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

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

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> Recent conversations with IT decision makers show a stark contrast between exiting 2023 versus the mindset when we were leaving 2022. CIOs are generally funding new initiatives by pushing off or cutting lower priority items, while security efforts are still being funded. Those that enable business initiatives that generate revenue or taking priority over cleaning up legacy technical debt. The bottom line is, for the moment, at least, the mindset is not cut everything, rather, it's put a pause on cleaning up legacy hairballs and fund monetization. Hello, and welcome to this week's Wikibon Cube Insights powered by ETR. In this breaking analysis, we tap recent discussions from two primary sources, year-end ETR roundtables with IT decision makers, and CUBE conversations with data, cloud, and IT architecture practitioners. The sources of data for this breaking analysis come from the following areas. Eric Bradley's recent ETR year end panel featured a financial services DevOps and SRE manager, a CSO in a large hospitality firm, a director of IT for a big tech company, the head of IT infrastructure for a financial firm, and a CTO for global travel enterprise, and for our upcoming Supercloud2 conference on January 17th, which you can register free by the way, at supercloud.world, we've had CUBE conversations with data and cloud practitioners, specifically, heads of data in retail and financial services, a cloud architect and a biotech firm, the director of cloud and data at a large media firm, and the director of engineering at a financial services company. Now we've curated commentary from these sources and now we share them with you today as anecdotal evidence supporting what we've been reporting on in the marketplace for these last couple of quarters. On this program, we've likened the economy to the slingshot effect when you're driving, when you're cruising along at full speed on the highway, and suddenly you see red brake lights up ahead, so, you tap your own brakes and then you speed up again, and traffic is moving along at full speed, so, you think nothing of it, and then, all of a sudden, the same thing happens. You slow down to a crawl and you start wondering, "What the heck is happening?" And you become a lot more cautious about the rate of acceleration when you start moving again. Well, that's the trend in IT spend right now. Back in June, we reported that despite the macro headwinds, CIOs were still expecting 6% to 7% spending growth for 2022. Now that was down from 8%, which we reported at the beginning of 2022. That was before Ukraine, and Fed tightening, but given those two factors, you know that that seemed pretty robust, but throughout the fall, we began reporting consistently declining expectations where CIOs are now saying Q4 will come in at around 3% growth relative to last year, and they're expecting, or should we say hoping that it pops back up in 2023 to 4% to 5%. The recent ETR panelists, when they heard this, are saying based on their businesses and discussions with their peers, they could see low single digit growth for 2023, so, 1%, 2%, 3%, so, this sort of slingshotting, or sometimes we call it a seesaw economy, has caught everyone off guard. Amazon is a good example of this, and there are others, but Amazon entered the pandemic with around 800,000 employees. It doubled that workforce during the pandemic. Now, right before Thanksgiving in 2022, Amazon announced that it was laying off 10,000 employees, and, Jassy, the CEO of Amazon, just last week announced that number is now going to grow to 18,000. Now look, this is a rounding error at Amazon from a headcount standpoint and their headcount remains far above 2019 levels. Its stock price, however, does not and it's back down to 2019 levels. The point is that visibility is very poor right now and it's reflected in that uncertainty. We've seen a lot of layoffs, obviously, the stock market's choppy, et cetera. Now importantly, not everything is on hold, and this downturn is different from previous tech pullbacks in that the speed at which new initiatives can be rolled out is much greater thanks to the cloud, and if you can show a fast return, you're going to get funding. Organizations are pausing on the cleanup of technical debt, unless it's driving fast business value. They're holding off on modernization projects. Those business enablement initiatives are still getting funded. CIOs are finding the money by consolidating redundant vendors, and they're stealing from other pockets of budget, so, it's not surprising that cybersecurity remains the number one technology priority in 2023. We've been reporting that for quite some time now. It's specifically cloud, cloud native security container and API security. That's where all the action is, because there's still holes to plug from that forced march to digital that occurred during COVID. Cloud migration, kind of showing here on number two on this chart, still a high priority, while optimizing cloud spend is definitely a strategy that organizations are taking to cut costs. It's behind consolidating redundant vendors by a long shot. There's very little evidence that cloud repatriation, i.e., moving workloads back on prem is a major cost cutting trend. The data just doesn't show it. What is a trend is getting more real time with analytics, so, companies can do faster and more accurate customer targeting, and they're really prioritizing that, obviously, in this down economy. Real time, we sometimes lose it, what's real time? Real time, we sometimes define as before you lose the customer. Now in the hiring front, customers tell us they're still having a hard time finding qualified site reliability engineers, SREs, Kubernetes expertise, and deep analytics pros. These job markets remain very tight. Let's stay with security for just a moment. We said many times that, prior to COVID, zero trust was this undefined buzzword, and the joke, of course, is, if you ask three people, "What is zero trust?" You're going to get three different answers, but the truth is that virtually every security company that was resisting taking a position on zero trust in an attempt to avoid... They didn't want to get caught up in the buzzword vortex, but they're now really being forced to go there by CISOs, so, there are some good quotes here on cyber that we want to share that came out of the recent conversations that we cited up front. The first one, "Zero trust is the highest ROI, because it enables business transformation." In other words, if I can have good security, I can move fast, it's not a blocker anymore. Second quote here, "ZTA," zero trust architecture, "Is more than securing the perimeter. It encompasses strong authentication and multiple identity layers. It requires taking a software approach to security instead of a hardware focus." The next one, "I'd love to have a security data lake that I could apply to asset management, vulnerability management, incident management, incident response, and all aspects for my security team. I see huge promise in that space," and the last one, I see NLP, natural language processing, as the foundation for email security, so, instead of searching for IP addresses, you can now read emails at light speed and identify phishing threats, so, look at, this is a small snapshot of the mindset around security, but I'll add, when you talk to the likes of CrowdStrike, and Zscaler, and Okta, and Palo Alto Networks, and many other security firms, they're listening to these narratives around zero trust. I'm confident they're working hard on skating to this puck, if you will. A good example is this idea of a security data lake and using analytics to improve security. We're hearing a lot about that. We're hearing architectures, there's acquisitions in that regard, and so, that's becoming real, and there are many other examples, because data is at the heart of digital business. This is the next area that we want to talk about. It's obvious that data, as a topic, gets a lot of mind share amongst practitioners, but getting data right is still really hard. It's a challenge for most organizations to get ROI and expected return out of data. Most companies still put data at the periphery of their businesses. It's not at the core. Data lives within silos or different business units, different clouds, it's on-prem, and increasingly it's at the edge, and it seems like the problem is getting worse before it gets better, so, here are some instructive comments from our recent conversations. The first one, "We're publishing events onto Kafka, having those events be processed by Dataproc." Dataproc is a Google managed service to run Hadoop, and Spark, and Flank, and Presto, and a bunch of other open source tools. We're putting them into the appropriate storage models within Google, and then normalize the data into BigQuery, and only then can you take advantage of tools like ThoughtSpot, so, here's a company like ThoughtSpot, and they're all about simplifying data, democratizing data, but to get there, you have to go through some pretty complex processes, so, this is a good example. All right, another comment. "In order to use Google's AI tools, we have to put the data into BigQuery. They haven't integrated in the way AWS and Snowflake have with SageMaker. Moving the data is too expensive, time consuming, and risky," so, I'll just say this, sharing data is a killer super cloud use case, and firms like Snowflake are on top of it, but it's still not pretty across clouds, and Google's posture seems to be, "We're going to let our database product competitiveness drive the strategy first, and the ecosystem is going to take a backseat." Now, in a way, I get it, owning the database is critical, and Google doesn't want to capitulate on that front. Look, BigQuery is really good and competitive, but you can't help but roll your eyes when a CEO stands up, and look, I'm not calling out Thomas Kurian, every CEO does this, and talks about how important their customers are, and they'll do whatever is right by the customer, so, look, I'm telling you, I'm rolling my eyes on that. Now let me also comment, AWS has figured this out. They're killing it in database. If you take Redshift for example, it's still growing, as is Aurora, really fast growing services and other data stores, but AWS realizes it can make more money in the long-term partnering with the Snowflakes and Databricks of the world, and other ecosystem vendors versus sub optimizing their relationships with partners and customers in order to sell more of their own homegrown tools. I get it. It's hard not to feature your own product. IBM chose OS/2 over Windows, and tried for years to popularize it. It failed. Lotus, go back way back to Lotus 1, 2, and 3, they refused to run on Windows when it first came out. They were running on DEC VAX. Many of you young people in the United States have never even heard of DEC VAX. IBM wanted to run every everything only in its cloud, the same with Oracle, originally. VMware, as you might recall, tried to build its own cloud, but, eventually, when the market speaks and reveals what seems to be obvious to analysts, years before, the vendors come around, they face reality, and they stop wasting money, fighting a losing battle. "The trend is your friend," as the saying goes. All right, last pull quote on data, "The hardest part is transformations, moving traditional Informatica, Teradata, or Oracle infrastructure to something more modern and real time, and that's why people still run apps in COBOL. In IT, we rarely get rid of stuff, rather we add on another coat of paint until the wood rots out or the roof is going to cave in. All right, the last key finding we want to highlight is going to bring us back to the cloud repatriation myth. Followers of this program know it's a real sore spot with us. We've heard the stories about repatriation, we've read the thoughtful articles from VCs on the subject, we've been whispered to by vendors that you should investigate this trend. It's really happening, but the data simply doesn't support it. Here's the question that was posed to these practitioners. If you had unlimited budget and the economy miraculously flipped, what initiatives would you tackle first? Where would you really lean into? The first answer, "I'd rip out legacy on-prem infrastructure and move to the cloud even faster," so, the thing here is, look, maybe renting infrastructure is more expensive than owning, maybe, but if I can optimize my rental with better utilization, turn off compute, use things like serverless, get on a steeper and higher performance over time, and lower cost Silicon curve with things like Graviton, tap best of breed tools in AI, and other areas that make my business more competitive. Move faster, fail faster, experiment more quickly, and cheaply, what's that worth? Even the most hard-o CFOs understand the business benefits far outweigh the possible added cost per gigabyte, and, again, I stress "possible." Okay, other interesting comments from practitioners. "I'd hire 50 more data engineers and accelerate our real-time data capabilities to better target customers." Real-time is becoming a thing. AI is being injected into data and apps to make faster decisions, perhaps, with less or even no human involvement. That's on the rise. Next quote, "I'd like to focus on resolving the concerns around cloud data compliance," so, again, despite the risks of data being spread out in different clouds, organizations realize cloud is a given, and they want to find ways to make it work better, not move away from it. The same thing in the next one, "I would automate the data analytics pipeline and focus on a safer way to share data across the states without moving it," and, finally, "The way I'm addressing complexity is to standardize on a single cloud." MonoCloud is actually a thing. We're hearing this more and more. Yes, my company has multiple clouds, but in my group, we've standardized on a single cloud to simplify things, and this is a somewhat dangerous trend, because it's creating even more silos and it's an opportunity that needs to be addressed, and that's why we've been talking so much about supercloud is a cross-cloud, unifying, architectural framework, or, perhaps, it's a platform. In fact, that's a question that we will be exploring later this month at Supercloud2 live from our Palo Alto Studios. Is supercloud an architecture or is it a platform? And in this program, we're featuring technologists, analysts, practitioners to explore the intersection between data and cloud and the future of cloud computing, so, you don't want to miss this opportunity. Go to supercloud.world. You can register for free and participate in the event directly. All right, thanks for listening. That's a wrap. I'd like to thank Alex Myerson, who's on production and manages our podcast, Ken Schiffman as well, Kristen Martin and Cheryl Knight, they helped get the word out on social media, and in our newsletters, and Rob Hof is our editor-in-chief over at siliconangle.com. He does some great editing. Thank you, all. Remember, all these episodes are available as podcasts wherever you listen. All you've got to do is search "breaking analysis podcasts." I publish each week on wikibon.com and siliconangle.com where you can email me directly at david.vellante@siliconangle.com or DM me, @Dante, or comment on our LinkedIn posts. By all means, check out etr.ai. They get the best survey data in the enterprise tech business. We'll be doing our annual predictions post in a few weeks, once the data comes out from the January survey. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, everybody, and we'll see you next time on "Breaking Analysis." (upbeat music)

>>Hey everyone, welcome to the Cube's coverage of AWS Reinvent 2022. Lisa Martin here with you with Subaru ier, one of our alumni who's now the CEO of Aerospike. Sabu. Great to have you on the program. Thank you for joining us. >>Great as always, to be on the cube. Luisa, good to meet you. >>So, you know, every company these days has got to be a data company, whether it's a retailer, a manufacturer, a grocer, a automotive company. But for a lot of companies, data is underutilized, yet a huge asset that is value added. Why do you think companies are struggling so much to make data a value added asset? >>Well, you know, we, we see this across the board when I talk to customers and prospects. There's a desire from the business and from it actually to leverage data to really fuel newer applications, newer services, newer business lines, if you will, for companies. I think the struggle is one, I think one the, you know, the plethora of data that is created, you know, surveys say that over the next three years data is gonna be, you know, by 2025, around 175 zetabytes, right? A hundred and zetabytes of data is gonna be created. And that's really a, a, a growth of north of 30% year over year. But the more important, and the interesting thing is the real time component of that data is actually growing at, you know, 35% cagr. And what enterprises desire is decisions that are made in real time or near real time. >>And a lot of the challenges that do exist today is that either the infrastructure that enterprises have in place was never built to actually manipulate data in real time. The second is really the ability to actually put something in place which can handle spikes yet be cost efficient if you'll, so you can build for really peak loads, but then it's very expensive to operate that particular service at normal loads. So how do you build something which actually works for you, for both you, both users, so to speak? And the last point that we see out there is even if you're able to, you know, bring all that data, you don't have the processing capability to run through that data. So as a result, most enterprises struggle with one, capturing the data, you know, making decisions from it in real time and really operating it at the cost point that they need to operate it at. >>You know, you bring up a great point with respect to real time data access. And I think one of the things that we've learned the last couple of years is that access to real time data, it's not a nice to have anymore. It's business critical for organizations in any industry. Talk about that as one of the challenges that organizations are facing. >>Yeah. When, when, when we started Aerospike, right when the company started, it started with the premise that data is gonna grow, number one, exponentially. Two, when applications open up to the internet, there's gonna be a flood of users and demands on those applications. And that was true primarily when we started the company in the ad tech vertical. So ad tech was the first vertical where there was a lot of data both on the supply side and the demand side from an inventory of ads that were available. And on the other hand, they had like microseconds or milliseconds in which they could make a decision on which ad to put in front of you and I so that we would click or engage with that particular ad. But over the last three to five years, what we've seen is as digitization has actually permeated every industry out there, the need to harness data in real time is pretty much present in every industry. >>Whether that's retail, whether that's financial services, telecommunications, e-commerce, gaming and entertainment. Every industry has a desire. One, the innovative companies, the small companies rather, are innovating at a pace and standing up new businesses to compete with the larger companies in each of these verticals. And the larger companies don't wanna be left behind. So they're standing up their own competing services or getting into new lines of business that really harness and are driven by real time data. So this compelling pressures, one, the customer exp you know, customer experience is paramount and we as customers expect answers in, you know, an instant in real time. And on the other hand, the way they make decisions is based on a large data set because you know, larger data sets actually propel better decisions. So there's competing pressures here, which essentially drive the need. One from a business perspective, two from a customer perspective to harness all of this data in real time. So that's what's driving an inces need to actually make decisions in real or near real time. >>You know, I think one of the things that's been in short supply over the last couple of years is patients we do expect as consumers, whether we're in our business lives, our personal lives that we're going to be getting, be given information and data that's relevant, it's personal to help us make those real time decisions. So having access to real time data is really business critical for organizations across any industries. Talk about some of the main capabilities that modern data applications and data platforms need to have. What are some of the key capabilities of a modern data platform that need to be delivered to meet demanding customer expectations? >>So, you know, going back to your initial question Lisa, around why is data really a high value but underutilized or underleveraged asset? One of the reasons we see is a lot of the data platforms that, you know, some of these applications were built on have been then around for a decade plus and they were never built for the needs of today, which is really driving a lot of data and driving insight in real time from a lot of data. So there are four major capabilities that we see that are essential ingredients of any modern data platform. One is really the ability to, you know, operate at unlimited scale. So what we mean by that is really the ability to scale from gigabytes to even petabytes without any degradation in performance or latency or throughput. The second is really, you know, predictable performance. So can you actually deliver predictable performance as your data size grows or your throughput grows or your concurrent user on that application of service grows? >>It's really easy to build an application that operates at low scale or low throughput or low concurrency, but performance usually starts degrading as you start scaling one of these attributes. The third thing is the ability to operate and always on globally resilient application. And that requires a, a really robust data platform that can be up on a five, nine basis globally, can support global distribution because a lot of these applications have global users. And the last point is, goes back to my first answer, which is, can you operate all of this at a cost point? Which is not prohibitive, but it makes sense from a TCO perspective. Cuz a lot of times what we see is people make choices of data platforms and as ironically their service or applications become more successful and more users join their journey, the revenue starts going up, the user base starts going up, but the cost basis starts crossing over the revenue and they're losing money on the service, ironically, as the service becomes more popular. So really unlimited scale, predictable performance always on, on a globally resilient basis and low tco. These are the four essential capabilities of any modern data platform. >>So then talk to me with those as the four main core functionalities of a modern data platform. How does aerospace deliver that? >>So we were built, as I said, from the from day one to operate at unlimited scale and deliver predictable performance. And then over the years as we work with customers, we build this incredible high availability capability which helps us deliver the always on, you know, operations. So we have customers who are, who have been on the platform 10 years with no downtime for example, right? So we are talking about an amazing continuum of high availability that we provide for customers who operate these, you know, globally resilient services. The key to our innovation here is what we call the hybrid memory architecture. So, you know, going a little bit technically deep here, essentially what we built out in our architecture is the ability on each node or each server to treat a bank of SSDs or solid state devices as essentially extended memory. So you're getting memory performance, but you're accessing these SSDs, you're not paying memory prices, but you're getting memory performance as a result of that. >>You can attach a lot more data to each node or each server in your distributed cluster. And when you kind of scale that across basically a distributed cluster you can do with aerospike, the same things at 60 to 80% lower server count and as a result 60 to 80% lower TCO compared to some of the other options that are available in the market. Then basically, as I said, that's the key kind of starting point to the innovation. We layer around capabilities like, you know, replication change, data notification, you know, synchronous and asynchronous replication. The ability to actually stretch a single cluster across multiple regions. So for example, if you're operating a global service, you can have a single aerospace cluster with one node in San Francisco, one northern New York, another one in London. And this would be basically seamlessly operating. So that, you know, this is strongly consistent. >>Very few no SQL data platforms are strongly consistent or if they are strongly consistent, they will actually suffer performance degradation. And what strongly consistent means is, you know, all your data is always available, it's guaranteed to be available, there is no data lost anytime. So in this configuration that I talked about, if the node in London goes down, your application still continues to operate, right? Your users see no kind of downtime and you know, when London comes up, it rejoins the cluster and everything is back to kind of the way it was before, you know, London left the cluster so to speak. So the op, the ability to do this globally resilient, highly available kind of model is really, really powerful. A lot of our customers actually use that kind of a scenario and we offer other deployment scenarios from a higher availability perspective. So everything starts with HMA or hybrid memory architecture and then we start building out a lot of these other capabilities around the platform. >>And then over the years, what our customers have guided us to do is as they're putting together a modern kind of data infrastructure, we don't live in a silo. So aerospace gets deployed with other technologies like streaming technologies or analytics technologies. So we built connectors into Kafka, pulsar, so that as you're ingesting data from a variety of data sources, you can ingest them at very high ingest speeds and store them persistently into Aerospike. Once the data is in Aerospike, you can actually run spark jobs across that data in a, in a multithreaded parallel fashion to get really insight from that data at really high, high throughput and high speed, >>High throughput, high speed, incredibly important, especially as today's landscape is increasingly distributed. Data centers, multiple public clouds, edge IOT devices, the workforce embracing more and more hybrid these days. How are you ex helping customers to extract more value from data while also lowering costs? Go into some customer examples cause I know you have some great ones. >>Yeah, you know, I think we have, we have built an amazing set of customers and customers actually use us for some really mission critical applications. So, you know, before I get into specific customer examples, let me talk to you about some of kind of the use cases which we see out there. We see a lot of aerospace being used in fraud detection. We see us being used in recommendations and since we use get used in customer data profiles or customer profiles, customer 360 stores, you know, multiplayer gaming and entertainment, these are kind of the repeated use case digital payments. We power most of the digital payment systems across the globe. Specific example from a, from a specific example perspective, the first one I would love to talk about is PayPal. So if you use PayPal today, then you know when you actually paying somebody your transaction is, you know, being sent through aero spike to really decide whether this is a fraudulent transaction or not. >>And when you do that, you know, you and I as a customer not gonna wait around for 10 seconds for PayPal to say yay or me, we expect, you know, the decision to be made in an instant. So we are powering that fraud detection engine at PayPal for every transaction that goes through PayPal before us, you know, PayPal was missing out on about 2% of their SLAs, which was essentially millions of dollars, which they were losing because, you know, they were letting transactions go through and taking the risk that it, it's not a fraudulent transaction with the aerospace. They can now actually get a much better sla and the data set on which they compute the fraud score has gone up by, you know, several factors. So by 30 x if you will. So not only has the data size that is powering the fraud engine actually grown up 30 x with Aerospike. Yeah. But they're actually making decisions in an instant for, you know, 99.95% of their transactions. So that's, >>And that's what we expect as consumers, right? We want to know that there's fraud detection on the swipe regardless of who we're interacting with. >>Yes. And so that's a, that's a really powerful use case and you know, it's, it's a great customer, great customer success story. The other one I would talk about is really Wayfair, right? From retail and you know, from e-commerce. So everybody knows Wayfair global leader in really, you know, online home furnishings and they use us to power their recommendations engine and you know, it's basically if you're purchasing this, people who bought this but also bought these five other things, so on and so forth, they have actually seen the card size at checkout go by up to 30% as a result of actually powering their recommendations in G by through Aerospike. And they, they were able to do this by reducing the server count by nine x. So on one ninth of the servers that were there before aerospace, they're now powering their recommendation engine and seeing card size checkout go up by 30%. Really, really powerful in terms of the business outcome and what we are able to, you know, drive at Wayfair >>Hugely powerful as a business outcome. And that's also what the consumer wants. The consumer is expecting these days to have a very personalized, relevant experience that's gonna show me if I bought this, show me something else that's related to that. We have this expectation that needs to be really fueled by technology. >>Exactly. And you know, another great example you asked about, you know, customer stories, Adobe, who doesn't know Adobe, you know, they, they're on a, they're on a mission to deliver the best customer experience that they can and they're talking about, you know, great customer 360 experience at scale and they're modernizing their entire edge compute infrastructure to support this. With Aerospike going to Aerospike, basically what they have seen is their throughput go up by 70%, their cost has been reduced by three x. So essentially doing it at one third of the cost while their annual data growth continues at, you know, about north of 30%. So not only is their data growing, they're able to actually reduce their cost to actually deliver this great customer experience by one third to one third and continue to deliver great customer 360 experience at scale. Really, really powerful example of how you deliver Customer 360 in a world which is dynamic and you know, on a dataset which is constantly growing at north, north of 30% in this case. >>Those are three great examples, PayPal, Wayfair, Adobe talking about, especially with Wayfair when you talk about increasing their cart checkout sizes, but also with Adobe increasing throughput by over 70%. I'm looking at my notes here. While data is growing at 32%, that's something that every organization has to contend with data growth is continuing to scale and scale and scale. >>Yep. I, I'll give you a fun one here. So, you know, you may not have heard about this company, it's called Dream 11 and it's a company based out of India, but it's a very, you know, it's a fun story because it's the world's largest fantasy sports platform and you know, India is a nation which is cricket crazy. So you know, when, when they have their premier league going on, you know, there's millions of users logged onto the dream alone platform building their fantasy lead teams and you know, playing on that particular platform, it has a hundred million users, a hundred million plus users on the platform, 5.5 million concurrent users and they have been growing at 30%. So they are considered a, an amazing success story in, in terms of what they have accomplished and the way they have architected their platform to operate at scale. And all of that is really powered by aerospace where think about that they are able to deliver all of this and support a hundred million users, 5.5 million concurrent users all with you know, 99 plus percent of their transactions completing in less than one millisecond. Just incredible success story. Not a brand that is you know, world renowned but at least you know from a what we see out there, it's an amazing success story of operating at scale. >>Amazing success story, huge business outcomes. Last question for you as we're almost out of time is talk a little bit about Aerospike aws, the partnership GRAVITON two better together. What are you guys doing together there? >>Great partnership. AWS has multiple layers in terms of partnerships. So you know, we engage with AWS at the executive level. They plan out, really roll out of new instances in partnership with us, making sure that, you know, those instance types work well for us. And then we just released support for Aerospike on the graviton platform and we just announced a benchmark of Aerospike running on graviton on aws. And what we see out there is with the benchmark, a 1.6 x improvement in price performance and you know, about 18% increase in throughput while maintaining a 27% reduction in cost, you know, on graviton. So this is an amazing story from a price performance perspective, performance per wat for greater energy efficiencies, which basically a lot of our customers are starting to kind of talk to us about leveraging this to further meet their sustainability target. So great story from Aero Aerospike and aws, not just from a partnership perspective on a technology and an executive level, but also in terms of what joint outcomes we are able to deliver for our customers. >>And it sounds like a great sustainability story. I wish we had more time so we would talk about this, but thank you so much for talking about the main capabilities of a modern data platform, what's needed, why, and how you guys are delivering that. We appreciate your insights and appreciate your time. >>Thank you very much. I mean, if, if folks are at reinvent next week or this week, come on and see us at our booth. We are in the data analytics pavilion. You can find us pretty easily. Would love to talk to you. >>Perfect. We'll send them there. So Ira, thank you so much for joining me on the program today. We appreciate your insights. >>Thank you Lisa. >>I'm Lisa Martin. You're watching The Cubes coverage of AWS Reinvent 2022. Thanks for watching.

