(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)

>> 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)

(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)

(upbeat music) >> Hello, welcome back to theCUBE's AWS RE:Invent 2022 Coverage. I'm John Furrier, host of theCUBE. Got a great lineup here, Itamar Ankorion SVP Technology Alliance at Qlik and Peter McDonald, vice President, cloud partnerships and business development Snowflake. We're going to talk about bringing SAP data to life, for joint Snowflake, Qlik and AWS Solution. Gentlemen, thanks for coming on theCUBE Really appreciate it. >> Thank you. >> Thank you, great meeting you John. >> Just to get started, introduce yourselves to the audience, then going to jump into what you guys are doing together, unique relationship here, really compelling solution in cloud. Big story about applications and scale this year. Let's introduce yourselves. Peter, we'll start with you. >> Great. I'm Peter MacDonald. I am vice president of Cloud Partners and business development here at Snowflake. On the Cloud Partner side, that means I manage AWS relationship along with Microsoft and Google Cloud. What we do together in terms of complimentary products, GTM, co-selling, things like that. Importantly, working with other third parties like Qlik for joint solutions. On business development, it's negotiating custom commercial partnerships, large companies like Salesforce and Dell, smaller companies at most for our venture portfolio. >> Thanks Peter and hi John. It's great to be back here. So I'm Itamar Ankorion and I'm the senior vice president responsible for technology alliances here at Qlik. With that, own strategic alliances, including our key partners in the cloud, including Snowflake and AWS. I've been in the data and analytics enterprise software market for 20 plus years, and my main focus is product management, marketing, alliances, and business development. I joined Qlik about three and a half years ago through the acquisition of Attunity, which is now the foundation for Qlik data integration. So again, we focus in my team on creating joint solution alignment with our key partners to provide more value to our customers. >> Great to have both you guys, senior executives in the industry on theCUBE here, talking about data, obviously bringing SAP data to life is the theme of this segment, but this reinvent, it's all about the data, big data end-to-end story, a lot about data being intrinsic as the CEO says on stage around in the organizations in all aspects. Take a minute to explain what you guys are doing as from a company standpoint. Snowflake and Qlik and the solutions, why here at AWS? Peter, we'll start with you at Snowflake, what you guys do as a company, your mission, your focus. >> That was great, John. Yeah, so here at Snowflake, we focus on the data platform and until recently, data platforms required expensive on-prem hardware appliances. And despite all that expense, customers had capacity constraints, inexpensive maintenance, and had limited functionality that all impeded these organizations from reaching their goals. Snowflake is a cloud native SaaS platform, and we've become so successful because we've addressed these pain points and have other new special features. For example, securely sharing data across both the organization and the value chain without copying the data, support for new data types such as JSON and structured data, and also advance in database data governance. Snowflake integrates with complimentary AWS services and other partner products. So we can enable holistic solutions that include, for example, here, both Qlik and AWS SageMaker, and comprehend and bring those to joint customers. Our customers want to convert data into insights along with advanced analytics platforms in AI. That is how they make holistic data-driven solutions that will give them competitive advantage. With Snowflake, our approach is to focus on customer solutions that leverage data from existing systems such as SAP, wherever they are in the cloud or on-premise. And to do this, we leverage partners like Qlik native US to help customers transform their businesses. We provide customers with a premier data analytics platform as a result. Itamar, why don't you talk about Qlik a little bit and then we can dive into the specific SAP solution here and some trends >> Sounds great, Peter. So Qlik provides modern data integration and analytics software used by over 38,000 customers worldwide. Our focus is to help our customers turn data into value and help them close the gap between data all the way through insight and action. We offer click data integration and click data analytics. Click data integration helps to automate the data pipelines to deliver data to where they want to use them in real-time and make the data ready for analytics and then Qlik data analytics is a robust platform for analytics and business intelligence has been a leader in the Gartner Magic Quadrant for over 11 years now in the market. And both of these come together into what we call Qlik Cloud, which is our SaaS based platform. So providing a more seamless way to consume all these services and accelerate time to value with customer solutions. In terms of partnerships, both Snowflake and AWS are very strategic to us here at Qlik, so we have very comprehensive investment to ensure strong joint value proposition to we can bring to our mutual customers, everything from aligning our roadmaps through optimizing and validating integrations, collaborating on best practices, packaging joint solutions like the one we'll talk about today. And with that investment, we are an elite level, top level partner with Snowflake. We fly that our technology is Snowflake-ready across the entire product set and we have hundreds of joint customers together and with AWS we've also partnered for a long time. We're here to reinvent. We've been here with the first reinvent since the inaugural one, so it kind of gives you an idea for how long we've been working with AWS. We provide very comprehensive integration with AWS data analytics services, and we have several competencies ranging from data analytics to migration and modernization. So that's our focus and again, we're excited about working with Snowflake and AWS to bring solutions together to market. >> Well, I'm looking forward to unpacking the solutions specifically, and congratulations on the continued success of both your companies. We've been following them obviously for a very long time and seeing the platform evolve beyond just SaaS and a lot more going on in cloud these days, kind of next generation emerging. You know, we're seeing a lot of macro trends that are going to be powering some of the things we're going to get into real quickly. But before we get into the solution, what are some of those power dynamics in the industry that you're seeing in trends specifically that are impacting your customers that are taking us down this road of getting more out of the data and specifically the SAP, but in general trends and dynamics. What are you hearing from your customers? Why do they care? Why are they going down this road? Peter, we'll start with you. >> Yeah, I'll go ahead and start. Thanks. Yeah, I'd say we continue to see customers being, being very eager to transform their businesses and they know they need to leverage technology and data to do so. They're also increasingly depending upon the cloud to bring that agility, that elasticity, new functionality necessary to react in real-time to every evolving customer needs. You look at what's happened over the last three years, and boy, the macro environment customers, it's all changing so fast. With our partnerships with AWS and Qlik, we've been able to bring to market innovative solutions like the one we're announcing today that spans all three companies. It provides a holistic solution and an integrated solution for our customer. >> Itamar let's get into it, you've been with theCUBE, you've seen the journey, you have your own journey, many, many years, you've seen the waves. What's going on now? I mean, what's the big wave? What's the dynamic powering this trend? >> Yeah, in a nutshell I'll call it, it's all about time. You know, it's time to value and it's about real-time data. I'll kind of talk about that a bit. So, I mean, you hear a lot about the data being the new oil, but it's definitely, we see more and more customers seeing data as their critical enabler for innovation and digital transformation. They look for ways to monetize data. They look as the data as the way in which they can innovate and bring different value to the customers. So we see customers want to use more data so to get more value from data. We definitely see them wanting to do it faster, right, than before. And we definitely see them looking for agility and automation as ways to accelerate time to value, and also reduce overall costs. I did mention real-time data, so we definitely see more and more customers, they want to be able to act and make decisions based on fresh data. So yesterday's data is just not good enough. >> John: Yeah. >> It's got to be down to the hour, down to the minutes and sometimes even lower than that. And then I think we're also seeing customers look to their core business systems where they have a lot of value, like the SAP, like mainframe and thinking, okay, our core data is there, how can we get more value from this data? So that's key things we see all the time with customers. >> Yeah, we did a big editorial segment this year on, we called data as code. Data as code is kind of a riff on infrastructure as code and you start to see data becoming proliferating into all aspects, fresh data. It's not just where you store it, it's how you share it, it's how you turn it into an application intrinsically involved in all aspects. This is the big theme this year and that's driving all the conversations here at RE:Invent. And I'm guaranteeing you, it's going to happen for another five and 10 years. It's not stopping. So I got to get into the solution, you guys mentioned SAP and you've announced the solution by Qlik, Snowflake and AWS for your customers using SAP. Can you share more about this solution? What's unique about it? Why is it important and why now? Peter, Itamar, we'll start with you first. >> Let me jump in, this is really, I'll jump because I'm excited. We're very excited about this solution and it's also a solution by the way and again, we've seen proven customer success with it. So to your point, it's ready to scale, it's starting, I think we're going to see a lot of companies doing this over the next few years. But before we jump to the solution, let me maybe take a few minutes just to clarify the need, why we're seeing, why we're seeing customers jump to do this. So customers that use SAP, they use it to manage the core of their business. So think order processing, management, finance, inventory, supply chain, and so much more. So if you're running SAP in your company, that data creates a great opportunity for you to drive innovation and modernization. So what we see customers want to do, they want to do more with their data and more means they want to take SAP with non-SAP data and use it together to drive new insights. They want to use real-time data to drive real-time analytics, which they couldn't do to date. They want to bring together descriptive with predictive analytics. So adding machine learning in AI to drive more value from the data. And naturally they want to do it faster. So find ways to iterate faster on their solutions, have freedom with the data and agility. And I think this is really where cloud data platforms like Snowflake and AWS, you know, bring that value to be able to drive that. Now to do that you need to unlock the SAP data, which is a lot of also where Qlik comes in because typical challenges these customers run into is the complexity, inherent in SAP data. Tens of thousands of tables, proprietary formats, complex data models, licensing restrictions, and more than, you have performance issues, they usually run into how do we handle the throughput, the volumes while maintaining lower latency and impact. Where do we find knowledge to really understand how to get all this done? So these are the things we've looked at when we came together to create a solution and make it unique. So when you think about its uniqueness, because we put together a lot, and I'll go through three, four key things that come together to make this unique. First is about data delivery. How do you have the SAP data delivery? So how do you get it from ECC, from HANA from S/4HANA, how do you deliver the data and the metadata and how that integration well into Snowflake. And what we've done is we've focused a lot on optimizing that process and the continuous ingestion, so the real-time ingestion of the data in a way that works really well with the Snowflake system, data cloud. Second thing is we looked at SAP data transformation, so once the data arrives at Snowflake, how do we turn it into being analytics ready? So that's where data transformation and data worth automation come in. And these are all elements of this solution. So creating derivative datasets, creating data marts, and all of that is done by again, creating an optimized integration that pushes down SQL based transformations, so they can be processed inside Snowflake, leveraging its powerful engine. And then the third element is bringing together data visualization analytics that can also take all the data now that in organizing inside Snowflake, bring other data in, bring machine learning from SageMaker, and then you go to create a seamless integration to bring analytic applications to life. So these are all things we put together in the solution. And maybe the last point is we actually took the next step with this and we created something we refer to as solution accelerators, which we're really, really keen about. Think about this as prepackaged templates for common business analytic needs like order to cash, finance, inventory. And we can either dig into that a little more later, but this gets the next level of value to the customers all built into this joint solution. >> Yeah, I want to get to the accelerators, but real quick, Peter, your reaction to the solution, what's unique about it? And obviously Snowflake, we've been seeing the progression data applications, more developers developing on top of Snowflake, data as code kind of implies developer ecosystem. This is kind of interesting. I mean, you got partnering with Qlik and AWS, it's kind of a developer-like thinking real solution. What's unique about this SAP solution that's, that's different than what customers can get anywhere else or not? >> Yeah, well listen, I think first of all, you have to start with the idea of the solution. This are three companies coming together to build a holistic solution that is all about, you know, creating a great opportunity to turn SAP data into value this is Itamar was talking about, that's really what we're talking about here and there's a lot of technology underneath it. I'll talk more about the Snowflake technology, what's involved here, and then cover some of the AWS pieces as well. But you know, we're focusing on getting that value out and accelerating time to value for our joint customers. As Itamar was saying, you know, there's a lot of complexity with the SAP data and a lot of value there. How can we manage that in a prepackaged way, bringing together best of breed solutions with proven capabilities and bringing this to market quickly for our joint customers. You know, Snowflake and AWS have been strong partners for a number of years now, and that's not only on how Snowflake runs on top of AWS, but also how we integrate with their complementary analytics and then all products. And so, you know, we want to be able to leverage those in addition to what Qlik is bringing in terms of the data transformations, bringing data out of SAP in the visualization as well. All very critical. And then we want to bring in the predictive analytics, AWS brings and what Sage brings. We'll talk about that a little bit later on. Some of the technologies that we're leveraging are some of our latest cutting edge technologies that really make things easier for both our partners and our customers. For example, Qlik leverages Snowflakes recently released Snowpark for Python functionality to push down those data transformations from clicking the Snowflake that Itamar's mentioning. And while we also leverage Snowpark for integrations with Amazon SageMaker, but there's a lot of great new technology that just makes this easy and compelling for customers. >> I think that's the big word, easy button here for what may look like a complex kind of integration, kind of turnkey, really, really compelling example of the modern era we're living in, as we always say in theCUBE. You mentioned accelerators, SAP accelerators. Can you give an example of how that works with the technology from the third party providers to deliver this business value Itamar, 'cause that was an interesting comment. What's the example? Give an example of this acceleration. >> Yes, certainly. I think this is something that really makes this truly, truly unique in the industry and again, a great opportunity for customers. So we kind talked earlier about there's a lot of things that need to be done with SP data to turn it to value. And these accelerator, as the name suggests, are designed to do just that, to kind of jumpstart the process and reduce the time and the risk involved in such project. So again, these are pre-packaged templates. We basically took a lot of knowledge, and a lot of configurations, best practices about to get things done and we put 'em together. So think about all the steps, it includes things like data extraction, so already knowing which tables, all the relevant tables that you need to get data from in the contexts of the solution you're looking for, say like order to cash, we'll get back to that one. How do you continuously deliver that data into Snowflake in an in efficient manner, handling things like data type mappings, metadata naming conventions and transformations. The data models you build all the way to data mart definitions and all the transformations that the data needs to go through moving through steps until it's fully analytics ready. And then on top of that, even adding a library of comprehensive analytic dashboards and integrations through machine learning and AI and put all of that in a way that's in pre-integrated and tested to work with Snowflake and AWS. So this is where again, you get this entire recipe that's ready. So take for example, I think I mentioned order to cash. So again, all these things I just talked about, I mean, for those who are not familiar, I mean order to cash is a critical business process for every organization. So especially if you're in retail, manufacturing, enterprise, it's a big... This is where, you know, starting with booking a sales order, following by fulfilling the order, billing the customer, then managing the accounts receivable when the customer actually pays, right? So this all process, you got sales order fulfillment and the billing impacts customer satisfaction, you got receivable payments, you know, the impact's working capital, cash liquidity. So again, as a result this order to cash process is a lifeblood for many businesses and it's critical to optimize and understand. So the solution accelerator we created specifically for order to cash takes care of understanding all these aspects and the data that needs to come with it. So everything we outline before to make the data available in Snowflake in a way that's really useful for downstream analytics, along with dashboards that are already common for that, for that use case. So again, this enables customers to gain real-time visibility into their sales orders, fulfillment, accounts receivable performance. That's what the Excel's are all about. And very similarly, we have another one for example, for finance analytics, right? So this will optimize financial data reporting, helps customers get insights into P&L, financial risk of stability or inventory analytics that helps with, you know, improve planning and inventory management, utilization, increased efficiencies, you know, so in supply chain. So again, these accelerators really help customers get a jumpstart and move faster with their solutions. >> Peter, this is the easy button we just talked about, getting things going, you know, get the ball rolling, get some acceleration. Big part of this are the three companies coming together doing this. >> Yeah, and to build on what Itamar just said that the SAP data obviously has tremendous value. Those sales orders, distribution data, financial data, bringing that into Snowflake makes it easily accessible, but also it enables it to be combined with other data too, is one of the things that Snowflake does so well. So you can get a full view of the end-to-end process and the business overall. You know, for example, I'll just take one, you know, one example that, that may not come to mind right away, but you know, looking at the impact of weather conditions on supply chain logistics is relevant and material and have interest to our customers. How do you bring those different data sets together in an easy way, bringing the data out of SAP, bringing maybe other data out of other systems through Qlik or through Snowflake, directly bringing data in from our data marketplace and bring that all together to make it work. You know, fundamentally organizational silos and the data fragmentation exist otherwise make it really difficult to drive modern analytics projects. And that in turn limits the value that our customers are getting from SAP data and these other data sets. We want to enable that and unleash. >> Yeah, time for value. This is great stuff. Itamar final question, you know, what are customers using this? What do you have? I'm sure you have customers examples already using the solution. Can you share kind of what these examples look like in the use cases and the value? >> Oh yeah, absolutely. Thank you. Happy to. We have customers across different, different sectors. You see manufacturing, retail, energy, oil and gas, CPG. So again, customers in those segments, typically sectors typically have SAP. So we have customers in all of them. A great example is like Siemens Energy. Siemens Energy is a global provider of gas par services. You know, over what, 28 billion, 30 billion in revenue. 90,000 employees. They operate globally in over 90 countries. So they've used SAP HANA as a core system, so it's running on premises, multiple locations around the world. And what they were looking for is a way to bring all these data together so they can innovate with it. And the thing is, Peter mentioned earlier, not just the SAP data, but also bring other data from other systems to bring it together for more value. That includes finance data, these logistics data, these customer CRM data. So they bring data from over 20 different SAP systems. Okay, with Qlik data integration, feeding that into Snowflake in under 20 minutes, 24/7, 365, you know, days a year. Okay, they get data from over 20,000 tables, you know, over million, hundreds of millions of records daily going in. So it is a great example of the type of scale, scalability, agility and speed that they can get to drive these kind of innovation. So that's a great example with Siemens. You know, another one comes to mind is a global manufacturer. Very similar scenario, but you know, they're using it for real-time executive reporting. So it's more like feasibility to the production data as well as for financial analytics. So think, think, think about everything from audit to texts to innovate financial intelligence because all the data's coming from SAP. >> It's a great time to be in the data business again. It keeps getting better and better. There's more data coming. It's not stopping, you know, it's growing so fast, it keeps coming. Every year, it's the same story, Peter. It's like, doesn't stop coming. As we wrap up here, let's just get customers some information on how to get started. I mean, obviously you're starting to see the accelerators, it's a great program there. What a great partnership between the two companies and AWS. How can customers get started to learn about the solution and take advantage of it, getting more out of their SAP data, Peter? >> Yeah, I think the first place to go to is talk to Snowflake, talk to AWS, talk to our account executives that are assigned to your account. Reach out to them and they will be able to educate you on the solution. We have packages up very nicely and can be deployed very, very quickly. >> Well gentlemen, thank you so much for coming on. Appreciate the conversation. Great overview of the partnership between, you know, Snowflake and Qlik and AWS on a joint solution. You know, getting more out of the SAP data. It's really kind of a key, key solution, bringing SAP data to life. Thanks for coming on theCUBE. Appreciate it. >> Thank you. >> Thank you John. >> Okay, this is theCUBE coverage here at RE:Invent 2022. I'm John Furrier, your host of theCUBE. Thanks for watching. (upbeat music)

