(gentle synth music) (music ends) >> Welcome back to Supercloud 2, an open industry collaboration between technologists, consultants, analysts, and of course practitioners to help shape the future of cloud. At this event, one of the key areas we're exploring is the intersection of cloud and data. And how building value on top of hyperscale clouds and across clouds is evolving, a concept of course we call "Supercloud". And we're pleased to welcome our friends from Enterprise Technology research, Erik Bradley and Darren Brabham. Guys, thanks for joining us, great to see you. we love to bring the data into these conversations. >> Thank you for having us, Dave, I appreciate it. >> Yeah, thanks. >> You bet. And so, let me do the setup on what is Supercloud. It's a concept that we've floated, Before re:Invent 2021, based on the idea that cloud infrastructure is becoming ubiquitous, incredibly powerful, but there's a lack of standards across the big three clouds. That creates friction. So we defined over the period of time, you know, better part of a year, a set of essential elements, deployment models for so-called supercloud, which create this common experience for specific cloud services that, of course, again, span multiple clouds and even on-premise data. So Erik, with that as background, I wonder if you could add your general thoughts on the term supercloud, maybe play proxy for the CIO community, 'cause you do these round tables, you talk to these guys all the time, you gather a lot of amazing information from senior IT DMs that compliment your survey. So what are your thoughts on the term and the concept? >> Yeah, sure. I'll even go back to last year when you and I did our predictions panel, right? And we threw it out there. And to your point, you know, there's some haters. Anytime you throw out a new term, "Is it marketing buzz? Is it worth it? Why are you even doing it?" But you know, from my own perspective, and then also speaking to the IT DMs that we interview on a regular basis, this is just a natural evolution. It's something that's inevitable in enterprise tech, right? The internet was not built for what it has become. It was never intended to be the underlying infrastructure of our daily lives and work. The cloud also was not built to be what it's become. But where we're at now is, we have to figure out what the cloud is and what it needs to be to be scalable, resilient, secure, and have the governance wrapped around it. And to me that's what supercloud is. It's a way to define operantly, what the next generation, the continued iteration and evolution of the cloud and what its needs to be. And that's what the supercloud means to me. And what depends, if you want to call it metacloud, supercloud, it doesn't matter. The point is that we're trying to define the next layer, the next future of work, which is inevitable in enterprise tech. Now, from the IT DM perspective, I have two interesting call outs. One is from basically a senior developer IT architecture and DevSecOps who says he uses the term all the time. And the reason he uses the term, is that because multi-cloud has a stigma attached to it, when he is talking to his business executives. (David chuckles) the stigma is because it's complex and it's expensive. So he switched to supercloud to better explain to his business executives and his CFO and his CIO what he's trying to do. And we can get into more later about what it means to him. But the inverse of that, of course, is a good CSO friend of mine for a very large enterprise says the concern with Supercloud is the reduction of complexity. And I'll explain, he believes anything that takes the requirement of specific expertise out of the equation, even a little bit, as a CSO worries him. So as you said, David, always two sides to the coin, but I do believe supercloud is a relevant term, and it is necessary because the cloud is continuing to be defined. >> You know, that's really interesting too, 'cause you know, Darren, we use Snowflake a lot as an example, sort of early supercloud, and you think from a security standpoint, we've always pushed Amazon and, "Are you ever going to kind of abstract the complexity away from all these primitives?" and their position has always been, "Look, if we produce these primitives, and offer these primitives, we we can move as the market moves. When you abstract, then it becomes harder to peel the layers." But Darren, from a data standpoint, like I say, we use Snowflake a lot. I think of like Tim Burners-Lee when Web 2.0 came out, he said, "Well this is what the internet was always supposed to be." So in a way, you know, supercloud is maybe what multi-cloud was supposed to be. But I mean, you think about data sharing, Darren, across clouds, it's always been a challenge. Snowflake always, you know, obviously trying to solve that problem, as are others. But what are your thoughts on the concept? >> Yeah, I think the concept fits, right? It is reflective of, it's a paradigm shift, right? Things, as a pendulum have swung back and forth between needing to piece together a bunch of different tools that have specific unique use cases and they're best in breed in what they do. And then focusing on the duct tape that holds 'em all together and all the engineering complexity and skill, it shifted from that end of the pendulum all the way back to, "Let's streamline this, let's simplify it. Maybe we have budget crunches and we need to consolidate tools or eliminate tools." And so then you kind of see this back and forth over time. And with data and analytics for instance, a lot of organizations were trying to bring the data closer to the business. That's where we saw self-service analytics coming in. And tools like Snowflake, what they did was they helped point to different databases, they helped unify data, and organize it in a single place that was, you know, in a sense neutral, away from a single cloud vendor or a single database, and allowed the business to kind of be more flexible in how it brought stuff together and provided it out to the business units. So Snowflake was an example of one of those times where we pulled back from the granular, multiple points of the spear, back to a simple way to do things. And I think Snowflake has continued to kind of keep that mantle to a degree, and we see other tools trying to do that, but that's all it is. It's a paradigm shift back to this kind of meta abstraction layer that kind of simplifies what is the reality, that you need a complex multi-use case, multi-region way of doing business. And it sort of reflects the reality of that. >> And you know, to me it's a spectrum. As part of Supercloud 2, we're talking to a number of of practitioners, Ionis Pharmaceuticals, US West, we got Walmart. And it's a spectrum, right? In some cases the practitioner's saying, "You know, the way I solve multi-cloud complexity is mono-cloud, I just do one cloud." (laughs) Others like Walmart are saying, "Hey, you know, we actually are building an abstraction layer ourselves, take advantage of it." So my general question to both of you is, is this a concept, is the lack of standards across clouds, you know, really a problem, you know, or is supercloud a solution looking for a problem? Or do you hear from practitioners that "No, this is really an issue, we have to bring together a set of standards to sort of unify our cloud estates." >> Allow me to answer that at a higher level, and then we're going to hand it over to Dr. Brabham because he is a little bit more detailed on the realtime streaming analytics use cases, which I think is where we're going to get to. But to answer that question, it really depends on the size and the complexity of your business. At the very large enterprise, Dave, Yes, a hundred percent. This needs to happen. There is complexity, there is not only complexity in the compute and actually deploying the applications, but the governance and the security around them. But for lower end or, you know, business use cases, and for smaller businesses, it's a little less necessary. You certainly don't need to have all of these. Some of the things that come into mind from the interviews that Darren and I have done are, you know, financial services, if you're doing real-time trading, anything that has real-time data metrics involved in your transactions, is going to be necessary. And another use case that we hear about is in online travel agencies. So I think it is very relevant, the complexity does need to be solved, and I'll allow Darren to explain a little bit more about how that's used from an analytics perspective. >> Yeah, go for it. >> Yeah, exactly. I mean, I think any modern, you know, multinational company that's going to have a footprint in the US and Europe, in China, or works in different areas like manufacturing, where you're probably going to have on-prem instances that will stay on-prem forever, for various performance reasons. You have these complicated governance and security and regulatory issues. So inherently, I think, large multinational companies and or companies that are in certain areas like finance or in, you know, online e-commerce, or things that need real-time data, they inherently are going to have a very complex environment that's going to need to be managed in some kind of cleaner way. You know, they're looking for one door to open, one pane of glass to look at, one thing to do to manage these multi points. And, streaming's a good example of that. I mean, not every organization has a real-time streaming use case, and may not ever, but a lot of organizations do, a lot of industries do. And so there's this need to use, you know, they want to use open-source tools, they want to use Apache Kafka for instance. They want to use different megacloud vendors offerings, like Google Pub/Sub or you know, Amazon Kinesis Firehose. They have all these different pieces they want to use for different use cases at different stages of maturity or proof of concept, you name it. They're going to have to have this complexity. And I think that's why we're seeing this need, to have sort of this supercloud concept, to juggle all this, to wrangle all of it. 'Cause the reality is, it's complex and you have to simplify it somehow. >> Great, thanks you guys. All right, let's bring up the graphic, and take a look. Anybody who follows the breaking analysis, which is co-branded with ETR Cube Insights powered by ETR, knows we like to bring data to the table. ETR does amazing survey work every quarter, 1200 plus 1500 practitioners that that answer a number of questions. The vertical axis here is net score, which is ETR's proprietary methodology, which is a measure of spending momentum, spending velocity. And the horizontal axis here is overlap, but it's the presence pervasiveness, and the dataset, the ends, that table insert on the bottom right shows you how the dots are plotted, the net score and then the ends in the survey. And what we've done is we've plotted a bunch of the so-called supercloud suspects, let's start in the upper right, the cloud platforms. Without these hyperscale clouds, you can't have a supercloud. And as always, Azure and AWS, up and to the right, it's amazing we're talking about, you know, 80 plus billion dollar company in AWS. Azure's business is, if you just look at the IaaS is in the 50 billion range, I mean it's just amazing to me the net scores here. Anything above 40% we consider highly elevated. And you got Azure and you got Snowflake, Databricks, HashiCorp, we'll get to them. And you got AWS, you know, right up there at that size, it's quite amazing. With really big ends as well, you know, 700 plus ends in the survey. So, you know, kind of half the survey actually has these platforms. So my question to you guys is, what are you seeing in terms of cloud adoption within the big three cloud players? I wonder if you could could comment, maybe Erik, you could start. >> Yeah, sure. Now we're talking data, now I'm happy. So yeah, we'll get into some of it. Right now, the January, 2023 TSIS is approaching 1500 survey respondents. One caveat, it's not closed yet, it will close on Friday, but with an end that big we are over statistically significant. We also recently did a cloud survey, and there's a couple of key points on that I want to get into before we get into individual vendors. What we're seeing here, is that annual spend on cloud infrastructure is expected to grow at almost a 70% CAGR over the next three years. The percentage of those workloads for cloud infrastructure are expected to grow over 70% as three years as well. And as you mentioned, Azure and AWS are still dominant. However, we're seeing some share shift spreading around a little bit. Now to get into the individual vendors you mentioned about, yes, Azure is still number one, AWS is number two. What we're seeing, which is incredibly interesting, CloudFlare is number three. It's actually beating GCP. That's the first time we've seen it. What I do want to state, is this is on net score only, which is our measure of spending intentions. When you talk about actual pervasion in the enterprise, it's not even close. But from a spending velocity intention point of view, CloudFlare is now number three above GCP, and even Salesforce is creeping up to be at GCPs level. So what we're seeing here, is a continued domination by Azure and AWS, but some of these other players that maybe might fit into your moniker. And I definitely want to talk about CloudFlare more in a bit, but I'm going to stop there. But what we're seeing is some of these other players that fit into your Supercloud moniker, are starting to creep up, Dave. >> Yeah, I just want to clarify. So as you also know, we track IaaS and PaaS revenue and we try to extract, so AWS reports in its quarterly earnings, you know, they're just IaaS and PaaS, they don't have a SaaS play, a little bit maybe, whereas Microsoft and Google include their applications and so we extract those out and if you do that, AWS is bigger, but in the surveys, you know, customers, they see cloud, SaaS to them as cloud. So that's one of the reasons why you see, you know, Microsoft as larger in pervasion. If you bring up that survey again, Alex, the survey results, you see them further to the right and they have higher spending momentum, which is consistent with what you see in the earnings calls. Now, interesting about CloudFlare because the CEO of CloudFlare actually, and CloudFlare itself uses the term supercloud basically saying, "Hey, we're building a new type of internet." So what are your thoughts? Do you have additional information on CloudFlare, Erik that you want to share? I mean, you've seen them pop up. I mean this is a really interesting company that is pretty forward thinking and vocal about how it's disrupting the industry. >> Sure, we've been tracking 'em for a long time, and even from the disruption of just a traditional CDN where they took down Akamai and what they're doing. But for me, the definition of a true supercloud provider can't just be one instance. You have to have multiple. So it's not just the cloud, it's networking aspect on top of it, it's also security. And to me, CloudFlare is the only one that has all of it. That they actually have the ability to offer all of those things. Whereas you look at some of the other names, they're still piggybacking on the infrastructure or platform as a service of the hyperscalers. CloudFlare does not need to, they actually have the cloud, the networking, and the security all themselves. So to me that lends credibility to their own internal usage of that moniker Supercloud. And also, again, just what we're seeing right here that their net score is now creeping above AGCP really does state it. And then just one real last thing, one of the other things we do in our surveys is we track adoption and replacement reasoning. And when you look at Cloudflare's adoption rate, which is extremely high, it's based on technical capabilities, the breadth of their feature set, it's also based on what we call the ability to avoid stack alignment. So those are again, really supporting reasons that makes CloudFlare a top candidate for your moniker of supercloud. >> And they've also announced an object store (chuckles) and a database. So, you know, that's going to be, it takes a while as you well know, to get database adoption going, but you know, they're ambitious and going for it. All right, let's bring the chart back up, and I want to focus Darren in on the ecosystem now, and really, we've identified Snowflake and Databricks, it's always fun to talk about those guys, and there are a number of other, you know, data platforms out there, but we use those too as really proxies for leaders. We got a bunch of the backup guys, the data protection folks, Rubric, Cohesity, and Veeam. They're sort of in a cluster, although Rubric, you know, ahead of those guys in terms of spending momentum. And then VMware, Tanzu and Red Hat as sort of the cross cloud platform. But I want to focus, Darren, on the data piece of it. We're seeing a lot of activity around data sharing, governed data sharing. Databricks is using Delta Sharing as their sort of place, Snowflakes is sort of this walled garden like the app store. What are your thoughts on, you know, in the context of Supercloud, cross cloud capabilities for the data platforms? >> Yeah, good question. You know, I think Databricks is an interesting player because they sort of have made some interesting moves, with their Data Lakehouse technology. So they're trying to kind of complicate, or not complicate, they're trying to take away the complications of, you know, the downsides of data warehousing and data lakes, and trying to find that middle ground, where you have the benefits of a managed, governed, you know, data warehouse environment, but you have sort of the lower cost, you know, capability of a data lake. And so, you know, Databricks has become really attractive, especially by data scientists, right? We've been tracking them in the AI machine learning sector for quite some time here at ETR, attractive for a data scientist because it looks and acts like a lake, but can have some managed capabilities like a warehouse. So it's kind of the best of both worlds. So in some ways I think you've seen sort of a data science driver for the adoption of Databricks that has now become a little bit more mainstream across the business. Snowflake, maybe the other direction, you know, it's a cloud data warehouse that you know, is starting to expand its capabilities and add on new things like Streamlit is a good example in the analytics space, with apps. So you see these tools starting to branch and creep out a bit, but they offer that sort of neutrality, right? We heard one IT decision maker we recently interviewed that referred to Snowflake and Databricks as the quote unquote Switzerland of what they do. And so there's this desirability from an organization to find these tools that can solve the complex multi-headed use-case of data and analytics, which every business unit needs in different ways. And figure out a way to do that, an elegant way that's governed and centrally managed, that federated kind of best of both worlds that you get by bringing the data close to the business while having a central governed instance. So these tools are incredibly powerful and I think there's only going to be room for growth, for those two especially. I think they're going to expand and do different things and maybe, you know, join forces with others and a lot of the power of what they do well is trying to define these connections and find these partnerships with other vendors, and try to be seen as the nice add-on to your existing environment that plays nicely with everyone. So I think that's where those two tools are going, but they certainly fit this sort of label of, you know, trying to be that supercloud neutral, you know, layer that unites everything. >> Yeah, and if you bring the graphic back up, please, there's obviously big data plays in each of the cloud platforms, you know, Microsoft, big database player, AWS is, you know, 11, 12, 15, data stores. And of course, you know, BigQuery and other, you know, data platforms within Google. But you know, I'm not sure the big cloud guys are going to go hard after so-called supercloud, cross-cloud services. Although, we see Oracle getting in bed with Microsoft and Azure, with a database service that is cross-cloud, certainly Google with Anthos and you know, you never say never with with AWS. I guess what I would say guys, and I'll I'll leave you with this is that, you know, just like all players today are cloud players, I feel like anybody in the business or most companies are going to be so-called supercloud players. In other words, they're going to have a cross-cloud strategy, they're going to try to build connections if they're coming from on-prem like a Dell or an HPE, you know, or Pure or you know, many of these other companies, Cohesity is another one. They're going to try to connect to their on-premise states, of course, and create a consistent experience. It's natural that they're going to have sort of some consistency across clouds. You know, the big question is, what's that spectrum look like? I think on the one hand you're going to have some, you know, maybe some rudimentary, you know, instances of supercloud or maybe they just run on the individual clouds versus where Snowflake and others and even beyond that are trying to go with a single global instance, basically building out what I would think of as their own cloud, and importantly their own ecosystem. I'll give you guys the last thought. Maybe you could each give us, you know, closing thoughts. Maybe Darren, you could start and Erik, you could bring us home on just this entire topic, the future of cloud and data. >> Yeah, I mean I think, you know, two points to make on that is, this question of these, I guess what we'll call legacy on-prem players. These, mega vendors that have been around a long time, have big on-prem footprints and a lot of people have them for that reason. I think it's foolish to assume that a company, especially a large, mature, multinational company that's been around a long time, it's foolish to think that they can just uproot and leave on-premises entirely full scale. There will almost always be an on-prem footprint from any company that was not, you know, natively born in the cloud after 2010, right? I just don't think that's reasonable anytime soon. I think there's some industries that need on-prem, things like, you know, industrial manufacturing and so on. So I don't think on-prem is going away, and I think vendors that are going to, you know, go very cloud forward, very big on the cloud, if they neglect having at least decent connectors to on-prem legacy vendors, they're going to miss out. So I think that's something that these players need to keep in mind is that they continue to reach back to some of these players that have big footprints on-prem, and make sure that those integrations are seamless and work well, or else their customers will always have a multi-cloud or hybrid experience. And then I think a second point here about the future is, you know, we talk about the three big, you know, cloud providers, the Google, Microsoft, AWS as sort of the opposite of, or different from this new supercloud paradigm that's emerging. But I want to kind of point out that, they will always try to make a play to become that and I think, you know, we'll certainly see someone like Microsoft trying to expand their licensing and expand how they play in order to become that super cloud provider for folks. So also don't want to downplay them. I think you're going to see those three big players continue to move, and take over what players like CloudFlare are doing and try to, you know, cut them off before they get too big. So, keep an eye on them as well. >> Great points, I mean, I think you're right, the first point, if you're Dell, HPE, Cisco, IBM, your strategy should be to make your on-premise state as cloud-like as possible and you know, make those differences as minimal as possible. And you know, if you're a customer, then the business case is going to be low for you to move off of that. And I think you're right. I think the cloud guys, if this is a real problem, the cloud guys are going to play in there, and they're going to make some money at it. Erik, bring us home please. >> Yeah, I'm going to revert back to our data and this on the macro side. So to kind of support this concept of a supercloud right now, you know Dave, you and I know, we check overall spending and what we're seeing right now is total year spent is expected to only be 4.6%. We ended 2022 at 5% even though it began at almost eight and a half. So this is clearly declining and in that environment, we're seeing the top two strategies to reduce spend are actually vendor consolidation with 36% of our respondents saying they're actively seeking a way to reduce their number of vendors, and consolidate into one. That's obviously supporting a supercloud type of play. Number two is reducing excess cloud resources. So when I look at both of those combined, with a drop in the overall spending reduction, I think you're on the right thread here, Dave. You know, the overall macro view that we're seeing in the data supports this happening. And if I can real quick, couple of names we did not touch on that I do think deserve to be in this conversation, one is HashiCorp. HashiCorp is the number one player in our infrastructure sector, with a 56% net score. It does multiple things within infrastructure and it is completely agnostic to your environment. And if we're also speaking about something that's just a singular feature, we would look at Rubric for data, backup, storage, recovery. They're not going to offer you your full cloud or your networking of course, but if you are looking for your backup, recovery, and storage Rubric, also number one in that sector with a 53% net score. Two other names that deserve to be in this conversation as we watch it move and evolve. >> Great, thank you for bringing that up. Yeah, we had both of those guys in the chart and I failed to focus in on HashiCorp. And clearly a Supercloud enabler. All right guys, we got to go. Thank you so much for joining us, appreciate it. Let's keep this conversation going. >> Always enjoy talking to you Dave, thanks. >> Yeah, thanks for having us. >> All right, keep it right there for more content from Supercloud 2. This is Dave Valente for John Ferg and the entire Cube team. We'll be right back. (gentle synth music) (music fades)

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

Upbeat music plays >> Voice Over: TheCUBE presents Ignite 22, brought to you by Palo Alto Networks. >> Good morning everyone. Welcome to theCUBE. Lisa Martin here with Dave Vellante. We are live at Palo Alto Networks Ignite. This is the 10th annual Ignite. There's about 3,000 people here, excited to really see where this powerhouse organization is taking security. Dave, it's great to be here. Our first time covering Ignite. People are ready to be back. They.. and security is top. It's a board level conversation. >> It is the other Ignite, I like to call it cuz of course there's another big company has a conference name Ignite, so I'm really excited to be here. Palo Alto Networks, a company we've covered for a number of years, as we just wrote in our recent breaking analysis, we've called them the gold standard but it's not just our opinion, we've backed it up with data. The company's on track. We think to do close to 7 billion in revenue by 2023. That's double it's 2020 revenue. You can measure it with execution, market cap M and A prowess. I'm super excited to have the CEO here. >> We have the CEO here, Nikesh Arora joins us from Palo Alto Networks. Nikesh, great to have you on theCube. Thank you for joining us. >> Well thank you very much for having me Lisa and Dave >> Lisa: It was great to see your keynote this morning. You said that, you know fundamentally security is a data problem. Well these days every company has to be a data company. Grocery stores, gas stations, car dealers. How is Palo Alto networks making customers, these data companies, more secure? >> Well Lisa, you know, (coughs) I've only done cybersecurity for about four, four and a half years so when I came to the industry I was amazed to see how security is so reactive as opposed to proactive. We should be able to stop bad threats, right? as they're happening. But I think a lot of threats get through because we don't have the right infrastructure and the right tooling and right products in there. So I think we've been working hard for the last four and a half years to turn it around so we can have consistent data flow across an enterprise and then mine that data for threats and anomalous behavior and try and protect our customers. >> You know the problem, I wrote this, this weekend, the problem in cybersecurity is well understood, you put up that Optiv graph and it's like 8,000 companies >> Yes >> and I think you mentioned your keynote on average, you know 30 to 40 tools, maybe 50, at least 20, >> Yes. >> from the folks that I talked to. So, okay, great, but actually solving that problem is not trivial. To be a consolidator, I mean, everybody wants to consolidate tools. So in your three to four years and security as you well know, it's, you can't fake security. It's a really, really challenging topic. So when you joined Palo Alto Networks and you heard that strategy, I know you guys have been thinking about this for some time, what did you see as the challenges to actually executing on that and how is it that you've been able to sort of get through that knot hole. >> So Dave, you know, it's interesting if you look at the history of cybersecurity, I call them the flavor of the decade, a flare, you know a new threat vector gets created, very large market gets created, a solution comes through, people flock, you get four or five companies will chase that opportunity, and then they become leaders in that space whether it's firewalls or endpoints or identity. And then people stick to their swim lane. The problem is that's a very product centric approach to security. It's not a customer-centric approach. The customer wants a more secure enterprise. They don't want to solve 20 different solutions.. problems with 20 different point solutions. But that's kind of how the industry's grown up, and it's been impossible for a large security company in one category, to actually have a substantive presence in the next category. Now what we've been able to do in the last four and a half years is, you know, from our firewall base we had resources, we had intellectual capability from a security perspective and we had cash. So we used that to pay off our technical debt. We acquired a bunch of companies, we created capability. In the last three years, four years we've created three incremental businesses which are all on track to hit a billion dollars the next 12 to 18 months. >> Yeah, so it's interesting on Twitter last night we had a little conversation about acquirers and who was a good, who was not so good. It was, there was Oracle, they came up actually very high, they'd done pretty, pretty good Job, VMware was on the list, IBM, Cisco, ServiceNow. And if you look at IBM and Cisco's strategy, they tend to be very services heavy, >> Mm >> right? How is it that you have been able to, you mentioned get rid of your technical debt, you invested in that. I wonder if you could, was it the, the Cloud, even though a lot of the Cloud was your own Cloud, was that a difference in terms of your ability to integrate? Because so many companies have tried it in the past. Oracle I think has done a good job, but it took 'em 10 to 12 years, you know, to, to get there. What was the sort of secret sauce? Is it culture, is it just great engineering? >> Dave it's a.. thank you for that. I think, look, it's, it's a mix of everything. First and foremost, you know, there are certain categories we didn't play in so there was nothing to integrate. We built a capability in a category in automation. We didn't have a product, we acquired a company. It's a net new capability in instant response. We didn't have a capability. It was net new capability. So there was, there was, other than integrating culturally and into the organization into our core to market processes there was no technical integration needed. Most of our technical integration was needed in our Cloud platform, which we bought five or six companies, we integrated then we just bought one recently called cyber security as well, which is going to get integrated in the Cloud platform. >> Dave: Yeah. >> And the thing is like, the Cloud platform is net new in the industry. We.. nobody's created a Cloud security platform yet, so we're working hard to create it because we don't want to replicate the mistakes of the past, that were made in enterprise security, in Cloud security. So it's a combination of cultural integration it's a combination of technical integration. The two things we do differently I think, than most people in the industry is look, we have no pride of, you know of innovations. Like, if somebody else has done it, we respect it and we'll acquire it, but we always want to acquire number one or number two in their category. I don't want number three or four. There's three or four for a reason and there still leaves one or two out there to compete with. So we've always acquired one or two, one. And the second thing, which is as important is most of these companies are in the early stage of development. So it's very important for the founding team to be around. So we spend a lot of time making sure they stick around. We actually make our people work for them. My principle is, listen, if they beat us in the open market with all our resources and our people, then they deserve to run this as opposed to us. So most of our new product categories are run by founders of companies required. >> So a little bit of Jack Welch, a little bit of Franks Lubens is a, you know always deference to the founders. But go ahead Lisa. >> Speaking of cultural transformation, you were mentioning your keynote this morning, there's been a significant workforce transformation at Palo Alto Networks. >> Yeah >> Talk a little bit about that, cause that's a big challenge, for many organizations to achieve. Sounds like you've done it pretty well. >> Well you know, my old boss, Eric Schmidt, used to say, 'revenue solves all known problems'. Which kind of, you know, it is a part joking, part true, but you know as Dave mentioned, we've doubled or two and a half time the revenues in the last four and a half years. That allows you to grow, that allows you to increase headcount. So we've gone from four and a half thousand people to 14,000 people. Good news is that's 9,500 people are net new to the company. So you can hire a whole new set of people who have new skills, new capabilities and there's some attrition four and a half thousand, some part of that turns over in four and a half years, so we effectively have 80% net new people, and the people we have, who are there from before, are amazing because they've built a phenomenal firewall business. So it's kind of been right sized across the board. It's very hard to do this if you're not growing. So you got to focus on growing. >> Dave: It's like winning in sports. So speaking of firewalls, I got to ask you does self-driving cars need brakes? So if I got a shout out to my friend Zeus Cararvela so like that's his line about why you need firewalls, right? >> Nikesh: Yes. >> I mean you mentioned it in your keynote today. You said it's the number one question that you get. >> and I don't get it why P industry observers don't go back and say that's, this is ridiculous. The network traffic is doubling or tripling. (clears throat) In fact, I gave an interesting example. We shut down our data centers, as I said, we are all on Google Cloud and Amazon Cloud and then, you know our internal team comes in, we'd want a bigger firewall. I'm like, why do you want a bigger firewall? We shut down our data centers as well. The traffic coming in and out of our campus is doubled. We need a bigger firewall. So you still need a firewall even if you're in the Cloud. >> So I'm going to come back to >> Nikesh: (coughs) >> the M and A strategy. My question is, can you be both best of breed and develop a comprehensive suite number.. part one and part one A of that is do you even have to, because generally sweets win out over best of breed. But what, how do you, how do you respond? >> Well, you know, this is this age old debate and people get trapped in that, I think in my mind, and let me try and expand the analogy which I tried to do up in my keynote. You know, let's assume that Oracle, Microsoft, Dynamics and Salesforce did not exist, okay? And you were running a large company of 50,000 people and your job was to manage the customer process which easier to understand than security. And I said, okay, guess what? I have a quoting system and a lead system but the lead system doesn't talk to my coding system. So I get leads, but I don't know who those customers. And I write codes for a whole new set of customers and I have a customer database. Then when they come as purchase orders, I have a new database with all the customers who've bought something from me, and then when I go get them licensing I have a new database and when I go have customer support, I have a fifth database and there are customers in all five databases. You'll say Nikesh you're crazy, you should have one customer database, otherwise you're never going to be able to make this work. But security is the same problem. >> Dave: Mm I should.. I need consistency in data from suit to nuts. If it's in Cloud, if you're writing code, I need to understand the security flaws before they go into deployment, before they go into production. We for somehow ridiculously have bought security like IT. Now the difference between IT and security is, IT is required to talk to each other, so a Dell server and HP server work very similarly but a Palo Alto firewall and a Checkpoint firewall Fortnight firewall work formally differently. And then how that transitions into endpoints is a whole different ball game. So you need consistency in data, as Lisa was saying earlier, it's a data problem. You need consistency as you traverse to the enterprise. And that's why that's the number one need. Now, when you say best of breed, (coughs) best of breed, if it's fine, if it's a specific problem that you're trying to solve. But if you're trying to make sure that's the data flow that happens, you need both best of breed, you know, technology that stops things and need integration on data. So what we are trying to do is we're trying to give people best to breed solutions in the categories they want because otherwise they won't buy us. But we're also trying to make sure we stitch the data. >> But that definition of best of breed is a little bit of nuance than different in security is what I'm hearing because that consistency >> Nikesh: (coughs) Yes, >> across products. What about across Cloud? You mentioned Google and Amazon. >> Yeah so that's great question. >> Dave: Are you building the security super Cloud, I call it, above the Cloud? >> It's, it's not, it's, less so a super Cloud, It's more like Switzerland and I used to work at Google for 10 years, not a secret. And we used to sell advertising and we decided to go into pub into display ads or publishing, right. Now we had no publishing platform so we had to be good at everybody else's publishing platform >> Dave: Mm >> but we never were able to search ads for everybody else because we only focus on our own platform. So part of it is when the Cloud guys they're busy solving security for their Cloud. Google is not doing anything about Amazon Cloud or Microsoft Cloud, Microsoft's Azure, right? AWS is not doing anything about Google Cloud or Azure. So what we do is we don't have a Cloud. Our job in providing Cloud securities, be Switzerland make sure it works consistently across every Cloud. Now if you try to replicate what we offer Prisma Cloud, by using AWS, Azure and GCP, you'd have to first of all, have three panes of glass for all three of them. But even within them they have four panes of glass for the capabilities we offer. So you could end up with 12 different interfaces to manage a development process, we give you one. Now you tell me which is better. >> Dave: Sounds like a super Cloud to me Lisa (laughing) >> He's big on super Cloud >> Uber Cloud, there you >> Hey I like that, Uber Cloud. Well, so I want to understand Nikesh, what's realistic. You mentioned in your keynote Dave, brought it up that the average organization has 30 to 50 tools, security tools. >> Nikesh: Yes, yes >> On their network. What is realistic for from a consolidation perspective where Palo Alto can come in and say, let me make this consistent and simple for you. >> Well, I'll give you your own example, right? (clears throat) We're probably sub 10 substantively, right? There may be small things here and there we do. But on a substantive protecting the enterprise perspective you be should be down to eight or 10 vendors, and that is not perfect but it's a lot better than 50, >> Lisa: Right? >> because don't forget 50 tools means you have to have capability to understand what those 50 tools are doing. You have to have the capability to upgrade them on a constant basis, learn about their new capabilities. And I just can't imagine why customers have two sets of firewalls right. Now you got to learn both the files on how to deploy both them. That's silly because that's why we need 7 million more people. You need people to understand, so all these tools, who work for companies. If you had less tools, we need less people. >> Do you think, you know I wrote about this as well, that the security industry is anomalous and that the leader has, you know, single digit, low single digit >> Yes >> market shares. Do you think that you can change that? >> Well, you know, when I started that was exactly the observation I had Dave, which you highlighted in your article. We were the largest by revenue, by small margin. And we were one and half percent of the industry. Now we're closer to three, three to four percent and we're still at, you know, like you said, going to be around $7 billion. So I see a path for us to double from here and then double from there, and hopefully as we keep doubling and some point in time, you know, I'd like to get to double digits to start with. >> One of the things that I think has to happen is this has to grow dramatically, the ecosystem. I wonder if you could talk about the ecosystem and your strategy there. >> Well, you know, it's a matter of perspective. I think we have to get more penetrated in our largest customers. So we have, you know, 1800 of the top 2000 customers in the world are Palo Alto customers. But we're not fully penetrated with all our capabilities and the same customers set, so yes the ecosystem needs to grow, but the pandemic has taught us the ecosystem can grow wherever they are without having to come to Vegas. Which I don't think is a bad thing to be honest. So the ecosystem is growing. You are seeing new players come to the ecosystem. Five years ago you didn't see a lot of systems integrators and security. You didn't see security offshoots of telecom companies. You didn't see the Optivs, the WWTs, the (indistinct) of the world (coughs) make a concerted shift towards consolidation or services and all that is happening >> Dave: Mm >> as we speak today in the audience you will find people from Google, Amazon Microsoft are sitting in the audience. People from telecom companies are sitting in the audience. These people weren't there five years ago. So you are seeing >> Dave: Mm >> the ecosystem's adapting. They're, they want to be front and center of solving the customer's problem around security and they want to consolidate capability, they need. They don't want to go work with a hundred vendors because you know, it's like, it's hard. >> And the global system integrators are key. I always say they like to eat at the trough and there's a lot of money in security. >> Yes. >> Dave: (laughs) >> Well speaking of the ecosystem, you had Thomas Curry and Google Cloud CEO in your fireside chat in the keynote. Talk a little bit about how Google Cloud plus Palo Alto Networks, the Zero Trust Partnership and what it's enable customers to achieve. >> Lisa, that's a great question. (clears his throat) Thank you for bringing it up. Look, you know the, one of the most fundamental shifts that is happening is obviously the shift to the Cloud. Now when that shift fully, sort of, takes shape you will realize if your network has changed and you're delivering everything to the Cloud you need to go figure out how to bring the traffic to the Cloud. You don't have to bring it back to your data center you can bring it straight to the Cloud. So in that context, you know we use Google Cloud and Amazon Cloud, to be able to carry our traffic. We're going from a product company to a services company in addition, right? Cuz when we go from firewalls to SASE we're not carrying your traffic. When we carry our traffic, we need to make sure we have underlying capability which is world class. We think GCP and AWS and Azure run some of the biggest and best networks in the world. So our partnership with Google is such that we use their public Cloud, we sit on top of their Cloud, they give us increased enhanced functionality so that our customers SASE traffic gets delivered in priority anywhere in the world. They give us tooling to make sure that there's high reliability. So you know, we partner, they have Beyond Corp which is their version of Zero Trust which allows you to take unmanaged devices with browsers. We have SASE, which allows you to have managed devices. So the combination gives our collective customers the ability for Zero Trust. >> Do you feel like there has to be more collaboration within the ecosystem, the security, you know, landscape even amongst competitors? I mean I think about Google acquires Mandiant. You guys have Unit 42. Should and will, like, Wendy Whitmore and maybe they already are, Kevin Mandia talk more and share more data. If security's a data problem is all this data >> Nikesh: Yeah look I think the industry shares threat data, both in private organizations as well as public and private context, so that's not a problem. You know the challenge with too much collaboration in security is you never know. Like you know, the moment you start sharing your stuff at third parties, you go out of Secure Zone. >> Lisa: Mm >> Our biggest challenge is, you know, I can't trust a third party competitor partner product. I have to treat it with as much suspicion as anything else out there because the only way I can deliver Zero Trust is to not trust anything. So collaboration in Zero Trust are a bit of odds with each other. >> Sounds like another problem you can solve >> (laughs) >> Nikesh last question for you. >> Yes >> Favorite customer or example that you think really articulates the value of what Palo Alto was delivering? >> Look you know, it's a great question, Lisa. I had this seminal conversation with a customer and I explained all those things we were talking about and the customer said to me, great, okay so what do I need to do? I said, fun, you got to trust me because you know, we are on a journey, because in the past, customers have had to take the onus on themselves of integrating everything because they weren't sure a small startup will be independent, be bought by another cybersecurity company or a large cybersecurity company won't get gobbled up and split into pieces by private equity because every one of the cybersecurity companies have had a shelf life. So you know, our aspiration is to be the evergreen cybersecurity company. We will always be around and we will always tackle innovation and be on the front line. So the customer understood what we're doing. Over the last three years we've been working on a transformation journey with them. We're trying to bring them, or we have brought them along the path of Zero Trust and we're trying to work with them to deliver this notion of reducing their meantime to remediate from days to minutes. Now that's an outcome based approach that's a partnership based approach and we'd like, love to have more and more customers of that kind. I think we weren't ready to be honest as a company four and a half years ago, but I think today we're ready. Hence my keynote was called The Perfect Storm. I think we're at the right time in the industry with the right capabilities and the right ecosystem to be able to deliver what the industry needs. >> The perfect storm, partners, customers, investors, employees. Nikesh, it's been such a pleasure having you on theCUBE. Thank you for coming to talk to Dave and me right after your keynote. We appreciate that and we look forward to two days of great coverage from your executives, your customers, and your partners. Thank you. >> Well, thank you for having me, Lisa and Dave and thank you >> Dave: Pleasure >> for what you guys do for our industry. >> Our pleasure. For Nikesh Arora and Dave Vellante, I'm Lisa Martin, you're watching theCUBE live at MGM Grand Hotel in Las Vegas, Palo Alto Ignite 22. Stick around Dave and I will be joined by our next guest in just a minute. (cheerful music plays out)