(slow upbeat music) >> Good afternoon and welcome back to Sin City. We're here at AWS reInvent with wall-to-wall coverage on theCUBE. My name is Savannah Peterson, joined with Dave Vellante, and very excited to have two exciting guests from Qlik and AARP with us. Molly and Samir, thank you so much for being here. Welcome to the show. >> Thank you for having us. >> Thank you for having us. >> How's it been so far for you, Molly? >> It's been a great show so far. We've got a big booth presence out here. We've had a lot of people coming by, doing demo stations and just really, really coming to the voice of the customer, so we've really enjoyed the event. >> Ah, love a good VOC conversation myself. How about for you, Samir? >> Oh, it's been great meeting a lot of product folks, meeting a lot of other people, trying to do similar things that we're doing, getting confirmation we're doing the right thing, and learning new things. And obviously, you know, here with Molly, it's been a highlight of my experience. >> What's the best thing you learned from your peers, this week? >> You know, some of the things, that we're all talking about, is how do we get data in the right place at the right time? And, you know, that's something that people are now starting to think about. >> Very hot topic. >> You know, doing it, and then not only getting it to the right place, but taking insights and taking action on it as it's getting there. So those are the conversations that are getting around, in the circle I've been hanging around with. >> You hearing the same thing at the booth or? >> Yeah, absolutely. >> And how are you guys responding? >> Well, I think, as a company, and the shifts in the market, people are really trying to determine what workloads belong in which Cloud, what belongs on-prem? And so talking about those realtime transformations, the integration points, the core systems they're coming from, and really how to unlock that data, is just really powerful and meaningful. So that's been a pretty consistent theme throughout the conference, and a lot of conversations that we have on a regular basis. >> I believe that, Molly, let's stick with you for a second. Just in case the audience isn't familiar, tell us a little more about Qlik. >> Yeah, so Qlik is a robust, end-to-end data pipeline. Starting with really looking at all of your source systems whether it's mainframe, SAP, relational database, kind of name your flavor as it's related to sources. Getting those sources over into the target landing spot whether it be Amazon, or other cloud players, or even if you're, if you're managing hybrid workloads. So that's kind of one piece of the end-to-end platform. And then the second piece is really having all that data, analytics ready, coming right through that real-time data pipeline, and really being able to use the data, to monetize the data, to make sense of the data. And then Qlik really does all that data preparation work underneath the visualization layer, which is where all the work happens. And then you get to see the output of that through the visualization of Qlik, which is, you know, the dashboards, the things that our people, people are used to seeing. >> I love that! So at AARP, what are you using Qlik for? What sort of dashboards are you pulling together? >> So when we started our journey to AWS, we knew that, you know, we're going to have our applications, they're distributed in the Cloud, but again, how do we get the data there, in the right place at the right time? So, as members are, taking action, they're calling into the call center, using our website, using our mobile apps. We want to want it to be able to take that information stream it, so we use Qlik, to take those changes when they happen as they happen, be able to stream it to Kafka and then push that data out to the applications that need it in the time that they needed it. So, instead of waiting for a batch job to happen overnight, we're able to now push this data in real time. And by doing that, we're able to personalize the engagement for our members. So if you come in, we know what you're doing, we can personalize the value that we put in front of you, and just make that engagement a lot more engaging for you. >> Yeah. >> And in the channel that you choose to want to come in with, right? Rather than a channel that we are trying to push to you. >> Everyone wants that personalized experience as we discussed, I love AARP, I've done a lot of work with AARP, I look forward to being a member, but in case the audience isn't familiar, you have the largest membership database of any company on Earth that I'm aware of. How many members does AARP have? >> We have nearly 38 million members, and 66,000 volunteers, and 2300 employees across every state in the United States. >> It's a perfect use case for Qlik, right? 'Cause you've been around for a while. You've got data in the million different places. You're trying to get, you've got a mainframe, right? You know, I hear Amazon's trying to put all the mainframes in the Cloud, but I'm guessing the business case isn't there for you. But you want the data that's coming out of that mainframe to be part of that data pipeline, right? So can you paint a picture, of how, what Molly was describing about the data pipeline, how that fits with AARP? >> Yeah, it's actually, it was a perfect use case. And you know, when we engaged with Qlik, what we wanted to be able to do is take that data in the mainframe, and get it distributed into the Cloud, accurately, securely, and make sure that we can track the lineage, and be able to say, hey, application A only needs name and address, application B needs, name, address, and payment. So we were able to do all of that within a couple of weeks, right? And getting that data out there, knowing that it's going to the right place, knowing it's secure, and knowing it's accurate, regardless of the application it goes to, we don't have to worry about seeking data across different applications. Now we know that there's a source of truth, and everything is done through the pipeline, and it's controlled in a way that, we can measure everything that's going through, how it's going through, and how it's being used by the applications, that are consuming it? >> So you've got the providence and the lineage of that data and that's what Qlik ensures, is that right? Is that your role or is that a partner role, combined? >> No, yes, that's absolutely Qlik's role. So for our new offering, Qlik Cloud data integration, it's a comprehensive solution, delivered as a service, delivers real time, automates, transformations, catalog and lineage, all extremely important. And in the case of Samir and AARP, they're trying to unlock the most valuable assets of their data in SAP and mainframe. And surprisingly, sometimes most valuable data in an organization is the hardest to actually get access to. >> Sure. >> So be, you know, just statistically, 70% of Fortune 500 companies still rely on mainframe. So when you think about that, and even when Samir and I are talking about it. >> That's a lot. >> Yeah. >> And that's a lot of scale, that's a lot of data. >> It's a lot of data. >> Yeah. >> So, you know, mainframe isn't a thing of the past. Companies are still relying on it. People have been saying that for years but when we're talking about getting the complex data out of there to really make something meaningful for AARP, we're really proud of the results, and the opportunity that we've been able to provide to really improve the member experience. And how people are able to consume AARP, and all the different offerings that they have? Kind of like you mentioned Savannah, and the way that you go about it. >> Well, it's also the high risk data. High value data, high risk data. You don't want to mess with it. You want to make sure that you've got that catalog to be able to say, okay, this is what we did with that data, this is where it came from. And then you essentially publish to other tools, analytic tools in the Cloud. Can you paint a picture of how that extends to the Cloud? >> Sure, so there's a couple of different things that we do with it. So once we get the data, into our streaming apps, we can publish it over to like our website. We can publish it to the call center, to mobile apps, to our data warehouse, where we can run analytics and AI on it. And then obviously a lot of our journeys, we use a journey orchestration tool, and we've built a CDP, a customer data platform, to get those insights in there, to drive, you know, personalization and experience. >> I'm smiling as you're talking, Samir, because I'm thinking of all the personalized experiences that my mother has with AARP, and it is so fun to learn about the technology that's serving that to her. >> Exactly. >> This segment actually becoming a bit more personal for me than I expected for a couple of reasons. So this is great. Molly, Qlik has been a part of the AWS ecosystem since the get go. How have things changed over the years? >> Yeah, so Qlik still remains the enterprise integration tool of choice for AWS especially- >> Let's call that a casual and just brag. >> Yeah. >> Because that's awesome. That's great, congratulations on that. >> Thank you for SAP and mainframe. So the relationship continues to evolve but we've been part of the ecosystem from since inception. So we look at, how we continue to evolve the partnership? And honestly, a lot of our customers landing spot is AWS. So the partnership evolves really on two fronts. One with Amazon itself, in a partnership lane, and two, with our customers, and what we're doing with them, and how we're able to really optimize what that looks like? And then secondly, earlier this year we announced an offering Amazon and Qlik, called Qlik Ramp, where we can come in and do, a half day architecture deep dive, look at SAP mainframe, and how they get to the Amazon landing spots, whether it's S3, Redshift, or EMR? So we got a lot of different things kind of going on in the Amazon ecosystem, whether it's customer forward and first, and how can we maximize the relationship spend et cetera, with Amazon. And then also how can we deliver, you know, kind of a shorter time to value throughout that process with something like a Qlik ramp, because we want to qualify, and solve customers needs, as equally as we want to you know, say when we're not the right fit. >> So data is a complicated- >> Love that honesty and transparency. >> Data is a complicated situation for most companies, right? And there's a lack of resource, lack of talent. There's hyper specialization. And you were just talking about the evolution of the Cloud and the relationship. How does automation fit into the equation? Are you able to automate a lot of that data integration through the pipeline? >> Yeah. >> Is it was a, what's your journey look like there? Were you resistant to that at first? 'Cause you got to trust the data. Take us through that. >> Yeah, so the first thing, we wanted to make sure is security right? We've got a lot of data, we're going to make sure privacy- >> Very personal data too. >> Exactly. And privacy and security is number one. So we want to make sure anything that we're doing with the data is secure, and it's not given out anywhere. In terms of automation, so what we've been able to do is being able to take these changes, and you know, in technology, the one thing you can guarantee is it's going to break. Network's going to go down, or a server goes down, a database goes down, and that's the only guarantee we have. And by using the product that we have today, we're able to take those outages, and minimize them because there's retry processes, there's ways of going back and saying, hey, I've missed this much data. How do we bring it back in? You don't want data to get out of sync because that causes downstream problems. >> Yeah. >> So all of that is done through the product, right? We don't have to worry about it. You know, we get notifications, but it's not like, oh, I've got to pay someone at two o'clock in the morning because the network's gone down and how's the data sync going to come back up, when it comes back up? All of that's done for us. >> Yeah, and just to add to that, automation, is a key component. I mean, the data engineering teams definitely see the value of automation and how we're able to deliver that. So, improving the experience but also the overall landscape of the environment is critical. >> Yeah, we've seen the stats, data scientists, data pro spend, you know, 80% of their time wrangling data, 20% of their time. >> Data preparation. >> You know extracting value from it. So. >> Yeah, it's so sad. It's such a waste of human capital, and you're obviously relieving that, and letting folks do their job more efficiently. >> The thing is too, you know, as I'm somebody who's love data you dive into the data, you get really excited then after a while you're like, Ugh! >> I'm still here. >> I'm slogging through this data. Taking a bath in it. >> But I think. >> I want to get to the insights. >> I think that world's changing a little bit. >> Yes, definitely. >> So as we're starting to get data that's coming through it's got high fidelity, and richness, right? So in the old days we'd put in a database, normalize it, and then, you know we'd go and do our magic, and hopefully, you know something comes out, and the least of frustration, you just spoke about. Well now, because it's moving in real time, and we can send the data to areas in the way we want it, and add automation, and machine learning on top of that, so that, now it becomes a commodity to massage that data into the in the format that you want it. Then you can concentrate on the value work, right? Which is really where people should be spending the time, rather than, oh, I've got to manipulate the data, make sure it's done in a consistent way, and then make sure it's compliant and done, the same way every single time. >> It may be too early to, you know quantify the business impact, but have you seen, for example, you know, what I was describing creates data silos. 'Cause nobody's going to use the data if it's not trusted. So what happens is it goes to a silo, they put a brick wall around it, and then, you know, they do their thing with it. They trust it for that one use case and then they don't share it. Has that begun to change as you've seen more integration that's automated and augmented? >> Absolutely. I mean, you know, if you're bringing in data and you're showing that it's consistent, and this is where governance and compliance comes in, right? So as long as you have a data catalog, you can make sure that this data's coming through with the lineage that you said is going to, here's the source, here's the target, here's who gets what they only need rather than giving them everything. And by being able to document that, in a way, that's automated rather than somebody going in, and running a report, it's key. Because that's where the trust comes in, rather than, oh, Samir has to go in and manipulate this stream so that, you know, Molly can get the reports she wants. Instead, hey, it's all going in there, the reports are coming out, they're audited, and that's where the trust factor comes. >> And that enables scale. >> Yeah. >> Cloud confidence and scale. Big topics of the show this week. >> Yep. >> It's been the whole thing. Molly, what's next for Qlik? >> Yeah, Qliks on a big journey. So we've released a lot of things most recently, Qlik Cloud data integration as a service, but we're just continuing to grow from a customer base, from a capabilities perspective. We also recently just became HIPAA compliant and went through some other services. >> Congratulations, that is not an easy process. >> Thank you, thank you. >> Yeah. >> And so for us it's really just about expanding and having, that same level of fidelity of the data, and really just getting all of that pushed out to the market so everybody really sees the full value of Qlik, and that we can make your data Qlik. And just for a minute, back to your earlier point. >> Beautiful pun drop there, Molly. Just going to see that. >> Thank you Savannah. >> Yeah. >> But back to your earlier point, just about the time that people are spending, when you're able to automate, and you're getting data delivered in real time, and operational systems are able to see that. 'Cause you're trying to create the least amount of disruption you can, right? 'Cause that's a critical part of the business. When you start to automate and relieve that burden then people have time to spend time on the real things. >> Right. >> Future forward, prescriptive analytics, machine learning, not data preparation, solving problems, fixing soft gaps. >> Staring a spreadsheet, yeah. >> Right? It's actually the full end-to-end pipeline. And so that's really where I feel like the power is unleashed. And as more sources and targets come to light, right? They're all over the showroom floor, so we don't have to mention any of 'em by name, but it's just continuing, to move into that world to have more SaaS integrations. And to be able to serve the customer, and meet them exactly where they're at, at the place that they want to be. And for Samir, and what we did in the transformation there, unlocking that data for mainframe and SAP, getting it into Qlik Cloud, has been a huge business driver for them. And so, because of partners like AWS and Samir and AARP, we're constantly evolving. And really trying to listen to the voice of the customer, to become better for all of you. >> Excellent. >> Love that community first attitude. Very clear that you both have it, both AARP and Qlik with that attitude. We have a new challenge this year to reInvent on theCUBE, little prompt here. >> Okay. >> We're going to put 30 seconds on the clock, although I'm not super crazy about watching the clock. So, feel comfortable with whatever however much time you need. >> Whatever works. >> Yeah, yeah, yeah, yeah, whatever works. But we're looking for equivocally, your Instagram reel, your hot take, your thought leadership, sizzle, with the key theme from this year's show. Molly, your smile is platinum and perfect. So I'm going to start with you. I feel like you've got this. >> Okay, great. >> Yeah. >> Just the closing statement is what you're looking for. >> Sure, yeah, sexy little sound bite. What do you, what's going to be your big takeaway from your experience here in Vegas this week? >> Yeah, so the experience at Vegas this week has been great but I think it's more than just the experience at Vegas, it's really the experience of the year, where we're at with the technology shift. And we're continuing to see, the need for Cloud, the move to Cloud, mixed workloads, hybrid workloads, unlocking core data, making sure that we're getting insights analytics, and value out of that. And really just working through that, kind of consistent evolution, which is exactly what it is. It's never, you never get to a point where, that's it, there's a bow on it, and it's perfect. It's continuously involving, evolving. >> Yeah. >> And I think that's the most important part that you have to take away. Samir's got his environment in a great place today but in six months, there may be some new things or transformations that he wants to look at, and we want to be there at the ready to work with him, roll up our sleeves, and kind of get into that. So the shift of the Cloud is here to stay. Qlik is a hundred percent here to stay. Here ready to serve our customers in any capacity that we can. And I think that's really my big takeaway from this week. And I've loved it, like this has been a great, this has been great with both of you. You both are super high energy. >> Aw, thank you. >> And Samir and I have had a great time over the event as well. >> Well, nailed it. You absolutely nailed it. All right, Samir, shoot your shot. >> So. >> Savannah. >> What I would say, I'm pretty, so. (laughing) >> I like to keep the smiles organic on stage, my perverse sense of humor, everyone just tolerates. >> Yeah, the one thing I think, I'm hearing a lot is, we have to look at data in motion. Streaming data is the way it's going to go. Whether it's customer data, operational data, it doesn't matter, right? We can't have these silos that you spoke about. Those days are gone, right? And if we really want to make a difference, and utilize all of the technology that's being built out there, all of the new features that were, you know, just in the keynotes. We can't have these separate silos, and the data has to go across, trusted data, it has to go across. The second thing I think we're all talking about is, we have to look at things differently. Unlearning the old is harder than learning the new. So we were just talking about event driven architecture. >> Understatement of the century. Sidebar, that was, yeah. >> So, you know, a lot of us techies are used to calling APIs. Well, now we have to push the data out, instead of pulling it. That just means retraining our brains, retraining our architects, retraining our developers, to think in a different way. And then the last thing I think I've learned is, us technology folks have put the customer first right? >> Yes, absolutely. >> What does a customer want? How do they want to feel when they engage with you? Because if we don't do that, none of this technology matters. And you know, we have to get away from the day where the IT guys go in the back black room, (laughing) coat up and then, you know, push something out, and don't think about what am I doing, and how am I impacting your mother? >> Yes, the end customer. It's no longer the person at the end of a terminal. Look at the green screen. >> And just one last thing. I think also it's fit for purpose transformations. And that's how we have to start thinking about how we're doing business. 'Cause there's a paradigm shift, right? From ETL to ELT, right? Extract, Load, Transform your data. And so as we're seeing that, I think it's really just about that fit for purpose, and looking at the transformations, the right transformations. And what's going to move the needle for the business. >> What a great closing note! Molly, Samir, thank you both for being here. >> Both: Thank you! >> This was a really fantastic chat, love where we took it. And thank all of you for tuning in to our live coverage from AWS reInvent here in fabulous Las Vegas, Nevada. I just want to give my mom a quick shout out, since she got a holler throughout this segment, as well as Stacy and all of my friends at AARP, I missed you all. My name's Savannah Peterson, joined with Dave Vellante. You're watching theCUBE. We are the technology leader in coverage for events like this. (slow upbeat music)

>>And good afternoon. Welcome back here on the Cub as to continue our coverage at aws Reinvent 22, win the Venetian here in Las Vegas, day two, it's Wednesday. Thanks. Still rolling. Quite a along. We have another segment for you as part of the Global Startup program, which is under the AWS Startup Showcase. I'm joined now by Vink at Viera, who is the CEO and co-founder of R Set. And good to see you, >>Sir. Thanks for having me here. Yeah, >>No, a real pleasure. Looking forward to it. So first off, for some of, for yours who might not be familiar with Roxette, I know you've been on the cube a little bit, so you're, you're an alum, but, but why don't you set the stage a little bit for Rock set and you know, where you're engaged with in terms of, with aws? >>Definitely. Rock Set is a realtime analytics database that is built for the cloud. You know, we make realtime applications possible in the cloud. You know, realtime applications need high concurrency, low latency query processing data needs to be fresh, your analytic needs to be fast. And, you know, we built on aws and that's why we are here. We are very, very proud partners of aws. We are in the AWS Accelerate program, and also we are in the startup program of aws. We are strategic ISV partner. And so yeah, we make real time analytics possible without all the cost and complexity barriers that are usually associated with it. And very, very happy to be part of this movement from batch to real time that is happening in the world. >>Right. Which is certainly an exciting trend. Right. I know great news for you, you made news yesterday, had an announcement involved with the intel with aws, who wants to share some of that >>With us too? Definitely. So, you know, one, one question that I always ask people is like, you know, if you go perspective that I share is like, if you go ask a hundred people, do you want fast analytics on fresh data or slow analytics on stale data? You know, a hundred out of a hundred would say fast and fresh, right? Sure. So then the question is, why hasn't this happened already? Why is this still a new trend that is emerging as opposed to something that everybody's taking for granted? It really comes down to compute efficiency, right? I think, you know, at the end of the day, real time analytics was always in using, you know, technologies that are, let's say 10 years ago using let's say processors that were available 10 years ago to, you know, three cloud, you know, days. There was a lot of complexity barriers associated with realtime analytics and also a lot of cost and, and performance barriers associated with it. >>And so Rox said from the, you know, from the very beginning, has been obsessing about building the most compute efficient realtime database in the world. And, you know, AWS on one hand, you know, allows us to make a consumption based pricing model. So you only pay for what you use. Sure. And that shatters all the cost barriers. But in terms of computer efficiency, what we announced yesterday is the Intel's third generation Zon scalable processors, it's code named Intel Ice Lake. When we port it over Rock said to that architecture, taking advantage of some of the instructions sets that Intel has, we got an 84% performance boost, 84, 84, 84. >>It's, it's incredible, right? >>It's, it's an incredible charts, it's an incredible milestone. It reduces the barrier even more in terms of cost and, you know, and, and pushes the efficiency and sets a, a really new record for how efficient realtime, you know, data processing can be in the cloud. And, and it's very, very exciting news. And so we used to benchmark ourselves against some of our other, you know, realtime, you know, did up providers and we were already faster and now we've set a, a much, much higher bar for other people to follow. >>Yep. And, and so what is, or what was it about real time that, that, you know, was such a barrier because, and now you've got the speed of, of course, obviously, and maybe that's what it was, but I think cost is probably part of that too, right? That's all part of that equation. I mean, real time, so elusive. >>Yeah. So real time has this inherent pattern that your data never stops coming. And when your data never stops coming, and you can now actually do analytics on that. Now, initially people start with saying, oh, I just want a real time dashboard. And then very quickly they realize, well, the dashboard is actually in real time. I'm not gonna be staring at the 24 7. Can you tap on my shoulder when something is off, something needs to be looked at. So in which case you're constantly also asking the question, is everything okay? Is everything all right? Do I need to, is is that something that I need to be, you know, double clicking on and, and following up on? So essentially very quickly in real time analytics, what happens is your queries never stop. The questions that you're asking on your data never stops. And it's often a program asking the question to detect anomalies and things like that. >>And your data never stops coming. And so compute is running 24 7. If you look at traditional data warehouses and data lakes, they're not really optimized for these kinds of workloads. They're optimized to store massive volumes of data and in a storage efficient format. And when an analyst comes and asks a question to generate a report, you can spin up a whole bunch of compute, generate the report and tear it all down when you're done. Well, that is not compute running 24 7 to continuously, you know, you know, keep ingesting the data or continuously keep answering questions. So the compute efficiency that is needed is, is much, much, much higher. Right? And that is why, you know, Rox was born. So from the very beginning, we're only built, you know, for these use cases, we have a, an extremely powerful SQL engine that can give you full feature SQL analytics in a very, very compute efficient way in the cloud. >>Right. So, so let's talk about the leap that you've made, say in the last two years and, and, and what's been the spur of that? What has been allowed you to, to create this, you know, obviously a, a different kind of an array for your customers from which to choose, but, but what's been the spark you think >>We touched upon