity, which is built underneath the cover which is completely hidden from the user perspective, right? So when I'm building an application, I'm still getting the power of the cloud, I'm getting the power of our underlying platform, the scalability, reliability, the security, the integration, all that kind of stuff is brought into this tooling without you having to learn any of those things. And that really is where the power comes in and it's flexible enough that you can kind of pretty much do any kind of application deployment. I will not build a full blown eCommerce site with it, but I can do a lot of typical day to day kind of applications like vacation approval or things you might want to do for mortgage lending, understanding a telehealth app for doctors. And so we're seeing a lot of the, we had customers who were doing this for hospital bed tracking during the COVID current crisis going on, right? Where they want to know what kind of PPE is available? How many beds are empty? So tracking that at the hospital level, at the health care departments, all that kind of stuff we're done very quickly and powerfully than they otherwise would have. >> Is there a concern amongst your customers about privacy, governance, compliance, security with all these citizen developers? How do you ensure that those fundamental edicts of the organization are preserved? >> Yeah, I think this is a similar thing than any other system we will make available to our customers in the cloud. We guarantee that all the data is only available to the people who are allowed to based on the privileges and the security profiles and everything else. So there's no really any kind of fear from the system perspective that you will get access to something which you're not allowed to. You do log in, you do have to have an account, you do have to have all the relevant credentials before you get access to it. Same thing with privacy. We make sure that nothing is shared with anybody who's not allowed to. So we apply the same tenant, same kind of rules to any kind of data or information we keep in the cloud for any other application development. All we're doing is abstracting it out and making it easier so that everybody who wants to build things don't have to learn 20 other things to kind of get going. So the ability to do this in faster and quickly is there but all the underlying philosophy and principles still remain intact into our products as well. >> Right, makes sense. You guys obviously you have this API first mentality. I've heard about things like API gateway, Apogee, data capabilities, automating AppSheets. Can you bring us up to date on some of those innovations? >> You will see a lot of updates in this area. So we've been innovating very aggressively. Of course, we have a product called Apogee which is a market leading API management product in the industry today. It does the full life cycle of APIs, including testing, development, publishing, monetization, security, all that kind of stuff for API. And we have thousands of customers using it today. Beyond that, what we've done is we've added a lot of ability from that Stack to kind of expose APIs and consume them through AppSheet. So we have an API data source for AppSheet. So it's easy for you to find APIs and build an app is one. Second, we also released something called API gateway, which is a very high performance, low latency cloud native gateway running on serverless. So a lot of applications are built on serverless platform nowadays. And if you want to now manage that to an API layer, we provide a gateway on top of Google cloud. So anybody can also use it very quickly and easily as well. So that's another area which we added. And the third thing which we are announcing is something called actually AppSheet automation. So as I talked about AppSheet for app development, we're also now adding a lot of workflow and business process automation underneath the covers as part of AppSheet. That's something we're making available to our customers so they can automate a business process and connect things together very quickly but also get the value of the automation in their application as well. So those are new innovations, new releases we're adding to our platform as part of business application offering so that anybody can take advantage of it. >> I mean, I love this trend because to the extent you've been able, I mean, this is the Holy grail. If you can enable business users, they're closer obviously to what's going on, closer to the customer and they can respond much more quickly. Are you seeing, for instance a user builds an app using an AppSheet, are you seeing because of the API richness, are you seeing other innovation around those occurring? Are we at that point yet? Or are they still kind of islands of- >> No, i think The scope of usage is growing very fast, right? We have more than 400,000 users on AppSheet are building applications. Thousands of thousands of applications been built on it, millions of users kind of using it at the end from the logging in and using those applications as well. So I think the innovation is happening very fast, where they're connecting different things, as well as now building an ecosystem, even in Solvay as example, I was giving you. The multiple apps are built by multiple departments, and they're kind of bringing those ecosystem together into a reuse, be able to kind of find new use cases around it, those kinds of things as well. >> Are organization's coming back to say, hey, we love this? But remember when we first started spinning up VMs, it was so easy. Are you seeing organizations say, hey, we need better line of sight on it. It could be in a catalog of what we're doing or marketplace. Are you seeing demand for that? >> Yeah, so we seeing a lot. I think there's a lot of reuse. Like we have partners who also build a build applications and put that into our marketplace as well and then we're also seeing a lot of interest from solution providers who build applications on top of what you might have as modules and deliver to our end customers as well. So now there's a lot of interest in that regards and there's a lot of good examples coming out and we're seeing a lot of ways of bringing some of these things together as well. >> I mean, how does machine intelligence, AI, how does it fit into your whole agenda and strategy? And what does it mean for a customer? >> Yeah, I think as you know, Google has been innovating and has been one of the top AIML vendor out in the marketplace today. And we have definitely taken a lot of advantage of that innovation and experience in that. So for example, when I talked about automation, a lot of the automation in AppSheet is being done using AIML technologies Google has built in terms of predicting the way the customer is going to use the application, how they're going to be able to take a business process and connect them together. A lot of that things have been built using AIML technologies at Google cloud. Beyond that on API management for our operational dashboards and operational monitoring. So make sure that we can give you five nines of availability. We kind of really use lot of AIML technologies to understand anomalies, figure out where the issues might be and predict those things and make sure that we kind of fixing those things in advance before things go down, right? Same thing in security, abuse, usage, make any kind of DDoS kind of things or whatever may be the security issues as well. We use a lot of AIML capabilities to make sure we're monitoring and securing our systems as well. So we're in the middle of everything. >> Right. Has the pandemic, you know, the last 150 days, obviously it's changed things and we've talked about digital transformation being accelerated. How are you thinking about sort of the go forward as a result of the post isolation era? >> Yeah, I think this is probably going to be... I don't think this is good. Once we get out of the COVID situation whenever that happens, some of the way we work and where we operate will definitely change than what it used to be pretty much in a way. So I do expect a lot more of video conferencing, for example I do expect a lot of digitalization. I do expect a lot of automation requirements, everybody trying to be more efficient and sharing things and working remotely. Those kinds of things will continue as a trend. So from our perspective, the work we're doing around API management, around digitalization, around digital transformation, around AppSheet automation, all those things are probably right things for the right kind of future where these technologies and tech offerings we do in Google cloud as well as other things we are doing broadly will make a big difference for everyone. >> Yeah recently, I want to kind of end just to get your industry perspectives. Recently, I wrote a piece that a video just on the enterprise app space, kind of the systems of record. And, you know, these are entrenched companies and even you see some of the new SaaS startups, but they're large companies and done very well. I was trying to sort of noodle on where does the potential of disruption come? Where's the new innovation? And I think some of the things that we're talking about here, this no-code, cloud. I mean, obviously you guys play in the application space but it seems like a part of your strategy is to enable developers to really build new types of applications. And maybe that's where the next wave of disruption comes, perhaps in vertical industries, perhaps with this no code. What are your thoughts on that? >> I know, you're right. I think the productivity in the collaboration space, no doubt is going through a huge transformation and change. I mean, Google being in the forefront of it with G Suite. If you look at some of the numbers and the metrics in terms of video conferencing and this collaboration in general has been going through the roof in terms of usage. AppSheet combination with that, for example, right? So if you're building an application, you're doing video conferencing, I might be able to build a telehealth app very quickly and easily. So that's where the no-code and collaboration, for example and productivity becomes part that story. Similarly, as you said, the industry solutions where you probably heard some of the innovation we're doing in that area by specific industry with business processes. Again, adding an API layer underneath the covers to connect different systems together, and then publishing that to an application through AppSheet becomes, again, a very much a great thought out solution and very easy to kind of provide that to our customers as well. So changes in productivity and collaboration, changes in no code app development, having a platform to connect all these things and make it easy to adopt is really a big part of our story as we move forward. And that's the reason why we're kind of increasing our investment in the Business Application Platform and just kind of pour to a lot of things we're doing. We did an acquisition on Looker, for example, for business intelligence. And that's an important part as part of business application platform, to be able to provide intelligence to what people are doing, what data you have to be able to do self service reporting, and then publish that to on a dashboard as well, which might be created through AppSheet or custom doesn't matter. But we provide you that whole end to end onto it. And then technology like Anthos ties it together to give you multicloud as well as a hybrid kind of delivery mechanism. So you have flexibility of choice how you deliver and run those systems. >> Yeah, I love that Looker example for sure. We're basically seeing the democratization of business apps. Amit, thanks so much for coming back in theCUBE. It's great to see you. Hopefully sometime soon we can see each other face to face. >> Yeah. I look forward to it and thank you again for having me. >> And thank you for watching our continuous coverage on theCUBE with Google's Next OnAir nine weeks of coverage. Keep it right there. Be right back after this short break. (upbeat music)