(bright upbeat music) >> Welcome back everyone to theCUBE's live coverage here in Las Vegas for wall-to-wall coverage. It is re:Invent 2022, our 10th year with theCUBE. Dave and I started this journey 10 years ago here at re:Invent. There are two sets, here, a set upstairs. Great content, I'm here with Paul Gillin, my cohost. Paul's out reporting on the floor, doing some interviews. Paul, what do you think so far? It's pretty crazy activity going on here. >> Well, the activity hasn't declined at all. I mean here we are in day three of the show and it's just as busy out there as it was in day one. And there's just an energy here that you can feel, it's palpable. There is a lot of activity around developers, a lot around data. Which actually brings us a good segue into our next guest because one of the leaders in data management in the cloud is MariaDB. And John Bakke is the CRO at MariaDB, and here to talk to us about your cloud version and how open source is going for you. >> Yeah, thanks for having me. >> Paul: Thanks for joining us. >> To get the update on the product, what do you guys do on the relation to AWS? How's that going? Give us a quick update. >> In the relational database? >> No, no. The relationship with AWS >> Oh, with AWS? >> And SkySQL, what's the update? >> There's no relationship that we have that's more important than the AWS relationship. We're building our cloud, our premier cloud service called SkySQL on AWS. And they offer the best in class infrastructure for a SaaS company to build what they're building. And for us, it's a database service, right? And then beyond that, they help you from the business side, right? They try to get you lined up in the marketplace and make it possible for you to work best with customers. And then from a customer perspective, they're super helpful in not only finding prospective customers, but making that customer successful. 'Cause everybody's got a vested interest in the outcome. Right? >> Yeah, a little tongue twister there. Relational data-based relationship. We've got relational databases, we've got unstructured, data is at the center of the value proposition. Swami's keynote today and the Adam CEO's keynote, data and security dominated the keynotes >> John: Yes. >> and the conversations. So, this is real. The customers are really wanting to accelerate the developer experience, >> John: Yep. >> Developer pipe lining, more code faster, more horsepower under the hood. But this data conversation, it just never goes away. The world's keeping on coming around. >> John: It never goes away. I've been in this business for almost 30 years and we're still talking about the same key factors, right? Reliability, availability, performance, security. These things are pervasive in the data management because it's such a critical aspect to success. >> Yeah, in this case of SkySQL, you have both a transactional and an analytical engine in one. >> John: That's correct. >> Right? >> John: Yep. >> And that was a, what has the customer adoption been like of that hybrid, or I guess not a hybrid, but a dual function? >> Yeah. So the thing that makes that important is that instead of having siloed services, you have integrated data services. And a lot of times when you ask a question that's analytical it might depend on a transaction. And so, that makes the entire experience best for the developer, right? So, to take that further, we also, in SkySQL, offer a geospatial offering that integrates with all of that. And then we even take it further than that with distributed database with Xpand or ready to be Xpand. >> A lot of discussion. Geospatial announcement today on stage, just the diversity of data, and your experience in the industry. There's not the one database that rule them all anymore. There's a lot of databases out there. How are customers dealing with, I won't say database for all, 'Cause you need databases. And then you've got real time transactional, you got batch going on, you got streaming data, all kinds of data use cases now, all kind of having to be rolled together. What's your reaction? What's your take on the state of data and databases? >> Yeah, yeah, yeah. So when I started in this business, there were four databases, and now there's 400 databases. And the best databases really facilitate great application development. So having as many of those services in real time or in analytics as possible means that you are a database for everyone or for all users, right? And customers don't want to use multiple databases. Sometimes they feel like they're forced to do that, but if you're like MariaDB, then you offer all of those capabilities in an integrated way that makes the developer move faster. >> Amazon made a number of announcements this morning in the data management area, including geospatial support on RDS, I believe. How do you, I guess, coordinate yourself, your sales message with their sales message, given that you are partners, but they are competing with you in some ways? >> Yeah, there's always some cooperatition, I guess, that happens with AWS in the various product silos that they're offering their customers. For us, we're one of thousands of obviously partners that they have. And we're out there trying to do what our customers want, which is to have those services integrated, not glued together with a variety of different integration software. We want it integrated in the service so that it's one data provision, data capability for the application developer. It makes for a better experience for the developer in the end. >> On the customer side, what's the big activity? I mean, you got the on-premises database, you've got the cloud. When should a customer decide, or what's the signals to them that they should either move to the cloud, or change, be distributed? What are some of the forcing functions? What does the mark look like? >> Yeah, I've come a long way on this, but my opinion is that every customer should be in the cloud. And the reason simply is the economies that are involved, the pace of execution, the resilience and dependability of the cloud, Amazon being the leader in that space. So if you were to ask me, right now is the time to be in SkySQL because it's the premier data service in the cloud. So I would take my customer out of their on-prem and put them all in AWS, on SkySQL, if I could. Not everybody's ready for that, but my opinion is that the security is there, the reliability, the privacy, all of the things that maybe are legacy concerns, it's all been proven to be adequate and probably even better because of all of the economies of scale that you get out of being in the cloud just generally. >> Now, MariaDB, you started on-premise though. You still have a significant customer base on-premise. What, if anything are you doing to encourage them to migrate to the cloud? >> Well, so we have hundreds and hundreds of customers as MariaDB, and we weren't the first database company to put their database in the cloud, but watching it unfold helped us realize that we're going to put MariaDB in its best form factor in SkySQL. It's the only place you could get the enterprise version of MariaDB in a cloud service, right? So when we look at our customers on-prem, we're constantly telling them, obviously, that we have a cloud service. When they subscribe, we show them the efficiencies and the economies, and we do get customers that are moving. We had a customer go to Telefonica over in the UK that moved from an on-premise to manage their wifi services across Europe. And they're very happy. They were one of our very first SkySQL customers. And that has routinely proven itself to be a path towards not only a better operation for the customer, they're up more, they have fewer outages because they're not inflicting their own self wounds that they have in their own data center. They're running on world class infrastructure on world class databases. >> What are some of those self wounds? Is it personnel, kind of manual mistakes, just outages, reliability? What's the real cause, and then what's the benefit alternative in the cloud that is outside? >> Yeah. I mean, I think, when you repeat the same database implementation over and over on the infrastructure, it gets tested thousands and thousands of times. Whereas if I'm a database team and I install it once, I've tested it one time, and I can't account for all of the things that might happen in the future. So the benefit of the cloud is that you just get that repeat ability that happens and all of the sort of the kinks and bugs and issues are worked out of the system. And that's why it's just fundamentally better. We get 99.9999% uptime because all of those mistakes have been made, solved, and fixed. >> Fully managed, obviously. >> Yes. Right. >> Huge benefit. >> John: Right. >> And people are moving, it's just a great benefit. >> John: Yeah. >> So I'm a fan obviously. I think it's a great way to go. I got to ask about the security though, because big conversation here is security. What's the security posture? What's the security story to customers with SkySQL and MariaDB? >> Right, right, right. So we've taken the server, which was the initial product that MariaDB was founded upon, right? And we've come a long way over the several years that we've been in business. In SkySQL, we have SOC 2 compliance, for example. So we've gone through commercial certifications to make sure that customers can depend that we are following processes, we have technology in place in order to secure and protect their data. And in that environment, it is repeatable. So every time a customer uses our DBaaS infrastructure, databases a service infrastructure called SkySQL, they're benefiting from all of the testing that's been done. They go there and do that themselves, they would've to go through months and months of processes in order to reach the same level of protection. >> Now MariaDB is distributed by design. Is that right? >> Yes. So we have a distributed database, it's called Xpand, MariaDB Xpand. And it's an option inside of SkySQL. It's the same cost as MariaDB server, but Xpand is distributed. And the easiest way to understand what distributed database is is to understand what it is not first. What it is not is like every other cloud database. So most of the databases strangely in the cloud are not distributed databases. They have one single database node in a cluster that is where all of the changes and rights happen. And that creates a bottleneck in the database. And that's why there's difficulties in scale. AWS actually talked about this in the keynote which is the difficulty around multi writer in the cloud. And that's what Xpand does. And it spreads out the reads and the rights to make it scalable, more performant, and more resilient. One node goes down, still stays up, but you get the benefit of the consistency and the parallelization that happens in Xpand. >> So when would a customer choose Xpand versus SkySQL Vanilla? >> So we have, I would say a lot of times, but the profile of our customers are typically like financial services, trade stores. We have Samsung Cloud, 500,000 transactions per second in an expand cluster where they run sort of their Samsung cloud for their mobile device unit. We have many customers like that where it's a commercial facing website often or a service where the brand depends on uptime. Okay. So if you're in exchange or if you are a mobile device company or an IOT company, you need those databases to be working all the time and scale broadly and have high performance. >> So you have resiliency built in essentially? >> Yes, yeah. And that's the major benefit of it. It hasn't been solved by anybody other than us in the cloud to be quite honest with you. >> That's a differentiator for sure. >> It is a huge differentiator, and there are a lot of interested parties. We're going to see that be the next discussion probably next year when we come back is, what's the state of distributed database? Because it's really become really the tip of the spear with the database industry right now. >> And what's the benefits of that? Just quickly describe why that's important? >> Obviously the performance and the resilience are the two we just talked about, but also the efficiency. So if you have a multi-node cluster of a single master database, that gets replicated four times, five times over, five times the cost. And so we're taking cost out, adding performance in. And so, you're really seeing a revolution there because you're getting a lot more for a lot less. And whenever you do that, you win the game. Right? >> Awesome. Yeah, that's true. And it seems like, okay, that might be more costly but you're not replicating. >> That's right. >> That's the key. >> Replicating just enough to be resilient but not excessively to be overly redundant. Right. >> Yeah. I find that the conversation this year is starting to unpack some of these cloud native embedded capabilities inside AWS. So are you guys doing more around, on the customer side, around marketplace? Are you guys, how do people consume products? >> Yeah. It's really both. So sometimes they come to us from AWS. AWS might say, "Hey, you know what," "we don't really have an answer." And that's specifically true on the expand side. They don't really have that in their list of databases yet. Right. Hopefully, we'll get out in front of them. But they oftentimes come through our front door where they're a MariaDB customer already, right? There's over a hundred thousand production systems with MariaDB in the world, and hundreds of thousands of users of the database. So they know our brand, not quite as well as AWS, but they know our brand... >> You've got a customer base. >> We do. Right. I mean people love MariaDB. They just think it's the database that they use for application development all the time. And when they see us release an offering like Xpand just a few years ago, they're interested, they want to use that. They want to see how that works. And then when they take it into production and it works as advertised, of course, success happens. Right? >> Well great stuff, John. Great to have you on theCUBE. Paul, I guess time we do the Insta challenge here. New format on theCUBE, we usually say at the end, summarize what's most important story for you or show, what's the bumper sticker? We kind of put it around more of an Instagram reel. What's your sizzle reel? What's your thought leadership statement? 30 seconds >> John: Thought leadership. >> John? >> So the thought leadership is really in scaling the cloud to the next generation. We believe MariaDB's Xpand product will be the the technology that fronts the next wave of database solutions in the cloud. And AWS has become instrumental in helping us do that with their infrastructure and all the help that they give us, I think at the end of the day, when the story on Xpand is written, it's going to be a very fun ride over the next few years. >> John, thank you. CRO, chief revenue officer of MariaDB, great to have you on. >> Thank you. >> 34-year veteran or so in databases. (laughs) >> You're putting a lot of age on me. I'm 29. I'm 29 again. (all laugh) >> I just graduated high school and I've been doing this for 10 years. Great to have you on theCUBE. Thanks for coming on. >> Thanks guys. Yeah. >> Thanks for sharing. >> Appreciate it. >> I'm John Furrier with Paul Gillin here live on the floor, wall-to-wall coverage. We're already into like 70 videos already. Got a whole another day, finish out day three. Keep watching theCUBE, thanks for watching. We'll be right back. (calm music)

(soft techno upbeat music) >> Hi there. Welcome back to Las Vegas. This is Dave Villante with Paul Gillon. Reinvent day one and a half. We started last night, Monday, theCUBE after dark. Now we're going wall to wall. Today. Today was of course the big keynote, Adam Selipsky, kind of the baton now handing, you know, last year when he did his keynote, he was very new. He was sort of still getting his feet wet and finding his guru swing. Settling in a little bit more this year, learning a lot more, getting deeper into the tech, but of course, sharing the love with other leaders like Peter DeSantis. Tomorrow's going to be Swamy in the keynote. Adrian Cockcroft is here. Former AWS, former network Netflix CTO, currently an analyst. You got your own firm now. You're out there. Great to see you again. Thanks for coming on theCUBE. >> Yeah, thanks. >> We heard you on at Super Cloud, you gave some really good insights there back in August. So now as an outsider, you come in obviously, you got to be impressed with the size and the ecosystem and the energy. Of course. What were your thoughts on, you know what you've seen so far, today's keynotes, last night Peter DeSantis, what stood out to you? >> Yeah, I think it's great to be back at Reinvent again. We're kind of pretty much back to where we were before the pandemic sort of shut it down. This is a little, it's almost as big as the, the largest one that we had before. And everyone's turned up. It just feels like we're back. So that's really good to see. And it's a slightly different style. I think there were was more sort of video production things happening. I think in this keynote, more storytelling. I'm not sure it really all stitched together very well. Right. Some of the stories like, how does that follow that? So there were a few things there and some of there were spelling mistakes on the slides, you know that ELT instead of ETL and they spelled ZFS wrong and something. So it just seemed like there was, I'm not quite sure just maybe a few things were sort of rushed at the last minute. >> Not really AWS like, was it? It's kind of remind the Patriots Paul, you know Bill Belichick's teams are fumbling all over the place. >> That's right. That's right. >> Part of it may be, I mean the sort of the market. They have a leader in marketing right now but they're going to have a CMO. So that's sort of maybe as lack of a single threaded leader for this thing. Everything's being shared around a bit more. So maybe, I mean, it's all fixable and it's mine. This is minor stuff. I'm just sort of looking at it and going there's a few things that looked like they were not quite as good as they could have been in the way it was put together. Right? >> But I mean, you're taking a, you know a year of not doing Reinvent. Yeah. Being isolated. You know, we've certainly seen it with theCUBE. It's like, okay, it's not like riding a bike. You know, things that, you know you got to kind of relearn the muscle memories. It's more like golf than is bicycle riding. >> Well I've done AWS keynotes myself. And they are pretty much scrambled. It looks nice, but there's a lot of scrambling leading up to when it actually goes. Right? And sometimes you can, you sometimes see a little kind of the edges of that, and sometimes it's much more polished. But you know, overall it's pretty good. I think Peter DeSantis keynote yesterday was a lot of really good meat there. There was some nice presentations, and some great announcements there. And today I was, I thought I was a little disappointed with some of the, I thought they could have been more. I think the way Andy Jesse did it, he crammed more announcements into his keynote, and Adam seems to be taking sort of a bit more of a measured approach. There were a few things he picked up on and then I'm expecting more to be spread throughout the rest of the day. >> This was more poetic. Right? He took the universe as the analogy for data, the ocean for security. Right? The Antarctic was sort of. >> Yeah. It looked pretty, >> yeah. >> But I'm not sure that was like, we're not here really to watch nature videos >> As analysts and journalists, You're like, come on. >> Yeah, >> Give it the meat >> That was kind the thing, yeah, >> It has always been the AWS has always been Reinvent has always been a shock at our approach. 100, 150 announcements. And they're really, that kind of pressure seems to be off them now. Their position at the top of the market seems to be unshakeable. There's no clear competition that's creeping up behind them. So how does that affect the messaging you think that AWS brings to market when it doesn't really have to prove that it's a leader anymore? It can go after maybe more of the niche markets or fix the stuff that's a little broken more fine tuning than grandiose statements. >> I think so AWS for a long time was so far out that they basically said, "We don't think about the competition, we are listen to the customers." And that was always the statement that works as long as you're always in the lead, right? Because you are introducing the new idea to the customer. Nobody else got there first. So that was the case. But in a few areas they aren't leading. Right? You could argue in machine learning, not necessarily leading in sustainability. They're not leading and they don't want to talk about some of these areas and-- >> Database. I mean arguably, >> They're pretty strong there, but the areas when you are behind, it's like they kind of know how to play offense. But when you're playing defense, it's a different set of game. You're playing a different game and it's hard to be good at both. I think and I'm not sure that they're really used to following somebody into a market and making a success of that. So there's something, it's a little harder. Do you see what I mean? >> I get opinion on this. So when I say database, David Foyer was two years ago, predicted AWS is going to have to converge somehow. They have no choice. And they sort of touched on that today, right? Eliminating ETL, that's one thing. But Aurora to Redshift. >> Yeah. >> You know, end to end. I'm not sure it's totally, they're fully end to end >> That's a really good, that is an excellent piece of work, because there's a lot of work that it eliminates. There's are clear pain points, but then you've got sort of the competing thing, is like the MongoDB and it's like, it's just a way with one database keeps it simple. >> Snowflake, >> Or you've got on Snowflake maybe you've got all these 20 different things you're trying to integrate at AWS, but it's kind of like you have a bag of Lego bricks. It's my favorite analogy, right? You want a toy for Christmas, you want a toy formula one racing car since that seems to be the theme, right? >> Okay. Do you want the fully built model that you can play with right now? Or do you want the Lego version that you have to spend three days building. Right? And AWS is the Lego technique thing. You have to spend some time building it, but once you've built it, you can evolve it, and you'll still be playing those are still good bricks years later. Whereas that prebuilt to probably broken gathering dust, right? So there's something about having an vulnerable architecture which is harder to get into, but more durable in the long term. And so AWS tends to play the long game in many ways. And that's one of the elements that they do that and that's good, but it makes it hard to consume for enterprise buyers that are used to getting it with a bow on top. And here's the solution. You know? >> And Paul, that was always Andy Chassy's answer to when we would ask him, you know, all these primitives you're going to make it simpler. You see the primitives give us the advantage to turn on a dime in the marketplace. And that's true. >> Yeah. So you're saying, you know, you take all these things together and you wrap it up, and you put a snowflake on top, and now you've got a simple thing or a Mongo or Mongo atlas or whatever. So you've got these layered platforms now which are making it simpler to consume, but now you're kind of, you know, you're all stuck in that ecosystem, you know, so it's like what layer of abstractions do you want to tie yourself to, right? >> The data bricks coming at it from more of an open source approach. But it's similar. >> We're seeing Amazon direct more into vertical markets. They spotlighted what Goldman Sachs is doing on their platform. They've got a variety of platforms that are supposedly targeted custom built for vertical markets. How do successful do you see that play being? Is this something that the customers you think are looking for, a fully integrated Amazon solution? >> I think so. There's usually if you look at, you know the MongoDB or data stacks, or the other sort of or elastic, you know, they've got the specific solution with the people that really are developing the core technology, there's open source equivalent version. The AWS is running, and it's usually maybe they've got a price advantage or it's, you know there's some data integration in there or it's somehow easier to integrate but it's not stopping those companies from growing. And what it's doing is it's endorsing that platform. So if you look at the collection of databases that have been around over the last few years, now you've got basically Elastic Mongo and Cassandra, you know the data stacks as being endorsed by the cloud vendors. These are winners. They're going to be around for a very long time. You can build yourself on that architecture. But what happened to Couch base and you know, a few of the other ones, you know, they don't really fit. Like how you going to bait? If you are now becoming an also ran, because you didn't get cloned by the cloud vendor. So the customers are going is that a safe place to be, right? >> But isn't it, don't they want to encourage those partners though in the name of building the marketplace ecosystem? >> Yeah. >> This is huge. >> But certainly the platform, yeah, the platform encourages people to do more. And there's always room around the edge. But the mainstream customers like that really like spending the good money, are looking for something that's got a long term life to it. Right? They're looking for a long commitment to that technology and that it's going to be invested in and grow. And the fact that the cloud providers are adopting and particularly AWS is adopting some of these technologies means that is a very long term commitment. You can base, you know, you can bet your future architecture on that for a decade probably. >> So they have to pick winners. >> Yeah. So it's sort of picking winners. And then if you're the open source company that's now got AWS turning up, you have to then leverage it and use that as a way to grow the market. And I think Mongo have done an excellent job of that. I mean, they're top level sponsors of Reinvent, and they're out there messaging that and doing a good job of showing people how to layer on top of AWS and make it a win-win both sides. >> So ever since we've been in the business, you hear the narrative hardware's going to die. It's just, you know, it's commodity and there's some truth to that. But hardware's actually driving good gross margins for the Cisco's of the world. Storage companies have always made good margins. Servers maybe not so much, 'cause Intel sucked all the margin out of it. But let's face it, AWS makes most of its money. We know on compute, it's got 25 plus percent operating margins depending on the seasonality there. What do you think happens long term to the infrastructure layer discussion? Okay, commodity cloud, you know, we talk about super cloud. Do you think that AWS, and the other cloud vendors that infrastructure, IS gets commoditized and they have to go up market or you see that continuing I mean history would say that still good margins in hardware. What are your thoughts on that? >> It's not commoditizing, it's becoming more specific. We've got all these accelerators and custom chips now, and this is something, this almost goes back. I mean, I was with some micro systems 20,30 years ago and we developed our own chips and HP developed their own chips and SGI mips, right? We were like, the architectures were all squabbling of who had the best processor chips and it took years to get chips that worked. Now if you make a chip and it doesn't work immediately, you screwed up somewhere right? It's become the technology of building these immensely complicated powerful chips that has become commoditized. So the cost of building a custom chip, is now getting to the point where Apple and Amazon, your Apple laptop has got full custom chips your phone, your iPhone, whatever and you're getting Google making custom chips and we've got Nvidia now getting into CPUs as well as GPUs. So we're seeing that the ability to build a custom chip, is becoming something that everyone is leveraging. And the cost of doing that is coming down to startups are doing it. So we're going to see many, many more, much more innovation I think, and this is like Intel and AMD are, you know they've got the compatibility legacy, but of the most powerful, most interesting new things I think are going to be custom. And we're seeing that with Graviton three particular in the three E that was announced last night with like 30, 40% whatever it was, more performance for HPC workloads. And that's, you know, the HPC market is going to have to deal with cloud. I mean they are starting to, and I was at Supercomputing a few weeks ago and they are tiptoeing around the edge of cloud, but those supercomputers are water cold. They are monsters. I mean you go around supercomputing, there are plumbing vendors on the booth. >> Of course. Yeah. >> Right? And they're highly concentrated systems, and that's really the only difference, is like, is it water cooler or echo? The rest of the technology stack is pretty much off the shelf stuff with a few tweets software. >> You point about, you know, the chips and what AWS is doing. The Annapurna acquisition. >> Yeah. >> They're on a dramatically different curve now. I think it comes down to, again, David Floyd's premise, really comes down to volume. The arm wafer volumes are 10 x those of X 86, volume always wins. And the economics of semis. >> That kind of got us there. But now there's also a risk five coming along if you, in terms of licensing is becoming one of the bottlenecks. Like if the cost of building a chip is really low, then it comes down to licensing costs and do you want to pay the arm license And the risk five is an open source chip set which some people are starting to use for things. So your dis controller may have a risk five in it, for example, nowadays, those kinds of things. So I think that's kind of the the dynamic that's playing out. There's a lot of innovation in hardware to come in the next few years. There's a thing called CXL compute express link which is going to be really interesting. I think that's probably two years out, before we start seeing it for real. But it lets you put glue together entire rack in a very flexible way. So just, and that's the entire industry coming together around a single standard, the whole industry except for Amazon, in fact just about. >> Well, but maybe I think eventually they'll get there. Don't use system on a chip CXL. >> I have no idea whether I have no knowledge about whether going to do anything CXL. >> Presuming I'm not trying to tap anything confidential. It just makes sense that they would do a system on chip. It makes sense that they would do something like CXL. Why not adopt the standard, if it's going to be as the cost. >> Yeah. And so that was one of the things out of zip computing. The other thing is the low latency networking with the elastic fabric adapter EFA and the extensions to that that were announced last night. They doubled the throughput. So you get twice the capacity on the nitro chip. And then the other thing was this, this is a bit technical, but this scalable datagram protocol that they've got which basically says, if I want to send a message, a packet from one machine to another machine, instead of sending it over one wire, I consider it over 16 wires in parallel. And I will just flood the network with all the packets and they can arrive in any order. This is why it isn't done normally. TCP is in order, the packets come in order they're supposed to, but this is fully flooding them around with its own fast retry and then they get reassembled at the other end. So they're not just using this now for HPC workloads. They've turned it on for TCP for just without any change to your application. If you are trying to move a large piece of data between two machines, and you're just pushing it down a network, a single connection, it takes it from five gigabits per second to 25 gigabits per second. A five x speed up, with a protocol tweak that's run by the Nitro, this is super interesting. >> Probably want to get all that AIML that stuff is going on. >> Well, the AIML stuff is leveraging it underneath, but this is for everybody. Like you're just copying data around, right? And you're limited, "Hey this is going to get there five times faster, pushing a big enough chunk of data around." So this is turning on gradually as the nitro five comes out, and you have to enable it at the instance level. But it's a super interesting announcement from last night. >> So the bottom line bumper sticker on commoditization is what? >> I don't think so. I mean what's the APIs? Your arm compatible, your Intel X 86 compatible or your maybe risk five one day compatible in the cloud. And those are the APIs, right? That's the commodity level. And the software is now, the software ecosystem is super portable across those as we're seeing with Apple moving from Intel to it's really not an issue, right? The software and the tooling is all there to do that. But underneath that, we're going to see an arms race between the top providers as they all try and develop faster chips for doing more specific things. We've got cranium for training, that instance has they announced it last year with 800 gigabits going out of a single instance, 800 gigabits or no, but this year they doubled it. Yeah. So 1.6 terabytes out of a single machine, right? That's insane, right? But what you're doing is you're putting together hundreds or thousands of those to solve the big machine learning training problems. These super, these enormous clusters that they're being formed for doing these massive problems. And there is a market now, for these incredibly large supercomputer clusters built for doing AI. That's all bandwidth limited. >> And you think about the timeframe from design to tape out. >> Yeah. >> Is just getting compressed It's relative. >> It is. >> Six is going the other way >> The tooling is all there. Yeah. >> Fantastic. Adrian, always a pleasure to have you on. Thanks so much. >> Yeah. >> Really appreciate it. >> Yeah, thank you. >> Thank you Paul. >> Cheers. All right. Keep it right there everybody. Don't forget, go to thecube.net, you'll see all these videos. Go to siliconangle.com, We've got features with Adam Selipsky, we got my breaking analysis, we have another feature with MongoDB's, Dev Ittycheria, Ali Ghodsi, as well Frank Sluman tomorrow. So check that out. Keep it right there. You're watching theCUBE, the leader in enterprise and emerging tech, right back. (soft techno upbeat music)