(upbeat music) >> Hello, welcome back to theCUBE's AWS RE:Invent 2022 Coverage. I'm John Furrier, host of theCUBE. Got a great lineup here, Itamar Ankorion SVP Technology Alliance at Qlik and Peter McDonald, vice President, cloud partnerships and business development Snowflake. We're going to talk about bringing SAP data to life, for joint Snowflake, Qlik and AWS Solution. Gentlemen, thanks for coming on theCUBE Really appreciate it. >> Thank you. >> Thank you, great meeting you John. >> Just to get started, introduce yourselves to the audience, then going to jump into what you guys are doing together, unique relationship here, really compelling solution in cloud. Big story about applications and scale this year. Let's introduce yourselves. Peter, we'll start with you. >> Great. I'm Peter MacDonald. I am vice president of Cloud Partners and business development here at Snowflake. On the Cloud Partner side, that means I manage AWS relationship along with Microsoft and Google Cloud. What we do together in terms of complimentary products, GTM, co-selling, things like that. Importantly, working with other third parties like Qlik for joint solutions. On business development, it's negotiating custom commercial partnerships, large companies like Salesforce and Dell, smaller companies at most for our venture portfolio. >> Thanks Peter and hi John. It's great to be back here. So I'm Itamar Ankorion and I'm the senior vice president responsible for technology alliances here at Qlik. With that, own strategic alliances, including our key partners in the cloud, including Snowflake and AWS. I've been in the data and analytics enterprise software market for 20 plus years, and my main focus is product management, marketing, alliances, and business development. I joined Qlik about three and a half years ago through the acquisition of Attunity, which is now the foundation for Qlik data integration. So again, we focus in my team on creating joint solution alignment with our key partners to provide more value to our customers. >> Great to have both you guys, senior executives in the industry on theCUBE here, talking about data, obviously bringing SAP data to life is the theme of this segment, but this reinvent, it's all about the data, big data end-to-end story, a lot about data being intrinsic as the CEO says on stage around in the organizations in all aspects. Take a minute to explain what you guys are doing as from a company standpoint. Snowflake and Qlik and the solutions, why here at AWS? Peter, we'll start with you at Snowflake, what you guys do as a company, your mission, your focus. >> That was great, John. Yeah, so here at Snowflake, we focus on the data platform and until recently, data platforms required expensive on-prem hardware appliances. And despite all that expense, customers had capacity constraints, inexpensive maintenance, and had limited functionality that all impeded these organizations from reaching their goals. Snowflake is a cloud native SaaS platform, and we've become so successful because we've addressed these pain points and have other new special features. For example, securely sharing data across both the organization and the value chain without copying the data, support for new data types such as JSON and structured data, and also advance in database data governance. Snowflake integrates with complimentary AWS services and other partner products. So we can enable holistic solutions that include, for example, here, both Qlik and AWS SageMaker, and comprehend and bring those to joint customers. Our customers want to convert data into insights along with advanced analytics platforms in AI. That is how they make holistic data-driven solutions that will give them competitive advantage. With Snowflake, our approach is to focus on customer solutions that leverage data from existing systems such as SAP, wherever they are in the cloud or on-premise. And to do this, we leverage partners like Qlik native US to help customers transform their businesses. We provide customers with a premier data analytics platform as a result. Itamar, why don't you talk about Qlik a little bit and then we can dive into the specific SAP solution here and some trends >> Sounds great, Peter. So Qlik provides modern data integration and analytics software used by over 38,000 customers worldwide. Our focus is to help our customers turn data into value and help them close the gap between data all the way through insight and action. We offer click data integration and click data analytics. Click data integration helps to automate the data pipelines to deliver data to where they want to use them in real-time and make the data ready for analytics and then Qlik data analytics is a robust platform for analytics and business intelligence has been a leader in the Gartner Magic Quadrant for over 11 years now in the market. And both of these come together into what we call Qlik Cloud, which is our SaaS based platform. So providing a more seamless way to consume all these services and accelerate time to value with customer solutions. In terms of partnerships, both Snowflake and AWS are very strategic to us here at Qlik, so we have very comprehensive investment to ensure strong joint value proposition to we can bring to our mutual customers, everything from aligning our roadmaps through optimizing and validating integrations, collaborating on best practices, packaging joint solutions like the one we'll talk about today. And with that investment, we are an elite level, top level partner with Snowflake. We fly that our technology is Snowflake-ready across the entire product set and we have hundreds of joint customers together and with AWS we've also partnered for a long time. We're here to reinvent. We've been here with the first reinvent since the inaugural one, so it kind of gives you an idea for how long we've been working with AWS. We provide very comprehensive integration with AWS data analytics services, and we have several competencies ranging from data analytics to migration and modernization. So that's our focus and again, we're excited about working with Snowflake and AWS to bring solutions together to market. >> Well, I'm looking forward to unpacking the solutions specifically, and congratulations on the continued success of both your companies. We've been following them obviously for a very long time and seeing the platform evolve beyond just SaaS and a lot more going on in cloud these days, kind of next generation emerging. You know, we're seeing a lot of macro trends that are going to be powering some of the things we're going to get into real quickly. But before we get into the solution, what are some of those power dynamics in the industry that you're seeing in trends specifically that are impacting your customers that are taking us down this road of getting more out of the data and specifically the SAP, but in general trends and dynamics. What are you hearing from your customers? Why do they care? Why are they going down this road? Peter, we'll start with you. >> Yeah, I'll go ahead and start. Thanks. Yeah, I'd say we continue to see customers being, being very eager to transform their businesses and they know they need to leverage technology and data to do so. They're also increasingly depending upon the cloud to bring that agility, that elasticity, new functionality necessary to react in real-time to every evolving customer needs. You look at what's happened over the last three years, and boy, the macro environment customers, it's all changing so fast. With our partnerships with AWS and Qlik, we've been able to bring to market innovative solutions like the one we're announcing today that spans all three companies. It provides a holistic solution and an integrated solution for our customer. >> Itamar let's get into it, you've been with theCUBE, you've seen the journey, you have your own journey, many, many years, you've seen the waves. What's going on now? I mean, what's the big wave? What's the dynamic powering this trend? >> Yeah, in a nutshell I'll call it, it's all about time. You know, it's time to value and it's about real-time data. I'll kind of talk about that a bit. So, I mean, you hear a lot about the data being the new oil, but it's definitely, we see more and more customers seeing data as their critical enabler for innovation and digital transformation. They look for ways to monetize data. They look as the data as the way in which they can innovate and bring different value to the customers. So we see customers want to use more data so to get more value from data. We definitely see them wanting to do it faster, right, than before. And we definitely see them looking for agility and automation as ways to accelerate time to value, and also reduce overall costs. I did mention real-time data, so we definitely see more and more customers, they want to be able to act and make decisions based on fresh data. So yesterday's data is just not good enough. >> John: Yeah. >> It's got to be down to the hour, down to the minutes and sometimes even lower than that. And then I think we're also seeing customers look to their core business systems where they have a lot of value, like the SAP, like mainframe and thinking, okay, our core data is there, how can we get more value from this data? So that's key things we see all the time with customers. >> Yeah, we did a big editorial segment this year on, we called data as code. Data as code is kind of a riff on infrastructure as code and you start to see data becoming proliferating into all aspects, fresh data. It's not just where you store it, it's how you share it, it's how you turn it into an application intrinsically involved in all aspects. This is the big theme this year and that's driving all the conversations here at RE:Invent. And I'm guaranteeing you, it's going to happen for another five and 10 years. It's not stopping. So I got to get into the solution, you guys mentioned SAP and you've announced the solution by Qlik, Snowflake and AWS for your customers using SAP. Can you share more about this solution? What's unique about it? Why is it important and why now? Peter, Itamar, we'll start with you first. >> Let me jump in, this is really, I'll jump because I'm excited. We're very excited about this solution and it's also a solution by the way and again, we've seen proven customer success with it. So to your point, it's ready to scale, it's starting, I think we're going to see a lot of companies doing this over the next few years. But before we jump to the solution, let me maybe take a few minutes just to clarify the need, why we're seeing, why we're seeing customers jump to do this. So customers that use SAP, they use it to manage the core of their business. So think order processing, management, finance, inventory, supply chain, and so much more. So if you're running SAP in your company, that data creates a great opportunity for you to drive innovation and modernization. So what we see customers want to do, they want to do more with their data and more means they want to take SAP with non-SAP data and use it together to drive new insights. They want to use real-time data to drive real-time analytics, which they couldn't do to date. They want to bring together descriptive with predictive analytics. So adding machine learning in AI to drive more value from the data. And naturally they want to do it faster. So find ways to iterate faster on their solutions, have freedom with the data and agility. And I think this is really where cloud data platforms like Snowflake and AWS, you know, bring that value to be able to drive that. Now to do that you need to unlock the SAP data, which is a lot of also where Qlik comes in because typical challenges these customers run into is the complexity, inherent in SAP data. Tens of thousands of tables, proprietary formats, complex data models, licensing restrictions, and more than, you have performance issues, they usually run into how do we handle the throughput, the volumes while maintaining lower latency and impact. Where do we find knowledge to really understand how to get all this done? So these are the things we've looked at when we came together to create a solution and make it unique. So when you think about its uniqueness, because we put together a lot, and I'll go through three, four key things that come together to make this unique. First is about data delivery. How do you have the SAP data delivery? So how do you get it from ECC, from HANA from S/4HANA, how do you deliver the data and the metadata and how that integration well into Snowflake. And what we've done is we've focused a lot on optimizing that process and the continuous ingestion, so the real-time ingestion of the data in a way that works really well with the Snowflake system, data cloud. Second thing is we looked at SAP data transformation, so once the data arrives at Snowflake, how do we turn it into being analytics ready? So that's where data transformation and data worth automation come in. And these are all elements of this solution. So creating derivative datasets, creating data marts, and all of that is done by again, creating an optimized integration that pushes down SQL based transformations, so they can be processed inside Snowflake, leveraging its powerful engine. And then the third element is bringing together data visualization analytics that can also take all the data now that in organizing inside Snowflake, bring other data in, bring machine learning from SageMaker, and then you go to create a seamless integration to bring analytic applications to life. So these are all things we put together in the solution. And maybe the last point is we actually took the next step with this and we created something we refer to as solution accelerators, which we're really, really keen about. Think about this as prepackaged templates for common business analytic needs like order to cash, finance, inventory. And we can either dig into that a little more later, but this gets the next level of value to the customers all built into this joint solution. >> Yeah, I want to get to the accelerators, but real quick, Peter, your reaction to the solution, what's unique about it? And obviously Snowflake, we've been seeing the progression data applications, more developers developing on top of Snowflake, data as code kind of implies developer ecosystem. This is kind of interesting. I mean, you got partnering with Qlik and AWS, it's kind of a developer-like thinking real solution. What's unique about this SAP solution that's, that's different than what customers can get anywhere else or not? >> Yeah, well listen, I think first of all, you have to start with the idea of the solution. This are three companies coming together to build a holistic solution that is all about, you know, creating a great opportunity to turn SAP data into value this is Itamar was talking about, that's really what we're talking about here and there's a lot of technology underneath it. I'll talk more about the Snowflake technology, what's involved here, and then cover some of the AWS pieces as well. But you know, we're focusing on getting that value out and accelerating time to value for our joint customers. As Itamar was saying, you know, there's a lot of complexity with the SAP data and a lot of value there. How can we manage that in a prepackaged way, bringing together best of breed solutions with proven capabilities and bringing this to market quickly for our joint customers. You know, Snowflake and AWS have been strong partners for a number of years now, and that's not only on how Snowflake runs on top of AWS, but also how we integrate with their complementary analytics and then all products. And so, you know, we want to be able to leverage those in addition to what Qlik is bringing in terms of the data transformations, bringing data out of SAP in the visualization as well. All very critical. And then we want to bring in the predictive analytics, AWS brings and what Sage brings. We'll talk about that a little bit later on. Some of the technologies that we're leveraging are some of our latest cutting edge technologies that really make things easier for both our partners and our customers. For example, Qlik leverages Snowflakes recently released Snowpark for Python functionality to push down those data transformations from clicking the Snowflake that Itamar's mentioning. And while we also leverage Snowpark for integrations with Amazon SageMaker, but there's a lot of great new technology that just makes this easy and compelling for customers. >> I think that's the big word, easy button here for what may look like a complex kind of integration, kind of turnkey, really, really compelling example of the modern era we're living in, as we always say in theCUBE. You mentioned accelerators, SAP accelerators. Can you give an example of how that works with the technology from the third party providers to deliver this business value Itamar, 'cause that was an interesting comment. What's the example? Give an example of this acceleration. >> Yes, certainly. I think this is something that really makes this truly, truly unique in the industry and again, a great opportunity for customers. So we kind talked earlier about there's a lot of things that need to be done with SP data to turn it to value. And these accelerator, as the name suggests, are designed to do just that, to kind of jumpstart the process and reduce the time and the risk involved in such project. So again, these are pre-packaged templates. We basically took a lot of knowledge, and a lot of configurations, best practices about to get things done and we put 'em together. So think about all the steps, it includes things like data extraction, so already knowing which tables, all the relevant tables that you need to get data from in the contexts of the solution you're looking for, say like order to cash, we'll get back to that one. How do you continuously deliver that data into Snowflake in an in efficient manner, handling things like data type mappings, metadata naming conventions and transformations. The data models you build all the way to data mart definitions and all the transformations that the data needs to go through moving through steps until it's fully analytics ready. And then on top of that, even adding a library of comprehensive analytic dashboards and integrations through machine learning and AI and put all of that in a way that's in pre-integrated and tested to work with Snowflake and AWS. So this is where again, you get this entire recipe that's ready. So take for example, I think I mentioned order to cash. So again, all these things I just talked about, I mean, for those who are not familiar, I mean order to cash is a critical business process for every organization. So especially if you're in retail, manufacturing, enterprise, it's a big... This is where, you know, starting with booking a sales order, following by fulfilling the order, billing the customer, then managing the accounts receivable when the customer actually pays, right? So this all process, you got sales order fulfillment and the billing impacts customer satisfaction, you got receivable payments, you know, the impact's working capital, cash liquidity. So again, as a result this order to cash process is a lifeblood for many businesses and it's critical to optimize and understand. So the solution accelerator we created specifically for order to cash takes care of understanding all these aspects and the data that needs to come with it. So everything we outline before to make the data available in Snowflake in a way that's really useful for downstream analytics, along with dashboards that are already common for that, for that use case. So again, this enables customers to gain real-time visibility into their sales orders, fulfillment, accounts receivable performance. That's what the Excel's are all about. And very similarly, we have another one for example, for finance analytics, right? So this will optimize financial data reporting, helps customers get insights into P&L, financial risk of stability or inventory analytics that helps with, you know, improve planning and inventory management, utilization, increased efficiencies, you know, so in supply chain. So again, these accelerators really help customers get a jumpstart and move faster with their solutions. >> Peter, this is the easy button we just talked about, getting things going, you know, get the ball rolling, get some acceleration. Big part of this are the three companies coming together doing this. >> Yeah, and to build on what Itamar just said that the SAP data obviously has tremendous value. Those sales orders, distribution data, financial data, bringing that into Snowflake makes it easily accessible, but also it enables it to be combined with other data too, is one of the things that Snowflake does so well. So you can get a full view of the end-to-end process and the business overall. You know, for example, I'll just take one, you know, one example that, that may not come to mind right away, but you know, looking at the impact of weather conditions on supply chain logistics is relevant and material and have interest to our customers. How do you bring those different data sets together in an easy way, bringing the data out of SAP, bringing maybe other data out of other systems through Qlik or through Snowflake, directly bringing data in from our data marketplace and bring that all together to make it work. You know, fundamentally organizational silos and the data fragmentation exist otherwise make it really difficult to drive modern analytics projects. And that in turn limits the value that our customers are getting from SAP data and these other data sets. We want to enable that and unleash. >> Yeah, time for value. This is great stuff. Itamar final question, you know, what are customers using this? What do you have? I'm sure you have customers examples already using the solution. Can you share kind of what these examples look like in the use cases and the value? >> Oh yeah, absolutely. Thank you. Happy to. We have customers across different, different sectors. You see manufacturing, retail, energy, oil and gas, CPG. So again, customers in those segments, typically sectors typically have SAP. So we have customers in all of them. A great example is like Siemens Energy. Siemens Energy is a global provider of gas par services. You know, over what, 28 billion, 30 billion in revenue. 90,000 employees. They operate globally in over 90 countries. So they've used SAP HANA as a core system, so it's running on premises, multiple locations around the world. And what they were looking for is a way to bring all these data together so they can innovate with it. And the thing is, Peter mentioned earlier, not just the SAP data, but also bring other data from other systems to bring it together for more value. That includes finance data, these logistics data, these customer CRM data. So they bring data from over 20 different SAP systems. Okay, with Qlik data integration, feeding that into Snowflake in under 20 minutes, 24/7, 365, you know, days a year. Okay, they get data from over 20,000 tables, you know, over million, hundreds of millions of records daily going in. So it is a great example of the type of scale, scalability, agility and speed that they can get to drive these kind of innovation. So that's a great example with Siemens. You know, another one comes to mind is a global manufacturer. Very similar scenario, but you know, they're using it for real-time executive reporting. So it's more like feasibility to the production data as well as for financial analytics. So think, think, think about everything from audit to texts to innovate financial intelligence because all the data's coming from SAP. >> It's a great time to be in the data business again. It keeps getting better and better. There's more data coming. It's not stopping, you know, it's growing so fast, it keeps coming. Every year, it's the same story, Peter. It's like, doesn't stop coming. As we wrap up here, let's just get customers some information on how to get started. I mean, obviously you're starting to see the accelerators, it's a great program there. What a great partnership between the two companies and AWS. How can customers get started to learn about the solution and take advantage of it, getting more out of their SAP data, Peter? >> Yeah, I think the first place to go to is talk to Snowflake, talk to AWS, talk to our account executives that are assigned to your account. Reach out to them and they will be able to educate you on the solution. We have packages up very nicely and can be deployed very, very quickly. >> Well gentlemen, thank you so much for coming on. Appreciate the conversation. Great overview of the partnership between, you know, Snowflake and Qlik and AWS on a joint solution. You know, getting more out of the SAP data. It's really kind of a key, key solution, bringing SAP data to life. Thanks for coming on theCUBE. Appreciate it. >> Thank you. >> Thank you John. >> Okay, this is theCUBE coverage here at RE:Invent 2022. I'm John Furrier, your host of theCUBE. Thanks for watching. (upbeat music)

>> From theCUBE Studios in Palo Alto and Boston, bringing you data driven insights from theCUBE and ETR. This is breaking analysis with Dave Vellante. >> The technology spending outlook is not pretty and very much unpredictable right now. The negative sentiment is of course being driven by the macroeconomic factors in earnings forecasts that have been coming down all year in an environment of rising interest rates. And what's worse, is many people think earnings estimates are still too high. But it's understandable why there's so much uncertainty. I mean, technology is still booming, digital transformations are happening in earnest, leading companies have momentum and they got cash runways. And moreover, the CEOs of these leading companies are still really optimistic. But strong guidance in an environment of uncertainty is somewhat risky. Hello and welcome to this week's Wikibon CUBE Insights Powered by ETR. In this breaking analysis, we share takeaways from ETR'S latest spending survey, which was released to their private clients on October 21st. Today, we're going to review the macro spending data. We're going to share where CIOs think their cloud spend is headed. We're going to look at the actions that organizations are taking to manage uncertainty and then review some of the technology companies that have the most positive and negative outlooks in the ETR data set. Let's first look at the sample makeup from the latest ETR survey. ETR captured more than 1300 respondents in this latest survey. Its highest figure for the year and the quality and seniority of respondents just keeps going up each time we dig into the data. We've got large contributions as you can see here from sea level executives in a broad industry focus. Now the survey is still North America centric with 20% of the respondents coming from overseas and there is a bias toward larger organizations. And nonetheless, we're still talking well over 400 respondents coming from SMBs. Now ETR for those of you who don't know, conducts a quarterly spending intention survey and they also do periodic drilldowns. So just by the way of review, let's take a look at the expectations in the latest drilldown survey for IT spending. Before we look at the broader technology spending intentions survey data, followers of this program know that we reported on this a couple of weeks ago, spending expectations that peaked last December at 8.3% are now down to 5.5% with a slight uptick expected for next year as shown here. Now one CIO in the ETR community said these figures could be understated because of inflation. Now that's an interesting comment. Real GDP in the US is forecast to be around 1.5% in 2022. So these figures are significantly ahead of that. Nominal GDP is forecast to be significantly higher than what is shown in that slide. It was over 9% in June for example. And one would interpret that survey respondents are talking about real dollars which reflects inflationary factors in IT spend. So you might say, well if nominal GDP is in the high single digits this means that IT spending is below GDP which is usually not the case. But the flip side of that is technology tends to be deflationary because prices come down over time on a per unit basis, so this would be a normal and even positive trend. But it's mixed right now with prices on hard to find hardware, they're holding more firms. Software, you know, software tends to be driven by lock in and competition and switching costs. So you have those countervailing factors. Services can be inflationary, especially now as wages rise but certain sectors like laptops and semis and NAND are seeing less demand and maybe even some oversupply. So the way to look at this data is on a relative basis. In other words, IT buyers are reporting 280 basis point drop in spending sentiment from the end of last year. Now, something that we haven't shared from the latest drilldown survey which we will now is how IT bar buyers are thinking about cloud adoption. This chart shows responses from 419 IT execs from that drilldown and depicts the percentage of workloads their organizations have in the cloud today and what the expectation is through years from now. And you can see it's 27% today and it's nearly 50% in three years. Now the nuance is if you look at the question, that ETRS, it's they asked about IaaS and PaaS, which to some could include on-prem. Now, let me come back to that. In particular, financial services, IT, telco and retail and services industry cited expectations for the future for three years out that we're well above the average of the mean adoption levels. Regardless of how you interpret this data there's most certainly plenty of public cloud in the numbers. And whether you believe cloud is an operating environment or a place out there in the cloud, there's plenty of room for workloads to move into a cloud model well beyond mid this decade. So you know, as ho hum as we've been toward recent as-a-service models announced from the likes of HPE with GreenLake and Dell with APEX, the timing of those offerings may be pretty good actually. Now let's expand on some of the data that we showed a couple weeks ago. This chart shows responses from 282 execs on actions their organizations are taking over the next three months. And the Deltas are quite traumatic from the early part of this charter than the left hand side. The brown line is hiring freezes, the black line is freezing IT projects, and the green line is hiring increases and that red line is layoffs. And we put a box around the sort of general area of the isolation economy timeframe. And you can see the wild swings on this chart. By mid last summer, people were kickstarting things and more hiring was going on and the black line shows IT project freezes, you know, came way down. And now, or on the way back up as our hiring freezes. So we're seeing these wild swings in organizational actions and strategies which underscores the lack of predictability. As with supply chains around the world, this is likely due to the fact that organizations, pre pandemic they were optimized for efficiency, not a lot of waste rather than business resilience. Meaning, you know, there's again not a lot of fluff in the system or if there was it got flushed out during the pandemic. And so the need for productivity and automation is becoming increasingly important, especially as actions that solely rely on headcount changes are very, very difficult to manage. Now, let's dig into some of the vendor commentary and take a look at some of the names that have momentum and some of the others possibly facing headwinds. Here's a list of companies that stand out in the ETR survey. Snowflake, once again leads the pack with a positive spending outlook. HashiCorp, CrowdStrike, Databricks, Freshworks and ServiceNow, they round out the top six. Microsoft, they seem to always be in the mix, as do a number of other security and related companies including CyberArk, Zscaler, CloudFlare, Elastic, Datadog, Fortinet, Tenable and to a certain extent Akamai, you can kind of put them sort of in that group. You know, CDN, they got to worry about security. Everybody worries about security, but especially the CDNs. Now the other software names that are highlighted here include Workday and Salesforce. On the negative side, you can see Dynatrace saw some negatives in the latest survey especially around its analytics business. Security is generally holding up better than other sectors but it's still seeing greater levels of pressure than it had previously. So lower spend. And defections relative to its observability peers, that's really for Dynatrace. Now the other one that was somewhat surprising is IBM. You see the IBM was sort of in that negative realm here but IBM reported an outstanding quarter this past week with double digit revenue growth, strong momentum in software, consulting, mainframes and other infrastructure like storage. It's benefiting from the Kyndryl restructuring and it's on track IBM to deliver 10 billion in free cash flow this year. Red Hat is performing exceedingly well and growing in the very high teens. And so look, IBM is in the midst of a major