>>From around the globe covering Google cloud. Next on there. >>Hi, I'm Stu Miniman and this is the cube coverage of Google cloud. Next 20 on air it's week seven of nine. Google of course took their event that was supposed to be in person and Moscone, spread it out online. It's all available on demand. Every Tuesday they've been dropping it in the cube. We've got a great lineup that we're going to share with you of our coverage thought event. This is our analysis segment, joining me to help dig into where Google cloud is. Everything happening in the ecosystem. Having to bring in Dave Alante and John furrier, our co-founders co-CEOs and, uh, always hosts of the program, John and Dave. Uh, it was, uh, it was great last year being in the middle of the show floor, uh, with the whole team and the great glam beautiful booth that Google built well, we're remote, but we're still in the middle of all the topics, the big waves and everything like that. So thanks so much for joining me and look forward to digging into it. >>Hey Stu, great to see you remotely. We got to get these events back. His virtual events are nine weeks, three weeks for Ws all day events. DockerCon virtual orders, nobody ecosystem support. I mean, this is really an interesting time and I think Google has laid out an interesting experiment with their multi. I call it summer of cloud program nine weeks with just a sustained demand for your attention. It's going to been a challenge. >>The question always, John, can they keep their attention? John, you laid out, you know, the cube three 65 were, there is 365 days a year, help extract the signal from the noise, help engage with the community. So absolutely want to kind of peel back the onion and see what we think of the event. But let let's, let's start with Google. Dave, you know, you've been digging through the numbers as you always do. Uh, we're we're more than a year since Thomas Kurian came in and you know, what are you hearing? What's the data showing you as to, you know, where Google really sits in the marketplace? How are they doing >>Well still you're right. I mean, Thomas curious now I think he's about 18 months in and in one of my previous breaking analysis, I kind of laid out a four point plan for, for Google. And we can talk about sort of how they're doing there, but, but really the first one is product maturity and there's, there's a number of things that we can assess as it relates to product maturity. The second we talk about it all the time is, is, is go to market. I think the third one is really around differentiation. How does Google uniquely differentiate from the other cloud service providers? And I think the fourth and we saw this earlier this year with Looker is, you know, Google's got a war chest and you know, they can use that to really beef up the cloud. And I think if, if, if you, if you look at it, you know, Google's done a pretty good job with things like fed ramp. >>I mean, these are table stakes in the big cloud. You know, they're starting to do more things around SAP of VMware, uh, windows. I mean, again, these are basic things that you have to do as part of any large cloud provider. I think the other thing we talked about go to market, they've done a number of things there. Karen's really focused on partnerships. He wants to be a hundred percent channel, uh, at the same time they're hiring salespeople. I think they're up over 1500 salespeople right now, uh, which is, you know, we're getting there. I think it was less than that. Obviously when he came on, that's kind of the benchmark, although we don't really know exactly what, what the numbers are. They've kind of launched into public sector. They see what's happening with Amazon there, they see great opportunities. They see, you know, what, what Microsoft is doing. And so public sector, they have to put out bakeoffs so you gotta be in there and at differentiations still a lot of, okay, how can we leverage alphabet our search business and retail, our business and healthcare, um, and edge things like autonomous vehicles. There's, there's some opportunities there. And then as I said, they're doing some M and a two plus billion dollars for Looker, you know, great capability. So I think they're, they're executing on those four and we can talk about what that means in terms of, you know, revenue and position in the market. >>Well, yeah, Dave, maybe it makes sense to let let's, let's walk through the revenue, just so that people understand, you know, where they sit for the longest time it's been, you know, the number three or the number four where Alibaba said, uh, compared to them, but they are still far behind, uh, AWS and Azure. Uh, and have they been closing the gap at all? >>Well, if guys, if you could bring up that chart, that first one, uh, this is are, we really are estimates. You remember now AWS, every quarter gives us a clean number for their infrastructure as a service. And what we've got here is an estimate for full year 2018, 2019 that's calendar year, the growth rates, and then, uh, with a trailing 12 month view. And I think there's a couple of points here. One is you can see the growth. Google grew 89% last year. They were 70% in Q one 59% in Q two. So, so even though it's somewhat declining, they're growing faster than both Azure and AWS, of course, from a smaller base. I think the other thing, if you, if you go back and look at 2019, relative to AWS, Google was one 10th, the size of AWS. Now they're, you know, there's only eight X, so they're starting to close that gap, but still very much a, a quite a distance from the leaders. >>Yeah. Uh, John, maybe if we look at Google under Thomas Currian, of course there's been a real, uh, growth in hiring. So, you know, you're there in the Valley, John, we know lots of really smart people that have joined Google's great enterprise, uh, you know, pedigrees there as well as the ecosystem, uh, that, that wants to be able to partner with Google. You know, what are you seeing? What are you hearing? I like one of the interviews that you did, uh Suneel prody, uh, it was, it was the number two over at Nutanix. Uh, and now we've got an important role in Google cloud, >>Google hiring great people. I got to say, one of the things I'm impressed with is I've always liked the product people. They have great product chops. I'll ask the Google has come from a position of strength on the tech side being Google. Um, and, but the enterprise business is hard too, and they got to hire more enterprise DNA. They're trying to do that at the same time. They're trying to make the table stakes stuff done, move fast during the product side. And then at the same time, create the game changing product with like ant those for instance, um, and then have all those new features. So they're running as fast as they can. Um, they're building product as fast as they can. So you got, you know, developer and operator efficiency, which I love the strategy. However, when you run that fast, there's definitely debt. >>You take on both technical and market debt around trying to make a shortcut. So Google to me, the word in the Valley is great stuff with the people. Product is awesome, getting better, good product people, but still those enterprise features product reliability in terms of not sunsetting products early to, you know, making sure the right support levels are there. These are like the little details that make the difference between an enterprise player and someone who is essentially, you know, moving too fast, get new products being to agile. So yeah, it's a double edged sword for Google. We've said this all the time, but overall I'd give them a solid number three position and still haven't seen the breakout yet. I think ant those can be that if they keep pushing on this operator efficiency, but I just don't think the enterprise is ready for Google yet. And I think there's issues there. >>Yeah. John, you bring up a great point. I know the last couple of times we've been at the show, I feel like I'm scratching my head. It was like, wait, when did lift and shift become sexy? Yes, you want to meet the enterprises where they are, but how is that different from the message that we hear from Microsoft that we hear from AWS? Uh, one of the bigger announcements during the infrastructure week, uh, was about a new program, the rapid assessment and migration plan or ramp, uh, to help customers get from where they are, where they need to be. Uh, it's interesting because of course, if you, you know, for reinvent for years, we had all the systems integrators, helping customers move and migrate, uh, both AWS and Azure have lots of migration solutions out there. So, you know, how will Google differentiate themselves and make different there? >>Well, they don't, they don't really know. I mean, they have put stuff down on paper, but here's the problem that Google has to overcome to make it a truly a fast growing cloud player. They got to nail the product features that they need to be in the marketplace. And the ecosystem really wants to work with Google. I see retail is lay up for them and they're doubling down on that. They've got smart people working on this, but the ecosystem and adding product features are two major heavy lifts ecosystems about moneymaking. At the end of the day. I know that sounds kind of greedy in this era of empathy and missions and values, but at the end of the day, if you're not making your ecosystem money, which means keep products around support for a certain number of >>Years and have incentives economically for people to build software. They're not going to work on your platform. And I think Google needs to understand that. Clearly. I just don't see it. I mean, I just don't see people saying, I love Google so much. I'm making so much cash and success. Um, and they got some good products. You know, I, like I said, products on ecosystem are things they're going to ratchet up super fast. Well, there's a couple of places, a couple of partners they violated, like I said, durian wants to be a hundred percent channel-based channel fulfillment. And when you talk to the channel, they do tell you, yo Google there they're being aggressive. Deloitte, you know, they chart chart out as a big partner HCL. Now of course, those guys are all working with everybody, but they're starting to put resources around that in terms of training and certification. >>And of course, other, you know, much smaller resellers and partners. So that's, that's interesting, right? That being really super channel friendly, that's a differentiator to your point, John, that's making be do that because they're not coming from a position of strength channel. No, they are channel friendly. Can, you can say you're channel friendly, but if your product doesn't work, the channel will reject you instantly. They're, they're a, they're a tough critic and they need to have reliability. So again, this is not really a problem with Google. It's just a product is evolving fast at the same time, they're trying to roll out a channel. So if you want to have a good rental strategy, you gotta have a good one posture and programs, but the product has to be enabling and reliable. And if someone's building software on top of a cloud platform and stuff doesn't work or changes, that's more cost more cost means more training, more hiring. >>If someone leaves, how does it scale? These are like really important things around channel. Cause they have to sell to the customer and support their name's on the line. So again, channels and easy to say thing to do, but to actually do it with a product is hard. And I think Google has that challenge. And again, it's a challenge that they overcome. It will be a great opportunity. Well, and I think that's a good point because it wasn't, it was 2019 when I was like VMware SAP, full blown windows support. I mean, that's, that's really late to the game. And so as I say, product maturity is critical, but there are some, some winners there obviously in analytics, uh, I think big query as get, gets very, very high marks. So there's, there's some real pockets of, of, of positive positivity there. But you know, I would agree though, the maturity is a key factor for the channel to really go on. >>Well, right. If you look, John, you mentioned anthro Santos was the story last year. Uh, and it's, we're all talking about multicloud. Uh, much of the multi-cloud discussion has been, uh, due to Kubernetes. And if it wasn't for Google, we wouldn't have Kubernetes. The concern of course, is that Google took it, it open source. The CNCF took it as a foundation and customers went nuts with it and the other public cloud and even, you know, smaller cloud providers are getting as if not more value than Google is. So what you hear in the back channels, when you say, boy, Google brought this technology out district really help enable their platform. Well, AWS is still winning. AWS has plenty of solutions. They've got interesting