(bright music) >> Hi, everybody, this is Dave Vellante, welcome back to Supercloud 2. Last August, at the first Supercloud event, we invited the broader community to help further define Supercloud, we assessed its viability, and identified the critical elements and deployment models of the concept. The objectives here at Supercloud too are, first of all, to continue to tighten and test the concept, the second is, we want to get real world input from practitioners on the problems that they're facing and the viability of Supercloud in terms of applying it to their business. So on the program, we got companies like Walmart, Sachs, Western Union, Ionis Pharmaceuticals, NASDAQ, and others. And the third thing that we want to do is we want to drill into the intersection of cloud and data to project what the future looks like in the context of Supercloud. So in this segment, we want to explore the concept of data architectures and what's going to be required for Supercloud. And I'm pleased to welcome one of our Supercloud sponsors, ChaosSearch, Ed Walsh is the CEO of the company, with Thomas Hazel, who's the Founder, CTO, and Chief Scientist. Guys, good to see you again, thanks for coming into our Marlborough studio. >> Always great. >> Great to be here. >> Okay, so there's a little debate, I'm going to put you right in the spot. (Ed chuckling) A little debate going on in the community started by Bob Muglia, a former CEO of Snowflake, and he was at Microsoft for a long time, and he looked at the Supercloud definition, said, "I think you need to tighten it up a little bit." So, here's what he came up with. He said, "A Supercloud is a platform that provides a programmatically consistent set of services hosted on heterogeneous cloud providers." So he's calling it a platform, not an architecture, which was kind of interesting. And so presumably the platform owner is going to be responsible for the architecture, but Dr. Nelu Mihai, who's a computer scientist behind the Cloud of Clouds Project, he chimed in and responded with the following. He said, "Cloud is a programming paradigm supporting the entire lifecycle of applications with data and logic natively distributed. Supercloud is an open architecture that integrates heterogeneous clouds in an agnostic manner." So, Ed, words matter. Is this an architecture or is it a platform? >> Put us on the spot. So, I'm sure you have concepts, I would say it's an architectural or design principle. Listen, I look at Supercloud as a mega trend, just like cloud, just like data analytics. And some companies are using the principle, design principles, to literally get dramatically ahead of everyone else. I mean, things you couldn't possibly do if you didn't use cloud principles, right? So I think it's a Supercloud effect, you're able to do things you're not able to. So I think it's more a design principle, but if you do it right, you get dramatic effect as far as customer value. >> So the conversation that we were having with Muglia, and Tristan Handy of dbt Labs, was, I'll set it up as the following, and, Thomas, would love to get your thoughts, if you have a CRM, think about applications today, it's all about forms and codifying business processes, you type a bunch of stuff into Salesforce, and all the salespeople do it, and this machine generates a forecast. What if you have this new type of data app that pulls data from the transaction system, the e-commerce, the supply chain, the partner ecosystem, et cetera, and then, without humans, actually comes up with a plan. That's their vision. And Muglia was saying, in order to do that, you need to rethink data architectures and database architectures specifically, you need to get down to the level of how the data is stored on the disc. What are your thoughts on that? Well, first of all, I'm going to cop out, I think it's actually both. I do think it's a design principle, I think it's not open technology, but open APIs, open access, and you can build a platform on that design principle architecture. Now, I'm a database person, I love solving the database problems. >> I'm waited for you to launch into this. >> Yeah, so I mean, you know, Snowflake is a database, right? It's a distributed database. And we wanted to crack those codes, because, multi-region, multi-cloud, customers wanted access to their data, and their data is in a variety of forms, all these services that you're talked about. And so what I saw as a core principle was cloud object storage, everyone streams their data to cloud object storage. From there we said, well, how about we rethink database architecture, rethink file format, so that we can take each one of these services and bring them together, whether distributively or centrally, such that customers can access and get answers, whether it's operational data, whether it's business data, AKA search, or SQL, complex distributed joins. But we had to rethink the architecture. I like to say we're not a first generation, or a second, we're a third generation distributed database on pure, pure cloud storage, no caching, no SSDs. Why? Because all that availability, the cost of time, is a struggle, and cloud object storage, we think, is the answer. >> So when you're saying no caching, so when I think about how companies are solving some, you know, pretty hairy problems, take MySQL Heatwave, everybody thought Oracle was going to just forget about MySQL, well, they come out with Heatwave. And the way they solve problems, and you see their benchmarks against Amazon, "Oh, we crush everybody," is they put it all in memory. So you said no caching? You're not getting performance through caching? How is that true, and how are you getting performance? >> Well, so five, six years ago, right? When you realize that cloud object storage is going to be everywhere, and it's going to be a core foundational, if you will, fabric, what would you do? Well, a lot of times the second generation say, "We'll take it out of cloud storage, put in SSDs or something, and put into cache." And that adds a lot of time, adds a lot of costs. But I said, what if, what if we could actually make the first read hot, the first read distributed joins and searching? And so what we went out to do was said, we can't cache, because that's adds time, that adds cost. We have to make cloud object storage high performance, like it feels like a caching SSD. That's where our patents are, that's where our technology is, and we've spent many years working towards this. So, to me, if you can crack that code, a lot of these issues we're talking about, multi-region, multicloud, different services, everybody wants to send their data to the data lake, but then they move it out, we said, "Keep it right there." >> You nailed it, the data gravity. So, Bob's right, the data's coming in, and you need to get the data from everywhere, but you need an environment that you can deal with all that different schema, all the different type of technology, but also at scale. Bob's right, you cannot use memory or SSDs to cache that, that doesn't scale, it doesn't scale cost effectively. But if you could, and what you did, is you made object storage, S3 first, but object storage, the only persistence by doing that. And then we get performance, we should talk about it, it's literally, you know, hundreds of terabytes of queries, and it's done in seconds, it's done without memory caching. We have concepts of caching, but the only caching, the only persistence, is actually when we're doing caching, we're just keeping another side-eye track of things on the S3 itself. So we're using, actually, the object storage to be a database, which is kind of where Bob was saying, we agree, but that's what you started at, people thought you were crazy. >> And maybe make it live. Don't think of it as archival or temporary space, make it live, real time streaming, operational data. What we do is make it smart, we see the data coming in, we uniquely index it such that you can get your use cases, that are search, observability, security, or backend operational. But we don't have to have this, I dunno, static, fixed, siloed type of architecture technologies that were traditionally built prior to Supercloud thinking. >> And you don't have to move everything, essentially, you can do it wherever the data lands, whatever cloud across the globe, you're able to bring it together, you get the cost effectiveness, because the only persistence is the cheapest storage persistent layer you can buy. But the key thing is you cracked the code. >> We had to crack the code, right? That was the key thing. >> That's where the plans are. >> And then once you do that, then everything else gets easier to scale, your architecture, across regions, across cloud. >> Now, it's a general purpose database, as Bob was saying, but we use that database to solve a particular issue, which is around operational data, right? So, we agree with Bob's. >> Interesting. So this brings me to this concept of data, Jimata Gan is one of our speakers, you know, we talk about data fabric, which is a NetApp, originally NetApp concept, Gartner's kind of co-opted it. But so, the basic concept is, data lives everywhere, whether it's an S3 bucket, or a SQL database, or a data lake, it's just a node on the data mesh. So in your view, how does this fit in with Supercloud? Ed, you've said that you've built, essentially, an enabler for that, for the data mesh, I think you're an enabler for the Supercloud-like principles. This is a big, chewy opportunity, and it requires, you know, a team approach. There's got to be an ecosystem, there's not going to be one Supercloud to rule them all, so where does the ecosystem fit into the discussion, and where do you fit into the ecosystem? >> Right, so we agree completely, there's not one Supercloud in effect, but we use Supercloud principles to build our platform, and then, you know, the ecosystem's going to be built on leveraging what everyone else's secret powers are, right? So our power, our superpower, based upon what we built is, we deal with, if you're having any scale, or cost effective scale issues, with data, machine generated data, like business observability or security data, we are your force multiplier, we will take that in singularly, just let it, simply put it in your object storage wherever it sits, and we give you uniformity access to that using OpenAPI access, SQL, or you know, Elasticsearch API. So, that's what we do, that's our superpower. So I'll play it into data mesh, that's a perfect, we are a node on a data mesh, but I'll play it in the soup about how, the ecosystem, we see it kind of playing, and we talked about it in just in the last couple days, how we see this kind of possibly. Short term, our superpowers, we deal with this data that's coming at these environments, people, customers, building out observability or security environments, or vendors that are selling their own Supercloud, I do observability, the Datadogs of the world, dot dot dot, the Splunks of the world, dot dot dot, and security. So what we do is we fit in naturally. What we do is a cost effective scale, just land it anywhere in the world, we deal with ingest, and it's a cost effective, an order of magnitude, or two or three order magnitudes more cost effective. Allows them, their customers are asking them to do the impossible, "Give me fast monitoring alerting. I want it snappy, but I want it to keep two years of data, (laughs) and I want it cost effective." It doesn't work. They're good at the fast monitoring alerting, we're good at the long-term retention. And yet there's some gray area between those two, but one to one is actually cheaper, so we would partner. So the first ecosystem plays, who wants to have the ability to, really, all the data's in those same environments, the security observability players, they can literally, just through API, drag our data into their point to grab. We can make it seamless for customers. Right now, we make it helpful to customers. Your Datadog, we make a button, easy go from Datadog to us for logs, save you money. Same thing with Grafana. But you can also look at ecosystem, those same vendors, it used to be a year ago it was, you know, its all about how can you grow, like it's growth at all costs, now it's about cogs. So literally we can go an environment, you supply what your customer wants, but we can help with cogs. And one-on one in a partnership is better than you trying to build on your own. >> Thomas, you were saying you make the first read fast, so you think about Snowflake. Everybody wants to talk about Snowflake and Databricks. So, Snowflake, great, but you got to get the data in there. All right, so that's, can you help with that problem? >> I mean we want simple in, right? And if you have to have structure in, you're not simple. So the idea that you have a simple in, data lake, schema read type philosophy, but schema right type performance. And so what I wanted to do, what we have done, is have that simple lake, and stream that data real time, and those access points of Search or SQL, to go after whatever business case you need, security observability, warehouse integration. But the key thing is, how do I make that click, click, click answer, and do it quickly? And so what we want to do is, that first read has to be fast. Why? 'Cause then you're going to do all this siloing, layers, complexity. If your first read's not fast, you're at a disadvantage, particularly in cost. And nobody says I want less data, but everyone has to, whether they say we're going to shorten the window, we're going to use AI to choose, but in a security moment, when you don't have that answer, you're in trouble. And that's why we are this service, this Supercloud service, if you will, providing access, well-known search, well-known SQL type access, that if you just have one access point, you're at a disadvantage. >> We actually talked about Snowflake and BigQuery, and a different platform, Data Bricks. That's kind of where we see the phase two of ecosystem. One is easy, the low-hanging fruit is observability and security firms. But the next one is, what we do, our super power is dealing with this messy data that schema is changing like night and day. Pipelines are tough, and it's changing all the time, but you want these things fast, and it's big data around the world. That's the next point, just use us alongside, or inside, one of their platforms, and now we get the best of both worlds. Our superpower is keeping this messy data as a streaming, okay, not a batch thing, allow you to do that. So, that's the second one. And then to be honest, the third one, which plays you to Supercloud, it also plays perfectly in the data mesh, is if you really go to the ultimate thing, what we have done is made object storage, S3, GCS, and blob storage, we made it a database. Put, get, complex query with big joins. You know, so back to your original thing, and Muglia teed it up perfectly, we've done that. Now imagine if that's an ecosystem, who would want that? If it's, again, it's uniform available across all the regions, across all the clouds, and it's right next to where you are building a service, or a client's trying, that's where the ecosystem, I think people are going to use Superclouds for their superpowers. We're really good at this, allows that short term. I think the Snowflakes and the Data Bricks are the medium term, you know? And then I think eventually gets to, hey, listen if you can make object storage fast, you can just go after it with simple SQL queries, or elastic. Who would want that? I think that's where people are going to leverage it. It's not going to be one Supercloud, and we leverage the super clouds. >> Our viewpoint is smart object storage can be programmable, and so we agree with Bob, but we're not saying do it here, do it here. This core, fundamental layer across regions, across clouds, that everyone has? Simple in. Right now, it's hard to get data in for access for analysis. So we said, simply, we'll automate the entire process, give you API access across regions, across clouds. And again, how do you do a distributed join that's fast? How do you do a distributed join that doesn't cost you an arm or a leg? And how do you do it at scale? And that's where we've been focused. >> So prior, the cloud object store was a niche. >> Yeah. >> S3 obviously changed that. How standard is, essentially, object store across the different cloud platforms? Is that a problem for you? Is that an easy thing to solve? >> Well, let's talk about it. I mean we've fundamentally, yeah we've extracted it, but fundamentally, cloud object storage, put, get, and list. That's why it's so scalable, 'cause it doesn't have all these other components. That complexity is where we have moved up, and provide direct analytical API access. So because of its simplicity, and costs, and security, and reliability, it can scale naturally. I mean, really, distributed object storage is easy, it's put-get anywhere, now what we've done is we put a layer of intelligence, you know, call it smart object storage, where access is simple. So whether it's multi-region, do a query across, or multicloud, do a query across, or hunting, searching. >> We've had clients doing Amazon and Google, we have some Azure, but we see Amazon and Google more, and it's a consistent service across all of them. Just literally put your data in the bucket of choice, or folder of choice, click a couple buttons, literally click that to say "that's hot," and after that, it's hot, you can see it. But we're not moving data, the data gravity issue, that's the other. That it's already natively flowing to these pools of object storage across different regions and clouds. We don't move it, we index it right there, we're spinning up stateless compute, back to the Supercloud concept. But now that allows us to do all these other things, right? >> And it's no longer just cheap and deep object storage. Right? >> Yeah, we make it the same, like you have an analytic platform regardless of where you're at, you don't have to worry about that. Yeah, we deal with that, we deal with a stateless compute coming up -- >> And make it programmable. Be able to say, "I want this bucket to provide these answers." Right, that's really the hope, the vision. And the complexity to build the entire stack, and then connect them together, we said, the fabric is cloud storage, we just provide the intelligence on top. >> Let's bring it back to the customers, and one of the things we're exploring in Supercloud too is, you know, is Supercloud a solution looking for a problem? Is a multicloud really a problem? I mean, you hear, you know, a lot of the vendor marketing says, "Oh, it's a disaster, because it's all different across the clouds." And I talked to a lot of customers even as part of Supercloud too, they're like, "Well, I solved that problem by just going mono cloud." Well, but then you're not able to take advantage of a lot of the capabilities and the primitives that, you know, like Google's data, or you like Microsoft's simplicity, their RPA, whatever it is. So what are customers telling you, what are their near term problems that they're trying to solve today, and how are they thinking about the future? >> Listen, it's a real problem. I think it started, I think this is a a mega trend, just like cloud. Just, cloud data, and I always add, analytics, are the mega trends. If you're looking at those, if you're not considering using the Supercloud principles, in other words, leveraging what I have, abstracting it out, and getting the most out of that, and then build value on top, I think you're not going to be able to keep up, In fact, no way you're going to keep up with this data volume. It's a geometric challenge, and you're trying to do linear things. So clients aren't necessarily asking, hey, for Supercloud, but they're really saying, I need to have a better mechanism to simplify this and get value across it, and how do you abstract that out to do that? And that's where they're obviously, our conversations are more amazed what we're able to do, and what they're able to do with our platform, because if you think of what we've done, the S3, or GCS, or object storage, is they can't imagine the ingest, they can't imagine how easy, time to glass, one minute, no matter where it lands in the world, querying this in seconds for hundreds of terabytes squared. People are amazed, but that's kind of, so they're not asking for that, but they are amazed. And then when you start talking on it, if you're an enterprise person, you're building a big cloud data platform, or doing data or analytics, if you're not trying to leverage the public clouds, and somehow leverage all of them, and then build on top, then I think you're missing it. So they might not be asking for it, but they're doing it. >> And they're looking for a lens, you mentioned all these different services, how do I bring those together quickly? You know, our viewpoint, our service, is I have all these streams of data, create a lens where they want to go after it via search, go after via SQL, bring them together instantly, no e-tailing out, no define this table, put into this database. We said, let's have a service that creates a lens across all these streams, and then make those connections. I want to take my CRM with my Google AdWords, and maybe my Salesforce, how do I do analysis? Maybe I want to hunt first, maybe I want to join, maybe I want to add another stream to it. And so our viewpoint is, it's so natural to get into these lake platforms and then provide lenses to get that access. >> And they don't want it separate, they don't want something different here, and different there. They want it basically -- >> So this is our industry, right? If something new comes out, remember virtualization came out, "Oh my God, this is so great, it's going to solve all these problems." And all of a sudden it just got to be this big, more complex thing. Same thing with cloud, you know? It started out with S3, and then EC2, and now hundreds and hundreds of different services. So, it's a complex matter for a lot of people, and this creates problems for customers, especially when you got divisions that are using different clouds, and you're saying that the solution, or a solution for the part of the problem, is to really allow the data to stay in place on S3, use that standard, super simple, but then give it what, Ed, you've called superpower a couple of times, to make it fast, make it inexpensive, and allow you to do that across clouds. >> Yeah, yeah. >> I'll give you guys the last word on that. >> No, listen, I think, we think Supercloud allows you to do a lot more. And for us, data, everyone says more data, more problems, more budget issue, everyone knows more data is better, and we show you how to do it cost effectively at scale. And we couldn't have done it without the design principles of we're leveraging the Supercloud to get capabilities, and because we use super, just the object storage, we're able to get these capabilities of ingest, scale, cost effectiveness, and then we built on top of this. In the end, a database is a data platform that allows you to go after everything distributed, and to get one platform for analytics, no matter where it lands, that's where we think the Supercloud concepts are perfect, that's where our clients are seeing it, and we're kind of excited about it. >> Yeah a third generation database, Supercloud database, however we want to phrase it, and make it simple, but provide the value, and make it instant. >> Guys, thanks so much for coming into the studio today, I really thank you for your support of theCUBE, and theCUBE community, it allows us to provide events like this and free content. I really appreciate it. >> Oh, thank you. >> Thank you. >> All right, this is Dave Vellante for John Furrier in theCUBE community, thanks for being with us today. You're watching Supercloud 2, keep it right there for more thought provoking discussions around the future of cloud and data. (bright music)

(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)