transformation and it seems like a company that is really focused now with hybrid cloud being powered by Red Hat and consulting and a decade plus of AI investments finally paying off. Now the other big thing we'll add is, IBM was once an outstanding acquire of companies and it seems to be really getting its act together on the M&A front. Yes, Red Hat was a big pill to swallow but IBM has done a number of smaller acquisitions, I think seven this year. Like for example, Turbonomic, which is starting to pay off. Arvind Krishna has the company focused once again. And he and Jim J. Kavanaugh, IBM CFO, seem to be very confident on the guidance that they're giving in their business. So that's a real positive in our view for the industry. Okay, the last thing we'd like to do is take 12 of the companies from the previous chart and plot them in context. Now these companies don't necessarily compete with each other, some do. But they are standouts in the ETR survey and in the market. What we're showing here is a view that we like to often show, it's net score or spending velocity on the vertical axis. And it's a measure, that's a measure of the net percentage of customers that are spending more on a particular platform. So ETR asks, are you spending more or less? They subtract less from the mores. I mean I'm simplifying, but that's what net score is. Now in the horizontal axis, that is a measure of overlap which is which measures presence or pervasiveness in the dataset. So bigger the better. We've inserted a table that informs how the dots in the companies are positioned. These companies are all in the green in terms of net score. And that right most column in the table insert is indicative of their presence in the dataset, the end. So higher, again, is better for both columns. Two other notes, the red dotted line there you see at 40%. Anything over that indicates an highly elevated spending momentum for a given platform. And we purposefully took Microsoft out of the mix in this chart because it skews the data due to its large size. Everybody else would cluster on the left and Microsoft would be all alone in the right. So we take them out. Now as we noted earlier, Snowflake once again leads with a net score of 64%, well above the 40% line. Having said that, while adoption rates for Snowflake remains strong the company's spending velocity in the survey has come down to Earth. And many more customers are shifting from where they were last year and the year before in growth mode i.e. spending more year to year with Snowflake to now shifting more toward flat spending. So a plus or minus 5%. So that puts pressure on Snowflake's net score, just based on the math as to how ETR calculates, its proprietary net score methodology. So Snowflake is by no means insulated completely to the macro factors. And this was seen especially in the data in the Fortune 500 cut of the survey for Snowflake. We didn't show that here, just giving you anecdotal commentary from the survey which is backed up by data. So, it showed steeper declines in the Fortune 500 momentum. But overall, Snowflake, very impressive. Now what's more, note the position of Streamlit relative to Databricks. Streamlit is an open source python framework for developing data driven, data science oriented apps. And it's ironic that it's net score and shared in is almost identical to those of data bricks, as the aspirations of Snowflake and Databricks are beginning to collide. Now, however, the Databricks net score has held up very well over the past year and is in the 92nd percentile of its machine learning and AI peers. And while it's seeing some softness, like Snowflake in the Fortune 500, Databricks has steadily moved to the right on the X axis over the last several surveys even though it was unable to get to the public markets and do an IPO during the lockdown tech bubble. Let's come back to the chart. ServiceNow is impressive because it's well above the 40% mark and it has 437 shared in on this cut, the largest of any company that we chose to plot here. The only real negative on ServiceNow is, more large customers are keeping spending levels flat. That's putting a little bit pressure on its net score, but that's just conservatives. It's kind of like Snowflakes, you know, same thing but in a larger scale. But it's defections, the ServiceNow as in Snowflake as well. It's defections remain very, very low, really low churn below 2% for ServiceNow, in fact, within the dataset. Now it's interesting to also see Freshworks hit the list. You can see them as one of the few ITSM vendors that has momentum and can potentially take on ServiceNow. Workday, on this chart, it's the other big app player that's above the 40% line and we're only showing Workday HCM, FYI, in this graphic. It's Workday Financials, that offering, is below the 40% line just for reference. Now let's talk about CrowdStrike. We attended Falcon last month, CrowdStrike's user conference and we're very impressed with the product visio, the company's execution, it's growing partnerships. And you can see in this graphic, the ETR survey data confirms the company's stellar performance with a net score at 50%, well above the 40% mark. And importantly, more than 300 mentions. That's second only to ServiceNow, amongst the 12 companies that we've chosen to highlight here. Only Microsoft, which is not shown here, has a higher net score in the security space than CrowdStrike. And when it comes to presence, CrowdStrike now has caught up to Splunk in terms of pervasion in the survey. Now CyberArk and Zscaler are the other two security firms that are right at that 40% red dotted line. CyberArk for names with over a hundred citations in the security sector, is only behind Microsoft and CrowdStrike. Zscaler for its part in the survey is seeing strong momentum in the Fortune 500, unlike what we said for Snowflake. And its pervasion on the X-axis has been steadily increasing. Again, not that Snowflake and CrowdStrike compete with each other but they're too prominent names and it's just interesting to compare peers and business models. Cloudflare, Elastic and Datadog are slightly below the 40% mark but they made the sort of top 12 that we showed to highlight here and they continue to have positive sentiment in the survey. So, what are the big takeaways from this latest survey, this really quick snapshot that we've taken. As you know, over the next several weeks we're going to dig into it more and more. As we've previously reported, the tide is going out and it's taking virtually all the tech ships with it. But in many ways the current market is a story of heightened expectations coming down to Earth, miscalculations about the economic patterns and the swings and imperfect visibility. Leading Barclays analyst, Ramo Limchao ask the question to guide or not to guide in a recent research note he wrote. His point being, should companies guide or should they be more cautious? Many companies, if not most companies, are actually giving guidance. Indeed, when companies like Oracle and IBM are emphatic about their near term outlook and their visibility, it gives one confidence. On the other hand, reasonable people are asking, will the red hot valuations that we saw over the last two years from the likes of Snowflake, CrowdStrike, MongoDB, Okta, Zscaler, and others. Will they return? Or are we in for a long, drawn out, sideways exercise before we see sustained momentum? And to that uncertainty, we add elections and public policy. It's very hard to predict right now. I'm sorry to be like a two-handed lawyer, you know. On the one hand, on the other hand. But that's just the way it is. Let's just say for our part, we think that once it's clear that interest rates are on their way back down and we'll stabilize it under 4% and we have clarity on the direction of inflation, wages, unemployment and geopolitics, the wild swings and sentiment will subside. But when that happens is anyone's guess. If I had to peg, I'd say 18 months, which puts us at least into the spring of 2024. What's your prediction? You know, it's almost that time of year. Let's hear it. Please keep in touch and let us know what you think. Okay, that's it for now. Many thanks to Alex Myerson. He is on production and he manages the podcast for us. Ken Schiffman as well is our newest addition to the Boston Studio. Kristin Martin and Cheryl Knight, they help get the word out on social media and in our newsletters. And Rob Hoff is our EIC, editor-in-chief over at SiliconANGLE. He does some wonderful editing for us. Thank you all. Remember all these episodes, they are available as podcasts. Wherever you listen, just search breaking analysis podcast. I publish each week on wikibon.com and siliconangle.com. Or you can email me at david.vellante@siliconangle.com or DM me @dvellante. Or feel free to comment on our LinkedIn posts. And please do check out etr.ai. They've got the best survey data in the enterprise tech business. If you haven't checked that out, you should. It'll give you an advantage. This is Dave Vellante for theCUBE Insights Powered by ETR. Thanks for watching. Be well and we'll see you next time on Breaking Analysis. (soft upbeat music)

(upbeat music) >> Welcome back everyone to Supercloud22, I'm John Furrier, host of theCUBE here in Palo Alto. For this next ecosystem's segment we have Muddu Sudhakar, who is the co-founder and CEO of Aisera, a friend of theCUBE, Cube alumni, serial entrepreneur, multiple exits, been on multiple times with great commentary. Muddu, thank you for coming on, and supporting our- >> Also thank you for having me, John. >> Yeah, thank you. Great handshake there, I love to do it. One, I wanted you here because, two reasons, one is, congratulations on your new funding. >> Thank you. >> For $90 million, Series D funding. >> Series D funding. >> So, huge validation in this market. >> It is. >> You have been experienced software so, it's a real testament to your team. But also, you're kind of in the Supercloud vortex. This new wave that Supercloud is part of is, I call it the pretext to what's coming with multi-clouds. It is the next level. >> I see. >> Structural change and we have been reporting on it, Dave and I, and we are being challenged. So, we decided to open it up. >> Very good, I would love it. >> And have a conversation rather than waiting eight months to prove that we are right. Which, we are right, but that is a long story. >> You're always right. (both laughs) >> What do you think of Supercloud, that's going on? What is the big trend? Because its public cloud is great, so there is no conflict there. >> Right. >> It's got great business, it's integrated, IaaS, to SaaS, PaaS, all in the beginning, or the middle. All that is called good. Now you have on-premise high rate cloud. >> Right. >> Edge is right around the corner. Exploding in new capabilities. So, complexity is still here. >> That's right, I think, you nailed it. We talk about hybrid cloud, and multi cloud. Supercloud is kind of elevates the message even better. Because you still have to leave for some of our clouds, public clouds. There will be some of our clouds, still running on the Edge. That's where, the Edge cloud comes in. Some will still be on-prem. So, the Supercloud as a concept is beyond hybrid and multi cloud. To me, I will run some of our cloud on Amazon. Some could be on Aisera, some could be running only on Edge, right? >> Mm hm >> And we still have, what we call remote executors. Some leaders of service now. You have, what we call the mid-server, is what I think it was called. Where you put in a small code and run it. >> Yeah. >> So, I think all those things will be running on-prem environment and VMware cloud, et cetera. >> And if you look back at, I think it has been five years now, maybe four or five years since Andy Jassy at reInvent announced Outposts. Think that was the moment in time that Dave and I took this pause back and said "Okay, that's Amazon." who listens to their customers. Acknowledging Hybrid. >> Right. >> Then we saw the rise of Snowflakes, the Databricks, specialty clouds. You start to see people who are building on top of AWS. But at MongoDB, it is a database, now they are a full blown, large scale data platform. These companies took advantage of the public cloud to build, as Jerry Chen calls it, "Castles in the cloud." >> Right. >> That seems to be happening in all areas. What do you think about that? >> Right, so what is driving the cloud? To me, we talk about machine learning in AI, right? Versus clouded options. We used to call it lift and shift. The outposts and lift and shift. Initially this was to get the data into the cloud. I think if you see, the vendor that I like the most, is, I'm not picking any favorite but, Microsoft Azure, they're thinking like your Supercloud, right? Amazon is other things, but Azure is a lot more because they run on-prem. They are also on Azure CloudFront, Amazon CloudFront. So I think, Azure and Amazon are doing a lot more in the area of Supercloud. What is really helping is the machine learning environment, needs Superclouds. Because I will be running some on the Edge, some compute, some will be running on the public cloud, some could be running on my data center. So, I think the Supercloud is really suited for AI and automation really well. >> Yeah, it is a good point about Microsoft, too. And I think Microsoft's existing install base saved Azure. >> Okay. >> They brought Office 365, Sequel Server, cause their customers weren't leaving Microsoft. They had the productivity thing nailed down as well as the ability to catch up >> That's right. >> To AWS. So, natural extension to on-premise with Microsoft. >> I think... >> Tell us- >> Your Supercloud is what Microsoft did. Right? Azure. If you think of, like, they had an Office 365, their SharePoint, their Dynamics, taking all of those properties, running on the Azure. And still giving the migration path into a data center. Is Supercloud. So, the early days Supercloud came from Azure. >> Well, that's a good point, we will certainly debate that. I will also say that Snowflake built on AWS. >> That's right. >> Okay, and became a super powerhouse with the data business. As did Databricks. >> That's right. >> Then went to Azure >> That's right. >> So, you're seeing kind of the Playbook. >> Right. >> Go fast on Cloud Native, the native cloud. Get that fly wheel going, then get going, somewhere else. >> It is, and to that point I think you and me are talking, right? If you are to start at one cloud and go to another cloud, the amount of work as a vendor for us to use for implement. Today, like we use all three clouds, including the Gov Cloud. It's a lot of work. So, what will happen, the next toolkit we use? Even services like Elastic. People will not, the word commoditize, is not the word, but people will create an abstraction layer, even for S3. >> Explain that, explain that in detail. So, elastic? What do you mean by that? >> Yeah, so what that means is today, Elasticsearch, if you do an Elasticsearch on Amazon, if I go to Azure, I don't want enter another Elasticsearch layer. Ideally I want us to write an abstracted search layer. So, that when I move my services into a different cloud I don't want to re-compute and re-calculate everything. That's a lot of work. Particularly once you have a production customer, if I were to shift the workloads, even to the point of infrastructure, take S3, if I read infrastructure to S3 and tomorrow I go to Azure. Azure will have its own objects store. I don't want to re-validate that. So what will happen is digital component, Kubernetes is already there, we want storage, we want network layer, we want VPM services, elastic as well as all fundamental stuff, including MongoDB, should be abstracted to run. On the Superclouds. >> Okay, well that is a little bit of a unicorn fantasy. But let's break that down. >> Sure. >> Do you think that's possible? >> It is. Because I think, if I am on MongoDB, I should be able to give a horizontal layer to MongoDB that is optimized for all three of them. I don't want MongoDB. >> First of all, everyone will buy that. >> Sure. >> I'm skeptical that that's possible. Given where we are at right now. So, you're saying that a vendor will provide an abstraction layer. >> No, I'm saying that either MongoDB, itself will do it, or a third party layer will come as a service which will abstract all this layer so that we will write to an AP layer. >> So what do you guys doing? How do you handle multiple clouds? You guys are taking that burden on, because it makes sense, you should build the abstraction layer. Not rely on a third party vendor right? >> We are doing it because there is no third party available offer it. But if you offer a third party tomorrow, I will use that as a Supercloud service. >> If they're 100% reliable? >> That's right. That's exactly it. >> They have to do the work. >> They have to do the work because if today I am doing it because no one else is offering it- >> Okay so what people might not know is that you are an angel investor as well as an entrepreneur been very successful, so you're rich, you have a lot of money. If I were a startup and I said, Muddu, I want to build this abstraction layer. What would be funding advice that you would give me as an entrepreneur? As a company to do that? >> I would do it like an Apigee that Google acquired, you should create an Apigee-like layer, for infrastructure upfront services, I think that is a very good option. >> And you think that is viable? >> It is very much viable. >> Would that be part of Supercloud architecture, in your opinion? >> It is. Right? And that will abstract all the clouds to some level. Like it is like Kubernetes abstract, so that if I am running on Kubernetes I can transfer to any cloud. >> Yeah >> But that should go from computer into other infrastructures. >> It's seems to me, Muddu, and I want to get your thoughts about this whole Supercloud defacto standard opportunity. It feels like we are waiting for a moment where there is some sort of defacto unification, whether it is in the distraction layer, or a standards body. There is no W3C here going on. I mean, W3C was for web consortium, for world wide web. The Supercloud seems to be having the same impact the web had. Transformative, disruptive, re-factoring business operations. Is there a standardized body or an opportunity for a defacto? Like Kubernetes was a great example of a unification around something for orchestration. Is there a better version in the Supercloud model where we need a standard? >> Yes and no. The reason is because by the time you come to standard, take time to look what happened. First, we started with VMs, then became Docker and Containers then we came to Kubernetes. So it goes through a journey. I think the next few years will be stood on SuperCloud let's make customers happy, let's make enough services going, and then the standards will come. Standards will be almost 2-3 years later. So I don't think standards should happen right now. Right now, all we need is, we need enough start ups to create the super layer abstraction, with the goal in mind of AI automation. The reason, AI is because AI needs to be able to run that. Automated because running a work flow is, I can either run a workflow in the cloud services, I can run it on on-prem, I can run it on database, so you have two good applications, take AI and automation with Supercloud and make enough enough noise on that make enough applications, then the standards will come. >> On this project we have been with SuperCloud these past day we have heard a lot of people talking. The themes that developers are okay, they are doing great. Open source is booming. >> Yes >> Cloud Native's got major traction. Developers are going fast and they love it, shifting left, all these great things. They're putting a lot of data, DevOps and the security teams, they're the ones who are leveling up. We are hearing a lot of conversations around how they can be faster. What is your view on this as relative to that Supercloud nirvana getting there? How are DevOps and security teams leveling up to devs? >> A couple of things. I think that in the world of DevSecOps and security ops. The reason security is important, right? Given what is going on, but you don't need to do security the manual way. I think that whole new operation that you and me talked about, AI ops should happen. Where the AI ops is for service operation, for performance, for incident or for security. Nobody thinks of AI security. So, the DevOps people should think more world of AI ops, so that I can predict, prevent things before they happen. Then the security will be much better. So AI ops with Supercloud will probably be that nirvana. But that is what should happen. >> In the AI side of things, what you guys are doing, what are you learning, on scale, relative to data? Is there, you said machine learning needs data, it needs scale operation. What's your view on the automation piece of all this? >> I think to me, the data is the single, underrated, unsung kind of hero in the whole machine learning. Everyone talks about AI and machine learning algorithms. Algorithms are as important, but even more important is data. Lack of data I can't do algorithms. So my advice to customers is don't lose your data. That is why I see, Frank, my old boss, setting everything up into the data cloud, in Snowflake. Data is so important, store the data, analyze the data. Data is the new AI. You and me talk so many times- >> Yeah >> It's underrated, people are not anticipating how important it is. But the data is coming from logs, events, whether there is knowledge documents, any data in any form. I think keep the data, analyze the data, data patterns, and then things like SuperCloud can really take advantage of that. >> So, in the Supercloud equation one of the things that has come up is that the native clouds do great. Their IaaS to SaaS is interactions that solve a lot of problems. There is integration that is good. >> Right. >> Now when you go off cloud, you get regions, get latency issues- >> Right >> You have more complexity. So what's the trade off in the Supercloud journey, if you had to guess? And just thinking out loud here, what would be some of the architectural trade offs of how you do it, what's the sequence? What's the order of operations to get Superclouding going? >> Yeah, very good questions here. I think once you start going from the public cloud, the clouds there scale to lets say, even a regional data center onto an Edge, latency will kick in. The lack of computer function will kick in. So there I think everything should become asynchronous, right? You will run the application in a limited environment. You should anticipate for small memories, small compute, long latencies, but still following should happen. So some operations should become the old-school following, like, it's like the email. I send an email, it's an asynchronous thing, I made a sponsor, I think most of message passing should go back to the old-school architectures They should become asynchronous where thing can rely. I think, as long as algorithms can take that into Edge, I think that Superclouds can really bridge between the public cloud to the edge. >> Muddu, thanks for coming, we really appreciate your insights here. You've always been a great friend, great commentator. If you weren't the CEO and a famous angel investor, we would certainly love to have you as a theCUBE analyst, here on theCUBE. >> I am always available for you. (John laughs) >> When you retire, you can come back. Final point, we've got time left. We'll give you a chance to talk about the company. I'm really intrigued by the success of your ninety million dollar financing realm because we are in a climate where people aren't getting those kinds of investments. It's usually down-rounds. >> Okay >> 409 adjustments, people are struggling. You got an up-round and you got a big number. Why the success? What is going on with the company? Why are you guys getting such great validation? Goldman Sachs, Thoma Bravo, Zoom, these are big names, these are the next gen winners. >> It is. >> Why are they picking you? Why are they investing in you? >> I think it is not one thing, it is many things. First all, I think it is a four-year journey for us where we are right now. So, the company started late 2017. It is getting the right customers, partners, employees, team members. So it is a lot hard work went in. So a lot of thanks to the Aisera community for where we are. Why customers and where we are? Look, fundamentally there is a problem to solve. Like, what Aisera is trying to solve is can we automate customer service? Whether internal employees, external customer support. Do it for IT, HR, sales, marketing, all the way to ops. Like you talk about DevSecOps, I don't want thousands of tune ups for ops. If I can make that job better, >> Yeah >> I want to, any job I want to automate. I call it, elevate the human, right? >> Yeah. >> And that's the reason- >> 'Cause you're saying people have to learn specialty tools, and there are consequences to that. >> Right, and to me, people should focus on more important tasks and use AI as a tool to automate those things right? It's like thinking of offering Apple City as Alexa as a service, that is how we are trying to offer customer service, like, right? And if it can do that consistently, and reduce costs, cost is a big reason why customers like us a lot, we have eliminated the cost in this down economy, I will amplify our message even more, right? I am going to take a bite out of their expense. Whether it is tool expense, it's on resources. Second, is user productivity And finally, experience. People want experience. >> Final question, folks out there, first of all, what do you think about Supercloud? And if someone asks you what is this Supercloud thing? How would you answer? >> Supercloud, is, to me, beyond multi cloud and hybrid cloud. It is to bridge applications that are build in Supercloud can run on all clouds seamlessly. You don't need to compile them, re-clear them. Supercloud is one place to build, develop, and deploy. >> Great, Muddu. Thank you for coming on. Supercloud22 here breaking it down with the ecosystem commentary, we have a lot of people coming to the small group of experts in our network, bringing you in open conversation around the future of cloud computing and applications globally. And again, it is all about the next generation cloud. This is theCUBE, thanks for watching. (upbeat music)