things to get, you know, deep solutions, leveraging Kubernetes. Uh, and if you look at Google, they announced anthros last year, it's gone through some updates this year. >>Uh, you know, you both mentioned, uh, working with the partners. One of the things that jumped out at me, uh, there's now something called ant those attached clusters, which means that if I have somebody else's, you know, Kubernetes that is fully certified, I can, I can plug that in and work with Anthem. It was one of the gaps that I saw last year. You hear Google saying, we're partnering with VMware, we're partnering pivotal, but here's. And if you want to use OpenShift or PKS, you know, you need to come over to work with Anthem here. We are understanding that customers are going to have multiple environments and often multiple different Kubernetes solutions out there. Uh, you know, Dave, you mentioned like VMware, of course is a really important solution. VMware moving along and supporting more Kubernetes. Uh, and the, the update for the solution is the Google cloud VMware engine. >>And absolutely the number one use case they talked about is take your VMs, get them in the cloud and then start using those data and analytic services that are in the public cloud. So we're seeing some maturity here, but you know, Dave, if we look at the multicloud market, you know, it, Google's not the first company that typically comes to mind, you know, VMware, red hat, even Microsoft probably are a little bit higher on people's thoughts. You know, what have you been seeing? It's an area we've been spending a lot of time last couple of years hybrid and multicloud. >>Well, we have some data on this guys, if you would pull up that next graphic and this, this is observing data from our data partner ETR and what this shows on the vertical axis is the spending momentum. So are you spending more or less? And then it's really a net score, which in other words, to subtract the less from the morning when we have leftover that's, the vertical axis high is higher, is better. And then the horizontal axis is markets, bear really presence in the data set, and you can see the hyperscaler guys, you know, that's where you want to be Microsoft AWS. They're always sort of separating from the pack. You'd love to see Google. Is there a hyperscaler out there with those guys, but they're not one of the interesting things that we're seeing in the dataset Stu and John VMware cloud on AWS has really popped up. >>So this thing of this notion of hybrid as part of the cloud ecosystem and multi-cloud is really starting to have legs. And you can also see red hat with, with open shift and believe it or not even OpenStack as a telco, you see in that pop up as well as VMware cloud, which is comprises cloud foundation and other components. So you see that hybrid and multicloud zone. And I think, I think you got to put Google, you know, right there, you can see where IBM and Oracle are for just for context, they don't have the momentum, they don't have the market presence in cloud, but they have a cloud. So that's kind of how the landscape is. And I think Google, from a standpoint of ant dos, they, again, they have to be trying to be open, leverage their Coopernetties chops and try to differentiate from certainly AWS. I think your point is right on, I think Microsoft has a pretty strong story there, but Google's got a clean story and they're investing and I think it's a good position for them. Not as, not as good as the other two, but you're when you're coming from behind, you have to try to differentiate and they are. >>Yeah, well, Dave, you've always said the rich get richer when these markets, but now with COVID that they are getting richer. Amazon honestly, stock I'm billion trillion, $2 billion valuation for Apple Google on the cloud side. This is, I think that if they had more product leadership in certain areas, I think they'd be doing more, more with their cloud, but they have some IP that could come out of this post COVID growth strategy for them, where it could be a game changer. So if you look at security and you look at identity, and one of the things that caught my attention in the anthesis announcement was this, uh, this, uh, identity service that they have, which is like, uh, open ID kind of connect thing. Identity will be critical because Google has so much IP around, um, you know, um, user login information around the mobile on the mobile side. >>I mean, Jennifer Lynn on this many times that they could leverage that and really helped the edge secure. And from a user access standpoint, having identification in the Anthem would be great. And this whole modern application trend is kind of where the puck is going. So you're there kind of skating to that puck area. And also they're focused on operators. This multicloud thing hits a home run with operators, because if you can create an abstraction layer between multiple clouds and have this modern kind of top layer to it, you're in a good position, but the insiders here in Silicon Valley and in the industry that I talked, they were all saying that Google has huge IP in their network. They have a very solid network. So what's interesting to me, as a Google can take leverage some of those network pain points and then bring anthesis that connective tissue. They got a real opportunity, but they've got to pull it off, right? So covert hitting, probably the worst thing that could have happened to Google because they were just a couple feet from the goal line on this, on this market in terms of really exploding. But I think they're well positioned. I'm not down on Google at all. >>I think that, you know, I'm glad you brought that up, John, because I think Cove was a two edged sword for them. I just published last week in my breaking analysis this weekend, actually that there were three big tailwinds insecurity as a result of coal go away. And identity was one of those cloud of course, was, was the other one. And then endpoint security was the third. And so that's a, that's, that's a, you know, kind of good news for, for, for identity. The flip side of it is if you go back and look at where Amazon and Microsoft were in terms of their growth, relative to where Google is now, Amazon and Microsoft appear to have been growing larger. Now these things go in an S curve, you know, it's kind of an old guy that starts out slow and then gets really steep. So we may actually see Google accelerate. Uh, but >>I think you wait in that it may have to wait until after COVID. So it's really a Jewish store, good news on the identity side. And Google's well positioned, but necessarily bad news from a growth standpoint. Well, there's three areas to that. You know, you and I have been riffing on lately and we've, haven't published a lot yet because we're going to wait until we have our event cube con event in October. But there's three areas, I think ant those points too. And they even say this kind of in their own way, um, multicloud, which is customers, connecting customers anywhere and finding device and whatnot. So customer connection points, customer enterprises, improved developer, modernized developer, the developer market, and then three operators, three areas that are all moving trains. They're all shifting under their feet. So I think they're doing great on developer side because they have great traction. >>We've covered that with coop con and other areas have done amazing work operator efficiency, no problem. I think they got a lot of great credit there and are building and adding new stuff. It's the customer piece that's weak. They, I think they really got to continue to double down on what is the customer deployment, because let's face it, enterprise customers aren't as savvy as Google or the hyperscaler. So when you roll into main street enterprise, especially with Cova Dave, as you pointed out, are they sitting there really grokking Coobernetti's on bare metal? And at those they're like, shit, how do I keep my network alive? So it, I just think isn't a long yet operationally on the customer side. And I think that is a weakness, um, and on Google's formula and they got to just make that easier. >>Yeah, no, no great, great points there. Absolutely. In, in talking to a lot of the cloud customers, if they already have an existing relationship that's expanding or accelerating, that is a lot easier than choosing a new environment. So as Dave said, the rich get richer. Um, I mentioned that at, at the start, this is week seven of nine of what Google is doing. Um, we want to get both your, your viewpoints on this event, how they laid it out nine weeks, it's all done on demand. I know when they had the opening keynote, there was a decent rally point. You saw the usual Twitter stream out there. They had a nice median analyst program that kicked off at the beginning. For me personally, there's been some stuff that I've gone back infrastructure week. I watch this week for app modernization. There's definitely some announcements that I'm digging into, but I think overall what I see out there is people rallied at the beginning and then they kind of forgot that the event was going on. Um, you know, what are you seeing? You know, what, what's the new best practice on, you know, how long should an event be? How do you deliver it? How do you get engagement? >>Well, I mean, just to, you know, Tim, Dave will weigh in, but I'm pretty hardcore on my criticism of most of these virtual events, mainly because virtual event platforms and virtual event executions or whatnot, well known as a first kind of generation problem. No one's really been under this kind of disruption when they got to replicate their business value as quickly in an environment they weren't optimized or have the personnel for. So you're seeing a lot of gaps in these virtuals, kinda like multi-cloud and high, where you have tens of different definitions of how to do it. I think Google went to nine weeks cause they really didn't know what to do. And they left a lot of their ecosystem hanging out there because normally Google next is a huge show with great content presentations. Everything's up on YouTube anyway. So on demand is not a build value. >>The real value of Google next was the face to face interactions. The show floor, the ecosystem, the expo hall that is completely absent from the show here. And this is consistent with other events. And honestly, it's over nine weeks, Amazon re-invent, it's going to be over three weeks. And last year they had a music festival. How are they going to replicate that again, this is a huge negative shift for these vendors because they rely so much on these events to get the word out. So it's really hard. Um, so I, I I'm really impressed with the nine week program and the sense of kind of staging it out and kind of the summer of cloud, I would have done things a little bit differently if I was them in terms of making it more exciting, but it's just really difficult to command attention for the audience over nine weeks. >>And I think that's, if they had to go back and do a Mulligan, I would've, they would've probably would've done more activation around the digital rather than a bunch of on demand video. So at least I did something and didn't cancel now the good news is there's a slew of news. We can collaborate on, um, the virtual spaces, the internet. So people are talking, it's just that it's all distributed. No one knows who's there, right? So it's not like an industry event. It's just an online collection of videos like on YouTube. So I felt that lack of intimacy was probably my, my biggest critique. Um, but again, I think he just wanted to move forward and get this behind them. >>I think you nailed it, John. I mean, on the one hand they made it harder for themselves stretching it out over nine weeks. On the other hand, they kind of took the easy way out is putting it up on all on demand. I guess they have analyst programs too, but I felt like they, weren't certainly not even close to what you have in physical. And it's really hard to obviously replicate physical, but I've seen other programs where the intimacy with the analyst and the journalist was much higher and opportunities to have interactions with executives. I felt it was just a little bit removed, actually quite a bit removed would have loved to have seen just a more intimate one-on-one activity. Maybe not one-on-one, but, but one, one to many with a smaller group of analysts and journalists, I think that would have gone a long way. Um, and that, that was missing for me