(gentle music) >> Okay, welcome back to Supercloud 2, live here in Palo Alto, California for our live stage performance. Supercloud 2 is our second Supercloud event. We're going to get these out as fast as we can every couple months. It's our second one, you'll see two and three this year. I'm John Furrier, my co-host, Dave Vellante. A panel here to break down the Supercloud momentum, the wave, and the developer impact that we bringing back Vittorio Viarengo, who's a VP for Cross-Cloud Services at VMware. Sarbjeet Johal, industry influencer and Analyst at StackPayne, his company, Cube alumni and Influencer. Sarbjeet, great to see you. Vittorio, thanks for coming back. >> Nice to be here. >> My pleasure. >> Vittorio, you just gave a keynote where we unpacked the cross-cloud services, what VMware is doing, how you guys see it, not just from VMware's perspective, but VMware looking out broadly at the industry and developers came up and you were like, "Developers, developer, developers", kind of a goof on the Steve Ballmer famous meme that everyone's seen. This is a huge star, sorry, I mean a big piece of it. The developers are the canary in the coal mines. They're the ones who are being asked to code the digital transformation, which is fully business transformation and with the market the way it is right now in terms of the accelerated technology, every enterprise grade business model's changing. The technology is evolving, the builders are kind of, they want go faster. I'm saying they're stuck in a way, but that's my opinion, but there's a lot of growth. >> Yeah. >> The impact, they got to get released up and let it go. Those developers need to accelerate faster. It's been a big part of productivity, and the conversations we've had. So developer impact is huge in Supercloud. What's your, what do you guys think about this? We'll start with you, Sarbjeet. >> Yeah, actually, developers are the masons of the digital empires I call 'em, right? They lay every brick and build all these big empires. On the left side of the SDLC, or the, you know, when you look at the system operations, developer is number one cost from economic side of things, and from technology side of things, they are tech hungry people. They are developers for that reason because developer nights are long, hours are long, they forget about when to eat, you know, like, I've been a developer, I still code. So you want to keep them happy, you want to hug your developers. We always say that, right? Vittorio said that right earlier. The key is to, in this context, in the Supercloud context, is that developers don't mind mucking around with platforms or APIs or new languages, but they hate the infrastructure part. That's a fact. They don't want to muck around with servers. It's friction for them, it is like they don't want to muck around even with the VMs. So they want the programmability to the nth degree. They want to automate everything, so that's how they think and cloud is the programmable infrastructure, industrialization of infrastructure in many ways. So they are happy with where we are going, and we need more abstraction layers for some developers. By the way, I have this sort of thinking frame for last year or so, not all developers are same, right? So if you are a developer at an ISV, you behave differently. If you are a developer at a typical enterprise, you behave differently or you are forced to behave differently because you're not writing software.- >> Well, developers, developers have changed, I mean, Vittorio, you and I were talking earlier on the keynote, and this is kind of the key point is what is a developer these days? If everything is software enabled, I mean, even hardware interviews we do with Nvidia, and Amazon and other people building silicon, they all say the same thing, "It's software on a chip." So you're seeing the role of software up and down the stack and the role of the stack is changing. The old days of full stack developer, what does that even mean? I mean, the cloud is a half a stack kind of right there. So, you know, developers are certainly more agile, but cloud native, I mean VMware is epitome of operations, IT operations, and the Tan Zoo initiative, you guys started, you went after the developers to look at them, and ask them questions, "What do you need?", "How do you transform the Ops from virtualization?" Again, back to your point, so this hardware abstraction, what is software, what is cloud native? It's kind of messy equation these days. How do you guys grokel with that? >> I would argue that developers don't want the Supercloud. I dropped that up there, so, >> Dave: Why not? >> Because developers, they, once they get comfortable in AWS or Google, because they're doing some AI stuff, which is, you know, very trendy right now, or they are in IBM, any of the IPA scaler, professional developers, system developers, they love that stuff, right? Yeah, they don't, the infrastructure gets in the way, but they're just, the problem is, and I think the Supercloud should be driven by the operators because as we discussed, the operators have been left behind because they're busy with day-to-day jobs, and in most cases IT is centralized, developers are in the business units. >> John: Yeah. >> Right? So they get the mandate from the top, say, "Our bank, they're competing against". They gave teenagers or like young people the ability to do all these new things online, and Venmo and all this integration, where are we? "Oh yeah, we can do it", and then build it, and then deploy it, "Okay, we caught up." but now the operators are back in the private cloud trying to keep the backend system running and so I think the Supercloud is needed for the primarily, initially, for the operators to get in front of the developers, fit in the workflow, but lay the foundation so it is secure.- >> So, so I love this thinking because I love the rift, because the rift points to what is the target audience for the value proposition and if you're a developer, Supercloud enables you so you shouldn't have to deal with Supercloud. >> Exactly. >> What you're saying is get the operating environment or operating system done properly, whether it's architecture, building the platform, this comes back to architecture platform conversations. What is the future platform? Is it a vendor supplied or is it customer created platform? >> Dave: So developers want best to breed, is what you just said. >> Vittorio: Yeah. >> Right and operators, they, 'cause developers don't want to deal with governance, they don't want to deal with security, >> No. >> They don't want to deal with spinning up infrastructure. That's the role of the operator, but that's where Supercloud enables, to John's point, the developer, so to your question, is it a platform where the platform vendor is responsible for the architecture, or there is it an architectural standard that spans multiple clouds that has to emerge? Based on what you just presented earlier, Vittorio, you are the determinant of the architecture. It's got to be open, but you guys determine that, whereas the nirvana is, "Oh no, it's all open, and it just kind of works." >> Yeah, so first of all, let's all level set on one thing. You cannot tell developers what to do. >> Dave: Right, great >> At least great developers, right? Cannot tell them what to do. >> Dave: So that's what, that's the way I want to sort of, >> You can tell 'em what's possible. >> There's a bottle on that >> If you tell 'em what's possible, they'll test it, they'll look at it, but if you try to jam it down their throat, >> Yeah. >> Dave: You can't tell 'em how to do it, just like your point >> Let me answer your answer the question. >> Yeah, yeah. >> So I think we need to build an architect, help them build an architecture, but it cannot be proprietary, has to be built on what works in the cloud and so what works in the cloud today is Kubernetes, is you know, number of different open source project that you need to enable and then provide, use this, but when I first got exposed to Kubernetes, I said, "Hallelujah!" We had a runtime that works the same everywhere only to realize there are 12 different distributions. So that's where we come in, right? And other vendors come in to say, "Hey, no, we can make them all look the same. So you still use Kubernetes, but we give you a place to build, to set those operation policy once so that you don't create friction for the developers because that's the last thing you want to do." >> Yeah, actually, coming back to the same point, not all developers are same, right? So if you're ISV developer, you want to go to the lowest sort of level of the infrastructure and you want to shave off the milliseconds from to get that performance, right? If you're working at AWS, you are doing that. If you're working at scale at Facebook, you're doing that. At Twitter, you're doing that, but when you go to DMV and Kansas City, you're not doing that, right? So your developers are different in nature. They are given certain parameters to work with, certain sort of constraints on the budget side. They are educated at a different level as well. Like they don't go to that end of the degree of sort of automation, if you will. So you cannot have the broad stroking of developers. We are talking about a citizen developer these days. That's a extreme low, >> You mean Low-Code. >> Yeah, Low-Code, No-code, yeah, on the extreme side. On one side, that's citizen developers. On the left side is the professional developers, when you say developers, your mind goes to the professional developers, like the hardcore developers, they love the flexibility, you know, >> John: Well app, developers too, I mean. >> App developers, yeah. >> You're right a lot of, >> Sarbjeet: Infrastructure platform developers, app developers, yes. >> But there are a lot of customers, its a spectrum, you're saying. >> Yes, it's a spectrum >> There's a lot of customers don't want deal with that muck. >> Yeah. >> You know, like you said, AWS, Twitter, the sophisticated developers do, but there's a whole suite of developers out there >> Yeah >> That just want tools that are abstracted. >> Within a company, within a company. Like how I see the Supercloud is there shouldn't be anything which blocks the developers, like their view of the world, of the future. Like if you're blocked as a developer, like something comes in front of you, you are not developer anymore, believe me, (John laughing) so you'll go somewhere else >> John: First of all, I'm, >> You'll leave the company by the way. >> Dave: Yeah, you got to quit >> Yeah, you will quit, you will go where the action is, where there's no sort of blockage there. So like if you put in front of them like a huge amount of a distraction, they don't like it, so they don't, >> Well, the idea of a developer, >> Coming back to that >> Let's get into 'cause you mentioned platform. Get year in the term platform engineering now. >> Yeah. >> Platform developer. You know, I remember back in, and I think there's still a term used today, but when I graduated my computer science degree, we were called "Software engineers," right? Do people use that term "Software engineering", or is it "Software development", or they the same, are they different? >> Well, >> I think there's a, >> So, who's engineering what? Are they engineering or are they developing? Or both? Well, I think it the, you made a great point. There is a factor of, I had the, I was blessed to work with Adam Bosworth, that is the guy that created some of the abstraction layer, like Visual Basic and Microsoft Access and he had so, he made his whole career thinking about this layer, and he always talk about the professional developers, the developers that, you know, give him a user manual, maybe just go at the APIs, he'll build anything, right, from system engine, go down there, and then through obstruction, you get the more the procedural logic type of engineers, the people that used to be able to write procedural logic and visual basic and so on and so forth. I think those developers right now are a little cut out of the picture. There's some No-code, Low-Code environment that are maybe gain some traction, I caught up with Adam Bosworth two weeks ago in New York and I asked him "What's happening to this higher level developers?" and you know what he is told me, and he is always a little bit out there, so I'm going to use his thought process here. He says, "ChapGPT", I mean, they will get to a point where this high level procedural logic will be written by, >> John: Computers. >> Computers, and so we may not need as many at the high level, but we still need the engineers down there. The point is the operation needs to get in front of them >> But, wait, wait, you seen the ChatGPT meme, I dunno if it's a Dilbert thing where it's like, "Time to tic" >> Yeah, yeah, yeah, I did that >> "Time to develop the code >> Five minutes, time to decode", you know, to debug the codes like five hours. So you know, the whole equation >> Well, this ChatGPT is a hot wave, everyone's been talking about it because I think it illustrates something that's NextGen, feels NextGen, and it's just getting started so it's going to get better. I mean people are throwing stones at it, but I think it's amazing. It's the equivalent of me seeing the browser for the first time, you know, like, "Wow, this is really compelling." This is game-changing, it's not just keyword chat bots. It's like this is real, this is next level, and I think the Supercloud wave that people are getting behind points to that and I think the question of Ops and Dev comes up because I think if you limit the infrastructure opportunity for a developer, I think they're going to be handicapped. I mean that's a general, my opinion, the thesis is you give more aperture to developers, more choice, more capabilities, more good things could happen, policy, and that's why you're seeing the convergence of networking people, virtualization talent, operational talent, get into the conversation because I think it's an infrastructure engineering opportunity. I think this is a seminal moment in a new stack that's emerging from an infrastructure, software virtualization, low-code, no-code layer that will be completely programmable by things like the next Chat GPT or something different, but yet still the mechanics and the plumbing will still need engineering. >> Sarbjeet: Oh yeah. >> So there's still going to be more stuff coming on. >> Yeah, we have, with the cloud, we have made the infrastructure programmable and you give the programmability to the programmer, they will be very creative with that and so we are being very creative with our infrastructure now and on top of that, we are being very creative with the silicone now, right? So we talk about that. That's part of it, by the way. So you write the code to the particle's silicone now, and on the flip side, the silicone is built for certain use cases for AI Inference and all that. >> You saw this at CES? >> Yeah, I saw at CES, the scenario is this, the Bosch, I spoke to Bosch, I spoke to John Deere, I spoke to AWS guys, >> Yeah. >> They were showcasing their technology there and I was spoke to Azure guys as well. So the Bosch is a good example. So they are building, they are right now using AWS. I have that interview on camera, I will put it some sometime later on there online. So they're using AWS on the back end now, but Bosch is the number one, number one or number two depending on what day it is of the year, supplier of the componentry to the auto industry, and they are creating a platform for our auto industry, so is Qualcomm actually by the way, with the Snapdragon. So they told me that customers, their customers, BMW, Audi, all the manufacturers, they demand the diversity of the backend. Like they don't want all, they, all of them don't want to go to AWS. So they want the choice on the backend. So whatever they cook in the middle has to work, they have to sprinkle the data for the data sovereign side because they have Chinese car makers as well, and for, you know, for other reasons, competitive reasons and like use. >> People don't go to, aw, people don't go to AWS either for political reasons or like competitive reasons or specific use cases, but for the most part, generally, I haven't met anyone who hasn't gone first choice with either, but that's me personally. >> No, but they're building. >> Point is the developer wants choice at the back end is what I'm hearing, but then finish that thought. >> Their developers want the choice, they want the choice on the back end, number one, because the customers are asking for, in this case, the customers are asking for it, right? But the customers requirements actually drive, their economics drives that decision making, right? So in the middle they have to, they're forced to cook up some solution which is vendor neutral on the backend or multicloud in nature. So >> Yeah, >> Every >> I mean I think that's nirvana. I don't think, I personally don't see that happening right now. I mean, I don't see the parody with clouds. So I think that's a challenge. I mean, >> Yeah, true. >> I mean the fact of the matter is if the development teams get fragmented, we had this chat with Kit Colbert last time, I think he's going to come on and I think he's going to talk about his keynote in a few, in an hour or so, development teams is this, the cloud is heterogenous, which is great. It's complex, which is challenging. You need skilled engineering to manage these clouds. So if you're a CIO and you go all in on AWS, it's hard. Then to then go out and say, "I want to be completely multi-vendor neutral" that's a tall order on many levels and this is the multicloud challenge, right? So, the question is, what's the strategy for me, the CIO or CISO, what do I do? I mean, to me, I would go all in on one and start getting hedges and start playing and then look at some >> Crystal clear. Crystal clear to me. >> Go ahead. >> If you're a CIO today, you have to build a platform engineering team, no question. 'Cause if we agree that we cannot tell the great developers what to do, we have to create a platform engineering team that using pieces of the Supercloud can build, and let's make this very pragmatic and give examples. First you need to be able to lay down the run time, okay? So you need a way to deploy multiple different Kubernetes environment in depending on the cloud. Okay, now we got that. The second part >> That's like table stakes. >> That are table stake, right? But now what is the advantage of having a Supercloud service to do that is that now you can put a policy in one place and it gets distributed everywhere consistently. So for example, you want to say, "If anybody in this organization across all these different buildings, all these developers don't even know, build a PCI compliant microservice, They can only talk to PCI compliant microservice." Now, I sleep tight. The developers still do that. Of course they're going to get their hands slapped if they don't encrypt some messages and say, "Oh, that should have been encrypted." So number one. The second thing I want to be able to say, "This service that this developer built over there better satisfy this SLA." So if the SLA is not satisfied, boom, I automatically spin up multiple instances to certify the SLA. Developers unencumbered, they don't even know. So this for me is like, CIO build a platform engineering team using one of the many Supercloud services that allow you to do that and lay down. >> And part of that is that the vendor behavior is such, 'cause the incentive is that they don't necessarily always work together. (John chuckling) I'll give you an example, we're going to hear today from Western Union. They're AWS shop, but they want to go to Google, they want to use some of Google's AI tools 'cause they're good and maybe they're even arguably better, but they're also a Snowflake customer and what you'll hear from them is Amazon and Snowflake are working together so that SageMaker can be integrated with Snowflake but Google said, "No, you want to use our AI tools, you got to use BigQuery." >> Yeah. >> Okay. So they say, "Ah, forget it." So if you have a platform engineering team, you can maybe solve some of that vendor friction and get competitive advantage. >> I think that the future proximity concept that I talk about is like, when you're doing one thing, you want to do another thing. Where do you go to get that thing, right? So that is very important. Like your question, John, is that your point is that AWS is ahead of the pack, which is true, right? They have the >> breadth of >> Infrastructure by a lot >> infrastructure service, right? They breadth of services, right? So, how do you, When do you bring in other cloud providers, right? So I believe that you should standardize on one cloud provider, like that's your primary, and for others, bring them in on as needed basis, in the subsection or sub portfolio of your applications or your platforms, what ever you can. >> So yeah, the Google AI example >> Yeah, I mean, >> Or the Microsoft collaboration software example. I mean there's always or the M and A. >> Yeah, but- >> You're going to get to run Windows, you can run Windows on Amazon, so. >> By the way, Supercloud doesn't mean that you cannot do that. So the perfect example is say that you're using Azure because you have a SQL server intensive workload. >> Yep >> And you're using Google for ML, great. If you are using some differentiated feature of this cloud, you'll have to go somewhere and configure this widget, but what you can abstract with the Supercloud is the lifecycle manage of the service that runs on top, right? So how does the service get deployed, right? How do you monitor performance? How do you lifecycle it? How you secure it that you can abstract and that's the value and eventually value will win. So the customers will find what is the values, obstructing in making it uniform or going deeper? >> How about identity? Like take identity for instance, you know, that's an opportunity to abstract. Whether I use Microsoft Identity or Okta, and I can abstract that. >> Yeah, and then we have APIs and standards that we can use so eventually I think where there is enough pain, the right open source will emerge to solve that problem. >> Dave: Yeah, I can use abstract things like object store, right? That's pretty simple. >> But back to the engineering question though, is that developers, developers, developers, one thing about developers psychology is if something's not right, they say, "Go get fixing. I'm not touching it until you fix it." They're very sticky about, if something's not working, they're not going to do it again, right? So you got to get it right for developers. I mean, they'll maybe tolerate something new, but is the "juice worth the squeeze" as they say, right? So you can't go to direct say, "Hey, it's, what's a work in progress? We're going to get our infrastructure together and the world's going to be great for you, but just hang tight." They're going to be like, "Get your shit together then talk to me." So I think that to me is the question. It's an Ops question, but where's that value for the developer in Supercloud where the capabilities are there, there's less friction, it's simpler, it solves the complexity problem. I don't need these high skilled labor to manage Amazon. I got services exposed. >> That's what we talked about earlier. It's like the Walmart example. They basically, they took away from the developer the need to spin up infrastructure and worry about all the governance. I mean, it's not completely there yet. So the developer could focus on what he or she wanted to do. >> But there's a big, like in our industry, there's a big sort of flaw or the contention between developers and operators. Developers want to be on the cutting edge, right? And operators want to be on the stability, you know, like we want governance. >> Yeah, totally. >> Right, so they want to control, developers are like these little bratty kids, right? And they want Legos, like they want toys, right? Some of them want toys by way. They want Legos, they want to build there and they want make a mess out of it. So you got to make sure. My number one advice in this context is that do it up your application portfolio and, or your platform portfolio if you are an ISV, right? So if you are ISV you most probably, you're building a platform these days, do it up in a way that you can say this portion of our applications and our platform will adhere to what you are saying, standardization, you know, like Kubernetes, like slam dunk, you know, it works across clouds and in your data center hybrid, you know, whole nine yards, but there is some subset on the next door systems of innovation. Everybody has, it doesn't matter if you're DMV of Kansas or you are, you know, metaverse, right? Or Meta company, right, which is Facebook, they have it, they are building something new. For that, give them some freedom to choose different things like play with non-standard things. So that is the mantra for moving forward, for any enterprise. >> Do you think developers are happy with the infrastructure now or are they wanting people to get their act together? I mean, what's your reaction, or you think. >> Developers are happy as long as they can do their stuff, which is running code. They want to write code and innovate. So to me, when Ballmer said, "Developer, develop, Developer, what he meant was, all you other people get your act together so these developers can do their thing, and to me the Supercloud is the way for IT to get there and let developer be creative and go fast. Why not, without getting in trouble. >> Okay, let's wrap up this segment with a super clip. Okay, we're going to do a sound bite that we're going to make into a short video for each of you >> All right >> On you guys summarizing why Supercloud's important, why this next wave is relevant for the practitioners, for the industry and we'll turn this into an Instagram reel, YouTube short. So we'll call it a "Super clip. >> Alright, >> Sarbjeet, you want, you want some time to think about it? You want to go first? Vittorio, you want. >> I just didn't mind. (all laughing) >> No, okay, okay. >> I'll do it again. >> Go back. No, we got a fresh one. We'll going to already got that one in the can. >> I'll go. >> Sarbjeet, you go first. >> I'll go >> What's your super clip? >> In software systems, abstraction is your friend. I always say that. Abstraction is your friend, even if you're super professional developer, abstraction is your friend. We saw from the MFC library from C++ days till today. Abstract, use abstraction. Do not try to reinvent what's already being invented. Leverage cloud, leverage the platform side of the cloud. Not just infrastructure service, but platform as a service side of the cloud as well, and Supercloud is a meta platform built on top of these infrastructure services from three or four or five cloud providers. So use that and embrace the programmability, embrace the abstraction layer. That's the key actually, and developers who are true developers or professional developers as you said, they know that. >> Awesome. Great super clip. Vittorio, another shot at the plate here for super clip. Go. >> Multicloud is awesome. There's a reason why multicloud happened, is because gave our developers the ability to innovate fast and ever before. So if you are embarking on a digital transformation journey, which I call a survival journey, if you're not innovating and transforming, you're not going to be around in business three, five years from now. You have to adopt the Supercloud so the developer can be developer and keep building great, innovating digital experiences for your customers and IT can get in front of it and not get in trouble together. >> Building those super apps with Supercloud. That was a great super clip. Vittorio, thank you for sharing. >> Thanks guys. >> Sarbjeet, thanks for coming on talking about the developer impact Supercloud 2. On our next segment, coming up right now, we're going to hear from Walmart enterprise architect, how they are building and they are continuing to innovate, to build their own Supercloud. Really informative, instructive from a practitioner doing it in real time. Be right back with Walmart here in Palo Alto. Thanks for watching. (gentle music)

(light music) >> Hi everybody. Welcome to this Cube Conversation. I'm really pleased to announce a collaboration with Rob Strechay. He's a guest cube analyst, and we'll be working together to extract the signal from the noise. Rob is a long-time product pro, working at a number of firms including AWS, HP, HPE, NetApp, Snowplow. I did a stint as an analyst at Enterprise Strategy Group. Rob, good to see you. Thanks for coming into our Marlboro Studios. >> Well, thank you for having me. It's always great to be here. >> I'm really excited about working with you. We've known each other for a long time. You've been in the Cube a bunch. You know, you're in between gigs, and I think we can have a lot of fun together. Covering events, covering trends. So. let's get into it. What's happening out there? We're sort of exited the isolation economy. Things were booming. Now, everybody's tapping the brakes. From your standpoint, what are you seeing out there? >> Yeah. I'm seeing that people are really looking how to get more out of their data. How they're bringing things together, how they're looking at the costs of Cloud, and understanding how are they building out their SaaS applications. And understanding that when they go in and actually start to use Cloud, it's not only just using the base services anymore. They're looking at, how do I use these platforms as a service? Some are easier than others, and they're trying to understand, how do I get more value out of that relationship with the Cloud? They're also consolidating the number of Clouds that they have, I would say to try to better optimize their spend, and getting better pricing for that matter. >> Are you seeing people unhook Clouds, or just reduce maybe certain Cloud activities and going maybe instead of 60/40 going 90/10? >> Correct. It's more like the 90/10 type of rule where they're starting to say, Hey I'm not going to get rid of Azure or AWS or Google. I'm going to move a portion of this over that I was using on this one service. Maybe I got a great two-year contract to start with on this platform as a service or a database as a service. I'm going to unhook from that and maybe go with an independent. Maybe with something like a Snowflake or a Databricks on top of another Cloud, so that I can consolidate down. But it also gives them more flexibility as well. >> In our last breaking analysis, Rob, we identified six factors that were reducing Cloud consumption. There were factors and customer tactics. And I want to get your take on this. So, some of the factors really, you got fewer mortgage originations. FinTech, obviously big Cloud user. Crypto, not as much activity there. Lower ad spending means less Cloud. And then one of 'em, which you kind of disagreed with was less, less analytics, you know, fewer... Less frequency of calculations. I'll come back to that. But then optimizing compute using Graviton or AMD instances moving to cheaper storage tiers. That of course makes sense. And then optimize pricing plans. Maybe going from On Demand, you know, to, you know, instead of pay by the drink, buy in volume. Okay. So, first of all, do those make sense to you with the exception? We'll come back and talk about the analytics piece. Is that what you're seeing from customers? >> Yeah, I think so. I think that was pretty much dead on with what I'm seeing from customers and the ones that I go out and talk to. A lot of times they're trying to really monetize their, you know, understand how their business utilizes these Clouds. And, where their spend is going in those Clouds. Can they use, you know, lower tiers of storage? Do they really need the best processors? Do they need to be using Intel or can they get away with AMD or Graviton 2 or 3? Or do they need to move in? And, I think when you look at all of these Clouds, they always have pricing curves that are arcs from the newest to the oldest stuff. And you can play games with that. And understanding how you can actually lower your costs by looking at maybe some of the older generation. Maybe your application was written 10 years ago. You don't necessarily have to be on the best, newest processor for that application per se. >> So last, I want to come back to this whole analytics piece. Last June, I think it was June, Dev Ittycheria, who's the-- I call him Dev. Spelled Dev, pronounced Dave. (chuckles softly) Same pronunciation, different spelling. Dev Ittycheria, CEO of Mongo, on the earnings call. He was getting, you know, hit. Things were starting to get a little less visible in terms of, you know, the outlook. And people were pushing him like... Because you're in the Cloud, is it easier to dial down? And he said, because we're the document database, we support transaction applications. We're less discretionary than say, analytics. Well on the Snowflake earnings call, that same month or the month after, they were all over Slootman and Scarpelli. Oh, the Mongo CEO said that they're less discretionary than analytics. And Snowflake was an interesting comment. They basically said, look, we're the Cloud. You can dial it up, you can dial it down, but the area under the curve over a period of time is going to be the same, because they get their customers to commit. What do you say? You disagreed with the notion that people are running their calculations less frequently. Is that because they're trying to do a better job of targeting customers in near real time? What are you seeing out there? >> Yeah, I think they're moving away from using people and more expensive marketing. Or, they're trying to figure out what's my Google ad spend, what's my Meta ad spend? And what they're trying to do is optimize that spend. So, what is the return on advertising, or the ROAS as they would say. And what they're looking to do is understand, okay, I have to collect these analytics that better understand where are these people coming from? How do they get to my site, to my store, to my whatever? And when they're using it, how do they they better move through that? What you're also seeing is that analytics is not only just for kind of the retail or financial services or things like that, but then they're also, you know, using that to make offers in those categories. When you move back to more, you know, take other companies that are building products and SaaS delivered products. They may actually go and use this analytics for making the product better. And one of the big reasons for that is maybe they're dialing back how many product managers they have. And they're looking to be more data driven about how they actually go and build the product out or enhance the product. So maybe they're, you know, an online video service and they want to understand why people are either using or not using the whiteboard inside the product. And they're collecting a lot of that product analytics in a big way so that they can go through that. And they're doing it in a constant manner. This first party type tracking within applications is growing rapidly by customers. >> So, let's talk about who wins in that. So, obviously the Cloud guys, AWS, Google and Azure. I want to come back and unpack that a little bit. Databricks and Snowflake, we reported on our last breaking analysis, it kind of on a collision course. You know, a couple years ago we were thinking, okay, AWS, Snowflake and Databricks, like perfect sandwich. And then of course they started to become more competitive. My sense is they still, you know, compliment each other in the field, right? But, you know, publicly, they've got bigger aspirations, they get big TAMs that they're going after. But it's interesting, the data shows that-- So, Snowflake was off the charts in terms of spending momentum and our EPR surveys. Our partner down in New York, they kind of came into line. They're both growing in terms of market presence. Databricks couldn't get to IPO. So, we don't have as much, you know, visibility on their financials. You know, Snowflake obviously highly transparent cause they're a public company. And then you got AWS, Google and Azure. And it seems like AWS appears to be more partner friendly. Microsoft, you know, depends on what market you're in. And Google wants to sell BigQuery. >> Yeah. >> So, what are you seeing in the public Cloud from a data platform perspective? >> Yeah. I think that was pretty astute in what you were talking about there, because I think of the three, Google is definitely I think a little bit behind in how they go to market with their partners. Azure's done a fantastic job of partnering with these companies to understand and even though they may have Synapse as their go-to and where they want people to go to do AI and ML. What they're looking at is, Hey, we're going to also be friendly with Snowflake. We're also going to be friendly with a Databricks. And I think that, Amazon has always been there because that's where the market has been for these developers. So, many, like Databricks' and the Snowflake's have gone there first because, you know, Databricks' case, they built out on top of S3 first. And going and using somebody's object layer other than AWS, was not as simple as you would think it would be. Moving between those. >> So, one of the financial meetups I said meetup, but the... It was either the CEO or the CFO. It was either Slootman or Scarpelli talking at, I don't know, Merrill Lynch or one of the other financial conferences said, I think it was probably their Q3 call. Snowflake said 80% of our business goes through Amazon. And he said to this audience, the next day we got a call from Microsoft. Hey, we got to do more. And, we know just from reading the financial statements that Snowflake is getting concessions from Amazon, they're buying in volume, they're renegotiating their contracts. Amazon gets it. You know, lower the price, people buy more. Long term, we're all going to make more money. Microsoft obviously wants to get into that game with Snowflake. They understand the momentum. They said Google, not so much. And I've had customers tell me that they wanted to use Google's AI with Snowflake, but they can't, they got to go to to BigQuery. So, honestly, I haven't like vetted that so. But, I think it's true. But nonetheless, it seems like Google's a little less friendly with the data platform providers. What do you think? >> Yeah, I would say so. I think this is a place that Google looks and wants to own. Is that now, are they doing the right things long term? I mean again, you know, you look at Google Analytics being you know, basically outlawed in five countries in the EU because of GDPR concerns, and compliance and governance of data. And I think people are looking at Google and BigQuery in general and saying, is it the best place for me to go? Is it going to be in the right places where I need it? Still, it's still one of the largest used databases out there just because it underpins a number of the Google services. So you almost get, like you were saying, forced into BigQuery sometimes, if you want to use the tech on top. >> You do strategy. >> Yeah. >> Right? You do strategy, you do messaging. Is it the right call by Google? I mean, it's not a-- I criticize Google sometimes. But, I'm not sure it's the wrong call to say, Hey, this is our ace in the hole. >> Yeah. >> We got to get people into BigQuery. Cause, first of all, BigQuery is a solid product. I mean it's Cloud native and it's, you know, by all, it gets high marks. So, why give the competition an advantage? Let's try to force people essentially into what is we think a great product and it is a great product. The flip side of that is, they're giving up some potential partner TAM and not treating the ecosystem as well as one of their major competitors. What do you do if you're in that position? >> Yeah, I think that that's a fantastic question. And the question I pose back to the companies I've worked with and worked for is, are you really looking to have vendor lock-in as your key differentiator to your service? And I think when you start to look at these companies that are moving away from BigQuery, moving to even, Databricks on top of GCS in Google, they're looking to say, okay, I can go there if I have to evacuate from GCP and go to another Cloud, I can stay on Databricks as a platform, for instance. So I think it's, people are looking at what platform as a service, database as a service they go and use. Because from a strategic perspective, they don't want that vendor locking. >> That's where Supercloud becomes interesting, right? Because, if I can run on Snowflake or Databricks, you know, across Clouds. Even Oracle, you know, they're getting into business with Microsoft. Let's talk about some of the Cloud players. So, the big three have reported. >> Right. >> We saw AWSs Cloud growth decelerated down to 20%, which is I think the lowest growth rate since they started to disclose public numbers. And they said they exited, sorry, they said January they grew at 15%. >> Yeah. >> Year on year. Now, they had some pretty tough compares. But nonetheless, 15%, wow. Azure, kind of mid thirties, and then Google, we had kind of low thirties. But, well behind in terms of size. And Google's losing probably almost $3 billion annually. But, that's not necessarily a bad thing by advocating and investing. What's happening with the Cloud? Is AWS just running into the law, large numbers? Do you think we can actually see a re-acceleration like we have in the past with AWS Cloud? Azure, we predicted is going to be 75% of AWS IAS revenues. You know, we try to estimate IAS. >> Yeah. >> Even though they don't share that with us. That's a huge milestone. You'd think-- There's some people who have, I think, Bob Evans predicted a while ago that Microsoft would surpass AWS in terms of size. You know, what do you think? >> Yeah, I think that Azure's going to keep to-- Keep growing at a pretty good clip. I think that for Azure, they still have really great account control, even though people like to hate Microsoft. The Microsoft sellers that are out there making those companies successful day after day have really done a good job of being in those accounts and helping people. I was recently over in the UK. And the UK market between AWS and Azure is pretty amazing, how much Azure there is. And it's growing within Europe in general. In the states, it's, you know, I think it's growing well. I think it's still growing, probably not as fast as it is outside the U.S. But, you go down to someplace like Australia, it's also Azure. You hear about Azure all the time. >> Why? Is that just because of the Microsoft's software state? It's just so convenient. >> I think it has to do with, you know, and you can go with the reasoning they don't break out, you know, Office 365 and all of that out of their numbers is because they have-- They're in all of these accounts because the office suite is so pervasive in there. So, they always have reasons to go back in and, oh by the way, you're on these old SQL licenses. Let us move you up here and we'll be able to-- We'll support you on the old version, you know, with security and all of these things. And be able to move you forward. So, they have a lot of, I guess you could say, levers to stay in those accounts and be interesting. At least as part of the Cloud estate. I think Amazon, you know, is hitting, you know, the large number. Laws of large numbers. But I think that they're also going through, and I think this was seen in the layoffs that they were making, that they're looking to understand and have profitability in more of those services that they have. You know, over 350 odd services that they have. And you know, as somebody who went there and helped to start yet a new one, while I was there. And finally, it went to beta back in September, you start to look at the fact that, that number of services, people, their own sellers don't even know all of their services. It's impossible to comprehend and sell that many things. So, I think what they're going through is really looking to rationalize a lot of what they're doing from a services perspective going forward. They're looking to focus on more profitable services and bringing those in. Because right now it's built like a layer cake where you have, you know, S3 EBS and EC2 on the bottom of the layer cake. And then maybe you have, you're using IAM, the authorization and authentication in there and you have all these different services. And then they call it EMR on top. And so, EMR has to pay for that entire layer cake just to go and compete against somebody like Mongo or something like that. So, you start to unwind the costs of that. Whereas Azure, went and they build basically ground up services for the most part. And Google kind of falls somewhere in between in how they build their-- They're a sort of layer cake type effect, but not as many layers I guess you could say. >> I feel like, you know, Amazon's trying to be a platform for the ecosystem. Yes, they have their own products and they're going to sell. And that's going to drive their profitability cause they don't have to split the pie. But, they're taking a piece of-- They're spinning the meter, as Ziyas Caravalo likes to say on every time Snowflake or Databricks or Mongo or Atlas is, you know, running on their system. They take a piece of the action. Now, Microsoft does that as well. But, you look at Microsoft and security, head-to-head competitors, for example, with a CrowdStrike or an Okta in identity. Whereas, it seems like at least for now, AWS is a more friendly place for the ecosystem. At the same time, you do a lot of business in Microsoft. >> Yeah. And I think that a lot of companies have always feared that Amazon would just throw, you know, bodies at it. And I think that people have come to the realization that a two pizza team, as Amazon would call it, is eight people. I think that's, you know, two slices per person. I'm a little bit fat, so I don't know if that's enough. But, you start to look at it and go, okay, if they're going to start out with eight engineers, if I'm a startup and they're part of my ecosystem, do I really fear them or should I really embrace them and try to partner closer with them? And I think the smart people and the smart companies are partnering with them because they're realizing, Amazon, unless they can see it to, you know, a hundred million, $500 million market, they're not going to throw eight to 16 people at a problem. I think when, you know, you could say, you could look at the elastic with OpenSearch and what they did there. And the licensing terms and the battle they went through. But they knew that Elastic had a huge market. Also, you had a number of ecosystem companies building on top of now OpenSearch, that are now domain on top of Amazon as well. So, I think Amazon's being pretty strategic in how they're doing it. I think some of the-- It'll be interesting. I think this year is a payout year for the cuts that they're making to some of the services internally to kind of, you know, how do we take the fat off some of those services that-- You know, you look at Alexa. I don't know how much revenue Alexa really generates for them. But it's a means to an end for a number of different other services and partners. >> What do you make of this ChatGPT? I mean, Microsoft obviously is playing that card. You want to, you want ChatGPT in the Cloud, come to Azure. Seems like AWS has to respond. And we know Google is, you know, sharpening its knives to come up with its response. >> Yeah, I mean Google just went and talked about Bard for the first time this week and they're in private preview or I guess they call it beta, but. Right at the moment to select, select AI users, which I have no idea what that means. But that's a very interesting way that they're marketing it out there. But, I think that Amazon will have to respond. I think they'll be more measured than say, what Google's doing with Bard and just throwing it out there to, hey, we're going into beta now. I think they'll look at it and see where do we go and how do we actually integrate this in? Because they do have a lot of components of AI and ML underneath the hood that other services use. And I think that, you know, they've learned from that. And I think that they've already done a good job. Especially for media and entertainment when you start to look at some of the ways that they use it for helping do graphics and helping to do drones. I think part of their buy of iRobot was the fact that iRobot was a big user of RoboMaker, which is using different models to train those robots to go around objects and things like that, so. >> Quick touch on Kubernetes, the whole DevOps World we just covered. The Cloud Native Foundation Security, CNCF. The security conference up in Seattle last week. First time they spun that out kind of like reinforced, you know, AWS spins out, reinforced from reinvent. Amsterdam's coming up soon, the CubeCon. What should we expect? What's hot in Cubeland? >> Yeah, I think, you know, Kubes, you're going to be looking at how OpenShift keeps growing and I think to that respect you get to see the momentum with people like Red Hat. You see others coming up and realizing how OpenShift has gone to market as being, like you were saying, partnering with those Clouds and really making it simple. I think the simplicity and the manageability of Kubernetes is going to be at the forefront. I think a lot of the investment is still going into, how do I bring observability and DevOps and AIOps and MLOps all together. And I think that's going to be a big place where people are going to be looking to see what comes out of CubeCon in Amsterdam. I think it's that manageability ease of use. >> Well Rob, I look forward to working with you on behalf of the whole Cube team. We're going to do more of these and go out to some shows extract the signal from the noise. Really appreciate you coming into our studio. >> Well, thank you for having me on. Really appreciate it. >> You're really welcome. All right, keep it right there, or thanks for watching. This is Dave Vellante for the Cube. And we'll see you next time. (light music)