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

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

>> Welcome back to Supercloud22, #Supercloud22. This is Dave Vellante. In 2019 Oracle and Microsoft announced a collaboration to bring interoperability between OCI, Oracle Cloud Infrastructure and Azure Clouds. It was Oracle's initial foray into so-called multi-cloud and we're joined by Karan Batta, who's the Vice President for Product Management at OCI. And Kris Rice is the Vice President of Software Development at Oracle Database. And we're going to talk about how this technology's evolving and whether it fits our view of what we call supercloud. Welcome gentlemen, thank you. >> Thanks for having us. >> So you recently just last month announced the new service. It extends on the initial partnership with Microsoft Oracle interconnect with Azure, and you refer to this as a secure private link between the two clouds, it cross 11 regions around the world, under two milliseconds data transmission sounds pretty cool. It enables customers to run Microsoft applications against data stored in Oracle databases without any loss in efficiency or presumably performance. So we use this term supercloud to describe a service or sets of services built on hyper scale infrastructure that leverages the core primitives and APIs of an individual cloud platform, but abstracts that underlying complexity to create a continuous experience across more than one cloud. Is that what you've done? >> Absolutely. I think it starts at the top layer in terms of just making things very simple for the customer, right. I think at the end of the day we want to enable true workloads running across two different clouds where you're potentially running maybe the app layer in one and the database layer or the back in another. And the integration I think starts with, you know, making it ease of use. Right. So you can start with things like, okay can you log into your second or your third cloud with the first cloud provider's credentials? Can you make calls against another cloud using another cloud's APIs? Can you peer the networks together? Can you make it seamless? I think those are all the components that are sort of, they're kind of the ingredients to making a multi-cloud or supercloud experience successful. >> Oh, thank you for that, Karan. So I guess there's a question for Chris is I'm trying to understand what you're really solving for? What specific customer problems are you focused on? What's the service optimized for presumably it's database but maybe you could double click on that. >> Sure. So, I mean, of course it's database. So it's a super fast network so that we can split the workload across two different clouds leveraging the best from both, but above the networking, what we had to do do is we had to think about what a true multi-cloud or what you're calling supercloud experience would be it's more than just making the network bites flow. So what we did is we took a look as Karan hinted at right, is where is my identity? Where is my observability? How do I connect these things across how it feels native to that other cloud? >> So what kind of engineering do you have to do to make that work? It's not just plugging stuff together. Maybe you could explain a little bit more detail, the the resources that you had to bring to bear and the technology behind the architecture. >> Sure. I think, it starts with actually, what our goal was, right? Our goal was to actually provide customers with a fully managed experience. What that means is we had to basically create a brand new service. So, we have obviously an Azure like portal and an experience that allows customers to do this but under the covers, we actually have a fully managed service that manages the networking layer, the physical infrastructure, and it actually calls APIs on both sides of the fence. It actually manages your Azure resources, creates them but it also interacts with OCI at the same time. And under the covers this service actually takes Azure primitives as inputs. And then it sort of like essentially translates them to OCI action. So, we actually truly integrated this as a service that's essentially built as a PaaS layer on top of these two clouds. >> So, the customer doesn't really care or know maybe they know cuz they might be coming through, an Azure experience, but you can run work on either Azure and or OCI. And it's a common experience across those clouds. Is that correct? >> That's correct. So like you said, the customer does know that they know there is a relationship with both clouds but thanks to all the things we built there's this thing we invented we created called a multi-cloud control plane. This control plane does operate against both clouds at the same time to make it as seamless as possible so that maybe they don't notice, you know, the power of the interconnect is extremely fast networking, as fast as what we could see inside a single cloud. If you think about how big a data center might be from edge to edge in that cloud, going across the interconnect makes it so that that workload is not important that it's spanning two clouds anymore. >> So you say extremely fast networking. I remember I used to, I wrote a piece a long time ago. Larry Ellison loves InfiniBand. I presume we've moved on from them, but maybe not. What is that interconnect? >> Yeah, so it's funny you mentioned interconnect you know, my previous history comes from Edge PC where we actually inside OCI today, we've moved from Infinite Band as is part of Exadata's core to what we call Rocky V two. So that's just another RDMA network. We actually use it very successfully, not just for Exadata but we use it for our standard computers that we provide to high performance computing customers. >> And the multi-cloud control plane runs. Where does that live? Does it live on OCI? Does it live on Azure? Yes? >> So it does it lives on our side. Our side of the house as part of our Oracle OCI control plane. And it is the veneer that makes these two clouds possible so that we can wire them together. So it knows how to take those Azure primitives and the OCI primitives and wire them at the appropriate levels together. >> Now I want to talk about this PaaS layer. Part of supercloud, we said to actually make it work you're going to have to have a super PaaS. I know we're taking this this term a little far but it's still it's instructive in that, what we surmised was you're probably not going to just use off the shelf, plain old vanilla PaaS, you're actually going to have a purpose built PaaS to solve for the specific problem. So as an example, if you're solving for ultra low latency, which I think you're doing, you're probably no offense to my friends at Red Hat but you're probably not going to develop this on OpenShift, but tell us about that PaaS layer or what we call the super PaaS layer. >> Go ahead, Chris. >> Well, so you're right. We weren't going to build it out on OpenShift. So we have Oracle OCI, you know, the standard is Terraform. So the back end of everything we do is based around Terraform. Today, what we've done is we built that control plane and it will be API drivable, it'll be drivable from the UI and it will let people operate and create primitives across both sides. So you can, you mentioned developers, developers love automation, right, because it makes our lives easy. We will be able to automate a multi-cloud workload from ground up config is code these days. So we can config an entire multi-cloud experience from one place. >> So, double click Chris on that developer experience. What is that like? They're using the same tool set irrespective of, which cloud we're running on is, and it's specific to this service or is it more generic, across other Oracle services? >> There's two parts to that. So one is the, we've only onboarded a portion. So the database portfolio and other services will be coming into this multi-cloud. For the majority of Oracle cloud, the automation, the config layer is based on Terraform. So using Terraform, anyone can configure everything from a mid-tier to an Exadata, all the way soup to nuts from smallest thing possible to the largest. What we've not done yet is integrated truly with the Azure API, from command line drivable. That is coming in the future. It is on the roadmap, it is coming. Then they could get into one tool but right now they would have half their automation for the multi-cloud config on the Azure tool set and half on the OCI tool set. >> But we're not crazy saying from a roadmap standpoint that will provide some benefit to developers and is a reasonable direction for the industry generally but Oracle and Microsoft specifically. >> Absolutely. I'm a developer at heart. And so one of the things we want to make sure is that developers' lives are as easy as possible. >> And is there a metadata management layer or intelligence that you've built in to optimize for performance or low latency or cost across the respective clouds? >> Yeah, definitely. I think, latency's going to be an important factor. The service that we've initially built isn't going to serve, the sort of the tens of microseconds but most applications that are sort of in, running on top of the enterprise applications that are running on top of the database are in the several millisecond range. And we've actually done a lot of work on the networking pairing side to make sure that when we launch these resources across the two clouds we actually picked the right trial site. We picked the right region we pick the right availability zone or domain. So we actually do the due diligence under the cover so the customer doesn't have to do the trial and error and try to find the right latency range. And this is actually one of the big reasons why we only launch the service on the interconnect regions. Even though we have close to, I think close to 40 regions at this point in OCI, this service is only built for the regions that we have an interconnect relationship with Microsoft. >> Okay, so you started with Microsoft in 2019. You're going deeper now in that relationship, is there any reason that you couldn't, I mean technically what would you have to do to go to other clouds? You talked about understanding the primitives and leveraging the primitives of Azure. Presumably if you wanted to do this with AWS or Google or Alibaba, you would have to do similar engineering work, is that correct? Or does what you've developed just kind of poured over to any cloud? >> Yeah, that's absolutely correct Dave. I think Chris talked a lot about the multi-cloud control plane, right? That's essentially the control plane that goes and does stuff on other clouds. We would have to essentially go and build that level of integration into the other clouds. And I think, as we get more popularity and as more products come online through these services I think we'll listen to what customers want. Whether it's, maybe it's the other way around too, Dave maybe it's the fact that they want to use Oracle cloud but they want to use other complimentary services within Oracle cloud. So I think it can go both ways. I think, the market and the customer base will dictate that. >> Yeah. So if I understand that correctly, somebody from another cloud Google cloud could say, Hey we actually want to run this service on OCI cuz we want to expand our market. And if TK gets together with his old friends and figures that out but then we're just, hypothesizing here. But, like you said, it can go both ways. And then, and I have another question related to that. So, multi clouds. Okay, great. Supercloud. How about the Edge? Do you ever see a day where that becomes part of the equation? Certainly the near Edge would, you know, a Home Depot or Lowe's store or a bank, but what about the far Edge, the tiny Edge. Can you talk about the Edge and where that fits in your vision? >> Yeah, absolutely. I think Edge is a interestingly, it's getting fuzzier and fuzzier day by day. I think, the term. Obviously every cloud has their own sort of philosophy in what Edge is, right. We have our own. It starts from, if you do want to do far Edge, we have devices like red devices, which is our ruggedized servers that talk back to our control plane in OCI. You could deploy those things unlike, into war zones and things like that underground. But then we also have things like clouded customer where customers can actually deploy components of our infrastructure like compute or Exadata into a facility where they only need that certain capability. And then a few years ago we launched, what's now called Dedicated Region. And that actually is a different take on Edge in some sense where you get the entire capability of our public commercial region, but within your facility. So imagine if a customer was to essentially point a finger on a commercial map and say, Hey, look, that region is just mine. Essentially that's the capability that we're providing to our customers, where if you have a white space if you have a facility, if you're exiting out of your data center space, you could essentially place an OCI region within your confines behind your firewall. And then you could interconnect that to a cloud provider if you wanted to, and get the same multi-cloud capability that you get in a commercial region. So we have all the spectrums of possibilities here. >> Guys, super interesting discussion. It's very clear to us that the next 10 years of cloud ain't going to be like the last 10. There's a whole new layer. Developing, data is a big key to that. We see industries getting involved. We obviously didn't get into the Oracle Cerner acquisitions. It's a little too early for that but we've actually predicted that companies like Cerner and you're seeing it with Goldman Sachs and Capital One they're actually building services on the cloud. So this is a really exciting new area and really appreciate you guys coming on the Supercloud22 event and sharing your insights. Thanks for your time. >> Thanks for having us. >> Okay. Keep it right there. #Supercloud22. We'll be right back with more great content right after this short break. (lighthearted marimba music)

(digital music) >> And we're back live in New York. It's theCUBE. It's not SNL, it's better than SNL. Lisa Martin and John Furrier here with about 10,000 to 12,000 folks. (John chuckles) There is a ton of energy here. There's a ton of interest in what's going on. But one of the things that we know that AWS is really well-known for is its massive ecosystem. And one of its ecosystem partners is joining us. Please welcome Domenic Ravita, the VP of Product Marketing from SingleStore. Dominic, great to have you on the program. >> Well, thank you. Glad to be here. >> It's a nice opening, wasn't it? (Lisa and John laughing) >> I love SNL. Who doesn't? >> Right? I know. So some big news came out today. >> Yes. >> Funding. Good number. Talk to us a little bit about that before we dig in to SingleStore and what you guys are doing with AWS. >> Right, yeah. Thank you. We announced this morning our latest round, 116 million. We're really grateful to our customers and our investors and the partners and employees and making SingleStore a success to go on this journey of, really, to fulfill our mission to unify and simplify modern, real time data. >> So talk to us about SingleStore. Give us the value prop, the key differentiators, 'cause obviously customers have choice. Help us understand where you're nailing it. >> SingleStore is all about, what we like to say, the moments that matter. When you have an analytical question about what's happening in the moment, SingleStore is your best way to solve that cost-effectively. So that is for, in the case of Thorn, where they're helping to protect and save children from online trafficking or in the case of True Digital, which early in the pandemic, was a company in Southeast Asia that used anonymized phone pings to identify real time population density changes and movements across Thailand to have a proactive response. So really real time data in the moment can help to save lives quite literally. But also it does things that are just good commercially that gives you an advantage like what we do with Uber to help real time pricing and things like this. >> It's interesting this data intensity happening right now. We were talking earlier on theCUBE with another guest and we said, "Why is it happening now?" The big data has been around since the dupe days. That was hard to work with, then data lakes kicked in. But we seem to be, in the past year, everyone's now aware like, "Wow, I got a lot of data." Is it the pandemic? Now we're seeing customers understand the consequences. So how do you look at that? Because is it just timing, evolution? Are they now getting it or is the technology better? Is machine learning better? What's the forces driving the massive data growth acceleration in terms of implementing and getting stuff out, done? (chuckles) >> We think it's the confluence of a lot of those things you mentioned there. First of all, we just celebrate the 15-year anniversary of the iPhone, so that is like wallpaper now. It's just faded into our daily lives. We don't even think of that as a separate thing. So there's an expectation that we all have instant information and not just for the consumer interactions, for the business interactions. That permeates everything. I think COVID with the pandemic forced everyone, every business to try to move to digital first and so that put pressure on the digital service economy to mature even faster and to be digital first. That is what drives what we call data intensity. And more generally, the economic phenomenon is the data intensive era. It's a continuous competition and game for customers. In every moment in every location, in every dimension, the more data hat you have, the better value prop you can give. And so SingleStore is uniquely positioned to and focused on solving this problem of data intensity by bringing and unifying data together. >> What's the big customer success story? Can you share any examples that highlight that? What are some cool things that are happening that can illustrate this new, I won't say bit that's been flipped, that's been happening for a while, but can you share some cutting edge customer successes? >> It's happening across a lot of industries. So I would say first in financial services, FinTech. FinTech is always at the leading edge of these kind of technology adaptions for speeds and things like that. So we have a customer named IEX Cloud and they're focused on providing real time financial data as an API. So it's a data product, API-first. They're providing a lot of historical information on instruments and that sort of thing, as well as real time trending information. So they have customers like Seeking Alpha, for instance, who are providing real time updates on massive, massive data sets. They looked at lots of different ways to do this and there's the traditional, transactionals, LTP database and then maybe if you want to scale an API like theirs, you might have a separate end-memory cache and then yet another database for analytics. And so we bring all that together and simplify that and the benefit of simplification, but it's also this unification and lower latency. Another example is GE who basically uses us to bring together lots of financial information to provide quicker close to the end-of-month process across many different systems. >> So we think about special purpose databases, you mentioned one of the customers having those. We were in the keynote this morning where AWS is like, "We have the broadest set of special purpose databases," but you're saying the industry can't afford them anymore. Why and would it make SingleStore unique in terms of what you deliver? >> It goes back to this data intensity, in that the new business models that are coming out now are all about giving you this instant context and that's all data-driven and it's digital and it's also analytical. And so the reason that's you can't afford to do this, otherwise, is data's getting so big. Moving that data gets expensive, 'cause in the cloud you pay for every byte you store, every byte you process, every byte you move. So data movement is a cost in dollars and cents. It's a cost in time. It's also a cost in skill sets. So when you have many different specialized data sets or data-based technologies, you need skilled people to manage those. So that's why we think the industry needs to be simplified and then that's why you're seeing this unification trend across the database industry and other parts of the stack happening. With AWS, I mean, they've been a great partner of ours for years since we launched our first cloud database product and their perspective is a little bit different. They're offering choice of the specialty, 'cause many people build this way. But if you're going after real time data, you need to bring it. They also offer a SingleStore as a service on AWS. We offer it that way. It's in the AWS Marketplace. So it's easily consumable that way. >> Access to real time data is no longer a nice-to-have for any company, it's table stakes. We saw that especially in the last 20 months or so with companies that needed to pivot so quickly. What is it about SingleStore that delivers, that you talked about moments that matter? Talk about the access to real time data. How that's a differentiator as well? >> I think businesses need to be where their customers are and in the moments their customers are interacting. So that is the real time business-driver. As far as technology wise, it's not easy to do this. And you think about what makes a database fast? A major way of what makes it fast is how you store the data. And so since 2014, when we first released this, what Gartner called at the time, hybrid transaction/analytical processing or HTAP, where we brought transactional data and analytical data together. Fast forward five years to 2019, we released this innovation called Universal Storage, which does that in a single unified table type. Why that matters is because, I would say, basically cost efficiency and better speed. Again, because you pay for the storage and you pay for the movement. If you're not duplicating that data, moving it across different stores, you're going to have a better experience. >> One of the things you guys pioneered is unifying workloads. You mentioned some of the things you've done. Others are now doing it. Snowflake, Google and others. What does that mean for you guys? I mean, 'cause are they copying you? Are they trying to meet the functionality? >> I think. >> I mean, unification. I mean, people want to just store things and make it, get all the table stakes, check boxes, compliance, security and just keep coding and keep building. >> We think it's actually great 'cause they're validating what we've been seeing in the market for years. And obviously, they see that it's needed by customers. And so we welcome them to the party in terms of bringing these unified workloads together. >> Is it easy or hard? >> It's a difficult thing. We started this in 2014. And we've now have lots of production workloads on this. So we know where all the production edge cases are and that capability is also a building block towards a broader, expansive set of capabilities that we've moved onto that next phase and tomorrow actually we have an event called, The Real Time Data Revolution, excuse me, where we're announcing what's in that new product of ours. >> Is that a physical event or virtual? >> It's a virtual event. >> So we'll get the URL on the show notes, or if you know, just go to the new site. >> Absolutely. SingleStore Real Time Data Revolution, you'll find it. >> Can you tease us with the top three takeaways from Revolution tomorrow? >> So like I said, what makes a database fast? It's the storage and we completed that functionality three years ago with Universal Storage. What we're now doing for this next phase of the evolution is making enterprise features available and Workspaces is one of the foundational capabilities there. What SingleStore Workspaces does is it allows you to have this isolation of compute between your different workloads. So that's often a concern to new users to SingleStore. How can I combine transactions and analytics together? That seems like something that might be not a good thing. Well, there are multiple ways we've been doing that with resource governance, workload management. Workspaces offers another management capability and it's also flexible in that you can scale those workloads independently, or if you have a multi-tenant application, you can segment your application, your customer tenant workloads by each workspace. Another capability we're releasing is called Wasm, which is W-A-S-M, Web Assembly. This is something that's really growing in the open source community and SingleStore's contributing to that open source scene, CF project with WASI and Wasm. Where it's been mentioned mostly in the last few years has been in the browser as a more efficient way to run code in the browser. We're adapting that technology to allow you to run any language of your choice in the database and why that's important, again, it's for data movement. As data gets large in petabyte sizes, you can't move it in and out of Pandas in Python. >> Great innovation. That's real valuable. >> So we call this Code Engine with Wasm and- >> What do you call it? >> Code Engine Powered by Wasm. >> Wow. Wow. And that's open source? >> We contribute to the Wasm open source community. >> But you guys have a service that you- >> Yes. It's our implementation and our database. But Wasm allows you to have code that's portable, so any sort of runtime, which is... At release- >> You move the code, not the data. >> Exactly. >> With the compute. (chuckles) >> That's right, bring the compute to the data is what we say. >> You mentioned a whole bunch of great customer examples, GE, Uber, Thorn, you talked about IEX Cloud. When you're in customer conversations, are you dealing mostly with customers that are looking to you to help replace an existing database that was struggling from a performance perspective? Or are you working with startups who are looking to build a product on SingleStore? Is it both? >> It is a mix of both. I would say among SaaS scale up companies, their API, for instance, is their product or their SaaS application is their product. So quite literally, we're the data engine and the database powering their scale to be able to sign that next big customer or to at least sleep at night to know that it's not going to crash if they sign that next big costumer. So in those cases, we're mainly replacing a lot of databases like MySQL, Postgre, where they're typically starting, but more and more we're finding, it's free to start with SingleStore. You can run it in production for free. And in our developer community, we see a lot of customers running in that way. We have a really interesting community member who has a Minecraft server analytics that he's building based on that SingleStore free tier. In the enterprise, it's different, because there are many incumbent databases there. So it typically is a case where there is a, maybe a new product offering, they're maybe delivering a FinTech API or a new SaaS digital offering, again, to better participate in this digital service economy and they're looking for a better price performance for that real time experience in the app. That's typically the starting point, but there are replacements of traditional incumbent databases as well. >> How has the customer conversation evolved the last couple of years? As we talked about, one of the things we learned in the pandemic was access to real time data and those moments that matter isn't a nice-to-have anymore for businesses. There was that force march to digital. We saw the survivors, we're seeing the thrivers, but want to get your perspective on that. From the customers, how has the conversation evolved or elevated, escalated within an organization as every company has to be a data company? >> It really depends on their business strategy, how they are adapting or how they have adapted to this new digital first orientation and what does that mean for them in the direct interaction with their customers and partners. Often, what it means is they realize that they need to take advantage of using more data in the customer and partner interaction and when they come to those new ideas for new product introductions, they find that it's complicated and expensive to build in the old way. And if you're going to have these real time interactions, interactive applications, APIs, with all this context, you're going to have to find a better, more cost-effective approach to get that to market faster, but also not to have a big sprawling data-based technology infrastructure. We find that in those situations, we're replacing four or five different database technologies. A specialized database for key value, a specialized database for search- >> Because there's no unification before? Is that one of the reasons? >> I think it's an awareness thing. I think technology awareness takes a little bit of time, that there's a new way to do things. I think the old saying about, "Don't pave cow paths when the car..." You could build a straight road and pave it. You don't have to pave along the cow path. I think that's the natural course of technology adaption and so as more- >> And the- pandemic, too, highlighted a lot of the things, like, "Do we really need that?" (chuckles) "Who's going to service that?" >> That's right. >> So it's an awakening moment there where it's like, "Hey, let's look at what's working." >> That's right. >> Double down on it. >> Absolutely. >> What are you excited about new round of funding? We talked about, obviously, probably investments in key growth areas, but what excites you about being part of SingleStore and being a partner of AWS? >> SingleStore is super exciting. I've been in this industry a long time as an engineer and an engineering leader. At the time, we were MemSQL, came into SingleStore. And just that unification and simplification, the systems that I had built as a system engineer and helped architect did the job. They could get the speed and scale you needed to do track and trace kinds of use cases in real time, but it was a big trade off you had to make in terms of the complexity, the skill sets you needed and the cost and just hard to maintain. What excites me most about SingleStore is that it really feels like the iPhone moment for databases because it's not something you asked for, but once your friend has it and shows it to you, why would you have three different devices in your pocket with a flip phone, a calculator? (Lisa and Domenic chuckles) Remember these days? >> Yes. >> And a Blackberry pager. (all chuckling) You just suddenly- >> Or a computer. That's in there. >> That's right. So you just suddenly started using iPhone and that is sort of the moment. It feels like we're at it in the database market where there's a growing awareness and those announcements you mentioned show that others are seeing the same. >> And your point earlier about the iPhone throwing off a lot of data. So now you have data explosions at levels that unprecedented, we've never seen before and the fact that you want to have that iPhone moment, too, as a database. >> Absolutely. >> Great stuff. >> The other part of your question, what excites us about AWS. AWS has been a great partner since the beginning. I mean, when we first released our database, it was the cloud database. It was on AWS by customer demand. That's where our customers were. That's where they were building other applications. And now we have integrations with other native services like AWS Glue and we're in the Marketplace. We've expanded, that said we are a multi-cloud system. We are available in any cloud of your choice and on premise and in hybrid. So we're multi-cloud, hybrid and SaaS distribution. >> Got it. All right. >> Got it. So the event is tomorrow, Revolution. Where can folks go to register? What time does it start? >> 1:00 PM Eastern and- >> 1:00 PM. Eastern. >> Just Google SingleStore Real Time Data Revolution and you'll find it. Love for everyone to join us. >> All right. We look forward to it. Domenic, thank you so much for joining us, talking about SingleStore, the value prop, the differentiators, the validation that's happening in the market and what you guys are doing with AWS. We appreciate it. >> Thanks so much for having me. >> Our pleasure. For Domenic Ravita and John Furrier, I'm Lisa Martin. You're watching theCUBE, live from New York at AWS Summit 22. John and I are going to be back after a short break, so come back. (digital pulsing music)