anyway. >>I mean, they could have done nine micro events every week with like a rallying point is to pointed out, um, just really a difficult, I mean, who, who was executing this event? I mean, they have an events team that's used to doing physical events, Moscone and whatever. It's just, they didn't, I don't think had the time to figure it out. Be honest with you. I mean, Google is a company known for search relevance, find what you're looking for and uh, organizing content. I just think they didn't do a good job at all. And I think I didn't have any much attention cycles to it because I was kind of keying in the news, but I didn't know where my friends were. Who's rallying is Stu there. I didn't even, do's tweeting, I'm not following it. Or I missed his tweet. So there's a lot of asynchronous, um, stuff going on with was no, you know, gravity around a community or ecosystem kind of moment where I could schedule an hour at 10 o'clock or multiple times >>Does the day to check in and go to the watering hole or some stuff, >>You know, hub or instance like that. So, you know, something that we're thinking a lot about David's, you know, and I think this is a moving, moving target, but what's clear is that you can create synchronicity and still have the asynchronous programs. So at least we learned that with the Docker con event that we did and the software that we're building. So, you know, virtual events, isn't about just the events, but what happens on inside the event, outside the event, after the event, I think people are too hung up on this. I got to have a portal walled garden model. So I think it's going to be a learning curve for everybody. And I think Google may or may not do nine weeks. We'll see what re-invent does with three weeks. How do you keep people's attention? But three weeks when they're not in Vegas? >>Well, you know, no, I think that physical or virtual, it's your opportunity to write the narrative, to set the tone or set the narrative. And you're seeing this with the conventions, with the political conventions, you know, they're, they're actually, you know, you don't necessarily watch the whole thing, but you get a good sense, you know, post virtual event, what the narrative is. And I think that's cause you know, the media picks it up and I think it's, it's imperative to really do a good job of interacting with the media. You know, the analysts, the ecosystem, the partners, I haven't talked to a ton of partners who have been totally engaged other than, you know, their one-on-one activity. So I think there's an opportunity there to, to really write that narrative, to set that narrative and keep it alive and that, that entices people to go back and watch the man. Then I didn't feel that hook here. >>Yeah, here's the problem that I see with has Google has this problem and Docker con did not have the problem and you know, self-serving, we did that software, but we designed it for this purpose. When I go to an event, you do guys too. But personally, when I go to an event face to face, I like to get a sense of what the collective group at the event is thinking. I fly there, I'm present. I can see the presentation. I can see the pack breakout sessions. I know it's not back. I can get a sense visually. And with my senses on what the collective voice of the group is at an event, does it suck? Is it good? How's the band? What it's, what's the hallway conversation. So I can feel that I had none of that with Google next. Okay. Like, I didn't know, five, no, I had no other than some random things on Twitter, I had no sense what the collective ecosystem thought of the event. >>And I think a lot of the events have that problem where you can do both. You could have the rallying moment where there's a group collective coming together and send people to do that and still have the asynchronous consumption, organizing the content. But that's one of the main benefits. What is what's, what's going on with it? What's the voice of this collective? How are people thinking about this? And who's there? Who can I connect with and maybe follow up with, I didn't feel that this was simply a bunch of videos posted fundamental. Yeah, absolutely. John, >>If you can't feel that energy, is there a Slack channel, is there some chat group, uh, is there some way that, that you can be involved? Uh, definitely a missed opportunity, especially Google's got great collaboration tools. They're tied into all of our calendars would have been something that they could, uh, make ways that we could engage and find out. All right, John and Dave, thank you so much for helping us, uh, you know, really dig through a lot, going on. As we said, this nine week event, uh, we we've got a playlist, uh, that we're, we're going to be broadcasting for some of the key executives. Got, got a lot of the news here. And after this week, which was at modernization, we do have a couple other interviews that will be, uh, coming out, uh, when we have them, but be sure to check out the cube.net, uh, for all the upcoming, as well as search, to be able to find the previous, uh, content there, reach out to at furrier at diva launch date, or meet at Stu for any feedback or comments. We'd love to get your feedback, especially in these times when we can't all be together. So thanks John and Dave for joining and I'm Stu Miniman. Thank you for watching the cube.

>> From theCUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is theCUBE Conversation. >> Hey, everybody, this is Dave Vellante, and welcome to this CXO Series. As you know, I've been running this series discussing major trends and CXOs, how they've navigated through the pandemic. And we've got some good news and some bad news today. And Ed Walsh is here to talk about that. Ed, how you doing? Great to see you. >> Great seeing you, thank you for having me on. I really appreciate it. So the bad news is Ed Walsh is leaving IBM as the head of the storage division (indistinct). But the good news is, he's joining a new startup as CEO, and we're going to talk about that, but Ed, always a pleasure to have you. You're quite a run at at IBM. You really have done a great job there. So, let's start there if we can before we get into the other part of the news. So, you give us the update. You're coming off another strong quarter for the storage business. >> I would say listen, they're sweet, heartily, but to be honest, we're leaving them in a really good position where they have sustainable growth. So they're actually IBM storage in a very good position. I think you're seeing it in the numbers as well. So, yeah, listen, I think the team... I'm very proud of what they were able to pull off. Four years ago, they kind of brought me in, hey, can we get IBM storage back to leadership? They were kind of on their heels, not quite growing, or not growing but falling back in market share. You know, kind of a distant third place finisher, and basically through real innovation that mattered to clients which that's a big deal. It's the right innovation that matters to the clients. We really were able to dramatically grow, grow all different four segments of the portfolio. But also get things like profitability growing, but also NPS growing. It really allowed us to go into a sustainable model. And it's really about the team. You heard I've talked about team all the time, which is you get a good team and they really nailed great client experiences. And they take the right offerings and go to market and merge it. And I'll tell you, I'm very proud of what the IBM team put together. And I'm still the number one fan and inside or outside IBM. So it might be bittersweet, but I actually think they're ready for quite some growth. >> You know Ed, when you came in theCUBE, right after you had joined IBM, a lot of people are saying, Ed Walsh joined an IBM storage division to sell the division. And I asked you on theCUBE, are you there to sell division? And you said, no, absolutely not. So it's always it seemed to me, well, hey, it's good. It's a good business, good cash flow business, got a big customer base, so why would IBM sell it? Never really made sense to me. >> I think it's integral to what IBM does, I think it places their client base in a big way. And under my leadership, really, we got more aligned with what IBM is doing from the big IBM right. What we're doing around Red Hat hybrid multi cloud and what we're doing with AI. And those are big focuses of the storage portfolio. So listen, I think IBM as a company is in a position where they're really innovating and thriving, and really customer centric. And I think IBM storage is benefiting from that. And vice versa. I think it's a good match. >> So one of the thing I want to bring up before we move on. So you had said you were seeing a number. So I want to bring up a chart here. As you know, we've been using a lot of data and sharing data reporting from our partner. ETR, Enterprise Technology Research, they do quarterly surveys. They have a very tight methodology, it's similar to NPS. But it's a net score, we call it methodology. And every quarter they go out and what we're showing here is the results from the last three quarter, specific to IBM storage and IBM net score in storage. And net scores is essentially, we ask people are you spending more, are you spending less, we subtract the less from the more and that's the net score. And you can see when you go back to the October 19, survey, you know, low single digits and then it dipped in the April survey, which was the height of the pandemic. So this was this is forward looking. So in the height of the pa, the lockdown people were saying, maybe I'm going to hold off on budgets. But then now look at the July survey. Huge, huge up check. And I think this is testament to a couple of things. One is, as you mentioned, the team. But the other is, you guys have done a good job of taking R&D, building a product pipeline and getting it into the field. And I think that shows up in the numbers. That was really a one of the hallmarks of your leadership. >> Yeah, I mean, they're the innovation. IBM is there's almost an embarrassment of riches inside. It's how do you get in the pipeline? We went from a typically about for four years, four and a half year cycles, not a two year cycle product cycle. So we're able to innovate and bring it to market much quicker. And I think that's what clients are looking for. >> Yeah, so I mean, you brought a startup mentality to the division and of course now, cause your startup guy, let's face it. Now you're going back to the startup world. So the other part of the news is Ed Walsh is joining ChaosSearch as the CEO. ChaosSearches is a local Boston company, they're focused on log analytics but more on we're going to talk about that. So first of all, congratulations. And tell us about your decision. Why ChaosSearch? And you know where you're out there? >> Yeah, listen, if you can tell from the way I describe IBM, I mean, it was a hard decision to leave IBM, but it was a very, very easy decision to go to Chaos, right. So I knew the founder, I knew what he was working on for the last seven years, right. Last five years as a company, and I was just blown away at their fundamental innovation, and how they're really driving like how to get insights at scale from your data lake in the cloud. But also and also instead, and statements slash cost dramatically. And they make it so simple. Simply put your data in your S3 or really Cloud object storage. But right now, it's, Amazon, they'll go the rest of clouds, but just put your data in S3. And what we'll do is we'll index it, give you API so you can search it and query it. And it literally brings a way to do at scale data analysts. And also login analytics on everything you just put into S3 basically bucket. It makes it very simple. And because they're really fundamental, we can go through it. Fundamental on hard technology that data layer, but they kept all the API. So you're using your normal tools that we did for Elastic Search API's. You want to do Glyfada, you want to do Cabana, or you want to do SQL or you want to do use Looker, Tableau, all those work. Which is that's a part of it. It's really revolutionary what they're doing as far as the value prop and we can explain it. But also they made it evolution, it's very easy for clients to go. Just run in parallel, and then they basically turn off what they currently have running. >> So data lakes, really the term became popular during the sort of early big data, Hadoop era. And, Hadoop obviously