(upbeat music beginning) >> From the Cube Studios in Palo Alto and Boston, bringing you data-driven insights from the Cube and ETR, this is "Breaking Analysis" with Dave Vellante. >> Making predictions about the future of enterprise tech is more challenging if you strive to lay down forecasts that are measurable. In other words, if you make a prediction, you should be able to look back a year later and say, with some degree of certainty, whether the prediction came true or not, with evidence to back that up. Hello and welcome to this week's Wikibon Cube Insights, powered by ETR. In this breaking analysis, we aim to do just that, with predictions about the macro IT spending environment, cost optimization, security, lots to talk about there, generative AI, cloud, and of course supercloud, blockchain adoption, data platforms, including commentary on Databricks, snowflake, and other key players, automation, events, and we may even have some bonus predictions around quantum computing, and perhaps some other areas. To make all this happen, we welcome back, for the third year in a row, my colleague and friend Eric Bradley from ETR. Eric, thanks for all you do for the community, and thanks for being part of this program. Again. >> I wouldn't miss it for the world. I always enjoy this one. Dave, good to see you. >> Yeah, so let me bring up this next slide and show you, actually come back to me if you would. I got to show the audience this. These are the inbounds that we got from PR firms starting in October around predictions. They know we do prediction posts. And so they'll send literally thousands and thousands of predictions from hundreds of experts in the industry, technologists, consultants, et cetera. And if you bring up the slide I can show you sort of the pattern that developed here. 40% of these thousands of predictions were from cyber. You had AI and data. If you combine those, it's still not close to cyber. Cost optimization was a big thing. Of course, cloud, some on DevOps, and software. Digital... Digital transformation got, you know, some lip service and SaaS. And then there was other, it's kind of around 2%. So quite remarkable, when you think about the focus on cyber, Eric. >> Yeah, there's two reasons why I think it makes sense, though. One, the cybersecurity companies have a lot of cash, so therefore the PR firms might be working a little bit harder for them than some of their other clients. (laughs) And then secondly, as you know, for multiple years now, when we do our macro survey, we ask, "What's your number one spending priority?" And again, it's security. It just isn't going anywhere. It just stays at the top. So I'm actually not that surprised by that little pie chart there, but I was shocked that SaaS was only 5%. You know, going back 10 years ago, that would've been the only thing anyone was talking about. >> Yeah. So true. All right, let's get into it. First prediction, we always start with kind of tech spending. Number one is tech spending increases between four and 5%. ETR has currently got it at 4.6% coming into 2023. This has been a consistently downward trend all year. We started, you know, much, much higher as we've been reporting. Bottom line is the fed is still in control. They're going to ease up on tightening, is the expectation, they're going to shoot for a soft landing. But you know, my feeling is this slingshot economy is going to continue, and it's going to continue to confound, whether it's supply chains or spending. The, the interesting thing about the ETR data, Eric, and I want you to comment on this, the largest companies are the most aggressive to cut. They're laying off, smaller firms are spending faster. They're actually growing at a much larger, faster rate as are companies in EMEA. And that's a surprise. That's outpacing the US and APAC. Chime in on this, Eric. >> Yeah, I was surprised on all of that. First on the higher level spending, we are definitely seeing it coming down, but the interesting thing here is headlines are making it worse. The huge research shop recently said 0% growth. We're coming in at 4.6%. And just so everyone knows, this is not us guessing, we asked 1,525 IT decision-makers what their budget growth will be, and they came in at 4.6%. Now there's a huge disparity, as you mentioned. The Fortune 500, global 2000, barely at 2% growth, but small, it's at 7%. So we're at a situation right now where the smaller companies are still playing a little bit of catch up on digital transformation, and they're spending money. The largest companies that have the most to lose from a recession are being more trepidatious, obviously. So they're playing a "Wait and see." And I hope we don't talk ourselves into a recession. Certainly the headlines and some of their research shops are helping it along. But another interesting comment here is, you know, energy and utilities used to be called an orphan and widow stock group, right? They are spending more than anyone, more than financials insurance, more than retail consumer. So right now it's being driven by mid, small, and energy and utilities. They're all spending like gangbusters, like nothing's happening. And it's the rest of everyone else that's being very cautious. >> Yeah, so very unpredictable right now. All right, let's go to number two. Cost optimization remains a major theme in 2023. We've been reporting on this. You've, we've shown a chart here. What's the primary method that your organization plans to use? You asked this question of those individuals that cited that they were going to reduce their spend and- >> Mhm. >> consolidating redundant vendors, you know, still leads the way, you know, far behind, cloud optimization is second, but it, but cloud continues to outpace legacy on-prem spending, no doubt. Somebody, it was, the guy's name was Alexander Feiglstorfer from Storyblok, sent in a prediction, said "All in one becomes extinct." Now, generally I would say I disagree with that because, you know, as we know over the years, suites tend to win out over, you know, individual, you know, point products. But I think what's going to happen is all in one is going to remain the norm for these larger companies that are cutting back. They want to consolidate redundant vendors, and the smaller companies are going to stick with that best of breed and be more aggressive and try to compete more effectively. What's your take on that? >> Yeah, I'm seeing much more consolidation in vendors, but also consolidation in functionality. We're seeing people building out new functionality, whether it's, we're going to talk about this later, so I don't want to steal too much of our thunder right now, but data and security also, we're seeing a functionality creep. So I think there's further consolidation happening here. I think niche solutions are going to be less likely, and platform solutions are going to be more likely in a spending environment where you want to reduce your vendors. You want to have one bill to pay, not 10. Another thing on this slide, real quick if I can before I move on, is we had a bunch of people write in and some of the answer options that aren't on this graph but did get cited a lot, unfortunately, is the obvious reduction in staff, hiring freezes, and delaying hardware, were three of the top write-ins. And another one was offshore outsourcing. So in addition to what we're seeing here, there were a lot of write-in options, and I just thought it would be important to state that, but essentially the cost optimization is by and far the highest one, and it's growing. So it's actually increased in our citations over the last year. >> And yeah, specifically consolidating redundant vendors. And so I actually thank you for bringing that other up, 'cause I had asked you, Eric, is there any evidence that repatriation is going on and we don't see it in the numbers, we don't see it even in the other, there was, I think very little or no mention of cloud repatriation, even though it might be happening in this in a smattering. >> Not a single mention, not one single mention. I went through it for you. Yep. Not one write-in. >> All right, let's move on. Number three, security leads M&A in 2023. Now you might say, "Oh, well that's a layup," but let me set this up Eric, because I didn't really do a great job with the slide. I hid the, what you've done, because you basically took, this is from the emerging technology survey with 1,181 responses from November. And what we did is we took Palo Alto and looked at the overlap in Palo Alto Networks accounts with these vendors that were showing on this chart. And Eric, I'm going to ask you to explain why we put a circle around OneTrust, but let me just set it up, and then have you comment on the slide and take, give us more detail. We're seeing private company valuations are off, you know, 10 to 40%. We saw a sneak, do a down round, but pretty good actually only down 12%. We've seen much higher down rounds. Palo Alto Networks we think is going to get busy. Again, they're an inquisitive company, they've been sort of quiet lately, and we think CrowdStrike, Cisco, Microsoft, Zscaler, we're predicting all of those will make some acquisitions and we're thinking that the targets are somewhere in this mess of security taxonomy. Other thing we're predicting AI meets cyber big time in 2023, we're going to probably going to see some acquisitions of those companies that are leaning into AI. We've seen some of that with Palo Alto. And then, you know, your comment to me, Eric, was "The RSA conference is going to be insane, hopping mad, "crazy this April," (Eric laughing) but give us your take on this data, and why the red circle around OneTrust? Take us back to that slide if you would, Alex. >> Sure. There's a few things here. First, let me explain what we're looking at. So because we separate the public companies and the private companies into two separate surveys, this allows us the ability to cross-reference that data. So what we're doing here is in our public survey, the tesis, everyone who cited some spending with Palo Alto, meaning they're a Palo Alto customer, we then cross-reference that with the private tech companies. Who also are they spending with? So what you're seeing here is an overlap. These companies that we have circled are doing the best in Palo Alto's accounts. Now, Palo Alto went and bought Twistlock a few years ago, which this data slide predicted, to be quite honest. And so I don't know if they necessarily are going to go after Snyk. Snyk, sorry. They already have something in that space. What they do need, however, is more on the authentication space. So I'm looking at OneTrust, with a 45% overlap in their overall net sentiment. That is a company that's already existing in their accounts and could be very synergistic to them. BeyondTrust as well, authentication identity. This is something that Palo needs to do to move more down that zero trust path. Now why did I pick Palo first? Because usually they're very inquisitive. They've been a little quiet lately. Secondly, if you look at the backdrop in the markets, the IPO freeze isn't going to last forever. Sooner or later, the IPO markets are going to open up, and some of these private companies are going to tap into public equity. In the meantime, however, cash funding on the private side is drying up. If they need another round, they're not going to get it, and they're certainly not going to get it at the valuations they were getting. So we're seeing valuations maybe come down where they're a touch more attractive, and Palo knows this isn't going to last forever. Cisco knows that, CrowdStrike, Zscaler, all these companies that are trying to make a push to become that vendor that you're consolidating in, around, they have a chance now, they have a window where they need to go make some acquisitions. And that's why I believe leading up to RSA, we're going to see some movement. I think it's going to pretty, a really exciting time in security right now. >> Awesome. Thank you. Great explanation. All right, let's go on the next one. Number four is, it relates to security. Let's stay there. Zero trust moves from hype to reality in 2023. Now again, you might say, "Oh yeah, that's a layup." A lot of these inbounds that we got are very, you know, kind of self-serving, but we always try to put some meat in the bone. So first thing we do is we pull out some commentary from, Eric, your roundtable, your insights roundtable. And we have a CISO from a global hospitality firm says, "For me that's the highest priority." He's talking about zero trust because it's the best ROI, it's the most forward-looking, and it enables a lot of the business transformation activities that we want to do. CISOs tell me that they actually can drive forward transformation projects that have zero trust, and because they can accelerate them, because they don't have to go through the hurdle of, you know, getting, making sure that it's secure. Second comment, zero trust closes that last mile where once you're authenticated, they open up the resource to you in a zero trust way. That's a CISO of a, and a managing director of a cyber risk services enterprise. Your thoughts on this? >> I can be here all day, so I'm going to try to be quick on this one. This is not a fluff piece on this one. There's a couple of other reasons this is happening. One, the board finally gets it. Zero trust at first was just a marketing hype term. Now the board understands it, and that's why CISOs are able to push through it. And what they finally did was redefine what it means. Zero trust simply means moving away from hardware security, moving towards software-defined security, with authentication as its base. The board finally gets that, and now they understand that this is necessary and it's being moved forward. The other reason it's happening now is hybrid work is here to stay. We weren't really sure at first, large companies were still trying to push people back to the office, and it's going to happen. The pendulum will swing back, but hybrid work's not going anywhere. By basically on our own data, we're seeing that 69% of companies expect remote and hybrid to be permanent, with only 30% permanent in office. Zero trust works for a hybrid environment. So all of that is the reason why this is happening right now. And going back to our previous prediction, this is why we're picking Palo, this is why we're picking Zscaler to make these acquisitions. Palo Alto needs to be better on the authentication side, and so does Zscaler. They're both fantastic on zero trust network access, but they need the authentication software defined aspect, and that's why we think this is going to happen. One last thing, in that CISO round table, I also had somebody say, "Listen, Zscaler is incredible. "They're doing incredibly well pervading the enterprise, "but their pricing's getting a little high," and they actually think Palo Alto is well-suited to start taking some of that share, if Palo can make one move. >> Yeah, Palo Alto's consolidation story is very strong. Here's my question and challenge. Do you and me, so I'm always hardcore about, okay, you've got to have evidence. I want to look back at these things a year from now and say, "Did we get it right? Yes or no?" If we got it wrong, we'll tell you we got it wrong. So how are we going to measure this? I'd say a couple things, and you can chime in. One is just the number of vendors talking about it. That's, but the marketing always leads the reality. So the second part of that is we got to get evidence from the buying community. Can you help us with that? >> (laughs) Luckily, that's what I do. I have a data company that asks thousands of IT decision-makers what they're adopting and what they're increasing spend on, as well as what they're decreasing spend on and what they're replacing. So I have snapshots in time over the last 11 years where I can go ahead and compare and contrast whether this adoption is happening or not. So come back to me in 12 months and I'll let you know. >> Now, you know, I will. Okay, let's bring up the next one. Number five, generative AI hits where the Metaverse missed. Of course everybody's talking about ChatGPT, we just wrote last week in a breaking analysis with John Furrier and Sarjeet Joha our take on that. We think 2023 does mark a pivot point as natural language processing really infiltrates enterprise tech just as Amazon turned the data center into an API. We think going forward, you're going to be interacting with technology through natural language, through English commands or other, you know, foreign language commands, and investors are lining up, all the VCs are getting excited about creating something competitive to ChatGPT, according to (indistinct) a hundred million dollars gets you a seat at the table, gets you into the game. (laughing) That's before you have to start doing promotion. But he thinks that's what it takes to actually create a clone or something equivalent. We've seen stuff from, you know, the head of Facebook's, you know, AI saying, "Oh, it's really not that sophisticated, ChatGPT, "it's kind of like IBM Watson, it's great engineering, "but you know, we've got more advanced technology." We know Google's working on some really interesting stuff. But here's the thing. ETR just launched this survey for the February survey. It's in the field now. We circle open AI in this category. They weren't even in the survey, Eric, last quarter. So 52% of the ETR survey respondents indicated a positive sentiment toward open AI. I added up all the sort of different bars, we could double click on that. And then I got this inbound from Scott Stevenson of Deep Graham. He said "AI is recession-proof." I don't know if that's the case, but it's a good quote. So bring this back up and take us through this. Explain this chart for us, if you would. >> First of all, I like Scott's quote better than the Facebook one. I think that's some sour grapes. Meta just spent an insane amount of money on the Metaverse and that's a dud. Microsoft just spent money on open AI and it is hot, undoubtedly hot. We've only been in the field with our current ETS survey for a week. So my caveat is it's preliminary data, but I don't care if it's preliminary data. (laughing) We're getting a sneak peek here at what is the number one net sentiment and mindshare leader in the entire machine-learning AI sector within a week. It's beating Data- >> 600. 600 in. >> It's beating Databricks. And we all know Databricks is a huge established enterprise company, not only in machine-learning AI, but it's in the top 10 in the entire survey. We have over 400 vendors in this survey. It's number eight overall, already. In a week. This is not hype. This is real. And I could go on the NLP stuff for a while. Not only here are we seeing it in open AI and machine-learning and AI, but we're seeing NLP in security. It's huge in email security. It's completely transforming that area. It's one of the reasons I thought Palo might take Abnormal out. They're doing such a great job with NLP in this email side, and also in the data prep tools. NLP is going to take out data prep tools. If we have time, I'll discuss that later. But yeah, this is, to me this is a no-brainer, and we're already seeing it in the data. >> Yeah, John Furrier called, you know, the ChatGPT introduction. He said it reminded him of the Netscape moment, when we all first saw Netscape Navigator and went, "Wow, it really could be transformative." All right, number six, the cloud expands to supercloud as edge computing accelerates and CloudFlare is a big winner in 2023. We've reported obviously on cloud, multi-cloud, supercloud and CloudFlare, basically saying what multi-cloud should have been. We pulled this quote from Atif Kahn, who is the founder and CTO of Alkira, thanks, one of the inbounds, thank you. "In 2023, highly distributed IT environments "will become more the norm "as organizations increasingly deploy hybrid cloud, "multi-cloud and edge settings..." Eric, from one of your round tables, "If my sources from edge computing are coming "from the cloud, that means I have my workloads "running in the cloud. "There is no one better than CloudFlare," That's a senior director of IT architecture at a huge financial firm. And then your analysis shows CloudFlare really growing in pervasion, that sort of market presence in the dataset, dramatically, to near 20%, leading, I think you had told me that they're even ahead of Google Cloud in terms of momentum right now. >> That was probably the biggest shock to me in our January 2023 tesis, which covers the public companies in the cloud computing sector. CloudFlare has now overtaken GCP in overall spending, and I was shocked by that. It's already extremely pervasive in networking, of course, for the edge networking side, and also in security. This is the number one leader in SaaSi, web access firewall, DDoS, bot protection, by your definition of supercloud, which we just did a couple of weeks ago, and I really enjoyed that by the way Dave, I think CloudFlare is the one that fits your definition best, because it's bringing all of these aspects together, and most importantly, it's cloud agnostic. It does not need to rely on Azure or AWS to do this. It has its own cloud. So I just think it's, when we look at your definition of supercloud, CloudFlare is the poster child. >> You know, what's interesting about that too, is a lot of people are poo-pooing CloudFlare, "Ah, it's, you know, really kind of not that sophisticated." "You don't have as many tools," but to your point, you're can have those tools in the cloud, Cloudflare's doing serverless on steroids, trying to keep things really simple, doing a phenomenal job at, you know, various locations around the world. And they're definitely one to watch. Somebody put them on my radar (laughing) a while ago and said, "Dave, you got to do a breaking analysis on CloudFlare." And so I want to thank that person. I can't really name them, 'cause they work inside of a giant hyperscaler. But- (Eric laughing) (Dave chuckling) >> Real quickly, if I can from a competitive perspective too, who else is there? They've already taken share from Akamai, and Fastly is their really only other direct comp, and they're not there. And these guys are in poll position and they're the only game in town right now. I just, I don't see it slowing down. >> I thought one of your comments from your roundtable I was reading, one of the folks said, you know, CloudFlare, if my workloads are in the cloud, they are, you know, dominant, they said not as strong with on-prem. And so Akamai is doing better there. I'm like, "Okay, where would you want to be?" (laughing) >> Yeah, which one of those two would you rather be? >> Right? Anyway, all right, let's move on. Number seven, blockchain continues to look for a home in the enterprise, but devs will slowly begin to adopt in 2023. You know, blockchains have got a lot of buzz, obviously crypto is, you know, the killer app for blockchain. Senior IT architect in financial services from your, one of your insight roundtables said quote, "For enterprises to adopt a new technology, "there have to be proven turnkey solutions. "My experience in talking with my peers are, "blockchain is still an open-source component "where you have to build around it." Now I want to thank Ravi Mayuram, who's the CTO of Couchbase sent in, you know, one of the predictions, he said, "DevOps will adopt blockchain, specifically Ethereum." And he referenced actually in his email to me, Solidity, which is the programming language for Ethereum, "will be in every DevOps pro's playbook, "mirroring the boom in machine-learning. "Newer programming languages like Solidity "will enter the toolkits of devs." His point there, you know, Solidity for those of you don't know, you know, Bitcoin is not programmable. Solidity, you know, came out and that was their whole shtick, and they've been improving that, and so forth. But it, Eric, it's true, it really hasn't found its home despite, you know, the potential for smart contracts. IBM's pushing it, VMware has had announcements, and others, really hasn't found its way in the enterprise yet. >> Yeah, and I got to be honest, I don't think it's going to, either. So when we did our top trends series, this was basically chosen as an anti-prediction, I would guess, that it just continues to not gain hold. And the reason why was that first comment, right? It's very much a niche solution that requires a ton of custom work around it. You can't just plug and play it. And at the end of the day, let's be very real what this technology is, it's a database ledger, and we already have database ledgers in the enterprise. So why is this a priority to move to a different database ledger? It's going to be very niche cases. I like the CTO comment from Couchbase about it being adopted by DevOps. I agree with that, but it has to be a DevOps in a very specific use case, and a very sophisticated use case in financial services, most likely. And that's not across the entire enterprise. So I just think it's still going to struggle to get its foothold for a little bit longer, if ever. >> Great, thanks. Okay, let's move on. Number eight, AWS Databricks, Google Snowflake lead the data charge with Microsoft. Keeping it simple. So let's unpack this a little bit. This is the shared accounts peer position for, I pulled data platforms in for analytics, machine-learning and AI and database. So I could grab all these accounts or these vendors and see how they compare in those three sectors. Analytics, machine-learning and database. Snowflake and Databricks, you know, they're on a crash course, as you and I have talked about. They're battling to be the single source of truth in analytics. They're, there's going to be a big focus. They're already started. It's going to be accelerated in 2023 on open formats. Iceberg, Python, you know, they're all the rage. We heard about Iceberg at Snowflake Summit, last summer or last June. Not a lot of people had heard of it, but of course the Databricks crowd, who knows it well. A lot of other open source tooling. There's a company called DBT Labs, which you're going to talk about in a minute. George Gilbert put them on our radar. We just had Tristan Handy, the CEO of DBT labs, on at supercloud last week. They are a new disruptor in data that's, they're essentially making, they're API-ifying, if you will, KPIs inside the data warehouse and dramatically simplifying that whole data pipeline. So really, you know, the ETL guys should be shaking in their boots with them. Coming back to the slide. Google really remains focused on BigQuery adoption. Customers have complained to me that they would like to use Snowflake with Google's AI tools, but they're being forced to go to BigQuery. I got to ask Google about that. AWS continues to stitch together its bespoke data stores, that's gone down that "Right tool for the right job" path. David Foyer two years ago said, "AWS absolutely is going to have to solve that problem." We saw them start to do it in, at Reinvent, bringing together NoETL between Aurora and Redshift, and really trying to simplify those worlds. There's going to be more of that. And then Microsoft, they're just making it cheap and easy to use their stuff, you know, despite some of the complaints that we hear in the community, you know, about things like Cosmos, but Eric, your take? >> Yeah, my concern here is that Snowflake and Databricks are fighting each other, and it's allowing AWS and Microsoft to kind of catch up against them, and I don't know if that's the right move for either of those two companies individually, Azure and AWS are building out functionality. Are they as good? No they're not. The other thing to remember too is that AWS and Azure get paid anyway, because both Databricks and Snowflake run on top of 'em. So (laughing) they're basically collecting their toll, while these two fight it out with each other, and they build out functionality. I think they need to stop focusing on each other, a little bit, and think about the overall strategy. Now for Databricks, we know they came out first as a machine-learning AI tool. They were known better for that spot, and now they're really trying to play catch-up on that data storage compute spot, and inversely for Snowflake, they were killing it with the compute separation from storage, and now they're trying to get into the MLAI spot. I actually wouldn't be surprised to see them make some sort of acquisition. Frank Slootman has been a little bit quiet, in my opinion there. The other thing to mention is your comment about DBT Labs. If we look at our emerging technology survey, last survey when this came out, DBT labs, number one leader in that data integration space, I'm going to just pull it up real quickly. It looks like they had a 33% overall net sentiment to lead data analytics integration. So they are clearly growing, it's fourth straight survey consecutively that they've grown. The other name we're seeing there a little bit is Cribl, but DBT labs is by far the number one player in this space. >> All right. Okay, cool. Moving on, let's go to number nine. With Automation mixer resurgence in 2023, we're showing again data. The x axis is overlap or presence in the dataset, and the vertical axis is shared net score. Net score is a measure of spending momentum. As always, you've seen UI path and Microsoft Power Automate up until the right, that red line, that 40% line is generally considered elevated. UI path is really separating, creating some distance from Automation Anywhere, they, you know, previous quarters they were much closer. Microsoft Power Automate came on the scene in a big way, they loom large with this "Good enough" approach. I will say this, I, somebody sent me a results of a (indistinct) survey, which showed UiPath actually had more mentions than Power Automate, which was surprising, but I think that's not been the case in the ETR data set. We're definitely seeing a shift from back office to front soft office kind of workloads. Having said that, software testing is emerging as a mainstream use case, we're seeing ML and AI become embedded in end-to-end automations, and low-code is serving the line of business. And so this, we think, is going to increasingly have appeal to organizations in the coming year, who want to automate as much as possible and not necessarily, we've seen a lot of layoffs in tech, and people... You're going to have to fill the gaps with automation. That's a trend that's going to continue. >> Yep, agreed. At first that comment about Microsoft Power Automate having less citations than UiPath, that's shocking to me. I'm looking at my chart right here where Microsoft Power Automate was cited by over 60% of our entire survey takers, and UiPath at around 38%. Now don't get me wrong, 38% pervasion's fantastic, but you know you're not going to beat an entrenched Microsoft. So I don't really know where that comment came from. So UiPath, looking at it alone, it's doing incredibly well. It had a huge rebound in its net score this last survey. It had dropped going through the back half of 2022, but we saw a big spike in the last one. So it's got a net score of over 55%. A lot of people citing adoption and increasing. So that's really what you want to see for a name like this. The problem is that just Microsoft is doing its playbook. At the end of the day, I'm going to do a POC, why am I going to pay more for UiPath, or even take on another separate bill, when we know everyone's consolidating vendors, if my license already includes Microsoft Power Automate? It might not be perfect, it might not be as good, but what I'm hearing all the time is it's good enough, and I really don't want another invoice. >> Right. So how does UiPath, you know, and Automation Anywhere, how do they compete with that? Well, the way they compete with it is they got to have a better product. They got a product that's 10 times better. You know, they- >> Right. >> they're not going to compete based on where the lowest cost, Microsoft's got that locked up, or where the easiest to, you know, Microsoft basically give it away for free, and that's their playbook. So that's, you know, up to UiPath. UiPath brought on Rob Ensslin, I've interviewed him. Very, very capable individual, is now Co-CEO. So he's kind of bringing that adult supervision in, and really tightening up the go to market. So, you know, we know this company has been a rocket ship, and so getting some control on that and really getting focused like a laser, you know, could be good things ahead there for that company. Okay. >> One of the problems, if I could real quick Dave, is what the use cases are. When we first came out with RPA, everyone was super excited about like, "No, UiPath is going to be great for super powerful "projects, use cases." That's not what RPA is being used for. As you mentioned, it's being used for mundane tasks, so it's not automating complex things, which I think UiPath was built for. So if you were going to get UiPath, and choose that over Microsoft, it's going to be 'cause you're doing it for more powerful use case, where it is better. But the problem is that's not where the enterprise is using it. The enterprise are using this for base rote tasks, and simply, Microsoft Power Automate can do that. >> Yeah, it's interesting. I've had people on theCube that are both Microsoft Power Automate customers and UiPath customers, and I've asked them, "Well you know, "how do you differentiate between the two?" And they've said to me, "Look, our users and personal productivity users, "they like Power Automate, "they can use it themselves, and you know, "it doesn't take a lot of, you know, support on our end." The flip side is you could do that with UiPath, but like you said, there's more of a focus now on end-to-end enterprise automation and building out those capabilities. So it's increasingly a value play, and that's going to be obviously the challenge going forward. Okay, my last one, and then I think you've got some bonus ones. Number 10, hybrid events are the new category. Look it, if I can get a thousand inbounds that are largely self-serving, I can do my own here, 'cause we're in the events business. (Eric chuckling) Here's the prediction though, and this is a trend we're seeing, the number of physical events is going to dramatically increase. That might surprise people, but most of the big giant events are going to get smaller. The exception is AWS with Reinvent, I think Snowflake's going to continue to grow. So there are examples of physical events that are growing, but generally, most of the big ones are getting smaller, and there's going to be many more smaller intimate regional events and road shows. These micro-events, they're going to be stitched together. Digital is becoming a first class citizen, so people really got to get their digital acts together, and brands are prioritizing earned media, and they're beginning to build their own news networks, going direct to their customers. And so that's a trend we see, and I, you know, we're right in the middle of it, Eric, so you know we're going to, you mentioned RSA, I think that's perhaps going to be one of those crazy ones that continues to grow. It's shrunk, and then it, you know, 'cause last year- >> Yeah, it did shrink. >> right, it was the last one before the pandemic, and then they sort of made another run at it last year. It was smaller but it was very vibrant, and I think this year's going to be huge. Global World Congress is another one, we're going to be there end of Feb. That's obviously a big big show, but in general, the brands and the technology vendors, even Oracle is going to scale down. I don't know about Salesforce. We'll see. You had a couple of bonus predictions. Quantum and maybe some others? Bring us home. >> Yeah, sure. I got a few more. I think we touched upon one, but I definitely think the data prep tools are facing extinction, unfortunately, you know, the Talons Informatica is some of those names. The problem there is that the BI tools are kind of including data prep into it already. You know, an example of that is Tableau Prep Builder, and then in addition, Advanced NLP is being worked in as well. ThoughtSpot, Intelius, both often say that as their selling point, Tableau has Ask Data, Click has Insight Bot, so you don't have to really be intelligent on data prep anymore. A regular business user can just self-query, using either the search bar, or even just speaking into what it needs, and these tools are kind of doing the data prep for it. I don't think that's a, you know, an out in left field type of prediction, but it's the time is nigh. The other one I would also state is that I think knowledge graphs are going to break through this year. Neo4j in our survey is growing in pervasion in Mindshare. So more and more people are citing it, AWS Neptune's getting its act together, and we're seeing that spending intentions are growing there. Tiger Graph is also growing in our survey sample. I just think that the time is now for knowledge graphs to break through, and if I had to do one more, I'd say real-time streaming analytics moves from the very, very rich big enterprises to downstream, to more people are actually going to be moving towards real-time streaming, again, because the data prep tools and the data pipelines have gotten easier to use, and I think the ROI on real-time streaming is obviously there. So those are three that didn't make the cut, but I thought deserved an honorable mention. >> Yeah, I'm glad you did. Several weeks ago, we did an analyst prediction roundtable, if you will, a cube session power panel with a number of data analysts and that, you know, streaming, real-time streaming was top of mind. So glad you brought that up. Eric, as always, thank you very much. I appreciate the time you put in beforehand. I know it's been crazy, because you guys are wrapping up, you know, the last quarter survey in- >> Been a nuts three weeks for us. (laughing) >> job. I love the fact that you're doing, you know, the ETS survey now, I think it's quarterly now, right? Is that right? >> Yep. >> Yep. So that's phenomenal. >> Four times a year. I'll be happy to jump on with you when we get that done. I know you were really impressed with that last time. >> It's unbelievable. This is so much data at ETR. Okay. Hey, that's a wrap. Thanks again. >> Take care Dave. Good seeing you. >> All right, many thanks to our team here, Alex Myerson as production, he manages the podcast force. Ken Schiffman as well is a critical component of our East Coast studio. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hoof is our editor-in-chief. He's at siliconangle.com. He's just a great editing for us. Thank you all. Remember all these episodes that are available as podcasts, wherever you listen, podcast is doing great. Just search "Breaking analysis podcast." Really appreciate you guys listening. I publish each week on wikibon.com and siliconangle.com, or you can email me directly if you want to get in touch, david.vellante@siliconangle.com. That's how I got all these. I really appreciate it. I went through every single one with a yellow highlighter. It took some time, (laughing) but I appreciate it. You could DM me at dvellante, or comment on our LinkedIn post and please check out etr.ai. Its data is amazing. Best survey data in the enterprise tech business. This is Dave Vellante for theCube Insights, powered by ETR. Thanks for watching, and we'll see you next time on "Breaking Analysis." (upbeat music beginning) (upbeat music ending)