this a little earlier, right? This spark is really, you know, the world going from batch to real time. So if you look at mainstream adoption of technologies like Apache, Kafka and Confluent doing a really good job at that. In, in, in growing that community and, and use cases, now businesses are now acquiring business data, really important business data in real time. Now they want to operationalize it, right? So, you know, extract based static reports and bi you know, business intelligence is getting replaced in all modern enterprises with what we call operational intelligence, right? Don't tell me what happened last quarter and how to plan this quarter better. Tell me what's happening today, what's happening right now. And it's, it's your business operations using data to make day to day decisions better that either grows your top line, compresses your bottom line, eliminates risk that are inherently creeping up in your business. >>Sure. You know, eliminate potential churn from a customer or fraud, you know, deduction and, and getting on top of, you know, that, you know, a minute into this, into, into an outage as opposed to an hour into the outage. Right? And so essentially I think businesses are now realizing that operational intelligence and operational analytics really, you know, allows them to leverage data and especially real time data to make their, you know, to grow their businesses faster and more efficiently. And especially in this kind of macro environment that is, you know, more important to have better unit economics in your business than ever before. Sure. And so that is really, I think that is the real market movement happening. And, and we are here to just serve that market. We are making it much, much easier for companies that have already adopted, you know, streaming technologies like Kafka and, and, and knows Canis MSK and all these technologies. Now businesses are acquiring these data in real time now. They can also get realtime analytics on the other end of it. Sure. >>You know, you just touched on this and, and I'd like to hear your thoughts about this, about, about the economic environment because it does drive decisions, right? And it does motivate people to look for efficiencies and maybe costs, you know, right. Cutting costs. What are you seeing right now in terms of that, that kind of looming influence, right? That the economy can have in terms of driving decisions about where investments are being made and what expectations are in terms of delivering value, more value for the buck? >>Exactly. I think we see across the board, all of our customers come back and tell us, we don't want to manage data infrastructure and we don't want to do kind of DIY open source clusters. We don't wanna manage and scale and build giant data ops and DevOps teams to manage that, because that is not really, you know, in their business. You know, we have car rental companies want to be better at car rentals, we want airlines to be a better airline, and they don't, don't want their, you know, a massive investment in DevOps and data ops, which is not really their core business. And they really want to leverage, you know, you know, fully managed and, you know, cloud offerings like Rock said, you know, built on aws, massively scalable in the cloud with zero operational overhead, very, very easy to get started and scale. >>And so that completely removes all the operational overhead. And so they can invest the resources they have, the manpower, they have, the calories that they have on actually growing their businesses because that is what really gonna allow them to have better unit economics, right? So everybody that is on my payroll is helping me grow my top line or shrink my bottom line, eliminate risk in my business and, and, and, and churn and, and fraud and other, and eliminate all those risks that are inherent in my business. So, so that is where I think a lot of the investments going. So gone are the days where, you know, you're gonna have these in like five to 10% team managing a very hard to operate, you know, open source data management clusters on EC two nodes in, in AWS and, and kind of DIYing it their way because those 10 people, you know, if all they do is just operational maintenance of infrastructure, which is a means to an end, you're way better off, you know, using a cloud, you know, a bond in the cloud built for the cloud solution like rock and eliminate all that cost and, and replace that with an operationally much, much simpler, you know, system to op, you know, to to work with such as, such as rock. >>So that is really the big trend that we are seeing why, you know, not only real time is going more and more mainstream cloud native solutions or the real future even when it comes to real time because the complexity barrier needs to be shattered and only cloud native solutions can actually, >>You get the two Cs cost and complexity, right. That you, you need to address. Exactly. Yeah, for sure. You know, what is it about building trust with your, with your clients, with your partners? Because you, you're talking about this cloud environment that, that everyone is talking about, right? Not everyone's made that commitment. There are still some foot draggers out there. How are you going about establishing confidence and establishing trust and, and, and providing them with really concrete examples of the values and the benefits that you can provide, you know, with, with these opportunities? >>So, you know, I grew up, so there's a few ways to to, to answer this question. I'll, I'll, I'll come, I'll cover all the angles. So in, in order to establish trust, you have to create value. They, you know, your customer has to see that with you. They were able to solve the problem faster, better, cheaper, and they're able to, you know, have a, the business impact they were looking for, which is why they started the project in the first place. And so establishing that and proving that, I think there's no equivalence to that. And, you know, I grew up at, at, you know, at Facebook back in the day, you know, I was managing online data infrastructure, okay. For Facebook from 2007 and 2015. And internally we always had this kind of culture of all the product teams building on top of the infrastructure that my team was responsible for. >>And so they were not ever, there was never a, a customer vendor relationship internally within Facebook that we're all like, we're all part of the same team. We're partnering here to have you, you know, to help you have a successful product launch. There's a very similar DNA that, that exists in Rock said, when our customers work with us and they come to us and we are there to make them successful, our consumption based pricing model also forces us to say they're not gonna really use Rock said and consume more. I mean, we don't make money until they consume, right? And so their success is very much integral part of our, our success. And so that I think is one really important angle on, you know, give us a shot, come and do an evaluation, and we will work with you to build the most efficient way to solve your problem. >>And then when you succeed, we succeed. So that I think is a very important aspect. The second one is AWS partnership. You know, we are an ISV partner, you know, AWS a lot of the time. That really helps us establish trust. And a lot of the time, one of the, the, the people that they look up to, when a customer comes in saying, Hey, what is, who is Rock? Said? You know, who are your friends? Yeah. Who are your friends? And then, you know, and then the AWS will go like, oh, you know, we'll tell you, you know, all these other successful case studies that R has, you know, you know, built up on, you know, the world's largest insurance provider, Europe's largest insurance provider. We have customers like, you know, JetBlue Airlines to Klarna, which is a big bator company. And so, so all these case studies help and, and, and, and platform and partners like AWS helps us, helps you amplify that, that, you know, and, and, and, and, and give more credibility. And last but not least, compliance matters. You know, being Soto type two compliant is, is a really important part of establishing trust. We are hip hop compliant now so that, you know, we can, you know, pi I phi data handling that. And so I think that will continue to be a part, a big part of our focus in improving the security, you know, functionality and, and capabilities that R set has in the cloud, and also compliance and, and the set of com, you know, you know, standards that we are gonna be compliant against. >>Well, I'm glad you hit on the AWS too, cause I did wanna bring that up. I, I appreciate that and I know they appreciate the relationship as well. Thanks for the time here. It's been a pleasure. Awesome. Learning about Rockette and what you're up to. Thank you. >>You bet. >>It's a pleasure. Thank you. Vi ka. All right. You are watching the cube coverage here at AWS Reinvent 22. And on the cube, of course, the leader, the leader in high tech coverage.

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

>> And it'll be the fastest 15 minutes of your day from there. >> In three- >> We go Lisa. >> Wait. >> Yes >> Wait, wait, wait. I'm sorry I didn't pin the right speed. >> Yap, no, no rush. >> There we go. >> The beauty of not being live. >> I think, in the background. >> Fantastic, you all ready to go there, Lisa? >> Yeah. >> We are speeding around the horn and we are coming to you in five, four, three, two. >> Hey everyone, welcome to theCUBE's coverage of AWS re:Invent 2022. Lisa Martin here with you with Subbu Iyer one of our alumni who's now the CEO of Aerospike. Subbu, great to have you on the program. Thank you for joining us. >> Great as always to be on theCUBE Lisa, good to meet you. >> So, you know, every company these days has got to be a data company, whether it's a retailer, a manufacturer, a grocer, a automotive company. But for a lot of companies, data is underutilized yet a huge asset that is value added. Why do you think companies are struggling so much to make data a value added asset? >> Well, you know, we see this across the board. When I talk to customers and prospects there is a desire from the business and from IT actually to leverage data to really fuel newer applications, newer services newer business lines if you will, for companies. I think the struggle is one, I think one the, the plethora of data that is created. Surveys say that over the next three years data is going to be you know by 2025 around 175 zettabytes, right? A hundred and zettabytes of data is going to be created. And that's really a growth of north of 30% year over year. But the more important and the interesting thing is the real time component of that data is actually growing at, you know 35% CAGR. And what enterprises desire is decisions that are made in real time or near real time. And a lot of the challenges that do exist today is that either the infrastructure that enterprises have in place was never built to actually manipulate data in real time. The second is really the ability to actually put something in place which can handle spikes yet be cost efficient to fuel. So you can build for really peak loads, but then it's very expensive to operate that particular service at normal loads. So how do you build something which actually works for you for both users, so to speak. And the last point that we see out there is even if you're able to, you know bring all that data you don't have the processing capability to run through that data. So as a result, most enterprises struggle with one capturing the data, making decisions from it in real time and really operating it at the cost point that they need to operate it at. >> You know, you bring up a great point with respect to real time data access. And I think one of the things that we've learned the last couple of years is that access to real time data it's not a nice to have anymore. It's business critical for organizations in any industry. Talk about that as one of the challenges that organizations are facing. >> Yeah, when we started Aerospike, right? When the company started, it started with the premise that data is going to grow, number one exponentially. Two, when applications open up to the internet there's going to be a flood of users and demands on those applications. And that was true primarily when we started the company in the ad tech vertical. So ad tech was the first vertical where there was a lot of data both on the supply set and the demand side from an inventory of ads that were available. And on the other hand, they had like microseconds or milliseconds in which they could make a decision on which ad to put in front of you and I so that we would click or engage with that particular ad. But over the last three to five years what we've seen is as digitization has actually permeated every industry out there the need to harness data in real time is pretty much present in every industry. Whether that's retail, whether that's financial services telecommunications, e-commerce, gaming and entertainment. Every industry has a desire. One, the innovative companies, the small companies rather are innovating at a pace and standing up new businesses to compete with the larger companies in each of these verticals. And the larger companies don't want to be left behind. So they're standing up their own competing services or getting into new lines of business that really harness and are driven by real time data. So this compelling pressures, one, you know customer experience is paramount and we as customers expect answers in you know an instant, in real time. And on the other hand, the way they make decisions is based on a large data set because you know larger data sets actually propel better decisions. So there's competing pressures here which essentially drive the need one from a business perspective, two from a customer perspective to harness all of this data in real time. So that's what's driving an incessant need to actually make decisions in real or near real time. >> You know, I think one of the things that's been in short supply over the last couple of years is patience. We do expect as consumers whether we're in our business lives our personal lives that we're going to be getting be given information and data that's relevant it's personal to help us make those real time decisions. So having access to real time data is really business critical for organizations across any industries. Talk about some of the main capabilities that modern data applications and data platforms need to have. What are some of the key capabilities of a modern data platform that need to be delivered to meet demanding customer expectations? >> So, you know, going back to your initial question Lisa around why is data really a high value but underutilized or under-leveraged asset? One of the reasons we see is a lot of the data platforms that, you know, some of these applications were built on have been then around for a decade plus. And they were never built for the needs of today, which is really driving a lot of data and driving insight in real time from a lot of data. So there are four major capabilities that we see that are essential ingredients of any modern data platform. One is really the ability to, you know, operate at unlimited scale. So what we mean by that is really the ability to scale from gigabytes to even petabytes without any degradation in performance or latency or throughput. The second is really, you know, predictable performance. So can you actually deliver predictable performance as your data size grows or your throughput grows or your concurrent user on that application of service grows? It's really easy to build an application that operates at low scale or low throughput or low concurrency but performance usually starts degrading as you start scaling one of these attributes. The third thing is the ability to operate and always on globally resilient application. And that requires a really robust data platform that can be up on a five nine basis globally, can support global distribution because a lot of these applications have global users. And the last point is, goes back to my first answer which is, can you operate all of this at a cost point which is not prohibitive but it makes sense from a TCO perspective. 'Cause a lot of times what we see is people make choices of data platforms and as ironically their service or applications become more successful and more users join their journey the revenue starts going up, the user base starts going up but the cost basis starts crossing over the revenue and they're losing money on the service, ironically as the service becomes more popular. So really unlimited scale predictable performance always on a globally resilient basis and low TCO. These are the four essential capabilities of any modern data platform. >> So then talk to me with those as the four main core functionalities of a modern data platform, how does Aerospike deliver that? >> So we were built, as I said from day one to operate at unlimited scale and deliver predictable performance. And then over the years as we work with customers we build this incredible high availability capability which helps us deliver the always on, you know, operations. So we have customers who are who have been on the platform 10 years with no downtime for example, right? So we are talking about an amazing continuum of high availability that we provide for customers who operate these, you know globally resilient services. The key to our innovation here is what we call the hybrid memory architecture. So, you know, going a little bit technically deep here essentially what we built out in our architecture is the ability on each node or each server to treat a bank of SSDs or solid-state devices as essentially extended memory. So you're getting memory performance but you're accessing these SSDs. You're not paying memory prices but you're getting memory performance. As a result of that you can attach a lot more data to each node or each server in a distributed cluster. And when you kind of scale that across basically a distributed cluster you can do with Aerospike the same things at 60 to 80% lower server count. And as a result 60 to 80% lower TCO compared to some of the other options that are available in the market. Then basically, as I said that's the key kind of starting point to the innovation. We lay around capabilities like, you know replication, change data notification, you know synchronous and asynchronous replication. The ability to actually stretch a single cluster across multiple regions. So for example, if you're operating a global service you can have a single Aerospike cluster with one node in San Francisco one node in New York, another one in London and this would be basically seamlessly operating. So that, you know, this is strongly consistent, very few no SQL data platforms are strongly consistent or if they are strongly consistent they will actually suffer performance degradation. And what strongly consistent means is, you know all your data is always available it's guaranteed to be available there is no data lost any time. So in this configuration that I talked about if the node in London goes down your application still continues to operate, right? Your users see no kind of downtime and you know, when London comes up it rejoins the cluster and everything is back to kind of the way it was before, you know London left the cluster so to speak. So the ability to do this globally resilient highly available kind of model is really, really powerful. A lot of our customers actually use that kind of a scenario and we offer other deployment scenarios from a higher availability perspective. So everything starts with HMA or Hybrid Memory Architecture and then we start building a lot of these other capabilities around the platform. And then over the years what our customers have guided us to do is as they're putting together a modern kind of data infrastructure, we don't live in the silo. So Aerospike gets deployed with other technologies like streaming technologies or analytics technologies. So we built connectors into Kafka, Pulsar, so that as you're ingesting data from a variety of data sources you can ingest them at very high ingest speeds and store them persistently into Aerospike. Once the data is in Aerospike you can actually run Spark jobs across that data in a multi-threaded parallel fashion to get really insight from that data at really high throughput and high speed. >> High throughput, high speed, incredibly important especially as today's landscape is increasingly distributed. Data centers, multiple public clouds, Edge, IoT devices, the workforce embracing more and more hybrid these days. How are you helping customers to extract more value from data while also lowering costs? Go into some customer examples 'cause I know you have some great ones. >> Yeah, you know, I think, we have built an amazing set of customers and customers actually use us for some really mission critical applications. So, you know, before I get into specific customer examples let me talk to you about some of kind of the use cases which we see out there. We see a lot of Aerospike being used in fraud detection. We see us being used in recommendations engines we get used in customer data profiles, or customer profiles, Customer 360 stores, you know multiplayer gaming and entertainment. These are kind of the repeated use case, digital payments. We power most of the digital payment systems across the globe. Specific example from a specific example perspective the first one I would love to talk about is PayPal. So if you use PayPal today, then you know when you're actually paying somebody your transaction is, you know being sent through Aerospike to really decide whether this is a fraudulent transaction or not. And when you do that, you know, you and I as a customer are not going to wait around for 10 seconds for PayPal to say yay or nay. We expect, you know, the decision to be made in an instant. So we are powering that fraud detection engine at PayPal. For every transaction that goes through PayPal. Before us, you know, PayPal was missing out on about 2% of their SLAs which was essentially millions of dollars which they were losing because, you know, they were letting transactions go through and taking the risk that it's not a fraudulent transaction. With Aerospike they can now actually get a much better SLA and the data set on which they compute the fraud score has gone up by you know, several factors. So by 30X if you will. So not only has the data size that is powering the fraud engine actually gone up 30X with Aerospike but they're actually making decisions in an instant for, you know, 99.95% of their transactions. So that's- >> And that's what we expect as consumers, right? We want to know that there's fraud detection on the swipe regardless of who we're interacting with. >> Yes, and so that's a really powerful use case and you know, it's a great customer success story. The other one I would talk about is really Wayfair, right, from retail and you know from e-commerce. So everybody knows Wayfair global leader in really in online home furnishings and they use us to power their recommendations engine. And you know it's basically if you're purchasing this, people who bought this also bought these five other things, so on and so forth. They have actually seen their cart size at checkout go up by up to 30%, as a result of actually powering their recommendations engine through Aerospike. And they were able to do this by reducing the server count by 9X. So on one ninth of the servers that were there before Aerospike, they're now powering their recommendations engine and seeing cart size checkout go up by 30%. Really, really powerful in terms of the business outcome and what we are able to, you know, drive at Wayfair. >> Hugely powerful as a business outcome. And that's also what the consumer wants. The consumer is expecting these days to have a very personalized relevant experience that's going to show me if I bought this show me something else that's related to that. We have this expectation that needs to be really fueled by technology. >> Exactly, and you know, another great example you asked about you know, customer stories, Adobe. Who doesn't know Adobe, you know. They're on a mission to deliver the best customer experience that they can. And they're talking about, you know great Customer 360 experience at scale and they're modernizing their entire edge compute infrastructure to support this with Aerospike. Going to Aerospike basically what they have seen is their throughput go up by 70%, their cost has been reduced by 3X. So essentially doing it at one third of the cost while their annual data growth continues at, you know about north of 30%. So not only is their data growing they're able to actually reduce their cost to actually deliver this great customer experience by one third to one third and continue to deliver great Customer 360 experience at scale. Really, really powerful example of how you deliver Customer 360 in a world which is dynamic and you know on a data set which is constantly growing at north of 30% in this case. >> Those are three great examples, PayPal, Wayfair, Adobe, talking about, especially with Wayfair when you talk about increasing their cart checkout sizes but also with Adobe increasing throughput by over 70%. I'm looking at my notes here. While data is growing at 32%, that's something that every organization has to contend with data growth is continuing to scale and scale and scale. >> Yap, I'll give you a fun one here. So, you know, you may not have heard about this company it's called Dream11 and it's a company based out of India but it's a very, you know, it's a fun story because it's the world's largest fantasy sports platform. And you know, India is a nation which is cricket crazy. So you know, when they have their premier league going on and there's millions of users logged onto the Dream11 platform building their fantasy league teams and you know, playing on that particular platform, it has a hundred million users a hundred million plus users on the platform, 5.5 million concurrent users and they have been growing at 30%. So they are considered an amazing success story in terms of what they have accomplished and the way they have architected their platform to operate at scale. And all of that is really powered by Aerospike. Think about that they're able to deliver all of this and support a hundred million users 5.5 million concurrent users all with, you know 99 plus percent of their transactions completing in less than one millisecond. Just incredible success story. Not a brand that is, you know, world renowned but at least you know from what we see out there it's an amazing success story of operating at scale. >> Amazing success story, huge business outcomes. Last question for you as we're almost out of time is talk a little bit about Aerospike AWS the partnership Graviton2 better together. What are you guys doing together there? >> Great partnership. AWS has multiple layers in terms of partnerships. So, you know, we engage with AWS at the executive level. They plan out, really roll out of new instances in partnership with us, making sure that, you know those instance types work well for us. And then we just released support for Aerospike on the Graviton platform and we just announced a benchmark of Aerospike running on Graviton on AWS. And what we see out there is with the benchmark a 1.6X improvement in price performance. And you know about 18% increase in throughput while maintaining a 27% reduction in cost, you know, on Graviton. So this is an amazing story from a price performance perspective, performance per watt for greater energy efficiencies, which basically a lot of our customers are starting to kind of talk to us about leveraging this to further meet their sustainability target. So great story from Aerospike and AWS not just from a partnership perspective on a technology and an executive level, but also in terms of what joint outcomes we are able to deliver for our customers. >> And it sounds like a great sustainability story. I wish we had more time so we would talk about this but thank you so much for talking about the main capabilities of a modern data platform, what's needed, why, and how you guys are delivering that. We appreciate your insights and appreciate your time. >> Thank you very much. I mean, if folks are at re:Invent next week or this week come on and see us at our booth and we are in the data analytics pavilion and you can find us pretty easily. Would love to talk to you. >> Perfect, we'll send them there. Subbu Iyer, thank you so much for joining me on the program today. We appreciate your insights. >> Thank you Lisa. >> I'm Lisa Martin, you're watching theCUBE's coverage of AWS re:Invent 2022. Thanks for watching. >> Clear- >> Clear cutting. >> Nice job, very nice job.