brought a lot of innovation, you know, leave the data where it is. Bring the compute to the data, really launched the Big Data initiative, but it was very complicated. You had, MapReduce and and elastic MapReduce in the cloud. And, it really was a big batch job, where storage was really kind of a second class citizen, if you will. There wasn't a lot of real time stuff going on. And then, Spark comes in. And still there's this very complicated situation. So it's sounds like, ChaosSearch is really attacking that problem. And the first use case, it's really going after is log analytics. Explain that a little bit more, please. >> Yeah, so listen, they finally went after it with this, it's called a data lake engine for scalable and we'll say log analytics firstly. It was the first use case to go after it. But basically, they allows for log analytics people, everyone does it, and everyone's kind of getting to scale with it, right. But if you asked your IT department, are you even challenged with scale, or cost, or retention levels, but also management overlay of what they're doing on log analytics or security log analytics, or all this machine data they're collecting? The answer be absolutely no, it's a nightmare. It starts easy and becomes a big, very costly application for our environments. And what Chaos does is because they deal with a real issue, which is the data layer, but keep the API's on top. And so people easily use the data insights at scale, what they're able to do is very simply run in parallel and we'll save 80% of your cost, but also get better data retention. Cause there's typically a trade off. Clients basically have this trade off, or it gets really expensive. It gets to scale. So I should just retain less. We have clients that went from nine day retention and security logs to literally four and five days. If they didn't catch it in that time, it was too late. Now what they're able to do is, they're able to go to our solution. Not change what they're doing applications, because you're using the same API's, but literally save 80% and this is millions and 10s of millions of dollars of savings, but also basically get 90 day retention. There's really limitless, whatever you put into your S3 bucket, we're going to give you access to. So that alone shows you that it's literally revolutions that CFO wins because they save money. The IT department wins because they don't that wrestle with this data technology that wasn't really built. It is really built 30 years ago, wasn't built for this volume and velocity of data coming in. And then the data analytics guys, hey, I keep my tool set but I get all the retention I want. No one's limiting me anymore. So it's kind of an easy win win. And it makes it really easy for clients to have this really big benefit for them. And dramatic cost savings. But also you get the scale, which really means a lot in security login or anything else. >> So let's dig into that a little bit. So Cloud Object Storage has kind of become the de facto bucket, if you will. Everybody wants it, because it's simple. It's a get put kind of paradigm. And it's cheap, but it's also got performance issues. So people will throw cash at the problem, they'll have to move data around. So is that the problem that you're solving? Is it a performance? You know, problem is it a cause problem or both? And explain that a little bit. >> Yeah, so it's all over. So basically, if you were building a data lake, they would like to just put all their data in one very cost effective, scalable, resilient environment. And that is Cloud Object Storage, or S3, or every cloud has around, right? You can do also on prem, everyone would love to do that. And then literally get their insights out of it. But they want to go after it with our tools. Is it Search or is it SQL, they want to go after their own tools. That's the vision everyone wants. But what everyone does now is because this is where the core special sauce what ChaosSearch provides, is we built from the ground up. The database, the indexing technology, the database technology, how to actually make your Cloud object storage a database. We don't move it somewhere, we don't cash it. You put it in the inside the bucket, we literally make the Cloud object storage, the database. And then around it, we basically built a Chaos fabric that allows you to spin up compute nodes to go at the data in different ways. We truly have separated that the data from the compute, but also if a worker nodes, beautiful, beauty of like containerization technology, a worker nodes goes away, nothing happens. It's not like what you do on Prem. And all sudden you have to rebuild clusters. So by fundamentally solving that data layer, but really what was interesting is they just published API's, you mentioned put and get. So the API's you're using cloud obvious sources of put and get. Imagine we just added to that API, your Search API from elastic, or your SQL interface. It's just all we're doing is extending. You put it in the bucket will extend your ability to get after it. Really is an API company, but it's a hard tech, putting that data layer together. So you have cost effectiveness, and scale simultaneously. But we can ask for instance, log analytics. We don't cash, nothing's on the SSD, nothing's on local storage. And we're as fast as you're running Elastic Search on SSDs. So we've solved the performance and scale issues simultaneously. And that's really the core fundamental technology. >> And you do that with math, with algorithms, with machine learning, what's the secret sauce? Yeah, we should really have I'll tell you, my founder, just has the right interesting way of looking at problems. And he really looked at this differently and went after how do you make a both, going after data. He really did it in a different way, and really a modern way. And the reason it differentiates itself is he built from the ground up to do this on object storage. Where basically everyone else is using 30 year old technology, right? So even really new up and coming companies, they're using Tableau, Looker, or Snowflake could be another example. They're not changing how the data stored, they always have to move it ETL at somewhere to go after it. We avoid all that. In fact, we're probably a pretty good ecosystem players for all those partners as we go forward. >> So your talking about Tom Hazel, you're founder and CTO and he's brought in the team and they've been working on this for a while. What's his background? >> Launched Telkom, building out God boxes. So he's always been in the database space. I can't do his in my first day of the job, I can't do justice to his deep technology. There's a really good white paper on our website that does that pretty well. But literally the patent technology is a Chaos index, which is a database that it makes your object storage, the database. And then it's really the chaos fabric that puts around in the chaos refinery that gives you virtual views. But that's one solution. And if you look for log analytics, you come in log in and you get all the tools you're used to. But underneath the covers, were just saving about 80% of overall cost, but also almost limitless retention. We see people going from literally have been reduced the number of logs are keeping because of cost, and complexity, and scale, down to literally a very small amount and going right back at nine days. You could do longer, but that's what we see most people go into when they go to our service. >> Let's talk about the market. I mean, as a startup person, you always look for large markets. Obviously, you got to have good tech, a great team. And you want large markets. So the, space that you're in, I mean, I would think it started, early days and kind of the decision support. Sort of morphed into the data warehouse, you mentioned ETL, that's kind of part of it. Business Intelligence, it's sort of all in there. If you look at the EDW market, it's probably around 18 to 20 billion. Small slice of that is data lakes, maybe a billion or a billion plus. And then you got this sort of BI layer on top, you mentioned a lot of those. You got ETL, you probably get up into the 30,35 billion just sort of off the top of my head and from my historical experience and looking at these markets. But I have to say these markets have traditionally failed to live up to the expectations. Things like 360 degree views of the customer, real time analytics, delivering insights and self service to the business. Those are promises that these industries made. And they ended up being cumbersome, slow, maybe requiring real experts, requiring a lot of infrastructure, the cloud is changing that. Is that right? Is that the way to look at the market that you're going after? You're a player inside of that very large team. >> Yeah, I think we're a key fundamental component underneath that whole ecosystem. And yes, you're seeing us build a full stack solution for log analytics, because there's really good way to prove just how game changing the technology is. But also how we publishing API's, and it's seamless for how you're using log analytics. Same thing can be applied as we go across the SQL and different BI and analytic type of platforms. So it's exactly how we're looking at the market. And it's those players that are all struggling with the same thing. How they add more value to clients? It's a big cost game, right? So if I can literally make your underlying how you store your data and mix it literally 80% more cost effective. that's a big deal or simultaneously saving 80% and give you much longer retention. Those two things are typically, Lily a trade off, you have to go through, and we don't have to do that. That's what really makes this kind of the underlying core technology. And really I look at log analytics is really the first application set. But or if you have any log analytics issues, if you talk to your teams and find out, scale, cost, management issues, it's a pretty we make it very easy. Just run in parallel, we'll do a PLC, and you'll see how easy it is you can just save 80% which is, 80% and better retention is really the value proposition you see at scale, right. >> So this is day zero for you. Give us the hundred day plan, what do you want to accomplish? Where are you going to focus your priorities? I mean, obviously, the company's been started, it's well funded, but where are you going to focus in the next 100 days? >> No, I think it's building out where are we taking the next? There's a lot of things we could do, there's degrees of freedom as far as where we'd go with this technology is pretty wide. You're going to see us be the best log analytic company there. We're getting, really a (mumbling) we, you saw the announcement, best quarter ever last quarter. And you're seeing this nice as a service ramp, you're going to see us go to VPC. So you can do as a service with us, but now we can put this same thing in your own virtual private data center. You're going to see us go to Google, Azure, and also IBM cloud. And the really, clients are driving this. It's not us driving it, but you're going to see actually the client. So we'll go into Google because we had a couple financial institutions that are saying they're driving us to go do exactly that. So it's more really working with our client sets and making sure we got the right roadmap to support what they're trying to do. And then the ecosystem is another play. How to, you know, my core technology is not necessarily competitive with anyone else. No one else is doing this. They're just kind of, hey, move it here, I'll put it on this, you know, a foundational DV or they'll put it on on a presto environment. They're not really worried about the bottom line economics, which is really that's the value prop and that's the hard tech and patented technology that we bring to this ecosystem. >> Well, people are definitely worried about their cloud bills. The the CFO saying, whoa, cause it's so easy to spin up, instances in the cloud. And so, Ed it really looks like you're going after a real problem. You got some great tech behind you. And of course, we love the fact that it's another Boston based company that you're joining, cause it's more Boston based startups. Better for us here at the East Coast Cube, so give us a give us your final thoughts. What should we look for? I'm sure we're going to be being touched and congratulations. >> No, hey, thank you for the time. I'm really excited about this. I really just think it's fundamental technology that allows us to get the most out of everything you're doing around analytics in the cloud. And if you look at a data lake model, I think that's our philosophy. And we're going to drive it pretty aggressively. And I think it's a good fundamental innovation for the space and that's the type of tech that I like. And I think we can also, do a lot of partnering across ecosystems to make it work for a lot of different people. So anyway, so I guess thank you very much for the time appreciate. >> Yeah, well, thanks for coming on theCUBE and best of luck. I'm sure we're going to be learning a lot more and hearing a lot more about ChaosSearch, Ed Walsh. This is Dave Vellante. Thank you for watching everybody, and we'll see you next time on theCUBE. (upbeat music)