(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)

Hello and welcome back to Supercloud 2 where we examine the intersection of cloud and data in the 2020s. My name is Dave Vellante. Our Supercloud panel, our power panel is back. Maribel Lopez is the founder and principal analyst at Lopez Research. Sanjeev Mohan is former Gartner analyst and principal at Sanjeev Mohan. And Keith Townsend is the CTO advisor. Folks, welcome back and thanks for your participation today. Good to see you. >> Okay, great. >> Great to see you. >> Thanks. Let me start, Maribel, with you. Bob Muglia, we had a conversation as part of Supercloud the other day. And he said, "Dave, I like the work, you got to simplify this a little bit." So he said, quote, "A Supercloud is a platform." He said, "Think of it as a platform that provides programmatically consistent services hosted on heterogeneous cloud providers." And then Nelu Mihai said, "Well, wait a minute. This is just going to create more stove pipes. We need more standards in an architecture," which is kind of what Berkeley Sky Computing initiative is all about. So there's a sort of a debate going on. Is supercloud an architecture, a platform? Or maybe it's just another buzzword. Maribel, do you have a thought on this? >> Well, the easy answer would be to say it's just a buzzword. And then we could just kill the conversation and be done with it. But I think the term, it's more than that, right? The term actually isn't new. You can go back to at least 2016 and find references to supercloud in Cornell University or assist in other documents. So, having said this, I think we've been talking about Supercloud for a while, so I assume it's more than just a fancy buzzword. But I think it really speaks to that undeniable trend of moving towards an abstraction layer to deal with the chaos of what we consider managing multiple public and private clouds today, right? So one definition of the technology platform speaks to a set of services that allows companies to build and run that technology smoothly without worrying about the underlying infrastructure, which really gets back to something that Bob said. And some of the question is where that lives. And you could call that an abstraction layer. You could call it cross-cloud services, hybrid cloud management. So I see momentum there, like legitimate momentum with enterprise IT buyers that are trying to deal with the fact that they have multiple clouds now. So where I think we're moving is trying to define what are the specific attributes and frameworks of that that would make it so that it could be consistent across clouds. What is that layer? And maybe that's what the supercloud is. But one of the things I struggle with with supercloud is. What are we really trying to do here? Are we trying to create differentiated services in the supercloud layer? Is a supercloud just another variant of what AWS, GCP, or others do? You spoken to Walmart about its cloud native platform, and that's an example of somebody deciding to do it themselves because they need to deal with this today and not wait for some big standards thing to happen. So whatever it is, I do think it's something. I think we're trying to maybe create an architecture out of it would be a better way of saying it so that it does get to those set of principles, but it also needs to be edge aware. I think whenever we talk about supercloud, we're always talking about like the big centralized cloud. And I think we need to think about all the distributed clouds that we're looking at in edge as well. So that might be one of the ways that supercloud evolves. >> So thank you, Maribel. Keith, Brian Gracely, Gracely's law, things kind of repeat themselves. We've seen it all before. And so what Muglia brought to the forefront is this idea of a platform where the platform provider is really responsible for the architecture. Of course, the drawback is then you get a a bunch of stove pipes architectures. But practically speaking, that's kind of the way the industry has always evolved, right? >> So if we look at this from the practitioner's perspective and we talk about platforms, traditionally vendors have provided the platforms for us, whether it's distribution of lineage managed by or provided by Red Hat, Windows, servers, .NET, databases, Oracle. We think of those as platforms, things that are fundamental we can build on top. Supercloud isn't today that. It is a framework or idea, kind of a visionary goal to get to a point that we can have a platform or a framework. But what we're seeing repeated throughout the industry in customers, whether it's the Walmarts that's kind of supersized the idea of supercloud, or if it's regular end user organizations that are coming out with platform groups, groups who normalize cloud native infrastructure, AWS multi-cloud, VMware resources to look like one thing internally to their developers. We're seeing this trend that there's a desire for a platform that provides the capabilities of a supercloud. >> Thank you for that. Sanjeev, we often use Snowflake as a supercloud example, and now would presumably would be a platform with an architecture that's determined by the vendor. Maybe Databricks is pushing for a more open architecture, maybe more of that nirvana that we were talking about before to solve for supercloud. But regardless, the practitioner discussions show. At least currently, there's not a lot of cross-cloud data sharing. I think it could be a killer use case, egress charges or a barrier. But how do you see it? Will that change? Will we hide that underlying complexity and start sharing data across cloud? Is that something that you think Snowflake or others will be able to achieve? >> So I think we are already starting to see some of that happen. Snowflake is definitely one example that gets cited a lot. But even we don't talk about MongoDB in this like, but you could have a MongoDB cluster, for instance, with nodes sitting in different cloud providers. So there are companies that are starting to do it. The advantage that these companies have, let's take Snowflake as an example, it's a centralized proprietary platform. And they are building the capabilities that are needed for supercloud. So they're building things like you can push down your data transformations. They have the entire security and privacy suite. Data ops, they're adding those capabilities. And if I'm not mistaken, it'll be very soon, we will see them offer data observability. So it's all works great as long as you are in one platform. And if you want resilience, then Snowflake, Supercloud, great example. But if your primary goal is to choose the most cost-effective service irrespective of which cloud it sits in, then things start falling sideways. For example, I may be a very big Snowflake user. And I like Snowflake's resilience. I can move from one cloud to another cloud. Snowflake does it for me. But what if I want to train a very large model? Maybe Databricks is a better platform for that. So how do I do move my workload from one platform to another platform? That tooling does not exist. So we need server hybrid, cross-cloud, data ops platform. Walmart has done a great job, but they built it by themselves. Not every company is Walmart. Like Maribel and Keith said, we need standards, we need reference architectures, we need some sort of a cost control. I was just reading recently, Accenture has been public about their AWS bill. Every time they get the bill is tens of millions of lines, tens of millions 'cause there are over thousand teams using AWS. If we have not been able to corral a usage of a single cloud, now we're talking about supercloud, we've got multiple clouds, and hybrid, on-prem, and edge. So till we've got some cross-platform tooling in place, I think this will still take quite some time for it to take shape. >> It's interesting. Maribel, Walmart would tell you that their on-prem infrastructure is cheaper to run than the stuff in the cloud. but at the same time, they want the flexibility and the resiliency of their three-legged stool model. So the point as Sanjeev was making about hybrid. It's an interesting balance, isn't it, between getting your lowest cost and at the same time having best of breed and scale? >> It's basically what you're trying to optimize for, as you said, right? And by the way, to the earlier point, not everybody is at Walmart's scale, so it's not actually cheaper for everybody to have the purchasing power to make the cloud cheaper to have it on-prem. But I think what you see almost every company, large or small, moving towards is this concept of like, where do I find the agility? And is the agility in building the infrastructure for me? And typically, the thing that gives you outside advantage as an organization is not how you constructed your cloud computing infrastructure. It might be how you structured your data analytics as an example, which cloud is related to that. But how do you marry those two things? And getting back to sort of Sanjeev's point. We're in a real struggle now where one hand we want to have best of breed services and on the other hand we want it to be really easy to manage, secure, do data governance. And those two things are really at odds with each other right now. So if you want all the knobs and switches of a service like geospatial analytics and big query, you're going to have to use Google tools, right? Whereas if you want visibility across all the clouds for your application of state and understand the security and governance of that, you're kind of looking for something that's more cross-cloud tooling at that point. But whenever you talk to somebody about cross-cloud tooling, they look at you like that's not really possible. So it's a very interesting time in the market. Now, we're kind of layering this concept of supercloud on it. And some people think supercloud's about basically multi-cloud tooling, and some people think it's about a whole new architectural stack. So we're just not there yet. But it's not all about cost. I mean, cloud has not been about cost for a very, very long time. Cloud has been about how do you really make the most of your data. And this gets back to cross-cloud services like Snowflake. Why did they even exist? They existed because we had data everywhere, but we need to treat data as a unified object so that we can analyze it and get insight from it. And so that's where some of the benefit of these cross-cloud services are moving today. Still a long way to go, though, Dave. >> Keith, I reached out to my friends at ETR given the macro headwinds, And you're right, Maribel, cloud hasn't really been about just about cost savings. But I reached out to the ETR, guys, what's your data show in terms of how customers are dealing with the economic headwinds? And they said, by far, their number one strategy to cut cost is consolidating redundant vendors. And a distant second, but still notable was optimizing cloud costs. Maybe using reserve instances, or using more volume buying. Nowhere in there. And I asked them to, "Could you go look and see if you can find it?" Do we see repatriation? And you hear this a lot. You hear people whispering as analysts, "You better look into that repatriation trend." It's pretty big. You can't find it. But some of the Walmarts in the world, maybe even not repatriating, but they maybe have better cost structure on-prem. Keith, what are you seeing from the practitioners that you talk to in terms of how they're dealing with these headwinds? >> Yeah, I just got into a conversation about this just this morning with (indistinct) who is an analyst over at GigaHome. He's reading the same headlines. Repatriation is happening at large scale. I think this is kind of, we have these quiet terms now. We have quiet quitting, we have quiet hiring. I think we have quiet repatriation. Most people haven't done away with their data centers. They're still there. Whether they're completely on-premises data centers, and they own assets, or they're partnerships with QTX, Equinix, et cetera, they have these private cloud resources. What I'm seeing practically is a rebalancing of workloads. Do I really need to pay AWS for this instance of SAP that's on 24 hours a day versus just having it on-prem, moving it back to my data center? I've talked to quite a few customers who were early on to moving their static SAP workloads onto the public cloud, and they simply moved them back. Surprising, I was at VMware Explore. And we can talk about this a little bit later on. But our customers, net new, not a lot that were born in the cloud. And they get to this point where their workloads are static. And they look at something like a Kubernetes, or a OpenShift, or VMware Tanzu. And they ask the question, "Do I need the scalability of cloud?" I might consider being a net new VMware customer to deliver this base capability. So are we seeing repatriation as the number one reason? No, I think internal IT operations are just naturally come to this realization. Hey, I have these resources on premises. The private cloud technologies have moved far along enough that I can just simply move this workload back. I'm not calling it repatriation, I'm calling it rightsizing for the operating model that I have. >> Makes sense. Yeah. >> Go ahead. >> If I missed something, Dave, why we are on this topic of repatriation. I'm actually surprised that we are talking about repatriation as a very big thing. I think repatriation is happening, no doubt, but it's such a small percentage of cloud migration that to me it's a rounding error in my opinion. I think there's a bigger problem. The problem is that people don't know where the cost is. If they knew where the cost was being wasted in the cloud, they could do something about it. But if you don't know, then the easy answer is cloud costs a lot and moving it back to on-premises. I mean, take like Capital One as an example. They got rid of all the data centers. Where are they going to repatriate to? They're all in the cloud at this point. So I think my point is that data observability is one of the places that has seen a lot of traction is because of cost. Data observability, when it first came into existence, it was all about data quality. Then it was all about data pipeline reliability. And now, the number one killer use case is FinOps. >> Maribel, you had a comment? >> Yeah, I'm kind of in violent agreement with both Sanjeev and Keith. So what are we seeing here? So the first thing that we see is that many people wildly overspent in the big public cloud. They had stranded cloud credits, so to speak. The second thing is, some of them still had infrastructure that was useful. So why not use it if you find the right workloads to what Keith was talking about, if they were more static workloads, if it was already there? So there is a balancing that's going on. And then I think fundamentally, from a trend standpoint, these things aren't binary. Everybody, for a while, everything was going to go to the public cloud and then people are like, "Oh, it's kind of expensive." Then they're like, "Oh no, they're going to bring it all on-prem 'cause it's really expensive." And it's like, "Well, that doesn't necessarily get me some of the new features and functionalities I might want for some of my new workloads." So I'm going to put the workloads that have a certain set of characteristics that require cloud in the cloud. And if I have enough capability on-prem and enough IT resources to manage certain things on site, then I'm going to do that there 'cause that's a more cost-effective thing for me to do. It's not binary. That's why we went to hybrid. And then we went to multi just to describe the fact that people added multiple public clouds. And now we're talking about super, right? So I don't look at it as a one-size-fits-all for any of this. >> A a number of practitioners leading up to Supercloud2 have told us that they're solving their cloud complexity by going in monocloud. So they're putting on the blinders. Even though across the organization, there's other groups using other clouds. You're like, "In my group, we use AWS, or my group, we use Azure. And those guys over there, they use Google. We just kind of keep it separate." Are you guys hearing this in your view? Is that risky? Are they missing out on some potential to tap best of breed? What do you guys think about that? >> Everybody thinks they're monocloud. Is anybody really monocloud? It's like a group is monocloud, right? >> Right. >> This genie is out of the bottle. We're not putting the genie back in the bottle. You might think your monocloud and you go like three doors down and figure out the guy or gal is on a fundamentally different cloud, running some analytics workload that you didn't know about. So, to Sanjeev's earlier point, they don't even know where their cloud spend is. So I think the concept of monocloud, how that's actually really realized by practitioners is primary and then secondary sources. So they have a primary cloud that they run most of their stuff on, and that they try to optimize. And we still have forked workloads. Somebody decides, "Okay, this SAP runs really well on this, or these analytics workloads run really well on that cloud." And maybe that's how they parse it. But if you really looked at it, there's very few companies, if you really peaked under the hood and did an analysis that you could find an actual monocloud structure. They just want to pull it back in and make it more manageable. And I respect that. You want to do what you can to try to streamline the complexity of that. >> Yeah, we're- >> Sorry, go ahead, Keith. >> Yeah, we're doing this thing where we review AWS service every day. Just in your inbox, learn about a new AWS service cursory. There's 238 AWS products just on the AWS cloud itself. Some of them are redundant, but you get the idea. So the concept of monocloud, I'm in filing agreement with Maribel on this that, yes, a group might say I want a primary cloud. And that primary cloud may be the AWS. But have you tried the licensed Oracle database on AWS? It is really tempting to license Oracle on Oracle Cloud, Microsoft on Microsoft. And I can't get RDS anywhere but Amazon. So while I'm driven to desire the simplicity, the reality is whether be it M&A, licensing, data sovereignty. I am forced into a multi-cloud management style. But I do agree most people kind of do this one, this primary cloud, secondary cloud. And I guarantee you're going to have a third cloud or a fourth cloud whether you want to or not via shadow IT, latency, technical reasons, et cetera. >> Thank you. Sanjeev, you had a comment? >> Yeah, so I just wanted to mention, as an organization, I'm complete agreement, no organization is monocloud, at least if it's a large organization. Large organizations use all kinds of combinations of cloud providers. But when you talk about a single workload, that's where the program arises. As Keith said, the 238 services in AWS. How in the world am I going to be an expert in AWS, but then say let me bring GCP or Azure into a single workload? And that's where I think we probably will still see monocloud as being predominant because the team has developed its expertise on a particular cloud provider, and they just don't have the time of the day to go learn yet another stack. However, there are some interesting things that are happening. For example, if you look at a multi-cloud example where Oracle and Microsoft Azure have that interconnect, so that's a beautiful thing that they've done because now in the newest iteration, it's literally a few clicks. And then behind the scene, your .NET application and your Oracle database in OCI will be configured, the identities in active directory are federated. And you can just start using a database in one cloud, which is OCI, and an application, your .NET in Azure. So till we see this kind of a solution coming out of the providers, I think it's is unrealistic to expect the end users to be able to figure out multiple clouds. >> Well, I have to share with you. I can't remember if he said this on camera or if it was off camera so I'll hold off. I won't tell you who it is, but this individual was sort of complaining a little bit saying, "With AWS, I can take their best AI tools like SageMaker and I can run them on my Snowflake." He said, "I can't do that in Google. Google forces me to go to BigQuery if I want their excellent AI tools." So he was sort of pushing, kind of tweaking a little bit. Some of the vendor talked that, "Oh yeah, we're so customer-focused." Not to pick on Google, but I mean everybody will say that. And then you say, "If you're so customer-focused, why wouldn't you do a ABC?" So it's going to be interesting to see who leads that integration and how broadly it's applied. But I digress. Keith, at our first supercloud event, that was on August 9th. And it was only a few months after Broadcom announced the VMware acquisition. A lot of people, myself included said, "All right, cuts are coming." Generally, Tanzu is probably going to be under the radar, but it's Supercloud 22 and presumably VMware Explore, the company really... Well, certainly the US touted its Tanzu capabilities. I wasn't at VMware Explore Europe, but I bet you heard similar things. Hawk Tan has been blogging and very vocal about cross-cloud services and multi-cloud, which doesn't happen without Tanzu. So what did you hear, Keith, in Europe? What's your latest thinking on VMware's prospects in cross-cloud services/supercloud? >> So I think our friend and Cube, along host still be even more offended at this statement than he was when I sat in the Cube. This was maybe five years ago. There's no company better suited to help industries or companies, cross-cloud chasm than VMware. That's not a compliment. That's a reality of the industry. This is a very difficult, almost intractable problem. What I heard that VMware Europe were customers serious about this problem, even more so than the US data sovereignty is a real problem in the EU. Try being a company in Switzerland and having the Swiss data solvency issues. And there's no local cloud presence there large enough to accommodate your data needs. They had very serious questions about this. I talked to open source project leaders. Open source project leaders were asking me, why should I use the public cloud to host Kubernetes-based workloads, my projects that are building around Kubernetes, and the CNCF infrastructure? Why should I use AWS, Google, or even Azure to host these projects when that's undifferentiated? I know how to run Kubernetes, so why not run it on-premises? I don't want to deal with the hardware problems. So again, really great questions. And then there was always the specter of the problem, I think, we all had with the acquisition of VMware by Broadcom potentially. 4.5 billion in increased profitability in three years is a unbelievable amount of money when you look at the size of the problem. So a lot of the conversation in Europe was about industry at large. How do we do what regulators are asking us to do in a practical way from a true technology sense? Is VMware cross-cloud great? >> Yeah. So, VMware, obviously, to your point. OpenStack is another way of it. Actually, OpenStack, uptake is still alive and well, especially in those regions where there may not be a public cloud, or there's public policy dictating that. Walmart's using OpenStack. As you know in IT, some things never die. Question for Sanjeev. And it relates to this new breed of data apps. And Bob Muglia and Tristan Handy from DBT Labs who are participating in this program really got us thinking about this. You got data that resides in different clouds, it maybe even on-prem. And the machine polls data from different systems. No humans involved, e-commerce, ERP, et cetera. It creates a plan, outcomes. No human involvement. Today, you're on a CRM system, you're inputting, you're doing forms, you're, you're automating processes. We're talking about a new breed of apps. What are your thoughts on this? Is it real? Is it just way off in the distance? How does machine intelligence fit in? And how does supercloud fit? >> So great point. In fact, the data apps that you're talking about, I call them data products. Data products first came into limelight in the last couple of years when Jamal Duggan started talking about data mesh. I am taking data products out of the data mesh concept because data mesh, whether data mesh happens or not is analogous to data products. Data products, basically, are taking a product management view of bringing data from different sources based on what the consumer needs. We were talking earlier today about maybe it's my vacation rentals, or it may be a retail data product, it may be an investment data product. So it's a pre-packaged extraction of data from different sources. But now I have a product that has a whole lifecycle. I can version it. I have new features that get added. And it's a very business data consumer centric. It uses machine learning. For instance, I may be able to tell whether this data product has stale data. Who is using that data? Based on the usage of the data, I may have a new data products that get allocated. I may even have the ability to take existing data products, mash them up into something that I need. So if I'm going to have that kind of power to create a data product, then having a common substrate underneath, it can be very useful. And that could be supercloud where I am making API calls. I don't care where the ERP, the CRM, the survey data, the pricing engine where they sit. For me, there's a logical abstraction. And then I'm building my data product on top of that. So I see a new breed of data products coming out. To answer your question, how early we are or is this even possible? My prediction is that in 2023, we will start seeing more of data products. And then it'll take maybe two to three years for data products to become mainstream. But it's starting this year. >> A subprime mortgages were a data product, definitely were humans involved. All right, let's talk about some of the supercloud, multi-cloud players and what their future looks like. You can kind of pick your favorites. VMware, Snowflake, Databricks, Red Hat, Cisco, Dell, HP, Hashi, IBM, CloudFlare. There's many others. cohesive rubric. Keith, I wanted to start with CloudFlare because they actually use the term supercloud. and just simplifying what they said. They look at it as taking serverless to the max. You write your code and then you can deploy it in seconds worldwide, of course, across the CloudFlare infrastructure. You don't have to spin up containers, you don't go to provision instances. CloudFlare worries about all that infrastructure. What are your thoughts on CloudFlare this approach and their chances to disrupt the current cloud landscape? >> As Larry Ellison said famously once before, the network is the computer, right? I thought that was Scott McNeley. >> It wasn't Scott McNeley. I knew it was on Oracle Align. >> Oracle owns that now, owns that line. >> By purpose or acquisition. >> They should have just called it cloud. >> Yeah, they should have just called it cloud. >> Easier. >> Get ahead. >> But if you think about the CloudFlare capability, CloudFlare in its own right is becoming a decent sized cloud provider. If you have compute out at the edge, when we talk about edge in the sense of CloudFlare and points of presence, literally across the globe, you have all of this excess computer, what do you do with it? First offering, let's disrupt data in the cloud. We can't start the conversation talking about data. When they say we're going to give you object-oriented or object storage in the cloud without egress charges, that's disruptive. That we can start to think about supercloud capability of having compute EC2 run in AWS, pushing and pulling data from CloudFlare. And now, I've disrupted this roach motel data structure, and that I'm freely giving away bandwidth, basically. Well, the next layer is not that much more difficult. And I think part of CloudFlare's serverless approach or supercloud approaches so that they don't have to commit to a certain type of compute. It is advantageous. It is a feature for me to be able to go to EC2 and pick a memory heavy model, or a compute heavy model, or a network heavy model, CloudFlare is taken away those knobs. and I'm just giving code and allowing that to run. CloudFlare has a massive network. If I can put the code closest using the CloudFlare workers, if I can put that code closest to where the data is at or residing, super compelling observation. The question is, does it scale? I don't get the 238 services. While Server List is great, I have to know what I'm going to build. I don't have a Cognito, or RDS, or all these other services that make AWS, GCP, and Azure appealing from a builder's perspective. So it is a very interesting nascent start. It's great because now they can hide compute. If they don't have the capacity, they can outsource that maybe at a cost to one of the other cloud providers, but kind of hiding the compute behind the surplus architecture is a really unique approach. >> Yeah. And they're dipping their toe in the water. And they've announced an object store and a database platform and more to come. We got to wrap. So I wonder, Sanjeev and Maribel, if you could maybe pick some of your favorites from a competitive standpoint. Sanjeev, I felt like just watching Snowflake, I said, okay, in my opinion, they had the right strategy, which was to run on all the clouds, and then try to create that abstraction layer and data sharing across clouds. Even though, let's face it, most of it might be happening across regions if it's happening, but certainly outside of an individual account. But I felt like just observing them that anybody who's traditional on-prem player moving into the clouds or anybody who's a cloud native, it just makes total sense to write to the various clouds. And to the extent that you can simplify that for users, it seems to be a logical strategy. Maybe as I said before, what multi-cloud should have been. But are there companies that you're watching that you think are ahead in the game , or ones that you think are a good model for the future? >> Yes, Snowflake, definitely. In fact, one of the things we have not touched upon very much, and Keith mentioned a little bit, was data sovereignty. Data residency rules can require that certain data should be written into certain region of a certain cloud. And if my cloud provider can abstract that or my database provider, then that's perfect for me. So right now, I see Snowflake is way ahead of this pack. I would not put MongoDB too far behind. They don't really talk about this thing. They are in a different space, but now they have a lakehouse, and they've got all of these other SQL access and new capabilities that they're announcing. So I think they would be quite good with that. Oracle is always a dark forest. Oracle seems to have revived its Cloud Mojo to some extent. And it's doing some interesting stuff. Databricks is the other one. I have not seen Databricks. They've been very focused on lakehouse, unity, data catalog, and some of those pieces. But they would be the obvious challenger. And if they come into this space of supercloud, then they may bring some open source technologies that others can rely on like Delta Lake as a table format. >> Yeah. One of these infrastructure players, Dell, HPE, Cisco, even IBM. I mean, I would be making my infrastructure as programmable and cloud friendly as possible. That seems like table stakes. But Maribel, any companies that stand out to you that we should be paying attention to? >> Well, we already mentioned a bunch of them, so maybe I'll go a slightly different route. I'm watching two companies pretty closely to see what kind of traction they get in their established companies. One we already talked about, which is VMware. And the thing that's interesting about VMware is they're everywhere. And they also have the benefit of having a foot in both camps. If you want to do it the old way, the way you've always done it with VMware, they got all that going on. If you want to try to do a more cross-cloud, multi-cloud native style thing, they're really trying to build tools for that. So I think they have really good access to buyers. And that's one of the reasons why I'm interested in them to see how they progress. The other thing, I think, could be a sleeping horse oddly enough is Google Cloud. They've spent a lot of work and time on Anthos. They really need to create a certain set of differentiators. Well, it's not necessarily in their best interest to be the best multi-cloud player. If they decide that they want to differentiate on a different layer of the stack, let's say they want to be like the person that is really transformative, they talk about transformation cloud with analytics workloads, then maybe they do spend a good deal of time trying to help people abstract all of the other underlying infrastructure and make sure that they get the sexiest, most meaningful workloads into their cloud. So those are two people that you might not have expected me to go with, but I think it's interesting to see not just on the things that might be considered, either startups or more established independent companies, but how some of the traditional providers are trying to reinvent themselves as well. >> I'm glad you brought that up because if you think about what Google's done with Kubernetes. I mean, would Google even be relevant in the cloud without Kubernetes? I could argue both sides of that. But it was quite a gift to the industry. And there's a motivation there to do something unique and different from maybe the other cloud providers. And I'd throw in Red Hat as well. They're obviously a key player and Kubernetes. And Hashi Corp seems to be becoming the standard for application deployment, and terraform, or cross-clouds, and there are many, many others. I know we're leaving lots out, but we're out of time. Folks, I got to thank you so much for your insights and your participation in Supercloud2. Really appreciate it. >> Thank you. >> Thank you. >> Thank you. >> This is Dave Vellante for John Furrier and the entire Cube community. Keep it right there for more content from Supercloud2.