(upbeat techno music) >> Hello, wonderful cloud community, and welcome to theCUBE's continuing coverage of AWS re:Invent. My name is Savannah Peterson, and I am very excited to be joined by two brilliant gentlemen today. Please welcome Keith from Cockroach Labs and Jeff from AMD. Thank you both for tuning in, coming in from the East coast. How you doing? >> Not too bad. A little cold, but we're going >> Doing great. >> Love that and I love the enthusiasm Keith, you're definitely bringing the heat in the green room before we got on, so I'm going to open this up with you. Cockroach Labs puts out a pretty infamous and useful cloud report each year. Can you tell us a little bit about that, the approach and the data that you report on? >> Yeah, so Cockroach Labs builds a distributed SQL database that we are able to run across multiple cloud regions, multiple sites, multiple data centers. Frequently is running a hybrid kind of a use case and it's important for our customers to be able to compare the performance of configurations when they don't have exact the same hardware available to them in every single location. So since we were already doing this internally for ourselves and for our customers, we decided to turn it into something we shared with the greater community. And it's been a great experience for us. A lot of people come and ask us every year, "Hey, when's the new cloud report coming out?" Because they want to read it. It's been a great win for us. >> How many different things are you looking at? I mean, when you're comparing configurations I imagine there's a lot of different complex variables there. Just how much are you taking into consideration when you publish this report? >> Yeah, so we look at micro benchmarks around CPU network and storage. And then our flagship benchmark is we use the database itself where we have the most expertise to create a real world benchmark on across all of these instances. This year I think we tested over 150 different discrete configurations and it's a bit of a labor of love for us because we then not only do we consume it for best practices for our own as a service offering, but we share it with our customers. We use it internally to make all kinds of different decisions. >> Yeah, 150 different comparisons is not a small number. And Jeff, I know that AMD's position in this cloud report is really important. Where do you fit into all of this and what does it mean for you? >> Right, so what it means for us and for our customers is, there's a good breath and depth of testing that has gone of from the lab. And you look at this cloud report and it helps them traverse this landscape of, why to go on instance A, B, or C on certain workloads. And it really is very meaningful because they now have the real data across all those dimensional kinds of tests. So this definitely helps not only the customers but also for ourselves. So we can now look at ourselves more independently for feedback loops and say, "Hey, here's where we're doing well, here's where we're doing okay, here's where we need to improve on." All those things are important for us. So love seeing the lab present out such a great report as I've seen, very comprehensive, so I very much appreciate it. >> And specifically I love that you're both fans of each other, obviously, specifically digging in there, what does it mean that AMD had the best performance ratio tested on AWS instances? >> Yeah, so when we're looking at instances, we're not just looking at how fast something is, we're also looking at how much it costs to get that level of performance because CockroachDB as a distributed system has the opportunity to scale up and out. And so rather than necessarily wanting the fastest single instance performance, which is an important metric for certain use cases for sure, the comparison of price for performance when you can add notes to get more performance can be a much more economical thing for a lot of our customers. And so AMD has had a great showing on the price performance ratio for I think two years now. And it makes it hard to justify other instance types in a lot of circumstances simply because it's cheaper to get, for each transaction per second that you need, it's cheaper to use an AMD instance than it would be a competitive instance from another vendor. >> I mean, everyone I think no matter their sector wants to do things faster and cheaper and you're able to achieve both, it's easy to see why it's a choice that many folks would like to make. So what do these results mean for CIOs and CTOs? I can imagine there's a lot of value here in the FinOps world. >> Yep. Oh, I'll start a few of 'em. So from the C-suite when they're really looking at the problem statement, think of it as less granular, but higher level. So they're really looking at CapEx, OpEx, sustainability, security, sort of ecosystem on there. And then as Keith pointed out, hey, there's this TCO conversation that has to happen. In other words, as they're moving from sort of this lift and shift from their on-prem into the cloud, what does that mean to them for spend? So now if you're looking at the consistency around sort of the performance and the total cost of running this to their insights, to the conclusions, less time, more money in their pocket and maybe a reduction for their own customers so they can provide better for the customer side. What you're actually seeing is that's the challenge that they're facing in that landscape that they're driving towards that they need guidance and help with towards that. And we find AMD lends itself well to that scale out architecture that connects so well with how cloud microservices are run today. >> It's not surprising to hear that. Keith, what other tips and tricks do you have for CIOs and CTOs trying to reduce FinOps and continue to excel as they're building out? >> Yeah, so there were a couple of other insights that we learned this year. One of those two insights that I'd like to mention is that it's not always obvious what size and shape infrastructure you need to acquire to maximize your cost productions, right? So we found that smaller instance types were by and large had a better TCO than larger instances even across the exact same configurations, we kept everything else the same. Smaller instances had a better price performance ratio than the larger instances. The other thing that we discovered this year that was really interesting, we did a bit of a cost analysis on networking. And largely because we're distributed system, we can scan span across availability zones, we can span across regions, right? And one of the things we discovered this year is the amount of cost for transferring data between availability zones and the amount of cost for transferring data across regions at least in the United States was the same. So you could potentially get more resiliency by spanning your infrastructure across regions, then you would necessarily just spanning across availability zones. So you could be across multiple regions at the same cost as you were across availability zones, which for something like CockroachDB, we were designed to support those workloads is a really big and important thing for us. Now you have to be very particular about where you're purchasing your infrastructure and where those regions are. Because those data transfer rates change depending on what the source and the target is. But at least within the United States, we found that there was a strong correlation to being more survivable if you were in a multi-region deployment and the cost stayed pretty flat. >> That's interesting. So it's interesting to see what the correlation is between things and when you think there may be relationship between variables and when there maybe isn't. So on that note, since it seems like you're both always learning, I can imagine, what are you excited to test or learn about looking forward? Jeff, let's start with you actually. >> For sort of future testing. One of those things is certainly those more scale out sort of workloads with respect to showing scale. Meaning as I'm increasing the working set, as I'm increasing the number of connections, variability is another big thing of showing that minimization from run to run because performance is interesting but consistency is better. And as the lower side is from the instant sizes as I was talking about earlier, a (indistinct) architecture lends itself so well to it because they have the local caching and the CCDs that you can now put a number of vCPUs that will benefit from that delivery of the local caching and drive better performance at the lower side for that scale out sort of architecture, which is so consistent with the microservices. So I would be looking for more of those dimensional testings variability across a variety of workloads that you can go from memory intense workloads to database persistence store as well as a blend of the two, Kafka, et cetera. So there's a great breath and depth of testing that I am looking for and to more connect with sort of the CTOs and CIOs, the higher level that really show them that that CapEx, OpEx, sustainability and provide a bit more around that side of it because those are are the big things that they're focused on as well as security, the fact that based on working sets et cetera, AMD has the ability with confidential compute around those kind of offerings that can start to drive to those outcomes and help from what the CTOs and CIOs are looking for from compliance as well. So set them out (indistinct). >> So you're excited about a lot. No, that's great. That means you're very excited about the future. >> It's a journey that continues as Keith knows, there's always something new. >> Yeah, absolutely. What about you Keith? What is the most excited on the journey? >> Yeah, there are a couple of things I'd like to see us test next year. One of those is to test a multi-region CockroachDB config. We have a lot of customers running in that configuration and production but we haven't scaled that testing up to the same breadth that we we do with our single region testing which is what we've based the cloud report on for the past four years. The other thing that I'd really love to see us do,, I'm a Kubernetes SME, at least that's kind of my technical background. I would love to see us get to a spot where we're comparing the performance of raw EC2 instances to using that same infrastructure running CockroachDB via EKS and kind of see what the differences are there. The vast majority of CockroachDB customers are running at least a portion of their infrastructure in Kubernetes. So I feel like that would be a real great value add to the report for the next time that we go around but go about publishing it. >> If I don't mind adding to that just to volley it back for a moment. And also as I was saying about the ScaleOut and how it leverages our AMD architecture so well with EKS specifically around the spin up, spin down. So you think of a whole development life cycle. As they grow and shrink the resources over time, time of those spin ups to spin downs are expensive. So that has to be as reduced as much as possible. And I think they'll see a lot of benefits in AMD's architecture with EKS running on it as well. >> The future is bright. There's a lot of hype about many of the technologies that you both just mentioned, so I'm very curious to see what the next cloud report looks like. Thank you Keith, and the team for the labor of love that you put into that every year. And Jeff, I hope that you continue to be as well positioned as everyone's innovation journey continues. Keith and Jeff, thank you so much for being on the show with us today. As you know, this is a continuation of our coverage of AWS re:Invent here on theCUBE. My name's Savannah Peterson and we'll see you for our next fascinating segment. (upbeat music)

>>Hey everyone. Welcome back to the Cube's coverage of Ansible Fest 2022. We are live in Chicago. This is day two of Waldo Wall coverage on the cube. John Fhrer here with me. Lisa Martin. John, today's a big news day. Yeah, >>Big time. I mean, we got the chief architect on this segments to be great. We have the lead product management. All the new stuff coming out really is a game changer. It's very cool and relevant. Very key to be relevant. And then, and being a part of the future. This is a changeover you see in the NextGen Cloud developer environment. Open source all coming together. So Ansible we've been covering for many, many years. We've always said they're in the middle of all the action and you're starting to see the picture. Yes. For me. So we're looking forward to a great segment. >>Yes. We've got two alumni back with us to unpack the news and all the great stuff that's going on here. Richard Hensel joins us Senior manager, Ansible Product Management, and Matthew Jones here, fresh from the keynote stage, Chief architect of Ansible Automation. Guys, great to have you on the program. Thanks >>For having us. Good to be here. >>So this morning was all about event driven Ansible. Unpack that. Talk about the impact that this is gonna have, The excitement, the buzz that you've heard on the show floor today. >>Yeah. You know, it's, it's exciting. We've been working on this for a while. We've been really excited to show this off because it's something that feels like the natural evolution of the platform and where it's going. Really being able to connect the automation with the sources of data and the actions that we know people want to use. We, we came into this knowing everybody here at this conference, this is something that everybody will be able to use. >>Talk about the innovations strategy. Cause we've always had these great conversations with Ansible. Oh yeah. The, the practitioners, they're, they're building the product with you. You guys are very hardcore on that. No secret. This is different. This is like a whole nother level of opportunity that's gonna take the, the community to new heights in terms of what they do in their job and free them up to do more creative development. >>Yeah, you're exactly right. You know, we, we know that people need to bring that sort of reactive and active automation to it. We've, we've done a lot of work to bring automation to everybody, to the masses. Now we need to meet them at the place where they are, where the, the where, where they have to do the most work and, and act in the most strategic and specific ways. >>All right. So now before we get into some of the deep dive, cause a ton of questions. This is really exciting product. Take a minute to explain what was the key announcement? Why, what specifically does this mean for the audience, watching customers and future customers? What's the big deal? To take a minute to explain what was announced. >>So this is about the, the evolution and the maturity of the automation that our users are doing. So, you know, you think about provisioning servers, you know, configuring networks, all that sort of, the stuff that we've established and everybody's been doing for a number of years. And then you go, Well, I've invested in that. I've done the heavy lifting, I've done the things that cost me agility. I think that cost me time. Well now I need to go further. So what can I go further into? And you move further at the stacks. You move away from the infrastructure, please. You move away from infrastructure as code. You move towards through configures code, up to officer's code. And you start to get into, well, I've got, I've got road tasks, I've got repetitive actions that I'm doing. I've got investigations, I've got remediations, I've got responses. >>Well, there's work that I do on a daily basis that is toil. Right. It's not efficient work. Right. Actually, we doing valuable work in the operation space as much as you were doing in, in the build space. And how do we move them up into that space? And it's, this is all based off observation. You can do this today, but how do we make it easier? We've gonna make it easier for them to do that and get, it's all about success. It's about the outcomes we're gonna drive users towards. They need to be successful as quickly as possible. How do we make that >>Happen? And Matt, I remember we talked in 2019 with Ansible, the word platform where we say, Hey, you know, platforms are super important. It's not a tool, tools and platforms as distinctions. You mentioned platform. This is now platform. A lot of people put a lot of work in into this Yeah. Claim what went on behind the scenes. So >>You're exactly right. And we've spent the last couple of years really taking that disparate set of tools that, that we've invested a lot of time in building that platform. It's been exciting to see it come together. We always knew that we wanted to capture more of, more of where people find automation and find they need automation, not just out on the edge, on the end of the, of the, of the actions and tasks that they need to do. They've got a lot of things coming in, a lot of things that they need to take care of. And the community is really what drives this for us. People who have been doing this for years and they've been asking us, Meet me halfway. Give me something. Give me a part of this platform and a capability that enables me to do this. So I I feel like we've done that and you did >>It. Yeah, exactly. For step one. >>And that must feel pretty good too, to be able to deliver what, you know, the masses are looking for and why they're looking >>For it. Yeah. This was, there was no question that we knew this was gonna deliver the kind of real value that people were looking for. >>Take us through the building blocks real quick. I know on stage you went through it in detail. What should people know about the core building blocks of, of this particular event driven >>Piece? Yeah. You know, I think the most important thing to understand at the, at the outset is the sources of data and events that come in. It's really easy to get lost in the details. Like, what do you mean a source? But, you know, we've shown examples using Kafka, but it's not just Kafka, right? It's, it's, it's web hooks, it's CI systems, it's any, any place that you can imagine an evict coming from your monitoring platforms. You can bring those together under the same umbrella. We're not requiring you to pick one or choose or what's your favorite one. You can bring, you can use them all and and condense them down into the, into the same place. >>There's a lot of data events everywhere now. There's more events. Yeah. Is there a standard interface? Is what's the, is there any kind of hook in there? Is what's, what's gonna limit? Or is there any limits? >>I I don't think there is a limit. I, you know, it's, and we can't even imagine where events and data are gonna come from, but we know we need to get them into the system in a way that makes the most sense for the, the customers. And then that, that drives through into the rule books. Like, okay, we have the data now, but what do we do with that data? How do we translate that into, into the action? What are the rules that need to follow? It's giving the, the, the person who is automating, who understands the data that's coming in and understands the task that they need to take. The, the rules are where they map those into it. And then the last part, of course is the playbook, the automation itself, which they already know. They're already experts in the system. So we've, we've, we've built this like eight lane highway. They get some right end of those actions. >>Let's talk about Richard, let's unpack those actions and the really kind of double click on the business outcomes that this is actually gonna enable organizations and any industry to achieve. >>Yeah, so >>I mean, it's, it, like Matt said, it's really hard to encapsulate everything that we see as possible. But if you just think about what happens when a system goes down, right? At that point in time, I'm potentially not making money, right? I'd say it's costing me time, it's costing me, that's a business impact. If I can speed up how quick I can resolve that problem, if I can reduce time in there, that's customer improvement, that's custom satisfaction. That's bottom line money for businesses, right? But it's also, it's also satisfaction for the users. You know, they're not involved in having the stressful get online, get quickly, activate whatever accounts you need to do, go and start doing discovery. You can detect a lot of that information for the discovery use case that we see, respond to an event, scan the system for that same logic that you would normally do as a user, as a human. >>And that's why the rules are important to add into ed. It's like, how do I take that human, that brain part that I would say, well, if I see this bit, oh, I'll go and have a look in this other log file. If I see this piece, I'll go and do something different. How do we translate that into Ansible so that you've got that conditional logic just to be able to say, if this do that, or if I see these three things, it means a certain outcome has happened. And then again, that defined, that's what's gonna help people like choose where it becomes useful. And that's how we, that's how we take that process >>Forward. I'm sure people are gonna get excited by this. I'm not sure the community already knows that, but as it's gonna attract more potential customers, what's different about it? Can you share the differentiation? Like wait minute, I already have that already. Do they have it already? What's different? What makes this different? What's, what's in it for them? >>Yeah. When we step up into a customer situation, an enterprise, an organization, what's really important becomes the, the ability to control where you do some of that work. So the control and the trust, You know, would you trust an automatic system to go and start making changes to hundreds of thousands of devices? And the answer is often not, not straight away. So how do we put this sort of sep the same separation of duties we have between dev and ops and all the nice structures we've done over the last number of years, and actually apply that to that programmatic access of automation that other systems do. So let's say a AIML systems that are detecting what's going on, observability platforms are, are much more intru or intrusive is the wrong word. They're much more observable of what's going on in the systems, right? But at the same time you go, I wanna make sure that I know that any point in time I can decide what, what is there and what can be run and who can run it and when they can run it. And that becomes an important dimension. >>The versatility seems like a big deal too. They can, Yeah. Any team could get >>Involved. And, and that's the, the same flexibility and the same extensibility of Ansible exists in this use case, right? The, the, the ability to take any of those tasks you wanna do in action, string them together, but what the way that it works for you, not the way that it works that we see, but the way that you see and you convert your operational DNA into how you do that automation and how that gets triggered as you see fit. >>Talk about this both of you. I'd like to get your perspectives on event driven Ansible as part of the automation journey that businesses are on. Obviously you can look at different industries and different businesses are, are at different places along that journey, but where does this fit in and kind of plugin to accelerating that journey? That's, >>That's a good question. You know, sometimes this ends up being like that last mile of we've adopted this automation, we've learned how to write automation. We even understand the things that we would need to automate, but how do we carry it over that last topic and connect it to our, our knowledge systems, our data stores, our data lakes, and how do we combine the expertise of the systems that we're managing with this automation that we've learned? Like you, you mentioned the, the, the community and the, the coalescing of data and information, the, the definition of the event rules and, and the event driven architecture. It lives alongside the automation that you've developed in the exact same place where you can feel that trust and ubiquity that we keep talking about. Right? It's there, it's certified. And we've talked a lot about secure supply chain recently. This gives you the ability to sign and certify that the rules and actions that we're taking and the sources that we're communicating with works exactly the same way. Yeah. And >>There's something we didn't, we didn't correlate this when we first started doing the work. We were, we were, we observe teams doing self-healing and you know, extending Ansible. And then over the last 18 months, what we've also seen is this movement, this platform engineering movement, the SRE teams becoming much more prominent. And this just nicely sits in as a type of use case for that type of transformation. You know, we've gotta remember that Ansible at is heart is also a transformative tool. Is like, how do you teach this behavior to a bunch of people? How do you upscale a larger base of engineers with what you want to be able to do? And I think this is such an important part that we, we just one say we stumbled into it, but it was a very, very nice, >>It was a natural progression. >>Exactly. >>Yeah. Yeah. Tom, Tom, when we were talking about Tom yesterday, Tom Anderson and he said, You guys bring up the SRE to you guys when you come on the cube. This is exactly a culture shift that we're talking about. I mean, SRE is really his legacy with Google. We all know that. Everyone kind of knows that, but it's become like a job title. Well they kind of, what does that even mean now if you're not Google, it means you're running stuff. DevOps has become a title. Yeah. So what that means is that's a cultural shift, not so much semantics Yeah. On title. This is kind of what you guys are targeting here, enabling people to run platforms, engineer them. Yeah. Like an architect and enable more co composability coding. >>And, and it's, so that's, that distinction is so important because one of the, you know, we see many customers come from different places. Many users from, you know, all the legacy or heritage of tools that have existed. And so often those processes are defined by the way that tool worked. Right? You had no other way that, that, and the, and it's, it happened 10 years ago, somebody implemented it, that's how it now works. And then they come and try and take something new and you go, well, you can't let the tool define your process. Now your culture and your objective has to define the process. So this is really, you know, how do we make sure we match that ability by giving them a flexible tool that let's say, Well what are you trying to achieve? I wanna achieve this outcome. That's the way you can do it. I >>Mean, that's how we match basically means my mind to get your reaction. It means I'm running stuff at scale. Yep. Engineer, I'm engineering and infrastructure at scale to enable, >>I'm responsible for it. And it's, it's my, it's my baby. It's my responsibility to do that. And how do we, how do we allow people to do that better? And you know, it, it's about, it's about freeing people up to focus on things that are really important and transformative. We can be transformative. And we do that by taking away the complexity and making things work