(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)

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

(bright intro music) >> Welcome to this "Cube Conversation". I'm your host, Lisa Martin. This is part of our third AWS Start-up Showcase. And I'm pleased to welcome two gentlemen from Rockset, Venkat Venkataramani is here, the CEO and co-founder and Dhruba Borthakur, CTO and co-founder. Gentlemen, welcome to the program. >> Thanks for having us. >> Thank you. >> Excited to learn more about Rockset, Venkat, talk to me about Rockset and how it's putting real-time analytics within the reach of every company. >> If you see the confluent IPO, if you see where the world is going in terms of analytics, I know, we look at this, real-time analytics is like the lost frontier. Everybody wants fast queries on fresh data. Nobody wants to say, "I don't need that. You know, give me slow queries on stale data," right? I think if you see what data warehouses and data lakes have done, especially in the cloud, they've really, really made batch analytics extremely accessible, but real-time analytics still seems too clumsy, too complex, and too expensive for most people. And we are on a mission to make, you know, real-time analytics, make it very, very easy and affordable for everybody to be able to take advantage of that. So that's our, that's what we do. >> But you're right, nobody wants a stale data or slower queries. And it seems like one of the things that we learned, Venkat, sticking with you in the last 18 months of a very strange world that we're living in, is that real-time is no longer a nice to have. It's really a differentiator and table stakes for businesses in every industry. How do you make it more affordable and accessible to businesses in so many different industries? >> I think that's a great question. I think there are, at a very high level, there are two categories of use cases we see. I think there is one full category of use cases where business teams and business units are demanding almost like business observability. You know, if you think about one domain that actually understood real-time and made everything work in real-time is the DevOps world, you know, metrics and monitoring coming out of like, you know, all these machines and because they really want to know as soon as something goes wrong, immediately, I want to, you know, be able to dive in and click and see what happens. But now businesses are demanding the same thing, right? Like a CEO wants to know, "Are we on track to hit our quarterly estimates or not? And tell me now what's happening," because you know, the larger the company, the more complex that have any operations dashboards are. And, you know, if you don't give them real-time visibility, the window of opportunity to do something about it disappears. And so they are really, businesses is really demanding that. And so that is one big use case we have. And the other strange thing we're also seeing is that customers are demanding real-time even from the products they are using. So you could be using a SaaS product for sales automation, support automation, marketing automation. Now I don't want to use a product if it doesn't have real-time analytics baked into the product itself. And so all these software companies, you know, providing a SaaS service to their cloud customers and clients, they are also looking to actually, you know, their proof of value really comes from the analytics that they can show within the product. And if that is not interactive and real-time, then they are also going to be left behind. So it's really a huge differentiator whether you're building a software product or your running a business, the real-time observability gives you a window of opportunity to actually do something about, you know, when something goes wrong, you can actually act on it very, very quickly. >> Right, which is absolutely critical. Dhruba, I want to get your take on this. As the CTO and co-founder as I introduced you, what were some of the gaps in the market back in 2016 that you saw that really necessitated the development of this technology? >> Yeah, for real-time analytics, the difference compared to what it was earlier is that all your things used to be a lot of batch processes. Again, the reason being because there was something called MapReduce, and that was a scanning system that was kind of a invention from Google, which talked about processing big data sets. And it was about scanning, scanning large data sets to give answers. Whereas for real-time analytics, the new trend is that how can you index these big datasets so that you can answer queries really fast? So this is what Rockset does as well, is that we have capabilities to index humongous amounts of data cheaply, efficiently, and economically feasible for our customers. And that's why query is the leverage the index to give fast (indistinct). This is one of the big changes. The other change obviously is that it has moved to the cloud, right? A lot of analytics have moved to the cloud. So Rockset is built natively for the cloud, which is why we can scale up, scale down resources when queries come and we can provide a great (indistinct) for people as data latency, and as far as query latencies comes on, both of these things. So these two trends, I think, are kind of the power behind moving, making people use more real-time analytics. >> Right, and as Venkat was talking about how it's an absolute differentiator for businesses, you know, last year we saw this really, this quick, all these quick pivots to survive and ultimately thrive. And we're seeing the businesses now coming out of this, that we're able to do that, and we're able to pivot to digital, to be successful and to out-compete those who maybe were not as fast. I saw that recently, Venkat, you guys had a new product release a few weeks ago, major product release, that is making real-time analytics on streaming data sources like Apache Kafka, Amazon Kinesis, Amazon DynamoDB, and data lakes a lot more accessible and affordable. Breakdown that launch for me, and how is it doing the accessibility and affordability that you talked about before? >> Extremely good question. So we're really excited about what we call SQL-based roll-ups, is what we call that release. So what does that do? So if you think about real-time analytics and even teeing off the previous question you asked on what is the gap in the market? The gap in the market is really, all that houses and lakes are built for batch. You know, they're really good at letting people accumulate huge volumes of data, and once a week, analyst asking a question, generating a report, and everybody's looking at it. And with real-time, the data never stops coming. The queries never stop coming. So how do you, if I want real-time metrics on all this huge volumes of data coming in, now if I drain it into a huge data lake and then I'm doing analytics on that, it gets very expensive and very complex very quickly. And so the new release that we had is called SQL-based roll-ups, where simply using SQL, you can define any real-time metric that you want to track across any dimensions you care about. It could be geo demographic and other dimensions you care about that and Rockset will automatically maintain all those real-time metrics for you in real-time in a highly accurate fashion. So you never have to doubt whether the metrics are valid and it will be accurate up to the second. And the best part is you don't have to learn a new language. You can actually use SQL to define those metrics and Rockset will automatically maintain that and scale that for you in the cloud. And that, I think, reduces the barrier. So like if somebody wants to build a real-time, you know, track something for their business in real-time, you know, you have to duct tape together multiple, disparate components and systems that were never meant to work with each other. Now you have a real-time database built for the cloud that is fully, you know, supports full feature SQL. So you can do this in a matter of minutes, which would probably take you days or weeks with alternate technologies. >> That's a dramatic X reduction in time