>>Good morning from New York city, Lisa Martin and John furrier with the cube. We are at AWS summit NYC. This is a series of summits this year, about 15 summit globally. And we're excited to be here, John, with about 10,000 folks. >>It's crowded. New York is packed big showing here at 80 of us summit. So it's super exciting, >>Super exciting. Just a little bit before the keynote. And we have our first guest, Kevin Farley joins us the director of strategic alliances at Maria DB. Kevin, welcome to >>The program. Thank you very much. Appreciate you guys having us. >>So all of us out from California to NYC. Yeah, lots of eyes. We got keynote with Warner Vogels coming up. We should be some good news, hopefully. Yep. But talk to us about Maria DB Skys cloud native version released a couple years ago. What's going on? >>Yeah, well, it's, you know, Skys SQL for us is really a be on the future. I think when we think about like the company's real mission is it's just creating a database for everyone. It's it's any cloud, any scale, um, any size of performance and really making sure that we're able to deliver on something that really kind of takes advantage of everything we've done in the market to date. If you think about it, there's not very many startups that have a billion downloads and 75% of the fortune 500 already using our service. So what we're really thinking about is how do we bridge that gap? How do we create a natural path for all of these customers? And if you think about not just Maria DB, but anyone else using the sequel query language, all the, my people, what I think most Andy jazzy TK, anyone says, you know, it's about 10% of the market currently is in the clouds. That's 90% of a total addressable market that hasn't done it yet. So creating cloud modernization for us, I think is just a huge opportunity. Do >>You guys have a great history with AWS? I want to just step back, you mentioned some stats on, on success. Can you scope the size and track record of Maria DB for us real quick and set the table? Because I think there's a bigger picture going on that we've been tracking for the past 13 years we address is the role of the database has always been one of those things where they didn't believe a one database fits all things, right. You guys have been part of that track record scope, the size and scale of Maria DB, the usage, the use cases and some of the successes. >>Yeah. I mean, like I said, some of the stats are already threw out there. So, you know, it is pervasive, I think is the best way to put it. I think what you look at what the database market really became is very siloed. Right? I think there was a lot of unique solutions that were built and delivered that had promise, but they also had compromise. And I think once you look at the landscape of a lot of fortune 500 companies, they have probably 10 to 15 different database solutions, right? And they're all doing unique things. They're difficult to manage. They're very costly. So what Marie DB is always kind of focused on is how do we continue to build more and more functionality into the database itself and allow that to be a single source of truth where application developers can seamlessly integrate applications. >>So then the theme of this event in New York city, which is scale dot, dot, dot, anything must align quite well with Maria and your >>Objectives. I mean, honestly, I think when I think of the problems that most database, um, companies, um, face customers, I should say it, it really comes down to performance and scale. Most of them like Maria DB, like you said, they it's like the car, you know, and love you've been driving it for years. You're an expert at it. It works great, but it doesn't have enough range. It doesn't go fast enough. It's hitting walls. That modern data requirements are just breaking. So scale for me is the favorite thing to talk about because what we launched as MariaDB expand, which is a plugable storage engine that is integrated into Skye, and it really gives you dynamic scale. So you can scale in, you can scale out, it's not costly compute to try to get for seasonality. So you can make your black Friday numbers. It's really about the dexterity to be able to come in and out as you need in a share, nothing architecture with full failover sale healing, high availability, married to the cloud for full cloud scale. And that's really the beauty of the AWS partnership. >>Can you elaborate a bit more on the partnership? How long have you guys been partners? Where is it now anything exciting coming out? >>Yeah, it it's, it's actually been a wonderful ride. They've really invested from the very beginning we went for the satisfactory. So they really brought a lot of resources to bear. And I think if you're looking at why it works, um, it's probably two things. I think the number one thing is that we share one of the core tenants and it's customer obsession in a, in a, in an environment where there is co-opetition right. You have to find paths for how do you get the best thing for the customer? And the second is pretty obvious, but if you look at any major cloud, their number one priority is getting large mission critical workloads into their cloud because the revenue is exponential on the backside. So what do we own? Large mission critical workloads. So if you marry that objective with AWS, the partnership is absolutely perfect for driving true revenue, growth scale, and, and revenue across, across both entities in the partner ecosystem. >>So Kevin talk about the, um, the hybrid strategy, cuz you're seeing cloud operations. Yep. Go hybrid. Amazon announced AWS announced outpost like four years ago. Right now edge is super hot. Yeah. So you're seeing like most of the enterprise is saying mm-hmm <affirmative> okay. Love cloud love the cloud database, but I got the on-prem hybrid cloud operations. Right. So it's not just proprietary operations. It's cloud ops. Yeah. How do you guys fit into that? What's the story. >>We, we actually it's. I mean, there's, there's all these new deliverables outposts, you know, come out with a promise. What we have is a reality right now, um, one of the largest, um, networking companies, which I can't mention yet publicly, um, we want a really big sky SQL deal, but what they had manufacturing plants, they needed to have on-prem deployments. So Maria DB naturally syncs with sky SQL. It's the same technology. It works in perfect harmony. So we really already deliver on the promise of hybrid, but of course there's a lot more we can grow in that area. And certainly thinking about app posts and other solutions, um, is definitely on the, the longer term roadmap of what could make sense for in our customer. What, >>What are some of the latest things that, that you guys are doing now that you weren't doing a few years ago that customers should know about the audience should know about? >>I mean, I think the game changer, we're always innovating. I mean, when you're the company that writes the code owns the code, you know, we can do hot fixes, we can do security patches, we can always do the things that give you real time access to what you need. But I think the game changer is what I mentioned a little bit earlier. And I think it's really the, the holy grail of the cloud. It's like, how can we take the, the SQL query language, which is well over 50% of the open source market. Right. And how do we convert that seamlessly into the cloud? How do we help you modernize on that journey? And expand gives you the ability to say, I can be the small, I can be a small startup. I got my C round. I don't wanna manage databases. I can use the exact same service as the largest fortune 100 company that has massive global scale and needs to be able to drive that across globe. Yeah. So I think that's the beauty is that it's really a democratization of the database, >>At least that, you know, we've been covering the big data space for 10 years. Remember all those different conversations had do those days and oh, they have big data and right. But then it's like too hard to set up. Then you had that kind of period where you saw a spark and data lakes emerge. Yeah. Then you, now it almost seems, seems like now more than ever, there's a data revolutions back. Right. It was almost like a lull in the, in, in the, in the market a little bit. Yeah. I'm gonna democratize data science right now. You got data. So now it just seems to be an explosion at that level. What's your analysis on that? Because you you've been in, in, in the weeds and in the, in the, in this market for 10 years. Yeah. And nothing really changed. It's just now it's more ready. Yeah. I think what's your observation. Why >>Is that? I think that's a really good question. And I love it cuz I mean, what the promise of things like could do and net new technologies sort of, it was always out there, but it required this whole net new lift and how do I do it? How do I manage it? How do I optimize it? The beauty of what we can do with Maria DB is that sky SQLs, which you already know and love. Right? And now we can Del you can deliver a data lake on S3, right? You can pull that data. And we also have the ability to do both analytical data and transactional data from the same database. So you can write applications that can pull column, store data up into, um, your application, but you can also have all of your asset transactions, which are absolutely required for all of your mission critical business. So I think that we're seeing more and more adoption. You've seen other companies start to talk about bringing the different elements in, but we're the only ones that really >>Do it and SQL standardizing that front end. Yeah. Even better than ever before. All the stuff under the covers is all being connected. >>That's the awesome part is right. Is you're literally doing what you already know how to do, but you blow it out on the back end, married to the cloud. And that I think is the real revolution of what makes usability real in the data space. And I think that's what was always the problem before >>When you're in partner conversations, you mentioned co-opetition. Yeah. <laugh> so I think when you're in partner conversations and customer conversations, there is a lot of the, the there's a lot of competition out there. Absolutely. Everyone's got their own key messages. What are the key differentiators that you're saying AWS Marie to be together better? And here's why, >>Yeah. I, I think that certainly you, you start with the global footprint of AWS, right? So what we rely on the most is having the ability to truly deal with global customers in availability zones, they're gonna optimize performance from them. But then when we look at what we do that really changes the game, it comes down to scale and performance. We actually just ran, um, a suspense test against cockroach that also does distributed sequel. Absolutely. You know, the results were off the chart. So we went public and said, we have an open challenge. Anyone that wants to try to beat, um, expand and Skye will we'll if you can, we'll put $25,000 towards charity. So we really are putting our money where our mouth is on that challenge. So we believe the performance cuz we've seen it and we know it's real, but then it's really always about data scale. Modern data requirements are breaking the mold of charting. They're breaking the mold of all these bandaids that people have put in these traditional services. And we give them future. We, we feature proof their investments, so they can say, Hey, I can start here. But if I end up being a startup that becomes Airbnb, I'm already built to blow it out on the back end. I can already use what I have. >>Speaking of startups, being the next Airbnb. If you look at behind us here, you can see, this is a really packed event in New York city events are back, but the ecosystem here is even flourishing. So Dave and I and Lisa were observing that we're still kind of in a growth mode, big time. So yeah, there's some market forces headwinds for the big unicorns, overfunded, you know, public companies, maybe the valuations are a little bit off, but there's still a surge of new innovations, new companies coming out of this. Um, and it's all around data and scale. It's all around new names. We've never heard of. Absolutely. What's your take on >>Reaction? Well, actually another awesome segues cuz in addition to the public clouds, I manage the ecosystem. And one of the things that we've really been focused on with Skys SQL is making it accessible API accessible. So if you're a company that has a huge Marine DB footprint change data capture might be the most important thing for you to say, we wanna do this, but we want you to stay in sync with our environments. Um, things like monitoring, things like BI, all of these are ecosystem plays and current partners that we have, um, that we really think about how do you holistically look at not only the database and what it can do, but how does it deliver value to different segments of your customer base or just your employee base that are using that stuff? So I think that's huge for us. >>Well, you know, one of the things that we talk often about is that every company, these days, regardless of industry, has to be a data company. Yep. You've gotta be able to access the data glean insights from an act on it quickly, whether it's manufacturing, retail, healthcare, are there any verticals in where Maria DB really excels? >>Um, so certainly we Excel in areas like financial services is huge DBS bank. Um, in APAC, one of our biggest customers, also one of the largest Oracle migrations, probably the, that we've ever done. A lot of people trying to get off Oracle, we make it seamless to get into Maria DB. Um, you can think about Samsung cloud and another, their entire consumer cloud is built on Maria DB, why it's integrated with expand right seasonality. So there's customers like that that really bring it home for us as far as ServiceNow tech sector. Right? So these are all different ones, but I think we're really strong in those >>Areas. So this brings up a good point. Dave and I a coined a term called super cloud at reinvent and Lisa and Dave were at multiple events we're together at events. And so a lot of people are getting behind this cuz it's multi-cloud sounds like something's broken. Yes. But so we call it super cloud because customers are building on top of ecosystems like Maria DB and others. Yeah. Not just AWS SOS does all the CapEx absolutely provide the value. So now people are having this new super cloud moment. We' saying we can get all the benefits of cloud scale mm-hmm <affirmative> without actually being a cloud. Right. So this is where the next gen layer comes. What's your reaction to, to super cloud. Do you think it's a thing? >>Well, I think it's a thing in the sense, from our perspective as an ISV, we're, we're laser focused on making sure that we support any cloud and we have a truly multicloud cloud platform. But the beauty of that as well is from a single UI, you're able to deploy databases in different clouds underneath that you're not looking at so you can have performance proximity, but you're still driving it through the same Skys UI. So for us it's, it's unequivocally true. Got it. And I think it's only ISVs like Maria DB that can deliver on that value because >>You're enabling, >>We're enabling it. Right. We partner, we build on top of everything. Right. So we can access everything underneath >>And they can then build on top of you. >>Sure, exactly. And that's exactly where it goes. Right? Yeah. So that, I think in that sense, the super cloud is actually already somewhat real. >>It's interesting. You look at the old, it spend, you take a big company. I won't say a name, but a leader in a, a vertical, they have such a big spend. Now they can leverage that spend in with the super cloud model. They then could become a service provider in the vertical. Absolutely capital one S doing it. Yeah. You're seeing, um, Goldman Sachs doing it. They have the power on the spend that they're leveraging in for their business and servicing their vertical and the smaller players. Do you see that trend? >>Well, I think that's the reality is that everyone is getting this place where if you're talking about sort of this broader super concept, you're talking about global scale, right? That's if in order to deliver a backbone that can service that model, you have to have the right data structure and the right database footprint to be able to scale. And I think that's what they all need to be able to do. And that's what we're really well positioned with Skys >>To enable companies, as we talked about a minute ago to truly become data companies. Yeah. And to be competitive and to scale on their own, where are your customer conversations? Are they at the C-suite level? Has that changed in the last couple of years? >>Uh, that's actually a really great way to state that question because I think you would've traditionally probably talked more to, um, the DBAs, right? They're the people that are having headaches. They're having problems. They're, they're trying to solve. We see a lot of developers now tons, right? They're thinking about, I have this, I have this new thing that I need to do to deliver this new application. And here's the requirements and the current model's broken. It doesn't optimize that it's a lot of work and it's hard to manage. So I think that we're in a great position to be able to take that to that next phase and deliver. And then of course, as you get deeper in with AWS, you're talking about, you know, CIO level, CISO level, they're they need to understand how do you fit into our larger paradigm. And many of these guys have, you know, hundreds of million dollar commits with AWS. So they think of their investment in the sense of the cloud stack. And we're part of that cloud stack, just like AWS services. So those conversations continue to happen certainly with our larger customers, cuz it truly is married. >>It is. And they continue to evolve. Kevin, thank you so much >>For joining. You're welcome. Great, >>John and me talking about what's going on with Maria >>D. Thank you, John. Thank you, Lisa. On behalf of Maria B, it was wonderful. Really >>Appreciate it. Fantastic as well for John furrier. I'm Lisa Martin. You're watching the cube live from New York city at AWS summit NYC, John and I we're back with our next guest in a minute.

>>Mhm. Mhm. Yeah. Hello, everybody. Welcome to the Cubes. Coverage of Mongo db World 2022. This is the first Mongo live mongo DB World. Since 2019, the Cube has covered a number of of mongo shows actually going back to when the company was called Engine. Some of you may recall Margo since then has done an i p o p o in 2017, it's It's been a rocket ship company. It's up. It'll probably do 1.2 billion in revenue this year. It's got a billion dollars in cash on the balance sheet. Uh, despite the tech clash, it's still got a 19 or $20 million valuation growing above 50% a year. Uh, company just had a really strong quarter, and and there seems to be hitting on all cylinders. My name is Dave Volonte. And here to kick it off with me as Sanjeev Mohan, who was the principal at Sanremo. So great to see you. You become a wonderful cube contributor, Former Gartner analyst. Really sharp? No, the database space in the data space generally really well, so thanks for coming back on >>you. You know, it's just amazing how exciting. The entire data space is like they used to say. Companies are All companies are software companies. All companies are data >>companies, >>so data has become the the foundation. >>They say software is eating the world. Data is eating software and a little little quips here. But this is a good size show. Four or 5000 people? I don't really know exactly. You know the numbers, but it's exciting. And of course, a lot of financial services were here at the Javits Centre. Um, let's let's lay down the basics for people of Mongo, DB is a is a document database, but they've been advancing. That's a document database as an alternative to R D. B M s. Explain that, but explain also how Mongo has broadened its capabilities and serving a lot more use cases. >>So that's my forte is like databases technology. But before even I talk about that, I have to say I am blown away by this mongo db world because mongo db uh, in beckons to all of us during the pandemic has really come of age, and it's a billion dollar company. Now we are in this brand new Javits Centre That's been built during the pandemic. And and now the company is holding this event the high 1000 people last year. So I think this company has really grown. And why has it drawn is because its offerings have grown to more developers than just a document database document databases. Revolution revolutionised the whole DBM s space where no sequel came up. Because for a change, you don't need a structured schema. You could start bringing data in this document model scheme, uh, like varying schema. But since then, they've added, uh, things like such. So they have you seen such? They added a geospatial. They had a time series last year, and this year they keep adding more and more so like, for example, they are going to add some column store indexes. So from being a purely transactional, they are now starting to address analytical. And they're starting to address more use cases, like, you know, uh, like what? What was announced this morning at keynote was faceted search. So they're expanding the going deeper and deeper into these other data >>structures. Taking Lucy made a search of first class citizens, but I want to ask you some basic questions about document database. So it's no fixed schemes. You put anything in there? Actually, so more data friendly. They're trying to simplify the use of data. Okay, that's that's pretty clear. >>What are the >>trade offs of a document database? >>So it's not like, you know, one technology has solved every problem. Every technology comes with its own tradeoffs. So in a document, you basically get rid of joining tables with primary foreign keys because you can have a flexible schemer and so and wouldn't sing single document. So it's very easy to write and and search. But when you have a lot of repeated elements and you start getting more and more complex, your document size can start expanding quite a bit because you're trying to club everything into a single space. So So that is where the complexity goes >>up. So what does that mean for for practitioner, it means they have to think about what? How they how they are ultimately gonna structure, how they're going to query so they can get the best performances that right. So they're gonna put some time in up front in order to make it pay back at the tail end, but clearly it's it's working. But is that the correct way of thinking about >>100% in, uh, the sequel world? You didn't care about the sequel. Analytical queries You just cared about how your data model was structured and then sequel would would basically such any model. But in the new sequel world, you have to know your patterns before you. You invest into the database so it's changed that equation where you come in knowing what you are signing up. >>So a couple of questions, if I can kind of Colombo questions so to Margo talks about how it's really supporting mission critical applications and at the same time, my understanding is the architecture of mongo specifically, or a document database in general. But specifically, you've got a a primary, uh, database, and you and that is the sort of the master, if you will, right and then you can create secondaries. But so help me square the circle between mission critical and really maybe a more of a focus on, say, consistency versus availability. Do customers have to sort of think about and design in that availability? How do they do that? How a Mongol customers handling that. >>So I have to say, uh, my experience of mongo db was was that the whole company, the whole ethos was developed a friendly. So, to be honest, I don't think Mongo DB was as much focused on high availability, disaster, recovery, even security. To some extent, they were more focused on developer productivity. >>And you've experienced >>simplicity. Make it simple, make the developers productive as fast as you can. What has really, uh, was an inflexion point for Mongo DB was the launch of Atlas because the atlas they were able to introduce all of these management features and hide it abstracted from the end users. So now they've got, you know, like 2014 is when Atlas came out and it was in four regions. But today they're in 100 regions, so they keep expanding, then every hyper scale cloud provider, and they've abstracted that whole managed. >>So Atlas, of course, is the managed database as a service in the cloud. And so it's those clouds, cloud infrastructure and cloud tooling that has allowed them to go after those high available application. My other question is when you talk about adding search, geospatial time series There are a lot of specialised databases that take time series persons. You have time series specialists that go deep into time series can accompany like Mongo with an all in one strategy. Uh, how close can they get to that functionality? Do they have to be? You know, it's kind of a classic Microsoft, you know, maybe not perfect, but good enough. I mean, can they compete with those other areas? Uh, with those other specialists? And what happens to those specialists if the answer is yes. What's your take on that? If that question >>makes sense So David, this is not a mongo db only issue This is this is an issue with, you know, anytime serious database, any graph database Should I put a graph database or should I put a multifunctional database multidimensional database? And and I really think there is no right or wrong answer. It just really comes down to your use case. If you have an extremely let's, uh, complex graph, you know, then maybe you should go with best of breed purpose built database. But more and more, we're starting to see that organisations are looking to simplify their environment by going in for maybe a unified database that has multiple data structures. Yeah, well, >>it's certainly it's interesting when you hear Mongo speak. They don't They don't call out Oracle specifically, but when they talk about legacy r d m r d B m s that don't scale and are complex and are expensive, they're talking about Oracle first. And of course, there are others. Um, And then when they talk about, uh, bespoke databases the horses for courses, databases that they show a picture of that that's like the poster child for Amazon. Of course, they don't call out Amazon. They're a great partner of Amazon's. But those are really the sort of two areas that mangoes going after, Um, now Oracle. Of course, we'll talk about their converged strategy, and they're taking a similar approach. But so help us understand the difference. There is just because they're sort of or close traditional r d B M s, and they have all the drawbacks associated with that. But by the way, there are some benefits as well. So how do you see that all playing >>out? So you know it. Really, uh, it's coming down to the the origins of these databases. Uh, I think they're converging to a point where they are offering similar services. And if you look at some of the benchmark numbers or you talk to users, I from a business point of view, I I don't think there's too much of a difference. Uh, technology writes. The difference is that Mongo DB started in the document space. They were more interested in availability rather than consistency. Oracle started in the relation database with focus on financial services, so asset compliance is what they're based on. And since then they've been adding other pieces, so so they differ from where they started. Oracle has been in the industry for some since 19 seventies, so they have that maturity. But then they have that legacy, >>you know, I love. Recently, Oracle announced the mongo db uh, kpi. So basically saying why? Why leave Oracle when you can just, you know, do the market? So that, to me, is a sign that Mongo DB is doing well because the Oracle calls you out, whether your workday or snowflake or mongo. You know, whoever that's a sign to me that you've got momentum and you're stealing share in that marketplace, and clearly Mongo is they're growing at 50 plus percent per year. So thinking about the early I mentioned 10 gen Early on, I remember that one of the first conferences I went to mongo conferences. It was just It was all developers. A lot of developers here as well. But they have really, since 2014, expanded the capabilities you talk about, Atlas, you talked about all these other you know, types of databases that they've added. If it seems like Mongo is becoming a platform company, uh, what are your thoughts on that in terms of them sort of up levelling the message there now, a billion dollar plus company. What's the next? You know, wave for Mongo. >>So, uh, Oracle announced mongo db a p i s a W s has document d. B has cost most db so they all have a p. I compatible a p. I s not the source code because, you know, mongo DB has its own SPL licence, so they have written their own layer on top. But at the end of the day, you know, if you if you these companies have to keep innovating to catch up with Mongo DB because we can announce a brand new capability, then all these other players have to catch up. So other cloud providers have 80% or so of capabilities, but they'll never have 100% of what Mongo DB has. So people who are diehard Mongo DB fans they prefer to stay on mongo db. They are now able to write more applications like you know, mongo DB bought realm, which is their front end. Uh, like, you know, like, if you're on social media kind of thing, you can build your applications and sink it with Atlas. So So mongo DB is now at a point where they are adding more capabilities that more like developers like, You know, five G is coming. Autonomous cars are coming, so now they can address Iot kind of use cases. So that's why it's becoming such a juggle, not because it's becoming a platform rather than a single document database. >>So atlases, the near the midterm future. Today it's about 60% of revenues, but they have what we call self serve, which is really the traditional on premise stuff. They're connecting those worlds. You're bringing up the point that. Of course, they go across clouds. You also bring up the point that they've got edge plays. We're gonna talk to Verizon later on today. And they're they've got, uh, edge edge activity going on with developers. I I call it Super Cloud. Right, This layer that floats above. Now, of course, a lot of the super Cloud concert says we're gonna hide the underlying complexity. But for developers, they wanna they might want to tap those primitives, so presumably will let them do that. But But that hybrid that what we call Super Cloud that is a new wave of innovation, is it not? And do you? Do you agree with that? And do you see that as a real opportunity from Mongo in terms of penetrating a new tan? >>Yes. So I see this is a new opportunity. In fact, one of the reasons mongo DB has grown so quickly is because they are addressing more markets than they had three pandemic. Um, Also, there are all gradations of users. Some users want full control. They want an eye as kind of, uh, someone passed. And some businesses are like, you know, we don't care. We don't want to deal with the database. So today we heard, uh, mongo db. Several went gear. So now they have surveillance capability, their past. But if you if you're more into communities, they have communities. Operator. So they're addressing the full stack of different types of developers different workloads, different geographical regions. So that that's why the market is expected. >>We're seeing abstraction layers, you know, throughout the started a physical virtual containers surveillance and eventually SuperClubs Sanjeev. Great analysis. Thanks so much for taking your time to come with the cube. Alright, Keep it right there. But right back, right after this short break. This is Dave Volonte from the Javits Centre. Mongo db World 2022. Thank you. >>Mm.