fast. >>And that's what people want. People in their daily jobs want to be able to deliver value to the organization. You wanna feel that. But something Richard that you were talking about that struck me a couple minutes ago is, was a venture of an Ansible. There's employee benefits, there's customer benefits, Those two are ex inextricably linked. But I liked how you were talking about what it facilitates for both Yes. And all the way to the customer satisfaction, brand reputation. That's an important Yeah. Element for any brand to >>Consider. And that, I mean, you know, think about what digital transformation was all about. I mean, as we evolve past all these initial terms that come about, you know, we actually start getting to the meat of what these things are. And that is it connecting what you do with actually what is the purpose of what your business is trying to achieve. And you can't, you can't almost put money on that. That's, that's the, that's the holy grail of what you're trying to get to. So how, you know, and again, it just comes back to how do we facilitate, how do we make it easy? If we don't make it easier, we're not doing it right. We've gotta make it easier. >>Right. Well, exciting news. I want to get your guys' reaction and if you don't mind sharing your opinion or your commentary on what's different now with Ansible this year than just a few years ago in terms of the scope of what's out there, what's been built, what you guys are doing for the, for the customer base and the community. What's changed? Obviously the people's roles looked that they're gonna expand and have more, I say more power, you know, more keys to the kingdom, however you wanna look at it. But things have changed. What's changed now from a few years >>Ago. It's, you know, it, it's funny because we've spent a lot of time over the last couple years setting up the capabilities that you're seeing us deliver right now. Right. We, we look back two or three years ago and we knew where we wanted to be. We wanted to build things like eda. We wanted to invest in systems like Project Wisdom and the, the types of content, the cloud journey that, that now we're on and we're enabling for folks. But we had to make some really big changes. And those changes take time and, and take investment. The move into last year, John, we talked about execution environments. Yeah. And separating the control plane from the execution plane. All of that work that we did and the investment into the platform and stability of the platform leads us now into what >>Cap. And that's architectural decision. That's the long game in mind. Exactly. Making things more cohesive, but decoupled, that's an operating system kind of thinking. >>It, it totally is. It's a systems engineering and system architecture thinking. And now we can start building on top of these things like what comes after ed, what does ED allow us to do within the platform? All of the dev tools that we focused on that we haven't spent a lot of time talking about that from the product side. But being, coming in with prescriptive and opinionated dev tools, now we can show you how to build it. We can show you how to use it and connect it to your systems. Where can we go next? I'm really excited. >>Yeah. Your customer base two has also been part of from the beginning and they solve their own problems and they rolled it up, grow with it, and now it's a full on platform. The question I then ask is, okay, you believe it's a platform, which it is, it's enabling. What do you guys see as that possible dots that could connect that might come on top of this from a creativity standpoint, from an ecosystem standpoint, from an Ansible standpoint, from maybe Red Hat. I mean, wisdom shows that you can go into the treasure trove of IBM's research, pull out some AI and some machine learning. Both that in or shim layered in whatever you do. >>I mean, what I'm starting to see much more, especially as I, the nice thing about being here is actually getting face to face with customers again and you know, actually hearing what they're talking about. But you know, we've moved away from a Ansible specific story where I'm talking about how I, I was always, I was looking to automate, I was looking to go to Ansible. Well now I've got the automation capability. Now we've enhanced the automation. Capabil wisdom enhances the automation capability further. What about all those, those broader set of management solutions that I've got that I would like to start connecting to each other. So we're starting to take the same like, you know, you mentioned as then software architecture, software design principles. We'll apply those same application design principles, apply them to your IT management because we've got data center with the pressures on there. We've got the expansion into cloud, we've got the expansion to the edge, right? Each adding a new layer of complexity and a new layer of, you know, more that you have to then look after. But there's still the same >>Number of people. So a thousand flower blooms kind of situation. >>Exactly. And so how do I, how do I constrain, how do I tame it, right? How do I sit there and go, I, I can control that now I can look after that. I contain that. I can, I can deal with what I wanna do. So I'm focusing on what's important and we are getting stuff done. >>We, we've been quoting Andy Grove on the cube lately. Let chaos, rain and then rain in the chaos. Yes. Right? I mean that's kind of every inflection point has complexity before it gets simpler. >>Yeah, that's right. >>Yeah. You can't, there's answer that one. That's >>Perfectly. >>Yeah. Yeah. What do you expect to see chief ar you gotta have the vision. What's gonna pop out? What's that low, low hanging fruit? What's gonna bloom first? What do you think's gonna come? >>I, you know, my overarching vision is that I just want to be able to automate more. Where, where can we bring back, So edge cloud, right? That's obvious, but what things run in the cloud and and on the edge, right? Devices, you heard Chad in the keynote this morning talk about programmable logic controllers, sensors, fans, motors, things like that. This is the, the sort of, this is the next frontier of automation is that connecting your data centers and your systems, your applications and needs all the way out to where your customers are. Gas stations, point of sale systems. >>It's instant. It's instant. It is what it is. It's like just add, Just >>Add faster and bigger. Yeah. >>But what happens if, I'll give you a tease. What I think is, is what happens if this happens? So I've got much more rich feature, rich diverse set of tools looking after my systems, observing what's going on. And they go through a whole filtering process and they say such and such has happened, right? Wisdom picks that up and decides from that natural language statement that comes outta the back of that system. That's the task I think is now appropriate to run. Where do you run that? You need a secure execution capability. Pass that to an support, that single task. And now we run inside the automation platform at any of those locations that you just mentioned, right? Stitching those things together and having that sequence of events all the way through where you, you predefine what's possible. You know, you start to bias the system towards what is your accepted standard and then let those clever systems do what you are investing in them for, which is to run your IT and make it >>Easier. Rich here was on earlier, I said, hey, about voice activated it. Provision the cluster. Yeah. >>Last question guys, before we run out of time for this. For customers who take advantage of this new frontier, how can they get started with the bench of an what's? >>That's a good question. You know, we, we've engaged our community because they trust us and we trust them to build really good products. ansible.com/events. Oh man, >>I did have the, I >>Had the cup, the landing page. >>Find somebody find that. >>Well it's on GitHub, right? GitHub It is. >>Yeah it >>Is. Absolutely ansible.com. It's probably a link somewhere if I on the front page. Exactly. On GitHub. The good code too. >>Right? Exactly. And so look at there, you can see where we're going on our roadmap, what we're capable of today. Examples, we're gonna be doing labs and blogs and demonstrations of it over the next day, week, month. Right. You'll be able to see this evolve. You get to be the, the sort of vanguard of support and actions on this and >>Cause we really want, we really want users to play with it, right? Of course. We've been doing this for a while. We've seen what we think is right. We want users to play with it. Tell us whether the syntax works, whether it makes sense, how does it run, how does it work? That's the exciting part. But at the same time, we want the partners, you know, we, we don't know all the technologies, right? We want the partners that we have that work with us already in the community to go and sort of, you know, do those integrations, do those triggers to their systems, define rules for their stuff cuz they'll talk to their customers about it as >>Well. Right? Right. It'll be exciting to see what unfolds over the next six to nine months or so with the partners getting involved, the community getting involved. Guys, congratulations on the big announcements. Sounds like a lot of work. I can tell. We can tell. Your excitement level is huge and job well done. Thank you so much for joining us on the Cube. Thank you very much. Thank you. Our pleasure. Just All right, for our guests and John Furrier, I'm Lisa Martin. You're watching The Cube Live from Chicago, Ansible Fest 22. John and I will be right back with our next guest of Stay tuned.

(soft music) >> Welcome back to Chicago guys and gals. Lisa Martin here with John Furrier. We have been covering Ansible Fest '22 for the last two days. This is our show wrap. We're going to leave you with some great insights into the things that we were able to dissect over the last two days. John, this has been an action packed two days. A lot of excitement, a lot of momentum. Good to be back in person. >> It's great to be back in person. It was the first time for you to do Ansible Fest. >> Yes. >> My first one was 2019 in person. That's the last time they had an event in person. So again, it's a very chill environment here, but it's content packed, great active loyal community and is growing. It's changing. Ansible now owned by Red Hat, and now Red Hat owned by IBM. Kind of see some game changing kind of movements here on the chess board, so to speak, in the industry. Ansible has always been a great product. It started in open source. It evolved configuration management configuring servers, networks. You know, really the nuts and bolts of IT. And became a fan favorite mainly because it was built by the fans and I think that never stopped. And I think you started to see an opportunity for Ansible to be not only just a, I won't say niche product or niche kind of use case to being the overall capabilities for large scale enterprise system architectures, system management. So it's very interesting. I mean I find it fascinating how, how it stays relevant and cool and continues to power through a massive shift >> A massive shift. They've done a great job though since the inception and through the acquisition of being still community first. You know, we talked a lot yesterday and today about helping organizations become automation first that Ansible has really stayed true to its roots in being community first, community driven and really that community flywheel was something that was very obvious the last couple of days. >> Yeah, I mean the community thing is is is their production system. I mean if you look at Red Hat, their open source, Ansible started open source, good that they're together. But what people may or may not know about Ansible is that they build their product from the community. So the community actually makes the suggestions. Ansible's just in listening modes. So when you have a system that's that efficient where you have direct working backwards from the customer like that, it's very efficient. Now, as a product manager you might want to worry about scope creep, but at the end of the day they do a good job of democratizing that process. So again, very strong product production system with open source, very relevant, solves the right problems. But this year the big story to me is the cultural shift of Ansible's relevance. And I think with multicloud on the horizon, operations is the new kind of developer kind of ground. DevOps has been around for a while. That's now shifted up to the developer themselves, the cloud native developer. But at cloud scale and hybrid computing, it's about the operations. It's about the data and the security. All of it's about the data. So to me there's a new ops configuration operating model that you're seeing people use, SRE and DevOps. That's the new culture, and the persona's changing. The operator of a large scale enterprise is going to be a lot different than it was past five, 10 years. So major cultural shift, and I think this community's going to step up to that position and fill that role. >> They seem to be having a lot of success meeting people where they are, meeting the demographics, delivering on how their community wants to work, how they want to collaborate. But yesterday you talked about operations. We talked a lot about Ops as code. Talk about what does that mean from your perspective, and what did you hear from our guests on the program with respect that being viable? >> Well great, that's a great point. Ops as code is the kind of their next layer of progression. Infrastructure is code. Configuration is code. Operations is code. To me that means running the company as software. So software influencing how operators, usually hardware in the past. Now it's infrastructure and software going to run things. So ops as code's, the next progression in how people are going to manage it. And I think most people think of that as enterprises get larger, when they hear words like SRE, which stands for Site Reliable Engineer. That came out of Google, and Google had all these servers that ran the search engine and at scale. And so one person managed boatload of servers and that was efficient. It was like a multiple 10x engineer, they used to call it. So that that was unique to Google but not everyone's Google. So it became language or parlance for someone who's running infrastructure but not everyone's that scale. So scale is a big issue. Ops as code is about scale and having that program ability as an operator. That's what Ops as code is. And that to me is a sign of where the scale meets the automation. Large scale is hard to do. Automating at large scale is even harder. So that's where Ansible fits in with their new automation platform. And you're seeing new things like signing code, making sure it's trusted and verified. So that's the software supply chain issue. So they're getting into the world where software, open source, automation are all happening at scale. So to me that's a huge concept of Ops as code. It's going to be very relevant, kind of the next gen positioning. >> Let's switch gears and talk about the partner ecosystem. We had Stefanie Chiras on yesterday, one of our longtime theCUBE alumni, talking about what they're doing with AWS in the marketplace. What was your take on that, and what's the "what's in it for me" for both Red Hat, Ansible and AWS? >> Yeah, so the big news on the automation platform was one. The other big news I thought was really, I won't say watered down, but it seems small but it's not. It's the Amazon Web Services relationship with Red Hat, now Ansible, where Ansible's now a product in AWS's marketplace. AWS marketplace is kind of hanging around. It's a catalog right now. It's not the most advanced technical system in the world, and it does over 2 billion plus revenue transactions. So even if it's just sitting there as a large marketplace, that's already doing massive amounts of disruption in the procurement, how software is bought. So we interviewed them in the past, and they're innovating on that. They're going to make that a real great platform. But the fact that Ansible's in the marketplace means that their sales are going to go up, number one. Number two, that means customers can consume it simply by clicking a button on their Amazon bill. That means they don't have to do anything. It's like getting a PO for free. It's like, hey, I'm going to buy Ansible, click, click, click. And then by the way, draw that down from their commitment to AWS. So that means Amazon's going into business with Ansible, and that is a huge revenue thing for Ansible, but also an operational efficiency thing that gives them more of an advantage over the competition. >> Talk what's in it for me as a customer. At Red Hat Summit a few months ago they announced similar partnership with Azure. Now we're talking about AWS. Customers are living in this hybrid cloud world, often by default. We're going to see that proliferate. What do you think this means for customers in terms of being able to- >> In the marketplace deal or Ansible? >> Yeah, the marketplace deal, but also what Red Hat and Ansible are doing with the hyperscalers to enable customers to live successfully in the hyper hybrid cloud world. >> It's just in the roots of the company. They give them the choice to consume the product on clouds that they like. So we're seeing a lot of clients that have standardized on AWS with their dev teams but also have productivity software on Azure. So you have the large enterprises, they sit on both clouds. So you know, Ansible, the customer wants to use Ansible anyway, they want that to happen. So it's a natural thing for them to work anywhere. I call that the Switzerland strategy. They'll play with all the clouds. Even though the clouds are fighting against each other, and they have to to differentiate, there's still going to be some common services. I think Ansible fits this shim layer between clouds but also a bolt on. Now that's a really a double win for them. They can bolt on to the cloud, Azure and bolt on to AWS and Google, and also be a shim layer technically in clouds as well. So there's two technical advantages to that strategy >> Can Ansible be a facilitator of hybrid cloud infrastructure for organizations, or a catalyst? >> I think it's going to be a gateway on ramp or gateway to multicloud or supercloud, as we call it, because Ansible's in that configuration layer. So you know, it's interesting to hear the IBM research story, which we're going to get to in a second around how they're doing the AI for Ansible with that wisdom project. But the idea of configuring stuff on the fly is really a concept that's needed for multicloud 'Cause programs don't want to have to configure anything. (he laughs) So standing up an application to run on Azure that's on AWS that spans both clouds, you're going to need to have that automation, and I think this is an opportunity whether they can get it or not, we'll see. I think Red Hat is probably angling on that hard, and I can see them kind of going there and some of the commentary kind of connects the dots for that. >> Let's dig into some of news that came out today. You just alluded to this. IBM research, we had on with Red Hat. Talk about what they call project wisdom, the value in that, what it also means for for Red Hat and IBM working together very synergistically. >> I mean, I think the project wisdom is an interesting dynamic because you got the confluence of the organic community of Ansible partnering with a research institution of IBM research. And I think that combination of practitioners and research groups is going to map itself out to academic and then you're going to see this kind of collaboration going forward. So I think it's a very nuanced story, but the impact to me is very clear that this is the new power brokers in the tech industry, because researchers have a lot of muscle in terms of deep research in the academic area, and the practitioners are the ones who are actually doing it. So when you bring those two forces together, that pretty much trumps any kind of standards bodies or anything else. So I think that's a huge signaling benefit to Ansible and Red Hat. I think that's an influence of Red Hat being bought by IBM. But the project itself is really amazing. It's taking AI and bringing it to Ansible, so you can do automated configurations. So for people who don't know how to code they can actually just automate stuff and know the process. I don't need to be a coder, I can just use the AI to do that. That's a low code, no code dynamic. That kind of helps with skill gaps, because I need to hire someone to do that. Today if I want to automate something, and I don't know how to code, I've got to get someone who codes. Here I can just do it and automate it. So if that continues to progress the way they want it to, that could literally be a game changer, 'cause now you have software configuring machines and that's pretty badass in my opinion. So that thought that was pretty cool. And again it's just an evolution of how AI is becoming more relevant. And I think it's directionally correct, and we'll see how it goes. >> And they also talked about we're nearing an inflection point in AI. You agree? >> Yeah I think AI is at an inflection point because it just falls short on the scale side. You see it with chatbots, NLP. You see what Amazon's doing. They're building these models. I think we're one step away from model scaling. I think the building the models is going to be one of these things where you're going to start to see marketplace and models and you start to composability of AI. That's where it's going to get very interesting to see which cloud is the best AI scale. So I think AI at scale's coming, and that's going to be something to watch really closely. >> Something exciting. Another thing that was big news today was the event driven Ansible. Talk about that, and that's something they've been working on in conjunction with the community for quite a while. They were very proud of that release and what that's going to enable organizations to do. >> Well I think that's more meat on the bone on the AI side 'cause in the big trend right now is MLAI ops. You hear that a lot. Oh, data ops or AI ops. What event driven automation does is allows you to take things that are going on in your world, infrastructure, triggers, alarms, notifications, data pipelining flows, things that go on in the plumbing of infrastructure. are being monitored and observed. So when events happen they trigger events. You want to stream something, you send a trigger and things happen. So these are called events. Events are wide ranging number of events. Kafka streaming for data. You got anything that produces data is an event. So harnessing that data into a pipeline is huge. So doing that at scale, that's where I think that product's a home run, and I think that's going to be a very valuable product, 'cause once you understand what the event triggers are, you then can automate that, and no humans involved. So that will save a lot of time for people in the the higher pay grade of MLAI ops automate some of that low level plumbing. They move their skill set to something more valuable or more impactful. >> And we talked about, speaking of impact, we talked about a lot of the business impact that organizations across industries are going to be able to likely achieve by using that. >> Yeah, I mean I think that you're going to see the community fill the gap on that. I mean the big part about all this is that their community builds the product and they have the the playbooks and they're shareable and they're reusable. So we produce content as a media company. They'd talk about content as is playbooks and documentation for people to use. So reuse and and reusing these playbooks is a huge part of it. So as they build up these catalogs and these playbooks and rules, it gets better by the community. So it's going to be interesting to see the adoption. That's going to be a big tell sign for what's going to happen. >> Yep, we get definitely are going to be watching that space. And the last thing, we got to talk to a couple of customers. We talked to Wells Fargo who says "We are a tech company that does banking," which I loved. We got to talk with Rockwell Automation. What are some of your takeaways from how the customers are leveraging Ansible and the technology to drive their businesses forward to meet demanding customers where they are? >> I think you're seeing the script flipping a little bit here, where the folks that used to use Ansible for configuration are flipping to be on the front edge of the innovation strategy where what process to automate is going to drive the profitability and scale. Cause you're talking about things like skill gaps, workflows. These are business constructs and people These are assets so they have economic value. So before it was just, IT serve the business, configure some servers, do some stuff. When you start getting into automation where you have expertise around what this means, that's economic value. So I think you're going to see the personas change significantly in this community where they're on the front lines, kind of like developers are. That's why ops as code is to me a developer kind of vibe. That's going to completely change how operations runs in IT. And I think that's going to be a very interesting cultural shift. And some will make it, some won't. That's going to be a big thing. Some people say, I'm going to retire. I'm old school storage server person, or no, I'm the new guard. I'm going to be the new team. I'm going be on the right side of history here. So they're clearly going down that right path in my opinion. >> What's your overall summary in the last minute of what this event delivered the last couple of days in terms of really talking about the transformation of enterprises and industries through automation? >> I think the big takeaway from me in listening and reading the tea leaves was the Ansible company and staff and the community together. It was really a call for arms. Like, hey, we've had it right from the beginning. We're on the right wave and the wave's getting bigger. So expand your scope, uplevel your skills. They're on the right side of history. And I think the message was engage more. Bring more people in because it is open source, and if they are on that track, you're going to see more of hey, we got it right, let's continue. So they got platform release. They got the key products coming out after years of work. So you know, they're doing their work. And the message I heard was, it's bigger than we thought. So I think that's interesting. We'll see what that means. We're going to unpack that after the event in series of showcases. But yeah, it was very positive, I thought. Very positive. >> Yeah, I think there was definitely some surprises in there for them. John, thank you so much. It's been a pleasure co-hosting with you the last couple of days, really uncovering what Ansible is doing, what they're enabling customers in every industry to achieve. >> Been fun. >> Yes. All right for my co-host, John Furrier, I'm Lisa Martin. You've been watching theCUBE's coverage of Ansible Fest 2022 live from Chicago. We hope you take good care and we'll see you soon.