there. I want to mention the Snowflake IPO since you guys mentioned the Confluent IPO. You say that Rockset does for real-time, what Snowflake did for batch. Dhruba, I want to get your perspective on that. Tell me about that. What do you mean by that? >> Yeah, so like we see this trend in the market where lot of analytics, which are very batch, they get a lot of value if they've moved more real-time, right? Like Venkat mentioned, when analytics powers, actual products, which need to use analytics into their, to make the product better. So Rockset very much plays in this area. So Rockset is the only solution. I shouldn't say solution. It's a database, it's a real-time database, which powers these kind of analytic systems. If you don't use Rockset, then you might be using maybe a warehouse or something, but you cannot get real-time because there is always a latency of putting data into the warehouse. It could be minutes, it could be hours. And then also you don't get too many people making concurrent queries on the warehouse. So this is another difference for real-time analytics because it powers applications, the query volume could be large. So that's why you need a real-time database and not a real-time warehouse or any other technologies for this. And this trend has really caught up because most people have either, are pretty much into this journey. You asked me this previous question about what has changed since 2016 as well. And this is a journey that most enterprises we see are already embarking upon. >> One thing too, that we're seeing is that more and more applications are becoming data intensive applications, right? We think of whether it's Instagram or DoorDash or whatnot, or even our banking app, we expect to have the information updated immediately. How do you help, Dhruba, sticking with you, how do you help businesses build and power those data intensive applications that the consumers are demanding? >> That's a great question. And we have booked, me and Venkat, we have seen these data applications at large scale when we were at Facebook earlier. We were both parts of the Facebook team. So we saw how real-time was really important for building that kind of a business, that was social media. But now we are taking the same kind of back ends, which can scale to like huge volumes of data to the enterprises as well. Venkat, do you have anything to add? >> Yeah, I think when you're trying to go from batch to real-time, you're 100% spot on that, a static report, a static dashboard actually becomes an application, becomes a data application, and it has to be interactive. So you're not just showing a newspaper where you just get to read. You want to click and deep dive, do slice and dice the data to not only understand what happened, but why it happened and come up with hypotheses to figure out what I want to do with it. So the interactivity is important and the real-timeliness now it becomes important. So the way we think about it is like, once you go into real-time analytics, you know, the data never stops coming. That's obvious. Data freshness is important. But the queries never stop coming also because one, when your dashboards and metrics are getting up to date real-time, you really want alerts and anomaly detection to be automatically built in. And so you don't even have to look at the graphs once a week. When something is off, the system will come and tap on your shoulder and say, "Hey, something is going on." And so that really is a real-time application at that point, because it's constantly looking at the data and querying on your behalf and only alerting you when something, actually, is interesting happening that you might need to look at. So yeah, the whole movement towards data applications and data intensive apps is a huge use case for us. I think most of our customers, I would say, are building a data application in one shape or form or another. >> And if I think of use cases like cutthroat customer 360, you know, as customers and consumers of whatever product or solution we're talking about, we expect that these brands know who we are, know what we've done with them, what we've bought, what to show me next is what I expect whether again, it's my bank or it's Instagram or something else. So that personalization approach is absolutely critical, and I imagine another big game changer, differentiator for the customers that use Rockset. What do you guys think about that? >> Absolutely, personalized recommendation is a huge use case. We see this all where we have, you know, Ritual is one of the customers. We have a case study on that, I think. They want to personalize. They generate offline recommendations for anything that the user is buying, but they want to use behavioral data from the product to personalize that experience and combine the two before they serve anything on the checkout lane, right? We also see in B2B companies, real-time analytics and data applications becoming a very important thing. And we have another customer, Command Alkon, who, you know, they have a supply chain platform for heavy construction and 80% of concrete in North America flows through their platform, for example. And what they want to know in real-time is reporting on how many concrete trucks are arriving at a big construction site, which ones are late and whatnot. And the real-time, you know, analytics needs to be accurate and needs to be, you know, up to the second, you know, don't tell me what trucks were, you know, coming like an hour ago. No, I need this right now. And so even in a B2B platform, we see that very similar trend trend where real-time reporting, real-time search, real-time indexing is actually a very, very important piece to the puzzle. And not just for B to C examples that you said, and the Instagram comment is also very appropriate because a hedge fund customer came to us and said, "I have kind of a dashboards built on top of like Snowflake. They're taking two to five seconds and I have certain parts of my dashboards, but I am actually having 50/60 visualizations. You do the math, it takes many minutes to load. And so they said, "Hey, you have some indexing deck. Can you make this faster?" Three weeks later, the queries that would take two to five seconds on a traditional warehouse or a cloud data warehouse came back in 18 milliseconds with Rockset. And so it is so fast that they said, you know, "If my internal dashboards are not as fast as Instagram, no one in my company uses it." These are their words. And so they are really, you know, the speed is really, really important. The scale is really, really important. Data freshness is important. If you combine all of these things and also make it simple for people to access with SQL-based, that's really the real unique value prop that we have a Rockset, which is what our customers love. >> You brought up something interesting, Venkat, that kind of made me think of the employee experience. You know, we always think of the customer 360. The customer experience with the employee experience, in my opinion, is inextricably linked. The employees have to have access to what they need to deliver and help these great customer relationships. And as you were saying, you know, the employees are expecting databases to be as fast as they see on Instagram, when they're, you know, surfing on their free time. Then adoption, I imagine, gets better, obviously, than the benefit from the end user and customers' perspective is that speed. Talk to me a little bit about how Rockset, and I would like to get both of your opinions here, is a facilitator of that employee productivity for your customers. >> This is a great question. In fact, the same hedge fund, you know, customer, I pushed them to go and measure how many times do people even look at all the data that you produce? (laughs) How many analysts and investors actually use your dashboards and ask them to go investigate at that. And one of the things that they eventually showed me was there was a huge uptake and their dashboards went from two to three second kind of like, you know, lags to 18 milliseconds. They almost got the daily active user for their own internal dashboards to be