>>Time series data is any data that's stamped in time in some way that could be every second, every minute, every five minutes, every hour, every nanosecond, whatever it might be. And typically that data comes from sources in the physical world like devices or sensors, temperature, gauges, batteries, any device really, or things in the virtual world could be software, maybe it's software in the cloud or data and containers or microservices or virtual machines. So all of these items, whether in the physical or virtual world, they're generating a lot of time series data. Now time series data has been around for a long time, and there are many examples in our everyday lives. All you gotta do is punch up any stock, ticker and look at its price over time and graphical form. And that's a simple use case that anyone can relate to and you can build timestamps into a traditional relational database. >>You just add a column to capture time and as well, there are examples of log data being dumped into a data store that can be searched and captured and ingested and visualized. Now, the problem with the latter example that I just gave you is that you gotta hunt and Peck and search and extract what you're looking for. And the problem with the former is that traditional general purpose databases they're designed as sort of a Swiss army knife for any workload. And there are a lot of functions that get in the way and make them inefficient for time series analysis, especially at scale. Like when you think about O T and edge scale, where things are happening super fast, ingestion is coming from many different sources and analysis often needs to be done in real time or near real time. And that's where time series databases come in. >>They're purpose built and can much more efficiently support ingesting metrics at scale, and then comparing data points over time, time series databases can write and read at significantly higher speeds and deal with far more data than traditional database methods. And they're more cost effective instead of throwing processing power at the problem. For example, the underlying architecture and algorithms of time series databases can optimize queries and they can reclaim wasted storage space and reuse it. At scale time, series databases are simply a better fit for the job. Welcome to moving the world with influx DB made possible by influx data. My name is Dave Valante and I'll be your host today. Influx data is the company behind InfluxDB. The open source time series database InfluxDB is designed specifically to handle time series data. As I just explained, we have an exciting program for you today, and we're gonna showcase some really interesting use cases. >>First, we'll kick it off in our Palo Alto studios where my colleague, John furrier will interview Evan Kaplan. Who's the CEO of influx data after John and Evan set the table. John's gonna sit down with Brian Gilmore. He's the director of IOT and emerging tech at influx data. And they're gonna dig into where influx data is gaining traction and why adoption is occurring and, and why it's so robust. And they're gonna have tons of examples and double click into the technology. And then we bring it back here to our east coast studios, where I get to talk to two practitioners, doing amazing things in space with satellites and modern telescopes. These use cases will blow your mind. You don't want to miss it. So thanks for being here today. And with that, let's get started. Take it away. Palo Alto. >>Okay. Today we welcome Evan Kaplan, CEO of influx data, the company behind influx DB. Welcome Evan. Thanks for coming on. >>Hey John, thanks for having me >>Great segment here on the influx DB story. What is the story? Take us through the history. Why time series? What's the story >><laugh> so the history history is actually actually pretty interesting. Um, Paul dicks, my partner in this and our founder, um, super passionate about developers and developer experience. And, um, he had worked on wall street building a number of time series kind of platform trading platforms for trading stocks. And from his point of view, it was always what he would call a yak shave, which means you had to do a ton of work just to start doing work, which means you had to write a bunch of extrinsic routines. You had to write a bunch of application handling on existing relational databases in order to come up with something that was optimized for a trading platform or a time series platform. And he sort of, he just developed this real clear point of view is this is not how developers should work. And so in 2013, he went through why Combinator and he built something for, he made his first commit to open source in flu DB at the end of 2013. And, and he basically, you know, from my point of view, he invented modern time series, which is you start with a purpose-built time series platform to do these kind of workloads. And you get all the benefits of having something right outta the box. So a developer can be totally productive right away. >>And how many people in the company what's the history of employees and stuff? >>Yeah, I think we're, I, you know, I always forget the number, but it's something like 230 or 240 people now. Um, the company, I joined the company in 2016 and I love Paul's vision. And I just had a strong conviction about the relationship between time series and IOT. Cuz if you think about it, what sensors do is they speak time, series, pressure, temperature, volume, humidity, light, they're measuring they're instrumenting something over time. And so I thought that would be super relevant over long term and I've not regretted it. >>Oh no. And it's interesting at that time, go back in the history, you know, the role of databases, well, relational database is the one database to rule the world. And then as clouds started coming in, you starting to see more databases, proliferate types of databases and time series in particular is interesting. Cuz real time has become super valuable from an application standpoint, O T which speaks time series means something it's like time matters >>Time. >>Yeah. And sometimes data's not worth it after the time, sometimes it worth it. And then you get the data lake. So you have this whole new evolution. Is this the momentum? What's the momentum, I guess the question is what's the momentum behind >>You mean what's causing us to grow. So >>Yeah, the time series, why is time series >>And the >>Category momentum? What's the bottom line? >>Well, think about it. You think about it from a broad, broad sort of frame, which is where, what everybody's trying to do is build increasingly intelligent systems, whether it's a self-driving car or a robotic system that does what you want to do or a self-healing software system, everybody wants to build increasing intelligent systems. And so in order to build these increasing intelligent systems, you have to instrument the system well, and you have to instrument it over time, better and better. And so you need a tool, a fundamental tool to drive that instrumentation. And that's become clear to everybody that that instrumentation is all based on time. And so what happened, what happened, what happened what's gonna happen? And so you get to these applications like predictive maintenance or smarter systems. And increasingly you want to do that stuff, not just intelligently, but fast in real time. So millisecond response so that when you're driving a self-driving car and the system realizes that you're about to do something, essentially you wanna be able to act in something that looks like real time, all systems want to do that, want to be more intelligent and they want to be more real time. And so we just happen to, you know, we happen to show up at the right time in the evolution of a >>Market. It's interesting near real time. Isn't good enough when you need real time. >><laugh> yeah, it's not, it's not. And it's like, and it's like, everybody wants, even when you don't need it, ironically, you want it. It's like having the feature for, you know, you buy a new television, you want that one feature, even though you're not gonna use it, you decide that your buying criteria real time is a buying criteria >>For, so you, I mean, what you're saying then is near real time is getting closer to real time as possible, as fast as possible. Right. Okay. So talk about the aspect of data, cuz we're hearing a lot of conversations on the cube in particular around how people are implementing and actually getting better. So iterating on data, but you have to know when it happened to get, know how to fix it. So this is a big part of how we're seeing with people saying, Hey, you know, I wanna make my machine learning algorithms better after the fact I wanna learn from the data. Um, how does that, how do you see that evolving? Is that one of the use cases of sensors as people bring data in off the network, getting better with the data knowing when it happened? >>Well, for sure. So, so for sure, what you're saying is, is, is none of this is non-linear, it's all incremental. And so if you take something, you know, just as an easy example, if you take a self-driving car, what you're doing is you're instrumenting that car to understand where it can perform in the real world in real time. And if you do that, if you run the loop, which is I instrumented, I watch what happens, oh, that's wrong? Oh, I have to correct for that. I correct for that in the software. If you do that for a billion times, you get a self-driving car, but every system moves along that evolution. And so you get the dynamic of, you know, of constantly instrumenting watching the system behave and do it. And this and sets up driving car is one thing. But even in the human genome, if you look at some of our customers, you know, people like, you know, people doing solar arrays, people doing power walls, like all of these systems are getting smarter. >>Well, let's get into that. What are the top applications? What are you seeing for your, with in, with influx DB, the time series, what's the sweet spot for the application use case and some customers give some >>Examples. Yeah. So it's, it's pretty easy to understand on one side of the equation that's the physical side is sensors are sensors are getting cheap. Obviously we know that and they're getting the whole physical world is getting instrumented, your home, your car, the factory floor, your wrist, watch your healthcare, you name it. It's getting instrumented in the physical world. We're watching the physical world in real time. And so there are three or four sweet spots for us, but, but they're all on that side. They're all about IOT. So they're think about consumer IOT projects like Google's nest todo, um, particle sensors, um, even delivery engines like rapid who deliver the Instacart of south America, like anywhere there's a physical location do and that's on the consumer side. And then another exciting space is the industrial side factories are changing dramatically over time. Increasingly moving away from proprietary equipment to develop or driven systems that run operational because what, what has to get smarter when you're building, when you're building a factory is systems all have to get smarter. And then, um, lastly, a lot in the renewables sustainability. So a lot, you know, Tesla, lucid, motors, Cola, motors, um, you know, lots to do with electric cars, solar arrays, windmills, arrays, just anything that's gonna get instrumented that where that instrumentation becomes part of what the purpose >>Is. It's interesting. The convergence of physical and digital is happening with the data IOT. You mentioned, you know, you think of IOT, look at the use cases there, it was proprietary OT systems. Now becoming more IP enabled internet protocol and now edge compute, getting smaller, faster, cheaper AI going to the edge. Now you have all kinds of new capabilities that bring that real time and time series opportunity. Are you seeing IOT going to a new level? What was the, what's the IOT where's the IOT dots connecting to because you know, as these two cultures merge yeah. Operations, basically industrial factory car, they gotta get smarter, intelligent edge is a buzzword, but I mean, it has to be more intelligent. Where's the, where's the action in all this. So the >>Action, really, it really at the core, it's at the developer, right? Because you're looking at these things, it's very hard to get an off the shelf system to do the kinds of physical and software interaction. So the actions really happen at the developer. And so what you're seeing is a movement in the world that, that maybe you and I grew up in with it or OT moving increasingly that developer driven capability. And so all of these IOT systems they're bespoke, they don't come out of the box. And so the developer, the architect, the CTO, they define what's my business. What am I trying to do? Am I trying to sequence a human genome and figure out when these genes express theself or am I trying to figure out when the next heart rate monitor's gonna show up on my apple watch, right? What am I trying to do? What's the system I need to build. And so starting with the developers where all of the good stuff happens here, which is different than it used to be, right. Used to be you'd buy an application or a service or a SA thing for, but with this dynamic, with this integration of systems, it's all about bespoke. It's all about building >>Something. So let's get to the developer real quick, real highlight point here is the data. I mean, I could see a developer saying, okay, I need to have an application for the edge IOT edge or car. I mean, we're gonna have, I mean, Tesla's got applications of the car it's right there. I mean, yes, there's the modern application life cycle now. So take us through how this impacts the developer. Does it impact their C I C D pipeline? Is it cloud native? I mean, where does this all, where does this go to? >>Well, so first of all, you're talking about, there was an internal journey that we had to go through as a company, which, which I think is fascinating for anybody who's interested is we went from primarily a monolithic software that was open sourced to building a cloud native platform, which means we had to move from an agile development environment to a C I C D environment. So to a degree that you are moving your service, whether it's, you know, Tesla monitoring your car and updating your power walls, right. Or whether it's a solar company updating the arrays, right. To degree that that service is cloud. Then increasingly remove from an agile development to a C I C D environment, which you're shipping code to production every day. And so it's not just the developers, all the infrastructure to support the developers to run that service and that sort of stuff. I think that's also gonna happen in a big way >>When your customer base that you have now, and as you see, evolving with infl DB, is it that they're gonna be writing more of the application or relying more on others? I mean, obviously there's an open source component here. So when you bring in kind of old way, new way old way was I got a proprietary, a platform running all this O T stuff and I gotta write, here's an application. That's general purpose. Yeah. I have some flexibility, somewhat brittle, maybe not a lot of robustness to it, but it does its job >>A good way to think about this is versus a new way >>Is >>What so yeah, good way to think about this is what, what's the role of the developer slash architect CTO that chain within a large, within an enterprise or a company. And so, um, the way to think about it is I started my career in the aerospace industry <laugh> and so when you look at what Boeing does to assemble a plane, they build very, very few of the parts. Instead, what they do is they assemble, they buy the wings, they buy the engines, they assemble, actually, they don't buy the wings. It's the one thing they buy the, the material for the w they build the wings, cuz there's a lot of tech in the wings and they end up being assemblers smart assemblers of what ends up being a flying airplane, which is pretty big deal even now. And so what, what happens with software people is they have the ability to pull from, you know, the best of the open source world. So they would pull a time series capability from us. Then they would assemble that with, with potentially some ETL logic from somebody else, or they'd assemble it with, um, a Kafka interface to be able to stream the data in. And so they become very good integrators and assemblers, but they become masters of that bespoke application. And I think that's where it goes, cuz you're not writing native code for everything. >>So they're more flexible. They have faster time to market cuz they're assembling way faster and they get to still maintain their core competency. Okay. Their wings in this case, >>They become increasingly not just coders, but designers and developers. They become broadly builders is what we like to think of it. People who start and build stuff by the way, this is not different than the people just up the road Google have been doing for years or the tier one, Amazon building all their own. >>Well, I think one of the things that's interesting is is that this idea of a systems developing a system architecture, I mean systems, uh, uh, systems have consequences when you make changes. So when you have now cloud data center on premise and edge working together, how does that work across the system? You can't have a wing that doesn't work with the other wing kind of thing. >>That's exactly. But that's where the that's where the, you know, that that Boeing or that airplane building analogy comes in for us. We've really been thoughtful about that because IOT it's critical. So our open source edge has the same API as our cloud native stuff that has enterprise on pre edge. So our multiple products have the same API and they have a relationship with each other. They can talk with each other. So the builder builds it once. And so this is where, when you start thinking about the components that people have to use to build these services is that you wanna make sure, at least that base layer, that database layer, that those components talk to each other. >>So I'll have to ask you if I'm the customer. I put my customer hat on. Okay. Hey, I'm dealing with a lot. >>That mean you have a PO for <laugh> >>A big check. I blank check. If you can answer this question only if the tech, if, if you get the question right, I got all this important operation stuff. I got my factory, I got my self-driving cars. This isn't like trivial stuff. This is my business. How should I be thinking about time series? Because now I have to make these architectural decisions, as you mentioned, and it's gonna impact my application development. So huge decision point for your customers. What should I care about the most? So what's in it for me. Why is time series >>Important? Yeah, that's a great question. So chances are, if you've got a business that was, you know, 20 years old or 25 years old, you were already thinking about time series. You probably didn't call it that you built something on a Oracle or you built something on IBM's DB two, right. And you made it work within your system. Right? And so that's what you started building. So it's already out there. There are, you know, there are probably hundreds of millions of time series applications out there today. But as you start to think about this increasing need for real time, and you start to think about increasing intelligence, you think about optimizing those systems over time. I hate the word, but digital transformation. Then you start with time series. It's a foundational base layer for any system that you're gonna build. There's no system I can think of where time series, shouldn't be the foundational base layer. If you just wanna store your data and just leave it there and then maybe look it up every five years. That's fine. That's not time. Series time series is when you're building a smarter, more intelligent, more real time system. And the developers now know that. And so the more they play a role in building these systems, the more obvious it becomes. >>And since I have a PO for you and a big check, yeah. What is, what's the value to me as I, when I implement this, what's the end state, what's it look like when it's up and running? What's the value proposition for me. What's an >>So, so when it's up and running, you're able to handle the queries, the writing of the data, the down sampling of the data, they're transforming it in near real time. So that the other dependencies that a system that gets for adjusting a solar array or trading energy off of a power wall or some sort of human genome, those systems work better. So time series is foundational. It's not like it's, you know, it's not like it's doing every action that's above, but it's foundational to build a really compelling, intelligent system. I think that's what developers and archs are seeing now. >>Bottom line, final word. What's in it for the customer. What's what, what's your, um, what's your statement to the customer? What would you say to someone looking to do something in time series on edge? >>Yeah. So, so it's pretty clear to clear to us that if you're building, if you view yourself as being in the build business of building systems that you want 'em to be increasingly intelligent, self-healing autonomous. You want 'em to operate in real time that you start from time series. But I also wanna say what's in it for us influx what's in it for us is people are doing some amazing stuff. You know, I highlighted some of the energy stuff, some of the human genome, some of the healthcare it's hard not to be proud or feel like, wow. Yeah. Somehow I've been lucky. I've arrived at the right time, in the right place with the right people to be able to deliver on that. That's that's also exciting on our side of the equation. >>Yeah. It's critical infrastructure, critical, critical operations. >>Yeah. >>Yeah. Great stuff, Evan. Thanks for coming on. Appreciate this segment. All right. In a moment, Brian Gilmore director of IOT and emerging technology that influx day will join me. You're watching the cube leader in tech coverage. Thanks for watching >>Time series data from sensors systems and applications is a key source in driving automation and prediction in technologies around the world. But managing the massive amount of timestamp data generated these days is overwhelming, especially at scale. That's why influx data developed influx DB, a time series data platform that collects stores and analyzes data influx DB empowers developers to extract valuable insights and turn them into action by building transformative IOT analytics and cloud native applications, purpose built and optimized to handle the scale and velocity of timestamped data. InfluxDB puts the power in your hands with developer tools that make it easy to get started quickly with less code InfluxDB is more than a database. It's a robust developer platform with integrated tooling. That's written in the languages you love. So you can innovate faster, run in flex DB anywhere you want by choosing the provider and region that best fits your needs across AWS, Microsoft Azure and Google cloud flex DB is fast and automatically scalable. So you can spend time delivering value to customers, not managing clusters, take control of your time series data. So you can focus on the features and functionalities that give your applications a competitive edge. Get started for free with influx DB, visit influx data.com/cloud to learn more. >>Okay. Now we're joined by Brian Gilmore director of IOT and emerging technologies at influx data. Welcome to the show. >>Thank you, John. Great to be here. >>We just spent some time with Evan going through the company and the value proposition, um, with influx DV, what's the momentum, where do you see this coming from? What's the value coming out of this? >>Well, I think it, we're sort of hitting a point where the technology is, is like the adoption of it is becoming mainstream. We're seeing it in all sorts of organizations, everybody from like the most well funded sort of advanced big technology companies to the smaller academics, the startups and the managing of that sort of data that emits from that technology is time series and us being able to give them a, a platform, a tool that's super easy to use, easy to start. And then of course will grow with them is, is been key to us. Sort of, you know, riding along with them is they're successful. >>Evan was mentioning that time series has been on everyone's radar and that's in the OT business for years. Now, you go back since 20 13, 14, even like five years ago that convergence of physical and digital coming together, IP enabled edge. Yeah. Edge has always been kind of hyped up, but why now? Why, why is the edge so hot right now from an adoption standpoint? Is it because it's just evolution, the tech getting better? >>I think it's, it's, it's twofold. I think that, you know, there was, I would think for some people, everybody was so focused on cloud over the last probably 10 years. Mm-hmm <affirmative> that they forgot about the compute that was available at the edge. And I think, you know, those, especially in the OT and on the factory floor who weren't able to take Avan full advantage of cloud through their applications, you know, still needed to be able to leverage that compute at the edge. I think the big thing that we're seeing now, which is interesting is, is that there's like a hybrid nature to all of these applications where there's definitely some data that's generated on the edge. There's definitely done some data that's generated in the cloud. And it's the ability for a developer to sort of like tie those two systems together and work with that data in a very unified uniform way. Um, that's giving them the opportunity to build solutions that, you know, really deliver value to whatever it is they're trying to do, whether it's, you know, the, the out reaches of outer space or whether it's optimizing the factory floor. >>Yeah. I think, I think one of the things you also mentions genome too, dig big data is coming to the real world. And I think I, OT has been kind of like this thing for OT and, and in some use case, but now with the, with the cloud, all companies have an edge strategy now. So yeah, what's the secret sauce because now this is hot, hot product for the whole world and not just industrial, but all businesses. What's the secret sauce. >>Well, I mean, I think part of it is just that the technology is becoming more capable and that's especially on the hardware side, right? I mean, like technology compute is getting smaller and smaller and smaller. And we find that by supporting all the way down to the edge, even to the micro controller layer with our, um, you know, our client libraries and then working hard to make our applications, especially the database as small as possible so that it can be located as close to sort of the point of origin of that data in the edge as possible is, is, is fantastic. Now you can take that. You can run that locally. You can do your local decision making. You can use influx DB as sort of an input to automation control the autonomy that people are trying to drive at the edge. But when you link it up with everything that's in the cloud, that's when you get all of the sort of cloud scale capabilities of parallelized, AI and machine learning and all of that. >>So what's interesting is the open source success has been something that we've talked about a lot in the cube about how people are leveraging that you guys have users in the enterprise users that IOT market mm-hmm <affirmative>, but you got developers now. Yeah. Kind of together brought that up. How do you see that emerging? How do developers engage? What are some of the things you're seeing that developers are really getting into with InfluxDB >>What's? Yeah. Well, I mean, I think there are the developers who are building companies, right? And these are the startups and the folks that we love to work with who are building new, you know, new services, new products, things like that. And, you know, especially on the consumer side of IOT, there's a lot of that, just those developers. But I think we, you gotta pay attention to those enterprise developers as well, right? There are tons of people with the, the title of engineer in, in your regular enterprise organizations. And they're there for systems integration. They're there for, you know, looking at what they would build versus what they would buy. And a lot of them come from, you know, a strong, open source background and they, they know the communities, they know the top platforms in those spaces and, and, you know, they're excited to be able to adopt and use, you know, to optimize inside the business as compared to just building a brand new one. >>You know, it's interesting too, when Evan and I were talking about open source versus closed OT systems, mm-hmm <affirmative> so how do you support the backwards compatibility of older systems while maintaining open dozens of data formats out there? Bunch of standards, protocols, new things are emerging. Everyone wants to have a control plane. Everyone wants to leverage the value of data. How do you guys keep track of it all? What do you guys support? >>Yeah, well, I mean, I think either through direct connection, like we have a product called Telegraph, it's unbelievable. It's open source, it's an edge agent. You can run it as close to the edge as you'd like, it speaks dozens of different protocols in its own, right? A couple of which MQTT B, C U a are very, very, um, applicable to these T use cases. But then we also, because we are sort of not only open source, but open in terms of our ability to collect data, we have a lot of partners who have built really great integrations from their own middleware, into influx DB. These are companies like ke wear and high bite who are really experts in those downstream industrial protocols. I mean, that's a business, not everybody wants to be in. It requires some very specialized, very hard work and a lot of support, um, you know, and so by making those connections and building those ecosystems, we get the best of both worlds. The customers can use the platforms they need up to the point where they would be putting into our database. >>What's some of customer testimonies that they, that share with you. Can you share some anecdotal kind of like, wow, that's the best thing I've ever used. This really changed my business, or this is a great tech that's helped me in these other areas. What are some of the, um, soundbites you hear from customers when they're successful? >>Yeah. I mean, I think it ranges. You've got customers who are, you know, just finally being able to do the monitoring of assets, you know, sort of at the edge in the field, we have a customer who's who's has these tunnel boring machines that go deep into the earth to like drill tunnels for, for, you know, cars and, and, you know, trains and things like that. You know, they are just excited to be able to stick a database onto those tunnel, boring machines, send them into the depths of the earth and know that when they come out, all of that telemetry at a very high frequency has been like safely stored. And then it can just very quickly and instantly connect up to their, you know, centralized database. So like just having that visibility is brand new to them. And that's super important. On the other hand, we have customers who are way far beyond the monitoring use case, where they're actually using the historical records in the time series database to, um, like I think Evan mentioned like forecast things. So for predictive maintenance, being able to pull in the telemetry from the machines, but then also all of that external enrichment data, the metadata, the temperatures, the pressure is who is operating the machine, those types of things, and being able to easily integrate with platforms like Jupyter notebooks or, you know, all of those scientific computing and machine learning libraries to be able to build the models, train the models, and then they can send that information back down to InfluxDB to apply it and detect those anomalies, which >>Are, I think that's gonna be an, an area. I personally think that's a hot area because I think if you look at AI right now, yeah. It's all about training the machine learning albums after the fact. So time series becomes hugely important. Yeah. Cause now you're thinking, okay, the data matters post time. Yeah. First time. And then it gets updated the new time. Yeah. So it's like constant data cleansing data iteration, data programming. We're starting to see this new use case emerge in the data field. >>Yep. Yeah. I mean, I think you agree. Yeah, of course. Yeah. The, the ability to sort of handle those pipelines of data smartly, um, intelligently, and then to be able to do all of the things you need to do with that data in stream, um, before it hits your sort of central repository. And, and we make that really easy for customers like Telegraph, not only does it have sort of the inputs to connect up to all of those protocols and the ability to capture and connect up to the, to the partner data. But also it has a whole bunch of capabilities around being able to process that data, enrich it, reform at it, route it, do whatever you need. So at that point you're basically able to, you're playing your data in exactly the way you would wanna do it. You're routing it to different, you know, destinations and, and it's, it's, it's not something that really has been in the realm of possibility until this point. Yeah. Yeah. >>And when Evan was on it's great. He was a CEO. So he sees the big picture with customers. He was, he kinda put the package together that said, Hey, we got a system. We got customers, people are wanting to leverage our product. What's your PO they're sell. He's selling too as well. So you have that whole CEO perspective, but he brought up this notion that there's multiple personas involved in kind of the influx DB system architect. You got developers and users. Can you talk about that? Reality as customers start to commercialize and operationalize this from a commercial standpoint, you got a relationship to the cloud. Yep. The edge is there. Yep. The edge is getting super important, but cloud brings a lot of scale to the table. So what is the relationship to the cloud? Can you share your thoughts on edge and its relationship to the cloud? >>Yeah. I mean, I think edge, you know, edges, you can think of it really as like the local information, right? So it's, it's generally like compartmentalized to a point of like, you know, a single asset or a single factory align, whatever. Um, but what people do who wanna pro they wanna be able to make the decisions there at the edge locally, um, quickly minus the latency of sort of taking that large volume of data, shipping it to the cloud and doing something with it there. So we allow them to do exactly that. Then what they can do is they can actually downsample that data or they can, you know, detect like the really important metrics or the anomalies. And then they can ship that to a central database in the cloud where they can do all sorts of really interesting things with it. Like you can get that centralized view of all of your global assets. You can start to compare asset to asset, and then you can do those things like we talked about, whereas you can do predictive types of analytics or, you know, larger scale anomaly detections. >>So in this model you have a lot of commercial operations, industrial equipment. Yep. The physical plant, physical business with virtual data cloud all coming together. What's the future for InfluxDB from a tech standpoint. Cause you got open. Yep. There's an ecosystem there. Yep. You have customers who want operational reliability for sure. I mean, so you got organic <laugh> >>Yeah. Yeah. I mean, I think, you know, again, we got iPhones when everybody's waiting for flying cars. Right. So I don't know. We can like absolutely perfectly predict what's coming, but I think there are some givens and I think those givens are gonna be that the world is only gonna become more hybrid. Right. And then, you know, so we are going to have much more widely distributed, you know, situations where you have data being generated in the cloud, you have data gen being generated at the edge and then there's gonna be data generated sort sort of at all points in between like physical locations as well as things that are, that are very virtual. And I think, you know, we are, we're building some technology right now. That's going to allow, um, the concept of a database to be much more fluid and flexible, sort of more aligned with what a file would be like. >>And so being able to move data to the compute for analysis or move the compute to the data for analysis, those are the types of, of solutions that we'll be bringing to the customers sort of over the next little bit. Um, but I also think we have to start thinking about like what happens when the edge is actually off the planet. Right. I mean, we've got customers, you're gonna talk to two of them, uh, in the panel who are actually working with data that comes from like outside the earth, like, you know, either in low earth orbit or you know, all the way sort of on the other side of the universe. Yeah. And, and to be able to process data like that and to do so in a way it's it's we gotta, we gotta build the fundamentals for that right now on the factory floor and in the mines and in the tunnels. Um, so that we'll be ready for that one. >>I think you bring up a good point there because one of the things that's common in the industry right now, people are talking about, this is kind of new thinking is hyper scale's always been built up full stack developers, even the old OT world, Evan was pointing out that they built everything right. And the world's going to more assembly with core competency and IP and also property being the core of their apple. So faster assembly and building, but also integration. You got all this new stuff happening. Yeah. And that's to separate out the data complexity from the app. Yes. So space genome. Yep. Driving cars throws off massive data. >>It >>Does. So is Tesla, uh, is the car the same as the data layer? >>I mean the, yeah, it's, it's certainly a point of origin. I think the thing that we wanna do is we wanna let the developers work on the world, changing problems, the things that they're trying to solve, whether it's, you know, energy or, you know, any of the other health or, you know, other challenges that these teams are, are building against. And we'll worry about that time series data and the underlying data platform so that they don't have to. Right. I mean, I think you talked about it, uh, you know, for them just to be able to adopt the platform quickly, integrate it with their data sources and the other pieces of their applications. It's going to allow them to bring much faster time to market on these products. It's gonna allow them to be more iterative. They're gonna be able to do more sort of testing and things like that. And ultimately it will, it'll accelerate the adoption and the creation of >>Technology. You mentioned earlier in, in our talk about unification of data. Yeah. How about APIs? Cuz developers love APIs in the cloud unifying APIs. How do you view view that? >>Yeah, I mean, we are APIs, that's the product itself. Like everything, people like to think of it as sort of having this nice front end, but the front end is B built on our public APIs. Um, you know, and it, it allows the developer to build all of those hooks for not only data creation, but then data processing, data analytics, and then, you know, sort of data extraction to bring it to other platforms or other applications, microservices, whatever it might be. So, I mean, it is a world of APIs right now and you know, we, we bring a very sort of useful set of them for managing the time series data. These guys are all challenged with. It's >>Interesting. You and I were talking before we came on camera about how, um, data is, feels gonna have this kind of SRE role that DevOps had site reliability engineers, which manages a bunch of servers. There's so much data out there now. Yeah. >>Yeah. It's like reigning data for sure. And I think like that ability to be like one of the best jobs on the planet is gonna be to be able to like, sort of be that data Wrangler to be able to understand like what the data sources are, what the data formats are, how to be able to efficiently move that data from point a to point B and you know, to process it correctly so that the end users of that data aren't doing any of that sort of hard upfront preparation collection storage's >>Work. Yeah. That's data as code. I mean, data engineering is it is becoming a new discipline for sure. And, and the democratization is the benefit. Yeah. To everyone, data science get easier. I mean data science, but they wanna make it easy. Right. <laugh> yeah. They wanna do the analysis, >>Right? Yeah. I mean, I think, you know, it, it's a really good point. I think like we try to give our users as many ways as there could be possible to get data in and get data out. We sort of think about it as meeting them where they are. Right. So like we build, we have the sort of client libraries that allow them to just port to us, you know, directly from the applications and the languages that they're writing, but then they can also pull it out. And at that point nobody's gonna know the users, the end consumers of that data, better than those people who are building those applications. And so they're building these user interfaces, which are making all of that data accessible for, you know, their end users inside their organization. >>Well, Brian, great segment, great insight. Thanks for sharing all, all the complexities and, and IOT that you guys helped take away with the APIs and, and assembly and, and all the system architectures that are changing edge is real cloud is real. Yeah, absolutely. Mainstream enterprises. And you got developer attraction too, so congratulations. >>Yeah. It's >>Great. Well, thank any, any last word you wanna share >>Deal with? No, just, I mean, please, you know, if you're, if you're gonna, if you're gonna check out influx TV, download it, try out the open source contribute if you can. That's a, that's a huge thing. It's part of being the open source community. Um, you know, but definitely just, just use it. I think when once people use it, they try it out. They'll understand very, >>Very quickly. So open source with developers, enterprise and edge coming together all together. You're gonna hear more about that in the next segment, too. Right. Thanks for coming on. Okay. Thanks. When we return, Dave LAN will lead a panel on edge and data influx DB. You're watching the cube, the leader in high tech enterprise coverage. >>Why the startup, we move really fast. We find that in flex DB can move as fast as us. It's just a great group, very collaborative, very interested in manufacturing. And we see a bright future in working with influence. My name is Aaron Seley. I'm the CTO at HBI. Highlight's one of the first companies to focus on manufacturing data and apply the concepts of data ops, treat that as an asset to deliver to the it system, to enable applications like overall equipment effectiveness that can help the factory produce better, smarter, faster time series data. And manufacturing's really important. If you take a piece of equipment, you have the temperature pressure at the moment that you can look at to kind of see the state of what's going on. So without that context and understanding you can't do what manufacturers ultimately want to do, which is predict the future. >>Influx DB represents kind of a new way to storm time series data with some more advanced technology and more importantly, more open technologies. The other thing that influx does really well is once the data's influx, it's very easy to get out, right? They have a modern rest API and other ways to access the data. That would be much more difficult to do integrations with classic historians highlight can serve to model data, aggregate data on the shop floor from a multitude of sources, whether that be P C U a servers, manufacturing execution systems, E R P et cetera, and then push that seamlessly into influx to then be able to run calculations. Manufacturing is changing this industrial 4.0, and what we're seeing is influx being part of that equation. Being used to store data off the unified name space, we recommend InfluxDB all the time to customers that are exploring a new way to share data manufacturing called the unified name space who have open questions around how do I share this new data that's coming through my UNS or my QTT broker? How do I store this and be able to query it over time? And we often point to influx as a solution for that is a great brand. It's a great group of people and it's a great technology. >>Okay. We're now going to go into the customer panel and we'd like to welcome Angelo Fasi. Who's a software engineer at the Vera C Ruben observatory in Caleb McLaughlin whose senior spacecraft operations software engineer at loft orbital guys. Thanks for joining us. You don't wanna miss folks this interview, Caleb, let's start with you. You work for an extremely cool company. You're launching satellites into space. I mean, there, of course doing that is, is highly complex and not a cheap endeavor. Tell us about loft Orbi and what you guys do to attack that problem. >>Yeah, absolutely. And, uh, thanks for having me here by the way. Uh, so loft orbital is a, uh, company. That's a series B startup now, uh, who and our mission basically is to provide, uh, rapid access to space for all kinds of customers. Uh, historically if you want to fly something in space, do something in space, it's extremely expensive. You need to book a launch, build a bus, hire a team to operate it, you know, have a big software teams, uh, and then eventually worry about, you know, a bunch like just a lot of very specialized engineering. And what we're trying to do is change that from a super specialized problem that has an extremely high barrier of access to a infrastructure problem. So that it's almost as simple as, you know, deploying a VM in, uh, AWS or GCP is getting your, uh, programs, your mission deployed on orbit, uh, with access to, you know, different sensors, uh, cameras, radios, stuff like that. >>So that's, that's kind of our mission. And just to give a really brief example of the kind of customer that we can serve. Uh, there's a really cool company called, uh, totem labs who is working on building, uh, IOT cons, an IOT constellation for in of things, basically being able to get telemetry from all over the world. They're the first company to demonstrate indoor T, which means you have this little modem inside a container container that you, that you track from anywhere in the world as it's going across the ocean. Um, so they're, it's really little and they've been able to stay a small startup that's focused on their product, which is the, uh, that super crazy complicated, cool radio while we handle the whole space segment for them, which just, you know, before loft was really impossible. So that's, our mission is, uh, providing space infrastructure as a service. We are kind of groundbreaking in this area and we're serving, you know, a huge variety of customers with all kinds of different missions, um, and obviously generating a ton of data in space, uh, that we've gotta handle. Yeah. >>So amazing Caleb, what you guys do, I, now I know you were lured to the skies very early in your career, but how did you kinda land on this business? >>Yeah, so, you know, I've, I guess just a little bit about me for some people, you know, they don't necessarily know what they wanna do like early in their life. For me, I was five years old and I knew, you know, I want to be in the space industry. So, you know, I started in the air force, but have, uh, stayed in the space industry, my whole career and been a part of, uh, this is the fifth space startup that I've been a part of actually. So, you know, I've, I've, uh, kind of started out in satellites, did spent some time in working in, uh, the launch industry on rockets. Then, uh, now I'm here back in satellites and you know, honestly, this is the most exciting of the difference based startups. That I've been a part of >>Super interesting. Okay. Angelo, let's, let's talk about the Ruben observatory, ver C Ruben, famous woman scientist, you know, galaxy guru. Now you guys the observatory, you're up way up high. You're gonna get a good look at the Southern sky. Now I know COVID slowed you guys down a bit, but no doubt. You continued to code away on the software. I know you're getting close. You gotta be super excited. Give us the update on, on the observatory and your role. >>All right. So yeah, Rubin is a state of the art observatory that, uh, is in construction on a remote mountain in Chile. And, um, with Rubin, we conduct the, uh, large survey of space and time we are going to observe the sky with, uh, eight meter optical telescope and take, uh, a thousand pictures every night with a 3.2 gig up peaks of camera. And we are going to do that for 10 years, which is the duration of the survey. >>Yeah. Amazing project. Now you, you were a doctor of philosophy, so you probably spent some time thinking about what's out there and then you went out to earn a PhD in astronomy, in astrophysics. So this is something that you've been working on for the better part of your career, isn't it? >>Yeah, that's that's right. Uh, about 15 years, um, I studied physics in college, then I, um, got a PhD in astronomy and, uh, I worked for about five years in another project. Um, the dark energy survey before joining rubing in 2015. >>Yeah. Impressive. So it seems like you both, you know, your organizations are looking at space from two different angles. One thing you guys both have in common of course is, is, is software. And you both use InfluxDB as part of your, your data infrastructure. How did you discover influx DB get into it? How do you use the platform? Maybe Caleb, you could start. >>Uh, yeah, absolutely. So the first company that I extensively used, uh, influx DBN was a launch startup called, uh, Astra. And we were in the process of, uh, designing our, you know, our first generation rocket there and testing the engines, pumps, everything that goes into a rocket. Uh, and when I joined the company, our data story was not, uh, very mature. We were collecting a bunch of data in LabVIEW and engineers were taking that over to MATLAB to process it. Um, and at first there, you know, that's the way that a lot of engineers and scientists are used to working. Um, and at first that was, uh, like people weren't entirely sure that that was a, um, that that needed to change, but it's something the nice thing about InfluxDB is that, you know, it's so easy to deploy. So as the, our software engineering team was able to get it deployed and, you know, up and running very quickly and then quickly also backport all of the data that we collected thus far into influx and what, uh, was amazing to see. >>And as kind of the, the super cool moment with influx is, um, when we hooked that up to Grafana Grafana as the visualization platform we used with influx, cuz it works really well with it. Uh, there was like this aha moment of our engineers who are used to this post process kind of method for dealing with their data where they could just almost instantly easily discover data that they hadn't been able to see before and take the manual processes that they would run after a test and just throw those all in influx and have live data as tests were coming. And, you know, I saw them implementing like crazy rocket equation type stuff in influx, and it just was totally game changing for how we tested. >>So Angelo, I was explaining in my open, you know, you could, you could add a column in a traditional RDBMS and do time series, but with the volume of data that you're talking about, and the example of the Caleb just gave you, I mean, you have to have a purpose built time series database, where did you first learn about influx DB? >>Yeah, correct. So I work with the data management team, uh, and my first project was the record metrics that measured the performance of our software, uh, the software that we used to process the data. So I started implementing that in a relational database. Um, but then I realized that in fact, I was dealing with time series data and I should really use a solution built for that. And then I started looking at time series databases and I found influx B. And that was, uh, back in 2018. The another use for influx DB that I'm also interested is the visits database. Um, if you think about the observations we are moving the telescope all the time in pointing to specific directions, uh, in the Skype and taking pictures every 30 seconds. So that itself is a time series. And every point in that time series, uh, we call a visit. So we want to record the metadata about those visits and flex to, uh, that time here is going to be 10 years long, um, with about, uh, 1000 points every night. It's actually not too much data compared to other, other problems. It's, uh, really just a different, uh, time scale. >>The telescope at the Ruben observatory is like pun intended, I guess the star of the show. And I, I believe I read that it's gonna be the first of the next gen telescopes to come online. It's got this massive field of view, like three orders of magnitude times the Hub's widest camera view, which is amazing, right? That's like 40 moons in, in an image amazingly fast as well. What else can you tell us about the telescope? >>Um, this telescope, it has to move really fast and it also has to carry, uh, the primary mirror, which is an eight meter piece of glass. It's very heavy and it has to carry a camera, which has about the size of a small car. And this whole structure weighs about 300 tons for that to work. Uh, the telescope needs to be, uh, very compact and stiff. Uh, and one thing that's amazing about it's design is that the telescope, um, is 300 tons structure. It sits on a tiny film of oil, which has the diameter of, uh, human hair. And that makes an almost zero friction interface. In fact, a few people can move these enormous structure with only their hands. Uh, as you said, uh, another aspect that makes this telescope unique is the optical design. It's a wide field telescope. So each image has, uh, in diameter the size of about seven full moons. And, uh, with that, we can map the entire sky in only, uh, three days. And of course doing operations everything's, uh, controlled by software and it is automatic. Um there's a very complex piece of software, uh, called the scheduler, which is responsible for moving the telescope, um, and the camera, which is, uh, recording 15 terabytes of data every night. >>Hmm. And, and, and Angela, all this data lands in influx DB. Correct. And what are you doing with, with all that data? >>Yeah, actually not. Um, so we are using flex DB to record engineering data and metadata about the observations like telemetry events and commands from the telescope. That's a much smaller data set compared to the images, but it is still challenging because, uh, you, you have some high frequency data, uh, that the system needs to keep up and we need to, to start this data and have it around for the lifetime of the price. Mm, >>Got it. Thank you. Okay, Caleb, let's bring you back in and can tell us more about the, you got these dishwasher size satellites. You're kind of using a multi-tenant model. I think it's genius, but, but tell us about the satellites themselves. >>Yeah, absolutely. So, uh, we have in space, some satellites already that as you said, are like dishwasher, mini fridge kind of size. Um, and we're working on a bunch more that are, you know, a variety of sizes from shoebox to, I guess, a few times larger than what we have today. Uh, and it is, we do shoot to have effectively something like a multi-tenant model where, uh, we will buy a bus off the shelf. The bus is, uh, what you can kind of think of as the core piece of the satellite, almost like a motherboard or something where it's providing the power. It has the solar panels, it has some radios attached to it. Uh, it handles the attitude control, basically steers the spacecraft in orbit. And then we build also in house, what we call our payload hub, which is, has all, any customer payloads attached and our own kind of edge processing sort of capabilities built into it. >>And, uh, so we integrate that. We launch it, uh, and those things, because they're in lower orbit, they're orbiting the earth every 90 minutes. That's, you know, seven kilometers per second, which is several times faster than a speeding bullet. So we've got, we have, uh, one of the unique challenges of operating spacecraft and lower orbit is that generally you can't talk to them all the time. So we're managing these things through very brief windows of time, uh, where we get to talk to them through our ground sites, either in Antarctica or, you know, in the north pole region. >>Talk more about how you use influx DB to make sense of this data through all this tech that you're launching into space. >>We basically previously we started off when I joined the company, storing all of that as Angelo did in a regular relational database. And we found that it was, uh, so slow in the size of our data would balloon over the course of a couple days to the point where we weren't able to even store all of the data that we were getting. Uh, so we migrated to influx DB to store our time series telemetry from the spacecraft. So, you know, that's things like, uh, power level voltage, um, currents counts, whatever, whatever metadata we need to monitor about the spacecraft. We now store that in, uh, in influx DB. Uh, and that has, you know, now we can actually easily store the entire volume of data for the mission life so far without having to worry about, you know, the size bloating to an unmanageable amount. >>And we can also seamlessly query, uh, large chunks of data. Like if I need to see, you know, for example, as an operator, I might wanna see how my, uh, battery state of charge is evolving over the course of the year. I can have a plot and an influx that loads that in a fraction of a second for a year's worth of data, because it does, you know, intelligent, um, I can intelligently group the data by, uh, sliding time interval. Uh, so, you know, it's been extremely powerful for us to access the data and, you know, as time has gone on, we've gradually migrated more and more of our operating data into influx. >>You know, let's, let's talk a little bit, uh, uh, but we throw this term around a lot of, you know, data driven, a lot of companies say, oh, yes, we're data driven, but you guys really are. I mean, you' got data at the core, Caleb, what does that, what does that mean to you? >>Yeah, so, you know, I think the, and the clearest example of when I saw this be like totally game changing is what I mentioned before at Astro where our engineer's feedback loop went from, you know, a lot of kind of slow researching, digging into the data to like an instant instantaneous, almost seeing the data, making decisions based on it immediately, rather than having to wait for some processing. And that's something that I've also seen echoed in my current role. Um, but to give another practical example, uh, as I said, we have a huge amount of data that comes down every orbit, and we need to be able to ingest all of that data almost instantaneously and provide it to the operator. And near real time, you know, about a second worth of latency is all that's acceptable for us to react to, to see what is coming down from the spacecraft and building that pipeline is challenging from a software engineering standpoint. >>Um, our primary language is Python, which isn't necessarily that fast. So what we've done is started, you know, in the, in the goal of being data driven is publish metrics on individual, uh, how individual pieces of our data processing pipeline are performing into influx as well. And we do that in production as well as in dev. Uh, so we have kind of a production monitoring, uh, flow. And what that has done is allow us to make intelligent decisions on our software development roadmap, where it makes the most sense for us to, uh, focus our development efforts in terms of improving our software efficiency. Uh, just because we have that visibility into where the real problems are. Um, it's sometimes we've found ourselves before we started doing this kind of chasing rabbits that weren't necessarily the real root cause of issues that we were seeing. Uh, but now, now that we're being a bit more data driven, there we are being much more effective in where we're spending our resources and our time, which is especially critical to us as we scale to, from supporting a couple satellites, to supporting many, many satellites at >>Once. Yeah. Coach. So you reduced those dead ends, maybe Angela, you could talk about what, what sort of data driven means to, to you and your teams? >>I would say that, um, having, uh, real time visibility, uh, to the telemetry data and, and metrics is, is, is crucial for us. We, we need, we need to make sure that the image that we collect with the telescope, uh, have good quality and, um, that they are within the specifications, uh, to meet our science goals. And so if they are not, uh, we want to know that as soon as possible and then, uh, start fixing problems. >>Caleb, what are your sort of event, you know, intervals like? >>So I would say that, you know, as of today on the spacecraft, the event, the, the level of timing that we deal with probably tops out at about, uh, 20 Hertz, 20 measurements per second on, uh, things like our, uh, gyroscopes, but the, you know, I think the, the core point here of the ability to have high precision data is extremely important for these kinds of scientific applications. And I'll give an example, uh, from when I worked at, on the rocket at Astra there, our baseline data rate that we would ingest data during a test is, uh, 500 Hertz. So 500 samples per second. And in some cases we would actually, uh, need to ingest much higher rate data, even up to like 1.5 kilohertz. So, uh, extremely, extremely high precision, uh, data there where timing really matters a lot. And, uh, you know, I can, one of the really powerful things about influx is the fact that it can handle this. >>That's one of the reasons we chose it, uh, because there's times when we're looking at the results of a firing where you're zooming in, you know, I talked earlier about how on my current job, we often zoom out to look, look at a year's worth of data. You're zooming in to where your screen is preoccupied by a tiny fraction of a second. And you need to see same thing as Angela just said, not just the actual telemetry, which is coming in at a high rate, but the events that are coming out of our controllers. So that can be something like, Hey, I opened this valve at exactly this time and that goes, we wanna have that at, you know, micro or even nanosecond precision so that we know, okay, we saw a spike in chamber pressure at, you know, at this exact moment, was that before or after this valve open, those kind of, uh, that kind of visibility is critical in these kind of scientific, uh, applications and absolutely game changing to be able to see that in, uh, near real time and, uh, with a really easy way for engineers to be able to visualize this data themselves without having to wait for, uh, software engineers to go build it for them. >>Can the scientists do self-serve or are you, do you have to design and build all the analytics and, and queries for your >>Scientists? Well, I think that's, that's absolutely from, from my perspective, that's absolutely one of the best things about influx and what I've seen be game changing is that, uh, generally I'd say anyone can learn to use influx. Um, and honestly, most of our users might not even know they're using influx, um, because what this, the interface that we expose to them is Grafana, which is, um, a generic graphing, uh, open source graphing library that is very similar to influx own chronograph. Sure. And what it does is, uh, let it provides this, uh, almost it's a very intuitive UI for building your queries. So you choose a measurement and it shows a dropdown of available measurements. And then you choose a particular, the particular field you wanna look at. And again, that's a dropdown, so it's really easy for our users to discover. And there's kind of point and click options for doing math aggregations. You can even do like perfect kind of predictions all within Grafana, the Grafana user interface, which is really just a wrapper around the APIs and functionality of the influx provides putting >>Data in the hands of those, you know, who have the context of domain experts is, is key. Angela, is it the same situation for you? Is it self serve? >>Yeah, correct. Uh, as I mentioned before, um, we have the astronomers making their own dashboards because they know what exactly what they, they need to, to visualize. Yeah. I mean, it's all about using the right tool for the job. I think, uh, for us, when I joined the company, we weren't using influx DB and we, we were dealing with serious issues of the database growing to an incredible size extremely quickly, and being unable to like even querying short periods of data was taking on the order of seconds, which is just not possible for operations >>Guys. This has been really formative it's, it's pretty exciting to see how the edge is mountaintops, lower orbits to be space is the ultimate edge. Isn't it. I wonder if you could answer two questions to, to wrap here, you know, what comes next for you guys? Uh, and is there something that you're really excited about that, that you're working on Caleb, maybe you could go first and an Angela, you can bring us home. >>Uh, basically what's next for loft. Orbital is more, more satellites, a greater push towards infrastructure and really making, you know, our mission is to make space simple for our customers and for everyone. And we're scaling the company like crazy now, uh, making that happen, it's extremely exciting and extremely exciting time to be in this company and to be in this industry as a whole, because there are so many interesting applications out there. So many cool ways of leveraging space that, uh, people are taking advantage of. And with, uh, companies like SpaceX and the now rapidly lowering cost, cost of launch, it's just a really exciting place to be. And we're launching more satellites. We are scaling up for some constellations and our ground system has to be improved to match. So there's a lot of, uh, improvements that we're working on to really scale up our control software, to be best in class and, uh, make it capable of handling such a large workload. So >>You guys hiring >><laugh>, we are absolutely hiring. So, uh, I would in we're we need, we have PE positions all over the company. So, uh, we need software engineers. We need people who do more aerospace, specific stuff. So, uh, absolutely. I'd encourage anyone to check out the loft orbital website, if there's, if this is at all interesting. >>All right. Angela, bring us home. >>Yeah. So what's next for us is really, uh, getting this, um, telescope working and collecting data. And when that's happen is going to be just, um, the Lu of data coming out of this camera and handling all, uh, that data is going to be really challenging. Uh, yeah. I wanna wanna be here for that. <laugh> I'm looking forward, uh, like for next year we have like an important milestone, which is our, um, commissioning camera, which is a simplified version of the, of the full camera it's going to be on sky. And so yeah, most of the system has to be working by them. >>Nice. All right, guys, you know, with that, we're gonna end it. Thank you so much, really fascinating, and thanks to influx DB for making this possible, really groundbreaking stuff, enabling value creation at the edge, you know, in the cloud and of course, beyond at the space. So really transformational work that you guys are doing. So congratulations and really appreciate the broader community. I can't wait to see what comes next from having this entire ecosystem. Now, in a moment, I'll be back to wrap up. This is Dave ante, and you're watching the cube, the leader in high tech enterprise coverage. >>Welcome Telegraph is a popular open source data collection. Agent Telegraph collects data from hundreds of systems like IOT sensors, cloud deployments, and enterprise applications. It's used by everyone from individual developers and hobbyists to large corporate teams. The Telegraph project has a very welcoming and active open source community. Learn how to get involved by visiting the Telegraph GitHub page, whether you want to contribute code, improve documentation, participate in testing, or just show what you're doing with Telegraph. We'd love to hear what you're building. >>Thanks for watching. Moving the world with influx DB made possible by influx data. I hope you learn some things and are inspired to look deeper into where time series databases might fit into your environment. If you're dealing with large and or fast data volumes, and you wanna scale cost effectively with the highest performance and you're analyzing metrics and data over time times, series databases just might be a great fit for you. Try InfluxDB out. You can start with a free cloud account by clicking on the link and the resources below. Remember all these recordings are gonna be available on demand of the cube.net and influx data.com. So check those out and poke around influx data. They are the folks behind InfluxDB and one of the leaders in the space, we hope you enjoyed the program. This is Dave Valante for the cube. We'll see you soon.