>>Hey everyone. Welcome to the cubes presentation of the AWS startup showcase. This is season two, episode four of our ongoing series. That's featuring exciting startups within the AWS ecosystem. This theme, cybersecurity protect and detect against threats. I'm your host. Lisa Martin. I've got two guests here with me. Please. Welcome back to the program. Sam Kam, a COO and co-founder of security scorecard and bar Roth. Charri team lead solutions marketing at confluent guys. It's great to have you on the program talking about cybersecurity. >>Thanks for having us, Lisa, >>Sam, let's go ahead and kick off with you. You've been on the queue before, but give the audience just a little bit of context about security scorecard or SSC as they're gonna hear it referred to. >>Yeah. AB absolutely. Thank you for that. Well, the easiest way to, to put it is when people wanna know about their credit risk, they consult one of the major credit scoring companies. And when companies wanna know about their cybersecurity risk, they turn to security scorecard to get that holistic view of, of, of the security posture. And the way it works is SSC is continuously 24 7 collecting signals from across the entire internet. I entire IPV four space and they're doing it to identify vulnerable and misconfigured digital assets. And we were just looking back over like a three year period. We looked from 2019 to 2022. We, we, we assessed through our techniques over a million and a half organizations and found that over half of them had at least one open critical vulnerability exposed to the internet. What was even more shocking was 20% of those organizations had amassed over a thousand vulnerabilities each. >>So SSC we're in the business of really building solutions for customers. We mine the data from dozens of digital sources and help discover the risks and the flaws that are inherent to their business. And that becomes increasingly important as companies grow and find new sources of risk and new threat vectors that emerge on the internet for themselves and for their vendor and business partner ecosystem. The last thing I'll mention is the platform that we provide. It relies on data collection and processing to be done in an extremely accurate and real time way. That's a key for that's allowed us to scale. And in order to comp, in order for us to accomplish this security scorecard engineering teams, they used a really novel combination of confluent cloud and confluent platform to build a really, really robust data for streaming pipelines and the data streaming pipelines enabled by confluent allow us at security scorecard to collect the data from a lot of various sources for risk analysis. Then they get feer further analyzed and provided to customers as a easy to understand summary of analytics. >>Rob, let's bring you into the conversation, talk about confluent, give the audience that overview and then talk about what you're doing together with SSC. >>Yeah, and I wanted to say Sam did a great job of setting up the context about what confluent is. So, so appreciate that, but a really simple way to think about it. Lisa is confident as a data streaming platform that is pioneering a fundamentally new category of data infrastructure that is at the core of what SSE does. Like Sam said, the key is really collect data accurately at scale and in real time. And that's where our cloud native offering really empowers organizations like SSE to build great customer experiences for their customers. And the other thing we do is we also help organizations build a sophisticated real time backend operations. And so at a high level, that's the best way to think about comfort. >>Got it. But I'll talk about data streaming, how it's being used in cyber security and what the data streaming pipelines enable enabled by confluent allow SSE to do for its customers. >>Yeah, I think Sam can definitely share his thoughts on this, but one of the things I know we are all sort of experiencing is the, is the rise of cyber threats, whether it's online from a business B2B perspective or as consumers just be our data and, and the data that they're generating and the companies that have access to it. So as the, the need to protect the data really grows companies and organizations really need to effectively detect, respond and protect their environments. And the best way to do this is through three ways, scale, speed, and cost. And so going back to the points I brought up earlier with conference, you can really gain real time data ingestion and enable those analytics that Sam talked about previously while optimizing for cost scale. So those are so doing all of this at the same time, as you can imagine, is, is not easy and that's where we Excel. >>And so the entire premise of data streaming is built on the concepts. That data is not static, but constantly moving across your organization. And that's why we call it data streams. And so at its core, we we've sort of built or leveraged that open source foundation of APA sheet Kafka, but we have rearchitected it for the cloud with a totally new cloud native experience. And ultimately for customers like SSE, we have taken a away the need to manage a lot of those operational tasks when it comes to Apache Kafka. The other thing we've done is we've added a ton of proprietary IP, including security features like role based access control. I mean, some prognosis talking about, and that really allows you to securely connect to any data no matter where it resides at scale at speed. And it, >>Can you talk about bar sticking with you, but some of the improvements, and maybe this is a actually question for Sam, some of the improvements that have been achieved on the SSC side as a result of the confluent partnership, things are much faster and you're able to do much more understand, >>Can I, can Sam take it away? I can maybe kick us off and then breath feel, feel free to chime in Lisa. The, the, the, the problem that we're talking about has been for us, it was a longstanding challenge. We're about a nine year old company. We're a high growth startup and data collection has always been in, in our DNA. It's at it's at the core of what we do and getting, getting the insights, the, and analytics that we synthesize from that data into customer's hands as quickly as possible is the, is the name of the game because they're trying to make decisions and we're empowering them to make those decisions faster. We always had challenges in, in the arena because we, well partners like confluent didn't didn't exist when we started scorecard when, when we we're a customer. But we, we, we think of it as a partnership when we found confluent technology and you can hear it from Barth's description. >>Like we, we shared a common vision and they understood some of the pain points that we were experiencing on a very like visceral and intimate level. And for us, that was really exciting, right? Just to have partners that are there saying, we understand your problem. This is exactly the problem that we're solving. We're, we're here to help what the technology has done for us since then is it's not only allowed us to process the data faster and get the analytics to the customer, but it's also allowed us to create more value for customers, which, which I'll talk about in a bit, including new products and new modules that we didn't have the capabilities to deliver before. >>And we'll talk about those new products in a second exciting stuff coming out there from SSC, bro. Talk about the partnership from, from confluence perspective, how has it enabled confluence to actually probably enhance its technology as a result of seeing and learning what SSC is able to do with the technology? >>Yeah, first of all, I, I completely agree with Sam it's, it's more of a partnership because like Sam said, we sort of shared the same vision and that is to really make sure that organizations have access to the data. Like I said earlier, no matter where it resides so that you can scan and identify the, the potential security security threads. I think from, from our perspective, what's really helped us from the perspective of partnering with SSE is just looking at the data volumes that they're working with. So I know a stat that we talked about recently was around scanning billions of records, thousands of ports on a daily basis. And so that's where, like I, like I mentioned earlier, our technology really excels because you can really ingest and amplify the volumes of data that you're processing so that you can scan and, and detect those threats in real time. >>Because I mean, especially the amount of volume, the data volume that's increasing on a year by basis, that aspect in order to be able to respond quickly, that is paramount. And so what's really helped us is just seeing what SSE is doing in terms of scanning the, the web ports or the data systems that are at are at potential risk. Being able to support their use cases, whether it's data sharing between their different teams internally are being able to empower customers, to be able to detect and scan their data systems. And so the learning for us is really seeing how those millions and billions of records get processed. >>Got it sounds like a really synergistic partnership that you guys have had there for the last year or so, Sam, let's go back over to you. You mentioned some new products. I see SSC just released a tax surface intelligence product. That's detecting thousands of vulnerabilities per minute. Talk to us about that, the importance of that, and another release that you're making. >>There are some really exciting products that we have released recently and are releasing at security scorecard. When we think about, when we think about ratings and risk, we think about it not just for our companies or our third parties, but we think about it in a, in a broader sense of an, of an ecosystem, because it's important to have data on third parties, but we also want to have the data on their third parties as well. No, nobody's operating in a vacuum. Everybody's operating in this hyper connected ecosystem and the risk can live not just in the third parties, but they might be storing processing data in a myriad of other technological solutions, which we want to understand, but it's really hard to get that visibility because today the way it's done is companies ask their third parties. Hey, send me a list of your third parties, where my data is stored. >>It's very manual, it's very labor intensive, and it's a trust based exercise that makes it really difficult to validate. What we've done is we've developed a technology called a V D automatic vendor detection. And what a V D does is it goes out and for any company, your own company or another business partner that you work with, it will go detect all of the third party connections that we see that have a live network connection or data connection to an organization. So that's like an awareness and discovery tool because now we can see and pull the veil back and see what the bigger ecosystem and connectivity looks like. Thus allowing the customers to go hold accountable, not just the third parties, but their fourth parties, fifth parties really end parties. And they, and they can only do that by using scorecard. The attack surface intelligence tool is really exciting for us because well, be before security scorecard people thought what we were doing was fairly, I impossible. >>It was really hard to get instant visibility on any company and any business partner. And at the same time, it was of critical importance to have that instant visibility into the risk because companies are trying to make faster decisions and they need the risk data to steer those decisions. So when I think about, when I think about that problem in, in managing sort of this evolving landscape, what it requires is it requires insightful and actionable, real time security data. And that relies on a couple things, talent and tech on the talent side, it starts with people. We have an amazing R and D team. We invest heavily. It's the heartbeat of what we do. That team really excels in areas of data collection analysis and scaling large data sets. And then we know on the tech side, well, we figured out some breakthrough techniques and it also requires partners like confluent to help with the real time streaming. >>What we realized was those capabilities are very desired in the market. And we created a new product from it called the tech surface intelligence. A tech surface intelligence focuses less on the rating. There's, there's a persona on users that really value the rating. It's easy to understand. It's a bridge language between technical and non-technical stakeholders. That's on one end of the spectrum on the other end of the spectrum. There's customers and users, very technical customers and users that may not have as much interest in a layman's rating, but really want a deep dive into the strong threat Intel data and capabilities and insights that we're producing. So we produced ASI, which stands for attack surface intelligence that allows customers to look at the surface area of attack all of the digital assets for any organization and see all of the threats, vulnerabilities, bad actors, including sometimes discoveries of zero day vulnerabilities that are, that are out in the wild and being exploited by bad guys. So we have a really strong pulse on what's happening on the internet, good and bad. And we created that product to help service a market that was interested in, in going deep into the data. >>So it's >>So critical. Go >>Ahead to jump in there real quick, because I think the points that Sam brought up, we had a great, great discussion recently while we were building on the case study that I think brings this to life, going back to the AVD product that Sam talked about and, and Sam can probably do a better job of walking through the story, but the way I understand it, one of security scorecards customers approached them and told them that they had an issue to resolve and what they ended up. So this customer was using an AVD product at the time. And so they said that, Hey, the car SSE, they said, Hey, your product shows that we used, you were using HubSpot, but we stopped using that age server. And so I think when SSE investigated, they did find a very recent HubSpot ping being used by the marketing team in this instance. And as someone who comes from that marketing background, I can raise my hand and said, I've been there, done that. So, so yeah, I mean, Sam can probably share his thoughts on this, but that's, I think the great story that sort of brings this all to life in terms of how actually customers go about using SSCs products. >>And Sam, go ahead on that. It sounds like, and one of the things I'm hearing that is a benefit is reduction in shadow. It, I'm sure that happens so frequently with your customers about Mar like a great example that you gave of, of the, the it folks saying we don't use HubSpot, have it in years marketing initiates an instance. Talk about that as some of the benefits in it for customers reducing shadow it, there's gotta be many more benefits from a security perspective. >>Yeah, the, there's a, there's a big challenge today because the market moved to the cloud and that makes it really easy for anybody in an organization to go sign, sign up, put in a credit card, or get a free trial to, to any product. And that product can very easily connect into the corporate system and access the data. And because of the nature of how cloud products work and how easy they are to sign up a byproduct of that is they sort of circumvent a traditional risk assessment process that, that organizations go through and organizations invest a, a lot of money, right? So there's a lot of time and money and energy that are invested in having good procurement risk management life cycles, and making sure that contracts are buttoned up. So on one side you have companies investing loads of energy. And then on the other side, any employee can circumvent that process by just going and with a few clicks, signing up and purchasing a product. >>And that's, and, and, and then that causes a, a disparity and Delta between what the technology and security team's understanding is of the landscape and, and what reality is. And we're trying to close that gap, right? We wanna close and reduce any windows of time or opportunity where a hacker can go discover some misconfigured cloud asset that somebody signed up for and maybe forgot to turn off. I mean, it's a lot of it is just human error and it, and it happens the example that Barra gave, and this is why understanding the third parties are so important. A customer contacted us and said, Hey, you're a V D detection product has an error. It's showing we're using a product. I think it was HubSpot, but we stopped using that. Right. And we don't understand why you're still showing it. It has to be a false positive. >>So we investigated and found that there was a very recent live HubSpot connection, ping being made. Sure enough. When we went back to the customer said, we're very confident the data's accurate. They looked into it. They found that the marketing team had started experimenting with another instance of HubSpot on the side. They were putting in real customer data in that instance. And it, it, you know, it triggered a security assessment. So we, we see all sorts of permutations of it, large multinational companies spin up a satellite office and a contractor setting up the network equipment. They misconfigure it. And inadvertently leave an administrator portal to the Cisco router exposed on the public internet. And they forget to turn off the administrative default credentials. So if a hacker stumbles on that, they can ha they have direct access to the network. We're trying to catch those things and surface them to the client before the hackers find it. >>So we're giving 'em this, this hacker's eye view. And without the continuous data analysis, without the stream processing, the customer wouldn't have known about those risks. But if you can automatically know about the risks as they happen, what that does is that prevents a million shoulder taps because the customer doesn't have to go tap on the marketing team's shoulder and go tap on employees and manually interview them. They have the data already, and that can be for their company. That can be for any company they're doing business with where they're storing and processing data. That's a huge time savings and a huge risk reduction, >>Huge risk reduction. Like you're taking blinders off that they didn't even know were there. And I can imagine Sam tune in the last couple of years, as SAS skyrocketed the use of collaboration tools, just to keep the lights on for organizations to be able to communicate. There's probably a lot of opportunity in your customer base and perspective customer base to engage with you and get that really full 360 degree view of their entire organization. Third parties, fourth parties, et cetera. >>Absolutely. Absolutely. CU customers are more engaged than they've ever been because that challenge of the market moving to the cloud, it hasn't stopped. We've been talking about it for a long time, but there's still a lot of big organizations that are starting to dip their toe in the pool and starting to cut over from what was traditionally an in-house data center in the basement of the headquarters. They're, they're moving over to the cloud. And then on, on top of that cloud providers like Azure, AWS, especially make it so easy for any company to go sign up, get access, build a product, and launch that product to the market. We see more and more organizations sitting on AWS, launching products and software. The, the barrier to entry is very, very low. And the value in those products is very, very high. So that's drawing the attention of organizations to go sign up and engage. >>The challenge then becomes, we don't know who has control over this data, right? We don't have know who has control and visibility of our data. We're, we're bringing that to surface and for vendors themselves like, especially companies that sit in AWS, what we see them doing. And I think Lisa, this is what you're alluding to. When companies engage in their own scorecard, there's a bit of a social aspect to it. When they look good in our platform, other companies are following them, right? So now all of the sudden they can make one motion to go look good, make their scorecard buttoned up. And everybody who's looking at them now sees that they're doing the right things. We actually have a lot of vendors who are customers, they're winning more competitive bakeoffs and deals because they're proving to their clients faster that they can trust them to store the data. >>So it's a bit of, you know, we're in a, two-sided kind of market. You have folks that are assessing other folks. That's fun to look at others and see how they're doing and hold them accountable. But if you're on the receiving end, that can be stressful. So what we've done is we've taken the, that situation and we've turned it into a really positive and productive environment where companies, whether they're looking at someone else or they're looking at themselves to prove to their clients, to prove to the board, it turns into a very productive experience for them >>One. Oh >>Yeah. That validation. Go ahead, bro. >>Really. I was gonna ask Sam his thoughts on one particular aspect. So in terms of the industry, Sam, that you're seeing sort of really moving to the cloud and like this need for secure data, making sure that the data can be trusted. Are there specific like verticals that are doing that better than the others? Or do you see that across the board? >>I think some industries have it easier and some industries have it harder, definitely in industries that are, I think, health, healthcare, financial services, a absolutely. We see heavier activity there on, on both sides, right? They they're, they're certainly becoming more and more proactive in their investments, but the attacks are not stopping against those, especially healthcare because the data is so valuable and historically healthcare was under, was an underinvested space, right. Hospitals. And we're always strapped for it folks. Now, now they're starting to wake up and pay very close attention and make heavier investments. >>That's pretty interesting. >>Tremendous opportunity there guys. I'm sorry. We are out of time, but this is such an interesting conversation. You see, we keep going, wanna ask you both where can, can prospective interested customers go to learn more on the SSC side, on the confluence side, through the AWS marketplace? >>I let some go first. >>Sure. Oh, thank thank, thank you. Thank you for on the security scorecard side. Well look, security scorecard is with the help of Colu is, has made it possible to instantly rate the security posture of any company in the world. We have 12 million organizations rated today and, and that, and that's going up every day. We invite any company in the world to try security scorecard for free and experience how, how easy it is to get your rating and see the security rating of, of any company and any, any company can claim their score. There's no, there's no charge. They can go to security, scorecard.com and we have a special, actually a special URL security scorecard.com/free-account/aws marketplace. And even better if someone's already on AWS, you know, you can view our security posture with the AWS marketplace, vendor insights, plugin to quickly and securely procure your products. >>Awesome. Guys, this has been fantastic information. I'm sorry, bro. Did you wanna add one more thing? Yeah. >>I just wanted to give quick call out leads. So anyone who wants to learn more about data streaming can go to www confluent IO. There's also an upcoming event, which has a separate URL. That's coming up in October where you can learn all about data streaming and that URL is current event.io. So those are the two URLs I just wanted to quickly call out. >>Awesome guys. Thanks again so much for partnering with the cube on season two, episode four of our AWS startup showcase. We appreciate your insights and your time. And for those of you watching, thank you so much. Keep it right here for more action on the, for my guests. I am Lisa Martin. We'll see you next time.