almost going from five people to the entire company, you know, so I think you're absolutely spot on. So it really goes back to, you know, really leveraging the data and actually doing something about it. Like, you know, if I ask a question and it's going to, you know, system is going to take 20 minutes to answer that, you know, I will probably not ask as many questions as I want to. When it becomes interactive and very, very fast, and all of a sudden, I not only start with a question and, you know, I can ask a follow-up question and then another follow-up question and make it really drive that to, you know, a conclusion and I can actually act upon it. And this really accelerates. So even if you kind of like, look at the macro, you hear these phrases, the world is going from batch to real-time, and in my opinion, when I look at this, people want to, you know, accelerate their growth. People want to make faster decisions. People want to get to, what can I do about this and get actionable insights. And that is not really going to come from systems that take 20 minutes to give a response. It's going to really come from systems that are interactive and real-time, and that's really the need for acceleration is what's really driving this movement from batch to real-time. And we're very happy to facilitate that and accelerate that moment. >> And it really drives the opportunity for your customers to monetize more and more data so that they can actually act on it, as you said, in real-time and do something about it, whether it's a positive experience or it is, you know, remediating a challenge. Last question guys, since we're almost out of time here, but I want to understand, talk to me about the Rockset-AWS partnership and what the value is for your customers. >> Okay, yeah. I'll get to that in a second, but I wanted to add something to your previous question. I think my opinion for all the customers that we see is that real-time analytics is addictive. Once they get used to it, they can go back to the old stuff. So this is what we have found with all our customers. So, yeah, for the AWS question, I think maybe Venkat can answer that better than me. >> Yeah, I mean, we love partnering with AWS. I think, they are the world's leader when it comes to public clouds. We have a lot of joint happy customers that are all AWS customers. Rockset is entirely built on top of AWS, and we love that. And there is a lot of integrations that Rockset natively comes with. So if you're already managing your data in AWS, you know, there are no data transfer costs or anything like that involved for you to also, you know, index that data in Rockset and actually build real-time applications and stream the data to Rockset. So I think the partnership goes in very, very deep in terms of like, we are an AWS customer, we are a partner and we, you know, our go-to market teams work with them. And so, yeah, we're very, very happy, you know, like, AWS fanboys here, yeah. >> Excellent, it sounds like a very great synergistic collaborative relationship, and I love, Dhruba, what you said. This is like, this is a great quote. "Real-time analytics is addictive." That sounds to me like a good addiction (all subtly laugh) for businesses and every industry to take out. Guys, it's been a pleasure talking to you. Thank you for joining me, talking to the audience about Rockset, what differentiates you, and how you're helping customers really improve their customer productivity, their employee productivity, and beyond. We appreciate your time. >> Thanks, Lisa. >> Thank you, thanks a lot. >> For my guests, I'm Lisa Martin. You're watching this "Cube Conversation". (bright ending music)

>> Announcer: Live from San Diego, California, it's theCUBE. Covering KubeCon and CloudNativeCon. Brought to you by Red Hat, the Cloud Native Computing Foundation and its ecosystem partners. >> Welcome back, I'm Stu Miniman and my Co-host is John Troyer. And you're watching theCUBE here in day two of our coverage of KubeCon and CloudNativeCon. And joining me is Tobi Knaup who is the co-founder and CTO of D2iQ. See what I did there, Tobi? >> That's right, I love it. >> Alright. So Tobi, first of all, KubeCon, of course D2iQ, last year when we were here it was Mesosphere, so give us a little bit, you've been to lots of customer meetings, 12,000 people in attendance, tell us a little bit about the energy and how your team's finding the show so far. >> Yeah, obviously biggest KubeCon so far and it's just amazing how far this community has come, how it's grown. How many projects are part of it now, how many vendors here, too. You know two expo halls with different booths and you know, I think it just shows how important this community, this ecosystem is. When customers come to us and say they want to work with Kubernetes the community's why they're really doing it. >> Yeah, it is a great community, great vibe for people that aren't already in it. It's easy to get started, but one of the big themes we're hearing here is simplicity, how to make it easier to get going and once they get going, what happens after day one? That's some of the rebranded pieces. So for our audience, explain a little bit, why the rebrand focus of the company, Day 2 operations, absolutely something that I hear a lot of discussion on and why is your team specifically well positioned for that environment. >> No absolutely, so the rebrand we did because obviously our old company named Mesosphere has Mesos in it. That's the open source product we started with. But we've been doing a lot more than that actually for many years, right? We help customers run Apache Kafka and Spark and Cassandra. We've been doing a lot with Kubernetes also for some time now and even more so now. So having one particular technology in the company name was holding us back, right. People just put us in that box but we're doing so much more. So that was the reason for the rebrand and so, we wanted a name that doesn't have a particular technology in it and so we're looking for what is really expressed, what we do, what we help our customers with? And we've always been focused on Day 2 operations, so everything that happens after the initial install. How do you monitor things properly, upgrade them and so on? So that's why we loved that Day 2 concept. And then the IQ really stands for a couple of things. First of all we try to put a lot of automation into our products, so make those products smart to help our customers. But more importantly too, when we look at the ecosystem as a whole, where are most customers at, where are most companies at. Well, they're still early in their cloud-native journey and they need to get up to speed, they need to get smart about cloud-native and about Day 2 operations and so that's the IQ piece. We want to help our customers become smart about this space, get educated and then learn to do cloud-native. >> So Tobi, one of the things that fascinates me about the Kubernetes ecosystem is that people bring stuff to the table. Kubernetes is here, that's evolving. Other companies, entities, projects are coming to the table with other open source concepts and solving problems that they have in the field. At D2iQ, when you were Mesosphere, you have years of experience dealing with production issues, scaling management, all these sort of really, really fascinating cloud-native problems, so you bring a lot of experience to the table. So one of the projects that you are now working on and working with your customers and partners and the bigger ecosystem on is a way of approaching operators. The concept of bringing this kind of lifecycle automation to applications and helping with all these Day 2 problems. Can you talk a little about so KUDO is the name of the framework, I guess. Can you talk a little bit about that and how you're bringing that here to sit at the table and what some people's experiences with that are and what they are using it for? >> Absolutely, yeah, so these data services, these stapler workloads like Kafka, Cassandra and Spark, that's been in our DNA for a very long time. In fact, a little known fact, Apache Spark was originally a demo application for Apache Mesos. That's how