(bright upbeat music) >> Welcome back, everyone, to the "Supercloud 22." I'm John Furrier, host of "The Cube." This is the Cloud-erati panel, the distinguished experts who have been there from day one, watching the cloud grow, from building clouds, and all open source stuff as well. Just great stuff. Good friends of "The Cube," and great to introduce back on "The Cube," Adrian Cockcroft, formerly with Netflix, formerly AWS, retired, now commentating here in "The Cube," as well as other events. Great to see you back out there, Adrian. Lori MacVittie, Cloud Evangelist with F5, also wrote a great blog post on supercloud, as well as Dave Vellante as well, setting up the supercloud conversation, which we're going to get into, and Chris Hoff, who's the CTO and CSO of LastPass who's been building clouds, and we know him from "The Cube" before with security and cloud commentary. Welcome, all, back to "The Cube" and supercloud. >> Thanks, John. >> Hi. >> All right, Lori, we'll start with you to get things going. I want to try to sit back, as you guys are awesome experts, and involved from building, and in the trenches, on the front lines, and Adrian's coming out of retirement, but Lori, you wrote the post setting the table on supercloud. Let's start with you. What is supercloud? What is it evolving into? What is the north star, from your perspective? >> Well, I don't think there's a north star yet. I think that's one of the reasons I wrote it, because I had a clear picture of this in my mind, but over the past, I don't know, three, four years, I keep seeing, in research, my own and others', complexity, multi-cloud. "We can't manage it. They're all different. "We have trouble. What's going on? "We can't do anything right." And so digging into it, you start looking into, "Well, what do you mean by complexity?" Well, security. Migration, visibility, performance. The same old problems we've always had. And so, supercloud is a concept that is supposed to overlay all of the clouds and normalize it. That's really what we're talking about, is yet another abstraction layer that would provide some consistency that would allow you to do the same security and monitor things correctly. Cornell University actually put out a definition way back in 2016. And they said, "It's an architecture that enables migration "across different zones or providers," and I think that's important, "and provides interfaces to everything, "makes it consistent, and normalizes the network," basically brings it all together, but it also extends to private clouds. Sometimes we forget about that piece of it, and I think that's important in this, so that all your clouds look the same. So supercloud, big layer on top, makes everything wonderful. It's unicorns again. >> It's interesting. We had multiple perspectives. (mumbles) was like Snowflake, who built on top of AWS. Jerry Chan, who we heard from earlier today, Greylock Penn's "Castles in the Cloud" saying, "Hey, you can have a moat, "you can build an advantage and have differentiation," so startups are starting to build on clouds, that's the native cloud view, and then, of course, they get success and they go to all the other clouds 'cause they got customers in the ecosystem, but it seems that all the cloud players, Chris, you commented before we came on today, is that they're all fighting for the customer's workloads on their infrastructure. "Come bring your stuff over to here, "and we'll make it run better." And all your developers are going to be good. Is there a problem? I mean, or is this something else happening here? Is there a real problem? >> Well, I think the north star's over there, by the way, Lori. (laughing) >> Oh, there it is. >> Right there. The supercloud north star. So indeed I think there are opportunities. Whether you call them problems or not, John, I think is to be determined. Most companies have, especially if they're a large enterprise, whether or not they've got an investment in private cloud or not, have spent time really trying to optimize their engineering and workload placement on a single cloud. And that, regardless of your choice, as we take the big three, whether it's Amazon, Google, or Microsoft, each of them have their pros and cons for various types of workloads. And so you'll see a lot of folks optimizing for a particular cloud, and it takes a huge effort up and down the stack to just get a single cloud right. That doesn't take into consideration integrations with software as a service, instantiated, oftentimes, on top of infrastructure of the service that you need to supplement where the obstruction layer ends in infrastructure of the service. You've seen most IS players starting to now move up-chain, as we predicted years ago, to platform as a service, but platforms of various types. So I definitely see it as an opportunity. Previous employers have had multiple clouds, but they were very specifically optimized for the types of workloads, for example, in, let's say, AWS versus GCP, based on the need for different types and optimized compute platforms that each of those providers ran. We never, in that particular case, thought about necessarily running the same workloads across both clouds, because they had different pricing models, different security models, et cetera. And so the challenge is really coming down to the fact that, what is the cost benefit analysis of thinking about multi-cloud when you can potentially engineer the resiliency or redundancy, all the in-season "ilities" that you might need to factor into your deployments on a single cloud, if they are investing at the pace in which they are? So I think it's an opportunity, and it's one that continues to evolve, but this just reminds me, your comments remind me, of when we were talking about OpenStack versus AWS. "Oh, if there were only APIs that existed "that everybody could use," and you saw how that went. So I think that the challenge there is, what is the impetus for a singular cloud provider, any of the big three, deciding that they're going to abstract to a single abstraction layer and not be able to differentiate from the competitors? >> Yeah, and that differentiation's going to be big. I mean, assume that the clouds aren't going to stay still like AWS and just not stop innovating. We see the devs are doing great, Adrian, open source is bigger and better than ever, but now that's been commercialized into enterprise. It's an ops problem. So to Chris's point, the cost benefit analysis is interesting, because do companies have to spin up multiple operations teams, each with specialized training and tooling for the clouds that they're using, and does that open up a can of worms, or is that a good thing? I mean, can you design for this? I mean, is there an architecture or taxonomy that makes it work, or is it just the cart before the horse, the solution before the problem? >> Yeah, well, I think that if you look at any large vendor... Sorry, large customer, they've got a bit of everything already. If you're big enough, you've bought something from everybody at some point. So then you're trying to rationalize that, and trying to make it make sense. And I think there's two ways of looking at multi-cloud or supercloud, and one is that the... And practically, people go best of breed. They say, "Okay, I'm going to get my email "from Google or Microsoft. "I'm going to run my applications on AWS. "Maybe I'm going to do some AI machine learning on Google, "'cause those are the strengths of the platforms." So people tend to go where the strength is. So that's multi-cloud, 'cause you're using multiple clouds, and you still have to move data and make sure they're all working together. But then what Lori's talking about is trying to make them all look the same and trying to get all the security architectures to be the same and put this magical layer, this unicorn magical layer that, "Let's make them all look the same." And this is something that the CIOs have wanted for years, and they keep trying to buy it, and you can sell it, but the trouble is it's really hard to deliver. And I think, when I go back to some old friends of ours at Enstratius who had... And back in the early days of cloud, said, "Well, we'll just do an API that abstracts "all the cloud APIs into one layer." Enstratius ended up being sold to Dell a few years ago, and the problem they had was that... They didn't have any problem selling it. The problem they had was, a year later, when it came up for renewal, the developers all done end runs around it were ignoring it, and the CIOs weren't seeing usage. So you can sell it, but can you actually implement it and make it work well enough that it actually becomes part of your core architecture without, from an operations point of view, without having the developers going directly to their favorite APIs around them? And I'm not sure that you can really lock an organization down enough to get them onto a layer like that. So that's the way I see it. >> You just defined- >> You just defined shadow shadow IT. (laughing) That's pretty- (crosstalk) >> Shadow shadow IT, yeah. >> Yeah, shadow shadow it. >> Yeah. >> Yeah. >> I mean, this brings up the question, I mean, is there really a problem? I mean, I guess we'll just jump to it. What is supercloud? If you can have the magic outcome, what is it? Enstratius rendered in with automation? The security issues? Kubernetes is hot. What is the supercloud dream? I guess that's the question. >> I think it's got easier than it was five, 10 years ago. Kubernetes gives you a bunch of APIs that are common across lots of different areas, things like Snowflake or MongoDB Atlas. There are SaaS-based services, which are across multiple clouds from vendors that you've picked. So it's easier to build things which are more portable, but I still don't think it's easy to build this magic API that makes them all look the same. And I think that you're going to have leaky abstractions and security being... Getting the security right's going to be really much more complex than people think. >> What about specialty superclouds, Chris? What's your view on that? >> Yeah, I think what Adrian is alluding to, those leaky abstractions, are interesting, especially from the security perspective, 'cause I think what you see is if you were to happen to be able to thin slice across a set of specific types of workloads, there is a high probability given today that, at least on two of the three major clouds, you could get SaaS providers that sit on those same infrastructure of the service clouds for you, string them together, and have a service that technically is abstracted enough from the things you care about to work on one, two, or three, maybe not all of them, but most SaaS providers in the security space, or identity space, data space, for example, coexist on at least Microsoft and AWS, if not all three, with Google. And so you could technically abstract a service to the point that you let that level of abstract... Like Lori said, no computer science problem could not be... So, no computer science problem can't be solved with more layers of abstraction or misdirection... Or redirection. And in that particular case, if you happen to pick the right vendors that run on all three clouds, you could possibly get close. But then what that really talks about is then, if you built your seven-layer dip model, then you really have specialty superclouds spanning across infrastructure of the service clouds. One for your identity apps, one for data and data layers, to normalize that, one for security, but at what cost? Because you're going to be charged not for that service as a whole, but based on compute resources, based on how these vendors charge across each cloud. So again, that cost-benefit ratio might start being something that is rather imposing from a budgetary perspective. >> Lori, weigh in on this, because the enterprise people love to solve complexity with more complexity. Here, we need to go the other way. It's a commodity. So there has to be a better way. >> I think I'm hearing two fundamental assumptions. One, that a supercloud would force the existing big three to implement some sort of equal API. Don't agree with that. There's no business case for that. There's no reason that could compel them to do that. Otherwise, we would've convinced them to do that, what? 10, 15 years ago when we said we need to be interoperable. So it's not going to happen there. They don't have a good reason to do that. There's no business justification for that. The other presumption, I think, is that we would... That it's more about the services, the differentiated services, that are offered by all of these particular providers, as opposed to treating the core IaaS as the commodity it is. It's compute, it's some storage, it's some networking. Look at that piece. Now, pull those together by... And it's not OpenStack. That's not the answer, it wasn't the answer, it's not the answer now, but something that can actually pull those together and abstract it at a different layer. So cloud providers don't have to change, 'cause they're not going to change, but if someone else were to build that architecture to say, "all right, I'm going to treat all of this compute "so you can run your workloads," as Chris pointed out, "in the best place possible. "And we'll help you do that "by being able to provide those cost benefit analysis, "'What's the best performance, what are you doing,' "And then provide that as a layer." So I think that's really where supercloud is going, 'cause I think that's what a lot of the market actually wants in terms of where they want to run their workloads, because we're seeing that they want to run workloads at the edge, "a lot closer to me," which is yet another factor that we have to consider, and how are you going to be moving individual workloads around? That's the holy grail. Let's move individual workloads to where they're the best performance, the security, cost optimized, and then one layer up. >> Yeah, I think so- >> John Considine, who ultimately ran CloudSwitch, that sold to Verizon, as well as Tom Gillis, who built Bracket, are both rolling in their graves, 'cause what you just described was exactly that. (Lori laughing) Well, they're not even dead yet, so I can't say they're rolling in their graves. Sorry, Tom. Sorry, John. >> Well, how do hyperscalers keep their advantage with all this? I mean, to that point. >> Native services and managed services on top of it. Look how many flavors of managed Kubernetes you have. So you have a choice. Roll your own, or go with a managed service, and then differentiate based on the ability to take away and simplify some of that complexity. Doesn't mean it's more secure necessarily, but I do think we're seeing opportunities where those guys are fighting tooth and nail to keep you on a singular cloud, even though, to Lori's point, I agree, I don't think it's about standardized APIs, 'cause I think that's never going to happen. I do think, though, that SaaS-y supercloud model that we were talking about, layering SaaS that happens to span all the three infrastructure of the service are probably more in line with what Lori was talking about. But I do think that portability of workload is given to you today within lots of ways. But again, how much do you manage, and how much performance do you give up by running additional abstraction layers? And how much security do you give up by having to roll your own and manage that? Because the whole point was, in many cases... Cloud is using other people's computers, so in many cases, I want to manage as little of it as I possibly can. >> I like this whole SaaS angle, because if you had the old days, you're on Amazon Web Services, hey, if you build a SaaS application that runs on Amazon, you're all great, you're born in the cloud, just like that generations of startups. Great. Now when you have this super pass layer, as Dave Vellante was riffing on his analysis, and Lori, you were getting into this pass layer that's kind of like SaaS-y, what's the SaaS equation look like? Because that, to me, sounds like a supercloud version of saying, "I have a workload that runs on all the clouds equally." I just don't think that's ever going to happen. I agree with you, Chris, on that one. But I do see that you can have an abstraction that says, "Hey, I don't really want to get in the weeds. "I don't want to spend a lot of ops time on this. "I just want it to run effectively, and magic happens," or, as you said, some layer there. How does that work? How do you see this super pass layer, if anything, enabling a different SaaS game? >> I think you hit on it there. The last like 10 or so years, we've been all focused on developers and developer productivity, and it's all about the developer experience, and it's got to be good for them, 'cause they're the kings. And I think the next 10 years are going to be very focused on operations, because once you start scaling out, it's not about developers. They can deliver fast or slow, it doesn't matter, but if you can't scale it out, then you've got a real problem. So I think that's an important part of it, is really, what is the ops experience, and what is the best way to get those costs down? And this would serve that purpose if it was done right, which, we can argue about whether that's possible or not, but I don't have to implement it, so I can say it's possible. >> Well, are we going to be getting into infrastructure as code moves into "everything is code," security, data, (laughs) applications is code? I mean, "blank" is code, fill in the blank. (Lori laughing) >> Yeah, we're seeing more of that with things like CDK and Pulumi, where you are actually coding up using a real language rather than the death by YAML or whatever. How much YAML can you take? But actually having a real language so you're not trying to do things in parsing languages. So I think that's an interesting trend. You're getting some interesting templates, and I like what... I mean, the counterexample is that if you just go deep on one vendor, then maybe you can go faster and it is simpler. And one of my favorite vendor... Favorite customers right now that I've been talking to is Liberty Mutual. Went very deep and serverless first on AWS. They're just doing everything there, and they're using CDK Patterns to do it, and they're going extremely fast. There's a book coming out called "The Value Flywheel" by Dave Anderson, it's coming out in a few months, to just detail what they're doing, but that's the counterargument. If you could pick one vendor, you can go faster, you can get that vendor to do more for you, and maybe get a bigger discount so you're not splitting your discounts across vendors. So that's one aspect of it. But I think, fundamentally, you're going to find the CIOs and the ops people generally don't like sitting on one vendor. And if that single vendor is a horizontal platform that's trying to make all the clouds look the same, now you're locked into whatever that platform was. You've still got a platform there. There's still something. So I think that's always going to be something that the CIOs want, but the developers are always going to just pick whatever the best tool for building the thing is. And a analogy here is that the developers are dating and getting married, and then the operations people are running the family and getting divorced. And all the bad parts of that cycle are in the divorce end of it. You're trying to get out of a vendor, there's lawyers, it's just a big mess. >> Who's the lawyer in this example? (crosstalk) >> Well... (laughing) >> Great example. (crosstalk) >> That's why ops people don't like lock-in, because they're the ones trying to unlock. They aren't the ones doing the lock-in. They're the ones unlocking, when developers, if you separate the two, are the ones who are going, picking, having the fun part of it, going, trying a new thing. So they're chasing a shiny object, and then the ops people are trying to untangle themselves from the remains of that shiny object a few years later. So- >> Aren't we- >> One way of fixing that is to push it all together and make it more DevOps-y. >> Yeah, that's right. >> But that's trying to put all the responsibilities in one place, like more continuous improvement, but... >> Chris, what's your reaction to that? Because you're- >> No, that's exactly what I was going to bring up, yeah, John. And 'cause we keep saying "devs," "dev," and "ops" and I've heard somewhere you can glue those two things together. Heck, you could even include "sec" in the middle of it, and "DevSecOps." So what's interesting about what Adrian's saying though, too, is I think this has a lot to do with how you structure your engineering teams and how you think about development versus operations and security. So I'm building out a team now that very much makes use of, thanks to my brilliant VP of Engineering, a "Team Topologies" approach, which is a very streamlined and product oriented way of thinking about, for example, in engineering, if you think about team structures, you might have people that build the front end, build the middle tier, and the back end, and then you have a product that needs to make use of all three components in some form. So just from getting stuff done, their ability then has to tie to three different groups, versus building a team that's streamlined that ends up having front end, middleware, and backend folks that understand and share standards but are able to uncork the velocity that's required to do that. So if you think about that, and not just from an engineering development perspective, but then you couple in operations as a foundational layer that services them with embedded capabilities, we're putting engineers and operations teams embedded in those streamlined teams so that they can run at the velocity that they need to, they can do continuous integration, they can do continuous deployment. And then we added CS, which is continuously secure, continuous security. So instead of having giant, centralized teams, we're thinking there's a core team, for example, a foundational team, that services platform, makes sure all the trains are running on time, that we're doing what we need to do foundationally to make the environments fully dev and operator and security people functional. But then ultimately, we don't have these big, monolithic teams that get into turf wars. So, to Adrian's point about, the operators don't like to be paned in, well, they actually have a say, ultimately, in how they architect, deploy, manage, plan, build, and operate those systems. But at the same point in time, we're all looking at that problem across those teams and go... Like if one streamline team says, "I really want to go run on Azure, "because I like their services better," the reality is the foundational team has a larger vote versus opinion on whether or not, functionally, we can satisfy all of the requirements of the other team. Now, they may make a fantastic business case and we play rock, paper, scissors, and we do that. Right now, that hasn't really happened. We look at the balance of AWS, we are picking SaaS-y, supercloud vendors that will, by the way, happen to run on three platforms, if we so choose to expand there. So we have a similar interface, similar capability, similar processes, but we've made the choice at LastPass to go all in on AWS currently, with respect to how we deliver our products, for all the reasons we just talked about. But I do think that operations model and how you build your teams is extremely important. >> Yeah, and to that point- >> And has the- (crosstalk) >> The vendors themselves need optionality to the customer, what you're saying. So, "I'm going to go fast, "but I need to have that optionality." I guess the question I have for you guys is, what is today's trade-off? So if the decision point today is... First of all, I love the go-fast model on one cloud. I think that's my favorite when I look at all this, and then with the option, knowing that I'm going to have the option to go to multiple clouds. But everybody wants lock-in on the vendor side. Is that scale, is that data advantage? I mean, so the lock-in's a good question, and then also the trade-offs. What do people have to do today to go on a supercloud journey to have an ideal architecture and taxonomy, and what's the right trade-offs today? >> I think that the- Sorry, just put a comment and then let Lori get a word in, but there's a lot of... A lot of the market here is you're building a product, and that product is a SaaS product, and it needs to run somewhere. And the customers that you're going to... To get the full market, you need to go across multiple suppliers, most people doing AWS and Azure, and then with Google occasionally for some people. But that, I think, has become the pattern that most of the large SaaS platforms that you'd want to build out of, 'cause that's the fast way of getting something that's going to be stable at scale, it's got functionality, you'd have to go invest in building it and running it. Those platforms are just multi-cloud platforms, they're running across them. So Snowflake, for example, has to figure out how to make their stuff work on more than one cloud. I mean, they started on one, but they're going across clouds. And I think that that is just the way it's going to be, because you're not going to get a broad enough view into the market, because there isn't a single... AWS doesn't have 100% of the market. It's maybe a bit more than them, but Azure has got a pretty solid set of markets where it is strong, and it's market by market. So in some areas, different people in some places in the world, and different vertical markets, you'll find different preferences. And if you want to be across all of them with your data product, or whatever your SaaS product is, you're just going to have to figure this out. So in some sense, the supercloud story plays best with those SaaS providers like the Snowflakes of this world, I think. >> Lori? >> Yeah, I think the SaaS product... Identity, whatever, you're going to have specialized. SaaS, superclouds. We already see that emerging. Identity is becoming like this big SaaS play that crosses all clouds. It's not just for one. So you get an evolution going on where, yes, I mean, every vendor who provides some kind of specific functionality is going to have to build out and be multi-cloud, as it were. It's got to work equally across them. And the challenge, then, for them is to make it simple for both operators and, if required, dev. And maybe that's the other lesson moving forward. You can build something that is heaven for ops, but if the developers won't use it, well, then you're not going to get it adopted. But if you make it heaven for the developers, the ops team may not be able to keep it secure, keep everything. So maybe we have to start focusing on both, make it friendly for both, at least. Maybe it won't be the perfect experience, but gee, at least make it usable for both sides of the equation so that everyone can actually work in concert, like Chris was saying. A more comprehensive, cohesive approach to delivery and deployment. >> All right, well, wrapping up here, I want to just get one final comment from you guys, if you don't mind. What does supercloud look like in five years? What's the Nirvana, what's the steady state of supercloud in five to 10 years? Or say 10 years, make it easier. (crosstalk) Five to 10 years. Chris, we'll start with you. >> Wow. >> Supercloud, what's it look like? >> Geez. A magic pane, a single pane of glass. (laughs) >> Yeah, I think- >> Single glass of pain. >> Yeah, a single glass of pain. Thank you. You stole my line. Well, not mine, but that's the one I was going to use. Yeah, I think what is really fascinating is ultimately, to answer that question, I would reflect on market consolidation and market dynamics that happens even in the SaaS space. So we will see SaaS companies combining in focal areas to be able to leverage the positions, let's say, in the identity space that somebody has built to provide a set of compelling services that help abstract that identity problem or that security problem or that instrumentation and observability problem. So take your favorite vendors today. I think what we'll end up seeing is more consolidation in SaaS offerings that run on top of infrastructure of the service offerings to where a supercloud might look like something I described before. You have the combination of your favorite interoperable identity, observability, security, orchestration platforms run across them. They're sold as a stack, whether it be co-branded by an enterprise vendor that sells all of that and manages it for you or not. But I do think that... You talked about, I think you said, "Is this an innovator's dilemma?" No, I think it's an integrator's dilemma, as it has always ultimately been. As soon as you get from Genesis to Bespoke Build to product to then commoditization, the cycle starts anew. And I think we've gotten past commoditization, and we're looking at niche areas. So I see just the evolution, not necessarily a revolution, of what we're dealing with today as we see more consolidation in the marketplace. >> Lori, what's your take? Five years, 10 years, what does supercloud look like? >> Part of me wants to take the pie in the sky unicorn approach. "No, it will be beautiful. "One button, and things will happen," but I've seen this cycle many times before, and that's not going to happen. And I think Chris has got it pretty close to what I see already evolving. Those different kinds of super services, basically. And that's really what we're talking about. We call them SaaS, but they're... X is a service. Everything is a service, and it's really a supercloud that can run anywhere, but it presents a different interface, because, well, it's easier. And I think that's where we're going to go, and that's just going to get more refined. And yes, a lot of consolidation, especially on the observability side, but that's also starting to consume the security side, which is really interesting to watch. So that could be a little different supercloud coming on there that's really focused on specific types of security, at least, that we'll layer across, and then we'll just hook them all together. It's an API first world, and it seems like that's going to be our standard for the next while of how we integrate everything. So superclouds or APIs. >> Awesome. Adrian... Adrian, take us home. >> Yeah, sure. >> What's your- I think, and just picking up on Lori's point that these are web services, meaning that you can just call them from anywhere, they don't have to run everything in one place, they can stitch it together, and that's really meant... It's somewhat composable. So in practice, people are going to be composable. Can they compose their applications on multiple platforms? But I think the interesting thing here is what the vendors do, and what I'm seeing is vendors running software on other vendors. So you have Google building platforms that, then, they will support on AWS and Azure and vice versa. You've got AWS's distro of Kubernetes, which they now give you as a distro so you can run it on another platform. So I think that trend's going to continue, and it's going to be, possibly, you pick, say, an AWS or a Google software stack, but you don't run it all on AWS, you run it in multiple places. Yeah, and then the other thing is the third tier, second, third tier vendors, like, I mean, what's IBM doing? I think in five years time, IBM is going to be a SaaS vendor running on the other clouds. I mean, they're already halfway there. To be a bit more controversial, I guess it's always fun to... Like I don't work for a corporate entity now. No one tells me what I can say. >> Bring it on. >> How long can Google keep losing a billion dollars a quarter? They've either got to figure out how to make money out of this thing, or they'll end up basically being a software stack on another cloud platform as their, likely, actual way they can make money on it. Because you've got to... And maybe Oracle, is that a viable cloud platform that... You've got to get to some level of viability. And I think the second, third tier of vendors in five, 10 years are going to be running on the primary platform. And I think, just the other final thing that's really driving this right now. If you try and place an order right now for a piece of equipment for your data center, key pieces of equipment are a year out. It's like trying to buy a new fridge from like Sub-Zero or something like that. And it's like, it's a year. You got to wait for these things. Any high quality piece of equipment. So you go to deploy in your data center, and it's like, "I can't get stuff in my data center. "Like, the key pieces I need, I can't deploy a whole system. "We didn't get bits and pieces of it." So people are going to be cobbling together, or they're going, "No, this is going to cloud, because the cloud vendors "have a much stronger supply chain to just be able "to give you the system you need. "They've got the capacity." So I think we're going to see some pandemic and supply chain induced forced cloud migrations, just because you can't build stuff anymore outside the- >> We got to accelerate supercloud, 'cause they have the supply. They are the chain. >> That's super smart. That's the benefit of going last. So I'm going to scoop in real quick. I can't believe we can call this "Web3 Supercloud," because none of us said "Web3." Don't forget DAO. (crosstalk) (indistinct) You have blockchain, blockchain superclouds. I mean, there's some very interesting distributed computing stuff there, but we'll have to do- >> (crosstalk) We're going to call that the "Cubeverse." The "Cubeverse" is coming. >> Oh, the "Cubeverse." All right. >> We will be... >> That's very meta. >> In the metaverse, Cubeverse soon. >> "Stupor cloud," perhaps. But anyway, great points, Adrian and Lori. Loved it. >> Chris, great to see you. Adrian, Lori, thanks for coming on. We've known each other for a long time. You guys are part of the cloud-erati, the group that has been in there from day one, and watched it evolve, and you get the scar tissue to prove it, and the experience. So thank you so much for sharing your commentary. We'll roll this up and make it open to everybody as additional content. We'll call this the "outtakes," the longer version. But really appreciate your time, thank you. >> Thank you. >> Thanks so much. >> Okay, we'll be back with more "Supercloud 22" right after this. (bright upbeat music)