>> 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)

(soft music) >> Welcome back, everyone. theCube's live coverage. Day two here at VMware Explore. Our 12th year covering VMware's annual conference formally called Vmworld, now it's VMware Explore. Exploring new frontiers multi-cloud and also bearing some of the fruit from all the investments in cloud native Tanzu and others. I'm John Furrier with Dave Vellante. We have the man who's in charge of a lot of that business and a lot of stuff coming out of the oven and hitting the market. Ajay Patel, senior vice president and general manager of the modern applications and management group at VMware, basically the modern apps. >> Absolutely. >> That's Tanzu. All the good stuff. >> And Aria now. >> And Aria, the management platform, which got social graph and all kinds of graph databases. Welcome back. >> Oh, thank you so much. Thanks for having me. >> Great to see you in person, been since 2019 when you were on. So, a lot's happened since 2019 in your area. Again, things get, the way VMware does it as we all know, they announce something and then you build it and then you ship it and then you announce it. >> I don't think that's true, but okay. (laughs) >> You guys had announced a lot of cool stuff. You bought Heptio, we saw that Kubernetes investment and all the cloud native goodness around it. Bearing fruit now, what's the status? Give us the update on the modern applications of the management, obviously the areas, the big announcement here on the management side, but in general holistically, what's the update? >> I think the first update is just the speed and momentum that containers and Kubernetes are getting in the marketplace. So if you take the market context, over 70% of organizations now have Kubernetes in production, not one or two clusters, but hundreds of clusters, sometimes tens of clusters. So, to me, that is a market opportunity that's coming to fruition. Sometimes people will come and say, Ajay, aren't you late to the market? I say, no, I'm just perfectly timing it. 'Cause where does our value come in? It's enterprise readiness. We're the company that people look to when you have complexity, you have scale, you need performance, you need security, you need the robustness. And so, Tanzu is really about making modern applications real, helping you design, develop, build and run these applications. And with Aria, we're fundamentally changing the game around multicloud management. So the one-two punch of Tanzu and Aria is I'm most excited about. >> Isn't it true that most of the Kubernetes, you know, today is people pulling down open source and banging away. And now, they're looking for, you know, like you say, more of a robust management capability. >> You know, last two years when I would go to many of the largest customers, like, you know, we're doing good. We've got a DIY platform, we're building this. And then you go to the customer a year later, he's got knocked 30, 40 teams and he has Log4j happen. And all of a sudden he is like, oh, I don't want to be in the business of patching this thing or updating it. And, you know, when's the next shoe going to fall? So, that maturity curve is what I was talking about. >> Yeah. Free like a puppy. >> Ajay, you know, mentioned readiness, enterprise readiness and the timing's perfect. You kind of included, not your exact words, but I'm paraphrasing. That's a lot to do with what's going on. I mean, I'll say Cloud Native, IWS, think of the hyper scale partner, big partner and Google and even Google said it today. You know, the market world's spinning in their direction. Especially with respect to VMware. You get the relationship with the hyperscalers. Cloud's been on everyone's agenda for a long time. So, it's always been ready. But enterprise, you are customer base at VMware, very cloud savvy in the sense they know it's there, there's some dabbling, there's some endeavors in the cloud, no problem. But from a business perspective and truly transforming the VMware value proposition, is already, they're ready and it's already time now for them, like, you can see the movement. And so, can you explain the timing of that? I mean, I get enterprise readiness, so we're ready to scale all that good stuff. But the timing of product market fit is important here. >> I think when Raghu talks about that cloud first to cloud chaos, to cloud smart, that's the transition we're seeing. And what I mean by that is, they're hitting that inflection point where it's not just about a single team. One of the guys, basically I talked to the CIO, he was like, look, let's assume hypothetically I have thousand developers. Hundred can talk about microservices, maybe 50 has built a microservice and three are really good at it. So how do I get my thousand developers productive? Right? And the other CIO says, this team comes to me and says, I should be able develop directly to the public cloud. And he goes, absolutely you can do that. You don't have to come through IT. But here's the book of security and compliance that you need to enforce to get that thing in production. >> Go for it. >> Go for it. >> Good luck with that. >> So that reality of how do I scale my dev developers is turning into a developer experience problem. We now have titles which says, head of developer experience. Imagine that two years ago. We didn't talk about it. People start, hey, containers Kubernetes. I'm good to go. I can go get all the open source technology you talked about. And now they're saying no. >> And also software supply chains, another board that you're think. This is a symptom of the growth. I mean, open source is the software industry. That is, I don't think debatable. >> Right. >> Okay. That's cool. But now integration becomes vetting, trust, trusting codes. It's very interesting software time right now. >> That's right. >> And how is that impacting the cloud native momentum in your mind? Accelerating it? What inning are we in? How would you peg the progress? >> You know, on that scale of 1 to 10, I think we're halfway marked now. And that moved pretty quickly. >> It really did. >> And if you sit back today, the kinds of applications we're involved in, I have a Chicago wealth management company. We're building the next generation wealth management application. It's a fundamental refactoring of the legacy application. If you go to a prescription company, they're building a brand new prescription platform. These are not just trivial. What they're learning is the lift and shift. Doesn't work for these major applications. They're having to refactor them which is the modernization. >> So how specifically, are they putting some kind of abstraction layer on that? Are they actually gutting it and rewriting it? >> There's always going to be brownfield. Remember the old days of SOA? >> Yeah, yeah. >> They are putting APIs in front of their main systems. They're not rewriting the core banking or the core platform, but the user experience, the business logic, the AIML capability to bring intelligence in the platform. It's surrounding the capability to make it much more intuitive, much more usable, much more declarative. That's where things are going. And so I'm seeing this mix of integration all over again. Showing my age now. But, you know, the new EAI so is now microservices and messaging and events with the same patterns. But again, being much more accelerated with cloud native services. >> And it is to the point, it's accelerated today. They're not having to freeze the code for six months or nine months and that which would kill the whole recipe for failure. So they're able to now to fast track their modernization. They have to prioritize 'cause they got limited resources. But how are you guys coming up to that? >> But the practice is changing as well, right? Well, the old days, it was 12, 18 months cycle or anything software. If you heard the CVS CIO, Rohan. >> Yeah. >> Three months where they started to engage with us in getting an app in production, right? If you look at the COVID, 10 days to get kind of a new application for getting small loans going with Pfizer, right? These are dramatically short term, but it's not rewriting the entire app. It's just putting these newer experiences, newer capability in front with newer modern developer practices. And they're saying, I need to do it not just once, but for 100, 200, 5,000 members. JPMC has 50,000 developers. Fifty thousand. They're not a bank anymore. >> We just have thousands of apps. >> Exactly. >> Ajay, I want to get your thoughts on something that we've been talking about on our super cloud event. I know we had an event a couple weeks ago, you guys were one of our sponsors, VMware was. It was called super cloud where we're defining that this next gen environment's a super cloud and every company will have a super cloud capability. And underneath that is cross cloud capabilities. So, super cloud is like a super set on top of a multi-cloud. And little word play or play on words is, ecosystem partners versus partners in the ecosystem. Because if you're coming down to the integration side of things, it's about knowing what goes what, it's almost like building an OS if you're a coder or an operating systems person. You got to put the pieces together right, not just go to the directory and say, okay, who's got the cheapest price in DR or air gaping or something or some solution. So ecosystem partners are truly partners. Partners in the ecosystem are a bunch of people out on a list. How do you see that? Because the trend we're seeing is, the development process includes partners at day one. >> That's right. Not bolt-on. >> Completely agree. >> Share your thoughts on that. >> So let's look at that. The first thing I'm hearing from my customers is, they're trying to use all the public clouds as a new IS. That's the first API or contract infrastructures code IS. From then on they're saying, I want more and more portable services. And if you see the success of some of the data vendors and the messaging vendors, you're starting to see best of breed becoming part of the platform. So you are to identify which of these are truly, you know, getting market momentum and are becoming kind of defacto leaders. So, Kafka goes hand in hand with streaming. RabbitMQ from my portfolio goes with messaging. Postgres for database. So these are the, in your definition, ecosystem partners, they're foundational. In the security space, you know, Snyk is a common player in terms of scanning or Aqua and Prisma even though we have Carbon Black. Those become partners from a container security perspective. So, what's happening is the industry stabilizing a handful of critical players that are becoming multi-cloud preference of choice in this. And our job is to bring it all together in a all coordinated, orchestrated manner to give them a platform. >> I mean, you guys always had ecosystem, but I think that priority more than ever. It wasn't really your job at VMware, even, Dave, 10 years ago to say, hey, this is the strategic role that you might play one partner. It was pretty much the partners all kind of fed off the momentum of VMware. Virtualization. And there's not a lot of nuance there. There's pretty much they plug in and you got. >> So what we're doing here is, since we're not the center of the universe, unfortunately, for the application world, things like Backstage is a developer portal from Spotify that became open source. That's becoming the place where everyone wants to provide a plugin. And so we took Backstage, we said, let's provide enterprise support for Backstage. If you take a technology like, you know, what we have with Spring. Every job where developer uses Spring, how do we make it modern with Spring cloud. We work with Microsoft to launch a service with Azure Spring Enterprise for Spring. So you're starting to see us taking communities where they have momentum and bringing the ecosystem around those technologies. Cluster API for Kubernetes, for have you managed stuff. >> Yeah. >> So it's about standard. >> Because the developers are voting with their clicks and their code repos. And so you're identifying the patterns that they like. >> That's right. >> And aligning with them and connecting with them rather than trying to sell against it. >> Exactly. It's the end story with everyone. I say stop competing. So people used to think Tanzu is Kubernetes. It's really Tanzu is the modern application platform that runs on any Kubernetes. So I've changed the narrative. When Heptio was here, we were trying to be a Kubernetes player. I'm like, Kubernetes is just another dial tone. You can use mine, you can use OpenShift. So this week we announced support for OpenShift by Tanzu application platform. The values moving up, it's around outcomes. So industry standards, taking lead and solving the problem. >> You know, we had a panel at super cloud. Dave, I know you got a question. I'll get to you in a second. But the panel was the innovator's dilemma. And then during the event, one of the panelists, Chris Hoff knows VMware very well, Beaker on Twitter, said it should be called the integrators dilemma. Because the innovations here, >> How do you put it all together? >> But the integration of the, putting the piece parts together, building the thing is the innovation. >> And we come back and say, it's a secure software supply chain. It starts with great content. Did you know, I published most of the open source content on every hyperscaler through my Bitnami acquisition. So I start with great content that's curated. Then I allow you to create your own golden images. Then I have a build service that secures and so on and so forth and we bring the part. So, that opinionated solution, but batteries included but you can change it is been one of our key differentiator. We recognize the roles is going to be modular, come back and solve for it. >> So I want to understand sort of relationship Tanzu and Aria, John was talking about, you know, super cloud before we had our event. We had an earlier session where we help people understand that Aria was not, you know, vRealize renamed. >> It's rebranded. >> And reason I bring that up is because we had said it around super cloud, that one of the defining characteristics was, sorry, super PaaS, which is a specific purpose built PaaS layer designed to support your objective for multi-cloud. And speaking to a lot of people this week, there's a federated architecture, there's graph relationships, there's real time ability to ingest and analyze. That's unique. And that's IP that is purpose built for what you're doing. >> Absolutely. When I think what came out of all that learning is after 20 years of Pivotal and BA and what we learned that you still need some abstraction layer. Kubernetes is too low level. So what are the developer problems? What are the delivery problems? What are the operations and management problems? Aria solves all the operations and management problem. Tanzu solves a super PaaS problems. >> Yes. Right. >> Of providing a consistent way to build great software and the secure software supply chain to run on any infrastructure. So the combination of Tanzu and Aria complete the value chain. >> And it's different. Again, we get a lot of heat for this, but we're saying, look, we're trying to describe, it's not just IAS, PaaS, and SaaS of last decade. There's something new that's happening. And we chose the name super cloud. >> And what's the difference? It's modular. It's pluggable. It fits into the way you operate. >> Whereas PaaS was very prescriptive. If you couldn't fit, you couldn't jump down to the next level. This is very much, you can stay at the abstraction level or go lower level. >> Oh, we got to add that to the attribute. >> We're recruiting him right now. (laughs) >> We'll give you credit. >> I mean, funny all the web service's background. Look at an app server. You well knew all about app servers. Basically the company is an app. So, if you believe that, say, Capital One is an application as a company and Amazon's providing all the CapEx, >> That's it. >> Okay. And they run all their quote, old IT spend millions, billions of dollars on operating expenses that's going to translate to the top line called the income statement. So, Dave always says, oh, it's on the balance sheet, but now they're going to go to the top line. So we're seeing dynamic. Ajay, I want to get your reaction to this where the business model shift if everything's tech enabled, the company is like an app server. >> Correct. >> So therefore, the revenue that's generated from the technology, making the app work has to get recognized in the income. Okay. But Amazon's doing all, or the cloud hyperscale is doing all the heavy lifting on the CapEx. So technically it's the cloud on top of a cloud. >> Yes and no. The way I look at it, >> I call that a super cloud. >> So I like the idea of super cloud, but I think we're mixing two different constructs. One is, the cloud is a new hardware, right? In terms of dynamic, elastic, always available, et cetera. And I believe when more and more customer I talk about, there's a service catalog of infrastructure services. That's emerging. This super cloud is the next set of PaaS super PaaS services. And the management service is to use the cloud. We spend so much time as VMware building clouds, the problem seems, how do you effectively use the cloud? What problems do we solve around digital where every company is a digital company and the product is this application, as you said. So everything starts with an application. And you look at from the lens of how you run the application, what it costs the application, what impact it's driving. And I think that's the change. So I agree with you in some way. That is a digital strategy. >> And that's the company. >> That's the company. The application is the company. >> That's the t-shirt. >> And API is the currency. >> So, Ajay, first of all, we love having you in theCube 'cause you're like a masterclass in multiple dimensions. So, I want to get your thoughts on the abstraction layer. 'Cause we were also talking earlier in theCube here as well as before. But abstraction layers happen when you have major movements in markets that are game changing or major inflection points because you've reached a complexity point where it's working so great, this new thing, that's too complex to reign it in. And we were quoting Andy Grove by saying, "let chaos reign then reign in the chaos". So, all major industry moments go back 30, 40 years happen with abstractions. So the question is is that, you can't be a vendor, we've observed you can't be a vendor and be the abstraction. Like, if Cisco's running routers, they can't be the abstraction layer. They have to be the benefit of the abstraction layer. And if you're on the other side of the abstraction layer, you can't be running that either. >> I like the way you're thinking about it. Yeah. Do you agree? >> I completely agree. And, you know, I'm an old middleware guy. And when I used to say this to my CEO, he's like, no, it's not middleware, it's just a new middleware. And what's middleware, right? It's a thing between app and infrastructure. You could define it whatever we want, right? And so this is the new distributed middleware. >> It's a metaphor and it's a good one because it does a purpose. >> It's a purpose. >> It creates a separation but then you have, it's like a DMZ zone or whatever you want to call it. It's an area that things happen. >> But the difference before last time was, you could always deploy it to a thing. The thing is now the cloud. The thing is a set of services. So now it's as much of a networking problem at the application layer is as much as security problem. It's how you build software, how we design. So APIs, become part of your development. You can't think of APIs after the fact, right? When you build an API, you got to publish API because the minute you publish it and if you change it, the API's out of. So you can't have it as a documentation process. So, the way you build software, you use software consume is all about it. So to me, digital product with an API as a currency is where we're headed towards. >> Yeah, that's a great observation. Want to make a mental note of that and make that a clip. I want to get your thoughts on software development. You mentioned that, obviously software development life cycles are changing. I'll say open sources now. I mean, it's unlimited codes, supply chain issue. What's in the code, I get that verified codes going to happen. Is software development coding as much or is coding changing the notion of writing code? Or is it more glue layer you're writing. >> I think you're onto something. I call software developments composition now. My son's at Facebook or Google. They have so many libraries. So you don't no longer start with the very similar primitive, you start with building blocks, components, services, libraries, open source technology. What are you really doing? You're composing these things from multiple artifacts. And how do you make sure those artifacts are good artifacts? So someone's not sticking in security in a vulnerability into it. So, the world is moving towards composition and there are few experts who build the core components. Most of the time we're just using those to build solutions. And so, the art here is, how do you provide that set of best practices? We call them patterns or building blocks or services that you can compose to build these next generation (indistinct) >> It's interesting. >> Cooking meals. >> I agree with you a hundred percent what you're thinking. I agree about that worldview. Here's a dilemma that I'm seeing. In the security world, you've got zero trust. You know, Which is, I don't know you, I don't trust you at all. And if you're going to go down this composed, we're going to have an orchestra of players with instruments, say to speak, Dave, metaphor. That's trust involved. >> Yes. >> So you have two spectrums of issues. >> Yes. >> If software's going trust and you're seeing Docker containers getting more verifications, software supply chain, and then you got hardware I call network guys, love zero trust. Where's the balance? How do you reconcile that? Is it just decoupled? Nuance? I mean, what's the point? >> No, no. I think it all comes together. And what I mean by that is, it starts with left shifting it all the way to hands of the developers, right? So, are you starting with good content? You have providence of the stuff you're using. Are you building it correctly? So you're not introducing bad things like solar winds along the process. Are you testing it along the way of the development process? And then once in production, do you know, half the time it's configurations of where you're running the stuff versus the software itself. So you can think of the two coming together. And the network security is protecting people from going laterally once they've got in there. So, a whole security solution requires all of the above, a secure software supply chain, the way to kind of monitor and look at configuration, we call posture management or workload management and the network security of SaaS-e for zero trust. That's a hard thing. And the boundary is the application. >> All right. >> So is it earned trust model sort of over time? >> No, it's designed in, it's been a thing. >> Okay. So it's not a, >> Because it developed. >> You can bolt in afterwards. >> Because the developers are driving it. They got to know what they're doing. >> And it's changing every week. If I'm putting a new code out every week. You can't, it can be changed to something else. >> Well, you guys got guardrails. The guardrails constant is a good example. >> It stops on the configuration side, but I also need the software. So, Tanzu is all about, the secure chain is about the development side of the house. Guardrails are on the operational side of the house. >> To make sure the developers don't stop. >> That's right. >> Things will always get out there. And I find out there's a CV that I use a library, I found after the fact. >> Okay. So again, while I got here again, this is great. I want to get test this thesis. So, we've been saying on theCube, talking about the new ops, the new kind of ops that emerging. DevOps, which we believe is cloud native. So DevOps moving infrastructure's code, that's happened, it's all good. Open source is growing. DevOps is done deal. It's done deal. Developers are doing that. That ops was IT. Then don't need the server, clouds my hardware. Check. That balances. The new ops is data and security which has to match up to the velocity of the developers. Do you believe that? >> Completely. That's why we call it DevSecOps. And the Sec is where all the action is. >> And data. And data too. >> And data is about making the data available where the app meets. So the problem was, you know, we had to move the logic to where the data is or you're going to move the data where the logic is. So data fabrics are going to become more and more interesting. I'll give you a simple example. I publish content today in a service catalog. My customer's saying, but my content catalog needs to be in 300 locations. How do I get the content to each of the repos that are running in 300 location? So I have a content distribution problem. So you call it a data problem. Yes, it's about getting the right data. Whether it's simple as even content, images available for use for deployment. >> So you think when I think about the application development stack and the analytics stack, the data stack, if I can call it that, they're separate, right? Are those worlds, I mean, people say, I want to inject data and AI intelligence into apps. Those worlds have deployment? I think about the insight from the historical being projected in the operational versus they all coming together. I have a Greenplum platform, it's a great analytics platform. I have a transactional platform. Do my customers buy the same? No, they're different buyers, they're different users. But the insight from that is being now plugged in so that at real time I can ask the question. So even this information is being made available on demand. So that's where I see it. And that's most coming together, but the insight is being incorporated in the operational use. So I can say, do I give the risk score? Do I give you credit? It's based on a whole bunch of historical analytics done. And at the real time, processing is happening, but the intelligence is behind it. >> It's a mind shift for sure because the old model was, I have a database, we're good. Now you have time series database, you got graphs. Each one has a role in the overall construct of the new thing. >> But it's about at the end. How do I make use of it? Someone built a smart AI model. I don't know how it was built, but I want to apply it for that particular purpose. >> Okay. So the final question for you, at least from my standpoint is, here at VMware Explore, you have a lot of the customers and so new people coming in that we've heard about, what's their core order of operations right now? Get on the bandwagon for modern apps. How do you see their world unfolding as they go back to the ranch, their places, and go back to their boss? Okay. We got the modern application. We're on the right track boss, full steam ahead. Or what change do they make? >> I think the biggest thing I saw was with some of the branding changes well and some of the new offerings. The same leader had two teams, the VMware team and the public cloud team. And they're saying, hey, maybe VMware's going to be the answer for both. And that's the world model. That's the biggest change I'm seeing. They were only thinking of us on the left column. Now they see us as a unifying player to play across cloud native and VMware, the uniquely set up to bring it all together. That's been really exciting this week. >> All right, Ajay, great to have you on. Great perspective. Worthy of great stuff. Congratulations on the success of all that investment coming to bear. >> Thank you. >> And on the new management platform. >> Yeah. Thank you. And thanks always for giving us all the support we need. It's always great. >> All right Cube coverage here. Getting all the data, getting inside the heads, getting all the specifics and all the new trends and actually connecting the dots here on theCube. I'm John Furrier with Dave Vellante. Stay tuned for more coverage from day two. Two sets, three days, Cube at VMware Explore. We'll be right back. (gentle music)