it started originally. Obviously, it took off. So, we've been doing that since even before we were a company. And we've been helping our customers on top of Mesos with running these complex data stacks and there's some equivalent of operators on top of Mesos called frameworks. So we've been building these frameworks and we realized it's a little too hard to build these things. We typically had to write thousands of lines of code, 10, 20,000 sometimes and it took too long. So what we actually did on Mesos many years ago is we extracted the common patterns from those frameworks and built it into a library and made it so you can actually build a framework with just configuration, with just YAML, so it's a language that allows you to essentially sequence your operations into phases and steps. kind of like you would write a run book that a human operator takes and then goes through, right? So when we looked at the Kubernetes Operator space, we saw some of those same challenges that we had faced years ago. Building a Kubernetes Operator requires to write a lot of code. Not every company has Go programmers, people that are skilled enough in Kubernetes that they can write an operator. And more importantly too, once you write those 10,000 lines of code or more, you also have to maintain it. You have to keep up with API changes and so, a lot of folks we talked to at KubeCon last year and to customers, said it's just too hard to build operators. The other side of that too, is folks said it's a little too hard to use those operators too because very common use cases, you build a data pipeline. That means you'll be using multiple different operators, say Kafka, Cassandra and Spark. So if those all have different APIs, that's pretty hard to manage. So we wanted to simplify that. We wanted to create an alternative way for building operators that doesn't require you to learn Go, doesn't require you to write code, it works with just this orchestration language that KUDO offers and then for the KUDO users, the API is the same across these different operators. It has a plugin for Kube Cuddle, so you can interface with all the different operators through that. So yeah, simplicity and a great developer experience are the keys here. >> Tobi, I was wondering maybe you bring us inside the personas you target with this type of solution. As we've seen the maturation of this space, first couple of years I came, it felt very infrastructure heavy. The last year or two, there's more of the AppDev discussion there. They don't always speak the same languages. Looks like you've got some tooling here to help simplify that environment and make it easier because of course your application developers don't want to worry about that stuff. That's the promise of things like serverless, or just we're going to take care of that and stats and whatnot, so where specifically do you target and what are you hearing from customers as to how they're sorting through these organizational changes? >> Yeah, so I think ultimately, everybody kind of wants a platform as a service in some way, right? If you're building an app for your business, you don't want to think about, how do I provision this database, how to do that? And obviously, I can go to a public cloud and I can use all those public cloud services but what a lot of folks are doing now is they're running on various different types of infrastructure. They're running on multiple public clouds. They're running on the Edge. We work with a lot of customers that have a need to deploy these data services, these operators in Edge locations, on the manufacturing floor in a factory, for instance. Or on a cruise ship, that's one company we're working with. So, how do you bring this API-driven deployment of these services to all these different types of locations? And so that's what we try to achieve with KUDO for the data services and then with our other products too, like Kommander, which is a multi-cluster control plane. It's about when organizations have all these different clusters. And very typically they get into the dozens or even hundreds of clusters fast. How do you then manage that? How do you apply configuration consistently across these clusters? Manage your secrets and RBAC rules and things like that? So those are all the Day 2 things that we try to help customers with. There's a little bit of a tension there sometimes, right? Because the great thing about Kubernetes is it's great for developers. It has a nice API, people love the API. People are very quick to adopt it, right? They try it out on their laptop, they setup their first cluster. That typically goes very fast and they very quickly have their first app running. So it happens organically, right? But every large organization also has a need to put the right governance in place, right? How I keep those clusters secure? How do I meet my regulatory requirements? How do I make sure I can upgrade those clusters fast, if I need to fix a security issue and so on? So there's that tension between the governance, the central IT and what the developers want to do. We try to strike a balance there with our products to give developers the agility that cloud-native promises but at the same time, give the IT folks the right controls so they can meet their requirements. >> Tobi, here at the show this year, obviously bigger and a lot more folks at different parts of their cloud-native journey. Again, with the experience you all have, as you talk to folks this year, obviously people are clearly in production. You talk about some of the governance issues, is there anything you can say about either what you think is going to make for a successful partnership with you and a successful customer? What qualities do you need to have by the time you're growing up in production and then also as they're making choices here, what should the end users be looking at? >> Right, so one of the things we realized over the years is actually cloud-native is a journey. Every organization is somewhere else on that journey. And you said partnership, I think that's the key word here. We want to partner with our customers because we realize that this stuff is complicated, right? And it's actually for us as a company, our journey has been kind of interesting because we started at this large scale spot, right? Before we were even a company, we were running these clusters with tens of thousands of notes. These large online services at Twitter and other companies, that's where we started and that's where our first product kind of landed. It's at that large scale is what we're known for but most organizations out there are much earlier in their journey to cloud-native. As so, what we realized is that we really need to partner with folks to even at the very first steps, where they're just getting educated about this space, right? What are containers? How are they different from VMs? What is this cluster management thing, right? How does this all fit together? So we try to hold our customers' hands, catch them where they are. Besides all of the software that we're building, we also offer trainings for example. And so we just try to have the conversation with the customer. Figure out what their needs are, whether that's training, whether that's services or different products. And the different products that come together in our Kubernetes product line, they're really designed to meet the customer at these different stages. There's Konway, that's our Kubernetes distribution, get your first project up and running. Then once you get a little bit more sophisticated, you probably want to do CI/CD. So we have an upcoming product for that, it's called Dispatch. Pretty excited about it. The data services with KUDO. Folks typically add that next and then very quickly you have these dozens of hundreds of clusters. Now, you need Kommander, right? So we try to fit that all together. Meet the customer where they are and I think education is a big piece of that. >> All right, Tobi, we want to give you the final word. You talked about some of the things coming out here, so just give us your viewpoint of the ecosystem broader as to what next things need to be done to help even