>> 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)

(upbeat music) >> Welcome back to Supercloud 2, which is an open industry collaboration between technologists, consultants, analysts, and of course, practitioners, to help shape the future of cloud. And 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 we call supercloud. And we're pleased to welcome Harvir Singh, who's the chief data architect and global head of data at Western Union. Harvir, it's good to see you again. Thanks for coming on the program. >> Thanks, David, it's always a pleasure to talk to you. >> So many things stand out from when we first met, and one of the most gripping for me was when you said to me, "When data moves, money moves." And that's the world we live in today, and really have for a long time. Money has moved as bits, and when it has to move, we want it to move quickly, securely, and in a governed manner. And the pressure to do so is only growing. So tell us how that trend is evolved over the past decade in the context of your industry generally, and Western Union, specifically. Look, I always say to people that we are probably the first ones to introduce digital currency around the world because, hey, somebody around the world needs money, we move data to make that happen. That trend has actually accelerated quite a bit. If you look at the last 10 years, and you look at all these payment companies, digital companies, credit card companies that have evolved, majority of them are working on the same principle. When data moves, money moves. When data is stale, the money goes away, right? I think that trend is continuing, and it's not just the trend is in this space, it's also continuing in other spaces, specifically around, you know, acquisition of customers, communication with customers. It's all becoming digital, and it's, at the end of the day, it's all data being moved from one place or another. At the end of the day, you're not seeing the customer, but you're looking at, you know, the data that he's consuming, and you're making actionable items on it, and be able to respond to what they need. So I think 10 years, it's really, really evolved. >> Hmm, you operate, Western Union operates in more than 200 countries, and you you have what I would call a pseudo federated organization. You're trying to standardize wherever possible on the infrastructure, and you're curating the tooling and doing the heavy lifting in the data stack, which of course lessens the burden on the developers and the line of business consumers, so my question is, in operating in 200 countries, how do you deal with all the diversity of laws and regulations across those regions? I know you're heavily involved in AWS, but AWS isn't everywhere, you still have some on-prem infrastructure. Can you paint a picture of, you know, what that looks like? >> Yeah, a few years ago , we were primarily mostly on-prem, and one of the biggest pain points has been managing that infrastructure around the world in those countries. Yes, we operate in 200 countries, but we don't have infrastructure in 200 countries, but we do have agent locations in 200 countries. United Nations says we only have like 183 are countries, but there are countries which, you know, declare themselves countries, and we are there as well because somebody wants to send money there, right? Somebody has an agent location down there as well. So that infrastructure is obviously very hard to manage and maintain. We have to comply by numerous laws, you know. And the last few years, specifically with GDPR, CCPA, data localization laws in different countries, it's been a challenge, right? And one of the things that we did a few years ago, we decided that we want to be in the business of helping our customers move money faster, security, and with complete trust in us. We don't want to be able to, we don't want to be in the business of managing infrastructure. And that's one of the reasons we started to, you know, migrate and move our journey to the cloud. AWS, obviously chosen first because of its, you know, first in the game, has more locations, and more data centers around the world where we operate. But we still have, you know, existing infrastructure, which is in some countries, which is still localized because AWS hasn't reached there, or we don't have a comparable provider there. We still manage those. And we have to comply by those laws. Our data privacy and our data localization tech stack is pretty good, I would say. We manage our data very well, we manage our customer data very well, but it comes with a lot of complexity. You know, we get a lot of requests from European Union, we get a lot of requests from Asia Pacific every pretty much on a weekly basis to explain, you know, how we are taking controls and putting measures in place to make sure that the data is secured and is in the right place. So it's a complex environment. We do have exposure to other clouds as well, like Google and Azure. And as much as we would love to be completely, you know, very, very hybrid kind of an organization, it's still at a stage where we are still very heavily focused on AWS yet, but at some point, you know, we would love to see a world which is not reliant on a single provider, but it's more a little bit more democratized, you know, as and when what I want to use, I should be able to use, and pay-per-use. And the concept started like that, but it's obviously it's now, again, there are like three big players in the market, and, you know, they're doing their own thing. Would love to see them come collaborate at some point. >> Yeah, wouldn't we all. I want to double-click on the whole multi-cloud strategy, but if I understand it correctly, and in a perfect world, everything on-premises would be in the cloud is, first of all, is that a correct statement? Is that nirvana for you or not necessarily? >> I would say it is nirvana for us, but I would also put a caveat, is it's very tricky because from a regulatory perspective, we are a regulated entity in many countries. The regulators would want to see some control if something happens with a relationship with AWS in one country, or with Google in another country, and it keeps happening, right? For example, Russia was a good example where we had to switch things off. We should be able to do that. But if let's say somewhere in Asia, this country decides that they don't want to partner with AWS, and majority of our stuff is on AWS, where do I go from there? So we have to have some level of confidence in our own infrastructure, so we do maintain some to be able to fail back into and move things it needs to be. So it's a tricky question. Yes, it's nirvana state that I don't have to manage infrastructure, but I think it's far less practical than it said. We will still own something that we call it our own where we have complete control, being a financial entity. >> And so do you try to, I'm sure you do, standardize between all the different on-premise, and in this case, the AWS cloud or maybe even other clouds. How do you do that? Do you work with, you know, different vendors at the various places of the stack to try to do that? Some of the vendors, you know, like a Snowflake is only in the cloud. You know, others, you know, whether it's whatever, analytics, or storage, or database, might be hybrid. What's your strategy with regard to creating as common an experience as possible between your on-prem and your clouds? >> You asked a question which I asked when I joined as well, right? Which question, this is one of the most important questions is how soon when I fail back, if I need to fail back? And how quickly can I, because not everything that is sitting on the cloud is comparable to on-prem or is backward compatible. And the reason I say backward compatible is, you know, there are, our on-prem cloud is obviously behind. We haven't taken enough time to kind of put it to a state where, because we started to migrate and now we have access to infrastructure on the cloud, most of the new things are being built there. But for critical application, I would say we have chronology that could be used to move back if need to be. So, you know, technologies like Couchbase, technologies like PostgreSQL, technologies like Db2, et cetera. We still have and maintain a fairly large portion of it on-prem where critical applications could potentially be serviced. We'll give you one example. We use Neo4j very heavily for our AML use cases. And that's an important one because if Neo4j on the cloud goes down, and it's happened in the past, again, even with three clusters, having all three clusters going down with a DR, we still need some accessibility of that because that's one of the biggest, you know, fraud and risk application it supports. So we do still maintain some comparable technology. Snowflake is an odd one. It's obviously there is none on-prem. But then, you know, Snowflake, I also feel it's more analytical based technology, not a transactional-based technology, at least in our ecosystem. So for me to replicate that, yes, it'll probably take time, but I can live with that. But my business will not stop because our transactional applications can potentially move over if need to. >> Yeah, and of course, you know, all these big market cap companies, so the Snowflake or Databricks, which is not public yet, but they've got big aspirations. And so, you know, we've seen things like Snowflake do a deal with Dell for on-prem object store. I think they do the same thing with Pure. And so over time, you see, Mongo, you know, extending its estate. And so over time all these things are coming together. I want to step out of this conversation for a second. I just ask you, given the current macroeconomic climate, what are the priorities? You know, obviously, people are, CIOs are tapping the breaks on spending, we've reported on that, but what is it? Is it security? Is it analytics? Is it modernization of the on-prem stack, which you were saying a little bit behind. Where are the priorities today given the economic headwinds? >> So the most important priority right now is growing the business, I would say. It's a different, I know this is more, this is not a very techy or a tech answer that, you know, you would expect, but it's growing the business. We want to acquire more customers and be able to service them as best needed. So the majority of our investment is going in the space where tech can support that initiative. During our earnings call, we released the new pillars of our organization where we will focus on, you know, omnichannel digital experience, and then one experience for customer, whether it's retail, whether it's digital. We want to open up our own experience stores, et cetera. So we are investing in technology where it's going to support those pillars. But the spend is in a way that we are obviously taking away from the things that do not support those. So it's, I would say it's flat for us. We are not like in heavily investing or aggressively increasing our tech budget, but it's more like, hey, switch this off because it doesn't make us money, but now switch this on because this is going to support what we can do with money, right? So that's kind of where we are heading towards. So it's not not driven by technology, but it's driven by business and how it supports our customers and our ability to compete in the market. >> You know, I think Harvir, that's consistent with what we heard in some other work that we've done, our ETR partner who does these types of surveys. We're hearing the same thing, is that, you know, we might not be spending on modernizing our on-prem stack. Yeah, we want to get to the cloud at some point and modernize that. But if it supports revenue, you know, we'll invest in that, and get the, you know, instant ROI. I want to ask you about, you know, this concept of supercloud, this abstracted layer of value on top of hyperscale infrastructure, and maybe on-prem. But we were talking about the integration, for instance, between Snowflake and Salesforce, where you got different data sources and you were explaining that you had great interest in being able to, you know, have a kind of, I'll say seamless, sorry, I know it's an overused word, but integration between the data sources and those two different platforms. Can you explain that and why that's attractive to you? >> Yeah, I'm a big supporter of action where the data is, right? Because the minute you start to move, things are already lost in translation. The time is lost, you can't get to it fast enough. So if, for example, for us, Snowflake, Salesforce, is our actionable platform where we action, we send marketing campaigns, we send customer communication via SMS, in app, as well as via email. Now, we would like to be able to interact with our customers pretty much on a, I would say near real time, but the concept of real time doesn't work well with me because I always feel that if you're observing something, it's not real time, it's already happened. But how soon can I react? That's the question. And given that I have to move that data all the way from our, let's say, engagement platforms like Adobe, and particles of the world into Snowflake first, and then do my modeling in some way, and be able to then put it back into Salesforce, it takes time. Yes, you know, I can do it in a few hours, but that few hours makes a lot of difference. Somebody sitting on my website, you know, couldn't find something, walked away, how soon do you think he will lose interest? Three hours, four hours, he'll probably gone, he will never come back. I think if I can react to that as fast as possible without too much data movement, I think that's a lot of good benefit that this kind of integration will bring. Yes, I can potentially take data directly into Salesforce, but I then now have two copies of data, which is, again, something that I'm not a big (indistinct) of. Let's keep the source of the data simple, clean, and a single source. I think this kind of integration will help a lot if the actions can be brought very close to where the data resides. >> Thank you for that. And so, you know, it's funny, we sometimes try to define real time as before you lose the customer, so that's kind of real time. But I want to come back to this idea of governed data sharing. You mentioned some other clouds, a little bit of Azure, a little bit of Google. In a world where, let's say you go more aggressively, and we know that for instance, if you want to use Google's AI tools, you got to use BigQuery. You know, today, anyway, they're not sort of so friendly with Snowflake, maybe different for the AWS, maybe Microsoft's going to be different as well. But in an ideal world, what I'm hearing is you want to keep the data in place. You don't want to move the data. Moving data is expensive, making copies is badness. It's expensive, and it's also, you know, changes the state, right? So you got governance issues. So this idea of supercloud is that you can leave the data in place and actually have a common experience across clouds. Let's just say, let's assume for a minute Google kind of wakes up, my words, not yours, and says, "Hey, maybe, you know what, partnering with a Snowflake or a Databricks is better for our business. It's better for the customers," how would that affect your business and the value that you can bring to your customers? >> Again, I would say that would be the nirvana state that, you know, we want to get to. Because I would say not everyone's perfect. They have great engineers and great products that they're developing, but that's where they compete as well, right? I would like to use the best of breed as much as possible. And I've been a person who has done this in the past as well. I've used, you know, tools to integrate. And the reason why this integration has worked is primarily because sometimes you do pick the best thing for that job. And Google's AI products are definitely doing really well, but, you know, that accessibility, if it's a problem, then I really can't depend on them, right? I would love to move some of that down there, but they have to make it possible for us. Azure is doing really, really good at investing, so I think they're a little bit more and more closer to getting to that state, and I know seeking our attention than Google at this point of time. But I think there will be a revelation moment because more and more people that I talk to like myself, they're also talking about the same thing. I'd like to be able to use Google's AdSense, I would like to be able to use Google's advertising platform, but you know what? I already have all this data, why do I need to move it? Can't they just go and access it? That question will keep haunting them (indistinct). >> You know, I think, obviously, Microsoft has always known, you know, understood ecosystems. I mean, AWS is nailing it, when you go to re:Invent, it's all about the ecosystem. And they think they realized they can make a lot more money, you know, together, than trying to have, and Google's got to figure that out. I think Google thinks, "All right, hey, we got to have the best tech." And that tech, they do have the great tech, and that's our competitive advantage. They got to wake up to the ecosystem and what's happening in the field and the go-to-market. I want to ask you about how you see data and cloud evolving in the future. You mentioned that things that are driving revenue are the priorities, and maybe you're already doing this today, but my question is, do you see a day when companies like yours are increasingly offering data and software services? You've been around for a long time as a company, you've got, you know, first party data, you've got proprietary knowledge, and maybe tooling that you've developed, and you're becoming more, you're already a technology company. Do you see someday pointing that at customers, or again, maybe you're doing it already, or is that not practical in your view? >> So data monetization has always been on the charts. The reason why it hasn't seen the light is regulatory pressure at this point of time. We are partnering up with certain agencies, again, you know, some pilots are happening to see the value of that and be able to offer that. But I think, you know, eventually, we'll get to a state where our, because we are trying to build accessible financial services, we will be in a state that we will be offering those to partners, which could then extended to their customers as well. So we are definitely exploring that. We are definitely exploring how to enrich our data with other data, and be able to complete a super set of data that can be used. Because frankly speaking, the data that we have is very interesting. We have trends of people migrating, we have trends of people migrating within the US, right? So if a new, let's say there's a new, like, I'll give you an example. Let's say New York City, I can tell you, at any given point of time, with my data, what is, you know, a dominant population in that area from migrant perspective. And if I see a change in that data, I can tell you where that is moving towards. I think it's going to be very interesting. We're a little bit, obviously, sometimes, you know, you're scared of sharing too much detail because there's too much data. So, but at the end of the day, I think at some point, we'll get to a state where we are confident that the data can be used for good. One simple example is, you know, pharmacies. They would love to get, you know, we've been talking to CVS and we are talking to Walgreens, and trying to figure out, if they would get access to this kind of data demographic information, what could they do be better? Because, you know, from a gene pool perspective, there are diseases and stuff that are very prevalent in one community versus the other. We could probably equip them with this information to be able to better, you know, let's say, staff their pharmacies or keep better inventory of products that could be used for the population in that area. Similarly, the likes of Walmarts and Krogers, they would like to have more, let's say, ethnic products in their aisles, right? How do you enable that? That data is primarily, I think we are the biggest source of that data. So we do take pride in it, but you know, with caution, we are obviously exploring that as well. >> My last question for you, Harvir, is I'm going to ask you to do a thought exercise. So in that vein, that whole monetization piece, imagine that now, Harvir, you are running a P&L that is going to monetize that data. And my question to you is a there's a business vector and a technology vector. So from a business standpoint, the more distribution channels you have, the better. So running on AWS cloud, partnering with Microsoft, partnering with Google, going to market with them, going to give you more revenue. Okay, so there's a motivation for multi-cloud or supercloud. That's indisputable. But from a technical standpoint, is there an advantage to running on multiple clouds or is that a disadvantage for you? >> It's, I would say it's a disadvantage because if my data is distributed, I have to combine it at some place. So the very first step that we had taken was obviously we brought in Snowflake. The reason, we wanted our analytical data and we want our historical data in the same place. So we are already there and ready to share. And we are actually participating in the data share, but in a private setting at the moment. So we are technically enabled to share, unless there is a significant, I would say, upside to moving that data to another cloud. I don't see any reason because I can enable anyone to come and get it from Snowflake. It's already enabled for us. >> Yeah, or if somehow, magically, several years down the road, some standard developed so you don't have to move the data. Maybe there's a new, Mogli is talking about a new data architecture, and, you know, that's probably years away, but, Harvir, you're an awesome guest. I love having you on, and really appreciate you participating in the program. >> I appreciate it. Thank you, and good luck (indistinct) >> Ah, thank you very much. This is Dave Vellante for John Furrier and the entire Cube community. Keep it right there for more great coverage from Supercloud 2. (uplifting music)

(upbeat music) >> Welcome back to Supercloud 2 this is Dave Vellante. We're here exploring the intersection of data and analytics and the future of cloud. And in this segment, we're going to look at the evolution of cloud, and try to test some of the Supercloud concepts and assumptions with Brian Gracely, is the founder and co-host along with Aaron Delp of the popular Cloudcast program. Amazing series, if you're not already familiar with it. The Cloudcast is one of the best ways to keep up with so many things going on in our industry. Enterprise tech, platform engineering, business models, obviously, cloud developer trends, crypto, Web 3.0. Sorry Brian, I know that's a sore spot, but Brian, thanks for coming >> That's okay. >> on the program, really appreciate it. >> Yeah, great to be with you, Dave. Happy New Year, and great to be back with everybody with SiliconANGLE again this year. >> Yeah, we love having you on. We miss working with you day-to-day, but I want to start with Gracely's theorem, which basically says, I'm going to paraphrase. For the most part, nothing new gets introduced in the enterprise tech business, patterns repeat themselves, maybe get applied in new ways. And you know this industry well, when something comes out that's new, if you take virtualization, for example, been around forever with mainframes, but then VMware applied it, solve a real problem in the client service system. And then it's like, "Okay, this is awesome." We get really excited and then after a while we pushed the architecture, we break things, introduce new things to fix the things that are broken and start adding new features. And oftentimes you do that through acquisitions. So, you know, has the cloud become that sort of thing? And is Supercloud sort of same wine, new bottle, following Gracely's theorem? >> Yeah, I think there's some of both of it. I hate to be the sort of, it depends sort of answer but, I think to a certain extent, you know, obviously Cloud in and of itself was, kind of revolutionary in that, you know, it wasn't that you couldn't rent things in the past, it was just being able to do it at scale, being able to do it with such amazing self-service. And then, you know, kind of proliferation of like, look at how many services I can get from, from one cloud, whether it was Amazon or Azure or Google. And then, you know, we, we slip back into the things that we know, we go, "Oh, well, okay, now I can get computing on demand, but, now it's just computing." Or I can get database on demand and it's, you know, it's got some of the same limitations of, of say, of database, right? It's still, you know, I have to think about IOPS and I have to think about caching, and other stuff. So, I think we do go through that and then we, you know, we have these sort of next paradigms that come along. So, you know, serverless was another one of those where it was like, okay, it seems sort of new. I don't have to, again, it was another level of like, I don't have to think about anything. And I was able to do that because, you know, there was either greater bandwidth available to me, or compute got cheaper. And what's been interesting is not the sort of, that specific thing, serverless in and of itself is just another way of doing compute, but the fact that it now gets applied as, sort of a no-ops model to, you know, again, like how do I provision a database? How do I think about, you know, do I have to think about the location of a service? Does that just get taken care of for me? So I think the Supercloud concept, and I did a thing and, and you and I have talked about it, you know, behind the scenes that maybe the, maybe a better name is Super app for something like Snowflake or other, but I think we're, seeing these these sort of evolutions over and over again of what were the big bottlenecks? How do we, how do we solve those bottlenecks? And I think the big thing here is, it's never, it's very rarely that you can take the old paradigm of what the thing was, the concept was, and apply it to the new model. So, I'll just give you an example. So, you know, something like VMware, which we all know, wildly popular, wildly used, but when we apply like a Supercloud concept of VMware, the concept of VMware has always been around a cluster, right? It's some finite number of servers, you sort of manage it as a cluster. And when you apply that to the cloud and you say, okay, there's, you know, for example, VMware in the cloud, it's still the same concept of a cluster of VMware. But yet when you look at some of these other services that would fit more into the, you know, Supercloud kind of paradigm, whether it's a Snowflake or a MongoDB Atlas or maybe what CloudFlare is doing at the edge, those things get rid of some of those old paradigms. And I think that's where stuff, you start to go, "Oh, okay, this is very different than before." Yes, it's still computing or storage, or data access, but there's a whole nother level of something that we didn't carry forward from the previous days. And that really kind of breaks the paradigm. And so that's the way I think I've started to think about, are these things really brand new? Yes and no, but I think it's when you can see that big, that thing that you didn't leave behind isn't there anymore, you start to get some really interesting new innovation come out of it. >> Yeah. And that's why, you know, lift and shift is okay, when you talk to practitioners, they'll say, "You know, I really didn't change my operating model. And so I just kind of moved it into the cloud. there were some benefits, but it was maybe one zero not three zeros that I was looking for." >> Right. >> You know, we always talk about what's great about cloud, the agility, and all the other wonderful stuff that we know, what's not working in cloud, you know, tie it into multi-cloud, you know, in terms of, you hear people talk about multi-cloud by accident, okay, that's true. >> Yep. >> What's not great about cloud. And then I want to get into, you know, is multi-cloud really a problem or is it just sort of vendor hype? But, but what's not working in cloud? I mean, you mentioned serverless and serverless is kind of narrow, right, for a lot of stateless apps, right? But, what's not great about cloud? >> Well, I think there's a few things that if you ask most people they don't love about cloud. I think, we can argue whether or not sort of this consolidation around a few cloud providers has been a good thing or a bad thing. I think, regardless of that, you know, we are seeing, we are hearing more and more people that say, look, you know, the experience I used to have with cloud when I went to, for example, an Amazon and there was, you know, a dozen services, it was easy to figure out what was going on. It was easy to figure out what my billing looked like. You know, now they've become so widespread, the number of services they have, you know, the number of stories you just hear of people who went, "Oh, I started a service over in US West and I can't find it anymore 'cause it's on a different screen. And I, you know, I just got billed for it." Like, so I think the sprawl of some of the clouds has gotten, has created a user experience that a lot of people are frustrated with. I think that's one thing. And we, you know, we see people like Digital Ocean and we see others who are saying, "Hey, we're going to be that simplified version." So, there's always that yin and yang. I think people are super frustrated at network costs, right? So, you know, and that's kind of at a lot of, at the center of maybe why we do or don't see more of these Supercloud services is just, you know, in the data center as an application owner, I didn't have to think about, well where, where does this go to? Where are my users? Yes, somebody took care of it, but when those things become front and center, that's super frustrating. That's the one area that we've seen absolutely no cost savings, cost reduction. So I think that frustrates people a lot. And then I think the third piece is just, you know, we're, we went from super centralized IT organizations, which, you know, for decades was how it worked. It was part of the reason why the cloud expanded and became a thing, right? Sort of shadow IT and I can't get things done. And then, now what we've seen is sort of this proliferation of little pockets of groups that are your IT, for lack of a better thing, whether they're called platform engineering or SRE or DevOps. But we have this, expansion, explosion if you will, of groups that, if I'm an app dev team, I go, "Hey, you helped me make this stuff run, but then the team next to you has another group and they have another group." And so you see this explosion of, you know, we don't have any standards in the company anymore. And, so sort of self-service has created its own nightmare to a certain extent for a lot of larger companies. >> Yeah. Thank you for that. So, you know, I want, I want to explore this multi-cloud, you know, by accident thing and is a real problem. You hear that a lot from vendors and we've been talking about Supercloud as this unifying layer across cloud. You know, but when you talk to customers, a lot of them are saying, "Yes, we have multiple clouds in our organization, but my group, we have mono cloud, we know the security, edicts, we know how to, you know, deal with the primitives, whether it's, you know, S3 or Azure Blob or whatever it is. And we're very comfortable with this." It's, that's how we're simplifying. So, do you think this is really a problem? Does it have merit that we need that unifying layer across clouds, or is it just too early for that? >> I think, yeah, I think what you, what you've laid out is basically how the world has played out. People have picked a cloud for a specific application or a series of applications. Yeah, and I think if you talk to most companies, they would tell you, you know, holistically, yes, we're multi-cloud, not, maybe not necessarily on, I don't necessarily love the phrase where people say like, well it happened by accident. I think it happened on purpose, but we got to multi-cloud, not in the way that maybe that vendors, you know, perceived, you know, kind of laid out a map for. So it was, it was, well you will lay out this sort of Supercloud framework. We didn't call it that back then, we just called it sort of multi-cloud. Maybe it was Kubernetes or maybe it was whatever. And different groups, because central IT kind of got disbanded or got fragmented. It turned into, go pick the best cloud for your application, for what you need to do for the business. And then, you know, multiple years later it was like, "Oh, hold on, I've got 20% in Google and 50% in AWS and I've got 30% in Azure. And, you know, it's, yeah, it's been evolution. I don't know that it's, I don't know if it's a mistake. I think it's now groups trying to figure out like, should I make sense of it? You know, should I try and standardize and I backwards standardize some stuff? I think that's going to be a hard thing for, for companies to do. 'cause I think they feel okay with where the applications are. They just happen to be in multiple clouds. >> I want to run something by you, and you guys, you and Aaron have talked about this. You know, still depending on who, which keynote you listen to, small percentage of the workloads are actually in cloud. And when you were with us at Wikibon, I think we called it true private cloud, and we looked at things like Nutanix and there were a lot of other examples of companies that were trying to replicate the hyperscale experience on Prem. >> Yeah. >> And, we would evaluate that, you know, beyond virtualization, and so we sort of defined that and, but I think what's, maybe what's more interesting than Supercloud across clouds is if you include that, that on Prem estate, because that's where most of the work is being done, that's where a lot of the proprietary tools have been built, a lot of data, a lot of software. So maybe there's this concept of sending that true private cloud to true hybrid cloud. So I actually think hybrid cloud in some cases is the more interesting use case for so-called Supercloud. What are your thoughts on that? >> Yeah, I think there's a couple aspects too. I think, you know, if we were to go back five or six years even, maybe even a little further and look at like what a data center looked like, even if it was just, "Hey we're a data center that runs primarily on VMware. We use some of their automation". Versus what you can, even what you can do in your data center today. The, you know, the games that people have seen through new types of automation through Kubernetes, through get ops, and a number of these things, like they've gotten significantly further along in terms of I can provision stuff really well, I can do multi-tenancy, I can do self-service. Is it, you know, is it still hard? Yeah. Because those things are hard to do, but there's been significant progress there. I don't, you know, I still look for kind of that, that killer application, that sort of, you know, lighthouse use case of, hybrid applications, you know, between data center and between cloud. I think, you know, we see some stuff where, you know, backup is a part of it. So you use the cloud for storage, maybe you use the cloud for certain kinds of resiliency, especially on maybe front end load balancing and stuff. But I think, you know, I think what we get into is, this being hung up on hybrid cloud or multi-cloud as a term and go like, "Look, what are you trying to measure? Are you trying to measure, you know, efficiency of of of IT usage? Are you trying to measure how quickly can I give these business, you know, these application teams that are part of a line of business resources that they need?" I think if we start measuring that way, we would look at, you know, you'd go, "Wow, it used to be weeks and months. Now we got rid of these boards that have to review everything every time I want to do a change management type of thing." We've seen a lot more self-service. I think those are the things we want to measure on. And then to your point of, you know, where does, where do these Supercloud applications fit in? I think there are a bunch of instances where you go, "Look, I have a, you know, global application, I have a thing that has to span multiple regions." That's where the Supercloud concept really comes into play. We used to do it in the data center, right? We'd had all sorts of technologies to help with that, I think you can now start to do it in the cloud. >> You know, one of the other things, trying to understand, your thoughts on this, do you think that you, you again have talked about this, like I'm with you. It's like, how is it that Google's losing, you know, 3 billion dollars a year, whatever. I mean, because when you go back and look at Amazon, when they were at that level of revenue where Google is today, they were making money, you know, and they were actually growing faster, by the way. So it's kind of interesting what's happened with Google. But, the reason I bring that up is, trying to understand if you think the hyperscalers will ever be motivated to create standards across clouds, and that may be a play for Google. I mean, obviously with Kubernetes it was like a Hail Mary and kind of made them relevant. Where would Google be without Kubernetes? But then did it achieve the objectives? We could have that conversation some other time, but do you think the hyperscalers will actually say, "Okay, we're going to lean in and create these standards across clouds." Because customers would love that, I would think, but it would sub-optimize their competitive advantage. What are your thoughts? >> I think, you know, on the surface, I would say they, they probably aren't. I think if you asked 'em the question, they would say, "Well, you know, first and foremost, you know, we do deliver standards, so we deliver a, you know, standard SQL interface or a SQL you know, or a standard Kubernetes API or whatever. So, in that, from that perspective, you know, we're not locking you into, you know, an Amazon specific database, or a Google specific database." You, you can argue about that, but I think to a certain extent, like they've been very good about, "Hey, we're going to adopt the standards that people want." A lot of times the open source standards. I think the problem is, let's say they did come up with a standard for it. I think you still have the problem of the costs of migration and you know, the longer you've, I think their bet is basically the longer you've been in some cloud. And again, the more data you sort of compile there, the data gravity concept, there's just going to be a natural thing that says, okay, the hurdle to get over to say, "Look, we want to move this to another cloud", becomes so cost prohibitive that they don't really have to worry about, you know, oh, I'm going to get into a war of standards. And so far I think they sort of realize like that's the flywheel that the cloud creates. And you know, unless they want to get into a world where they just cut bandwidth costs, like it just kind of won't happen. You know, I think we've even seen, and you know, the one example I'll use, and I forget the name of it off the top of my head, but there's a, there's a Google service. I think it's like BigQuery external or something along those lines, that allows you to say, "Look, you can use BigQuery against like S3 buckets and against other stuff." And so I think the cloud providers have kind of figured out, I'm never going to get the application out of that other guy's cloud or you know, the other cloud. But maybe I'm going to have to figure out some interesting ways to sort of work with it. And, you know, it's a little bit, it's a little janky, but that might be, you know, a moderate step that sort of gets customers where they want to be. >> Yeah. Or you know, it'd be interesting if you ever see AWS for example, running its database in other clouds, you started, even Oracle is doing that with, with with Azure, which is a form of Supercloud. My last question for you is, I want to get you thinking about sort of how the future plays out. You know, think about some of the companies that we've put forth this Supercloud, and by the way, this has been a criticism of the concept. Charles Fitzer, "Everything is Supercloud!" Which if true would defeat the purpose of course. >> Right. >> And so right with the community effort, we really tried to put some guardrails down on the essential characteristics, the deployment models, you know, so for example, running across multiple clouds with a purpose build pass, creating a common experience, metadata intelligence that solves a specific problem. I mean, the example I often use is Snowflake's governed data sharing. But yeah, Snowflake, Databricks, CloudFlare, Cohesity, you know, I just mentioned Oracle and Azure, these and others, they certainly claim to have that common experience across clouds. But my question is, again, I come back to, do customers need this capability? You know, is Mono Cloud the way to solve that problem? What's your, what are your thoughts on how this plays out in the future of, I guess, PAs, apps and cloud? >> Yeah, I think a couple of things. So, from a technology perspective, I think, you know, the companies you name, the services you've named, have sort of proven that the concept is viable and it's viable at a reasonable size, right? These aren't completely niche businesses, right? They're multi-billion dollar businesses. So, I think there's a subset of applications that, you know, maybe a a bigger than a niche set of applications that are going to use these types of things. A lot of what you talked about is very data centric, and that's, that's fine. That's that layer is, figuring that out. I think we'll see messaging types of services, so like Derek Hallison's, Caya Company runs a, sort of a Supercloud for messaging applications. So I think there'll be places where it makes a ton of sense. I think, the thing that I'm not sure about, and because again, we've been now 10 plus years of sort of super low, you know, interest rates in terms of being able to do things, is a lot of these things come out of research that have been done previously. Then they get turned into maybe somewhat of an open source project, and then they can become something. You know, will we see as much investment into the next Snowflake if, you know, the interest rates are three or four times that they used to be, do we, do we see VCs doing it? So that's the part that worries me a little bit, is I think we've seen what's possible. I think, you know, we've seen companies like what those services are. I think I read yesterday Snowflake was saying like, their biggest customers are growing at 30, like 50 or 60%. Like the, value they get out of it is becoming exponential. And it's just a matter of like, will the economics allow the next big thing to happen? Because some of these things are pretty, pretty costly, you know, expensive to get started. So I'm bullish on the idea. I don't know that it becomes, I think it's okay that it's still sort of, you know, niche plus, plus in terms of the size of it. Because, you know, if we think about all of IT it's still, you know, even microservices is a small part of bigger things. But I'm still really bullish on the idea. I like that it's been proven. I'm a little wary, like a lot of people have the economics of, you know, what might slow things down a little bit. But yeah, I, think the future is going to involve Supercloud somewhere, whatever people end up calling it. And you and I discussed that. (laughs) But I don't, I don't think it goes away. I don't think it's, I don't think it's a fad. I think it is something that people see tremendous value and it's just, it's got to be, you know, for what you're trying to do, your application specific thing. >> You're making a great point on the funding of innovation and we're entering a new era of public policy as well. R and D tax credit is now is shifting. >> Yeah. >> You know, you're going to have to capitalize that over five years now. And that's something that goes back to the 1950s and many people would argue that's at least in part what has helped the United States be so, you know, competitive in tech. But Brian, always great to talk to you. Thanks so much for participating in the program. Great to see you. >> Thanks Dave, appreciate it. Good luck with the rest of the show. >> Thank you. All right, this is Dave Vellante for John Furrier, the entire Cube community. Stay tuned for more content from Supercloud2.