>>Thanks. Hey, everyone, welcome back to the cubes. Continuing coverage of snowflakes. Summit 22 live from Caesars Forum in Las Vegas. Lisa Martin here. I have three guests here with me. We're gonna be talking about Snowflake Ventures and the snowflakes start up Challenge. That's in its second year. I've got Harry Glaser with me. Co founder and CEO of Model Bit Start Up Challenge finalist Damon Bryan joins us as well. The CTO and co founder of Hyper Affinity. Also a startup Challenge Finalists. And Stephane Williams to my left here, VP of Corporate development and snowflake Ventures. Guys, great to have you all on this little mini panel this morning. >>Thank you. >>Thank you. >>Let's go ahead, Harry, and we'll start with you. Talk to the audience about model. But what do you guys do? And then we'll kind of unpack the snowflake. The Snowflakes challenge >>Model bit is the easiest way for data scientists to deploy machine learning models directly into Snowflake. We make use of the latest snowflake functionality called Snow Park for python that allows those models to run adjacent to the data so that machine learning models can be much more efficient and much more powerful than they were before. >>Awesome. Damon. Give us an overview of hyper affinity. >>Yes, so hyper affinity were Decision Intelligence platform. So we helped. Specifically retailers and brands make intelligent decisions through the use of their own customer, data their product data and put data science in a I into the heart of the decision makers across their business. >>Nice Step seven. Tell us about the startup challenge. We talked a little bit about it yesterday with CMO Denise Pearson, but I know it's in its second year. Give us the idea of the impetus for it, what it's all about and what these companies embody. >>Yeah, so we This is the second year that we've done it. Um, we it was really out of, um Well, it starts with snowflake Ventures when we started to invest in companies, and we quickly realised that there's there's a massive opportunity for companies to be building on top of the Lego blocks, uh, of snowflake. And so, um, open up the competition. Last year it was the inaugural competition overlay analytics one, Um, and since then, you've seen a number of different functionalities and features as part of snowflakes snow part. Being one of them native applications is a really exciting one going forward. Um, the companies can really use to accelerate their ability to kind of deliver best in class applications using best in class technology to deliver real customer outcomes and value. Um, so we've we've seen tremendous traction across the globe, 250 applicants across 50. I think 70 countries was mentioned today, so truly global in nature. And it's really exciting to see how some of the start ups are taking snowflake to to to new and interesting use cases and new personas and new industries. >>So you had 200 over 250 software companies applied for this. How did you did you narrow it down to three? >>We did. Yeah, >>you do that. >>So, behind the scenes, we had a sub judging panel, the ones you didn't see up on stage, which I was luckily part of. We had kind of very distinct evaluation criteria that we were evaluating every company across. Um and we kind of took in tranches, right? We we took the first big garden, and we kind of try to get that down to a top 50 and top 50. Then we really went into the details and we kind of across, um, myself in ventures with some of my venture partners. Um, some of the market teams, some of the product and engineering team, all kind of came together and evaluated all of these different companies to get to the top 10, which was our semifinalists and then the semi finalists, or had a chance to present in front of the group. So we get. We got to meet over Zoom along the way where they did a pitch, a five minute pitch followed by a Q and A in a similar former, I guess, to what we just went through the startup challenge live, um, to get to the top three. And then here we are today, just coming out of the competition with with With folks here on the table. >>Wow, Harry talked to us about How did you just still down what model bit is doing into five minutes over Zoom and then five minutes this morning in person? >>I think it was really fun to have that pressure test where, you know, we've only been doing this for a short time. In fact model. It's only been a company for four or five months now, and to have this process where we pitch and pitch again and pitch again and pitch again really helped us nail the one sentence value proposition, which we hadn't done previously. So in that way, very grateful to step on in the team for giving us that opportunity. >>That helps tremendously. I can imagine being a 4 to 5 months young start up and really trying to figure out I've worked with those young start ups before. Messaging is challenging the narrative. Who are we? What do we do? How are we changing or chasing the market? What are our customers saying we are? That's challenging. So this was a good opportunity for you, Damon. Would you say the same as well for hyper affinity? >>Yeah, definitely conquer. It's really helped us to shape our our value proposition early and how we speak about that. It's quite complicated stuff, data science when you're trying to get across what you do, especially in retail, that we work in. So part of what our platform does is to help them make sense of data science and Ai and implement that into commercial decisions. So you have to be really kind of snappy with how you position things. And it's really helped us to do that. We're a little bit further down the line than than these guys we've been going for three years. So we've had the benefit of working with a lot of retailers to this point to actually identify what their problems are and shape our product and our proposition towards. >>Are you primarily working with the retail industry? >>Yes, Retail and CPG? Our primary use case. We have seen any kind of consumer related industries. >>Got it. Massive changes right in retail and CPG the last couple of years, the rise of consumer expectations. It's not going to go back down, right? We're impatient. We want brands to know who we are. I want you to deliver relevant content to me that if I if I bought a tent, go back on your website, don't show me more tense. Show me things that go with that. We have this expectation. You >>just explain the whole business. But >>it's so challenging because the brothers brands have to respond to that. How do you what is the value for retailers working with hyper affinity and snowflake together. What's that powerhouse? >>Yeah, exactly. So you're exactly right. The retail landscape is changing massively. There's inflation everywhere. The pandemic really impacted what consumers really value out of shopping with retailers. And those decisions are even harder for retailers to make. So that's kind of what our platform does. It helps them to make those decisions quickly, get the power of data science or democratise it into the hands of those decision makers. Um, so our platform helps to do that. And Snowflake really underpins that. You know, the scalability of snowflake means that we can scale the data and the capability that platform in tangent with that and snowflake have been innovating a lot of things like Snow Park and then the new announcements, announcements, uni store and a native APP framework really helping us to make developments to our product as quick as snowflakes are doing it. So it's really beneficial. >>You get kind of that tailwind from snowflakes acceleration. It sounds like >>exactly that. Yeah. So as soon as we hear about new things were like, Can we use it? You know, and Snow Park in particular was music to our ears, and we actually part of private preview for that. So we've been using that while and again some of the new developments will be. I'm on the phone to my guys saying, Can we use this? Get it, get it implemented pretty quickly. So yeah, >>fantastic. Sounds like a great aligned partnership there, Harry. Talk to us a little bit about model bit and how it's enabling customers. Maybe you've got a favourite customer example at model bit plus snowflake, the power that delivers to the end user customer? >>Absolutely. I mean, as I said, it allows you to deploy the M L model directly into snowflake. But sometimes you need to use the exact same machine learning model in multiple endpoints simultaneously. For example, one of our customers uses model bit to train and deploy a lead scoring model. So you know when somebody comes into your website and they fill out the form like they want to talk to a sales person, is this gonna be a really good customer? Do we think or maybe not so great? Maybe they won't pay quite as much, and that lead scoring model actually runs on the website using model bit so that you can deploy display a custom experience to that customer we know right away. If this is an A, B, C or D lead, and therefore do we show them a salesperson contact form? Do we just put them in the marketing funnel? Based on that lead score simultaneously, the business needs to know in the back office the score of the lead so that they can do things like routed to the appropriate salesperson or update their sales forecasts for the end of the quarter. That same model also runs in the in the snowflake warehouse so that those back office systems can be powered directly off of snowflake. The fact that they're able to train and deploy one model into two production environment simultaneously and manage all that is something they can only do with bottled it. >>Lead scoring has been traditionally challenging for businesses in every industry, but it's so incredibly important, especially as consumers get pickier and pickier with. I don't want I don't want to be measured. I want to opt out. What sounds like what model but is enabling is especially alignment between sales and marketing within companies, which is That's also a big challenge at many companies face for >>us. It starts with the data scientist, right? The fact that sales and marketing may not be aligned might be an issue with the source of truth. And do we have a source of truth at this company? And so the idea that we can empower these data scientists who are creating this value in the company by giving them best in class tools and resources That's our dream. That's our mission. >>Talk to me a little bit, Harry. You said you're only 4 to 5 months old. What were the gaps in the market that you and your co founders saw and said, Guys, we've got to solve this. And Snowflake is the right partner to help us do it. >>Absolutely. We This is actually our second start up, and we started previously a data Analytics company that was somewhat successful, and it got caught up in this big wave of migration of cloud tools. So all of data tools moved and are moving from on premise tools to cloud based tools. This is really a migration. That snowflake catalyst Snowflake, of course, is the ultimate in cloud based data platforms, moving customers from on premise data warehouses to modern cloud based data clouds that dragged and pulled the rest of the industry along with it. Data Science is one of the last pieces of the data industry that really hasn't moved to the cloud yet. We were almost surprised when we got done with our last start up. We were thinking about what to do next. The data scientists were still using Jupiter notebooks locally on their laptops, and we thought, This is a big market opportunity and we're We're almost surprised it hasn't been captured yet, and we're going to get in there. >>The other thing. I think it's really interesting on your business that we haven't talked about is just the the flow of data, right? So that the data scientist is usually taking data out of a of a of a day like something like Smoke like a data platform and the security kind of breaks down because then it's one. It's two, it's three, it's five, it's 20. Its, you know, big companies just gets really big. And so I think the really interesting thing with what you guys are doing is enabling the data to stay where it's at, not copping out keeping that security, that that highly governed environment that big companies want but allowing the data science community to really unlock that value from the data, which is really, really >>cool. Wonderful for small startups like Model Bit. Because you talk to a big company, you want them to become a customer. You want them to use your data science technology. They want to see your fed ramp certification. They want to talk to your C. So we're two guys in Silicon Valley with a dream. But if we can tell them the data is staying in snowflake and you have that conversation with Snowflake all the time and you trust them were just built on top. That is an easy and very smooth way to have that conversation with the customer. >>Would you both say that there's credibility like you got street cred, especially being so so early in this stage? Harry, with the partnership with With Snowflake Damon, we'll start with you. >>Yeah, absolutely. We've been using Snowflake from day one. We leave from when we started our company, and it was a little bit of an unknown, I guess maybe 23 years ago, especially in retail. A lot of retailers using all the legacy kind of enterprise software, are really starting to adopt the cloud now with what they're doing and obviously snowflake really innovating in that area. So what we're finding is we use Snowflake to host our platform and our infrastructure. We're finding a lot of retailers doing that as well, which makes it great for when they wanted to use products like ours because of the whole data share thing. It just becomes really easy. And it really simplifies it'll and data transformation and data sharing. >>Stephane, talk about the startup challenge, the innovation that you guys have seen, and only the second year I can. I can just hear it from the two of you. And I know that the winner is back in India, but tremendous amount of of potential, like to me the last 2.5 days, the flywheel that is snowflake is getting faster and faster and more and more powerful. What are some of the things that excite you about working on the start up challenge and some of the vision going forward that it's driving. >>I think the incredible thing about Snowflake is that we really focus as a company on the data infrastructure and and we're hyper focused on enabling and incubating and encouraging partners to kind of stand on top of a best of breed platform, um, unlocked value across the different, either personas within I T organisations or industries like hypothermia is doing. And so it's it's it's really incredible to see kind of domain knowledge and subject matter expertise, able to kind of plug into best of breed underlying data infrastructure and really divide, drive, drive real meaningful outcomes for for for our customers in the community. Um, it's just been incredible to see. I mean, we just saw three today. Um, there was 250 incredible applications that past the initial. Like, do they check all the boxes and then actually, wow, they just take you to these completely different areas. You never thought that the technology would go and solve. And yet here we are talking about, you know, really interesting use cases that have partners are taking us to two >>150. Did that surprise you? And what was it last year. >>I think it was actually close to close to 2 to 40 to 50 as well, and I think it was above to 50 this year. I think that's the number that is in my head from last year, but I think it's actually above that. But the momentum is, Yeah, it's there and and again, we're gonna be back next year with the full competition, too. So >>awesome. Harry, what is what are some of the things that are next for model bed as it progresses through its early stages? >>You know, one thing I've learned and I think probably everyone at this table has internalised this lesson. Product market fit really is everything for a start up. And so for us, it's We're fortunate to have a set of early design partners who will become our customers, who we work with every day to build features, get their feedback, make sure they love the product, and the most exciting thing that happened to me here this week was one of our early design partner. Customers wanted us to completely rethink how we integrate with gets so that they can use their CI CD workflows their continuous integration that they have in their own get platform, which is advanced. They've built it over many years, and so can they back, all of model, but with their get. And it was it was one of those conversations. I know this is getting a little bit in the weeds, but it was one of those conversations that, as a founder, makes your head explode. If we can have a critical mass of those conversations and get to that product market fit, then the flywheel starts. Then the investment money comes. Then you're hiring a big team and you're off to the races. >>Awesome. Sounds like there's a lot of potential and momentum there. Damon. Last question for you is what's next for hyper affinity. Obviously you've got we talked about the street cred. >>Yeah, what's >>next for the business? >>Well, so yeah, we we've got a lot of exciting times coming up, so we're about to really fully launch our products. So we've been trading for three years with consultancy in retail analytics and data science and actually using our product before it was fully ready to launch. So we have the kind of main launch of our product and we actually starting to onboard some clients now as we speak. Um, I think the climate with regards to trying to find data, science, resources, you know, a problem across the globe. So it really helps companies like ours that allow, you know, allow retailers or whoever is to democratise the use of data science. And perhaps, you know, really help them in this current climate where they're struggling to get world class resource to enable them to do that >>right so critical stuff and take us home with your overall summary of snowflake summit. Fourth annual, nearly 10,000 people here. Huge increase from the last time we were all in person. What's your bumper sticker takeaway from Summit 22 the Startup Challenge? >>Uh, that's a big closing statement for me. It's been just the energy. It's been incredible energy, incredible excitement. I feel the the products that have been unveiled just unlock a tonne, more value and a tonne, more interesting things for companies like the model bit I profanity and all the other startups here. And to go and think about so there's there's just this incredible energy, incredible excitement, both internally, our product and engineering teams, the partners that we have spoke. I've spoken here with the event, the portfolio companies that we've invested in. And so there's there's there's just this. Yeah, incredible momentum and excitement around what we're able to do with data in today's world, powered by underlying platform, like snowflakes. >>Right? And we've heard that energy, I think, through l 30 plus guests we've had on the show since Tuesday and certainly from the two of you as well. Congratulations on being finalist. We wish you the best of luck. You have to come back next year and talk about some of the great things. More great >>things hopefully will be exhibited next year. >>Yeah, that's a good thing to look for. Guys really appreciate your time and your insights. Congratulations on another successful start up challenge. >>Thank you so much >>for Harry, Damon and Stefan. I'm Lisa Martin. You're watching the cubes. Continuing coverage of snowflakes. Summit 22 live from Vegas. Stick around. We'll be right back with a volonte and our final guest of the day. Mhm, mhm

(Upbeat music) >> Hey everyone. Welcome back to theCUBE's continuing coverage day two Snowflake Summit '22 live from Caesar's forum in Las Vegas. Lisa Martin here with Dave Vellante, bringing you wall to wall coverage yesterday, today, and tomorrow. We're excited to welcome Matthew Carroll to the program. The CEO of Immuta, we're going to be talking about removing barriers to secure data access security. Matthew, welcome. >> Thank you for having me, appreciate it. >> Talk to the audience a little bit about Immuta you're a Snowflake premier technology partner, but give him an overview of Immuta what you guys do, your vision, all that good stuff. >> Yeah, absolutely, thanks. Yeah, if you think about what Immunta at it's core is, we're a data security platform for the modern data stack, right? So what does that mean? It means that we embed natively into a Snowflake and we enforce policies on data, right? So, the rules to be able to use it, to accelerate data access, right? So, that means connecting to the data very easily controlling it with any regulatory or security policy on it as well as contractual policies, and then being able to audit it. So, that way, any corporation of any size can leverage their data and share that data without risking leaking it or potentially violating a regulation. >> What are some of the key as we look at industry by industry challenges that Immuta is helping those customers address and obviously quickly since everything is accelerating. >> Yeah. And it's, you're seeing it 'cause the big guys like Snowflake are verticalizing, right? You're seeing a lot of industry specific, you know, concepts. With us, if you think of, like, where we live obviously policies on data regulated, right? So healthcare, how do we automate HIPAA compliance? How do we redesign clinical trial management post COVID, right? If you're going to have billions of users and you're collecting that data, pharmaceutical companies can't wait to collect that data. They need to remove those barriers. So, they need to be able to collect it, secure it, and be able to share it. Right? So, double and triple blinded studies being redesigned in the cloud. Government organizations, how do we share security information globally with different countries instantaneously? Right? So these are some of the examples where we're helping organizations transform and be able to kind of accelerate their adoption of data. >> Matt, I don't know if you remember, I mean, I know you remember coming to our office. But we had an interesting conversation and I was telling Lisa. Years ago I wrote a piece of you know, how to build on top of, AWS. You know, there's so much opportunity. And we had a conversation, at our office, theCUBE studios in Marlborough, Massachusetts. And we both, sort of, agreed that there was this new workload emerging. We said, okay, there's AWS, there's Snowflake at the time, we were thinking, and you bring machine learning, at time where we were using data bricks, >> Yeah. >> As the example, of course now it's been a little bit- >> Yeah. Careful. >> More of a battle, right, with those guys. But, and so, you see them going in their different directions, but the premise stands is that there's an ecosystem developing, new workloads developing, on top of the hyper scale infrastructure. And you guys play a part in that. So, describe what you're seeing there 'cause you were right on in that conversation. >> Yeah. Yeah. >> It's nice to be, right. >> Yeah. So when you think of this design pattern, right, is you have a data lake, you have a warehouse, and you have an exchange, right? And this architecture is what you're seeing around you now, is this is every single organization in the world is adopting this design pattern. The challenge that where we fit into kind of a sliver of this is, the way we used to do before is application design, right? And we would build lots of applications, and we would build all of our business logic to enforce security controls and policies inside each app. And you'd go through security and get it approved. In this paradigm, any user could potentially access any data. There's just too many data sources, too many users, and too many things that can go wrong. And to scale that is really hard. So, like, with Immuta, what we've done, versus what everyone else has done is we natively embedded into every single one of those compute partners. So ,Snowflake, data breaks, big query, Redshift, synapse on and on. Natively underneath the covers, so that was BI tools, those data science tools hit Snowflake. They don't have to rewrite any of their code, but we automatically enforce policy without them having to do anything. And then we consistently audit that. I call that the separation of policy from platform. So, just like in the world in big data, when we had to separate compute from storage, in this world, because we're global, right? So we're, we have a distributed workforce and our data needs to abide by all these new security rules and regulations. We provide a flexible framework for them to be able to operate at that scale. And we're the only ones in the world doing it. >> Dave Vellante: See the key there is, I mean, Snowflake is obviously building out its data cloud and the functions that it's building in are quite impressive. >> Yeah. >> Dave Vellante: But you know at some point a customer's going to say, look I have other stuff, whether it's in an Oracle database, or data lake or wherever, and that should just be a node on this global, whatever you want to call it, mesh or fabric. And then if I'm hearing you right, you participate in all of that. >> Correct? Yeah We kind of, we were able to just natively inject into each, and then be able to enforce that policy consistently, right? So, hey, can you access HIPAA data? Who are you? Are you authorized to use this? What's the purpose you want to query this data? Is it for fraud? Is it for marketing? So, what we're trying to do as part of this new design paradigm is ensure that we can automate nearly the entire data access process, but with the confidence and de-risk it, that's kind of the key thing. But the one thing I will mention is I think we talk a lot about the core compute, but I think, especially at this summit, data sharing is everything. Right? And this concept of no copy data sharing, because the data is too big and there's too many sets to share, that's the keys to the kingdom. You got to get your lake and your warehouse set with good policy, so you can effectively share it. >> Yeah, so, I wanted to just to follow up, if I may. So, you'd mentioned separating compute from storage and a lot of VC money poured into that. A lot of VC money poured into cloud database. How do you see, do you see Snowflake differentiating substantially from all the other cloud databases? And how so? >> I think it's the ease of use, right? Apple produces a phone that isn't much different than other competitors. Right? But what they do is, end to end, they provide an