further the journey that we're all on? >> Yeah, I think in terms of next things, there's a lot of interest around operators. Well, operators as the implementation but really what's happening is, people are running more and more different workloads on top of Kubernetes, right? And I think that's where a lot of the work is going to happen over the next year. There's some discussions in the CNCF now even. What is an operator? How do we define that? Is it something fairly broad? Is it something fairly specific? But Kubernetes is definitely the factor standard for doing cloud-native and people are putting it in a lot of different environments. They're putting it in Edge locations. So I think we need to figure out how do you have a sane sort of development workflow for these types of deployments? How do you define an application that might actually run on multiple different clusters? So I think there's going to be a lot of talk. Operators obviously, but also on the developers side, in a layer above Kubernetes, right? How can I just define my application in a way where I say maybe just run this thing at a highly available way on two different cloud providers, instead of saying specifically it needs to go here, it needs to go there? Or deploy this thing in a follow the sun model or whatever that is. So I think that's where a lot of the conversations are going to happen, is that level above. >> All right well Tobi, appreciate the updates. Congratulations on the progress and definitely look forward to catching more from you and D2iQ team in the near future. >> Thank you very much for having me. >> All right, for John Troyer, I'm Stu Miniman, lots more to come. Thanks for watching theCUBE. (light music)

>> Narrator: Live from San Jose it's theCUBE. Presenting Big Data Silicon Valley. Brought to you by SiliconANGLE Media and its ecosystem partners. (upbeat music) >> Welcome back to San Jose everybody, this is theCUBE, the leader in live tech coverage and you're watching BigData SV, my name is Dave Vellante. In the early days of Hadoop everything was batch oriented. About four or five years ago the market really started to focus on real time and streaming analytics to try to really help companies affect outcomes while things were still in motion. Steve Wilks is here, he's the co-founder and CTO of a company called Stream, a firm that's been in this business for around six years. Steve welcome to theCUBE, good to see you. Thanks for coming on. >> Thanks Dave it's a pleasure to be here. >> So tell us more about that, you started about six years ago, a little bit before the market really started talking about real time and streaming. So what led you to that conclusion that you should co-found Steam way ahead of its time? >> It's partly our heritage. So the four of us that founded Stream, we were executives at GoldenGate Software. In fact our CEO Ali Kutay was the CEO of GoldenGate Software. So when we were acquired by Oracle in 2009, after having to work for Oracle for a couple years, we were trying to work out what to do next. And GoldenGate was replication software right? So it's moving data from one place to another. But customers would ask us in customer advisory boards, that data seems valuable, it's moving. Can you look at it while it's moving and analyze it while it's moving, get value out of that moving data? And so that was kind of set in our heads. And then we were thinking about what to do next, that was kind of the genesis of the idea. So the concept around Stream when we first started the company was we can't just give people streaming data, we need to give them the ability to process that data, analyze it, visualize it, play with it and really truly understand the data. As well as being able to collect it and move it somewhere else. And so the goal from day one was always to build a full end-to-end platform that did everything customers needed to do for streaming integration analytics out of the box. And that's what we've done after six years. >> I got to ask a really basic question, so you're talking about your experience at GoldenGate moving data from point a to point b and somebody said well why don't we put that to work. But is there change data or was it static data? Why couldn't I just analyze it in place? >> GoldenGate works on change data. >> Okay so that's why, there was changes going through. Why wait until it hits its target, let's do some work in real time and learn from that, get greater productivity. And now you guys have taken that to a new level. That new level being what? Modern tools, modern technologies? >> A platform built from the ground up to be inherently distributed, scalable, reliable with exactly one's processing guarantees. And to be a complete end-to-end platform. There's a recognition that the first part of being able to do streaming data integration or analytics is that you need to be able to collect the data right? And while change data captured from databases is the way to get data out of databases in a streaming fashion, you also have to deal with files and devices and message queues and anywhere else the data can reside. So you need a large number of different data collectors that all turn the enterprise data sources into streaming data. And similarly if you want to store data somewhere you need a large collection of target adapters that deliver to things. Not just on premise but also in the cloud. So things like Amazon S3 or the cloud databases like Redshift and Google BigQuery. So the idea was really that we wanted to give customers everything they need and that everything they need isn't trivial. It's not just, well we take Apache Kafka and then we stuff things into it and then we take things out. Pretty often, for example, you need to be able to enrich data and that means you need to be able to join streaming data with additional context information, reference data. And that reference data may come form a database or from files or somewhere else. So you can't call out to the database and maintain the speeds of streaming data. We have customers that are doing hundreds of thousands of events per second. So you can't call out to a database for every event and ask for records to enrich it with. And you can't even do that with an external cache because it's just not fast enough. So we built in an in-memory data grid as part of our platform. So you can join streaming data with the context information in real time without slowing anything down. So when you're thinking about doing streaming integration, it's more than just moving data around. It's ability to process it and get it in the right form, to be able to analyze it, to be able to do things like complex event processing on that data. And also to be able to visualize it and play with it is an essential part of the whole platform. >> So I wanted to ask you about end-to-end. I've seen a lot of products from larger, maybe legacy companies that will say it's end-to-end but what it really is, is a cobbled together pieces that they bought in and then, this is our end-to-end platform, but it's not unified. Or I've seen others "Well we've got an end-to-end platform" oh really, can I see the visualization? "Well we don't have visualization "we use this third party for visualization". So convince me that you're end-to-end. >> So our platform when you start with it you go into a UI, you can start building data flows. Those data flows start from connectors, we have all the connectors that you need to get your enterprise data. We have wizards to help you build those. And so now you have a data stream. Now you want to start processing that, we have SQL-based processing so you can do everything from filtering, transformation, aggregation, enrichment of data. If you want to load reference data into memory you use a cache component to drag that in, configure that. You now have data in-memory you can join with your streams. If you want to now take the results of all that processing and write it somewhere, use one of our target connectors, drag that in so you've got a data flow that's getting bigger and bigger, doing more and more processing. So now you're writing some of that data out to Kafka, oh I'm going to also