(bright music) >> Okay, we're back in theCUBE. We said we were signing off for the night, but during the hallway track, we ran into old friend Stu Miniman who was the Director of Market Insights at Red Hat. Stu, friend of theCUBE done the thousands of CUBE interviews. >> Dave, it's great to be here. Thanks for pulling me on, you and I hosted Red Hat Summit before. It's great to see Paul here. I was actually, I was talking to some of the Red Hatters walking around Boston. It's great to have an event here. Boston's got strong presence and I understand, I think was either first or second year, they had it over... What's the building they're tearing down right down the road here. Was that the World Trade Center? I think that's where they actually held it, the first time they were here. We hosted theCUBE >> So they moved up. >> at the Hines Convention Center. We did theCUBE for summit at the BCEC next door. And of course, with the pandemic being what it was, we're a little smaller, nice intimate event here. It's great to be able to room the hall, see a whole bunch of people and lots watching online. >> It's great, it's around the same size as those, remember those Vertica Big Data events that we used to have here. And I like that you were commenting out at the theater and the around this morning for the keynotes, that was good. And the keynotes being compressed, I think, is real value for the attendees, you know? 'Cause people come to these events, they want to see each other, you know? They want to... It's like the band getting back together. And so when you're stuck in the keynote room, it's like, "Oh, it's okay, it's time to go." >> I don't know that any of us used to sitting at home where I could just click to another tab or pause it or run for, do something for the family, or a quick bio break. It's the three-hour keynote I hope has been retired. >> But it's an interesting point though, that the virtual event really is driving the physical and this, the way Red Hat marketed this event was very much around the virtual attendee. Physical was almost an afterthought, so. >> Right, this is an invite only for in-person. So you're absolutely right. It's optimizing the things that are being streamed, the online audience is the big audience. And we just happy to be in here to clap and do some things see around what you're doing. >> Wonderful see that becoming the norm. >> I think like virtual Stu, you know this well when virtual first came in, nobody had a clue with what they were doing. It was really hard. They tried different things, they tried to take the physical and just jam it into the virtual. That didn't work, they tried doing fun things. They would bring in a famous person or a comedian. And that kind of worked, I guess, but everybody showed up for that and then left. And I think they're trying to figure it out what this hybrid thing is. I've seen it both ways. I've seen situations like this, where they're really sensitive to the virtual. I've seen others where that's the FOMO of the physical, people want physical. So, yeah, I think it depends. I mean, reinvent last year was heavy physical. >> Yeah, with 15,000 people there. >> Pretty long keynotes, you know? So maybe Amazon can get away with it, but I think most companies aren't going to be able to. So what is the market telling you? What are these insights? >> So Dave just talking about Amazon, obviously, the world I live in cloud and that discussion of cloud, the journey that customers are going on is where we're spending a lot of the discussions. So, it was great to hear in the keynote, talked about our deep partnerships with the cloud providers and what we're doing to help people with, you like to call it super cloud, some call it hybrid, or multi-cloud... >> New name. (crosstalk) Meta-Cloud, come on. >> All right, you know if Che's my executive, so it's wonderful. >> Love it. >> But we'll see, if I could put on my VR Goggles and that will help me move things. But I love like the partnership announcement with General Motors today because not every company has the needs of software driven electric vehicles all over the place. But the technology that we build for them actually has ramifications everywhere. We've working to take Kubernetes and make it smaller over time. So things that we do at the edge benefit the cloud, benefit what we do in the data center, it's that advancement of science and technology just lifts all boats. >> So what's your take on all this? The EV and software on wheels. I mean, Tesla obviously has a huge lead. It's kind of like the Amazon of vehicles, right? It's sort of inspired a whole new wave of innovation. Now you've got every automobile manufacturer kind of go and after. That is the future of vehicles is something you followed or something you have an opinion on Stu? >> Absolutely. It's driving innovation in some ways, the way the DOS drove innovation on the desktop, if you remember the 64K DOS limit, for years, that was... The software developers came up with some amazing ways to work within that 64K limit. Then when it was gone, we got bloatware, but it actually does enforce a level of discipline on you to try to figure out how to make software run better, run more efficiently. And that has upstream impacts on the enterprise products. >> Well, right. So following your analogy, you talk about the enablement to the desktop, Linux was a huge influence on allowing the individual person to write code and write software, and what's happening in the EV, it's software platform. All of these innovations that we're seeing across industries, it's how is software transforming things. We go back to the mark end reasons, software's eating the world, open source is the way that software is developed. Who's at the intersection of all those? We think we have a nice part to play in that. I loved tha- Dave, I don't know if you caught at the end of the keynote, Matt Hicks basically said, "Our mission isn't just to write enterprise software. "Our mission is based off of open source because open source unlocks innovation for the world." And that's one of the things that drew me to Red Hat, it's not just tech in good places, but allowing underrepresented, different countries to participate in what's happening with software. And we can all move that ball forward. >> Well, can we declare victory for open source because it's not just open source products, but everything that's developed today, whether proprietary or open has open source in it. >> Paul, I agree. Open source is the development model period, today. Are there some places that there's proprietary? Absolutely. But I had a discussion with Deepak Singh who's been on theCUBE many times. He said like, our default is, we start with open source code. I mean, even Amazon when you start talking about that. >> I said this, the $70 billion business on open source. >> Exactly. >> Necessarily give it back, but that say, Hey, this is... All's fair in tech and more. >> It is interesting how the managed service model has sort of rescued open source, open source companies, that were trying to do the Red Hat model. No one's ever really successfully duplicated the Red Hat model. A lot of companies were floundering and failing. And then the managed service option came along. And so now they're all cloud service providers. >> So the only thing I'd say is that there are some other peers we have in the industry that are built off open source they're doing okay. The recent example, GitLab and Hashicorp, both went public. Hashi is doing some managed services, but it's not the majority of their product. Look at a company like Mongo, they've heavily pivoted toward the managed service. It is where we see the largest growth in our area. The products that we have again with Amazon, with Microsoft, huge growth, lots of interest. It's one of the things I spend most of my time talking on. >> I think Databricks is another interesting example 'cause Cloudera was the now company and they had the sort of open core, and then they had the proprietary piece, and they've obviously didn't work. Databricks when they developed Spark out of Berkeley, everybody thought they were going to do kind of a similar model. Instead, they went for all in managed services. And it's really worked well, I think they were ahead of that curve and you're seeing it now is it's what customers want. >> Well, I mean, Dave, you cover the database market pretty heavily. How many different open source database options are there today? And that's one of the things we're solving. When you look at what is Red Hat doing in the cloud? Okay, I've got lots of databases. Well, we have something called, it's Red Hat Open Database Access, which is from a developer, I don't want to have to think about, I've got six different databases, which one, where's the repository? How does all that happen? We give that consistency, it's tied into OpenShift, so it can help abstract some of those pieces. we've got same Kafka streaming and we've got APIs. So it's frameworks and enablers to help bridge that gap between the complexity that's out there, in the cloud and for the developer tool chain. >> That's really important role you guys play though because you had this proliferation, you mentioned Mongo. So many others, Presto and Starbursts, et cetera, so many other open source options out there now. And companies, developers want to work with multiple databases within the same application. And you have a role in making that easy. >> Yeah, so and that is, if you talk about the question I get all the time is, what's next for Kubernetes? Dave, you and I did a preview for KubeCon and it's automation and simplicity that we need to be. It's not enough to just say, "Hey, we've got APIs." It's like Dave, we used to say, "We've got standards? Great." Everybody's implementation was a little bit different. So we have API Sprawl today. So it's building that ecosystem. You've been talking to a number of our partners. We are very active in the community and trying to do things that can lift up the community, help the developers, help that cloud native ecosystem, help our customers move faster. >> Yeah API's better than scripts, but they got to be managed, right? So, and that's really what you guys are doing that's different. You're not trying to own everything, right? It's sort of antithetical to how billions and trillions are made in the IT industry. >> I remember a few years ago we talked here, and you look at the size that Red Hat is. And the question is, could Red Hat have monetized more if the model was a little different? It's like, well maybe, but that's not the why. I love that they actually had Simon Sinek come in and work with Red Hat and that open, unlocks the world. Like that's the core, it's the why. When I join, they're like, here's a book of Red Hat, you can get it online and that why of what we do, so we never have to think of how do we get there. We did an acquisition in the security space a year ago, StackRox, took us a year, it's open source. Stackrox.io, it's community driven, open source project there because we could have said, "Oh, well, yeah, it's kind of open source and there's pieces that are open source, but we want it to be fully open source." You just talked to Gunnar about how he's RHEL nine, based off CentOS stream, and now developing out in the open with that model, so. >> Well, you were always a big fan of Whitehurst culture book, right? It makes a difference. >> The open organization and right, Red Hat? That culture is special. It's definitely interesting. So first of all, most companies are built with the hierarchy in mind. Had a friend of mine that when he joined Red Hat, he's like, I don't understand, it's almost like you have like lots of individual contractors, all doing their things 'cause Red Hat works on thousands of projects. But I remember talking to Rackspace years ago when OpenStack was a thing and they're like, "How do you figure out what to work on?" "Oh, well we hired great people and they work on what's important to them." And I'm like, "That doesn't sound like a business." And he is like, "Well, we struggle sometimes to that balance." Red Hat has found that balance because we work on a lot of different projects and there are people inside Red Hat that are, you know, they care more about the project than they do the business, but there's the overall view as to where we participate and where we productize because we're not creating IP because it's all an open source. So it's the monetizations, the relationships we have our customers, the ecosystems that we build. And so that is special. And I'll tell you that my line has been Red Hat on the inside is even more Red Hat. The debates and the discussions are brutal. I mean, technical people tearing things apart, questioning things and you can't be thin skinned. And the other thing is, what's great is new people. I've talked to so many people that started at Red Hat as interns and will stay for seven, eight years. And they come there and they have as much of a seat at the table, and when I talk to new people, your job, is if you don't understand something or you think we might be able to do it differently, you better speak up because we want your opinion and we'll take that, everybody takes that into consideration. It's not like, does the decision go all the way up to this executive? And it's like, no, it's done more at the team. >> The cultural contrast between that and your parent, IBM, couldn't be more dramatic. And we talked earlier with Paul Cormier about has IBM really walked the walk when it comes to leaving Red Hat alone. Naturally he said, "Yes." Well what's your perspective. >> Yeah, are there some big blue people across the street or something I heard that did this event, but look, do we interact with IBM? Of course. One of the reasons that IBM and IBM Services, both products and services should be able to help get us breadth in the marketplace. There are times that we go arm and arm into customer meetings and there are times that customers tell us, "I like Red Hat, I don't like IBM." And there's other ones that have been like, "Well, I'm a long time IBM, I'm not sure about Red Hat." And we have to be able to meet all of those customers where they are. But from my standpoint, I've got a Red Hat badge, I've got a Red Hat email, I've got Red Hat benefits. So we are fiercely independent. And you know, Paul, we've done blogs and there's lots of articles been written is, Red Hat will stay Red Hat. I didn't happen to catch Arvin I know was on CNBC today and talking at their event, but I'm sure Red Hat got mentioned, but... >> Well, he talks about Red Hat all time. >> But in his call he's talking backwards. >> It's interesting that he's not here, greeting this audience, right? It's again, almost by design, right? >> But maybe that's supposed to be... >> Hundreds of yards away. >> And one of the questions being in the cloud group is I'm not out pitching IBM Cloud, you know? If a customer comes to me and asks about, we have a deep partnership and IBM will be happy to tell you about our integrations, as opposed to, I'm happy to go into a deep discussion of what we're doing with Google, Amazon, and Microsoft. So that's how we do it. It's very different Dave, from you and I watch really closely the VMware-EMC, VMware-Dell, and how that relationship. This one is different. We are owned by IBM, but we mostly, it does IBM fund initiatives and have certain strategic things that are done, absolutely. But we maintain Red Hat. >> But there are similarities. I mean, VMware crowd didn't want to talk about EMC, but they had to, they were kind of forced to. Whereas, you're not being forced to. >> And then once Dell came in there, it was joint product development. >> I always thought a spin in. Would've been the more effective, of course, Michael Dell and Egon wouldn't have gotten their $40 billion out. But I think a spin in was more natural based on where they were going. And it would've been, I think, a more dominant position in the marketplace. They would've had more software, but again, financially it wouldn't have made as much sense, but that whole dynamic is different. I mean, but people said they were going to look at VMware as a model and it's been largely different because remember, VMware of course was a separate company, now is a fully separate company. Red Hat was integrated, we thought, okay, are they going to get blue washed? We're watching and watching, and watching, you had said, well, if the Red Hat culture isn't permeating IBM, then it's a failure. And I don't know if that's happening, but it's definitely... >> I think a long time for that. >> It's definitely been preserved. >> I mean, Dave, I know I read one article at the beginning of the year is, can Arvin make IBM, Microsoft Junior? Follow the same turnaround that Satya Nadella drove over there. IBM I think making some progress, I mean, I read and watch what you and the team are all writing about it. And I'll withhold judgment on IBM. Obviously, there's certain financial things that we'd love to see IBM succeed. We worry about our business. We do our thing and IBM shares our results and they've been solid, so. >> Microsoft had such massive cash flow that even bomber couldn't screw it up. Well, I mean, this is true, right? I mean, you think about how were relevant Microsoft was in the conversation during his tenure and yet they never got really... They maintained a position so that when the Nadella came in, they were able to reascend and now are becoming that dominant player. I mean, IBM just doesn't have that cash flow and that luxury, but I mean, if he pulls it off, he'll be the CEO of the decade. >> You mentioned partners earlier, big concern when the acquisition was first announced, was that the Dells and the HP's and the such wouldn't want to work with Red Hat anymore, you've sort of been here through that transition. Is that an issue? >> Not that I've seen, no. I mean, the hardware suppliers, the ISVs, the GSIs are all very important. It was great to see, I think you had Accenture on theCUBE today, obviously very important partner as we go to the cloud. IBM's another important partner, not only for IBM Cloud, but IBM Services, deep partnership with Azure and AWS. So those partners and from a technology standpoint, the cloud native ecosystem, we talked about, it's not just a Red Hat product. I constantly have to talk about, look, we have a lot of pieces, but your developers are going to have other tools that they're going to use and the security space. There is no such thing as a silver bullet. So I've been having some great conversations here already this week with some of our partners that are helping us to round out that whole solution, help our customers because it has to be, it's an ecosystem. And we're one of the drivers to help that move forward. >> Well, I mean, we were at Dell Tech World last week, and there's a lot of talk about DevSecOps and DevOps and Dell being more developer friendly. Obviously they got a long way to go, but you can't have that take that posture and not have a relationship with Red Hat. If all you got is Pivotal and VMware, and Tansu >> I was thrilled to hear the OpenShift mention in the keynote when they talked about what they were doing. >> How could you not, how could you have any credibility if you're just like, Oh, Pivotal, Pivotal, Pivotal, Tansu, Tansu. Tansu is doing its thing. And they smart strategy. >> VMware is also a partner of ours, but that we would hope that with VMware being independent, that does open the door for us to do more with them. >> Yeah, because you guys have had a weird relationship with them, under ownership of EMC and then Dell, right? And then the whole IBM thing. But it's just a different world now. Ecosystems are forming and reforming, and Dell's building out its own cloud and it's got to have... Look at Amazon, I wrote about this. I said, "Can you envision the day where Dell actually offers competitive products in its suite, in its service offering?" I mean, it's hard to see, they're not there yet. They're not even close. And they have this high say/do ratio, or really it's a low say/do, they say high say/do, but look at what they did with Nutanix. You look over- (chuckles) would tell if it's the Cisco relationship. So it's got to get better at that. And it will, I really do believe. That's new thinking and same thing with HPE. And, I don't know about Lenovo that not as much of an ecosystem play, but certainly Dell and HPE. >> Absolutely. Michael Dell would always love to poke at HPE and HP really went very far down the path of their own products. They went away from their services organization that used to be more like IBM, that would offer lots of different offerings and very much, it was HP Invent. Well, if we didn't invent it, you're not getting it from us. So Dell, we'll see, as you said, the ecosystems are definitely forming, converging and going in lots of different directions. >> But your position is, Hey, we're here, we're here to help. >> Yeah, we're here. We have customers, one of the best proof points I have is the solution that we have with Amazon. Amazon doesn't do the engineering work to make us a native offering if they didn't have the customer demand because Amazon's driven off of data. So they came to us, they worked with us. It's a lot of work to be able to make that happen, but you want to make it frictionless for customers so that they can adopt that. That's a long path. >> All right, so evening event, there's a customer event this evening upstairs in the lobby. Microsoft is having a little shin dig, and then serves a lot of customer dinners going on. So Stu, we'll see you out there tonight. >> All right, thanks you. >> Were watching a brewing somewhere. >> Keynotes tomorrow, a lot of good sessions and enablement, and yeah, it's great to be in person to be able to bump some people, meet some people and, Hey, I'm still a year and a half in still meeting a lot of my peers in person for the first time. >> Yeah, and that's kind of weird, isn't it? Imagine. And then we kick off tomorrow at 10:00 AM. Actually, Stephanie Chiras is coming on. There she is in the background. She's always a great guest and maybe do a little kickoff and have some fun tomorrow. So this is Dave Vellante for Stu Miniman, Paul Gillin, who's my co-host. You're watching theCUBEs coverage of Red Hat Summit 2022. We'll see you tomorrow. (bright music)