experience that's very simple. Right? And so yes. Are there other warehouses? Are there other ways to, you know you heard about their analytic workloads now, you know through unistore, where they're going to be able to process analytical workloads as well as their ad hoc queries. I think other vendors are obviously going to have the same capabilities, but I think the user experience of Snowflake right now is top tier. Right? Is I can, whether I'm a small business, I can load my debt in there and build an app really quickly. Or if I'm a JP Morgan or, you know, a West Farmer's I can move legacy, you know monolithic architectures in there in months. I mean, these are six months transitions. When think about 20 years of work is now being transitioned to the cloud in six months. That's the difference. >> So measuring ease of views and time to value, time to market. >> Yeah. That's it's everything is time to value. No one wants to manage the infrastructure. In the Hudup world, no one wants to have expensive customized engineers that are, you know, keeping up your Hudup infrastructure any longer. Those days are completely over. >> Can you share an example of a joint customer, where really the joint value proposition that Immuta and Snowflake bring, are delivering some pretty substantial outcomes? >> Yeah. I, what we're seeing is and we're obviously highly incentivized to get them in there because it's easier on us, right? Because we can leverage their row and com level security. We can leverage their features that they've built in to provide a better experience to our customers. And so when we talk about large banks, they're trying to move Terra data workloads into Snowflake. When we talk about clinical trial management, they're trying to get away from physical copies of data, and leverage the exchanges of mechanism, so you can manage data contracts, right? So like, you know, when we think of even like a company like Latch, right? Like Latch uses us to be able to oversee all of the consumer data they have. Without like a Snowflake, what ends up happening is they end up having to double down and invest on their own people building out all their own infrastructure. And they don't have the capital to invest in third party tools like us that keep them safe, prevent data leaks, allow them to do more and get more value out of their data, which is what they're good at. >> So TCO reduction I'm hearing. >> Matthew Carroll: Yes, exactly. >> Matt, where are you as a company, you've obviously made a lot of progress since we last talked. Maybe give us the update on you know, the headcount, and fundraising, and- >> Yeah, we're just at about 250 people, which scares me every day, but it's awesome. But yeah, we've just raised 100 million dollars- >> Lisa Martin: Saw that, congratulations. >> Series E, thank you, with night dragon leading it. And night dragon was very tactical as well. We are moving, we found that data governance, I think what you're seeing in the market now is the catalog players are really maturing, and they're starting to add a suite of features around governance, right? So quality control, observability, and just traditional asset management around their data. What we are finding is is that there's a new gap in this space, right? So if you think about legacy it's we had infrastructure security we had the four walls and we protect our four walls. Then we moved to network security. We said, oh, the adversary is inside zero trust. So, let's protect all of our endpoints, right? But now we're seeing is data is the security flaw data could be, anyone could potentially access it in this organization. So how do we protect data? And so what we have matured into is a data security company. What we have found is, there's this next generation of data security products that are missing. And it's this blend between authentication like an, an Okta or an AuthO and auth- I'm sorry, authorization. Like Immuta, where we're authorizing certain access. And we have to pair together, with the modern observability, like a data dog, to provide an a layer above this modern data stack, to protect the data to analyze the users, to look for threats. And so Immuta has transformed with this capital. And we brought Dave DeWalt onto our board because he's a cybersecurity expert, he gives us that understanding of what is it like to sell into this modern cyber environment. So now, we have this platform where we can discover data, analyze it, tag it, understand its risk, secure it to author and enforce policies. And then monitor, the key thing is monitoring. Who is using the data? Why are they using the data? What are the risks to that? In order to enforce the security. So, we are a data security platform now with this raise. >> Okay. That, well, that's a new, you know, vector for you guys. I always saw you as an adjacency, but you're saying smack dab in the heart >> Matthew Carroll: Yes. Yeah. We're jumping right in. What we've seen is there is a massive global gap. Data is no longer just in one country. So it is, how do we automate policy enforcement of regulatory oversight, like GDPR or CCPA, which I think got this whole category going. But then we quickly realized is, well we have data jurisdiction. So, where does that data have to live? Where can I send it to? Because from Europe to us, what's the export treaty? We don't have defined laws anymore. So we needed a flexible framework to handle that. And now what we're seeing is data leaks, upon data leaks, and you know, the Snowflakes and the other cloud compute vendors, the last thing they ever want is a data leak out of their ecosystem. So, the security aspects are now becoming more and more important. It's going to be an insider threat. It's someone that already has access to that and has the rights to it. That's going to be the risk. And there is no pattern for a data scientist. There's no zero trust model for data. So we have to create that. >> How are you, last question, how are you going to be using a 100 million raised in series E funding, which you mentioned, how are you going to be leveraging that investment to turn the volume up on data security? >> Well, and we still have also another 80 million still in the bank from our last raise, so 180 million now, and potentially more soon, we'll kind of throw that out there. But, the first thing is M and A I believe in a recessing market, we're going to see these platforms consolidate. Larger customer of ours are driving us to say, Hey, we need less tools. We need to make this easier. So we can go faster. They're, even in a recessing market, these customers are not going to go slower. They're moving in the cloud as fast as possible, but it needs to be easier, right? It's going back to the mid nineties kind of Lego blocks, right? Like the IBM, the SAP, the Informatica, right? So that's number one. Number two is investing globally. Customer success, engineering, support, 24 by seven support globally. Global infrastructure on cloud, moving to true SaaS everywhere in the world. That's where we're going. So sales, engineering, and customer success globally. And the third is, is doubling down on R and D. That monitor capability, we're going to be building software around. How do we monitor and understand risk of users, third parties. So how do you handle data contracts? How do you handle data use agreements? So those are three areas we're focused on. >> Dave Vellante: How are you scaling go to market at this point? I mean, I presume you are. >> Yeah, well, I think as we're leveraging these types of engagements, so like our partners are the big cloud compute vendors, right? Those data clouds. We're injecting as much as we can into them and helping them get more workloads onto their infrastructure because it benefits us. And then obviously we're working with GSIs and then RSIs to kind of help with this transformation, but we're all in, we're actually deprecating support of legacy connectors. And we're all in on cloud compute. >> How did the pivot to all in on security, how did it affect your product portfolio? I mean, is that more positioning or was there other product extensions that where you had to test product market fit? >> Yeah. This comes out of customer drive. So we've been holding customer advisory boards across Europe, Asia and U.S. And what we just saw was a pattern of some of these largest banks and pharmaceutical companies and insurance companies in the world was, hey we need to understand who is actually on our data. We have a better understanding of our data now, but we don't actually understand why they're using our data. Why are they running these types of queries? Is this machine, you know logic, that we're running on this now, we invested all this money in AI. What's the risk? They just don't know. And so, yeah, it's going to change our product portfolio. We modularized our platform to the street components over the past year, specifically now, so we can start building custom applications on top of it, for specific users like the CSO, like, you know, the legal department, and like third party regulators to come in, as well as as going back to data sharing, to build data use agreements between one or many entities, right? So an SMP global can expose their data to third parties and have one consistent digital contract, no more long memo that you have to read the contract, like, Immuta can automate those data contracts between one or many entities. >> Dave Vellante: And make it a checkbox item. >> It's just a checkbox, but then you can audit it all, right? >> The key thing is this, I always tell people, there's negligence and gross negligence. Negligence, you can go back and fix something, gross negligence you don't have anything to put into controls. Regulators want you to be at least negligent, grossly negligent. They get upset. (laughs) >> Matthew, it sounds like great stuff is going on at Immuta, lots of money in the bank. And it sounds like a very clear and strategic vision and direction. We thank you so much for joining us on theCUBE this morning. >> Thank you so much >> For our guest and Dave Vellante, I'm Lisa Martin, you're watching theCUBE's coverage of day two, Snowflake Summit '22, coming at ya live, from the show floor in Las Vegas. Be right back with our next guest. (Soft music)

(intro music) >> Good evening. Welcome back to theCUBE's coverage of day one of Snowflake Summit 22 live from Caesar's Forum in Las Vegas. Lisa Martin, here with Dave Vellante. Dave, we have had an action-packed day one. A lot of news coming out this morning. We've talked to Snowflake folks. We've talked to partners, we've talked to customers. A lot going on today. >> It's our light day. Tomorrow it even gets more intense. >> I know. I'm a little scared. (Dave Vellante laughing) We've got another partner of Snowflakes onboard with us here. Please welcome, let me get this, Sudhir Chaturvedi, President and Executive Board Member at LTI. How did I do? >> Yeah, very well, actually. (laughing) >> Dave Vellante: Outstanding. >> Welcome to the program. Tell us a little bit about you and then talk to the audience about LTI and what you're doing with Snowflake. >> Sure. So, LTI is a global technology consulting and services firm. We had (indistinct) out of India. We're part of a large conglomerate, which is over 80 years old. Our founders were two Danish engineers who came to India and were essentially stuck when World War II broke out, and they created a company that's lasted 80 years. So we are very proud of our heritage. We come from an engineering background and frankly what we do with Snowflake is really bring that engineering DNA to Snowflake. So we are, we've been a partner of Snowflake. We are an elite partner of Snowflake, and we work with them across all regions in the world, actually. 50 plus customers today. So, we have great partnership for today. >> And I have a note here. It says you're the GSI Delivery Platform Partner of the Year. Congratulations. What does that entail? What are the requirements to get that award? >> Yeah, I know we are very proud that we are the Delivery Platform Partner of the Year this year. We were the Innovation Partner of the Year, last year. So it shows the journey from innovation to execution in showing delivery. I think what it entails is that we've been recognized for leadership and excellence in executing Snowflake programs at scale, the migration programs and the implementation programs that we've done for customers across the globe. >> Take us back, how did you first find Snowflake? When did you decide to lean in as a company? >> Yeah, it's a great question actually. You know, in fact, so we went public as a company in 2016 and at that time, how do I put it politely? People weren't expecting that much of us. They thought we'll be one amongst many other companies. And we decided that we will vector the company on data, digital, and cloud, and we'll make bets on partners that are perhaps unknown at that time. So in late 2017, early 2018, we started partnering with Snowflake. And since then I must, you know, hand it to Snowflake. We have an phenomenal partnership with them. I just met Frank this morning. Chris Degnan is their Chief Revenue Officer, Colleen Kapase. All of these people have been tremendous in terms of how they work together with us across the world to bring what essentially is phenomenal technology to our clients. >> What was the allure back then? It was, you know, cloud data warehouse, simplified data warehouse, the technically splitting storage from compute, you know, infinite, blah, blah, blah. Was that the allure and saying or did you have a broader vision? >> No, I think what happened was clients were struggling with data because data and applications in our world were sort of very tightly intertwined and they weren't really leveraging data for making realtime decisions. So the moment we saw the promise of Snowflake that you can create true data on cloud, which on sort of all data on cloud, you know what Frank was talking about this morning, and it's available in real time and you can do a lot of things on it. We said, this is technology of the future. It truly is because it separated storage and compute. It did many things that were not possible before. So I think the thing is when you see promising technology as a GSI, you always wonder, should we wait for it to be proven before we jump in? >> Dave Vellante: Right. >> Or should we jump in right up front and help them prove the model? And we decided to take the first approach where we jumped in right up front. >> Dave Vellante: You bet. >> And I think that's helped us earlier. >> Jumped in head first, pandemic hits, they go public. >> Yes. >> Lots of stuff going on. Talk to us about how you're leveraging the power this flywheel that Snowflake has created that I think is just getting bigger and faster. >> Sudhir: Absolutely. >> How are you leveraging the power of the technology to really deliver business outcomes for clients? >> No, that's a great question. And the thing with our initial focus was to get people onto data on cloud and with Snowflake, but now it's really around driving business outcomes from there. So we have a suite called Fosfor which is a data to decisions product suite, which is Snowflake ready. We've also launched PolarSled too which is based on business outcomes. So what we've done is we've done is we've actually created about 155 NorthStars. So various industry sectors, what business outcome do you want to achieve? We call that a NorthStar. And then we say, how do you achieve it with Snowflake? You know, so what we are doing is we're saying let's achieve the business outcome that's going to drive more consumption, but essentially, you know, we live in a difficult world, a increasingly difficult world. So we want to help people take better database decisions. >> Well, what are some of the more interesting ways in which your clients are using Snowflake? >> Yeah, I think when I look at, for example, we have a client in the financial services sector who was struggling with, you know, they're one of the largest asset management and fund management companies in the world. They're a household name, everybody knows them. And they probably have an EFT or some sort of 401k with them. And what they were struggling with was to say, how do I actually get various sources of data together in a way that I can make better asset, you know, better fund management decisions because otherwise it was left to a lot of very traditional equity research reporting and fund managers taking their expertise. Here, the data from multiple sources being available, running some AIML routines on it, we're able to show them patterns in various asset classes, on options, on investments that they hadn't seen before. And now that they've jumped headlong into it, 15 of their units across the world are using it now. So I think the power of once you see data in action that it's sort of, it's almost like the superpower that smart people get. It's like, yeah, like you suddenly arm them with so much more than they had previously. And then they get so much better at what they're doing. And ultimately consumers like us benefit from that. So, you know, that's really where we want to go. >> What's LTIs like best sweet spot where, you go into a client and you know, wow, this is a perfect fit for what we do? >> Yeah. So I think I would say banking and insurance is 47% of our business. We really understand that business extremely well. The other aspect of that is because we come from a manufacturing heritage. We've had that as well. And media is something we've done more recently. So, you know we've got a media cloud along with Snowflake. So I would say these are the sectors that we are, so we've been very domain focused as a client, as a company. You know, domain first, technology, we'll work with whatever technology the domain needs but that's really been helpful to us all. And this is where that whole point of NorthStar and Fosfor comes back in, which is, today, I think without the data on cloud you would've never achieved the kind of outcomes that we are able to achieve with our clients today. >> How did you feel about the recent sales pivot that Snowflake has made in terms of retail, but also healthcare and life sciences? Talk to me about that and is that enabling your joint customers to really leverage? >> Yeah, no, I think it's very exciting. We are working with clients on that. They like the new model. They're looking forward to, I think what clients are now doing is they're putting data perhaps ahead of even in these times where people are looking at, you know, we are seeing seven or eight very difficult macroeconomic trends. People are wondering, clients are wondering, what's this going to mean for their business in the future? So they're looking at spends and saying, what do I prioritize? But what I find is that that data spend only goes up, you know? So, our own data practice has sort of grown fourfold in the last six years, you know? So it's been just an exponential growth for us. And essentially Snowflake is our largest bet in that space even over every other technology that's out there. So I think clients, when they see that combination of how Snowflake is changing and what we can bring to them, I think the model works well for them. >> You know, ecosystem is one of the areas that we always pay attention to. You can see, just look around,. I mean, you compare 2019 to where we are today. What's the importance of ecosystem to LTI and how do you see it evolving? >> That's a great question. So, you know, it's like, I think in About a Boy, you know, Hugh Grant says that no man is an island. You know, and I think the same thing applies for companies. Any company, no matter what size they are, if they think that they can do everything themselves and I think they're not going to be successful in the long run. We believe that the ecosystem of partnerships is what drives all the best outcomes for our clients and our clients expect that today. They want (indistinct) partners to work together. And the thing with an ecosystem is, you know no one person can dominate an ecosystem, you know? The customer has to be at the center of the ecosystem and then everybody in the ecosystem is actually saying how best do I service the customer? So I think if you have that kind of customer centricity and you understand that ecosystems, you know, on your own you'll never be as good as an ecosystem. I think you nailed it, but it requires, a partnering ethos and that's what we really like about Snowflake. Such a strong partnering ethos. I still, I keep telling people if I text or message Chris or Colleen, I'll get a response in within 15, 20 minutes. You know, that's invaluable when you're trying to do great things for your joint clients, you know, so. >> Sounds like there's a lot of synergies there around the customer obsession, customer centricity. >> Absolutely. I think responsiveness in today's world is key. You know, I think the first people to respond, even if it's to say, you know what, I hear you I'm going to get back to you. I think, you know, people love that about you. It's easy to say customer centric. It's difficult to actually practice it in real life. And we believe that, for us, responsiveness is the key. We'll respond no matter what time of day or night. And the other thing is we'll respond even with our partners, right? We are not going to respond on our own and then bring everybody else along. Even things like, I don't know this but I can refer you to a partner who can help you do this. That's also a response. >> That responsiveness is so critical, especially in this day and age where I think one of the things that was in short supply during COVID and one of the many things is patience and tolerance. >> Correct. >> Right? On us as consumers and our business lives. So being able to respond even just to say we're checking, don't know yet, that builds trust between organizations with customers. >> Well, yeah, absolutely. In fact, you know, even the first year of the pandemic we grew nine and a half percent, year and year. >> In India, we were the fastest growing company that year. And if anybody asked me why did you grow nine and half percent when the industry grew at -1%, you know, in that financial. I think it was the speed at which we responded between February and June to client requests. We responded even before, I know I was in calls till 12:30 in the night working with clients to say, okay how do we fix this? How do we change this? How do we stop doing something? How do we cut costs, whatever they needed. And what we did in the first three months actually helped us our first four months when the first wave of the pandemic really hit. Actually clients were like these guys were on our side when times are tough. Let's sort of bet on them. And the data business actually grew. And I keep saying this, you know, whenever a big macro trend hits when there's more uncertainty, people look to the data because your judgment and experience is no longer applicable. Nobody in the world had any experience or judgment that could be applied in COVID times, right? So you need to now look at the data and say, okay, is the data telling me something that I would never come to know based on my own experience? And I think, you know, this is what I call the real database decisions is no company in the world will say we don't do it. But I think today's world, we are seeing real time data decisions being taken. We see it in the supply chain all the time. We see it in how banks are processing interest rate rises, et cetera. It's the speed at which they're acting would not be possible without a data first kind of approach they've taken. >> Right. And it has to be real time these days. >> It has to be. >> Every organization. That's no longer a nice to have. >> No, you know, and data is getting out of date also so quickly. I mean, in today's world, with the war in Ukraine I think the first thing we realized was that almost every parameter on commodity, whether it was oil or steel or shipping or whatever, it changed so rapidly that the only way to predict, many of our clients were not able to to tell their customers when they would be able to deliver products and service or products, especially manufacturing clients because they just didn't know when they would get their materials and go get their parts, et cetera. And we used data to say, okay, let's at least establish a base on which, because clients get disappointed, more customers get disappointed when you don't meet a delivery date. So we wanted to say, let's make it more predictable, even in unpredictable times. So we were able to manage expectations. We were able to do that better. Without the data there was no way it would've happened. There was just no way. And frankly, for us, Snowflake is the reason. For us it's our biggest bet in the data space. And that's how most of the work that we are doing in supply chain, in fact, I'm just headed to a manufacturing event that our team has organized, which is with Snowflake on data on cloud for manufacturing clients. So we've been slightly behind the curve compared to some of the others, but now seeing the promise and saying, hey let's go for this. >> There's a tremendous amount of potential. We're only scratching the surface. We thank you so much >> Sudhir: Thank you. >> For joining David me on the program, talking about LTI, the power of what you're doing together with Snowflake. We'll let you get to that manufacturing event. I'm sure that they are looking forward to talking to you. >> Yeah, no. Thank you so much. It was lovely to speak to you. Thank you so much. >> Likewise. My pleasure. For our guest and Dave Vellante, this is Lisa Martin signing off from the show floor of Snowflake Summit 22. Day one coverage is complete. Dave and I look forward to seeing you bright and early tomorrow for a jam packed day two. Thanks so much for watching. Take good care. (outro music)