(vibrant electronic music) >> Welcome back to SuperCloud2. You know, this event, and the Super Cloud initiative in general, it's an open industry-wide collaboration. Last August at SuperCloud22, we really honed in on the definition, which of course we've published. And there's this shared doc, which folks are still adding to and refining, in fact, just recently, Dr. Nelu Mihai added some critical points that really advanced some of the community's initial principles, and today at SuperCloud2, we're digging further into the topic with input from real world practitioners, and we're exploring that intersection of data, data mesh, and cloud, and importantly, the realities and challenges of deploying technology to drive new business capability, and I'm pleased to welcome Ash Naseer to the program. He's a Senior Director of Data Engineering at Warner Bros. Discovery. Ash, great to see you again, thanks so much for taking time with us. >> It's great to be back, these conversations are always very fun. >> I was so excited when we met last spring, I guess, so before we get started I wanted to play a clip from that conversation, it was June, it was at the Snowflake Summit in Las Vegas. And it's a comment that you made about your company but also data mesh. Guys, roll the clip. >> Yeah, so, when people think of Warner Bros., you always think of the movie studio. But we're more than that, right, I mean, you think of HBO, you think of TNT, you think of CNN. We have 30 plus brands in our portfolio, and each have their own needs. So the idea of a data mesh really helps us because what we can do is we can federate access across the company, so that CNN can work at their own pace, you know, when there's election season, they can ingest their own data. And they don't have to bump up against, as an example, HBO, if Game of Thrones is goin' on. >> So-- Okay, so that's pretty interesting, so you've got these sort of different groups that have different data requirements inside of your organization. Now data mesh, it's a relatively new concept, so you're kind of ahead of the curve. So Ash, my question is, when you think about getting value from data, and how that's changed over the past decade, you've had pre-Hadoop, Hadoop, what do you see that's changed, now you got the cloud coming in, what's changed? What had to be sort of fixed? What's working now, and where do you see it going? >> Yeah, so I feel like in the last decade, we've gone through quite a maturity curve. I actually like to say that we're in the golden age of data, because the tools and technology in the data space, particularly and then broadly in the cloud, they allow us to do things that we couldn't do way back when, like you suggested, back in the Hadoop era or even before that. So there's certainly a lot of maturity, and a lot of technology that has come about. So in terms of the good, bad, and ugly, so let me kind of start with the good, right? In terms of bringing value from the data, I really feel like we're in this place where the folks that are charged with unlocking that value from the data, they're actually spending the majority of their time actually doing that. And what do I mean by that? If you think about it, 10 years ago, the data scientist was the person that was going to sort of solve all of the data problems in a company. But what happened was, companies asked these data scientists to come in and do a multitude of things. And what these data scientists found out was, they were spending most of their time on, really, data wrangling, and less on actually getting the value out of the data. And in the last decade or so, I feel like we've made the shift, and we realize that data engineering, data management, data governance, those are as important practices as data science, which is sort of getting the value out of the data. And so what that has done is, it has freed up the data scientist and the business analyst and the data analyst, and the BI expert, to really focus on how to get value out of the data, and spend less time wrangling data. So I really think that that's the good. In terms of the bad, I feel like, there's a lot of legacy data platforms out there, and I feel like there's going to be a time where we'll be in that hybrid mode. And then the ugly, I feel like, with all the data and all the technology, creates another problem of itself. Because most companies don't have arms around their data, and making sure that they know who's using the data, what they're using for, and how can the company leverage the collective intelligence. That is a bigger problem to solve today than 10 years ago. And that's where technologies like the data mesh come in. >> Yeah, so when I think of data mesh, and I say, you're an early practitioner of data mesh, you mentioned legacy technology, so the concept of data mesh is inclusive. In theory anyway, you're supposed to be including the legacy technologies. Whether it's a data lake or data warehouse or Oracle or Snowflake or whatever it is. And when you think about Jamak Dagani's principles, it's domain-centric ownership, data as product. And that creates challenges around self-serve infrastructure and automated governance, and then when you start to combine these different technologies. You got legacy, you got cloud. Everything's different. And so you have to figure out how to deal with that, so my question is, how have you dealt with that, and what role has the cloud played in solving those problems, in particular, that self-serve infrastructure, and that automated governance, and where are we in terms of solving that problem from a practitioner's standpoint? >> Yeah, I always like to say that data is a team sport, and we should sort of think of it as such, and that's, I feel like, the key of the data mesh concept, is treating it as a team sport. A lot of people ask me, they're like, "Oh hey, Ash, I've heard about this thing called data mesh. "Where can I buy one?" or, "what's the technology that I use to get a data mesh? And the reality is that there isn't one technology, you can't really buy a data mesh. It's really a way of life, it's how organizations decide to approach data, like I said, back to a team sport analogy, making sure that everyone has the seat on the table, making sure that we embrace the fact that we have a lot of data, we have a lot of data problems to solve. And the way we'll be successful is to make everyone inclusive. You know, you think about the old days, Data silos or shadow IT, some might call it. That's been around for decades. And what hasn't changed was this notion that, hey, everything needs to be sort of managed centrally. But with the cloud and with the technologies that we have today, we have the right technology and the tooling to democratize that data, and democratize not only just the access, but also sort of building building blocks and sort of taking building blocks which are relevant to your product or your business. And adding to the overall data mesh. We've got all that technology. The challenge is for us to really embrace it, and make sure that we implement it from an organizational standpoint. >> So, thinking about super cloud, there's a layer that lives above the clouds and adds value. And you think about your brands you got 30 brands, you mentioned shadow IT. If, let's say, one of those brands, HBO or TNT, whatever. They want to go, "Hey, we really like Google's analytics tools," and they maybe go off and build something, I don't know if that's even allowed, maybe it's not. But then you build this data mesh. My question is around multi-cloud, cross cloud, super cloud if you will. Is that a advantage for you as a practitioner, or does that just make things more complicated? >> I really love the idea of a multi-cloud. I think it's great, I think that it should have been the norm, not the exception, I feel like people talk about it as if it's the exception. That should have been the case. I will say, though, I feel like multi-cloud should evolve organically, so back to your point about some of these different brands, and, you know, different brands or different business units. Or even in a merger and acquisitions situation, where two different companies or multiple different companies come together with different technology stacks. You know, I feel like that's an organic evolution, and making sure that we use the concepts and the technologies around the multi-cloud to bring everyone together. That's where we need to be, and again, it talks to the fact that each of those business units and each of those groups have their own unique needs, and we need to make sure that we embrace that and we enable that, rather than stifling everything. Now where I have a little bit of a challenge with the multi-cloud is when technology leaders try to build it by design. So there's a notion there that, "Hey, you need to sort of diversify "and don't put all your eggs in one basket." And so we need to have this multi-cloud thing. I feel like that is just sort of creating more complexity where it doesn't need to be, we can all sort of simplify our lives, but where it evolves organically, absolutely, I think that's the right way to go. >> But, so Ash, if it evolves organically don't you need some kind of cloud interpreter, to create a common experience across clouds, does that exist today? What are your thoughts on that? >> There is a lot of technology that exists today, and that helps go between these different clouds, a lot of these sort of cloud agnostic technologies that you talked about, the Snowflakes and the Databricks and so forth of the world, they operate in multiple clouds, they operate in multiple regions, within a given cloud and multiple clouds. So they span all of that, and they have the tools and technology, so, I feel like the tooling is there. There does need to be more of an evolution around the tooling and I think the market's need are going to dictate that, I feel like the market is there, they're asking for it, so, there's definitely going to be that evolution, but the technology is there, I think just making sure that we embrace that and we sort of embrace that as a challenge and not try to sort of shut all of that down and box everything into one. >> What's the biggest challenge, is it governance or security? Or is it more like you're saying, adoption, cultural? >> I think it's a combination of cultural as well as governance. And so, the cultural side I've talked about, right, just making sure that we give these different teams a seat at the table, and they actually bring that technology into the mix. And we use the modern tools and technologies to make sure that everybody sort of plays nice together. That is definitely, we have ways to go there. But then, in terms of governance, that is another big problem that most companies are just starting to wrestle with. Because like I said, I mean, the data silos and shadow IT, that's been around there, right? The only difference is that we're now sort of bringing everything together in a cloud environment, the collective organization has access to that. And now we just realized, oh we have quite a data problem at our hands, so how do we sort of organize this data, make sure that the quality is there, the trust is there. When people look at that data, a lot of those questions are now coming to the forefront because everything is sort of so transparent with the cloud, right? And so I feel like, again, putting in the right processes, and the right tooling to address that is going to be critical in the next years to come. >> Is sharing data across clouds, something that is valuable to you, or even within a single cloud, being able to share data. And my question is, not just within your organization, but even outside your organization, is that something that has sort of hit your radar or is it mature or is that something that really would add value to your business? >> Data sharing is huge, and again, this is another one of those things which isn't new. You know, I remember back in the '90s, when we had to share data externally, with our partners or our vendors, they used to physically send us stacks of these tapes, or physical media on some truck. And we've evolved since then, right, I mean, it went from that to sharing files online and so forth. But data sharing as a concept and as a concept which is now very frictionless, through these different technologies that we have today, that is very new. And that is something, like I said, it's always been going on. But that needs to be really embraced more as well. We as a company heavily leverage data sharing between our own different brands and business units, that helps us make that data mesh, so that when CNN, as an example, builds their own data model based on election data and the kinds of data that they need, compare that with other data in the rest of the company, sports, entertainment, and so forth and so on. Everyone has their unique data, but that data sharing capability brings it together wherever there is a need. So you think about having a Tiger Woods documentary, as an example, on HBO Max and making sure that you reach the audiences that are interested in golf and interested in sports and so forth, right? That all comes through the magic of data sharing, so, it's really critical, internally, for us. And then externally as well, because just understanding how our products are doing on our partners' networks and different distribution channels, that's important, and then just understanding how our consumers are consuming it off properties, right, I mean, we have brands that transcend just the screen, right? We have a lot of physical merchandise that you can buy in the store. So again, understanding who's buying the Batman action figures after the Batman movie was released, that's another critical insight. So it all gets enabled through data sharing, and something we rely heavily on. >> So I wanted to get your perspective on this. So I feel like the nirvana of data mesh is if I want to use Google BigQuery, an Oracle database, or a Microsoft database, or Snowflake, Databricks, Amazon, whatever. That that's a node on the mesh. And in the perfect world, you can share that data, it can be governed, I don't think we're quite there today, so. But within a platform, maybe it's within Google or within Amazon or within Snowflake or Databricks. If you're in that world, maybe even Oracle. You actually can do some levels of data sharing, maybe greater with some than others. Do you mandate as an organization that you have to use this particular data platform, or are you saying "Hey, we are architecting a data mesh for the future "where we believe the technology will support that," or maybe you've invented some technology that supports that today, can you help us understand that? >> Yeah, I always feel like mandate is a strong area, and it breeds the shadow IT and the data silos. So we don't mandate, we do make sure that there's a consistent set of governance rules, policies, and tooling that's there, so that everyone is on the same page. However, at the same time our focus is really operating in a federated way, that's been our solution, right? Is to make sure that we work within a common set of tooling, which may be different technologies, which in some cases may be different clouds. Although we're not that multi-cloud. So what we're trying to do is making sure that everyone who has that technology already built, as long as it sort of follows certain standards, it's modern, it has the capabilities that will eventually allow us to be successful and eventually allow for that data sharing, amongst those different nodes, as you put it. As long as that's the case, and as long as there's a governance layer, a master governance layer, where we know where all that data is and who has access to what and we can sort of be really confident about the quality of the data, as long as that case, our approach to that is really that federated approach. >> Sorry, did I hear you correctly, you're not multi-cloud today? >> Yeah, that's correct. There are certain spots where we use that, but by and large, we rely on a particular cloud, and that's just been, like I said, it's been the evolution, it was our evolution. We decided early on to focus on a single cloud, and that's the direction we've been going in. >> So, do you want to go to a multi-cloud, or, you mentioned organic before, if a business unit wants to go there, as long as they're adhering to those standards that you put out, maybe recommendations, that that's okay? I guess my question is, does that bring benefit to your business that you'd like to tap, or do you feel like it's not necessary? >> I'll go back to the point of, if it happens organically, we're going to be open about it. Obviously we'll have to look at every situations, not all clouds are created equal as well, so there's a number of different considerations. But by and large, when it happens organically, the key is time to value, right? How do you quickly bring those technologies in, as long as you could share the data, they're interconnected, they're secured, they're governed, we are confident on the quality, as long as those principles are met, we could definitely go in that direction. But by and large, we're sort of evolving in a singular direction, but even within a singular cloud, we're a global company. And we have audiences around the world, so making sure that even within a single cloud, those different regions interoperate as one, that's a bigger challenge that we're having to solve as well. >> Last question is kind of to the future of data and cloud and how it's going to evolve, do you see a day when companies like yours are increasingly going to be offering data, their software, services, and becoming more of a technology company, sort of pointing your tooling and your proprietary knowledge at the external world, as an opportunity, as a business opportunity? >> That's a very interesting concept, and I know companies have done that, and some of them have been extremely successful, I mean, Amazon is the biggest example that comes to mind, right-- >> Yeah. >> When they launched AWS, something that they had that expertise they had internally, and they offered it to the world as a product. But by and large, I think it's going to be far and few between, especially, it's going to be focused on companies that have technology as their DNA, or almost like in the technology sector, building technology. Most other companies have different markets that they are addressing. And in my opinion, a lot of these companies, what they're trying to do is really focus on the problems that we can solve for ourselves, I think there are more problems than we have people and expertise. So my guess is that most large companies, they're going to focus on solving their own problems. A few, like I said, more tech-focused companies, that would want to be in that business, would probably branch out, but by and large, I think companies will continue to focus on serving their customers and serving their own business. >> Alright, Ash, we're going to leave it there, Ash Naseer. Thank you so much for your perspectives, it was great to see you, I'm sure we'll see you face-to-face later on this year. >> This is great, thank you for having me. >> Ah, you're welcome, alright. Keep it right there for more great content from SuperCloud2. We'll be right back. (gentle percussive music)

>> Narrator: TheCUBE presents Ignite '22, brought to you by Palo Alto Networks. >> Greetings from the MGM Grand Hotel in beautiful Las Vegas. It's theCUBE Live Day two of our coverage of Palo Alto Networks, ignite 22. Lisa Martin, Dave Vellante. Dave, what can I say? This has been a great couple of days. The amount of content we have created and shared with our viewers on theCUBE is second to none. >> Well, the cloud has completely changed the way that people think about security. >> Yeah. You know at first it was like, oh, the cloud, how can that be secure? And they realized, wow actually cloud is pretty secure if we do it right. And so shared responsibility model and partnerships are critical. >> Partnerships are critical, especially as more and more organizations are multicloud by default. Right? These days we're going to be bring Google into the conversation. Josh Haslet joins us. Strategic Partnership Manager at Google. Welcome. Great to have you Josh. >> Hi Lisa, thanks for having me here. >> So you are a secret squirrel from Palo Alto Networks. Talk to me a little bit about your background and about your role at Google in terms of partnership management. >> Sure, I feel like we need to add that to my title. [Lisa] You should, secret squirrel. >> Great. Yeah, so as a matter of fact, I've been at Google for two and a half years. Prior to that, I was at Palo Alto Networks. I was managing the business development relationship with Google, and I was kind of at the inception of when the cash came in and, and decided that we needed to think about how to do security in a new way from a platform standpoint, right? And so it was exciting because when I started with the partnership, we were focusing on still securing you know, workloads in the cloud with next generation firewall. And then as we went through acquisitions the Palo Alto added it expanded the capabilities of what we could do from cloud security. And so it was very exciting, you know, to, to make sure that we could onboard with Google Cloud, take a look at how not only Palo Alto was enhancing their solutions as they built those and delivered those from Google Cloud. But then how did we help customers adopt cloud in a more easy fashion by making things, you know more tightly integrated? And so that's really been a lot of what I've been involved in, which has been exciting to see the growth of both organizations as we see customers shifting to cloud transformation. And then how do they deploy these new methodologies and tools from a security perspective to embrace this new way of working and this new way of, you know creating applications and doing digital transformation. >> Important, since work is no longer a place, it's an activity. Organizations have have to be able to cater to the distributed workforce. Of course, the, the, the workforce has to be able to access everything that they need to, but it has to be done in a secure way regardless of what kind of company you are. >> Yeah, you're right, Lisa. It's interesting. I mean, the pandemic has really changed and accelerated that transformation. I think, you know really remote working has started previous to that. And I think Nikesh called that out in the keynote too right? He, he really said that this has been ongoing for a while, but I think, you know organizations had to figure out how to scale and that was something that they weren't as prepared for. And a lot of the technology that was deployed for VPN connectivity or supporting remote work that was fixed hardware. And so cloud deployment and cloud architecture specifically with Prisma access really enabled this transformation to happen in a much faster, you know, manner. And where we've come together is how do we make sure that customers, no matter what device, what user what application you're accessing. As we take a look at ZTNA, Zero Trust Network Access 2.0, how can we come together to partner to make sure the customers have that wide range of coverage and capability? >> How, how do you how would you describe Josh Google's partner strategy generally and specifically, you know, in the world of cyber and what makes it unique and different? >> Yeah, so that's a great question. I think, you know, from Google Cloud perspective we heard TK mention this in the keynote with Nikesh. You know, we focus on on building a secure platform first and foremost, right? We want to be a trusted cloud for customers to deploy on. And so, you know, we find that as customers do one of two things, they're looking at, you know, reducing cost as they move to cloud and consolidate workloads or as they embrace innovation and look at, you know leveraging things like BigQuery for analytics and you know machine learning for the way that they want to innovate and stay ahead of the competition. They have to think about how do they secure in a new way. And so, not only do we work on how do we secure our own platform, we work with trusted partners to make sure that customers have you mentioned it earlier, Dave the shared security model, right? How do they take a look at their applications and their workloads and this new way of working as they go to CI/CD pipelines, they start thinking about DevSecOps. How do they integrate tooling that is frictionless and seamless for their, for their teams to deploy but allows them to quickly embrace that cloud transformation journey. And so, yes, partners are critical to that. The other thing is, you know we find that, you mentioned earlier, Lisa that customers are multicloud, right? That's kind of the the new normal as we look at enterprises today. And so Google Cloud's going to do a great job at securing our platform, but we need partners that can help customers deploy policy that embraces not only the things that they put in Google Cloud but as they're in their transformation journey. How that embraces the estates that are in data centers the things that are still on-prem. And really this is about making sure that the applications no matter where they are, the databases no matter where they are, and the users no matter where they are are all secure in that new framework of deploying and embracing innovation on public cloud. >> One of the things that almost everybody from Palo Alto Networks talks about is their partnering strategy their acquisition strategy integrations. And I was doing some research. There's over 50 joint integrations that Google Cloud and Palo Alto Networks. Have you talked about Zero Trust Network Access 2.0 that was announced yesterday. >> Correct. >> Give us a flavor of what that is and what does it deliver that 1.0 did not? >> Well, great. And what I'd like to do is touch a little bit on those 50 integrations because it's been, you know, a a building rolling thunder, shall we say as far as how have we taken a look at customers embracing the cloud. The first thing was we took a look at at how do we make sure that Palo Alto solutions are easier for customers to deploy and to orchestrate in Google Cloud making their journey to embracing cloud seamless and easy. The second thing was how could we make that deployment and the infrastructure even more easy to adopt by doing first party integrations? So earlier this year we announced cloud IDS intrusion detection system where we actually have first party directly in our console of customers being able to simply select, they want to turn on inspection of the traffic that's running on Google Cloud and it leverages the threat detection capability from Palo Alto Networks. So we've gone from third party integration alone to first party integration. And that really takes us to, you know, the direction of what we're seeing customers need to embrace now which is, this is your Zero Trusts strategy and Zero Trust 2.0 helps customers do a number of things. The first is, you know, we don't want to just verify a user and their access into the environment once. It needs to be continuous inspection, right? Cause their state could change. I think, you know, the, the teams we're talking about some really good ways of addressing, you know for instance, TSA checkpoints, right? And how does that experience look? We need to make sure that we're constantly evaluating that user's access into the environment and then we need to make sure that the content that's being accessed or, you know, loaded into the environment is inspected. So we need continuous content inspection. And that's where our partnership really comes together very well, is not only can we take care of any app any device, any user, and especially as we take a look at you know, embracing contractor like use cases for instance where we have managed devices and unmanaged devices we bring together beyond Corp and Prisma access to take a look at how can we make sure any device, any user any application is secure throughout. And then we've got content inspection of how that ZTNA 2.0 experience looks like. >> Josh, that threat data that you just talked about. >> Yeah. >> Who has access to that? Is it available to any partner, any customer, how... it seems like there's gold in them, NAR hills, so. >> There is. But, this could be gold going both ways. So how, how do you adjudicate and, how do you make sure that first of all that that data's accessible for, for good and not in how do you protect it against, you know, wrong use? >> Well, this is one of the great things about partnering with Palo Alto because technically the the threat intelligence is coming from their ingestion of malware, known threats, and unknown threats right into their technology. Wildfire, for instance, is a tremendous example of this where unit 42 does, you know, analysis on unknown threats based upon what Nikesh said on stage. They've taken their I think he said 27 days to identification and remediation down to less than a minute, right? So they've been able to take the intelligence of what they ingest from all of their existing customers the unknown vulnerabilities that are identified quickly assessing what those look like, and then pushing out information to the rest of their customers so that they can remediate and protect against those threats. So we get this shared intelligence from the way that Palo Alto leverages that capability and we've brought that natively into Google Cloud with cloud intrusion detection. >> So, okay, so I'm, I'm I dunno why I have high frequency trading in my mind cause it used to be, you know, like the norm was, oh it's going to take a year to identify an intrusion. And, and, and now it's down to, you know take was down to 27 days. Now it's down to a minute. Now it's not. That's best practice. And I'm, again, I'm thinking high frequency trading how do I beat the speed of light? And that's kind of where we're headed, right? >> Right. >> And so that's why he said one minute's not enough. We have to keep going. >> That's right. >> So guys got your best people working on that? >> Well, as a matter of fact, so Palo Alto Networks, you know when we take a look at what Nikesh said from stage, he talked about using machine learning and AI to get ahead of what we what they look at as far as predictability not only about behaviors in the environment so things that are not necessarily known threats but things that aren't behaving properly in the environment. And you can start to detect based on that. The second piece of it then is a lot of that technology is built on Google Cloud. So we're leveraging, their leveraging the capabilities that come together with you know, aggregation of, of logs the file stitching across the entire environment from the endpoint through to cloud operations the things that they detect for network content inspection putting all those files together to understand, you know where has the threat vector entered how has it gone lateral inside the environment? And then how do you make sure that you remediate all of those points of intrusion. And so yeah it's been exciting to see how our product teams have worked together to continue to advance the capabilities for speed for customers. >> And secure speed is critical. We had the opportunity this morning to speak with Lee Claridge, the chief product officer, and you know one of the things that I had heard about Lee is that despite all of the challenges in cybersecurity and the amorphous expansion of the threat network and the sophistication of the adversaries he's really optimistic about what it's going to enable organizations to do. I see you smiling. Do you share that optimism? >> I, I do. I think, you know, when you bring, when you bring leaders together to tackle big problems, I think, you know we've got the right teams working on the right things and we understand the problems that the customers are facing. And so, you know, from a a Google cloud perspective we understand that partnering with Palo Alto Networks helps to make sure that that optimism continues. You know, we work on continuous innovation when it comes to Google Cloud security framework, but then partnering with Palo Alto brings additional capabilities to the table. >> Vision for the, for the partnership. Where do you want to see it go? What's... we're two to five years down the road, what's it look like? Maybe two to three years. Let's go. >> Well, it was interesting. I, I think neer was the one that mentioned on stage about, you know how AI is going to start replacing us in our main jobs, right? I I think there's a lot of truth to that. I think as we look forward, we see that our teams are going to continue to help with automation remediation and we're going to have the humans working on things that are more interesting and important. And so that's an exciting place to go because today the reality is that we are understaffed in cybersecurity across the industry and we just can't hire enough people to make sure that we can detect, remediate and secure, you know every user endpoint and environment out there. So it's exciting to see that we've got a capability to move in a direction to where we can make sure that we get ahead of the threat actors. >> Yeah. So he said within five years your SOC will be AI based and and basically he elaborated saying there's a lot of stuff that you're doing today that you're not going to be doing tomorrow. >> That's true. >> And that's going to continue to be a moving target I would think Google is probably ahead in that game and ahead of most, right? I mean, you guys were there early. I mean, I remember when Hadoop was all the rage like just at the beginning you guys like, yeah, you know Google's like, no, no, no, we're not doing Hadoop anymore. That's like old news. So you tended to be, I don't know, at least five maybe seven years ahead of the industry. So I imagine you using a lot of those AI techniques in your own business today. >> Absolutely. I mean, I think you see it in our consumer products, and you certainly see it in the the capabilities we make available to enterprise as far as how they can innovate on our cloud. And we want to make sure that we continue to provide those capabilities, you know not only for the tools that we build but the tools that customers use. >> What's the, as we kind of get towards the end of our conversation here, we we talk about zero trust as, as a journey, as an approach. It's not a product, it's not a tool. What is the, who's involved in the zero trust journey from the customers perspective? Is this solely with the CSO, CSO, CIOs or is this at the CEO level going, we have to be a data company but we have to be a secure data company 24/7. >> It's interesting as you've seen malware, phishing, ransomware attacks. >> Yeah. >> This is not only just a CSO CIO conversation it's a board level conversation. And so, you know the way to address this new way of working where we have very distributed environments where you can't create a perimeter anymore. You need to strategize with zero trust. And so continuously, when we're talking to customers we're hearing that as a main initiative, you know from the CIO's office and from the board level. >> Got it, last question. The upgrade path for existing customers from 1., ZTNA 1.0 to 2.0. How simple is that? >> It's easy. You know, when we take- >> Is there an easy button? >> So here's the great thing [Dave] If you're feeling lucky. [Lisa] Yeah. (group laughs) >> Well, Palo Alto, right? Billing prisma access has really taken what was traditional security that was an on-prem or a data center deployed strategy to cloud-based. And so we've worked with customers like Princeton University who had to quickly transition from in-person learning to distance learning find a way to ramp their staff their faculty and their students. And we were able to, you know Palo Alto deploy it on Google Cloud's, you know network that solution in very quick order and had those, you know, everybody back up and running. So deployment and upgrade path is, is simple when you look at cloud deployed architectures to address zero trusts network. >> That's awesome. Some of those, some of those use cases that came out of the pandemic were mind blowing but also really set the table for other organizations to go, yes, this can be done. And it doesn't have to take forever because frankly where security is concerned, we don't have time. >> That's right. And it's so much faster than traditional architectures where you had to procure hardware. >> Yeah. >> Deploy it, configure it, and then, you know push agents out to all the endpoints and and get your users provisioned. In this case, we're talking about cloud delivered, right? So I've seen, you know, with Palo Alto deploying for customers that run on Google Cloud they've deployed tens of thousands of users in a very short order. You know, we're talking It was, it's not months anymore. It's not weeks anymore. It's days >> Has to be days. Josh, it's been such a pleasure having you on the program. Thank you for stopping by and talking with Dave and me about Google Cloud, Palo Alto Networks in in addition to secret squirrel. I feel like when you were describing your background that you're like the love child of Palo Alto Networks and Google Cloud, you might put that on your cartoon. >> That is a huge compliment. I really appreciate that, Lisa, thank you so much. >> Thanks so much, Josh. [Josh] It's been a pleasure being here with you. [Dave] Thank you >> Oh, likewise. For Josh Haslett and Dave, I'm Lisa Martin. You're watching theCUBE, the leader in live coverage for emerging and enterprise tech. (upbeat outro music)