(upbeat music) >> Okay, welcome back everyone to theCUBE's coverage of AWS Summit 2022, here at Moscone Center live on the floor, I'm John Furry host of theCUBE, all the action day two, remember AWS Summit in New York City is coming in the summer. We'll be there as well. Got a great guest Murli Murli who's the VP and GM of Cloud Native Business Unit Portworx, been in theCUBE multiple times. We were just talking about the customer he had on Ford from Detroit, where kubernetes will be this year. >> That's right. >> Great to see you. >> Yeah, same here, John. Great to see. >> So, what's the update? Quickly this, before we get into the country, give the update on what's going on in the company, what's happening? >> Well, you know, we've been acquired by Pure Storage it's well over a year. So we've had one full year of being inside of Pure. It's been wonderful, right? So we've had a great ride so far, The products have been renewed. We've got a bunch of integrations with Pure. We more than doubled our business and more than doubled our head count. So things are going great. >> I always had a, congratulations by the way. And I was going to ask about the integration but before I get there, yeah, we've been always like play some jokes on theCUBE and because serverless is so hot, I've been using storage lists and actually saw a startup yesterday had the word networking lists in their title. So this idea of like making things easier, but me, I mean serverless of this is basically servers that make it easier. >> Yeah, yeah >> So this is kind of where we see Cloud Native going. Can you share your thoughts on how Pure and Portworx are bringing this together? Because you can almost connect the dots in my mind. So say specifically what is the Cloud Native angle with Pure? >> Yeah. So look, I'll kind of start by being captain an obvious, I guess. Just sort of stating some obvious stuff and then get to what I hope will be a little bit more new and interesting. So the obvious stuff to start with is just the fact that Cloud Native is exploding. Containers are exploding. It's kind of a well known fact that 85% of the enterprise organizations around the world are pretty much going to be deploying containers, if not already in the next couple of years, right? So one it's really happening. The, buzz is now, it's not just in the future, the hype is now. The second part of that is it's really part of that is things are going production. 56% of these organizations are in production already. And that's the number is going to climb to 80 fairly quickly. So not only is this stuff being deployed as being deployed in sort of fairly mission critical, especially Greenfield applications. So that's kind of one, right? Now, the second thing that we're seeing is as they go in into production, John, the migraines are starting, right? Customer migraines, right? It's always happens in stuff that they have not looked around the corner and anticipated. So one of them is, again, a fairly obvious one is as they go into production, they need to be able to kind of recover from some oops that happens, right? And the kinds of think about this, right? John, this stuff is rapidly changing, right? Look at how many versions of kubernetes come out on a regular basis. On top of that, you got all these app, virgins, new database virgins, new stuff, vendors like us, ourselves have new virgins. So with all these new virgins, when you put it all together the stack, sometimes misbehave. So you got to kind of, "Hey, let me go recover." Right? You have outages. So essentially the whole area of data protection becomes a lot more critical. That's the migraine that people are beginning to get now, right? They can feel the migraine coming on. The good news is this is not new stuff. People know on- >> John: The DevOps. >> Yeah. Well, and in fact it is that transition from DevOps to ITOps, right? People know that they're going into production, that they need backup and data protection and disaster recovery. So in a way it's kind of good news, bad news, the good news is they know that they need it. The bad news is, it turns out that it's kind of interesting as they go Cloud Native, the technology stack has changed. So 82% of customers who are kind of deploying Cloud Native are worried about data protection. And in fact, I'll go one step further 67% of those people have actually kind of looked at what they can get from existing vendors and are going, "Hey, this is not it. This is not going to do my stuff for me." >> And by the way, just to throw a little bit more gas on that fire is ransomware attacks. So any kind of vulnerability opening? Maybe make people are scared. >> Murli: Absolutely. >> So with- >> Murli: Its a board level topic, right? >> Yeah, and then you bring down the DevOps, which is we all know the innovation formula launch in iterate, pivot, iterate, pivot, then innovation you get the formula, all your metrics, but it's a system. >> Correct. >> Storage is part now of a system when you bring Cloud Native into it, you have a consequence if something changes. >> Murli: Correct. >> So I see that. And the question I have for you is, where are we in the stability side of it? Are we close to getting there and what's coming out to help that, is it more tooling? Because the trend is people are building tools around their Cloud Native thing. I was just talking to MongoDB and they got a database, now that's all tooling. Vertically integrate into the asset or the product, because it integrates with APIs, right? So that makes total sense. >> So I think there's kind of again, a good news, bad news there, right? There's a lot of good news, right? In the world of containers and kubernetes what are some of the good news items, right? A lot of the APIs have settled down have been defined well, CNCF has done a great job promoting that, right? So the APIs are stable, right? Second, the product feature set, have become more stable, particularly sort of the the core kubernetes product security kind of stuff, right? Now what's the bad news. The bad news is, while these things are stable they are not ready for scale in every case yet, right? And when you integrate at scale, so and typically the tipping point is around 20 to 30 nodes, right? So typically when you go beyond 20 to 30 nodes then the stuff starts to come a apart, right? Like, the wheels come off of the train and all of that. And that's typically because there's a lot of the products that were designed for DevOps, are not well suited for ITOps. So really there is a new- >> And the talent culture. >> Exactly. >> Talent and culture sometimes aren't ready or are changing. >> So it's a whole bunch of people trying to use kind of a maturing product set with skill sets that are pretty low, right? So when we get into production, then other factors come into play, high availability, right? Security, you talk about ransomware, disaster recovery backup. So these are things that are sort of, I would say not 101 problems, but 201 problems, so right? This is natural as we go to that part of the thing. And that's the kind of stuff that, Portworx and Pure Storage have been kind of focused on solving. And that's kind of been how we've made our mark in the industry, right? We've helped people really get to production on some of these different points. >> Expectation on both companies have been strong, high quality, obviously performance on Pure side from day one, just did a great job with the products. Now, when you go into Cloud Native you have now this connection okay. To the customer, again I think huge point on the changing landscape. How do you see that IT to DevOps emerging? Because the trend that we're seeing is, abstracting way the complexities of management. So I won't say managed services are more of a trend, they've always been around but the notion of making it easier for customers. >> Yep, absolutely right. >> Super important. So can you guys share what you guys are doing to make it easier because not everyone has a DevOps team. >> Yeah, so look, the number one way things are made more easy, is to make it more consumable by making it as a service. So this is one of the things, here we are, at AWS Summit, right? And delighted to be here by the way. And we have a strategic alliance with with AWS, and specifically, what we're here to announce really is that we're announcing a backup as a SaaS product. Coming up in a few weeks we're going to be giing running on AWS as a service integrated with AWS. So essentially what happens is, if you have a containerized set of applications you're deploying it on EKS, ECS, AWS, what have you. We will automatically provide the ability for that to be backed up scaled and to be very, very container granular, very app specific, right? Yeah, so it's designed specifically for kubernetes. Now here's the kind of key thing to say, right? Backup's been around for a long time. You've interviewed, tons of backup people in the past. But traditional backup is just not going to work for kubernetes. And it's very simple if you think about it, John. >> John: And why is that? >> It's a very simple thing, right? Traditional backup focuses on apps and data, right? Those are the two kind of legs of that. And they create catalogs and then do a great job there. Well, here's, what's happened with Cloud Native. You have a thing inserted in the middle called kubernetes. So when you take a snapshot, I'm now kind of going into a specific kind of, world of storage, right? When you take a snapshot, what Portworx does is we take a 3D snapshot. What you really need to recover, from a backup situation where, you want to go back to the earlier stage to be kubernetes specific, you need a app snapshot, snapshot of the kubernetes spec, pod spec, And third of snapshot of the data. Well, traditional, backup folks are not taking that middle snapshot. So we do a 3D snapshot and we recover all three which is really what you need to be able to kind of like get backed up, get recovered in minutes. >> Okay and so the alternative to not doing that is what? What will happen? >> You To do that, to do your old machine level backup? So what happens with traditional backups are typically VM level or machine level, right? So you're taking a snapshot of the whole kind of machine and server or VM setup and then you recover all of that, and then you run kubernetes on that and then you try to recover it- >> John: To either stand everything up again. >> Yeah, yeah. >> John: Pretty much. >> Yeah. Whereas, what do most people want to do? This is a very different use case, by the way, right? How does this work? What people are doing for kubernetes is they're not doing archival kind of backup. What they're doing is real time, right? You're running an ops. Like I said, you got an oops, "Hey, a new release for one of the new databases then work right? Boom! I want to just go back to like yesterday, right? So how do I do that? Well, here you can just go back for that one database, one app, and recover back to that. So it's operational backup and recovery as opposed to archival backup and recovery. So for that, to be able to recover in seconds, right? You need to be, he kind of want integrated with AWS which is what we are. So it's integrated, it's automated, and it's very, very container granular. And so these three things are the things that make it sort of, very specific way. >> I love the integration story. 'Cause I think that's the big mega trend we're seeing now is is that integrating in. And, but again, it's a systems concept. It's not standalone storage, detached storage. >> Murli: Exactly. >> It's always, even though it might be decoupled a little bit it's glued together through say- >> John, you said it right. The easy button is for the system, right? Not for the individual component. Look, all of us vendors in this ecosystem are going around framing, having a being easy. But when we say that, what do we mean? We mean, oh, I'm easy to use. Well that doesn't help the user. Who's got to put all this stuff together. So it's really kind of making that stack work. >> This is easy to use, but it made these things more complex. This is what we do in the enterprise solve complexity with more complexity. >> Putting the problem to the other guy. Yeah. So it's that end to end ease of use is kind of what I would say, is the number one benefit, right? One it's container specific and designed for kubernetes. And second, it really, really is easy. >> Well, I really like the whole thing and I want to get your thoughts as we close out, what should people know about Pure and Portworx's relationship now and in the Amazon integration, what's the new narrative the north Star's still the same? High performance store, backup, securely recover and deliver the data in whatever mechanism we can. That north Star's clear, never changes, which is great. I feel love about Pure and Cloud Native. It's just taking the blockers away- >> I think the single biggest thing I would say, is all of these things, what we're turning into it is as a service offering. So if we're going to backup as a service our Portworx product now is going to be the Portworx enterprise Pure Storage product is going to be offered as a service. So with, as a service, it's easy to consume. It's easy to deploy. It's fully automated. That's the kind of the single biggest aha! Especially for the folks who are deploying on AWS today, AWS is well known for being easy to use. It's kind of fully automated. Well here, now you have this functionality for Cloud Native workloads. >> Final question, real quick, customer reaction so far, I'm assuming marketplace integration, buying terms, join selling, go to market? >> So yeah, it is integrated billing and all of that is part of that kind of offering, right? So when we say easy, it's not just about being easy to use it's about being easy to buy. It's being easy to expand all of that and scaling. Yeah. And being able to kind of automatically or automagically as I like to say, scale it, right? So all of that is absolutely part of it, right? So it is really kind of... It's not about having the basics anymore. We've been in the market now for six, seven years, so right? We have sort of an advanced offering that not only knows what customer want but anticipates what ones can expect and that's a key difference. >> I was talking to Dr. Matt Wood real quick. I know we got to wrap up on the schedule, but earlier today about AI and business analytics division's running and we were talking about serverless and the impact of serverless. And he really kind of came down the same lines where you are with the storage and the cloud data which is, "Hey, some people just want storage and the elastic leap analytics without all the under the cover stuff." Some people want to look under the covers, fine whatever choice. So really two things, so. >> Yeah, yeah. All the way from you can buy the individual components or you can buy the as a service offering, which just packages it all up in a on easy to consume kind of solution, right? >> Final, final question. What's it like at Pure everything going well, things good? >> We love it, man. I'll tell you these folks have welcomed us with open arms. And look, I've been acquired twice before. And I say this, that one of the key linchpins to a successful integration or acquisition is not just the strategic intent that always exists but really around a common culture. And, we've been blessed. I think the two companies have a strong common culture of being customer first, product excellence, and team wins every time. And these three things kind of have pulled us together. It's been a pleasure. >> One of the benefits of doing the queue for 13 years is that you get the seats things. Scott came on the queue to announce Pure Storage on theCUBE, cuz he was a nobody else. There was, oh, you're never going to get escape Velocity, EMC's going to kill, you never owned you. Nope. >> Well, we're talking about marketplaces and theCUBE is the marketplace of big announcements, John. So this is, delighted- >> Announcements. >> Yeah. Yeah. Well that was the AWS announcement. Yeah. So that's, that is big >> Final words, share the audience. What's what to expect in the next year for you guys? What's the big come news coming down? What's coming around the corner? >> I think you can expect from from Pure and Portworx the as a service set of offerings around, HADR backup, but also a brand new stuff, keep an eye out. We'll be back with John. I hope that talking about this is data services. So we have a Portworx data service product that is going to be announced. And it's magic. It's allowing people to deploy databases in a very, very, it's the easy button for database deployment. >> Congratulations on all your success. The VP and General Manager of the Cloud Native Business Unit. >> You make it sound bigger than it actually is, John. >> Thanks for coming on. Appreciate it. >> Thanks. >> Okay theCUBE coverage be back for more coverage. You're watching theCUBE here, live in Moscone on the ground at an event AWS Summit 2022. I'm John Furrier. Thanks for watching. (upbeat music)

>>Hi, everyone. Welcome to the cubes presentation of the AWS startup showcase the theme. This episode is data as code, and this is season two, episode two of the ongoing series covering exciting startups from the ecosystem in cloud and the future of data analytics. I'm your host, John furry. You're getting great featured panel here with AWS heroes, Lynn blankets, the CEO of Lindbergh Lega consulting, Peter Hanson's, founder of cloud Cedar and Alex debris, principal of debris advisory. Great to see all of you here and, uh, remotely and look forward to see you in person at the next re-invent or other event. >>Thanks for having us. >>So Lynn, you're doing a lot of work in healthcare, Peter you're in the middle of all the action as data as code Alex. You're in deep on the databases. We've got a good round up of, of topics here ranging from healthcare to getting under the hood on databases. So as we'll start with you, what are you working on right now? What trends do you see in the database space? >>Yeah, sure. So I do, uh, I do a lot of consulting work working with different people and, you know, often with, with dynamo DB or, or just general serverless technology type stuff. Um, if you want to talk about trends that I'm seeing right now, I would say trends you're seeing as a lot, just more serverless native databases or cloud native databases where you're seeing these cool databases come out that really take advantage of, uh, this new cloud environment, right? Where you have scalability, you have plasticity of the clouds. So you're not having, you know, instant space environments anymore. You're paying for capacity, you're paying for throughput. You're able to scale up and down. You're not managing individual instances. So a lot of cool stuff that we're seeing, you know, um, with this new generation of, of infrastructure and in particular database is taking advantage of this, this new cloud world >>And really lot deep into the database side in terms of like cloud native impact, diversity of database types, when to use certain databases that also a big deal. >>Yeah, absolutely. I like, I totally agree. I love seeing the different types of databases and, you know, AWS has this whole, uh, purpose-built database strategy. And I think that, that makes a lot of sense. Um, you know, I want to go too far with it. I would, I would more think about purpose-built categories and things like that, you know, specialize in an OLTB database within your, within your organization, whether that's dynamo DB or document DB or relational database Aurora or something like that. But then also choose some sort of analytics database, you know, if it's drew it or Redshift or Athena, and then, you know, if you have some specialized needs, you want to show some real time stuff to your users, check out rock site. If you want to, uh, you know, do some graph analytics, fraud detection, checkout tiger graph, a lot of cool stuff that we're seeing from the startup showcase here. >>Looking forward to unpacking that Lynn you've been in love now, a healthcare action with cloud ops, the pandemic pushes hard core on everybody. What are you working on? >>Yeah, it's all COVID data all the time. Uh, before the pandemic, I was supporting research groups for cancer genomics, which I still do, but, um, what's, uh, impactful is the explosive data volumes. You know, when you there's big data and there's genomic data, you know, I've worked with clients that have broken data centers, broken public cloud provider data centers because of the daily volume they're putting in. So there's this volume aspect. And then there's a collaboration, particularly around COVID research because of pandemic. And so you have this explosive volume, you have this, um, need for, uh, computational complexity. And that means cloud the challenge is it, you know, put the pedal to the metal. So you've got all these bioinformatics researchers that are used to single machine. Suddenly they have to deal with distributed compute. So it's a wild time to be in this space. >>What was the big change that you've seen with the, uh, the pandemic and in genomic cloud genomic specifically what's the big change has happened. >>The amount of data that is being put into the public cloud, um, previously people would have their data on their local, uh, capacity, and then they would publish their paper and the data may or may not become available for, uh, reproducing the research, uh, to accelerate for drug discovery and even variant identification. The data sets are being pushed to public cloud repositories, which is a whole new set of concerns. You have not only dealing with the volume and cost, but security, you know, there's federated security is non-trivial and not well understood by this domain. So there's so much work available here. >>Awesome. Peter, you're doing a lot with the data as a platform kind of view and platform engineering data as code is, is something that's being kicked around. What are you working on and how does platform engineering change as data becomes so much more prevalent in its value proposition? >>Yeah. So I'm the founder of cloud Cedar and, um, we sort of built this company out, this consultancy all around the challenges that a lot of companies have got with getting their data sorted, getting it organized, getting it ready for other use cases, such as analytics and machine learning, um, AI workloads and the like. So typically a platform engineering team will look after the organization of a company infrastructure, making sure that it's coherent across the company and a data platform, engineering teams doing something similar in that sense where they're, they're looking at making sure that, uh, data teams have a solid foundation to build upon, uh, that everything's quite predictable and what that enables is a faster velocity and the ability to use data as code as a way of specifying and onboarding data, building that, translating it, transforming it out into its specific domains and then on to data products. >>I have to ask you while you're here. Um, there's a big trend around data meshes right now. You're hearing, we've had a lot of stuff on the cube. Um, what are practical that people are using data mesh, first of all, is it relevant and how are people looking at this data mesh conversation? >>I think it becomes more and more relevant, uh, the bigger the organization that you're dealing with. So, you know, often times in the enterprise, you've got, uh, projects with timelines of five to 10 years often outlasting technology life cycles. The technology that you're building on is probably irrelevant by the time that you complete it. And what we're seeing is that data engineering teams and data teams more broadly, this organizational bottleneck and data mesh is all about, uh, breaking down that, um, bottleneck and decentralizing the work, shifting that work back onto, uh, development teams who oftentimes have got more of the context and a centralized data engineering team. And we're seeing a lot of, uh, Philocity increases as a result of that. >>It's interesting. There's so many different aspects of how data is changing the world. Lynn talks about the volume with the cloud and genomics. We're hearing data engineering at a platform level. You're talking about slicing and dicing and real-time information. You mentioned rock set, Alex. So I'd like to ask each of you to answer this next question, which is how has the team dynamics changed with data engineering because every single company's impacted. So if you're researchers, Lynn, you're pumping more data into the cloud, that's got a little bit of data engineering to it. Do they even understand that is that impacting them? So how has data changed the responsibilities or roles in this new emerging area of data engineering or whatever you want to call it? Lynn, we'll start with you. What do you, what do you see this impact? >>Well, you know, I mean, dev ops becomes data ops and ML ops and, uh, you know, this is a whole emergent area of work and it starts with an understanding of container technologies, which, you know, in different verticals like FinTech, that's a given, right, but in bioinformatics building an appropriately optimized Docker container is something I'm still working with customers now on because they have the concept of a Docker container is just a virtual machine, which obviously it isn't, or shouldn't be. So, um, you have, again, as I mentioned previously, this humongous skill gap, um, concepts like D, which are prevalent in ad tech FinTech, that's not available yet for most of my customers. So those are the things that I'm building. So the whole ops space is, um, this a wide open area. And really it's a question of practicality. Um, you know, I have, uh, a lot of experience with data lakes and, you know, containerizing and using the data lake platform. But a lot of my customers are going to move to like an interim pass based solutions. If they're using spark, for example, they might use to use a managed spark solution as an interim, um, step up to the cloud before they build their own containers. Because the amount of knowledge to do that effectively is non-trivial >>Peter, you mentioned data, you mentioned data lakes, onboarding data into lake house architectures, for instance, something that you're familiar with. Um, this is not obvious to some verticals obvious to others. What do you see this data engineering impact from a personnel standpoint? And then ultimately how things get built, >>You know, are you directing that to me, >>Peter? >>Yeah. So I think, um, first and foremost, you know, the workload that data engineering teams are dealing with is ever increasing. Usually there's a 10 X ratio of, um, software engineers to data engineers within a business and usually double the amount of analysts to data engineers again. And so they're, they're fighting it ever increasing backload. And, uh, so they're fighting an ever increasing backlog of, of, uh, tasks to do and tickets to, to, to churn through. And so what we're seeing is that data engineering teams are becoming data platform engineering teams where they're building capability instead of constantly hamster wheels spinning if you will. And so with that in mind, with onboarding data into, uh, a Lakehouse architecture or a data lake where data engineering teams, uh, uh, getting wins is developing a very good baseline of structure where they're getting the categorization, the data tagging, whether this data is of a particular domain, does it contain some, um, PII data, for instance, uh, and, and, and, and then the security aspects, and also, you know, the mechanisms on which to do the data transformations, >>Alex, on the database side, those are known personas in an enterprise, a them, the database team, but now the scale is so big. Um, and there's so much going on in databases. How does the data engineering impact organizations from your standpoint? >>Yeah, absolutely. I think definitely, you know, gone are the days where you have a single relational database that is serving operational queries for your users, and you can also serve analytics queries, you know, for your internal teams. It's, it's now split up into those purpose-built databases, like we've said. Uh, but now you've got two different teams managing it and they're, they're designing their data model for different things. You know? So L LLTP might have a more de-normalized model, something that works for very fast operations and it's optimized for that, but now you need to suck that data out and get it elsewhere so that your, your PM or your business analyst, or whoever can crunch through some of that. And, you know, now it needs to be in a more normalized format. How do you sort of bridge that gap? That's a tough one. I think you need to, you know, build empathy on each side of, of what each side is doing and, and build the tools to say, Hey, this is going to help you, uh, you know, LLTP team, if we know what, what users are actually doing, and, and if you can get us into the right format there, so that then I can, you know, we can analyze it, um, on the backend. >>So I think, I think building empathy across those teams is helpful. >>When I left to come back to, you mentioned a health and informatics is coming back. Um, but it's interesting, you know, I look at a database world and you look at the solutions that are out there. A lot of companies that build data solutions don't have a data problem. They've never, they're not swimming in a lot of data, but then you look at like the field that you're working in right now with the genomics and health and, and quantum, they're always, they're dealing with data all the time. So you have people who deal with a lot of data all the time are breaking through New Zealand. People who are don't have that experience are now becoming data full, right? So people are now either it's a first time problem, or they've always been swimming in a ton of data. So it's more of what's the new playbook. And then, wow, I've never had to deal with a lot of data before. What's your take? >>It's interesting. Cause they know, uh, bioinformatics hires, um, uh, grad students. So grad students, you know, use their, our scripts with their file on their laptop. And so, um, to get those folks to understand distributed container-based computing is like I said, a not non-trivial problem. What's been really interesting with the money pouring in to COVID research is when I first started, some of the workflows would take, you know, literally 500 hours and that was just okay. And coming out of FinTech, I was, uh, I could, I was blown away like FinTech is like, could that please take a millisecond rather than a second? Right. And so what has now happened, which makes it, you know, like I said, even more fun to work in this domain is, uh, the research dollars have really gone up because of the pandemic. And so there are, there are, there's this blending of people like me with more of a big data background coming into bioinformatics and working side by side. >>So it's this interesting sort of translation because you have the whole taxonomy of bioinformatics with genomics and sequencers and all the weird file types that you get. And then you have the whole taxonomy of dev ops data ops, you know, containers and Kubernetes and all that. And trying to get that into pipelines that can actually, you know, be efficient, given the constraints. Of course, we, on the tech side, we always want to make it super optimized. I had a customer that we got it down from 500 hours to minutes, but they wanted to stay with the past solution because it was easier for them to go from 500 hours to five hours was good enough, but you know, the techies want to get it down to five minutes. >>This is, this is, we've seen this movie before dev ops, um, edge and op operations, you know, IOT, world scenes, the convergence of cultures. Now you have data and then old, old school operations kind of coming up. So this kind of supports the thesis. That data as code is the next infrastructure as code. What do you guys, what's the reaction there for you guys? What do you think about that? What does data's code mean? If infrastructure's code was cloud and dev ops, what is data as code? What does that mean? >>I could take it if you like. I think, um, data teams, organizations, um, have been long been this bottleneck within the organization and there's like this dark matter of untapped energy and potential waiting to be unleashed a data with the advent of open source projects like DBT, um, have been slowly sort of embracing software development, lifecycle practices. And this is really sort of seeing a, a big steep increase in, um, in their velocity. And, and this is only going to increase and improve as we're seeing data teams, um, embrace starter as code. I think it's, uh, the future is bright for data. So I'm very excited. >>Lynn Peter reaction. I mean, agility data is code is developer concept CICB pipeline. You mentioned it new operational workflows coming into traditional operations reaction. >>Yeah. I mean, I think Peter's right on there. I'd say, you know, some of those tools we're seeing come in from, from software, like, like DBT, basically giving you that infrastructure as code, but applied to that data realm. Also there have been a few, like get for data type things, pack a derm, I believe is one and a few other ones where you bring that in and you also see a lot of immutability concepts flowing into the data realm. So I think just seeing some of those software engineering concepts come over to the data world has, has been pretty interesting >>What we'll literally just versioning datasets and the identification of what's in a data set. What's not in a data set. Some of this is around ethical AI as well, um, is a whole, uh, area that has come out of research groups. Um, mostly AI research groups, but is being applied to medical data and needs to be obviously, um, so this, this, this, um, metadata and versioning around data sets is really, I think, a very of the moment area. >>Yeah, I think we, we, you guys are bringing up a really good kind of direction that's happening in data. And that is something that you're seeing on the software side, open source and now dev ops. And now going to data is that the supply chain challenges of we've been talking about it here on the cube and this, this, um, this episode is, you know, we've seen Ukraine war, but some open source, you know, malware hitting datasets is data secure. What is that going to look like? So you starting to get into this what's the supply chain, is it verified data sets if data sets have to be managed a whole nother level of data supply chain comes up, what do you guys think about that? >>I'll jump in. Oh, sorry. I'll jump in again. I think that, you know, there's, there's, um, some, some of the compliance requirements, um, around financial data are going to be applied to other types of data, probably health data. So immutability reproducibility, um, that is, uh, legally required. Um, also some of the privacy requirements that originated in Europe with GDPR are going to be replicated as more and more, um, types of data. And again, I'm always going to speak for health, but there's other types as well coming out of personal devices and that kind of stuff. So I think, you know, this idea of data as code is it's, it goes down to versioning and controlling and, um, that's, uh, that's sort of a real succinct way to say it that we didn't used to think about that. We just put it in our, you know, relational database and we were good to go, but, um, versioning and controlling in the global ecosystem is kind of, uh, where I'm focusing my efforts. >>It brings up a good question. If databases, if data is going to be part of the development process has to be addressable, which means horizontally scalable. That means it has to be accessible and open. How do you make that work and not foreclose it with a lot of restrictions? >>I think the use of data catalogs and appropriate tagging and categorization, you know, I think, you know, everyone's heard of the term data swamp, and I think that just came about because that everyone saw like, oh, wow, S3, you know, infinite storage. We just, you know, throw whatever in there for as long as we want. And I think at times, you know, the proliferation of S3 buckets, um, and the like, you know, we've just seen, uh, perhaps security, not maintained as well as it could have been. And I think that's kind of where data platform engineering teams have really sort of, uh, come into the, for, you know, creating a governance set of buckets like formation on top. But I think that's kind of where we need to see a lot more work with appropriate tags and also the automatic publishing of metadata into data catalogs so that, um, folks can easily search and address particular data sets and also control the access. You know, for instance, you've got some PII data, perhaps really only your marketing folks should be looking at email addresses and the like not perhaps your finance folks. So I think, you know, there's, there's a lot to be leveraged there in formation and other solutions, >>Alex, let's back up and talk about what's in it for the customer, right. Let's zoom back and saying reality is I just got to get my data to make sure it's secure always on and not going to be hackable. And I just got to get my data available on river performance. So then, then I got to start thinking about, okay, how do I intersect it? So what should teams be thinking about right now as I look up all their data options or databases across their enterprise? >>Yeah, it's, it's a, it's a good question. I just, you know, I think Peter made some good points there and you can think of history as sort of ebbing and flowing between centralization and decentralization a lot of times. And you know, when storage was expensive, data was going to be sort of centralized and Maine maintained, sort of a, you know, by the, uh, the people that are in charge of it. But then when, when S3 comes along, it really decreases storage. Now we can do a lot more experiments on it. We can store a lot more of our data, keep it around and do different things on it. You know, now we've got regulations again, we were, we gotta, we gotta be more realistic about, about keeping that data secure and make sure we're, we're doing the right things with it. So it's, we're gonna probably go through a period of, of centralization as we work out some of this tooling around, you know, tagging and, and ethical AI that, that both Peter. And when we're talking about here and maybe get us into that, that next wearable world of de-centralization again. But I, I think that ebb and flow is going to be natural in response to, you know, the problems of the, the other extreme, >>Where are we in the market right now from progress standpoint, because data lakes don't want to be data swamps. You seeing lake formation as a data architecture, as an example, where are we with customers? What are they doing right now? Where would you put them in the progress bar of, of evolution towards the Nirvana of having this data sovereignty? And this data is code environment. Are they just now in the data lake store, everything real-time and historical? >>Well, I can jump in there. Um, SQL on files is the, is the driver. And so we know when Amazon got Athena, um, that really drove a lot of the customers to really realistically look at data lake technologies, but data warehouses are not going away. And the integration between the two is not seamless. No, we, we are partners with AWS, but we don't work for them. So we can tell you the truth here. Um, there's, there's work to it, but it really, for my customers, it really upped the ante around data lake, uh, because Athena and technologies like that, the serverless, um, SQL queries or the familiar quarry, um, uh, libraries really drove a movement away from either OLTB or OLAP, more expensive, more cumbersome structures, >>But they still need that. Oh, LTP, like if they have high latency issues, they want to be low latency. Can they have the best of both worlds? That's the question. >>I mean, I w I would say we're getting, you know, we're getting closer. We're always going to be, uh, you know, that technology is going to be moving forward, and then we'll just move the goalpost again, in terms of, of what we're asking from it. But I think, you know, the technology that's getting out there, you can get, get really well. And then, you know, just what I work in the dynamo DB world. So you can get really great low latency. So, you know, single digit millisecond LLTP response times on that. I think some of the analytics stuff has been a problem with that. And there, there are different solutions out there to where you can export dynamo to S3, and then you can be doing SQL on your FA your files with Athena Lakeland's talking about, or now you see, you know, rock set of partner here that that'll just ingest your dynamo, DB data, you know, make all those changes. So if you're doing a lot of, uh, changes to your data and dynamo is going to reflect in Roxanna, and then you can do analytics queries, you can do complex filters, different things like that. So, you know, I, I think we continue to push the envelope and then we moved the goalpost again. But, um, you know, I think we're in a, a lot better place than we were a few years ago, for sure. >>Where do you guys see this going relative to the next level? If data as code becomes that next agile, um, software defined environment with open source? Well, all of these new tools with serverless things happening with data lakes are built in with nice architectures with data warehouses, where does it go next? What happens next? If this becomes an agile environment, what's the impact? >>Well, I don't want to be so dominant, but I have, I feel strongly, so I'm going to jump in here. So, so I, um, I feel like, you know, now for my, my, my most computationally intensive workloads, I'm using GPS, I'm bursting to GPU for TensorFlow neural networks. So I've been doing quite a bit of exploration around Amazon bracket for QPS and it's early. Um, and it's specialty. It's not, you know, for everybody. And the learning curve again is pretty daunting, but, um, there are some use cases out there. I mean, I got ahold of a paper where some people did some, um, it was a Q CNN, um, quantum convolutional neural network for lung cancer images, um, from COVID patients and the, the, uh, the QP Hugh, um, algorithm pipeline performed more accurately and faster. So I think, um, bursting to quantum is something to pay attention to. >>Awesome. Peter, what's your take on what's next? >>Well, I think there's still, um, that, that was absolutely fascinating from Lynn, but I think also there's, there's, uh, you know, some more sort of low-level, uh, low-hanging fruit available in, in the data stack. I think there's a lot of, there's still a lot of challenges around the transformation there, getting our data from sort of raw landed data into business domains, and that sort of talks to a lot of what data mesh is all about. I think if we can somehow make that a little more frictionless, because that that's really where the like labor intensive work is. That's, that's kinda dominating, uh, data engineering teams and where we're sort of trying to push that, that workload back onto, um, you know, software engineering teams. >>Alice will give you the final word. What's the impact. What's the next step? What's it look like in the future? >>Yeah, for sure. I mean, I've never had the, uh, breaking a data center problem that wind's had, or the bursting the quantum problem, for sure. But, you know, if you're in that, you know, the pool I swim and of terabytes of data and below and things like that, I think it's a good time. It just like we saw, you know, like we were talking about dev ops and, and pushing, uh, you know, allowing software engineers to handle more of, of the operation stuff. I think the same thing with data can happen where, you know, software engineering teams can handle not just their code, not just, you know, deploying and operating it, but also thinking about their data around the code. And that doesn't mean you won't have people assist you within your organization. You won't have some specialists in there, but I think pushing more stuff, even onto the individual development teams where they have ownership of that. And they're thinking about it through all this different life cycle. I mean, I'm pretty bullish on that. And I think that's an exciting development >>Was that shift, what left with left is security. What does that mean to >>Shipped so much stuff left, but now, you know, the things that were at the end are back at the end again, but, uh, you know, at least we think we can think about that stuff early in the process, which is good, >>Great conversation, very provocative, very realistic and great impact on the future data as code is real, the developers I do believe will have a great operational role and the data stack concept and impacting things like quantum, it's all kind of lining up nicely. Um, and it's a great opportunity to be in this field from a science and policy standpoint. Um, data engineering is legit. It's going to continue to grow and thanks for unpacking that here on the queue. Appreciate it. Okay. Great panel D AWS heroes. They work with AWS and the ecosystem independently out there. They're in the trenches doing the front lines, cracking the code here with data as code season two, episode two of the ongoing series of the 80, but startups I'm John for your host. Thanks for watching.