(upbeat music) >> Hello everyone, welcome to this CUBE conversation. I'm John Furrier, your host of theCUBE here in Palo Alto, California in our studios. Got a great guest here, Rachel Obstler, Vice President, Head of Product at heap.io or Heap is the company name, heap.io is URL. Rachel, thanks for coming on. >> Thanks for having me, John. Great to be here. >> So you guys are as a company is heavily backed with some big time VCs and funders. The momentum is pretty significant. You see the accolades in the industry. It's a hot market for anyone who can collect data easily and make sense of it relative to everything being measured, which is the Nirvana. You can measure everything, but then what do you do with it? So you're at the center of it. You're heading up product for heap. This is what you guys do. And there's a lot of solutions, so let's get into it. Describe the company. What's your mission and what you guys do? >> Yeah, so let me start maybe with how Heap was even started and where the idea came from. So Heap was started by Matin Movassate, someone who was working at Facebook. And this is important 'cause it gets right at the problem that we are trying to solve, which is that he was a product manager at Facebook and he was spending a lot of money on pizza. The reason why he was spending a lot of money on pizza is because he wanted to be able to measure what the users were doing in the product that he was responsible for, and he couldn't get the data. And in order to get the data, he would have to go beg his engineers to put in all sorts of tracking code to collect data. And every time he did so, he had to bribe him with pizza because it's no one's favorite work, number one, and then people want to build new things. They don't want to just constantly be adding tracking code. And then the other thing he found is that even when he did that then it took a couple weeks to get it done. And then he had to wait to collect the data to see what data is. It takes a while to build up the data, and he just thought there must be a better way. And so he founded, he with a couple other co-founders, and the idea was that we could automatically collect data all the time. So it didn't matter if you launched something new, you didn't have to do anything. The data would be automatically collected. And so Heap's mission is really to make it easy to create amazing digital experiences. And we do that by firstly, just making sure you have all the data of what your users are doing because you would think you want to create a new digital experience. You could just do that and it would be perfect the first time, but that's not how it works and users are not predictable. >> Yeah, remember back in the day, big data, Hadoop and that kind of fell flap, but the idea of a data lake started there. You saw the rise of Databricks, the Snowflakes. So this idea that you can collect is there. It's here now, state of the art. Now I see that market. Now the business model comes in. Okay, I can collect everything. How fast can I turn around the insights becomes the next question. So what is the business model of the company? What does the product do? Is it SaaS? Is it a as a package software? How do you guys deploy? How do your customers consume and pay for the service? >> Yeah, so we are a SaaS company and we sell largely to, it could be a product manager. It could be someone in marketing, but it's someone who is responsible for a digital service or a digital product. So they're responsible for making sure that that they're hitting whatever targets they have. It could be revenue, it could be just usage, getting more users adopted, making sure they stay in the product. So that's who we sell to. And so basically our model is just around sessions. So how many sessions do you have? How much data are you collecting? How much traffic do you have? And that's how we charge. I think you were getting at something else though that was really interesting, which is this proliferation of data and then how do you get to an insight. And so one of the things that we've done is first of all, okay, collecting all the data and making sure that you have everything that you need, but then you have a lot of data. So that is indeed an issue. And so we've also built on top of Heap a data science layer that will automatically surface interesting points. So for instance, let's say that you have a very common user flow. Maybe it's your checkout flow. Maybe it's a signup flow and you know exactly what the major milestones are. Like you first fill out a form, you sign up, like maybe you get to do the first thing in the trial. You configure it, you get some value. So we're collecting not only those major milestones, we're collecting every single thing that happens in between. And then we'll automatically surface when there is an important drop off point, for instance, between two milestones so that you know exactly where things are going wrong. >> So you have these indicators. So it's a data driven business. I can see that clearly. And the value proposition in the pitch to the customer is ease of use. Is it accelerated time to value for insights? Is it eliminating IT? Is it the 10X marketer? Or all of those things? What is the core contract with the customer, the brand promise? >> That's exactly. So it's the ability to get to insight. First of all, that you may never have found on your own, or that would take you a long time to keep trialing an error of collecting data until you found something interesting. So getting to that insight faster and being able to understand very quickly, how you can drive impact with your business. And the other thing that we've done recently that adds a lot to this is we recently joined forces with a company named Auryc so we just announced this on Monday. So now on top of having all the data and automatically surfacing points of interest, like this is where you're having drop off, this is where you have an opportunity, we now allow you to watch it. So not only just see it analytically, see it in the numbers, but immediately click a show me button, and then just watch examples of users getting stuck in that place. And it really gives you a much better or clearer context for exactly what's happening. And it gives you a much better way to come up with ideas as to how to fix it as one of those digital builders or digital owners. >> You know, kind of dating myself when I mention this movie "Contact" where Jodie foster finds that one little nugget that opens up so much more insight. This is what you're getting at where if you can find that one piece that you didn't see before and bring it in and open it up and bring in that new data, it could change the landscape and lens of the entire data. >> Yeah. I can give you an example. So we have a customer, Casper. Most are familiar with that they sell mattresses online. So they're really a digital innovator for selling something online that previously you had to like go into a store to do. And they have a whole checkout flow. And what they discovered was that users that at the very end of the flow chose same day delivery were much more likely to convert and ultimately buy a mattress. They would not necessarily have looked at this. They wouldn't necessarily have looked at or decided to track like delivery mechanism. Like that's just not the most front and center thing, but because he collects all the data, they could look at it and say, oh, people who are choosing this converted a much higher rate. And so then they thought, well, okay, this is happening at the very end of the process. Like they've already gone through choosing what they want and putting it in their card and then it's like the very last thing they do. What if we made the fact that you could get same day delivery obvious at the beginning of the whole funnel. And so they tried that and it improved their conversion rate considerably. And so these are the types of things that you wouldn't necessarily anticipate. >> I got to have a mattress to sleep on. I want it today. Come on. >> Yeah, exactly. Like there's a whole market of people who are like, oh no, I need a mattress right now. >> This is exactly the point. I think this is why I love this opportunity that you guys are in. Every company now is digitalizing their business, aka digital transformation. But now they're going to have applications, they're going to have cloud native developers, they're going to be building modern applications. And they have to think like an eCommerce company, but it's not about brick and mortars anymore. It's just digital. So this is the new normal. This is an imperative. This is a fact. And so a lot of them don't know what to do. So like, wait a minute, who do we call? This is like a new problem for the mainstream. >> Yeah, and think about it too. Actually e-commerce has been doing this for quite a while, but think about all the B2B companies and B2B SaaS, like all the things that today, you do online. And that they're really having to start thinking more like e-commerce companies and really think about how do we drive conversion, even if conversion isn't the same thing or doesn't mean the same thing, but it means like a successful retained user. It's still important to understand what their journey is and where you going to help them. >> Recently, the pandemic has pulled forward this digital gap that every company's seeing, especially the B2B, which is virtual events, which is just an indicator of the convergence of physical and online. But it brings up billions of signals and I know we have an event software that people do as well. But when you're measuring everything, someone's in a chat, someone hit a web page, I mean there are billions of signals that need to get stored, and this is what you guys do. So I want to ask you, you run the product team. What's under the covers? What's the secret sauce for you guys at Heap? Because you got to store everything. That's one challenge. That's one problem you got to solve. Then you got to make it fast because most of the databases can't actually roll up data fast enough. So you're waiting for the graph forever when some people say. What's under the covers? What's the secret sauce? >> Well, it's a couple different things. So one is we designed the system from the very beginning for that purpose. For the purpose of bringing in all those different signals and then being able to cut the data lots of different ways. And then also to be able to apply data science to it in real time to be able to surface these important points that you should be looking at. So a lot of it is just about designing the system for the very beginning for that purpose. It was also designed to be easy for everyone to use. So what was a really important principle for us is a democratization of data. So in the past, you have these central data teams. You still have them today. Central data teams that are responsible for doing complex analysis. Well, we want to bring as much of that functionality to the digital builders, the product managers, the marketers, the ones that are making decisions about how to drive impact for their digital products and make it super easy for them to find these insights without having to go through a central team that could again take weeks and months to get an answer back from. >> Well, that's what brings up a good point. I want to dig into, if you don't mind, Rachel, this data engineering challenge. There's not enough talent out there. When I call data engineer, I'm talking about like the specialist person. She could be a unique engineer, but not a data scientist. We're talking about like hardcore data engineering, pipelining, streaming data, hardcore. There's not many people that fit that bill. So how do you scale that? Is that what you guys help do? >> We can help with that. Because, again, like if you put the power in the hands of the product people or the marketers or the people that are making those decisions, they can do their own analysis. Then you can really offload some of those central teams and they can do some of the much more complex work, but they don't have to spend their time constantly serving maybe the easier questions to answer. You have data that's self-service for everyone. >> Okay, before I get into the quick customer side of it, quickly while I have you on the product side. What are some of your priorities? You look at the roadmap, probably got tons of people calling. I can only imagine the customer base is diverse in its feature requests. Everyone has the same need, but they all have different businesses. So they want a feature here. They want a feature there. What's the priorities? How do you prioritize? What are some of your priorities for how you're going to build out and keep continuing the momentum? >> Yeah, so I mentioned earlier that we just joined forces with a company name Auryc that has session replay capabilities, as well as voice of customer. So one of our priorities is that we've noticed in this market, there's a real, it's very broken up in a strange way. I shouldn't say it's strange. It's probably because this is the way markets form, startups start, and they pick a technology and they build on top of it. So as a result, the way the market has formed is that you have analytics tools like Heap, and they look at very quantitative data, collecting all sorts of data and doing all sorts of quantitative cuts on it. And then you have tools that do things like session replay. So I just want to record sessions and watch and see exactly what the user's doing and follow their path through one at a time. And so one is aggregating data and the other one is looking at individual user journeys, but they're solving similar jobs and they're used by the same people. So a product manager, for example, wants to find a point of friction, wants to find an opportunity in their product that is significant, that is happening to a lot of people, that if they make a change will drive impact like a large impact for the business. So they'll identify that using the quant, but then to figure out how to fix it, they need the qual. They need to be able to watch it and really understand where people are getting stuck. They know where, but what does that really look like? Like, let me visualize this. And so our priority is really to bring these things together to have one platform where someone can just, in seconds, find this point of opportunity and then really understand it with a show me button so that they can watch examples of it and be like, I see exactly what's happening here and I have ideas of how to fix this. >> Yeah, something's happening at that intersection. Let's put some cameras on. Let's get some eyes on that. Let's look at it. >> Exactly. >> Oh, hey, let's put something. Let's fix that. So it makes a lot of sense. Now, customer attraction has been strong. I know it's been a lot of press and accolades online with when you guys are getting review wise. I mean, I can see DevOps and app people just using this easily, like signing up and I can collect all the data and seeing value, so I get that. What are some of the customer value propositions that are coming out of that, that you can share? And for the folks watching that don't know Heap, what's their problem that they're facing that you can solve, and what pain are they in or what problem do they solve? So example of some success that's coming out of the platform, enablement, the disruptive enablement, and then what's the problem, what's the customer's pain point, and when they know to call you guys or sign up. >> Yeah, so there's a couple different ways to look at it. When I was talking about is really for the user. There's this individual person who owns an outcome and this is where the market is going that the product managers, the marketers, they're not just there to build new features, they're there to drive outcomes for the business. And so in order to drive these outcomes, they need to figure out what are the most impactful things to do? Where are the investments that they need to make? And so Heap really helps them narrow down on those high impact areas and then be able to understand quickly as I was mentioning how to fix them. So that's one way to look at it. Another use case is coming from the other side. So talking in about session replay, you may have a singular problem. You may have a single support ticket. You may have someone complaining about something and you want to really understand, not only what is the problem, like what were they experiencing that caused them to file this ticket, but is this a singular problem, or is this something that is happening to many different people? And therefore, like we should prioritize fixing it very quickly. And so that's the other use case is let's start, not with the group, like the biggest impact and go to like exactly some examples, let's start with the singular and figure out if that gives you a path to the group. But the other use case that I think is really interesting is if you think about it from a macro point of view or from a product leader or a marketing leader's point of view, they're not just trying to drive impact. They're trying to make it easy for their team to drive that impact. So they're thinking about how do they make their whole organization a lot more data driven or insights driven? How do they change the culture, the process, not just the tool, but all of those things together so that they can have a bigger business impact and enable their team to be able to do this on their own? >> You guys are like a data department for developers and product managers. >> Essentially, like we are the complete dataset and the easy analysis that really helps you figure out, where do I invest? How do I justify my investments? And how do I measure how well my investments are doing? >> And this is where the iteration comes in. This is the model everyone's doing. You see a problem, you keep iterating. Got to look at the data, get some insight and keep looking back and making that product, get that flywheel going. Rachel, great stuff. Coming out here, real quick question for you to end the segment. What's the culture like over at Heap? If people are interested in joining the company or working with you guys. Every company has their own kind of DNA. What's the Heap culture like? >> That's a great question. So Heap is definitely a unique company that I've worked at and in a really good way. We find it really important to be respectful to each other. So one of our values is respectful candor. So you may be familiar with radical candor. We've kind of softened it a bit and said, look, it's good to be truthful and have candor, but let's do it in a respectful way. We really find important that everyone has a growth mindset. So we're always thinking about how do we improve? How do we get better? How do we grow faster? How do we learn? And then the other thing that I'll mention, another one of our values that I love, we call it, "taste the soup". Some people use to call it dogfooding, but we are in Heap all the time. We call it Heap on Heap. We really want to experience what our customers experience and constantly use our product to also get better and make our product better. >> A little more salt on the sauce, keep the soup, taste it a little bit. Good stuff. Rachel, thanks for coming on. Great insights and congratulations on a great product opportunity. Again, as world goes digital transformation, developers, product, all people want to instrument everything to then start figuring out how to improve their offering. So really hot market and hot company. Thanks for coming on. >> Thanks, John. Thanks for having me. >> This is theCUBE conversation. I'm John Furrier here in Palo Alto, California. Thanks for watching. (gentle music)

>> From The Cube studios in Palo Alto, in Boston, bringing you data driven insights from the cube and ETR. This is breaking analysis with Dave Vellante. >> Last year, we noted in a breaking analysis that the cloud ecosystem is innovating beyond the idea or notion of multi-cloud. We've said for years that multi-cloud is really not a strategy but rather a symptom of multi-vendor. And we coined this term supercloud to describe an abstraction layer that lives above the hyperscale infrastructure that hides the underlying complexities, the APIs, and the primitives of each of the respective clouds. It interconnects whether it's On-Prem, AWS, Azure, Google, stretching out to the edge and creates a value layer on top of that. So our vision is that supercloud is more than running an individual service in cloud native mode within an individual individual cloud rather it's this new layer that builds on top of the hyperscalers. And does things irrespective of location adds value and we'll get into that in more detail. Now it turns out that we weren't the only ones thinking about this, not surprisingly, the majority of the technology ecosystem has been working towards this vision in various forms, including some examples that actually don't try to hide the underlying primitives. And we'll talk about that, but give a consistent experience across the DevSecOps tool chain. Hello, and welcome to this week's Wikibon, Cube insights powered by ETR. In this breaking analysis, we're going to share some recent examples and direct quotes about supercloud from the many Cube guests that we've had on over the last several weeks and months. And we've been trying to test this concept of supercloud. Is it technically feasible? Is it business rational? Is there business case for it? And we'll also share some recent ETR data to put this into context with some of the players that we think are going after this opportunity and where they are in their supercloud build out. And as you can see I'm not in the studio, everybody's got COVID so the studios shut down temporarily but breaking analysis continues. So here we go. Now, first thing is we uncovered an article from earlier this year by Lori MacVittie, is entitled, Supercloud: The 22 Answer to Multi-Cloud Challenges. What a great title. Of course we love it. Now, what really interested us here is not just the title, but the notion that it really doesn't matter what it's called, who cares? Supercloud, distributed cloud, someone even called it Metacloud recently, and we'll get into that. But Lori is a technologist. She's a developer by background. She works at F-Five and she's partial to the supercloud definition that was put forth by Cornell. You can see it here. That's a cloud architecture that enables application migration as a service across different availability zones or cloud providers, et cetera. And that the supercloud provides interfaces to allocate, migrate and terminate resources... And can span all major public cloud providers as well as private clouds. Now, of course, we would take that as well to the edge. So sure. That sounds about right and provides further confirmation that something new is really happening out there. And that was our initial premise when we put this fourth last year. Now we want to dig deeper and hear from the many Cube guests that we've interviewed recently probing about this topic. We're going to start with Chuck Whitten. He's Dell's new Co-COO and most likely part of the Dell succession plan, many years down the road hopefully. He coined the phrase multi-cloud by default versus multi-cloud by design. And he provides a really good business perspective. He's not a deep technologist. We're going to hear from Chuck a couple of times today including one where John Furrier asks him about leveraging hyperscale CapEx. That's an important concept that's fundamental to supercloud. Now, Ashesh Badani heads products at Red Hat and he talks about what he calls Metacloud. Again, it doesn't matter to us what you call it but it's the ecosystem gathering and innovating and we're going to get his perspective. Now we have a couple of clips from Danny Allan. He is the CTO of Veeam. He's a deep technologist and super into the weeds, which we love. And he talks about how Veeam abstracts the cloud layer. Again, a concept that's fundamental to supercloud and he describes what a supercloud is to him. And we also bring with Danny the edge discussion to the conversation. Now the bottom line from Danny is we want to know is supercloud technically feasible? And is it a thing? And then we have Jeff Clarke. Jeff Clark is the Co-COO and Vice Chairman of Dell super experienced individual. He lays out his vision of supercloud and what John Furrier calls a business operating system. You're going to hear from John a couple times. And he, Jeff Clark has a dropped the mic moment, where he says, if we can do this X, we'll describe what X is, it's game over. Okay. So of course we wanted to then go to HPE, one of Dell's biggest competitors and Patrick Osborne is the vice president of the storage business unit at Hewlett Packet Enterprise. And so given Jeff Clarke's game over strategy, we want to understand how HPE sees supercloud. And the bottom line, according to Patrick Osborne is that it's real. So you'll hear from him. And now Raghu Raghuram is the CEO of VMware. He threw a curve ball at this supercloud concept. And he flat out says, no, we don't want to hide the underlying primitives. We want to give developers access to those. We want to create a consistent developer experience in that DevsSecOps tool chain and Kubernetes runtime environments, and connect all the elements in the application development stack. So that's a really interesting perspective that Raghu brings. And then we end on Itzik Reich. Itzik is a technologist and a technical team leader who's worked as a go between customers and product developers for a number of years. And we asked Itzik, is supercloud technically feasible and will it be a reality? So let's hear from these experts and you can decide for yourselves how real supercloud is today and where it is, run the sizzle >> Operative phrase is multi-cloud by default that's kind of the buzz from your keynote. What do you mean by that? >> Well, look, customers have woken up with multiple clouds, multiple public clouds, On-Premise clouds increasingly as the edge becomes much more a reality for customers clouds at the edge. And so that's what we mean by multi-cloud by default. It's not yet been designed strategically. I think our argument yesterday was, it can be and it should be. It is a very logical place for architecture to land because ultimately customers want the innovation across all of the hyperscale public clouds. They will see workloads and use cases where they want to maintain an On-Premise cloud, On-Premise clouds are not going away, I mentioned edge clouds, so it should be strategic. It's just not today. It doesn't work particularly well today. So when we say multi-cloud by default we mean that's the state of the world today. Our goal is to bring multi-cloud by design as you heard. >> Really great question, actually, since you and I talked, Dave, I've been spending some time noodling just over that. And you're right. There's probably some terminology, something that will get developed either by us or in collaboration with the industry. Where we sort of almost have the next almost like a Metacloud that we're working our way towards. >> So we manage both the snapshots and we convert it into the Veeam portable data format. And here's where the supercloud comes into play. Because if I can convert it into the Veeam portable data format, I can move that OS anywhere. I can move it from physical to virtual, to cloud, to another cloud, back to virtual, I can put it back on physical if I want to. It actually abstracts the cloud layer. There are things that we do when we go between cloud some use BIOS, some use UEFI, but we have the data in backup format, not snapshot format, that's theirs, but we have it in backup format that we can move around and abstract workloads across all of the infrastructure. >> And your catalog is control in control of that. Is that right? Am I thinking about that the right way? >> Yeah it is, 100%. And you know what's interesting about our catalog, Dave, the catalog is inside the backup. Yes. So here's, what's interesting about the edge, two things, on the edge you don't want to have any state, if you can help it. And so containers help with that You can have stateless environments, some persistent data storage But we not not only provide the portability in operating systems, we also do this for containers. And that's true. If you go to the cloud and you're using say EKS with relational database services RDS for the persistent data later, we can pick that up and move it to GKE or move it to OpenShift On-Premises. And so that's why I call this the supercloud, we have all of this data. Actually, I think you termed the term supercloud. >> Yeah. But thank you for... I mean, I'm looking for a confirmation from a technologist that it's technically feasible. >> It is technically feasible and you can do it today. >> You said also technology and business models are tied together and enabler. If you believe that then you have to believe that it's a business operating system that they want. They want to leverage whatever they can. And at the end of the day, they have to differentiate what they do. >> Well, that's exactly right. If I take that in what Dave was saying and I summarize it the following way, if we can take these cloud assets and capabilities, combine them in an orchestrated way to deliver a distributed platform, game over. >> We have a number of platforms that are providing whether it's compute or networking or storage, running those workloads that they plum up into the cloud they have an operational experience in the cloud and they now they have data services that are running in the cloud for us in GreenLake. So it's a reality, we have a number of platforms that support that. We're going to have a a set of big announcements coming up at HPE Discover. So we led with Electra and we have a block service. We have VM backup as a service and DR on top of that. So that's something that we're providing today. GreenLake has over, I think it's actually over 60 services right now that we're providing in the GreenLake platform itself. Everything from security, single sign on, customer IDs, everything. So it's real. We have the proofpoint for it. >> Yeah. So I want to clarify something that you said because this tends to be very commonly confused by customers. I use the word abstraction. And usually when people think of abstraction, they think it hides capabilities of the cloud providers. That's not what we are trying to do. In fact, that's the last thing we are trying to do. What we are trying to do is to provide a consistent developer experience regardless of where you want to build your application. So that you can use the cloud provider services if that's what you want to use. But the DevSecOp tool chain, the runtime environment which turns out to be Kubernetes and how you control the Kubernetes environment, how do you manage and secure and connect all of these things. Those are the places where we are adding the value. And so really the VMware value proposition is you can build on the cloud of your choice but providing these consistent elements, number one, you can make better use of us, your scarce developer or operator resources and expertise. And number two, you can move faster. And number three, you can just spend less as a result of this. So that's really what we are trying to do. We are not... So I just wanted to clarify the word abstraction. In terms of where are we? We are still, I would say, in the early stages. So if you look at what customers are trying to do, they're trying to build these greenfield applications. And there is an entire ecosystem emerging around Kubernetes. There is still, Kubernetes is not a developer platform. The developer experience on top of Kubernetes is highly inconsistent. And so those are some of the areas where we are introducing new innovations with our Tanzu Application Platform. And then if you take enterprise applications, what does it take to have enterprise applications running all the time be entirely secure, et cetera. >> Well, look, the multi-cloud by default today are isolated clouds. They don't work together. Your data is siloed. It's locked up and it is expensive to move and make sense of it. So I think the word you and I were batting around before, this is an interconnected tissue. That's what the world needs. They need the clouds to work together as a single platform. That's the problem that we're trying to solve. And you saw it in some of our announcements here that we're starting to