add in another target adaptor write some of it into Azure Blob Storage and some of it's going to Amazon Redshift. So now you have a much bigger data flow. But now you say okay well I also want to do some analytics on that. So you take the data stream, you build another data flow that is doing some aggregation of a Windows, maybe some complex event processing, and then you use that dashboard builder to build a dashboard to visualize all of that. And that's all in one product. So it literally is everything you need to get value immediately. And you're right, the big vendors they have multiple different products and they're very happy to sell you consulting to put them all together. Even if you're trying to build this from open source and you know, organizations try and do that, you need five or six major pieces of open source, a lot of support in libraries, and a huge team of developers to just build a platform that you can start to build applications on. And most organizations aren't software platform companies, they're finance companies, oil and gas companies, healthcare companies. And they really want to focus on solving business problems and not on reinventing the wheel by building a software platform. So we can just go in there and say look; value immediately. And that really, really helps. >> So what are some of your favorite use cases, examples, maybe customer examples that you can share with me? >> So one of the great examples, one of my customers they have a lot of data in our HP non-stop system. And they needed to be able to get visibility into that immediately. And this was like order processing, supply chain, ERP data. And it would've taken a very large amount of time to do analytics directly on the HP nonstop. And finding resources to do that is hard as well. So they needed to get the data out and they need to get it into the appropriate place. And they recognize that use the right technology to ask the right question. So they wanted some of it in Hadoop so they could do some machine learning on that. They wanted some of it to go into Kafka so they could get real time analytics. And they wanted some of it to go into HBase so they could query it immediately and use that for reference purposes. So they utilized us to do change data capture against the HP nonstop, deliver that datastream out immediately into Kafka and also push some of it into HEFS and some of it into HBase. So they immediately got value out of that, because then they could also build some real-time analytics on it. It would sent out alerts if things were taking too long in their order processing system. And allowed them to get visibility directly into their process that they couldn't get before with much fewer resources and more modern technologies than they could have used before. So that's one example. >> Can I ask you a question about that? So you talked about Kafka, HBase, you talk about a lot of different open source projects. You've integrated those or you've got entries and exits into those? >> So we ship with Kafka as part of our product. It's an optional messaging bus. So, our platform has two different ways of moving data around. We have a high-speed, in-memory only message bus and that works almost network speed and it's great for a lot of different use cases. And that is what backs our data streams. So when you build a data flow, you have streams in between each step, that is backed by an in-memory bus. Pretty often though, in use cases, you need to be able to potentially rewind data for recovery purposes or have different applications running at different speeds and that's where a persistent message bus like Kafka comes in but you don't want to use a persistent message bus for everything because it's doing IO and it's slowing things down. So you typically use that at the beginning, at the sources, especially things like IOT where you can't rewind into them. Things like databases and files, you can rewind into them and replay and recover but IOT sources, you can't do that. So you would push that into a Kafka backed stream and then subsequent processing is in-memory. So we have that as part of our product. We also have Elastic as part of our product for results storage. You can switch to other results storage but that's our default. And we have a few other key components that are part of our product but then on the periphery, we have adapters integrate with a lot of the other things that you mentioned. So we have adapters to read and write HDFS, Hive, HBase, Across, Cloudera, Autumn Works, even MapR. So we have the MapR versions of the file system and MapR streams and MapR DB and then there's lots of other more proprietary connectors like CVC from Oracle, and SQL server, and MySQL and MariaDB. And then database connectors for delivery to virtually any JDBC compliant database. >> I took you down a tangent before you had a chance. You were going to give us another example. We're pretty much out of time but if you can briefly share either that or the last word, I'll give it to you. >> I think the last word would be that that is one example. We have lots and lots of other types of use cases that we do including things like: migrating data from on-premise to the cloud, being able to distribute log data, and being able to analyze that log data being able to do in-memory analytics and get real-time insights immediately and send alerts. It's a very comprehensive platform but each one of those use cases are very easy to develop on their own and you can do them very quickly. And of course as the use case expands within a customer, they build more and more and so they end up using the same platform for lots of different use cases within the same account. >> And how large is the company? How many people? >> We are around 70 people right now. >> 70 People and you're looking for funding? What rounds are you in? Where are you at with funding and revenue and all that stuff? >> Well I'd have to defer to my CEO for those questions. >> All right, so you've been around for what, six years you said? >> Yeah, we have a number of rounds of funding. We had initial seed funding then we had the investment by Summit Partners that carried us through for a while. Then subsequent investment from Intel Capital, Dell EMC, Atlantic Bridge. And that's where we are right now. >> Good, excellent. Steve, thanks so much for coming on theCUBE, really appreciate your time. >> Great, it's awesome. Thank you Dave. >> Great to meet you. All right, keep it right there everybody, we'll be back with our next guest. This is theCUBE. We're live from BigData SV in San Jose. We'll be right back. (techno music)

>> Announcer: Live from Midtown Manhattan, it's theCUBE, covering Big Data, New York City, 2017. Brought to you by SiliconANGLE Media and its ecosystem sponsors. >> Okay, welcome back, everyone. Live in Manhattan, this is theCUBE's coverage of our fifth year doing Big Data, NYC; eighth year covering Hadoop World, which is now evolved into Strata Data which is right around the corner. We're doing that in conjunction with that event. This is, again, where we have the thought leaders, we have the experts, we have the entrepreneurs and CEOs come in, of course. The who's who in tech. And my next two guests, is Jagane Sundar, CUBE alumni, who was on yesterday. CTO of WANdisco, one of the hottest companies, most valuable companies in the space for their unique IP, and not a lot of people know what they're doing. So congratulations on that. But you're here with one of your partners, a company I've heard of, called Microsoft, also doing extremely well with Azure Cloud. We've got Pranav Rastogi, who's the program manager of Microsoft Cloud Azure. You guys have an event going on as well at Microsoft Ignite which has been creating a lot of buzz this year again. As usual, they have a good show, but this year the Cloud certainly has taken front and center. Welcome to theCUBE, and good to see you again. >> Thank you. >> Thank you. >> Alright, so talk about the partnership. You guys, Jagane deals with all the Cloud guys. You're here with Microsoft. What's going on with Microsoft? Obviously they've been, if you look at the stock price. From 20-something