(upbeat music) >> Hey, everyone, welcome back to Las Vegas, "theCube" is on our fourth day of covering AWS re:Invent, live from the Venetian Expo Center. This week has been amazing. We've created a ton of content, as you know, 'cause you've been watching. But, there's been north of 55,000 people here, hundreds of thousands online. We've had amazing conversations across the AWS ecosystem. Lisa Martin, Paul Gillan. Paul, what's your, kind of, take on day four of the conference? It's still highly packed. >> Oh, there's lots of people here. (laughs) >> Yep. Unusual for the final day of a conference. I think Werner Vogels, if I'm pronouncing it right kicked things off today when he talked about asymmetry and how the world is, you know, asymmetric. We build symmetric software, because it's convenient to do so, but asymmetric software actually scales and evolves much better. And I think that that was a conversation starter for a lot of what people are talking about here today, which is how the cloud changes the way we think about building software. >> Absolutely does. >> Our next guest, Holger Mueller, that's one of his key areas of focus. And Holger, welcome, thanks for joining us on the "theCube". >> Thanks for having me. >> What did you take away from the keynote this morning? >> Well, how do you feel on the final day of the marathon, right? We're like 23, 24 miles. Hit the ball yesterday, right? >> We are going strong Holger. And, of course, >> Yeah. >> you guys, we can either talk about business transformation with cloud or the World Cup. >> Or we can do both. >> The World Cup, hands down. World Cup. (Lisa laughs) Germany's out, I'm unbiased now. They just got eliminated. >> Spain is out now. >> What will the U.S. do against Netherlands tomorrow? >> They're going to win. What's your forecast? U.S. will win? >> They're going to win 2 to 1. >> What do you say, 2:1? >> I'm optimistic, but realistic. >> 3? >> I think Netherlands. >> Netherlands will win? >> 2 to nothing. >> Okay, I'll vote for the U.S.. >> Okay, okay >> 3:1 for the U.S.. >> Be optimistic. >> Root for the U.S.. >> Okay, I like that. >> Hope for the best wherever you work. >> Tomorrow you'll see how much soccer experts we are. >> If your prediction was right. (laughs) >> (laughs) Ja, ja. Or yours was right, right, so. Cool, no, but the event, I think the event is great to have 50,000 people. Biggest event of the year again, right? Not yet the 70,000 we had in 2019. But it's great to have the energy. I've never seen the show floor going all the way down like this, right? >> I haven't either. >> I've never seen that. I think it's a record. Often vendors get the space here and they have the keynote area, and the entertainment area, >> Yeah. >> and the food area, and then there's an exposition, right? This is packed. >> It's packed. >> Maybe it'll pay off. >> You don't see the big empty booths that you often see. >> Oh no. >> Exactly, exactly. You know, the white spaces and so on. >> No. >> Right. >> Which is a good thing. >> There's lots of energy, which is great. And today's, of course, the developer day, like you said before, right now Vogels' a rockstar in the developer community, right. Revered visionary on what has been built, right? And he's becoming a little professorial is my feeling, right. He had these moments before too, when it was justifying how AWS moved off the Oracle database about the importance of data warehouses and structures and why DynamoDB is better and so on. But, he had a large part of this too, and this coming right across the keynotes, right? Adam Selipsky talking about Antarctica, right? Scott against almonds and what went wrong. He didn't tell us, by the way, which often the tech winners forget. Scott banked on technology. He had motorized sleds, which failed after three miles. So, that's not the story to tell the technology. Let everything down. Everybody went back to ponies and horses and dogs. >> Maybe goes back to these asynchronous behavior. >> Yeah. >> The way of nature. >> And, yesterday, Swami talking about the bridges, right? The root bridges, right? >> Right. >> So, how could Werner pick up with his video at the beginning. >> Yeah. >> And then talk about space and other things? So I think it's important to educate about event-based architecture, right? And we see this massive transformation. Modern software has to be event based, right? Because, that's how things work and we didn't think like this before. I see this massive transformation in my other research area in other platforms about the HR space, where payrolls are being rebuilt completely. And payroll used to be one of the three peaks of ERP, right? You would size your ERP machine before the cloud to financial close, to run the payroll, and to do an MRP manufacturing run if you're manufacturing. God forbid you run those three at the same time. Your machine wouldn't be able to do that, right? So it was like start the engine, start the boosters, we are running payroll. And now the modern payroll designs like you see from ADP or from Ceridian, they're taking every payroll relevant event. You check in time wise, right? You go overtime, you take a day of vacation and right away they trigger and run the payroll, so it's up to date for you, up to date for you, which, in this economy, is super important, because we have more gig workers, we have more contractors, we have employees who are leaving suddenly, right? The great resignation, which is happening. So, from that perspective, it's the modern way of building software. So it's great to see Werner showing that. The dirty little secrets though is that is more efficient software for the cloud platform vendor too. Takes less resources, gets less committed things, so it's a much more scalable architecture. You can move the events, you can work asynchronously much better. And the biggest showcase, right? What's the biggest transactional showcase for an eventually consistent asynchronous transactional application? I know it's a mouthful, but we at Amazon, AWS, Amazon, right? You buy something on Amazon they tell you it's going to come tomorrow. >> Yep. >> They don't know it's going to come tomorrow by that time, because it's not transactionally consistent, right? We're just making every ERP vendor, who lives in transactional work, having nightmares of course, (Lisa laughs) but for them it's like, yes we have the delivery to promise, a promise to do that, right? But they come back to you and say, "Sorry, we couldn't make it, delivery didn't work and so on. It's going to be a new date. We are out of the product.", right? So these kind of event base asynchronous things are more and more what's going to scale around the world. It's going to be efficient for everybody, it's going to be better customer experience, better employee experience, ultimately better user experience, it's going to be better for the enterprise to build, but we have to learn to build it. So big announcement was to build our environment to build better eventful applications from today. >> Talk about... This is the first re:Invent... Well, actually, I'm sorry, it's the second re:Invent under Adam Selipsky. >> Right. Adam Selipsky, yep. >> But his first year. >> Right >> We're hearing a lot of momentum. What's your takeaway with what he delivered with the direction Amazon is going, their vision? >> Ja, I think compared to the Jassy times, right, we didn't see the hockey stick slide, right? With a number of innovations and releases. That was done in 2019 too, right? So I think it's a more pedestrian pace, which, ultimately, is good for everybody, because it means that when software vendors go slower, they do less width, but more depth. >> Yeah. >> And depth is what customers need. So Amazon's building more on the depth side, which is good news. I also think, and that's not official, right, but Adam Selipsky came from Tableau, right? >> Yeah. So he is a BI analytics guy. So it's no surprise we have three data lake offerings, right? Security data lake, we have a healthcare data lake and we have a supply chain data lake, right? Where all, again, the epigonos mentioned them I was like, "Oh, my god, Amazon's coming to supply chain.", but it's actually data lakes, which is an interesting part. But, I think it's not a surprise that someone who comes heavily out of the analytics BI world, it's off ringside, if I was pitching internally to him maybe I'd do something which he's is familiar with and I think that's what we see in the major announcement of his keynote on Tuesday. >> I mean, speaking of analytics, one of the big announcements early on was Amazon is trying to bridge the gap between Aurora. >> Yep. >> And Redshift. >> Right. >> And setting up for continuous pipelines, continuous integration. >> Right. >> Seems to be a trend that is common to all database players. I mean, Oracle is doing the same thing. SAP is doing the same thing. MariaDB. Do you see the distinction between transactional and analytical databases going away? >> It's coming together, right? Certainly coming together, from that perspective, but there's a fundamental different starting point, right? And with the big idea part, right? The universal database, which does everything for you in one system, whereas the suite of specialized databases, right? Oracle is in the classic Oracle database in the universal database camp. On the other side you have Amazon, which built a database. This is one of the first few Amazon re:Invents. It's my 10th where there was no new database announced. Right? >> No. >> So it was always add another one specially- >> I think they have enough. >> It's a great approach. They have enough, right? So it's a great approach to build something quick, which Amazon is all about. It's not so great when customers want to leverage things. And, ultimately, which I think with Selipsky, AWS is waking up to the enterprise saying, "I have all this different database and what is in them matters to me." >> Yeah. >> "So how can I get this better?" So no surprise between the two most popular database, Aurora and RDS. They're bring together the data with some out of the box parts. I think it's kind of, like, silly when Swami's saying, "Hey, no ETL.". (chuckles) Right? >> Yeah. >> There shouldn't be an ETL from the same vendor, right? There should be data pipes from that perspective anyway. So it looks like, on the overall value proposition database side, AWS is moving closer to the universal database on the Oracle side, right? Because, if you lift, of course, the universal database, under the hood, you see, well, there's different database there, different part there, you do something there, you have to configure stuff, which is also the case but it's one part of it, right, so. >> With that shift, talk about the value that's going to be in it for customers regardless of industry. >> Well, the value for customers is great, because when software vendors, or platform vendors, go in depth, you get more functionality, you get more maturity you get easier ways of setting up the whole things. You get ways of maintaining things. And you, ultimately, get lower TCO to build them, which is super important for enterprise. Because, here, this is the developer cloud, right? Developers love AWS. Developers are scarce, expensive. Might not be want to work for you, right? So developer velocity getting more done with same amount of developers, getting less done, less developers getting more done, is super crucial, super important. So this is all good news for enterprise banking on AWS and then providing them more efficiency, more automation, out of the box. >> Some of your customer conversations this week, talk to us about some of the feedback. What's the common denominator amongst customers right now? >> Customers are excited. First of all, like, first event, again in person, large, right? >> Yeah. >> People can travel, people meet each other, meet in person. They have a good handle around the complexity, which used to be a huge challenge in the past, because people say, "Do I do this?" I know so many CXOs saying, "Yeah, I want to build, say, something in IoT with AWS. The first reference built it like this, the next reference built it completely different. The third one built it completely different again. So now I'm doubting if my team has the skills to build things successfully, because will they be smart enough, like your teams, because there's no repetitiveness and that repetitiveness is going to be very important for AWS to come up with some higher packaging and version numbers.", right? But customers like that message. They like that things are working better together. They're not missing the big announcement, right? One of the traditional things of AWS would be, and they made it even proud, as a system, Jassy was saying, "If we look at the IT spend and we see something which is, like, high margin for us and not served well and we announced something there, right?" So Quick Start, Workspaces, where all liaisons where AWS went after traditional IT spend and had an offering. We haven't had this in 2019, we don't have them in 2020. Last year and didn't have it now. So something is changing on the AWS side. It's a little bit too early to figure out what, but they're not chewing off as many big things as they used in the past. >> Right. >> Yep. >> Did you get the sense that... Keith Townsend, from "The CTO Advisor", was on earlier. >> Yep. >> And he said he's been to many re:Invents, as you have, and he said that he got the sense that this is Amazon's chance to do a victory lap, as he called it. That this is a way for Amazon to reinforce the leadership cloud. >> Ja. >> And really, kind of, establish that nobody can come close to them, nobody can compete with them. >> You don't think that- >> I don't think that's at all... I mean, love Keith, he's a great guy, but I don't think that's the mindset at all, right? So, I mean, Jassy was always saying, "It's still the morning of the day in the cloud.", right? They're far away from being done. They're obsessed over being right. They do more work with the analysts. We think we got something right. And I like the passion, from that perspective. So I think Amazon's far from being complacent and the area, which is the biggest bit, right, the biggest. The only thing where Amazon truly has floundered, always floundered, is the AI space, right? So, 2018, Werner Vogels was doing more technical stuff that "Oh, this is all about linear regression.", right? And Amazon didn't start to put algorithms on silicon, right? And they have a three four trail and they didn't announce anything new here, behind Google who's been doing this for much, much longer than TPU platform, so. >> But they have now. >> They're keen aware. >> Yep. >> They now have three, or they own two of their own hardware platforms for AI. >> Right. >> They support the Intel platform. They seem to be catching up in that area. >> It's very hard to catch up on hardware, right? Because, there's release cycles, right? And just the volume that, just talking about the largest models that we have right now, to do with the language models, and Google is just doing a side note of saying, "Oh, we supported 50 less or 30 less, not little spoken languages, which I've never even heard of, because they're under banked and under supported and here's the language model, right? And I think it's all about little bit the organizational DNA of a company. I'm a strong believer in that. And, you have to remember AWS comes from the retail side, right? >> Yeah. >> Their roll out of data centers follows their retail strategy. Open secret, right? But, the same thing as the scale of the AI is very very different than if you take a look over at Google where it makes sense of the internet, right? The scale right away >> Right. >> is a solution, which is a good solution for some of the DNA of AWS. Also, Microsoft Azure is good. There has no chance to even get off the ship of that at Google, right? And these leaders with Google and it's not getting smaller, right? We didn't hear anything. I mean so much focused on data. Why do they focus so much on data? Because, data is the first step for AI. If AWS was doing a victory lap, data would've been done. They would own data, right? They would have a competitor to BigQuery Omni from the Google side to get data from the different clouds. There's crickets on that topic, right? So I think they know that they're catching up on the AI side, but it's really, really hard. It's not like in software where you can't acquire someone they could acquire in video. >> Not at Core Donovan. >> Might play a game, but that's not a good idea, right? So you can't, there's no shortcuts on the hardware side. As much as I'm a software guy and love software and don't like hardware, it's always a pain, right? There's no shortcuts there and there's nothing, which I think, has a new Artanium instance, of course, certainly, but they're not catching up. The distance is the same, yep. >> One of the things is funny, one of our guests, I think it was Tuesday, it was, it was right after Adam's keynote. >> Sure. >> Said that Adam Selipsky stood up on stage and talked about data for 52 minutes. >> Yeah. Right. >> It was timed, 52 minutes. >> Right. >> Huge emphasis on that. One of the things that Adam said to John Furrier when they were able to sit down >> Yeah >> a week or so ago at an event preview, was that CIOs and CEOs are not coming to Adam to talk about technology. They want to talk about transformation. They want to talk about business transformation. >> Sure, yes, yes. >> Talk to me in our last couple of minutes about what CEOs and CIOs are coming to you saying, "Holger, help us figure this out. We have to transform the business." >> Right. So we advise, I'm going quote our friends at Gartner, once the type A company. So we'll use technology aggressively, right? So take everything in the audience with a grain of salt, followers are the laggards, and so on. So for them, it's really the cusp of doing AI, right? Getting that data together. It has to be in the cloud. We live in the air of infinite computing. The cloud makes computing infinite, both from a storage, from a compute perspective, from an AI perspective, and then define new business models and create new best practices on top of that. Because, in the past, everything was fine out on premise, right? We talked about the (indistinct) size. Now in the cloud, it's just the business model to say, "Do I want to have a little more AI? Do I want a to run a little more? Will it give me the insight in the business?". So, that's the transformation that is happening, really. So, bringing your data together, this live conversation data, but not for bringing the data together. There's often the big win for the business for the first time to see the data. AWS is banking on that. The supply chain product, as an example. So many disparate systems, bring them them together. Big win for the business. But, the win for the business, ultimately, is when you change the paradigm from the user showing up to do something, to software doing stuff for us, right? >> Right. >> We have too much in this operator paradigm. If the user doesn't show up, doesn't find the click, doesn't find where to go, nothing happens. It can't be done in the 21st century, right? Software has to look over your shoulder. >> Good point. >> Understand one for you, autonomous self-driving systems. That's what CXOs, who're future looking, will be talked to come to AWS and all the other cloud vendors. >> Got it, last question for you. We're making a sizzle reel on Instagram. >> Yeah. >> If you had, like, a phrase, like, or a 30 second pitch that would describe re:Invent 2022 in the direction the company's going. What would that elevator pitch say? >> 30 second pitch? >> Yeah. >> All right, just timing. AWS is doing well. It's providing more depth, less breadth. Making things work together. It's catching up in some areas, has some interesting offerings, like the healthcare offering, the security data lake offering, which might change some things in the industry. It's staying the course and it's going strong. >> Ah, beautifully said, Holger. Thank you so much for joining Paul and me. >> Might have been too short. I don't know. (laughs) >> About 10 seconds left over. >> It was perfect, absolutely perfect. >> Thanks for having me. >> Perfect sizzle reel. >> Appreciate it. >> We appreciate your insights, what you're seeing this week, and the direction the company is going. We can't wait to see what happens in the next year. And, yeah. >> Thanks for having me. >> And of course, we've been on so many times. We know we're going to have you back. (laughs) >> Looking forward to it, thank you. >> All right, for Holger Mueller and Paul Gillan, I'm Lisa Martin. You're watching "theCube", the leader in live enterprise and emerging tech coverage. (upbeat music)