>>Okay, welcome back everyone to super cloud 22 here in our live studio performance. You're on stage in Palo Alto. I'm Sean fur. You're host with the queue with Dave ante. My co it's got a great industry ecosystem panel to discuss whether it's realer hype, David MC Janet CEO of Hashi Corp, hugely successful company as will LA forest field CTO, Colu and Victoria over yourgo from VMware guys. Thanks for coming on the queue. Appreciate it. Thanks for having us. So realer, hype, super cloud David. >>Well, I think it depends on the definition. >>Okay. How do you define super cloud start there? So I think we have a, >>I think we have a, like an inherently pragmatic view of super cloud of the idea of super cloud as you talk about it, which is, you know, for those of us that have been in the infrastructure world for a long time, we know there are really only six or seven categories of infrastructure. There's sort of the infrastructure security, networking databases, middleware, and, and, and, and really the message queuing aspects. And I think our view is that if the steady state of the world is multi-cloud, what you've seen is sort of some modicum of standardization across those different elements, you know, take, you know, take confluent. You know, I, I worked in the middleware world years ago, MQ series, and typical multicast was how you did message queuing. Well, you don't do that anymore. All the different cloud providers have their own message, queuing tech, there's, Google pub sub, and the equivalents across the different, different clouds. Kafka has provided a consistent way to do that. And they're not trying to project that. You can run everything connected. They're saying, Hey, you should standardize on Kafka for message cuing is that way you can have operational consistency. So I think to me, that's more how we think about it is sort of, there is sort of layer by layer of sort of de facto standardization for the lingo Franco. >>So a streaming super cloud is how you would think of it, or no, I just, or a component of >>Cloud that could be a super cloud. >>I just, I just think that there are like, if I'm gonna build an application message, queuing is gonna be a necessary element of it. I'm gonna use Kafka, not, you know, a native pub sub engine on one of the clouds, because operationally that's just the only way I can do it. So I think that's more, our view's much more pragmatic rather than trying to create like a single platform that you can run everywhere and deal with the networking realities of like network, you know, hops missing across those different worlds and have that be our responsibility. It's much more around, Hey, let's standardize each layer, operational >>Standardized layer that you can use to build a super cloud if that's in your, your intent or, yeah. Okay. >>And it reminds me of the web services days. You kind of go throwback there. I mean, we're kind of living the next gen of web services, the dream of that next level, because DevOps dev SecOps now is now gone mainstream. That's the big challenge we're hearing devs are doing great. Yep. But the ops teams and screen, they gotta go faster. This seems to be a core, I won't say blocker, but more of a drag to the innovation. >>Well, I I'll just get off, I'll hand it off to, to you guys. But I think the idea that like, you know, if I'm gonna have an app that's running on Amazon that needs to connect to a database that's running on, on the private data center, that's essentially the SOA notion, you know, w large that we're all trying to solve 20 years ago, but is much more complicated because you're brokering different identity models, different networking models. They're just much more complex. So that's where the ops bit is the constraint, you know, for me to build that app, not that complicated for the ops person to let it see traffic is another thing altogether. I think that's, that's the break point for so much of what looks easier to a developer is the operational reality of how you do that. And the good news is those are actually really well solved problems. They're just not broadly understood. >>Well, what's your take, you talk to customers all the time, field CTO, confluent, really doing well, streaming data. I mean, everyone's doing it now. They have to, yeah. These are new things that pop up that need solutions. You guys step up and doing more. What's your take on super cloud? >>Well, I mean, the way we address it honestly is we don't, it's gonna be honest. We don't think about super cloud much less is the fact that SAS is really being pushed down. Like if we rely on seven years ago and you took a look at SAS, like it was obvious if you were gonna build a product for an end consumer or business user, you'd do SAS. You'd be crazy not to. Right. But seven years ago, if you look at your average software company producing something for a developer that people building those apps, chances are you had an open source model. Yeah. Or, you know, self-managed, I think with the success of a lot of the companies that are here today, you know, snowflake data, bricks, Colu, it's, it's obvious that SaaS is the way to deliver software to the developers as well. And as such, because our product is provided that way to the developers across the clouds. That's, that's how they have a unifying data layer, right. They don't necessarily, you know, developers like many people don't necessarily wanna deal with the infrastructure. They just wanna consume cloud data services. Right. So that's how we help our customers span cloud. >>So we evenly that SAS was gonna be either built on a single cloud or in the case of service. Now they built their own cloud. Right. So increasingly we're seeing opportunities to build a Salesforce as well across clouds tap different, different, different services. So, so how does that evolve? Do you, some clouds have, you know, better capabilities in other clouds. So how does that all get sort of adjudicated, do you, do you devolve to the lowest common denominator? Or can you take the best of all of each? >>The whole point to that I think is that when you move from the business user and the personal consumer to the developer, you, you can no longer be on a cloud, right. There has to be locality to where applications are being developed. So we can't just deploy on a single cloud and have people send their data to that cloud. We have to be where the developer is. And our job is to make the most of each, an individual cloud to provide the same experience to them. Right. So yes, we're using the capabilities of each cloud, but we're hiding that to the developer. They don't shouldn't need to know or care. Right. >>Okay. And you're hiding that with the abstraction layer. We talked about this before Victoria, and that, that layer has what, some intelligence that has metadata knowledge that can adjudicate what, what, the best, where the best, you know, service is, or function of latency or data sovereignty. How do you see that? >>Well, I think as the, you need to instrument these applications so that you, you, you can get that data and then make the intelligent decision of where, where, where this, the deploy application. I think what Dave said is, is right. You know, the level of super cloud that they talking about is the standardization across messaging. And, and are you what's happening within the application, right? So you don't, you are not too dependent on the underlying, but then the application say that it takes the form of a, of a microservice, right. And you deploy that. There has to be a way for operator to say, okay, I see all these microservices running across clouds, and I can factor out how they're performing, how I, I, life lifecycle managed and all that. And so I think there is, there is, to me, there's the next level of the super cloud is how you factor this out. So an operator can actually keep up with the developers and make sense of all that and manage it. Like >>You guys that's time. Like its also like that's what Datadog does. So Datadog basically in allows you to instrument all those services, on-prem private data center, you know, all the different clouds to have a consistent view. I think that that's not a good example of a vendor that's created a, a sort of a level of standardization across a layer. And I think that's, that's more how we think about it. I think the notion of like a developer building an application, they can deploy and not have to worry where it exists. Yeah. Is more of a PAs kind of construct, you know, things like cloud Foundry have done a great job of, of doing that. But underneath that there's still infrastructure. There's still security. There's still networking underneath it. And I think that's where, you know, things like confluent and perhaps at the infrastructure layer have standardized, but >>You have off the shelf PAs, if I can call it that. Yeah. Kind of plain. And then, and then you have PAs and I think about, you mentioned snowflake, snowflake is with snow park, seems to be developing a PAs layer that's purpose built for their specific purpose of sharing data and governing data across multiple clouds call super paths. Is, is that a prerequisite of a super cloud you're building blocks. I'm hearing yeah. For super cloud. Is that a prerequisite for super cloud? That's different than PAs of 10 years ago. No, but I, >>But I think this is, there's just different layers. So it's like, I don't know how that the, the snowflake offering is built built, but I would guess it's probably built on Terraform and vault and cons underneath it. Cuz those are the ingredients with respect to how you would build a composite application that runs across multiple. And >>That's how Oracle that town that's how Oracle with the Microsoft announcement. They just, they just made if you saw that that was built on Terraform. Right. But, but they would claim that they, they did some special things within their past that were purpose built for, for sure. Low latency, for example, they're not gonna build that on, you know, open shift as an, as an example, they're gonna, you know, do their own little, you know, >>For sure, for sure. So I think what you're, you're pointing at and what Victoria was talking about is, Hey, can a vendor provided consistent experience across the application layer across these multiple clouds? And I would say, sure, just like, you know, you might build a mobile banking application that has a front end on Amazon in the back end running on vSphere on your private data center. Sure. But the ingredients you use to do that have to be, they can't be the cloud native aspects for how you do that. How do you think about, you know, the connectivity of, of like networking between that thing to this thing? Is it different on Amazon? Is it different on Azure? Is it different on, on Google? And so the, the, the, the companies that we all serve, that's what they're building, they're building composited applications. Snowflake is just an example of a company that we serve this building >>Composite. And, but, but, but don't those don't, you have to hide the complexity of that, those, those cloud native primitives that's your job, right. Is to actually it creates simplicity across clouds. Is it not? >>Why? Go ahead. You. >>Yeah, absolutely. I mean that in fact is what we're doing for developers that need to do event streaming, right. That need to process this data in real time. Now we're, we're doing the sort of things that Victoria was just talking about, like underneath the covers, of course, you know, we're using Kubernetes and we're managing the differences between the clouds, but we're hiding the, that, and we've become sort of a defacto standard across the cloud. So if I'm developing an app in any of those cloud, and I think we all know, and you were mentioning earlier every significant company's multi-cloud now all the large enterprises, I just got back from Brazil and like every single one of 'em have multiple clouds and on-prem right. So they need something that can span those. >>What's the challenge there. If you talk to those customers, because we're seeing the same thing, they have multiple clouds. Yeah. But it was kind of by default or they had some use case, either.net developers there with Azure, they'll do whatever cloud. And it kind of seems specialty relative to the cloud native that they're on what problems do they have because the complexity to run infrastructure risk code across clouds is hard. Right? So the trade up between native cloud and have better integration to complexity of multiple clouds seems to be a topic around super cloud. What are you seeing for, for issues that they might have or concerns? >>Yeah. I mean, honestly it is, it is hard to actually, so here's the thing that I think is kind of interesting though, by the way, is that I, I think we tend to, you know, if you're, if you're from a technical background, you tend to think of multicloud as a problem for the it organization. Like how do we solve this? How do we save money? But actually it's a business problem now, too, because every single one of these companies that have multiple clouds, they want to integrate their data, their products across these, and it it's inhibiting their innovation. It's hard to do, but that's where something like, you know, Hatchie Corp comes in right. Is to help solve that. So you can instrument it. It has to happen at each of these layers. And I suppose if it does happen at every single layer, then voila, we organically have something that amounts to Supercloud. Right. >>I love how you guys are representing each other's firms. And, but, but, and they also correct me if I'm a very similar, your customers want to, it is very similar, but your customers want to monetize, right. They want bring their tools, their software, their particular IP and their data and create, you know, every, every company's a software company, as you know, Andreesen says every company's becoming a cloud company to, to monetize in, in the future. Is that, is that a reasonable premise of super cloud? >>Yeah. I think, think everyone's trying to build composite applications to, to generate revenue. Like that's, that's why they're building applications. So yeah. One, 100%. I'm just gonna make it point cuz we see it as well. Like it's actually quite different by geography weirdly. So if you go to like different geographies, you see actually different cloud providers, more represented than others. So like in north America, Amazon's pretty dominant Japan. Amazon's pretty dominant. You go to Southeast Asia actually. It's not necessarily that way. Like it might be Google for, for whatever reason more hourly Bob. So this notion of multi's just the reality of one's everybody's dealing with. But yeah, I think everyone, everyone goes through the same process. What we've observed, they kind of go, there's like there's cloud V one and there's cloud V two. Yeah. Cloud V one is sort of the very tactical let's go build something on cloud cloud V two is like, whoa, whoa, whoa, whoa. And I have some stuff on Amazon, some stuff on Azure, some stuff on, on vSphere and I need some operational consistency. How do I think about zero trust across that way in a consistent way. And that's where this conversation comes into being. It's sort of, it's not like the first version of cloud it's actually when people step back and say, Hey, Hey, I wanna build composite applications to monetize. How am I gonna do that in an industrialized way? And that's the problem that you were for. It's >>Not, it's not as, it's not a no brainer like it was with cloud, go to the cloud, write an app. You're good here. It's architectural systems thinking, you gotta think about regions. What's the latency, you know, >>It's step back and go. Like, how are we gonna do this, this exactly. Like it's wanted to do one app, but how we do this at scale >>Zero trust is a great example. I mean, Amazon kind of had, was forced to get into the zero trust, you know, discussion that, that wasn't, you know, even a term that they used and now sort of, they're starting to talk about it, but within their domain. And so how do you do zero trust trust across cost to your point? >>I, I wonder if we're limiting our conversation too much to the, the very technical set of developers, cuz I'm thinking back at again, my example of C plus plus libraries C plus plus libraries makes it easier. And then visual BA visual basic. Right. And right now we don't have enough developers to build the software that we want to build. And so I want, and we are like now debating, oh, can we, do we hide that AI API from Google versus that SQL server API from, from Microsoft. I wonder at some point who cares? Right. You know, we, I think if we want to get really economy scale, we need to get to a level of abstraction for developers that really allows them to say, I don't need, for most of most of the procedural application that I need to build as a developer, as a, as a procedural developer, I don't care about this. Some, some propeller had, has done that for me. I just like plug it in my ID and, and I use it. And so I don't, I don't know how far we are from that, but if we don't get to that level, it fits me that we never gonna get really the, the economy or the cost of building application to the level. >>I was gonna ask you in the previous segment about low code, no code expanding the number of developers out there and you talking about propel heads. That's, that's what you guys all do. Yeah. You're the technical geniuses, right. To solve that problem so that, so you can have low code development is that I >>Don't think we have the right here. Cause I, we, we are still, you know, trying to solve that problem at that level. But, but >>That problem has to be solved first, right before we can address what you're talking about. >>Yeah. I, I worked very closely with one of my biggest mentors was Adam Bosworth that built, you know, all the APIs for visual basics and, and the SQL API to visual basic and all that stuff. And he always was on that front. In fact that his last job was at my, at AWS building that no code environment. So I'm a little detached from that. It just hit me as we were discussing this. It's like, maybe we're just like >>Creating, but I would, I would argue that you kind of gotta separate the two layers. So you think about the application platform layer that a developer interfaces to, you know, Victoria and I worked together years ago and one of the products we created was cloud Foundry, right? So this is the idea of like just, you know, CF push, just push this app artifact and I don't care. That's how you get the developer community written large to adopt something complicated by hiding all the complexity. And I think that that is one model. Yeah. Turns out Kubernetes is actually become a peer to that and perhaps become more popular. And that's what folks like Tanza are trying to do. But there's another layer underneath that, which is the infrastructure that supports it. Right? Yeah. Cause that's only needs to run on something. And I think that's, that's the separation we have to do. Yes. We're talking a little bit about the plumbing, but you know, we just easily be talking about the app layer. You need, both of them. Our point of view is you need to standardize at this layer just like you need standardize at this layer. >>Well, this is, this is infrastructure. This is DevOps V two >>Dev >>Ops. Yeah. And this is where I think the ops piece with open source, I would argue that open source is blooming more than ever. So I think there's plenty of developers coming. The automation question becomes interesting because I think what we're seeing is shift left is proving that there's app developers out there that wanna stay in their pipelining. They don't want to get in under the hood. They just want infrastructure as code, but then you got supply chain software issues there. We talked about the Docker on big time. So developers at the top, I think are gonna be fine. The question is what's the blocker. What's holding them back. And I don't see the devs piece Victoria as much. What do you guys think? Is it, is the, is the blocker ops or is it the developer experience? That's the blocker. >>It's both. There are enough people truthfully. >>That's true. Yeah. I mean, I think I sort of view the developer as sort of the engine of the digital innovation. So, you know, if you talk about creative destruction, that's, that was the economic equivalent of softwares, eating the world. The developers are the ones that are doing that innovation. It's absolutely essential that you make it super easy for them to consume. Right. So I think, you know, they're nerds, they want to deal with infrastructure to some degree, but I think they understand the value of getting a bag of Legos that they can construct something new around. And I think that's the key because honestly, I mean, no code may help for some things. Maybe I'm just old >>School, >>But I, I went through this before with like Delphy and there were some other ones and, and I hated it. Like I just wanted a code. Yeah. Right. So I think making them more efficient is, is absolutely good. >>But I think what, where you're going with that question is that the, the developers, they tend to stay ahead. They, they just, they're just gear, you know, wired that way. Right. So I think right now where there is a big bottleneck in developers, I think the operation team needs to catch up. Cuz I, I talk to these, these, these people like our customers all the time and I see them still stuck in the old world. Right. Gimme a bunch of VMs and I'll, I know how to manage well that world, you know, although as lag is gonna be there forever, so managing mainframe. But so if they, the world is all about microservices and containers and if the operation team doesn't get on top of it and the security team that then that they're gonna be a bottleneck. >>Okay. I want to ask you guys if the, if the companies can get through that knothole of having their ops teams and the dev teams work well together, what's the benefits of a Supercloud. How do you see the, the outcome if you kind of architect it, right? You think the big picture you zoom as saying what's the end game look like for Supercloud? Is that >>What I would >>Say? Or what's the Nirvana >>To me Nirvana is that you don't care. You just don't don't care. You know, you just think when you running building application, let's go back to the on-prem days. You don't care if it runs on HP or Dell or, you know, I'm gonna make some enemies here with my old, old family, but you know, you don't really care, right. What you want is the application is up and running and people can use it. Right. And so I think that Nirvana is that, you know, there is some, some computing power out there, some pass layer that allows me to deploy, build application. And I just like build code and I deploy it and I get value at a reasonable cost. I think one of the things that the super cloud for as far as we're concerned is cost. How do you manage monitor the cost across all this cloud? >>Make sure that you don't, the economics don't get outta whack. Right? How many companies we know that have gone to the cloud only to realize that holy crap, now I, I got the bill and, and you know, I, as a vendor, when I was in my previous company, you know, we had a whole team figuring out how to lower our cost on the one hyperscaler that we were using. So these are, you know, the, once you have in the super cloud, you don't care just you, you, you go with the path of least the best economics is. >>So what about the open versus closed debate will you were mentioning that we had snowflake here and data bricks is both ends of the spectrum. Yeah. You guys are building open standards across clouds. Clearly even the CLO, the walled gardens are using O open standards, but historically de facto standards have emerged and solved these problems. So the super cloud as a defacto standard, versus what data bricks is trying to do super cloud kind of as an, as an open platform, what are you, what are your thoughts on that? Can you actually have an, an open set of standards that can be a super cloud for a specific purpose, or will it just be built on open source technologies? >>Well, I mean, I, I think open source continues to be an important part of innovation, but I will say from a business model perspective, like the days, like when we started off, we were an open source company. I think that's really done in my opinion, because if you wanna be successful nowadays, you need to provide a cloud native SAS oriented product. It doesn't matter. What's running underneath the covers could be commercial closed source, open source. They just wanna service and they want to use it quite frankly. Now it's nice to have open source cuz the developers can download it and run on their laptop. But I, I can imagine in 10 years time actually, and you see most companies that are in the cloud providing SAS, you know, free $500 credit, they may not even be doing that. They'll just, you know, go whatever cloud provider that their company is telling them to use. They'll spin up their SAS product, they'll start playing with it. And that's how adoption will grow. Right? >>Yeah. I, I think, I mean my personal view is that it's, that it's infrastructure is pervasive enough. It exists at the bottom of everything that the standards emerge out of open source in my view. And you think about how something like Terraform is built, just, just pick one of the layers there's Terraform core. And then there's a plugin for everything you integrate with all of those are open source. There are over 2000 of these. We don't build them. Right. That's and it's the same way that drove Linux standardization years ago, like someone had to build the drivers for every piece of hardware in the world. The market does not do that twice. The market does that once. And so I, I I'm deeply convicted that opensource is the only way that this works at the infrastructure layer, because everybody relies on it at the application layer, you may have different kinds of databases. You may have different kind of runtime environments. And that's just the nature of it. You can't to have two different ways of doing network, >>Right? Because the stakes are so high, basically. >>Yeah. Cuz there's, there's an infinite number of the surface areas are so large. So I actually worked in product development years ago for middleware. And the biggest challenge was how do you keep the adapter ecosystem up to date to integrate with everything in the world? And the only way to do it in our view is through open source. And I think that's a fundamental philosophical view that it we're just, you know, grounded in. I think when people are making infrastructure decisions that span 20 years at the customer base, this is what they think about. They go which standard it will emerge based on the model of the vendor. And I don't think my personal view is, is it's not possible to do in a, in >>A, do you think that's a defacto standard kind of psychological perspective or is there actual material work being done or both in >>There it's, it's, it's a network effect thing. Right? So, so, you know, before Google releases a new service service on Google cloud, as part of the release checklist is does it support Terraform? They do that work, not us. Why? Because every one of their customers uses Terraform to interface with them and that's how it works. So see, so the philosophical view of, of the customers, okay, what am I making a standardize on for this layer for the next 30 years? It's kind of a no brainer. Philosophically. >>I tend, >>I think the standards are organically created based upon adoption. I mean, for instance, Terraform, we have a provider we're again, we're at the data layer that we created for you. So like, I don't think there's a board out there. I mean there are that creating standards. I think those days are kind of done to be honest, >>The, the Terraform provider for vSphere has been downloaded five and a half million times this year. Yeah. Right. Like, so, I >>Mean, these are unifying moments. This are like the de facto standards are really important process in these structural changes. I think that's something that we're looking at here at Supercloud is what's next? What has to unify look what Kubernetes has done? I mean, that's essentially the easy thing to orchestra, but people get behind it. So I see this is a big part of this next, the two. Totally. What do you guys see that's needed? What's the rallying unification point? Is it the past layer? Is it more infrastructure? I guess that's the question we're trying to, >>I think every layer will need that open source or a major traction from one of the proprietary vendor. But I, I agree with David, it's gonna be open source for the most part, but you know, going back to the original question of the whole panel, if I may, if this is reality of hype, look at the roster of companies that are presenting or participating today, these are all companies that have some sort of multi-cloud cross cloud, super cloud play. They're either public have real revenue or about to go public. So the answer to the question. Yeah, it's real. Yeah. >>And so, and there's more too, we had couldn't fit him in, but we, >>We chose super cloud on purpose cuz it kind of fun, John and I kind came up with it and, and but, but do you think it's, it hurts the industry to have this, try to put forth this new term or is it helpful to actually try to push the industry to define this new term? Or should it just be multi-cloud 2.0, >>I mean, conceptually it's different than multi-cloud right. I mean, in my opinion, right? So in that, in that respect, it has value, right? Because it's talking about something greater than just multi-cloud everyone's got multi-cloud well, >>To me multi-cloud is the, the problem I should say the opportunity. Yeah. Super cloud or we call it cross cloud is the solution to that channel. Let's >>Not call again. And we're debating that we're debating that in our cloud already panel where we're talking about is multi-cloud a problem yet that needs to get solved or is it not yet ready for a market to your point? Is it, are we, are we in the front end of coming into the true problem set, >>Give you definitely answer to that. The answer is yes. If you look at the customers that are there, they won, they have gone through the euphoria phase. They're all like, holy something, what, what are we gonna do about this? Right. >>And, but they don't know what to do. >>Yeah. And the more advanced ones as the vendor look at the end of the day, markets are created by vendors that build ed that customers wanna buy. Yeah. Because they get value >>And it's nuance. David, we were sort talking about before, but Goldman Sachs has announced they're analysis software vendor, right? Capital one is a software vendor. I've been really interested Liberty what Cerner does with what Oracle does with Cerner and in terms of them becoming super cloud vendors and monetizing that to me is that is their digital transformation. Do you guys, do you guys see that in the customer base? Am I way too far out of my, of my skis there or >>I think it's two different things. I think, I think basically it's the idea of building applications. If they monetize yeah. There and Cerner's gonna build those. And you know, I think about like, you know, IOT companies that sell that sell or, or you think people that sell like, you know, thermostats, they sell an application that monetizes those thermostats. Some of that runs on Amazon. Some of that runs a private data center. So they're basically in composite applications and monetize monetizing them for the particular vertical. I think that's what we ation every day. That's what, >>Yeah. You can, you can argue. That's not, not anything new, but what's new is they're doing that on the cloud and taking across multiple clouds. Multiple. Exactly. That's what makes >>Edge. And I think what we all participate in is, Hey, in order to do that, you need to drive standardization of how you do provisioning, how you do networking, how you do security to underpin those applications. I think that's what we're all >>Talking about, guys. It's great stuff. And I really appreciate you taking the time outta your day to help us continue the conversation to put out in the open. We wanna keep it out in the open. So in the last minute we have left, let's go down the line from a hash core perspective, confluent and VMware. What's your position on super cloud? What's the outcome that you would like to see from your standpoint, going out five years, what's it look like they will start with you? >>I just think people like sort under understanding that there is a layer by layer of view of how to interact across cloud, to provide operational consistency and decomposing it that way. Thinking about that way is the best way to enable people to build and run apps. >>We wanna help our customers work with their data in real time, regardless of where they're on primer in the cloud and super cloud can enable them to build applications that do that more effectively. That's that's great for us >>For tour you. >>I, my Niana for us is customers don't care, just that's computing out there. And it's a, it's a, it's a tool that allows me to grow my business and we make it all, all the differences and all the, the challenges, you know, >>Disappear, dial up, compute utility infrastructure, ISN >>Code. I open up the thought there's this water coming out? Yeah, I don't care. I got how I got here. I don't wanna care. Well, >>Thank you guys so much and congratulations on all your success in the marketplace, both of you guys and VMware and your new journey, and it's gonna be great to watch. Thanks for participating. Really appreciate it. Thank you, sir. Okay. This is super cloud 22, our events, a pilot. We're gonna get it out there in the open. We're gonna get the data we're gonna share with everyone out in the open on Silicon angle.com in the cube.net. We'll be back with more live coverage here in Palo Alto. After this short break.