to a complete changeover of the leadership of Satya Nadella. The company has mobilized. The Cloud has got traction, putting a dent in the universe. Certainly, Amazon feels a little bit of pain there. But, in general, a lot more work to do. What are you guys doing together? Share the relationship. >> So, we just announced a product that's a one-click deployment in the Microsoft Azure Cloud, off WANdisco's Fusion Replication technology. So, if you got some data assets, Hadoop or Cloud object stores on-premise and you want to create a hybrid or a Cloud environment with Azure and Picture, ours is the only way of doing Active/Active. >> Active/Active. And there is some stuff out there that's looking like Active/Active. DataPlane by Hortonworks. But it's fully not Active/Active. We talked a little bit about that yesterday. >> Jagane: Yes. >> Microsoft, you guys, what's interesting about these guys besides the Active/Active? It's a unique thing. It's an ingredient for you guys. >> Yes, the interesting thing for us is, the biggest problem that we think customers have for big data perspective is, if you look at the landscape of the ecosystem in terms of open source projects that are available it's very hard to a: figure out How do I use this software?, b: How do I install it? And, so what we have done is created an experience in Azure HDInsight where you can discover these applications, within the context of your cluster and you can install these applications by one-click install. Which installs the application, configures it, and then you're good to go. We think that this is going to sort of increase the productivity of users trying to get sense out of big data. The key challenges we think customers have today is setting up some sort of hybrid environment between how do you connect your on premise data to move it to the Cloud, and there are different use cases that you can have you can move parts of the data and you can do experiment easily in the Cloud. So what we've done is, we've enabled WANdisco as an application on our HDInsight application platform, where customers can install it using a single-click deploy connected with the data that's sitting on-prem, use the Active/Active feature to have both these environments running simultaneously and they're in sync. >> So one benefits the one-click thing, that's on your side, right? You guys are enabling that. So, okay, I get that. That's totally cool. We'll get to that in a second. I want to kind of drill down on that. But, what's the benefit to the customers, that you guys are having? So, I'm a customer, I one-click, I want some WANdisco Active/Active. Why am I doing it? What does the Cloud change? How does your Cloud change from that experience? >> One example that you can think about is going to change is in an on-premise environment you have a cluster running, but you're kind of limited on what you can do with the cluster, because you've already setup the number of nodes and the workloads your running is fairly finite, but what's happening in reality and today is, lots of users, especially in the machine learning space, and AI space, and the analytic space are using a lot of open source libraries and technologies and they're using it on top of Hadoop, and they're using it on top of Spark. However, in experimenting with these technologies is hard on-prem because it's a locked environment. So we believe, with the Cloud, especially with it offering WANdisco and HDInsight, once you move the data you can start spinning up clusters, you can start installing more open source libraries, experiment, and you can shut down the clusters when you're done. So it's going to increase your efficiency, it's going to allow you to experiment faster, and it's going to reduce for cost as well, because you don't have to have the cluster running all the time and once you are done with your experimentation, then you can decide which way do you want to go. So, it's going to remove the-- >> Jagane, what's your experience with Azure? A lot of people have been, some people have been critical, and rightfully so. You guys are moving as fast you can. You can only go as fast you can, but the success of the Cloud has been phenomenal. You guys have done a great job with the Cloud. Got to give you props on that. Your customers are benefiting, or Microsoft's customers are benefiting. How's the relationship? Are you getting more customers through these guys? Are you bringing customers from on-prem to Cloud? How's the customer flow going? >> Almost all of our customers who have on-prem instances of Hadoop are considering Cloud in one form or the other. Different Clouds have different strengths, as they've found-- >> Interviewer: And different technologies. >> Indeed. And Azure's strengths appear to be the HDInsight piece of it and as Pranam just mentioned, the cool thing is, you can replicate into the Cloud, start up a 50 node Spark cluster today to run a query, that may return results to you really fast. Now, remember this is data that you can write to both in the Cloud and on-premise. It's kept consistent by our technology, or tomorrow you may find that somebody tells you, Hive with the new Tez enhancements is faster, sure, spin up a hundred node Hive cluster in the Cloud, HDInsight supports that really well. You're getting consistent data and your queries will respond much faster than your on-premise. >> We've had Oliver Chu on, before with Hortonworks obviously they're partnering there. HDInsight's been getting a lot of traction lately. Where's that going? We've seen some good buzz on that. Good people talking about it. What's the latest update on your end? >> HDInsight is doing really good. The customers love the ease of creating a cluster using just a few clicks and the benefits that customers get, clusters are optimized for certain scenarios. So if you're doing data science, you can create a Spark cluster, install open source libraries. We have Microsoft R Server running on Spark, which is a unique offering to Microsoft, which lots of customers have appreciated. You also have streaming scenarios that you can do using open source technologies, like we have Apache Kafka running on a stack, which is becoming very popular from an ingestion perspective. Folks have been-- >> Has the Kupernetes craze come down to your group yet? Has it trickled down? It seems to be going crazy. You hired an amazing person from Google, Brendan Burns, we've interviewed before. He's part of the original Kubernetes spec he now works for Microsoft. What's the buzz on the Kubernetes container world there? >> In general, Microsoft Azure has seen great benefits out of it. We are seeing lots of traction in that space. From my role in particular, I focus more on the HDInsight big data space, which is kind of outside of what we do with Kubernetes' work. >> And your relationship is going strong with WANdisco? >> Pranav: Yes. >> Right. >> We just launched this offering just about yesterday is what we announced and we're looking forward to getting customers on to the stack. >> That's awesome. What's your take on the industry right now? Obviously, the partnerships are becoming clearer as people can see there's (mumbles). You're starting to see the notion of infrastructure and services are changing. More and more people want services and then you got the classic infrastructure which looks like it's going to be hybrid. That's pretty clear, we see that. Services versus infrastructure, how should customers think about how they architect their environments? So they can take advantage of the Active/Active and also have a robust, clean, not a lot of re-skilling going on, but more of a good organization from a personnel standpoint, but yet get to a hybrid architecture? >> So, it depends, the Cloud gives you lots of options to meet the customers where they are. Different customers have different kinds of requirements. Customers who have specialized, some of their applications will probably want to go more of an infrastructure route, but customers also love to have some of the past benefits where, you know, I have a service running where I don't have to worry about the infrastructure, how dispatching happen, how