(upbeat music) >> Once a staid and boring business dominated by IBM, Oracle, and at the time newcomer Microsoft, along with a handful of wannabes, the database business has exploded in the past decade and has become a staple of financial excellence, customer experience, analytic advantage, competitive strategy, growth initiatives, visualizations, not to mention compliance, security, privacy and dozens of other important use cases and initiatives. And on the vendor's side of the house, we've seen the rapid ascendancy of cloud databases. Most notably from Snowflake, whose massive raises leading up to its IPO in late 2020 sparked a spate of interest and VC investment in the separation of compute and storage and all that elastic resource stuff in the cloud. The company joined AWS, Azure and Google to popularize cloud databases, which have become a linchpin of competitive strategies for technology suppliers. And if I get you to put your data in my database and in my cloud, and I keep innovating, I'm going to build a moat and achieve a hugely attractive lifetime customer value in a really amazing marginal economics dynamic that is going to fund my future. And I'll be able to sell other adjacent services, not just compute and storage, but machine learning and inference and training and all kinds of stuff, dozens of lucrative cloud offerings. Meanwhile, the database leader, Oracle has invested massive amounts of money to maintain its lead. It's building on its position as the king of mission critical workloads and making typical Oracle like claims against the competition. Most were recently just yesterday with another announcement around MySQL HeatWave. An extension of MySQL that is compatible with on-premises MySQLs and is setting new standards in price performance. We're seeing a dramatic divergence in strategies across the database spectrum. On the far left, we see Amazon with more than a dozen database offerings each with its own API and primitives. AWS is taking a right tool for the right job approach, often building on open source platforms and creating services that it offers to customers to solve very specific problems for developers. And on the other side of the line, we see Oracle, which is taking the Swiss Army Knife approach, converging database functionality, enabling analytic and transactional workloads to run in the same data store, eliminating the need to ETL, at the same time adding capabilities into its platform like automation and machine learning. Welcome to this database Power Panel. My name is Dave Vellante, and I'm so excited to bring together some of the most respected industry analyst in the community. Today we're going to assess what's happening in the market. We're going to dig into the competitive landscape and explore the future of database and database platforms and decode what it means to customers. Let me take a moment to welcome our guest analyst today. Matt Kimball is a vice president and principal analysts at Moor Insights and Strategy, Matt. He knows products, he knows industry, he's got real world IT expertise, and he's got all the angles 25 plus years of experience in all kinds of great background. Matt, welcome. Thanks very much for coming on theCUBE. Holgar Mueller, friend of theCUBE, vice president and principal analyst at Constellation Research in depth knowledge on applications, application development, knows developers. He's worked at SAP and Oracle. And then Bob Evans is Chief Content Officer and co-founder of the Acceleration Economy, founder and principle of Cloud Wars. Covers all kinds of industry topics and great insights. He's got awesome videos, these three minute hits. If you haven't seen 'em, checking them out, knows cloud companies, his Cloud Wars minutes are fantastic. And then of course, Marc Staimer is the founder of Dragon Slayer Research. A frequent contributor and guest analyst at Wikibon. He's got a wide ranging knowledge across IT products, knows technology really well, can go deep. And then of course, Ron Westfall, Senior Analyst and Director Research Director at Futurum Research, great all around product trends knowledge. Can take, you know, technical dives and really understands competitive angles, knows Redshift, Snowflake, and many others. Gents, thanks so much for taking the time to join us in theCube today. It's great to have you on, good to see you. >> Good to be here, thanks for having us. >> Thanks, Dave. >> All right, let's start with an around the horn and briefly, if each of you would describe, you know, anything I missed in your areas of expertise and then you answer the following question, how would you describe the state of the database, state of platform market today? Matt Kimball, please start. >> Oh, I hate going first, but that it's okay. How would I describe the world today? I would just in one sentence, I would say, I'm glad I'm not in IT anymore, right? So, you know, it is a complex and dangerous world out there. And I don't envy IT folks I'd have to support, you know, these modernization and transformation efforts that are going on within the enterprise. It used to be, you mentioned it, Dave, you would argue about IBM versus Oracle versus this newcomer in the database space called Microsoft. And don't forget Sybase back in the day, but you know, now it's not just, which SQL vendor am I going to go with? It's all of these different, divergent data types that have to be taken, they have to be merged together, synthesized. And somehow I have to do that cleanly and use this to drive strategic decisions for my business. That is not easy. So, you know, you have to look at it from the perspective of the business user. It's great for them because as a DevOps person, or as an analyst, I have so much flexibility and I have this thing called the cloud now where I can go get services immediately. As an IT person or a DBA, I am calling up prevention hotlines 24 hours a day, because I don't know how I'm going to be able to support the business. And as an Oracle or as an Oracle or a Microsoft or some of the cloud providers and cloud databases out there, I'm licking my chops because, you know, my market is expanding and expanding every day. >> Great, thank you for that, Matt. Holgar, how do you see the world these days? You always have a good perspective on things, share with us. >> Well, I think it's the best time to be in IT, I'm not sure what Matt is talking about. (laughing) It's easier than ever, right? The direction is going to cloud. Kubernetes has won, Google has the best AI for now, right? So things are easier than ever before. You made commitments for five plus years on hardware, networking and so on premise, and I got gray hair about worrying it was the wrong decision. No, just kidding. But you kind of both sides, just to be controversial, make it interesting, right. So yeah, no, I think the interesting thing specifically with databases, right? We have this big suite versus best of breed, right? Obviously innovation, like you mentioned with Snowflake and others happening in the cloud, the cloud vendors server, where to save of their databases. And then we have one of the few survivors of the old guard as Evans likes to call them is Oracle who's doing well, both their traditional database. And now, which is really interesting, remarkable from that because Oracle it was always the power of one, have one database, add more to it, make it what I call the universal database. And now this new HeatWave offering is coming and MySQL open source side. So they're getting the second (indistinct) right? So it's interesting that older players, traditional players who still are in the market are diversifying their offerings. Something we don't see so much from the traditional tools from Oracle on the Microsoft side or the IBM side these days. >> Great, thank you Holgar. Bob Evans, you've covered this business for a while. You've worked at, you know, a number of different outlets and companies and you cover the competition, how do you see things? >> Dave, you know, the other angle to look at this from is from the customer side, right? You got now CEOs who are any sort of business across all sorts of industries, and they understand that their future success is going to be dependent on their ability to become a digital company, to understand data, to use it the right way. So as you outline Dave, I think in your intro there, it is a fantastic time to be in the database business. And I think we've got a lot of new buyers and influencers coming in. They don't know all this history about IBM and Microsoft and Oracle and you know, whoever else. So I think they're going to take a long, hard look, Dave, at some of these results and who is able to help these companies not serve up the best technology, but who's going to be able to help their business move into the digital future. So it's a fascinating time now from every perspective. >> Great points, Bob. I mean, digital transformation has gone from buzzword to imperative. Mr. Staimer, how do you see things? >> I see things a little bit differently than my peers here in that I see the database market being segmented. There's all the different kinds of databases that people are looking at for different kinds of data, and then there is databases in the cloud. And so database as cloud service, I view very differently than databases because the traditional way of implementing a database is changing and it's changing rapidly. So one of the premises that you stated earlier on was that you viewed Oracle as a database company. I don't view Oracle as a database company anymore. I view Oracle as a cloud company that happens to have a significant expertise and specialty in databases, and they still sell database software in the traditional way, but ultimately they're a cloud company. So database cloud services from my point of view is a very distinct market from databases. >> Okay, well, you gave us some good meat on the bone to talk about that. Last but not least-- >> Dave did Marc, just say Oracle's a cloud company? >> Yeah. (laughing) Take away the database, it would be interesting to have that discussion, but let's let Ron jump in here. Ron, give us your take. >> That's a great segue. I think it's truly the era of the cloud database, that's something that's rising. And the key trends that come with it include for example, elastic scaling. That is the ability to scale on demand, to right size workloads according to customer requirements. And also I think it's going to increase the prioritization for high availability. That is the player who can provide the highest availability is going to have, I think, a great deal of success in this emerging market. And also I anticipate that there will be more consolidation across platforms in order to enable cost savings for customers, and that's something that's always going to be important. And I think we'll see more of that over the horizon. And then finally security, security will be more important than ever. We've seen a spike (indistinct), we certainly have seen geopolitical originated cybersecurity concerns. And as a result, I see database security becoming all the more important. >> Great, thank you. Okay, let me share some data with you guys. I'm going to throw this at you and see what you think. We have this awesome data partner called Enterprise Technology Research, ETR. They do these quarterly surveys and each period with dozens of industry segments, they track clients spending, customer spending. And this is the database, data warehouse sector okay so it's taxonomy, so it's not perfect, but it's a big kind of chunk. They essentially ask customers within a category and buy a specific vendor, you're spending more or less on the platform? And then they subtract the lesses from the mores and they derive a metric called net score. It's like NPS, it's a measure of spending velocity. It's more complicated and granular than that, but that's the basis and that's the vertical axis. The horizontal axis is what they call market share, it's not like IDC market share, it's just pervasiveness in the data set. And so there are a couple of things that stand out here and that we can use as reference point. The first is the momentum of Snowflake. They've been off the charts for many, many, for over two years now, anything above that dotted red line, that 40%, is considered by ETR to be highly elevated and Snowflake's even way above that. And I think it's probably not sustainable. We're going to see in the next April survey, next month from those guys, when it comes out. And then you see AWS and Microsoft, they're really pervasive on the horizontal axis and highly elevated, Google falls behind them. And then you got a number of well funded players. You got Cockroach Labs, Mongo, Redis, MariaDB, which of course is a fork on MySQL started almost as protest at Oracle when they acquired Sun and they got MySQL and you can see the number of others. Now Oracle who's the leading database player, despite what Marc Staimer says, we know, (laughs) and they're a cloud player (laughing) who happens to be a leading database player. They dominate in the mission critical space, we know that they're the king of that sector, but you can see here that they're kind of legacy, right? They've been around a long time, they get a big install base. So they don't have the spending momentum on the vertical axis. Now remember this is, just really this doesn't capture spending levels, so that understates Oracle but nonetheless. So it's not a complete picture like SAP for instance is not in here, no Hana. I think people are actually buying it, but it doesn't show up here, (laughs) but it does give an indication of momentum and presence. So Bob Evans, I'm going to start with you. You've commented on many of these companies, you know, what does this data tell you? >> Yeah, you know, Dave, I think all these compilations of things like that are interesting, and that folks at ETR do some good work, but I think as you said, it's a snapshot sort of a two-dimensional thing of a rapidly changing, three dimensional world. You know, the incidents at which some of these companies are mentioned versus the volume that happens. I think it's, you know, with Oracle and I'm not going to declare my religious affiliation, either as cloud company or database company, you know, they're all of those things and more, and I think some of our old language of how we classify companies is just not relevant anymore. But I want to ask too something in here, the autonomous database from Oracle, nobody else has done that. So either Oracle is crazy, they've tried out a technology that nobody other than them is interested in, or they're onto something that nobody else can match. So to me, Dave, within Oracle, trying to identify how they're doing there, I would watch autonomous database growth too, because right, it's either going to be a big plan and it breaks through, or it's going to be caught behind. And the Snowflake phenomenon as you mentioned, that is a rare, rare bird who comes up and can grow 100% at a billion dollar revenue level like that. So now they've had a chance to come in, scare the crap out of everybody, rock the market with something totally new, the data cloud. Will the bigger companies be able to catch up and offer a compelling alternative, or is Snowflake going to continue to be this outlier. It's a fascinating time. >> Really, interesting points there. Holgar, I want to ask you, I mean, I've talked to certainly I'm sure you guys have too, the founders of Snowflake that came out of Oracle and they actually, they don't apologize. They say, "Hey, we not going to do all that complicated stuff that Oracle does, we were trying to keep it real simple." But at the same time, you know, they don't do sophisticated workload management. They don't do complex joints. They're kind of relying on the ecosystems. So when you look at the data like this and the various momentums, and we talked about the diverging strategies, what does this say to you? >> Well, it is a great point. And I think Snowflake is an example how the cloud can turbo charge a well understood concept in this case, the data warehouse, right? You move that and you find steroids and you see like for some players who've been big in data warehouse, like Sentara Data, as an example, here in San Diego, what could have been for them right in that part. The interesting thing, the problem though is the cloud hides a lot of complexity too, which you can scale really well as you attract lots of customers to go there. And you don't have to build things like what Bob said, right? One of the fascinating things, right, nobody's answering Oracle on the autonomous database. I don't think is that they cannot, they just have different priorities or the database is not such a priority. I would dare to say that it's for IBM and Microsoft right now at the moment. And the cloud vendors, you just hide that right through scripts and through scale because you support thousands of customers and you can deal with a little more complexity, right? It's not against them. Whereas if you have to run it yourself, very different story, right? You want to have the autonomous parts, you want to have the powerful tools to do things. >> Thank you. And so Matt, I want to go to you, you've set up front, you know, it's just complicated if you're in IT, it's a complicated situation and you've been on the customer side. And if you're a buyer, it's obviously, it's like Holgar said, "Cloud's supposed to make this stuff easier, but the simpler it gets the more complicated gets." So where do you place your bets? Or I guess more importantly, how do you decide where to place your bets? >> Yeah, it's a good question. And to what Bob and Holgar said, you know, the around autonomous database, I think, you know, part of, as I, you know, play kind of armchair psychologist, if you will, corporate psychologists, I look at what Oracle is doing and, you know, databases where they've made their mark and it's kind of, that's their strong position, right? So it makes sense if you're making an entry into this cloud and you really want to kind of build momentum, you go with what you're good at, right? So that's kind of the strength of Oracle. Let's put a lot of focus on that. They do a lot more than database, don't get me wrong, but you know, I'm going to short my strength and then kind of pivot from there. With regards to, you know, what IT looks at and what I would look at you know as an IT director or somebody who is, you know, trying to consume services from these different cloud providers. First and foremost, I go with what I know, right? Let's not forget IT is a conservative group. And when we look at, you know, all the different permutations of database types out there, SQL, NoSQL, all the different types of NoSQL, those are largely being deployed by business users that are looking for agility or businesses that are looking for agility. You know, the reason why MongoDB is so popular is because of DevOps, right? It's a great platform to develop on and that's where it kind of gained its traction. But as an IT person, I want to go with what I know, where my muscle memory is, and that's my first position. And so as I evaluate different cloud service providers and cloud databases, I look for, you know, what I know and what I've invested in and where my muscle memory is. Is there enough there and do I have enough belief that that company or that service is going to be able to take me to, you know, where I see my organization in five years from a data management perspective, from a business perspective, are they going to be there? And if they are, then I'm a little bit more willing to make that investment, but it is, you know, if I'm kind of going in this blind or if I'm cloud native, you know, that's where the Snowflakes of the world become very attractive to me. >> Thank you. So Marc, I asked Andy Jackson in theCube one time, you have all these, you know, data stores and different APIs and primitives and you know, very granular, what's the strategy there? And he said, "Hey, that allows us as the market changes, it allows us to be more flexible. If we start building abstractions layers, it's harder for us." I think also it was not a good time to market advantage, but let me ask you, I described earlier on that spectrum from AWS to Oracle. We just saw yesterday, Oracle announced, I think the third major enhancement in like 15 months to MySQL HeatWave, what do you make of that announcement? How do you think it impacts the competitive landscape, particularly as it relates to, you know, converging transaction and analytics, eliminating ELT, I know you have some thoughts on this. >> So let me back up for a second and defend my cloud statement about Oracle for a moment. (laughing) AWS did a great job in developing the cloud market in general and everything in the cloud market. I mean, I give them lots of kudos on that. And a lot of what they did is they took open source software and they rent it to people who use their cloud. So I give 'em lots of credit, they dominate the market. Oracle was late to the cloud market. In fact, they actually poo-pooed it initially, if you look at some of Larry Ellison's statements, they said, "Oh, it's never going to take off." And then they did 180 turn, and they said, "Oh, we're going to embrace the cloud." And they really have, but when you're late to a market, you've got to be compelling. And this ties into the announcement yesterday, but let's deal with this compelling. To be compelling from a user point of view, you got to be twice as fast, offer twice as much functionality, at half the cost. That's generally what compelling is that you're going to capture market share from the leaders who established the market. It's very difficult to capture market share in a new market for yourself. And you're right. I mean, Bob was correct on this and Holgar and Matt in which you look at Oracle, and they did a great job of leveraging their database to move into this market, give 'em lots of kudos for that too. But yesterday they announced, as you said, the third innovation release and the pace is just amazing of what they're doing on these releases on HeatWave that ties together initially MySQL with an integrated builtin analytics engine, so a data warehouse built in. And then they added automation with autopilot, and now they've added machine learning to it, and it's all in the same service. It's not something you can buy and put on your premise unless you buy their cloud customers stuff. But generally it's a cloud offering, so it's compellingly better as far as the integration. You don't buy multiple services, you buy one and it's lower cost than any of the other services, but more importantly, it's faster, which again, give 'em credit for, they have more integration of a product. They can tie things together in a way that nobody else does. There's no additional services, ETL services like Glue and AWS. So from that perspective, they're getting better performance, fewer services, lower cost. Hmm, they're aiming at the compelling side again. So from a customer point of view it's compelling. Matt, you wanted to say something there. >> Yeah, I want to kind of, on what you just said there Marc, and this is something I've found really interesting, you know. The traditional way that you look at software and, you know, purchasing software and IT is, you look at either best of breed solutions and you have to work on the backend to integrate them all and make them all work well. And generally, you know, the big hit against the, you know, we have one integrated offering is that, you lose capability or you lose depth of features, right. And to what you were saying, you know, that's the thing I found interesting about what Oracle is doing is they're building in depth as they kind of, you know, build that service. It's not like you're losing a lot of capabilities, because you're going to one integrated service versus having to use A versus B versus C, and I love that idea. >> You're right. Yeah, not only you're not losing, but you're gaining functionality that you can't get by integrating a lot of these. I mean, I can take Snowflake and integrate it in with machine learning, but I also have to integrate in with a transactional database. So I've got to have connectors between all of this, which means I'm adding time. And what it comes down to at the end of the day is expertise, effort, time, and cost. And so what I see the difference from the Oracle announcements is they're aiming at reducing all of that by increasing performance as well. Correct me if I'm wrong on that but that's what I saw at the announcement yesterday. >> You know, Marc, one thing though Marc, it's funny you say that because I started out saying, you know, I'm glad I'm not 19 anymore. And the reason is because of exactly what you said, it's almost like there's a pseudo level of witchcraft that's required to support the modern data environment right in the enterprise. And I need simpler faster, better. That's what I need, you know, I am no longer wearing pocket protectors. I have turned from, you know, break, fix kind of person, to you know, business consultant. And I need that point and click simplicity, but I can't sacrifice, you know, a depth of features of functionality on the backend as I play that consultancy role. >> So, Ron, I want to bring in Ron, you know, it's funny. So Matt, you mentioned Mongo, I often and say, if Oracle mentions you, you're on the map. We saw them yesterday Ron, (laughing) they hammered RedShifts auto ML, they took swipes at Snowflake, a little bit of BigQuery. What were your thoughts on that? Do you agree with what these guys are saying in terms of HeatWaves capabilities? >> Yes, Dave, I think that's an excellent question. And fundamentally I do agree. And the question is why, and I think it's important to know that all of the Oracle data is backed by the fact that they're using benchmarks. For example, all of the ML and all of the TPC benchmarks, including all the scripts, all the configs and all the detail are posted on GitHub. So anybody can look at these results and they're fully transparent and replicate themselves. If you don't agree with this data, then by all means challenge it. And we have not really seen that in all of the new updates in HeatWave over the last 15 months. And as a result, when it comes to these, you know, fundamentals in looking at the competitive landscape, which I think gives validity to outcomes such as Oracle being able to deliver 4.8 times better price performance than Redshift. As well as for example, 14.4 better price performance than Snowflake, and also 12.9 better price performance than BigQuery. And so that is, you know, looking at the quantitative side of things. But again, I think, you know, to Marc's point and to Matt's point, there are also qualitative aspects that clearly differentiate the Oracle proposition, from my perspective. For example now the MySQL HeatWave ML capabilities are native, they're built in, and they also support things such as completion criteria. And as a result, that enables them to show that hey, when you're using Redshift ML for example, you're having to also use their SageMaker tool and it's running on a meter. And so, you know, nobody really wants to be running on a meter when, you know, executing these incredibly complex tasks. And likewise, when it comes to Snowflake, they have to use a third party capability. They don't have the built in, it's not native. So the user, to the point that he's having to spend more time and it increases complexity to use auto ML capabilities across the Snowflake platform. And also, I think it also applies to other important features such as data sampling, for example, with the HeatWave ML, it's intelligent sampling that's being implemented. Whereas in contrast, we're seeing Redshift using random sampling. And again, Snowflake, you're having to use a third party library in order to achieve the same capabilities. So I think the differentiation is crystal clear. I think it definitely is refreshing. It's showing that this is where true value can be assigned. And if you don't agree with it, by all means challenge the data. >> Yeah, I want to come to the benchmarks in a minute. By the way, you know, the gentleman who's the Oracle's architect, he did a great job on the call yesterday explaining what you have to do. I thought that was quite impressive. But Bob, I know you follow the financials pretty closely and on the earnings call earlier this month, Ellison said that, "We're going to see HeatWave on AWS." And the skeptic in me said, oh, they must not be getting people to come to OCI. And then they, you remember this chart they showed yesterday that showed the growth of HeatWave on OCI. But of course there was no data on there, it was just sort of, you know, lines up and to the right. So what do you guys think of that? (Marc laughs) Does it signal Bob, desperation by Oracle that they can't get traction on OCI, or is it just really a smart tame expansion move? What do you think? >> Yeah, Dave, that's a great question. You know, along the way there, and you know, just inside of that was something that said Ellison said on earnings call that spoke to a different sort of philosophy or mindset, almost Marc, where he said, "We're going to make this multicloud," right? With a lot of their other cloud stuff, if you wanted to use any of Oracle's cloud software, you had to use Oracle's infrastructure, OCI, there was no other way out of it. But this one, but I thought it was a classic Ellison line. He said, "Well, we're making this available on AWS. We're making this available, you know, on Snowflake because we're going after those users. And once they see what can be done here." So he's looking at it, I guess you could say, it's a concession to customers because they want multi-cloud. The other way to look at it, it's a hunting expedition and it's one of those uniquely I think Oracle ways. He said up front, right, he doesn't say, "Well, there's a big market, there's a lot for everybody, we just want on our slice." Said, "No, we are going after Amazon, we're going after Redshift, we're going after Aurora. We're going after these users of Snowflake and so on." And I think it's really fairly refreshing these days to hear somebody say that, because now if I'm a buyer, I can look at that and say, you know, to Marc's point, "Do they measure up, do they crack that threshold ceiling? Or is this just going to be more pain than a few dollars savings is worth?" But you look at those numbers that Ron pointed out and that we all saw in that chart. I've never seen Dave, anything like that. In a substantive market, a new player coming in here, and being able to establish differences that are four, seven, eight, 10, 12 times better than competition. And as new buyers look at that, they're going to say, "What the hell are we doing paying, you know, five times more to get a poor result? What's going on here?" So I think this is going to rattle people and force a harder, closer look at what these alternatives are. >> I wonder if the guy, thank you. Let's just skip ahead of the benchmarks guys, bring up the next slide, let's skip ahead a little bit here, which talks to the benchmarks and the benchmarking if we can. You know, David Floyer, the sort of semiretired, you know, Wikibon analyst said, "Dave, this is going to force Amazon and others, Snowflake," he said, "To rethink actually how they architect databases." And this is kind of a compilation of some of the data that they shared. They went after Redshift mostly, (laughs) but also, you know, as I say, Snowflake, BigQuery. And, like I said, you can always tell which companies are doing well, 'cause Oracle will come after you, but they're on the radar here. (laughing) Holgar should we take this stuff seriously? I mean, or is it, you know, a grain salt? What are your thoughts here? >> I think you have to take it seriously. I mean, that's a great question, great point on that. Because like Ron said, "If there's a flaw in a benchmark, we know this database traditionally, right?" If anybody came up that, everybody will be, "Oh, you put the wrong benchmark, it wasn't audited right, let us do it again," and so on. We don't see this happening, right? So kudos to Oracle to be aggressive, differentiated, and seem to having impeccable benchmarks. But what we really see, I think in my view is that the classic and we can talk about this in 100 years, right? Is the suite versus best of breed, right? And the key question of the suite, because the suite's always slower, right? No matter at which level of the stack, you have the suite, then the best of breed that will come up with something new, use a cloud, put the data warehouse on steroids and so on. The important thing is that you have to assess as a buyer what is the speed of my suite vendor. And that's what you guys mentioned before as well, right? Marc said that and so on, "Like, this is a third release in one year of the HeatWave team, right?" So everybody in the database open source Marc, and there's so many MySQL spinoffs to certain point is put on shine on the speed of (indistinct) team, putting out fundamental changes. And the beauty of that is right, is so inherent to the Oracle value proposition. Larry's vision of building the IBM of the 21st century, right from the Silicon, from the chip all the way across the seven stacks to the click of the user. And that what makes the database what Rob was saying, "Tied to the OCI infrastructure," because designed for that, it runs uniquely better for that, that's why we see the cross connect to Microsoft. HeatWave so it's different, right? Because HeatWave runs on cheap hardware, right? Which is the breadth and butter 886 scale of any cloud provider, right? So Oracle probably needs it to scale OCI in a different category, not the expensive side, but also allow us to do what we said before, the multicloud capability, which ultimately CIOs really want, because data gravity is real, you want to operate where that is. If you have a fast, innovative offering, which gives you more functionality and the R and D speed is really impressive for the space, puts away bad results, then it's a good bet to look at. >> Yeah, so you're saying, that we versus best of breed. I just want to sort of play back then Marc a comment. That suite versus best of breed, there's always been that trade off. If I understand you Holgar you're saying that somehow Oracle has magically cut through that trade off and they're giving you the best of both. >> It's the developing velocity, right? The provision of important features, which matter to buyers of the suite vendor, eclipses the best of breed vendor, then the best of breed vendor is in the hell of a potential job. >> Yeah, go ahead Marc. >> Yeah and I want to add on what Holgar just said there. I mean the worst job in the data center is data movement, moving the data sucks. I don't care who you are, nobody likes it. You never get any kudos for doing it well, and you always get the ah craps, when things go wrong. So it's in- >> In the data center Marc all the time across data centers, across cloud. That's where the bleeding comes. >> It's right, you get beat up all the time. So nobody likes to move data, ever. So what you're looking at with what they announce with HeatWave and what I love about HeatWave is it doesn't matter when you started with it, you get all the additional features they announce it's part of the service, all the time. But they don't have to move any of the data. You want to analyze the data that's in your transactional, MySQL database, it's there. You want to do machine learning models, it's there, there's no data movement. The data movement is the key thing, and they just eliminate that, in so many ways. And the other thing I wanted to talk about is on the benchmarks. As great as those benchmarks are, they're really conservative 'cause they're underestimating the cost of that data movement. The ETLs, the other services, everything's left out. It's just comparing HeatWave, MySQL cloud service with HeatWave versus Redshift, not Redshift and Aurora and Glue, Redshift and Redshift ML and SageMaker, it's just Redshift. >> Yeah, so what you're saying is what Oracle's doing is saying, "Okay, we're going to run MySQL HeatWave benchmarks on analytics against Redshift, and then we're going to run 'em in transaction against Aurora." >> Right. >> But if you really had to look at what you would have to do with the ETL, you'd have to buy two different data stores and all the infrastructure around that, and that goes away so. >> Due to the nature of the competition, they're running narrow best of breed benchmarks. There is no suite level benchmark (Dave laughs) because they created something new. >> Well that's you're the earlier point they're beating best of breed with a suite. So that's, I guess to Floyer's earlier point, "That's going to shake things up." But I want to come back to Bob Evans, 'cause I want to tap your Cloud Wars mojo before we wrap. And line up the horses, you got AWS, you got Microsoft, Google and Oracle. Now they all own their own cloud. Snowflake, Mongo, Couchbase, Redis, Cockroach by the way they're all doing very well. They run in the cloud as do many others. I think you guys all saw the Andreessen, you know, commentary from Sarah Wang and company, to talk about the cost of goods sold impact of cloud. So owning your own cloud has to be an advantage because other guys like Snowflake have to pay cloud vendors and negotiate down versus having the whole enchilada, Safra Catz's dream. Bob, how do you think this is going to impact the market long term? >> Well, Dave, that's a great question about, you know, how this is all going to play out. If I could mention three things, one, Frank Slootman has done a fantastic job with Snowflake. Really good company before he got there, but since he's been there, the growth mindset, the discipline, the rigor and the phenomenon of what Snowflake has done has forced all these bigger companies to really accelerate what they're doing. And again, it's an example of how this intense competition makes all the different cloud vendors better and it provides enormous value to customers. Second thing I wanted to mention here was look at the Adam Selipsky effect at AWS, took over in the middle of May, and in Q2, Q3, Q4, AWS's growth rate accelerated. And in each of those three quotas, they grew faster than Microsoft's cloud, which has not happened in two or three years, so they're closing the gap on Microsoft. The third thing, Dave, in this, you know, incredibly intense competitive nature here, look at Larry Ellison, right? He's got his, you know, the product that for the last two or three years, he said, "It's going to help determine the future of the company, autonomous database." You would think he's the last person in the world who's going to bring in, you know, in some ways another database to think about there, but he has put, you know, his whole effort and energy behind this. The investments Oracle's made, he's riding this horse really hard. So it's not just a technology achievement, but it's also an investment priority for Oracle going forward. And I think it's going to form a lot of how they position themselves to this new breed of buyer with a new type of need and expectations from IT. So I just think the next two or three years are going to be fantastic for people who are lucky enough to get to do the sorts of things that we do. >> You know, it's a great point you made about AWS. Back in 2018 Q3, they were doing about 7.4 billion a quarter and they were growing in the mid forties. They dropped down to like 29% Q4, 2020, I'm looking at the data now. They popped back up last quarter, last reported quarter to 40%, that is 17.8 billion, so they more doubled and they accelerated their growth rate. (laughs) So maybe that pretends, people are concerned about Snowflake right now decelerating growth. You know, maybe that's going to be different. By the way, I think Snowflake has a different strategy, the whole data cloud thing, data sharing. They're not trying to necessarily take Oracle head on, which is going to make this next 10 years, really interesting. All right, we got to go, last question. 30 seconds or less, what can we expect from the future of data platforms? Matt, please start. >> I have to go first again? You're killing me, Dave. (laughing) In the next few years, I think you're going to see the major players continue to meet customers where they are, right. Every organization, every environment is, you know, kind of, we use these words bespoke in Snowflake, pardon the pun, but Snowflakes, right. But you know, they're all opinionated and unique and what's great as an IT person is, you know, there is a service for me regardless of where I am on my journey, in my data management journey. I think you're going to continue to see with regards specifically to Oracle, I think you're going to see the company continue along this path of being all things to all people, if you will, or all organizations without sacrificing, you know, kind of richness of features and sacrificing who they are, right. Look, they are the data kings, right? I mean, they've been a database leader for an awful long time. I don't see that going away any time soon and I love the innovative spirit they've brought in with HeatWave. >> All right, great thank you. Okay, 30 seconds, Holgar go. >> Yeah, I mean, the interesting thing that we see is really that trend to autonomous as Oracle calls or self-driving software, right? So the database will have to do more things than just store the data and support the DVA. It will have to show it can wide insights, the whole upside, it will be able to show to one machine learning. We haven't really talked about that. How in just exciting what kind of use case we can get of machine learning running real time on data as it changes, right? So, which is part of the E5 announcement, right? So we'll see more of that self-driving nature in the database space. And because you said we can promote it, right. Check out my report about HeatWave latest release where I post in oracle.com. >> Great, thank you for that. And Bob Evans, please. You're great at quick hits, hit us. >> Dave, thanks. I really enjoyed getting to hear everybody's opinion here today and I think what's going to happen too. I think there's a new generation of buyers, a new set of CXO influencers in here. And I think what Oracle's done with this, MySQL HeatWave, those benchmarks that Ron talked about so eloquently here that is going to become something that forces other companies, not just try to get incrementally better. I think we're going to see a massive new wave of innovation to try to play catch up. So I really take my hat off to Oracle's achievement from going to, push everybody to be better. >> Excellent. Marc Staimer, what do you say? >> Sure, I'm going to leverage off of something Matt said earlier, "Those companies that are going to develop faster, cheaper, simpler products that are going to solve customer problems, IT problems are the ones that are going to succeed, or the ones who are going to grow. The one who are just focused on the technology are going to fall by the wayside." So those who can solve more problems, do it more elegantly and do it for less money are going to do great. So Oracle's going down that path today, Snowflake's going down that path. They're trying to do more integration with third party, but as a result, aiming at that simpler, faster, cheaper mentality is where you're going to continue to see this market go. >> Amen brother Marc. >> Thank you, Ron Westfall, we'll give you the last word, bring us home. >> Well, thank you. And I'm loving it. I see a wave of innovation across the entire cloud database ecosystem and Oracle is fueling it. We are seeing it, with the native integration of auto ML capabilities, elastic scaling, lower entry price points, et cetera. And this is just going to be great news for buyers, but also developers and increased use of open APIs. And so I think that is really the key takeaways. Just we're going to see a lot of great innovation on the horizon here. >> Guys, fantastic insights, one of the best power panel as I've ever done. Love to have you back. Thanks so much for coming on today. >> Great job, Dave, thank you. >> All right, and thank you for watching. This is Dave Vellante for theCube and we'll see you next time. (soft music)