(gentle music) >> Hello, beautiful humans and welcome back to fabulous Las Vegas, where we are combating the dry air of the desert and all giggling about the rasp of our voice at this stage. We're theCUBE and we are live from AWS reinvent. I am Savannah Peterson, joined by the fabulous Paul Gillin. Paul, how are you holding up? How are your feet doing? >> My feet are, I can't feel them anymore. (both laugh) >> We can't feel much after these feet. >> Two miles. Just to get from, just to get to to the keynotes this morning. >> Did you do your cross training to prepare >> For, >> Apparently not well enough. (Savannah laughs) Not well enough. >> Well, it's great to have you here >> likewise. and I'm very excited for our next conversation. We've got Ramesh from Prosimo. >> Thank you. >> Savannah: Welcome to the show. How is the show going for you? How's your voice? >> Oh my God. I woke up this morning and I could not hear my own voice. I'm like, this is not me. I think it's the dry air here, so if I cough, I apologize in advance. But no, the show has been great. It's been nonstop at the booth. It's wonderful to see all the customers in one place so you don't have to schedule lots of meetings spread across three, four weeks. So you get to >> Savannah: Right. I, yeah >> So yesterday was like eight to six, nonstop and it was awesome, right? Because you get to meet all these guys. The other important thing is the focus on the right layer, right? Like, I loved the keynote from Adam. It was about applications, services, data. Nowhere in there was there like infrastructure. Like we are infrastructure, right? I actually love that because that's where the focus should be and that's what customers are caring about right? So it's, it's been great so far. >> Yeah. I'm so happy to hear your booth's packed. I know exactly what you mean. I mean, we're going to be talking about optimization. It's a theme, but we also optimize our time here >> Ramesh: Yeah. >> on the show floor by getting to engage with our community. Prosimo's been around for three years just in case folks aren't familiar, give us the pitch. >> Sure. We are in the cloud networking space, solving for two problems. What happens within the cloud as you bring up VPCs, vnet and workloads, how are they able to talk to each other, secure each other, and how to use those access workloads? Those are the two problems that we solve for. It stemmed from really us seeing a complete diversion in what cloud wants versus what network really focuses on. Cloud has been always focused on applications and speed of operations and network has always been about reliability, scalability, and robust architecture. And we didn't really see these things come together. So that's when prosimo was born. >> So what are some of the surprises newcomers to the cloud may encounter with networking, with cloud networking that was not a factor when they were fully on-prem? >> So the first thing is in the cloud, you can't deal with the workload the same way you dealt with in the data center. In the data center, you usually had pools of service. They were all allocated some level of addressing. And it was not about the workload, it was more about the identity, IP addresses and so forth. In the cloud, those things have completely gotten demolished, right? You have to refer to a S3 service as an S3 service. It's not an IP endpoint. IP endpoint comes and goes, right? >> Savannah: Yeah. >> And so you have to completely shift around that, right? >> Now, this actually challenges almost 10 years, 12, 20 years maybe, of networking that we knew about, right? So that's why cloud networking is almost night and day difference compared to regular networking right? And, we're seeing that and that's what we are really helping customers with. >> What are some of the trends that you're seeing? I, well actually, let me ask you this question. Do you, is there an industry or vertical you work with specifically? I would imagine most people across, >> Ramesh: The Yeah, across. >> Yeah. >> Anybody that has workloads in the cloud right? >> Yeah, right. >> Ramesh: That's, >> I mean I can't imagine any companies that would have that. >> Exactly. (Savannah laughs) >> What are some of the trends that you're seeing? I know we talk about time to value. We talk about cost optimization. Is that the top priority for your customers? >> Yeah. Up until end of last year, a lot of the focus was about speed of operations. And so people would look at what are the type of workloads? How do I enable things? How do I empower my development team? So, if I'm the cloud platform team responsible for connecting, securing and making sure my applications can get deployed smooth and fast, that was the primary focus. Fast forward to this year, we started to see this a little bit at the beginning of the year. Now it's in full force. It's about cost control, right? It's about egress charges coming out of the cloud. Suddenly the cloud bill and every single line item on the cloud bill is in focus, right? And so that has a direct impact on what does this mean for networking. Cloud networking for many may not be familiar, it's about 14% of the cloud bill. And so anything that materially moves the needle on the cloud networking costs can actually have a have a big impact, right? And so we have seen the focus on the speed of operations are still there but cloud cost control has become a big part of it. >> So where are the excesses? I mean, it's, it's a big part of the bill. Where can company, where do companies typically waste money in networking costs? >> So, if you bring a person who understands networking and networking architecture really, really well, they'll can build a solid architecture, but they'll not focus on operations and automation. If you bring a 25 year old, they will automate the heck out of it. They know python day in and day out. And so they'll automate the heck out of it but it will not be with a robust architecture, right? And so you, on one hand, you end up wasting because you do things very suboptimally. It's a solid architecture, it's a really good design but it's really bad for operations. In the other hand, with push of a button you can get anything done but underneath the covers, underneath the hood, if you look at it, it's a mess, right? And so you have more competence than necessary. And so, what customers want is really a best of both, right? You need solid architecture that has all the right principles but also you need the automation so that you don't employ four, five people and a whole toolkit in order to make things work, right? And that's where we see most of the efficiencies come from >> You said you were you were super busy at your booth. Do customers understand that this is a problem now? >> So more so now than I would say last year. The last reinvent when we had a session. >> Yeah. >> We had to educate a lot of people on these are the requirements for cloud networking. Thanks to Gartner, thanks to many of the sessions you guys have been doing as well. The focus and the education for what cloud networking requires has started to come about. Now, this is where the savviness of the customer is important, right? Like there are customers in different stages of their journey. Those that have been operating in the cloud for three years plus, know that they've crossed that initial phase, right? Like you have basic hygiene, you have certain things and moving from hundreds of VPCs to maybe about thousand, right? And so at that time, the set of challenges I need to work with are very, very different, right? So now increasingly we are seeing at the booth the challenges are, "Hey, I know how to operate in the cloud". Right? Like, "Don't talk to me about that." Right? "But how do I get from hundred to a thousand?" Because I have a gun to my head. My CIO has said, I need to decommission my data centers in the next couple of years and I need to go all in on cloud. Help me with that, right? And so it's the, I wouldn't call it like massive scale it's the scale from kind of the trivial to the next stage that's actually causing a lot of these problems to surface. >> It's that layer of transformation. >> Ramesh: Yeah. It's when you've made the commitment and now we've got to catch everything up >> [Ramesh} exactly. >> across the company locations and probably a variety of different silos doing different things. >> Ramesh: Exactly. Yeah. >> Super complex. So, how do folks get started with you? >> Yeah, so typically we start with like, even if the customer says, "Here's what my blueprint looks like." We say, "Bring two regions." That's it, two regions, a few workloads. We'll help you set up the connectivity, set up the secure access required, set up the foundational things There's a certain level of automation, right? Let's get to that point because governance is different. The cloud privileges are different so let's work through all of that, right? Usually this takes about a week or so. The actual proof of concept, proof of value can be done in a day, but getting permissions and what not takes about, about a week, right? And once you show two regions then it's actually game on, right? Then you go from 10 VPCs to a hundred to a thousand and it's just like one to one thing after another. So that's usually how we see customers get started. We have a full stack that covers kind of what does this mean for the network to application services to kind of layer seven and so forth. We tell the customer, as much as we want you to focus on the entire stack, let's start with one, right? Start baby steps, start with one. Because for many, cloud itself is, I wouldn't say new but they're in a region that's not comfortable, right? So you wannna, you don't want to throw too much at them. >> Savannah: Right. >> So we help them kind of progressively move towards different types of workplace. >> Savannah: Yeah. >> And you have a multicloud story as well. >> Ramesh: That's correct. >> So when companies begin to cross clouds with workloads, move them between clouds, what kinds of issues emerge then? >> Yeah, so there are two parts for this, right? There is the AWS and data center and then there is the AWS plus other clouds. Two different set of problems, actually, >> Paul: Hm-hmm. Hm-hmm. The AWS plus connectivity, back into my data center almost every single enterprise. We deal with kind of the global 2000. Every single one of them has that, right? And so we kind of, we go through a series of steps, come up with an architecture, deploy a solution. After that, it's, Hey, I have BigQuery in Google that needs to talk back to an S3 bucket out here. Like, no networking solution can help you with that. Like, you need like cloud native principles in order to come into the picture. So increasingly we are seeing requests for, hey I have a distributed workload. It's not, it's not that one single application is spread across multiple clouds, but I have these islands of workloads that all need to talk to each other. >> Paul: Right. And what I don't want to do is actually build highways that actually connect all these things together because that's a waste of time. I actually want to make sure that only these applications that care about the talking to each other, are allowed to talk to each other. So that's kind of one foundational thing that we see. A few others are around compliance and governance. So we say, Hey, if I'm a retailer, I need to have some workloads in Azure some in the GCP and so forth. So it depends on kind of the industry compliance, regulatory requirements and so forth. >> So many different needs >> Ramesh: Exactly. for so many different types of companies. But also, you know, creating that efficiency is so great. >> Ramesh: Yup. >> And especially that time to value tune, cost reduction >> Ramesh: Yup. doing a lot of great things for your customers. There's a note on my run sheet here that you've seen some success with Topgolf and I suspect we have some golfers in the audience. John even used to be a caddy. We had a caddy segment with someone who was a pro caddy. Drew, when we were at Cape Con. Tell us about that story. >> So it was a really wild idea. We said, okay people are going to be walking around 22,000 steps right? >> Savannah: Yeah. >> And so >> Like Paul, >> And, they're going to be talking to people, listening to sessions. So we said, let's, what do most others do? You set up some time in a restaurant, you come, you have a social time, and what not. We said, let's give people something different. So we reserve the Topgolf here and we opened it up. We initially paid for a certain number of things. It's actually gone three x of that right now. So we had in the Topgolf, can you give us like the entire thing? I think people just want to go do something different, right? >> Savannah: Yeah. >> And of course the topic is important but equally important is like, I just want to have a good time, right? >> Yeah. And if you, hit a few And there you go. >> It doesn't have to relate back to network >> Cloud, network. >> Yeah, exactly. And so >> Well, it's all about building community. >> Exactly. >> And especially right now, we all, you know, we're stronger together. >> Ramesh: Yup. We're entering a unique time, we're coming out of a unique time. >> Ramesh: Exactly. >> And, no, I think that's great. And we actually do a swag segment here on theCUBE, differentiating on the show floor. I mean, it's clear because of how thoughtful you are >> Ramesh: Yeah. there's a reason that your, that your booth is so busy. >> Ramesh: That's right. >> So what's next? What can you, can you give us a little sneak preview? What's coming out for you? >> Yeah, so, I'm sensitive and sympathetic to all the macroeconomic conditions that are happening but there's been, we have not skipped a beat. So our business is growing really well. Thanks to all the things that are happening in the cloud. Increasingly, folks are looking at, you know, how how do I move in mass into the cloud? And so a few themes have come about as a result. One, certainly around cost control. How do I, how do I make, how do we make sure that we help our customers in that journey, right? So we have a few things around those lines. Modernization, especially after you go through the first few workloads, the next few that come about are invariably modern workloads. And modern workloads is this sensitive thing where I think the ultra savvy developers know what to do but the infrastructure guys don't know what to do in order to serve, right? And so we have actually developed a set of capabilities to help with that kind of modernization, right? Because it's not enough if your apps are modernized, your infrastructure that serves the apps also need to be modernized. And so those are the, those are the things and certainly, getting our customers less than us. We want to get our customers to talk. And so you'll see quite a bit of that as well. >> I want to ask you about a statement that was in the notes that we were reading, running up this interview. Zero Trust network access is the next solution that will be disrupted. What do you mean by that? >> So, when we started the company about three years ago, zero test network access was there. It was about maybe two, three years old at that time. And so we said, it needs to be done differently in the cloud. Why? Because you are a user. You're trying to access an application in the cloud. Do you care what's in the middle? You really don't, you just want to be able to open up your laptop, go to dub dub something.com and you should be able to access, right? But that's not how the experience is today. There's invariably something that comes, a middle mile solution that comes in the middle, right? And then the guy needs to operationalize all of that. And that now passes on to you. You need to launch a an agent on your thing, connect into something. It just brings a lot of complexity, right? So we looked at that problem and we said, cloud has done really really a few things really, really well, right? It's literally at your doorstep. Cloud presence is literally at your doorstep. So as you open up your browser, connect from your home, I don't need anything in the middle. I am jumping straight into the cloud. And so when you do that, then you actually have the luxury of bringing a few capabilities to the entry point of the cloud so that security can be done better, posture control can be done better and so on and so forth. So we developed those capabilities almost three years ago. We have quite a few large enterprises that have deployed this. And we fundamentally believe on building on top of the hyperscale network because billions of tens of billions of dollars go into the investment here. And we want to be building a layer of value on top, right? And so we've been working closely with our AWS buddies here and actually built capabilities so that the infrastructure presence, the massive reach and also the underlying capabilities for zero trust are provided. But what the customer regains in terms of value is through our platform, right? And so we'll see a whole lot more innovation along these lines. Probably bad news for the Middle Mile provider who sit in the, in the middle because hey AWS is literally at your doorstep, so you have to rethink your strategy. >> Going to be a lot of agility >> Ramesh: Yes, absolutely. >> In a very different context than we normally use it in Nerdland. And no, I think that's great. So we have, it's an exciting time for you as a company. We have a new challenge here at Reinvent. >> Okay. >> On theCUBE. I know you're a venerable alumni. >> Yep. >> You have been on theCUBE multiple times with multiple companies which is very impressive. Which says a lot about you. Although given how fun this interview's been, I'm not surprised. Give us your 30 second, Instagram real highlight, sound bite on the biggest or most important theme or takeaway from this year's show. >> From this show? Yeah, so if you look across the keynotes in all the sessions, the focus is on data, services and the applications. So the biggest takeaway I would offer anybody is focus on that first because that's where the outcome needs to shine. The rest of the stuff is a means to an end. I am an infrastructure guy through and through, I have been for the last 20 years. It hurts me to say infrastructure is a means to end but it is, right. Let the people dealing with the infrastructure deal with the infrastructure. If you are a customer or a client of the service, focus on the outcome, focus on the apps, focus on the services focus on on the data. That would be the biggest takeaway. >> Savannah: I appreciate your >> Paul: Words of wisdom >> Savannah: transparency. Yeah, no, exactly. Words of wisdom and very honest words of wisdom. Really great to talk to you about intelligent infrastructure. >> Absolutely. >> Savannah: Thank you so much for being on the show, Ramesh. >> Thank you. >> Savannah: It's been, it's been awesome. Paul, it's always a pleasure. >> Likewise. Thank you all for tuning in today here live from the show floor at AWS, reinvent in beautiful sin city, in the high desert and the high end dry desert with Paul Gillin. My name is Savannah Peterson and you're watching theCUBE, the leader in high tech coverage. (gentle music)

(upbeat electronic music) >> Hello, and welcome to this Cube Conversation. My name is Dave Vellante, and we're here with Raj Gossain, who's the Chief Product Officer at Alation. We have some news. Hello, Raj. Thanks for coming on. >> Dave, it's great to be with you on theCUBE again. >> Yeah, good to see you. So, okay, we're going to talk about Alation Connected Sheets. You know, what is that? Talk to us about what it is, what it does, what it brings to customers. >> So we recognize, spreadsheets are really the dark matter of the data universe. And they're used by, over 78 million people use spreadsheets on a regular basis to drive critical business analysis. But there's a lot of challenges with spreadsheet usage. It brings risk to the organization. There's no visibility into where data comes from. And so we wanted to bring the power of the Alation Data Intelligence Platform to business users where they spend most of their time. And that's in a tool that they love, and that's spreadsheets. And so we're launching a brand new product next week called Alation Connected Sheets. >> So talk more about that. So yes, I get the lineage issue, like where did-- who did this, where's this data come from? I got different data. But talk more about the problems that Alation Connected Sheets solves, specifically for customers. >> Yeah, so the big challenges that we see when we talk to data organizations is how do they understand where the data came from? Is it trusted? Is it reusable? Should it be used in this format? And if you look at where most users that use spreadsheets get the data to power their spreadsheets, maybe it's a CSV download from a database, and then you have no idea where the data came from and where it's going. Or even worse, it's copying and pasting data from other spreadsheets. And so if you take those problems, how can we bring trusted data from governed sources like Snowflake and Redshift and put it in the hands of spreadsheet users, and give them the power and flexibility of Google Sheets or Microsoft Excel, but use trusted, reliable, well-governed data so that the data office feels great about them using spreadsheets and the end users, the business users, can take advantage of the tool that they know and love and do the work that they need to do quickly. >> So, okay. So I'm inferring from your comments there that you've got the ability to take data from you mentioned a couple, Snowflake and Redshift, other popular data warehouses. >> Yep. >> So talk about the key capabilities that you have, any specific features that we should know about. >> Sure. So, we built the leading data intelligence platform and the leading data catalog. And one of the benefits of that catalog is where you have visibility into all of the trusted, governed data sources that a data organization cares about, whether it's enterprise warehouses like Snowflake or Redshift, databases like SQL Server, Google BigQuery, what have you. So what we've done is we've brought the power of that data catalog directly into both Google Sheets as well as Excel. And the idea there is a user can log into their application, authenticate to Alation using the Alation Connected Sheets plugin into their spreadsheet tool, and browse those trusted data sets that are surfaced in the Alation catalog. They get trust signals, they get visibility into where this data came from. So lineage, insights, descriptive information. And then with one or two clicks, they can choose a data set from their warehouse, basically apply filtering conditions. So let's say I'm looking for customer data in Snowflake. I can find the right customer table. If I only want it for say, 2022, I can apply some filter conditions, I can reorder columns, push one button, authenticate to that data source. We want to maintain and ensure security is being applied, so only those users that have access to the warehouse can actually download that data set. But once they've authenticated, that data gets downloaded into their spreadsheet and there's a live connection that's maintained to that spreadsheet. So anytime you need to refresh the data, one push of a button and that data set gets updated. I can schedule the updates. So, you know, if I have to produce a report every Monday morning, I could have that data set refreshed at 8:00 a.m. Monday morning, or whatever schedule the user wants. And so it gives the user the data set they need, but the data organization, they can see where that data came from and they understand the lineage of the data as it is used in analysis in those spreadsheets themselves. >> So Raj, I know you're at the Super Bowl this week, a.k.a. re:Invent. >> Yes. >> And I know you got very close relationships with Snowflake, you've mentioned them a couple times with the data summit last spring. And I know you've done some integration work with those platforms and I'm sure others. So should we think of this as you're extending that sort of trust and governance out to spreadsheets, is that right? And stretching that out? >> That's exactly right. The way we talk about it is how do we bring data intelligence to business users in the tool that they know and love, which is the spreadsheet. And so, the data catalog and data intelligence platforms in general have really primarily been focused on servicing the needs of data users: data analysts, data scientists, data engineers. But you know, our vision, our aspiration at Alation is to really bring data intelligence to any business user. And so it's a big part of our strategy to make sure that the insights from the Alation catalog and platform can find their way into tools like Excel and Google Sheets. And so that's, what you highlighted, Dave, is exactly correct. We want to maximize the likelihood that a business user can have self-service access to trusted, governed data, do the work that they need to do, and ensure that the organization has a set of data assets in spreadsheets, frankly as opposed to liabilities, which is the way most data organizations look at spreadsheets is it's almost like a risk factor. We want to convert that risk, that liability, into an asset so that people can reuse data sets and they understand where this analysis is actually coming from. >> It's something that we've talked about for well over a decade on theCUBE. Is data an asset or is it a liability? >> Yeah, yeah. >> You obviously want to get value out of it, but if you can't share it, it's not trusted. So what people do is they lock it down and then that constricts value creation. >> Exactly. >> My understanding is this tech came out of an acquisition from a company, Kloudio. >> That's correct. >> Tell us about Kloudio. Why Kloudio? What's the fit there? >> Yeah, so Kloudio is a company, it's about five years old. We closed the acquisition of the company in March of this past year. And they had about 20 customers, 10 engineers. And we saw an opportunity with the spreadsheet tool that they'd created to really compliment our data intelligence strategy. And as you said, Dave, extend the value of data intelligence to business users. And so, we brought the Kloudio team into the fold. The thing I'm most excited about as a product guy, is within seven months of them joining Alation, we're actually shipping a brand new product that's going to drive revenue and meet the needs of tens of millions of users, ultimately. Like that's really our aspiration. And so, the tech they had was extremely modern. It reinforces the platform position that we have. You know, this microservices architecture that we've built Alation around, made it easy for that new team to come in and leverage existing APIs and capabilities from our platform and the tech that they brought into Alation to essentially connect the dots and deliver a brand new set of capabilities to an entirely new audience, to help our customers achieve their business objectives, which is really creating a data culture across their entire organization, inclusive of business users, not just, like I said, the data X users that are already taking advantage of solutions like Alation and cloud warehouses, et cetera. >> So I have two questions, follow up questions by me, and I think you might have answered the second one. The first one is what's the secret sauce behind Kloudio? How does the tech work? The second question is how does it fit into the Alation portfolio? How were you able to integrate it so quickly? Maybe that's the microservices architecture. But start with the secret sauce. What is it, what can you share with me? >> I think the thing that we saw with Kloudio that got us excited, and the fact that they, even though it was a small company, they had 20 customers, they were generating revenue, and they were delivering real value to business users, by really enabling business users to tap into the value of trusted, governed data, and frankly, get IT out of the way. You know, we almost refer to it as like smart self-service, which is, they could find a data asset and connect to that source, and just with a couple quick clicks, almost a low-code, no-code type of an experience, bring that sort of data into their spreadsheet so they could do the work that they needed to do. That opportunity, that tech that the Kloudio team had built out, the big gap that they had is, my goodness, what does it take to actually be aware of all the data sources that exist across an organization and connect to them? And that's what Alation does, right? That's why we built the platform that we built, so that we can basically understand all of a customer's data assets, whether they're on-prem or in the cloud. And so it was a little bit of, you know, that Reese's Peanut Butter Cup analogy. The chocolate and the peanut butter coming together. The Alation platform, the Alation catalog, coupled with the technology that Kloudio brought to us really was sort of a match made in heaven. And it's allowed us to bring this new capability to market that really is value-add on top of the platform and catalog investments that our customers have already made. >> Yeah, so they had this magic pixie dust, but it was sort of isolated, and then you've integrated it into your catalog. And that's the second part of my question. How were you able to do that so quickly? >> So, we've been on this evolution, enhancing the the Alation data intelligence platform. We've moved to a microservices architecture, we're fully multi-tenant in the cloud. And the fact that we'd made those investments over the past few years gave us the opportunity to make it easy for an acquired business like Kloudio, or you know, perhaps a future acquisition, or third party developers leveraging APIs that we expose to make it easy for them to integrate into the Alation platform. And so, I think it's a bit of foresight. We recognize that in starting with the catalog, the opportunity was much bigger than just providing a data catalog. We've added data governance, we've built out this platform and we recognize that more and more users can and should be benefiting from data intelligence. And so I think those platform investments have paid significant dividends and accelerated our ability to deliver Alation Connected Sheets as quickly as we have. >> Sounds like a great acquisition, like a diamond in the rough. I mean, I love big these big mega acquisitions 'cause the media company can write about 'em, but I really love the high, high return. You know, low denominator, high value. So, congratulations. >> Thank you. >> Where can people learn more about this? Maybe play around a little bit with it? >> Yeah, so we're going to be demoing Alation Connected Sheets at AWS re:Invent next week. And it's going to be available starting next week, so the 28th of November. And obviously you'll see it online, on social media, on our website as well. But folks that are going to be in Las Vegas next week, come to the Alation booth and you'll get a chance to see it directly. >> Awesome. Okay, Raj. Hey, thanks for spending some time with us today. Really appreciate it. >> Great, thanks so much, Dave. Great to see you. >> Hey, you're very welcome. And thank you for watching. This is Dave Vellante for theCUBE, your leader in enterprise and emerging tech coverage.

(atmospheric music) >> Welcome to theCUBE Coverage of Data Citizens 2022 Collibra's Customer event. My name is Dave Vellante. With us is Kirk Haslbeck, who's the Vice President of Data Quality of Collibra. Kirk, good to see you, welcome. >> Thanks for having me, Dave. Excited to be here. >> You bet. Okay, we're going to discuss data quality, observability. It's a hot trend right now. You founded a data quality company, OwlDQ, and it was acquired by Collibra last year. Congratulations. And now you lead data quality at Collibra. So we're hearing a lot about data quality right now. Why is it such a priority? Take us through your thoughts on that. >> Yeah, absolutely. It's definitely exciting times for data quality which you're right, has been around for a long time. So why now? And why is it so much more exciting than it used to be? I think it's a bit stale, but we all know that companies use more data than ever before, and the variety has changed and the volume has grown. And while I think that remains true there are a couple other hidden factors at play that everyone's so interested in as to why this is becoming so important now. And I guess you could kind of break this down simply and think about if Dave you and I were going to build a new healthcare application and monitor the heartbeat of individuals, imagine if we get that wrong, what the ramifications could be, what those incidents would look like. Or maybe better yet, we try to build a new trading algorithm with a crossover strategy where the 50 day crosses the 10 day average. And imagine if the data underlying the inputs to that is incorrect. We will probably have major financial ramifications in that sense. So, kind of starts there, where everybody's realizing that we're all data companies, and if we are using bad data we're likely making incorrect business decisions. But I think there's kind of two other things at play. I bought a car not too long ago and my dad called and said, "How many cylinders does it have?" And I realized in that moment, I might have failed him cause I didn't know. And I used to ask those types of questions about any lock breaks and cylinders, and if it's manual or automatic. And I realized, I now just buy a car that I hope works. And it's so complicated with all the computer chips. I really don't know that much about it. And that's what's happening with data. We're just loading so much of it. And it's so complex that the way companies consume them in the IT function is that they bring in a lot of data and then they syndicate it out to the business. And it turns out that the individuals loading and consuming all of this data for the company actually may not know that much about the data itself and that's not even their job anymore. So, we'll talk more about that in a minute, but that's really what's setting the foreground for this observability play and why everybody's so interested. It's because we're becoming less close to the intricacies of the data and we just expect it to always be there and be correct. >> You know, the other thing too about data quality, and for years we did the MIT, CDO, IQ event. We didn't do it last year at COVID, messed everything up. But the observation I would make there, your thoughts is, data quality used to be information quality, used to be this back office function, and then it became sort of front office with financial services, and government and healthcare, these highly regulated industries. And then the whole chief data officer thing happened and people were realizing, well they sort of flipped the bit from sort of a data as a risk to data as an asset. And now as we say, we're going to talk about observability. And so it's really become front and center, just the whole quality issue because data's so fundamental, hasn't it? >> Yeah, absolutely. I mean, let's imagine we pull up our phones right now and I go to my favorite stock ticker app, and I check out the Nasdaq market cap. I really have no idea if that's the correct number. I know it's a number, it looks large, it's in a numeric field. And that's kind of what's going on. There's so many numbers and they're coming from all of these different sources, and data providers, and they're getting consumed and passed along. But there isn't really a way to tactically put controls on every number and metric across every field we plan to monitor, but with the scale that we've achieved in early days, even before Collibra. And what's been so exciting is, we have these types of observation techniques, these data monitors that can actually track past performance of every field at scale. And why that's so interesting, and why I think the CDO is listening right intently nowadays to this topic is, so maybe we could surface all of these problems with the right solution of data observability and with the right scale, and then just be alerted on breaking trends. So we're sort of shifting away from this world of must write a condition and then when that condition breaks that was always known as a break record. But what about breaking trends and root cause analysis? And is it possible to do that with less human intervention? And so I think most people are seeing now that it's going to have to be a software tool and a computer system. It's not ever going to be based on one or two domain experts anymore. >> So how does data observability relate to data quality? Are they sort of two sides of the same coin? Are they cousins? What's your perspective on that? >> Yeah, it's super interesting. It's an emerging market. So the language is changing, a lot of the topic and areas changing. The way that I like to say it or break it down because the lingo is constantly moving, as a target on the space is really breaking records versus breaking trends. And I could write a condition when this thing happens it's wrong, and when it doesn't it's correct. Or I could look for a trend and I'll give you a good example. Everybody's talking about fresh data and stale data, and why would that matter? Well, if your data never arrived, or only part of it arrived, or didn't arrive on time, it's likely stale, and there will not be a condition that you could write that would show you all the good and the bads. That was kind of your traditional approach of data quality break records. But your modern day approach is you lost a significant portion of your data, or it did not arrive on time to make that decision accurately on time. And that's a hidden concern. Some people call this freshness, we call it stale data. But it all points to the same idea of the thing that you're observing may not be a data quality condition anymore. It may be a breakdown in the data pipeline. And with thousands of data pipelines in play for every company out there, there's more than a couple of these happening every day. >> So what's the Collibra angle on all this stuff? Made the acquisition, you got data quality, observability coming together. You guys have a lot of expertise in this area, but you hear providence of data. You just talked about stale data, the whole trend toward realtime. How is Collibra approaching the problem and what's unique about your approach? >> Well I think where we're fortunate is with our background. Myself and team, we sort of lived this problem for a long time in the Wall Street days about a decade ago. And we saw it from many different angles. And what we came up with, before it was called data observability or reliability, was basically the underpinnings of that. So we're a little bit ahead of the curve there when most people evaluate our solution. It's more advanced than some of the observation techniques that currently exist. But we've also always covered data quality and we believe that people want to know more, they need more insights. And they want to see break records and breaking trends together, so they can correlate the root cause. And we hear that all the time. "I have so many things going wrong just show me the big picture. Help me find the thing that if I were to fix it today would make the most impact." So we're really focused on root cause analysis, business impact, connecting it with lineage and catalog metadata. And as that grows you can actually achieve total data governance. At this point with the acquisition of what was a Lineage company years ago, and then my company OwlDQ, now Collibra Data Quality. Collibra may be the best positioned for total data governance and intelligence in the space. >> Well, you mentioned financial services a couple of times and some examples, remember the flash crash in 2010. Nobody had any idea what that was. They would just say, "Oh, it's a glitch." So they didn't understand the root cause of it. So this is a really interesting topic to me. So we know at Data Citizens 22 that you're announcing, you got to announce new products, right? It is your yearly event. What's new? Give us a sense as to what products are coming out but specifically around data quality and observability. >> Absolutely. There's this, there's always a next thing on the forefront. And the one right now is these hyperscalers in the cloud. So you have databases like Snowflake and BigQuery, and Databricks, Delta Lake and SQL Pushdown. And ultimately what that means is a lot of people are storing in loading data even faster in a SaaS like model. And we've started to hook into these databases, and while we've always worked with the same databases in the past they're supported today. We're doing something called Native Database pushdown, where the entire compute and data activity happens in the database. And why that is so interesting and powerful now? Is everyone's concerned with something called Egress. Did my data that I've spent all this time and money with my security team securing ever leave my hands, did it ever leave my secure VPC as they call it? And with these native integrations that we're building and about to unveil here as kind of a sneak peak for next week at Data Citizens, we're now doing all compute and data operations in databases like Snowflake. And what that means is with no install and no configuration you could log into the Collibra data quality app and have all of your data quality running inside the database that you've probably already picked as your go forward team selection secured database of choice. So we're really excited about that. And I think if you look at the whole landscape of network cost, egress cost, data storage and compute, what people are realizing is it's extremely efficient to do it in the way that we're about to release here next week. >> So this is interesting because what you just described, you mentioned Snowflake, you mentioned Google, oh actually you mentioned yeah, Databricks. You know, Snowflake has the data cloud. If you put everything in the data cloud, okay, you're cool. But then Google's got the open data cloud. If you heard, Google next. And now Databricks doesn't call it the data cloud, but they have like the open source data cloud. So you have all these different approaches and there's really no way, up until now I'm hearing, to really understand the relationships between all those and have confidence across, it's like yamarket AMI, you should just be a note on the mesh. I don't care if it's a data warehouse or a data lake, or where it comes from, but it's a point on that mesh and I need tooling to be able to have confidence that my data is governed and has the proper lineage, providence. And that's what you're bringing to the table. Is that right? Did I get that right? >> Yeah, that's right. And it's, for us, it's not that we haven't been working with those great cloud databases, but it's the fact that we can send them the instructions now we can send them the operating ability to crunch all of the calculations, the governance, the quality, and get the answers. And what that's doing, it's basically zero network cost, zero egress cost, zero latency of time. And so when you were to log into BigQuery tomorrow using our tool, or say Snowflake for example, you have instant data quality metrics, instant profiling, instant lineage in access, privacy controls, things of that nature that just become less onerous. What we're seeing is there's so much technology out there just like all of the major brands that you mentioned but how do we make it easier? The future is about less clicks, faster time to value, faster scale, and eventually lower cost. And we think that this positions us to be the leader there. >> I love this example because, we've got talks about well the cloud guys you're going to own the world. And of course now we're seeing that the ecosystem is finding so much white space to add value connect across cloud. Sometimes we call it super cloud and so, or inter clouding. Alright, Kirk, give us your final thoughts on the trends that we've talked about and data Citizens 22. >> Absolutely. Well I think, one big trend is discovery and classification. Seeing that across the board, people used to know it was a zip code and nowadays with the amount of data that's out there they want to know where everything is, where their sensitive data is, if it's redundant, tell me everything inside of three to five seconds. And with that comes, they want to know in all of these hyperscale databases how fast they can get controls and insights out of their tools. So I think we're going to see more one click solutions, more SaaS based solutions, and solutions that hopefully prove faster time to value on all of these modern cloud platforms. >> Excellent. All right, Kirk Haslbeck, thanks so much for coming on theCUBE and previewing Data Citizens 22. Appreciate it. >> Thanks for having me, Dave. >> You're welcome. All right. And thank you for watching. Keep it right there for more coverage from theCUBE. (atmospheric music)