does OS updates happen, how does maintenance happen. They want to sort of rely on the Microsoft Azure Cloud provider to take care of it. So that they can focus on their application specific logic, or business specific logic, or analytical workloads, and worry about optimizing those parts of the application because that is their core-- >> It's been great.I want to get your thoughts real quick. Share some color. What's going on inside Microsoft? Obviously, open source has become a really big part of the culture, even just at Ignite. More Linux news is coming. You guys have been involved in Linux. Obviously, open source with Azure, ton of stuff, I know is built in the Microsoft Cloud on open source. You're contributing now as to Kubernetes, as I mentioned earlier. Seems to be a good cultural shift at Microsoft. What's the vibe on the open source internally at Microsoft? Can you share, just some anecdotal insight into what's the vibe like inside, around open source? >> The vibe has increased quite a lot around open source. You rightly mentioned, just recently we've announced a SQL server on Linux as well, at the Ignite conference. You can also deploy a SQL server on a docker container, which is quite revolutionary if you think about how forward we have come. Open source is so pervasive it's almost used in a lot of these projects. Microsoft employees are contributing back to open source projects in terms of, bug fixes, feature requests, or documentation updates. It's a very, very active community and by and large I think customers are benefiting a lot, because there are so many folks working together on open source projects and making them successful and especially around the Azure stack, we also ensure that you can run these open source workloads lively in the Cloud. From an enterprise perspective, you get the best of both worlds. You get the latest innovations happening in open source, plus the reliability of the managed platform that Azure provides at an enterprise scale. >> So again, obviously Microsoft partnership is huge, all the Clouds as well. Where do you want to take the relationship with Microsoft? What happens next? You guys are just going to continue to do business, you're like expecting the one-click's nice, I have some questions on that. What happens next? >> So, I see our partnership becoming deeper. We see the value that HDInsight brings to the ecosystem and all of that value is captured by the data. At the end of the day, if you have stale data, if you have data that you can't rely on the applications are useless. So we see ourselves getting more and more deeply embedded in the system. We see of ourselves as an essential part of the data strategy for Azure. >> Yeah, we see continuous integration as a development concept, continuous analytics as a term, that's being kicked around. We were talking yesterday about, here in theCUBE, real time, I want some data real time and IT goes back, "Here it is, it's real time!" No, but the data's three weeks old. I mean, real time (laughs) is a word that doesn't mean I got to see it really fast, low latency response. Well, that's not the data I want. I meant the data in real time, not you giving me a real time query. So again, this brings up a mind shift in terms of the new way to do business in the Cloud and hybrid. It's changing the game. As customers scratch their heads and try to figure out how to make their organizations more DevOps oriented, what do you guys see for advice for those managers, who are really getting behind it, really want to make change, who kind of have to herd the cats a little bit, and maybe break out security and put it in it's own group? Or you come and say, okay IT guys we're going to change into our operating model, even on-prem, we'll use some burst in to the Cloud, Azure's got 365 on there, lot of coolness developing. What's the advice for the mindset of the change agents out there that are going to do the transformation? >> My advice would be, if you've done the same thing by hand over two times, it's time you automated it, but-- >> Interviewer: Two times?! >> Two times. >> No three rule? Three strikes you're out? >> You're saying two, contrarian. >> That's a careful statement. Because, if you try automating something that you've never actually tried by hand, that's a disaster as well. A couple times, so you know how it's supposed to work. >> Interviewer: Get a good groove on it. >> Right, then you optimize, you automate, and then you turn the knobs. So, you try a hundred node cluster, maybe that's going to be faster. Maybe after a certain point, you don't get any improvements, so you know how to-- >> So take some baby steps, and one easy way to do it is to automate something that you've done. >> Jagane: Yes, exactly. >> That's almost risk-free, relatively speaking. Thoughts, advice to change agents out there. This is your industry hat on. You can take your Microsoft hat off. >> Baby steps. So you start small, you get familiar with the environment and your toolsets are provided so that you get a consistent experience on what you were doing on-prem and sort of in a hybrid space. And the whole idea is as you get more comfortable the benefits of the Cloud far outweigh any sort of cultural changes that need to happen-- >> Guys, thanks for coming on theCUBE, really appreciate it. Thoughts on the Big Data NYC this week? What do you think? >> I think it's a conference that has a lot of Cloud hanging over it and people are scratching their heads. Including vendors, customers, everybody scratching their head, but there is a lot of Cloud in this conference, although this is not a Cloud conference. >> Yeah, they're trying to make it an AI conference. A lot of AI watching certainly we're seeing that everywhere. But again, nothing wrong hyping up AI. It's good for society. It really is cool, but still, that's talking about baby steps, AI is still not there. It seems like, AI from when I got my CS degree in the 80's, not a lot innovation, well machine learning is getting better, but, a lot more way to go on AI. Don't you think? >> Yes, you know a few of the announcements we've made in this week is all about making it easier for developers to get started with AI and machine learning and our whole hope is with these investments that we've done and Azure machine learning improvements and the companion app and the workbench, allows you to get started very easily with AI and machine learning models and you can apply and build these models, do a CICD process and deploy these models and be more effective in the space. >> Yeah and also the tooling market has kind of gotten out of control. We were just joking the other day, that there's this tool shed mindset where everything is in the tool shed and people bought a hammer and turned it into a lawnmower. So it's like, you got to be careful which tools you have. Think about a platform. Think holistically, but if you take the baby steps and implement it, certainly it's there. My personal opinion, I think the Cloud is the equalizer. Cloud can bring compute power that changes what a tool was built for. Even, go back six years, the tools that were out there even six years ago are completely changed by the impact of unlimited, potentially unlimited capacity horsepower. So, okay that resets a little bit. You agree? >> I do. I totally agree. >> Who wins, who loses on the reset? >> The Cloud is an equalizer, but there is a mindset shift that goes with that those who can adapt to the mindset shift, will win. Those who can not and are still clinging to their old practices will have a hard time. >> Yeah, it's exciting. If you're still reinventing Hadoop from 2011 then, probably not good shape right now. >> Jagane: Not a good place to be. >> Using Hadoop is great for Bash, but you can't make that be a lawnmower. That's my opinion. Okay, thanks for coming on. I appreciate it (laughs) You're smiling, you got something that you, no? >> Pranav: (laughs) Thank you so much for that comment. >> Yeah, tool sheds are out there, be careful. Guys do your job. Congratulations on your partnership, appreciate it. This is theCUBE, live in New York. More after this short break. We'll be right back.