(upbeat music) >> Announcer: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Everyone, welcome to this CUBE conversation. I'm John Furrier with theCUBE, host of theCUBE here in our Palo Alto office for remote interviews during this time of COVID-19. We're here with the quarantine crew here in our studio. We've got a great guest here from Google, Will Grannis, managing director, head of the office of the CTO with Google Cloud. Thanks for coming on, Will. Appreciate you spending some time with me. >> Oh, John, it's great to be with you. And as you said, in these times, more important than ever to stay connected. >> Yeah, and I'm really glad you came on because a couple of things. One, congratulations to Google Cloud for the success you guys had. Saw a lot of big wins under your belt, both on the momentum side, on the business side, but also on the technical side. Meet is available now for folks. Anthos is doing very, very well. Partner ecosystem's developing. Got some nice use cases in vertical markets, so I want to get in and unpack with you. But really, the bigger story here is that the world has seen the future before it was ready for it. And that is the at-scale challenge that the COVID-19 has shown everyone. We're seeing the future has been pulled forward. We're living in a virtualized environment. It's funny to say that, virtualization (laughs). Server virtualization is a tech term, but that enabled a lot of things. We're living in a virtualized world now 'cause we have to, but this is going to set in motion a series of new realities that you guys have been experiencing and supporting for many, many years. But now as a provider of Google Cloud, you guys have to operate at scale, you have. And now the whole world realizes that scale is a big deal. And so you guys have had some successes. I want to get your thoughts on the this at scale problem that the world now realizes. I mean, everyone's at home. That's a disruption that was unforecasted. Whether it's under-provisioning VPNs in IT to a surface area for security, to just work and play. And activities are now confined, so people aren't convening anymore and it's a huge issue. What's your take on all this? >> Well, I mean, to your point just now, the fact that we can have this conversation and we can have it fluidly from our respective remote locations just goes to show you the power of information technology that underlies so many of the things that we do today. And for Google Cloud, this is not a new thing. And for Google, this is not a new thing. For Google Cloud, we had a mission of trying to help companies accelerate their transformation and enable them in these new digital environments. And so many companies that we've been working with, they've already been on the path to operating in environments that are digital, that are fluid. And when you think about the cloud, that's one of the great benefits of cloud, is that scalability in common with the business demand. And it also helps the scale situation without having to do the typical, "Oh wait, "you need to find the procurement people. "We need to find the server vendors. "We need to get the storage lined up." It really allows a much more fluid response to unexpected and unforecasted situations. Whether that's customer demand or in this case a global pandemic. >> Yeah, one of the things I want to get in with you on, you have explained what your job is there 'cause obviously Google's got a new CEO now for over a year. Thomas Kurian came from Oracle, knows the enterprise up and down. You had Diane Greene before that. Again, another enterprise leader. Google Cloud has essentially rebuilt itself from the original Google Cloud to be very enterprise centric. You guys have great momentum, and this is a world where cloud-native is going to be required. I mean, everyone now sees it. The tide has been pulled out, everything's exposed, all the gaps in business from a tech standpoint is kind of exposed. And so the smart managers and companies are looking at things and saying, "Double down on that. "Let's kill that. "We don't want to pay that supplier. "They're not core to our business." This is going to be a very rapid acceleration of what I call a vetting of the new set of players that are going to emerge because the folks who don't adapt to this new cloud-native reality, whether it's app workloads for banking to whatever are going to have to reinvent themselves now and reset and tweak to come out of this crisis. So it's going to be very cloud-native. This is a big deal. Can you share your reaction to that? >> Absolutely. And so as you pointed out, there are kind of two worlds that exist right now. Companies that are moving to become more digital and transform, and you mentioned the momentum in Google Cloud just over the last year, greater than 50% revenue growth. And in a greater than $10 billion run rate business and adding customers at a really quick clip, including just yesterday, Splunk, and along the way, Telecom Italia, Major League Baseball, Vodafone, Lowe's, Wayfair, Activision Blizzard. This transformation and this digitization is not just for a few or just for any one industry. It's happening across the board. And then you add that to the implementations that have been happening across Shopify and the Spotify and HSBC, which was a early customer of ours in the cloud and it already has a little bit of a headstart into this transformation. So you see these new companies coming in and seeing the value of digital transformation. And then these other companies that have kind of lit the path for others to consider. And Shopify is a really good example of how seeing drastic uptick in demand, they're able to respond and keep roughly half a million shops up and running during a period of time where many retailers are trying to figure out how to stay online or even get online. >> Well, what is your role at Google? Obviously, you're the managing director. Title is managing director, head of the office of the CTO. We've seen these roles before, head of the CTO, obviously a technical role. Is it partnering with the CEO on strategy? Is it you're tire kicking new things? Are you overseeing any strategic initiatives? What is your role? >> So a little bit of all of those things combined into one. So I spent the first couple of decades of my career on the other side of the fence in the non-tech community, both in the enterprise. But we were still building technology and we were still digitally minded. But not the way that people view technology in Silicon Valley. And so spending a couple of decades in that environment really gave me insights into how to take technology and apply them to a specific problem. And when I came to Google five years ago, selfishly, it was because I knew the potential of Google's technology having been on the other side. And I was really interested in forming a better bridge between Google's technology and people like me who were CTOs of public companies and really wanted to leverage that technology for problems that I was solving. Whether it was aerospace, public sector, manufacturing, what have you. And so it's been great. It's the role of a lifetime. I've been able to build the team that I wanted as an enterprise technologist for decades and the entire span of technologies at our disposal. And we do two things. One is we help our most strategic customers accelerate their path to cloud. And two, we create these signals by working with the top companies moving to the cloud and digitally transforming. We learned so much, John, about what we need to build as an organization. So it also helps balance out the Google driven innovation with our customer driven innovation. >> Yeah, and I can attest. I've been watching you guys from day one. Hired a lot of great enterprise people that I personally know. So you get in the enterprise chops and stuff and you've seen some progress. I have to ask you though, because first of all, big fan of Google at scale from knowing them from when they were just a little search engine to what they are now. There was an expression a few years ago I heard from enterprise customers. It goes along the lines like this. "I want to be like Google," because you guys had a great network, you had large scale. You had all these things that were like awesome. And then they realized, "Well, we can't be like Google. "We don't have SREs. "We don't have large scale data centers." So there was a little bit of a translation, and I want to say a little bit of a overplay of the Google hand, and you guys had since realized that it wasn't just people are going to bang at your doorstep and be adopting Google Cloud because there was a little bit of a cultural disconnect from wanting to be like Google, then leveraging Google in their business as they transform. So as you guys have moved from that, what's changed? They still want to be like Google in the sense you have great security, got a great network, and you've got that scale. Enterprises are a little bit slower to adopt that, which you're focused on now. What is the story there? Because I think that's kind of the theme that I'm hearing. Okay, Google now understands me. They know I'm not as fast as Google. They got super great people (laughs). We are training our people. We're retraining them. This is the transformation that they're going through. So you might be a little bit ahead of them certainly, but now they need to level up. How do you respond to that? >> Well, a lot of this is the transformation that Thomas has been enacting over the last year plus. And it comes in kind of three very operational or tactical pillars that I think of. First, we expanded our customer and we continue to expand our customer facing teams. Three times what they were before because we need to be there. We need to be in those situations. We need to hear from the customer. We need to learn more about the problems they're trying to solve. So we don't just take a theoretical principle and try to overlay it onto a problem. We actually get very visceral understanding of what they're trying to solve. But you have to be there to gain that empathy and that understanding. And so one is showing up, and that has been mobilizing a much larger engine of customer facing personnel from Google. Second, it's also been really important that we evolve our own. Just as Google brought SRE principles and principles of distributed systems and software design out to the world, we also had a little bit to learn about transitioning from typical customer support and moving to more customer experience. So you've seen that evolution under Thomas as well with cloud changing... Moving from talking about support to talking about customer experience, that white glove experience that our customers get and our partners get from the beginning of their journey with us all the way through. And then finally making sure that our product roadmap has the solutions that are relevant across key priority industries for us. Again, that only comes from being present from having a focus in those industries and then developing the solutions that progress those companies. This isn't about taking a principle and trying to apply it blindly. This is about adding that connection, that really deep connection to our customers and our partners and letting that connection manifest the things that we have to do as a product company to best support them over a long period of time. I mean, look at some of these deals we've been announcing. These are 10-year, five-year, multi-year strategic partnerships that go across the canvas of all of Google. And those are the really exciting scaled partnerships. But to your point, you can't just take SRE from Google and apply it to company X, but you can things like error budgets or how we think about the principles of SRE, and you can apply them over the course of developing technology, collaborating, innovating together. >> Yeah, and I think cloud-native is going to be a key thing. It's just my opinion, but I think one of those situations where the better mouse trap will win. If you're cloud-native and you have APIs and you have the kind of services, people will beat it to your doorstep. So I got to ask you, with Thomas Kurian on board, obviously, we've been following his career as well at Oracle. He knows what he's doing. Comes into Google, it's being built out. It's like a rocket ship at this point. What bet is he making and what bet are you guys making on behalf of your customers? If you had to boil it down to Google Cloud's big bet, what is the bet on the technology side? And what's the bet on the business side? >> Sure. Well, I've already mentioned... I've already hinted at the big strategy that Thomas has brought in. And that's, again, those three pillars. Making sure that we show up and that we're present by having a scaled customer facing organization. Again, making sure that we transition from a typical support mindset into more of a customer experience mindset and then making sure that those solutions are tailored and available for our priority industries. If I was to add more color to that, I think one of the most important changes that Thomas has personally been driving is he's been converting us to a partner-led business and a partner-led organization. And this means a lot of investments in large global systems integrators like Accenture and Deloitte. But this also means that... Like the Splunk announcement from yesterday, that isn't just a sell to. This is a partnership that goes deep across go-to market product and sell to. And then we also bring in very specific partners like Temenos in Europe for financial services or a CETA or a Rackspace for migrations. And as a result, already, we're seeing really incredible lifts. So for example, nearly 200% year over year increase in partner influenced revenue in Google Cloud and almost like a 13X year over year increase in new customers won by partners. That's the kind of engine that builds a real hyper-scale business. >> Interesting you mentioned Splunk. I want to get to that in a second, but I also noticed there was a deal with TELUS Group on eSIM subscriptions, which kind of leads me into the edge piece. There's a real edge component here with Google Cloud, and I think I had a conversation with Jennifer Lynn a few years ago, really digging into the built-in security and the value of the Google network. I mean, a lot of the scuttlebutt around the Valley and the industry is Google's got an amazing network. Software-defined networking is going to be a hot programmable area. So you got programmable networking and you got edge and edge security. These are killer areas that need innovation. Could you comment on what you guys are doing there and do you agree? Obviously, you have a killer network and you're leveraging it. Can you just give some insight into what's going on in those two areas? Network and then the edge. >> Yeah, I think what you're seeing is the manifestation of the progression of cloud generally. And what do I mean by that? It started out as like get everything to the data center. We kind of had this thought that maybe we could take all the workloads and we could get them to these centralized hubs and that we could redistribute out the results and drive the latency down over time so we can expand the portfolio of applications and services that would become relevant over time. And what we've seen over the last decade really in cloud is an evolution to more of a layered architecture. And that layered architecture includes kind of core data centers. It includes CDN capacity, points of presence, it includes edge. And just in that list of customers over the last year I mentioned, there were at least three or four telcos in there. And you've also probably heard and seen quite a bit of telco momentum coming from us in recent announcements. I think that's an indication that a lot of us are thinking about, how can we take technology like Anthos, for example, and how could we orchestrate workloads, create a common control plane, manage services across those three shells, if you will, of the architecture? And that's a very strategic and important area for us. And I think generally for the cloud industry, is expanding beyond the data center as the place where everything happens. And you can look at Google Fi, you can look at Stadia. You can look at examples within Google that go well beyond cloud as to how we think about new ways to leverage that kind of criteria. >> All right, so we saw some earnings come out on Amazon side as Google, both groups and Microsoft as well, all three clouds are crushing it on the cloud side. That's a tailwind, I get that. But as it continues, we're expecting post-COVID some redistribution of development dollars in projects. Whether it's IT going cloud-native or whatever new workloads. We are predicting a Cambrian explosion of new things from core to edge. And this is going to create some lifts. So I want to get your thoughts on you guys' strategy with go-to market, as well as your customers as they now have the ability to build workloads and apps with AI and data. There seems to be a trend towards the verticalization of whether it's sales and go-to market and/or specialism because you have horizontal scalability with cloud and you now have data that has distinct (chuckles) value in these verticals. So it's really seems to be... I won't say ratification, but in a way, that seems to be the norm. Whether you come into a market and you have specialization, but the data is there so apps can be more agile. Are you guys seeing that? And is that something that you guys are considering from an organization standpoint? And how do customers think about targeting vertical industries and their customers? >> Yeah, I bring this to... And where you started going there at the end of the question is exactly the way that we think about it as well. Which is we've moved from, "Here are storage offers for everybody, "and here's basic infrastructure for everybody." And now we've said, "How can we make sure "that we have solutions that are tailored "to the very specific problems that customers "are trying to solve?" And we're getting to the point now where performance and variety of technologies are available to be able to impose very specific solutions. And if you think about the substrate that has to be there, we mentioned you have to have some really great partners, and you have to have a roadmap that is focused on priority solution. So for example, at Google Cloud, we're very focused on six priority vertical areas. So retail, financial services, healthcare, manufacturing and industrials, healthcare life sciences, public sector. And as a result of being very focused in those areas, we can make more targeted investments and also align our entire go-to market system and our entire partner ecosystem... Excuse me, ecosystem around those bare specific priority areas. So for example, we work with CETA and HDA Healthcare very recently to develop and maintain a national response portal for COVID-19. And that's to help better inform communities and hospitals. We can use Looker to help with like a Commonwealth Care Alliance nonprofit and that helps monitor patient symptoms and risk factors. So we're using a very specific focus in healthcare and a partner ecosystem to develop very tailored solutions. You can also look at... I mentioned Shopify earlier. That's another great example of how in retail, they can use something like Google Meet, inherent reliability, scalability, security, to connect their employees during these interesting times. But then they can also use GCP, Google Cloud Platform to scale out. And as they come up with new apps and experiences for their shoppers, for their shops, they can rapidly deploy, to your point. And those solutions and how the database performs and how those tiers perform, that's a very tight-knit feedback loop with our engineering teams. >> Yeah, one of the things I'm seeing obviously with the virtualization of the COVID is that when the world gets back to normal, it'll be a hybrid. And it'll be a hybrid between reality, not physical and a hundred percent virtual, hybrid. And that's going to impact events too, media, to everything. Every vertical will be impacted. And I want to point out the Splunk deal and bring that back in because I want you to comment on the relevance of the Splunk deal in context to Splunk has a cloud. And they've got a great slogan, "Data for everywhere." "Data to everywhere," I think it is. But theCUBE, we have a cloud. Every company will have a cloud scale. At some level, we'll progress to having some sort of cloud because they have data. How are you guys powering those clouds? Because I think the Splunk deal is interesting. Their partner, their stock price was up out on the news of the deal. Nice bump there for Splunk, shout out to those guys. But they're a data company and now they're cross-platform. But they're not Google, but they have a cloud. So you know what I'm saying? So they need to play in all the clouds, but they need infrastructure (laughs), they need support. So how do you guys talk to that customer that says, "Hey, the next pandemic that comes, "the next crisis that's going to cause some "either social disruption or workflow disruption "or supply chain disruption. "I need to be agile. "I need to have full cloud scale. "And so I need to talk to Google." What do you say to them? What's the pitch? And does the Splunk deal mirror some of those capabilities? Or tie that together for us, the Splunk deal and how it relates to how to proof themselves for the future. Sorry. >> For example, with the Splunk cloud deal, if you take a look at what Google is already really good at, data processing at scale, log analytics, and you take a look at what Splunk is doing with their events and security incident monitoring and the rest, it's a really great mashup because they see by platforming on Google Cloud, not only do they get highly performing infrastructure. But they also get the opportunity to leverage data tools, data analytics tools, machine learning and AI that can help them provide enhanced services. So not just about capacity going up and down through periods of demand, but also enhancing services and continuing to offer more value to their customers. And we see that as a really big trend. And this gets at something, John, a little bit bigger, which is kind of the two views of the world. And we talked about very tailored, focused solutions. Splunk is an example of taking a very methodical approach to a partnership, building a solution specifically with partners. And in this case, Splunk on the security event management side. But we're always going to provide our data processing platform, our infrastructure for companies across many different industries. And I think that addresses one part of the topic, which is, how do we make sure that in periods of demand rapidly changing, and this goes back to the foundational elements of infrastructure as a service and elasticity. We're going to provide a platform and infrastructure that can help companies move through periods of... It's hard to forecast, and/or demand may rise and fall in very interesting ways. But then there's going to be times where we... Because we're not necessarily a focused use case where it may just be generalized platform versus a focused solution. So for example, in the oil and gas industry, we don't develop custom AI, ML solutions that facilitate upstream extraction, for example. But what we do do is work with renewable energy companies to figure out how they might be able to leverage some of our AI machine learning algorithms from our own data centers to make their operations more efficient and to help those renewable energy companies learn from what we've learned building out what I consider to be a world leading renewable energy strategy and infrastructure. >> It's a classic enablement model where you're enabling your platform for your customers. Okay, so I've got to ask the question. I asked this to the Microsoft guys as well because Amazon has their own SaaS stuff. But really more of end to end. The better product's usually on the ecosystem side. You guys have some killer SaaS. G Suite, we're a customer. We use the G Suite really deeply. We also use some Bigtable as well. I want to build a cloud, we have a cloud, CUBE cloud. But you guys have Meet. So I want to build my product on Google Cloud. How do I know you're not going to compete with me? Do you guys have those conversations around the trade-off between the pure Google services, which provide great value for the areas where the ecosystem needs to develop those new areas that are going to be great markets, potentially huge markets that are out there. >> Well, this is the power of partnership. I mentioned earlier that one of the really big moves that Thomas has made has been developing a sense of partners. And it kind of blurs the line between traditional, what you would call a customer and what you would call a partner. And so having a really strong sense of which industries we're in, which we prioritize, plus having a really strong sense of where we want to add value and where our customers and partners want to add that value. That's the foundational, that's the beginning of that conversation that you just mentioned. And it's important that we have an ability to engage not just in a, "Here's the cloud infrastructure piece of the puzzle." But one of the things Thomas has also done and a key strategy of his has been to make sure that the Google Cloud relationship is also a way to access all amazing innovation happening across all of Google. And also help bring a strategic conversation in that includes multiple properties from across Google so that an HSBC and Google and have a conversation about how to move forward together that is comprehensive rather than having to wonder and have that uncertainty sit behind the projects that we're trying to get out and have high velocity on because they offer so much to retail bank, for example. >> Well, I've got a couple more questions and then I'll let you go. I know you got some other things going on. I really appreciate you taking the time, sharing this great insight and updates. As a builder, you've been on the other side of the table. Now you're at Google heading up the CTO. Also working with Thomas, understanding the go-to market across the board and the product mix. As you talk to customers and they're thinking... The good customers are thinking, "Hey, "I want to come out of this COVID on an upward trajectory "and I want to use this opportunity "to reset and realign for the future." What advice do you have for those enterprises? They could be small, medium-sized enterprises to the full large big guys. And obviously, cloud-native, we've talked some of that already, but what advice would you have for them as they start to really prioritize, as some things are now exposed? The collaboration, the tooling, the scale, all these things are out there. What have you seen and what advice would you give a CXO or CSO or a leader in the industry to think about and how they should come out of this thing, how they should plan, execute, and move forward? >> Well, I appreciate the question because this is the crux of most of my day job, which is interacting with the C-suite and boards of companies and partners around the world. And they're obviously very interested to learn or get a data point from someone at Google. And the advice generally goes in a couple of different directions. One, collaboration is part of the secret sauce that makes Google what it is. And I think you're seeing this right now across every industry, and whether you're a small, medium-sized business or you're a large company, the ability to connect people with each other to collaborate in very meaningful ways, to share information rapidly, to do it securely with high reliability, that's the foundation that enables all of the projects that you might choose to... Applications to build, services to enable, to actually succeed in production and over the long haul. Is that culture of innovation and collaboration. So absolutely number one is having a really strong sense of what they want to achieve from a cultural perspective and collaboration perspective and the people because that's the thing that fuels everything else. Second piece of advice, especially in these times where there's so much uncertainty, is where can you buy down uncertainty with...? You can learn without a high penalty. This is why cloud I think is really, really finding super scale. It was already on the rise, but what you're seeing now as you've laid back to me during this conversation, we're seeing the same thing, which is a high increase in demand of, "Let's get this implemented now. "How can we do this more? "This is clearly one way to move through uncertainty." And so look for those opportunities. I'll give you a really good example. Mainframes, (chuckles) one of the classic workloads of the on-premise enterprise. There are all sorts of potential magic solves for getting mainframes to the cloud and getting out of mainframes. But a practical consideration might be maybe you just front-end it with some Java. Or maybe you just get closer to other data centers within a certain amount of milliseconds that's required to have a performant workload. Maybe you start chunking at art and treat the workload a little bit differently rather than just one thing. But there are a lot of years and investments in our workload that might run on a mainframe. And that's a perfect example of how biting off too much might be a little bit dangerous, but there is a path to... So for example, we brought in a company called Cornerstone to help with those migrations. But we also have partnerships with data center providers and others globally plus our own built infrastructure to allow even a smaller step per se for more close proximity location of the workload. >> It's great. Everything kind of has a technical metaphor connection these days when you have a internet, digitally connected world. We're living in the notion of a digital business, was a research buzzword that's been kicked around for years. But I think now COVID-19, you're seeing the virtual or digital, it's really digital, but virtual reality, augmented reality is going to come fast too. Really get people to go, "Wow. "Virtualization of my business." So we've been kind of kicking around this term business virtualization just almost as a joke, but it's really more about, okay, this is about a new world, new opportunity to think about when we come out of this, we're going to still go back to our physical world. Now, the hybrid now kicks in. This kind of connects all aspects of business in every vertical. It's not like, "Hey, I'm targeting this industry." So there might be unique solutions in those industries, but now the world is virtualized. It's connected, it's a digital environment. These are huge concepts that I think has kind of been a lunatic fringe idea, but now it's brought mainstream. This is going to be a huge tailwind for you guys as well as developers and entrepreneurs and application software. This is going to be, we think, a big thing. What's your reaction to that? Based on your experience, what do you see happening? Do you agree with it? And do you have anything you might want to add to that? >> Maybe one kind of philosophical statement and then one more... I bruised my shins a lot in this world and maybe share some of the black and blue coloration. First from a philosophical standpoint, the greater the crisis, the more open-minded people become and the more creative people get. And so I'm really excited about the creativity that I'm seeing with all of the customers that I work with directly, plus our partners, Googlers. Everybody is rallying together to think about this world differently. So to your point, a shift in mindset, there are very few moments where you get this pronounced change and everyone is going through it all at the same time. So that creates an opportunity, a scenario where you're bold thinking new strategies, creativity. Bringing people in in new ways, collaborating in new ways and offer a lot of benefits. More practically speaking and from my experience, building technology for a couple decades, it has an interesting parallel to building tightly coupled, really large maybe monoliths versus microservices and the debate around, "Do we build small things "that can be reconfigured and built out by others "or built upon by others more easily? "Or do we create a golden path and a more understood development environment?" And I'm not here to answer the question of which one's better because that's still a raging debate. But I can tell you that the process of going through and taking a service or an application or a thing that we want to deliver to a customer, that one of our customers wants to deliver to their customer. And thinking about it so comprehensively that you're able to think about it in, what are its core functions? And then thinking methodically about how to enable those core functions. That's a real opportunity, and I think technology to your point is getting to the place where if you want to run across multiple clouds, this is the Anthos conversation were recently GA'ed. Global scale platform, multicloud platform, that's a pretty big moment in technology. And that opens up the aperture to think differently about architectures and that process of taking an application service and making it real. >> Well, I think you're right on the money. I think philosophically, it's a flashpoints opportunity. I think that's going to prove to be accelerating and to see people win faster and lose faster. You're going to to see that quickly happen. But to your point about the monolith versus service or decoupled based systems, I think we now live in a world where it's a systems view now. You can have a monolith combined with decoupled systems. That's distributed computing. I think this is the trend, it's a system. It's not one thing or the other. So I think the debate will continue just like VI versus Emacs (chuckles). We don't know, right? People are going to have the debate, but if you think about it as a system, the use case defines your architecture. That's the beautiful thing about the cloud. So great insight, I really appreciate it. And how's everything going over there at Google Cloud? You've got Meet that's available. How's your staff? What's it like inside the Googleplex and the Google Cloud team? Tell us what's going on over there. People still working, working remote? How's everyone doing? >> Well, as you can tell from my scenario here, my backdrop, yes, still part at work. And we take this as a huge responsibility. These moments as a huge responsibility because there are educators, loved ones, medical professionals, critical life services that run on services that Google provides. And so I can tell you we're humbled by the opportunity to provide the backbone and the platform and the people and the curiosity and the sincere desire to help. And I mentioned a couple of ways already just in this conversation where we've been able to leverage some of our investments technology to help form people that really gets at the root of who we are. So while we just like any other humans are going through a process of understanding our new reality, what really fires us up and what really charges us up is because this is a moment where what we do really well is very, very important for the world in every geo, in every vertical, in every use case, in every solution type. We're taking that responsibility very seriously. And at the same time, we're trying to make sure that all of our teams as well as all of the teams that we work with and our customers and partners are making it through the human moment, not just the technology moment. >> Well, congratulations and thanks for spending the time. Great insight, Will. Appreciate, Will Grannis, managing director, head of technology office of the CTO at Google Cloud. This certainly brings to the mainstream what we've been in the industry been into for a long time, which is DevOps, large scale, role of data and technology. Now we think it's going to be even more acute around societal benefits. And thank God we have all those services for the frontline workers. So thank you so much for all that effort and thanks for spending the time here in theCUBE Conversation. Appreciate it. >> Thanks for having me, John. >> Okay, I'm John Furrier here in Palo Alto studios for remote CUBE Conversation with Google Cloud, getting the update. Really looking at the future as it unfolds. We are going to see this moment in time as an opportunity to move to the next level, cloud-native and change not only the tech industry but society. I'm John Furrier, thanks for watching. (upbeat music)