make steps on that journey to make multi-cloud work together much simpler. >> It's interesting, you mentioned the hyperscalers and all that CapEx investments. Why wouldn't you want to take advantage of a cloud and build on the CapEx and then ultimately have the solutions machine learning as one area. You see some specialization with the clouds. But you start to see the rise of superclouds, Dave calls them, and that's where you can innovate on a cloud then go to the multiple clouds. Snowflakes is one, we see a lot of examples of supercloud... >> Project Alpine was another one. I mean, it's early, but it's its clearly where you're going. The technology is just starting to come around. I mean it's real. >> Yeah. I mean, why wouldn't you want to take advantage of all of the cloud innovation out there? >> Is that something that's, that supercloud idea is a reality from a technologist perspective. >> I think it is. So for example Katie Gordon, which I believe you've interviewed earlier this week, was demonstrating the Kubernetes data mobility aspect which is another project. That's exactly part of the it's rationale, the rationale of customers being able to move some of their Kubernetes workloads to the cloud and back and between different clouds. Why are we doing? Because customers wants to have the ability to move between different cloud providers, using a common API that will be able to orchestrate all of those things with a self-service that may be offered via the APEX console itself. So it's all around enabling developers and meeting them where they are today and also meeting them into tomorrow's world where they actually may have changed their mind to do those things. So yes we are walking on all of those different aspects. >> Okay. Let's take a quick look at some of the ETR data. This is an X-Y graph. You've seen it a number of times on breaking analysis, it plots the net score or spending momentum on the Y-axis and overlap or pervasiveness in the ETR dataset on the X-axis, used to be called market share. I think that term was off putting to some people, but anyway it's an indicator of presence in the dataset. Now that red dotted line that's rarefied air where anything above that line is considered highly elevated. Now you can see we've plotted Azure and AWS in the upper right. GCP is in there and Kubernetes. We've done that as reference points. They're not necessarily building supercloud platforms. We'll see if they ever want to do so. And Kubernetes of course not a company, but we put 'em in there for context. And we've cherry picked a few players that we believe are building out or are important for supercloud build out. Let's start with Snowflake. We've talked a lot about this company. You can see they're highly elevated on the vertical axis. We see the data cloud as a supercloud in the making. You've got pure storage in there. They made the public, the early part of its supercloud journey at Accelerate 2019 when it unveiled a hybrid block storage service inside of AWS, it connects its On-Prem to AWS and creates that singular experience for pure customers. We see Hashi, HashiCorp as an enabling infrastructure, as code. So they're enabling infrastructure as code across different clouds and different locations. You see Nutanix. They're embarking on their multi-cloud strategy but it's doing so in a way that we think is supercloud, like now. Now Veeam, we were just at VeeamON. And this company has tied Dell for the number one revenue player in data protection. That's according to IDC. And we don't think it won't be long before it holds that position alone at the top as it's growing faster than in Dell in the space. We'll see, Dell is kind of waking up a little bit and putting more resource on that. But Veeam, they're a pure play vendor in data protection. And you heard their CTO, Danny Allan's view on Supercloud, they're doing it today. And we heard extensive comments as well from Dell that's clearly where they're headed, project Alpine was an early example from Dell technologies world of Supercloud in our view. And HPE with GreenLake. Finally beginning to talk about that cross cloud experience. I think it in initially HPE has been more focused on the private cloud, we'll continue to probe. We'll be at HPE discover later on the spring, actually end of June. And we'll continue to probe to see what HPE is doing specifically with GreenLake. Now, finally, Cisco, we put them on the chart. We don't have direct quotes from recent shows and events but this data really shows you the size of Cisco's footprint within the ETR data set that's on the X-axis. Now the cut of this ETR data includes all sectors across the ETR taxonomy which is not something that we commonly show but you can see the magnitude of Cisco's presence. It's impressive. Now, they had better, Cisco that is, had better be building out a supercloud in our view or they're going to be left behind. And I'm quite certain that they're actually going to do so. So we have a lot of evidence that we're putting forth here and seeing in the marketplace what we said last year, the ecosystem is take taking shape, supercloud is forming and becoming a thing. And really in our view, is the future of cloud. But there are always risks to these predictive scenarios and we want to acknowledge those. So first, look, we could end up with a bunch of bespoke superclouds. Now one supercloud is better than three separate cloud native services that do fundamentally the same thing from the same vendor. One for AWS, one for GCP and one for Azure. So maybe that's not all that bad. But to point number two, we hope there evolves a set of open standards for self-service infrastructure, federated governance, and data sharing that will evolve as a horizontal layer versus a set of proprietary vendor specific tools. Now, maybe a company like Veeam will provide that as a data management layer or some of Veeam's competitors or maybe it'll emerge again as open source. As well, and this next point, we see the potential for edge disruptions, changing the economics of the data center. Edge in fact could evolve on its own, independent of the cloud. In fact, David Floria sees the edge somewhat differently from Danny Allan. Floria says he sees a requirement for distributed stateful environments that are ephemeral where recovery is built in. And I said, David, stateful? Ephemeral? Stateful ephemeral? Isn't that an oxymoron? And he responded that, look, if it's not ephemeral the costs are going to be prohibitive. He said the biggest mistake the companies could make is thinking that the edge is simply an extension of their current cloud strategies. We're seeing that a lot. Dell largely talks about the edge as retail. Now, and Telco is a little bit different, but back to Floria's comments, he feels companies have to completely reimagine an integrated file and recovery system which is much more data efficient. And he believes that the technology will evolve with massive volumes and eventually seep into enterprise cloud and distributed data centers with better economics. In other words, as David Michelle recently wrote, we're about 15 years into the most recent cloud cycle and history shows that every 15 years or so, something new comes along that is a blind spot and highly disruptive to existing leaders. So number four here is really important. Remember, in 2007 before AWS introduced the modern cloud, IBM outpost, sorry, IBM outspent Amazon and Google and RND and CapEx and was really comparable to Microsoft. But instead of inventing cloud, IBM spent hundreds of billions of dollars on stock buybacks and dividends. And so our view is that innovation rewards leaders. And while it's not without risks, it's what powers the technology industry it always has and likely always will. So we'll be watching that very closely, how companies choose to spend their free cash flow. Okay. That's it for now. Thanks for watching this episode of The Cube Insights, powered by ETR. Thanks to Stephanie Chan who does some of the background research? Alex Morrison is on production and is going to compile all this stuff. Thank you, Alex. We're all remote this week. Kristen Nicole and Cheryl Knight do Cube distribution and social distribution and get the word out, so thank you. Robert Hof is our editor in chief. Don't forget the checkout etr.ai for all the survey action. Remember I publish each week on wikibon.com and siliconangle.com and you can check out all the breaking analysis podcasts. All you can do is search breaking analysis podcast so you can pop in the headphones and listen while you're on a walk. You can email me at david.vellante@siliconangle.com. If you want to get in touch or DM me at DVellante, you can always hit me up into a comment on our LinkedIn posts. This is Dave Vellante. Thank you for watching this episode of break analysis, stay safe, be well and we'll see you next time. (upbeat music)

(upbeat music) >> Welcome to this CUBE Power Panel where we're going to talk about application modernization, also success templates, and take a look at some new survey data to see how CIOs are thinking about digital transformation, as we get deeper into the post isolation economy. And with me are three familiar VIP guests to CUBE audiences. Tony Bear, the principal at DB InSight, Doug Henschen, VP and principal analyst at Constellation Research and Sanjeev Mohan principal at SanjMo. Guys, good to see you again, welcome back. >> Thank you. >> Glad to be here. >> Thanks for having us. >> Glad to be here. >> All right, Doug. Let's get started with you. You know, this recent survey, which was commissioned by Couchbase, 650 CIOs and CTOs, and IT practitioners. So obviously very IT heavy. They responded to the following question, "In response to the pandemic, my organization accelerated our application modernization strategy and of course, an overwhelming majority, 94% agreed or strongly agreed." So I'm sure, Doug, that you're not shocked by that, but in the same survey, modernizing existing technologies was second only behind cyber security is the top investment priority this year. Doug, bring us into your world and tell us the trends that you're seeing with the clients and customers you work with in their modernization initiatives. >> Well, the survey, of course, is spot on. You know, any Constellation Research analyst, any systems integrator will tell you that we saw more transformation work in the last two years than in the prior six to eight years. A lot of it was forced, you know, a lot of movement to the cloud, a lot of process improvement, a lot of automation work, but transformational is aspirational and not every company can be a leader. You know, at Constellation, we focus our research on those market leaders and that's only, you know, the top 5% of companies that are really innovating, that are really disrupting their markets and we try to share that with companies that want to be fast followers, that these are the next 20 to 25% of companies that don't want to get left behind, but don't want to hit some of the same roadblocks and you know, pioneering pitfalls that the real leaders are encountering when they're harnessing new technologies. So the rest of the companies, you know, the cautious adopters, the laggards, many of them fall by the wayside, that's certainly what we saw during the pandemic. Who are these leaders? You know, the old saw examples that people saw at the Amazons, the Teslas, the Airbnbs, the Ubers and Lyfts, but new examples are emerging every year. And as a consumer, you immediately recognize these transformed experiences. One of my favorite examples from the pandemic is Rocket Mortgage. No disclaimer required, I don't own stock and you're not client, but when I wanted to take advantage of those record low mortgage interest rates, I called my current bank and some, you know, stall word, very established conventional banks, I'm talking to you Bank of America, City Bank, and they were taking days and weeks to get back to me. Rocket Mortgage had the locked in commitment that day, a very proactive, consistent communications across web, mobile, email, all customer touchpoints. I closed in a matter of weeks an entirely digital seamless process. This is back in the gloves and masks days and the loan officer came parked in our driveway, wiped down an iPad, handed us that iPad, we signed all those documents digitally, completely electronic workflow. The only wet signatures required were those demanded by the state. So it's easy to spot these transformed experiences. You know, Rocket had most of that in place before the pandemic, and that's why they captured 8% of the national mortgage market by 2020 and they're on track to hit 10% here in 2022. >> Yeah, those are great examples. I mean, I'm not a shareholder either, but I am a customer. I even went through the same thing in the pandemic. It was all done in digital it was a piece of cake and I happened to have to do another one with a different firm and stuck with that firm for a variety of reasons and it was night and day. So to your point, it was a forced merge to digital. If you were there beforehand, you had real advantage, it could accelerate your lead during the pandemic. Okay, now Tony bear. Mr. Bear, I understand you're skeptical about all this buzz around digital transformation. So in that same survey, the data shows that the majority of respondents said that their digital initiatives were largely reactive to outside forces, the pandemic compliance changes, et cetera. But at the same time, they indicated that the results while somewhat mixed were generally positive. So why are you skeptical? >> The reason being, and by the way, I have nothing against application modernization. The problem... I think the problem I ever said, it often gets conflated with digital transformation and digital transformation itself has become such a buzzword and so overused that it's really hard, if not impossible to pin down (coughs) what digital transformation actually means. And very often what you'll hear from, let's say a C level, you know, (mumbles) we want to run like Google regardless of whether or not that goal is realistic you know, for that organization (coughs). The thing is that we've been using, you know, businesses have been using digital data since the days of the mainframe, since the... Sorry that data has been digital. What really has changed though, is just the degree of how businesses interact with their customers, their partners, with the whole rest of the ecosystem and how their business... And how in many cases you take look at the auto industry that the nature of the business, you know, is changing. So there is real change of foot, the question is I think we need to get more specific in our goals. And when you look at it, if we can boil it down to a couple, maybe, you know, boil it down like really over simplistically, it's really all about connectedness. No, I'm not saying connectivity 'cause that's more of a physical thing, but connectedness. Being connected to your customer, being connected to your supplier, being connected to the, you know, to the whole landscape, that you operate in. And of course today we have many more channels with which we operate, you know, with customers. And in fact also if you take a look at what's happening in the automotive industry, for instance, I was just reading an interview with Bill Ford, you know, their... Ford is now rapidly ramping up their electric, you know, their electric vehicle strategy. And what they realize is it's not just a change of technology, you know, it is a change in their business, it's a change in terms of the relationship they have with their customer. Their customers have traditionally been automotive dealers who... And the automotive dealers have, you know, traditionally and in many cases by state law now have been the ones who own the relationship with the end customer. But when you go to an electric vehicle, the product becomes a lot more of a software product. And in turn, that means that Ford would have much more direct interaction with its end customers. So that's really what it's all about. It's about, you know, connectedness, it's also about the ability to act, you know, we can say agility, it's about ability not just to react, but to anticipate and act. And so... And of course with all the proliferation, you know, the explosion of data sources and connectivity out there and the cloud, which allows much more, you know, access to compute, it changes the whole nature of the ball game. The fact is that we have to avoid being overwhelmed by this and make our goals more, I guess, tangible, more strictly defined. >> Yeah, now... You know, great points there. And I want to just bring in some survey data, again, two thirds of the respondents said their digital strategies were set by IT and only 26% by the C-suite, 8% by the line of business. Now, this was largely a survey of CIOs and CTOs, but, wow, doesn't seem like the right mix. It's a Doug's point about, you know, leaders in lagers. My guess is that Rocket Mortgage, their digital strategy was led by the chief digital officer potentially. But at the same time, you would think, Tony, that application modernization is a prerequisite for digital transformation. But I want to go to Sanjeev in this war in the survey. And respondents said that on average, they want 58% of their IT spend to be in the public cloud three years down the road. Now, again, this is CIOs and CTOs, but (mumbles), but that's a big number. And there was no ambiguity because the question wasn't worded as cloud, it was worded as public cloud. So Sanjeev, what do you make of that? What's your feeling on cloud as flexible architecture? What does this all mean to you? >> Dave, 58% of IT spend in the cloud is a huge change from today. Today, most estimates, peg cloud IT spend to be somewhere around five to 15%. So what this number tells us is that the cloud journey is still in its early days, so we should buckle up. We ain't seen nothing yet, but let me add some color to this. CIOs and CTOs maybe ramping up their cloud deployment, but they still have a lot of problems to solve. I can tell you from my previous experience, for example, when I was in Gartner, I used to talk to a lot of customers who were in a rush to move into the cloud. So if we were to plot, let's say a maturity model, typically a maturity model in any discipline in IT would have something like crawl, walk, run. So what I was noticing was that these organizations were jumping straight to run because in the pandemic, they were under the gun to quickly deploy into the cloud. So now they're kind of coming back down to, you know, to crawl, walk, run. So basically they did what they had to do under the circumstances, but now they're starting to resolve some of the very, very important issues. For example, security, data privacy, governance, observability, these are all very big ticket items. Another huge problem that nav we are noticing more than we've ever seen, other rising costs. Cloud makes it so easy to onboard new use cases, but it leads to all kinds of unexpected increase in spikes in your operating expenses. So what we are seeing is that organizations are now getting smarter about where the workloads should be deployed. And sometimes it may be in more than one cloud. Multi-cloud is no longer an aspirational thing. So that is a huge trend that we are seeing and that's why you see there's so much increased planning to spend money in public cloud. We do have some issues that we still need to resolve. For example, multi-cloud sounds great, but we still need some sort of single pane of glass, control plane so we can have some fungibility and move workloads around. And some of this may also not be in public cloud, some workloads may actually be done in a more hybrid environment. >> Yeah, definitely. I call it Supercloud. People win sometimes-- >> Supercloud. >> At that term, but it's above multi-cloud, it floats, you know, on topic. But so you clearly identified some potholes. So I want to talk about the evolution of the application experience 'cause there's some potholes there too. 81% of their respondents in that survey said, "Our development teams are embracing the cloud and other technologies faster than the rest of the organization can adopt and manage them." And that was an interesting finding to me because you'd think that infrastructure is code and designing insecurity and containers and Kubernetes would be a great thing for organizations, and it is I'm sure in terms of developer productivity, but what do you make of this? Does the modernization path also have some potholes, Sanjeev? What are those? >> So, first of all, Dave, you mentioned in your previous question, there's no ambiguity, it's a public cloud. This one, I feel it has quite a bit of ambiguity because it talks about cloud and other technologies, that sort of opens up the kimono, it's like that's everything. Also, it says that the rest of the organization is not able to adopt and manage. Adoption is a business function, management is an IT function. So I feed this question is a bit loaded. We know that app modernization is here to stay, developing in the cloud removes a lot of traditional barriers or procuring instantiating infrastructure. In addition, developers today have so many more advanced tools. So they're able to develop the application faster because they have like low-code/no-code options, they have notebooks to write the machine learning code, they have the entire DevOps CI/CD tool chain that makes it easy to version control and push changes. But there are potholes. For example, are developers really interested in fixing data quality problems, all data, privacy, data, access, data governance? How about monitoring? I doubt developers want to get encumbered with all of these operationalization management pieces. Developers are very keen to deliver new functionality. So what we are now seeing is that it is left to the data team to figure out all of these operationalization productionization things that the developers have... You know, are not truly interested in that. So which actually takes me to this topic that, Dave, you've been quite actively covering and we've been talking about, see, the whole data mesh. >> Yeah, I was going to say, it's going to solve all those data quality problems, Sanjeev. You know, I'm a sucker for data mesh. (laughing) >> Yeah, I know, but see, what's going to happen with data mesh is that developers are now going to have more domain resident power to develop these applications. What happens to all of the data curation governance quality that, you know, a central team used to do. So there's a lot of open ended questions that still need to be answered. >> Yeah, That gets automated, Tony, right? With computational governance. So-- >> Of course. >> It's not trivial, it's not trivial, but I'm still an optimist by the end of the decade we'll start to get there. Doug, I want to go to you again and talk about the business case. We all remember, you know, the business case for modernization that is... We remember the Y2K, there was a big it spending binge and this was before the (mumbles) of the enterprise, right? CIOs, they'd be asked to develop new applications and the business maybe helps pay for it or offset the cost with the initial work and deployment then IT got stuck managing the sprawling portfolio for years. And a lot of the apps had limited adoption or only served a few users, so there were big pushes toward rationalizing the portfolio at that time, you know? So do I modernize, they had to make a decision, consolidate, do I sunset? You know, it was all based on value. So what's happening today and how are businesses making the case to modernize, are they going through a similar rationalization exercise, Doug? >> Well, the Y2K era experience that you talked about was back in the days of, you know, throw the requirements over the wall and then we had waterfall development that lasted months in some cases years. We see today's most successful companies building cross functional teams. You know, the C-suite the line of business, the operations, the data and analytics teams, the IT, everybody has a seat at the table to lead innovation and modernization initiatives and they don't start, the most successful companies don't start by talking about technology, they start by envisioning a business outcome by envisioning a transformed customer experience. You hear the example of Amazon writing the press release for the product or service it wants to deliver and then it works backwards to create it. You got to work backwards to determine the tech that will get you there. What's very clear though, is that you can't transform or modernize by lifting and shifting the legacy mess into the cloud. That doesn't give you the seamless processes, that doesn't give you data driven personalization, it doesn't give you a connected and consistent customer experience, whether it's online or mobile, you know, bots, chat, phone, everything that we have today that requires a modern, scalable cloud negative approach and agile deliver iterative experience where you're collaborating with this cross-functional team and course correct, again, making sure you're on track to what's needed. >> Yeah. Now, Tony, both Doug and Sanjeev have been, you know, talking about what I'm going to call this IT and business schism, and we've all done surveys. One of the things I'd love to see Couchbase do in future surveys is not only survey the it heavy, but also survey the business heavy and see what they say about who's leading the digital transformation and who's in charge of the customer experience. Do you have any thoughts on that, Tony? >> Well, there's no question... I mean, it's kind like, you know, the more things change. I mean, we've been talking about that IT and the business has to get together, we talked about this back during, and Doug, you probably remember this, back during the Y2K ERP days, is that you need these cross functional teams, we've been seeing this. I think what's happening today though, is that, you know, back in the Y2K era, we were basically going into like our bedrock systems and having to totally re-engineer them. And today what we're looking at is that, okay, those bedrock systems, the ones that basically are keeping the lights on, okay, those are there, we're not going to mess with that, but on top of that, that's where we're going to innovate. And that gives us a chance to be more, you know, more directed and therefore we can bring these related domains together. I mean, that's why just kind of, you know, talk... Where Sanjeev brought up the term of data mesh, I've been a bit of a cynic about data mesh, but I do think that work and work is where we bring a bunch of these connected teams together, teams that have some sort of shared context, though it's everybody that's... Every team that's working, let's say around the customer, for instance, which could be, you know, in marketing, it could be in sales, order processing in some cases, you know, in logistics and delivery. So I think that's where I think we... You know, there's some hope and the fact is that with all the advanced, you know, basically the low-code/no-code tools, they are ways to bring some of these other players, you know, into the process who previously had to... Were sort of, you know, more at the end of like a, you know, kind of a... Sort of like they throw it over the wall type process. So I do believe, but despite all my cynicism, I do believe there's some hope. >> Thank you. Okay, last question. And maybe all of you could answer this. Maybe, Sanjeev, you can start it off and then Doug and Tony can chime in. In the survey, about a half, nearly half of the 650 respondents said they could tangibly show their organizations improve customer experiences that were realized from digital projects in the last 12 months. Now, again, not surprising, but we've been talking about digital experiences, but there's a long way to go judging from our pandemic customer experiences. And we, again, you know, some were great, some were terrible. And so, you know, and some actually got worse, right? Will that improve? When and how will it improve? Where's 5G and things like that fit in in terms of improving customer outcomes? Maybe, Sanjeev, you could start us off here. And by the way, plug any research that you're working on in this sort of area, please do. >> Thank you, Dave. As a resident optimist on this call, I'll get us started and then I'm sure Doug and Tony will have interesting counterpoints. So I'm a technology fan boy, I have to admit, I am in all of all these new companies and how they have been able to rise up and handle extreme scale. In this time that we are speaking on this show, these food delivery companies would have probably handled tens of thousands of orders in minutes. So these concurrent orders, delivery, customer support, geospatial location intelligence, all of this has really become commonplace now. It used to be that, you know, large companies like Apple would be able to handle all of these supply chain issues, disruptions that we've been facing. But now in my opinion, I think we are seeing this in, Doug mentioned Rocket Mortgage. So we've seen it in FinTech and shopping apps. So we've seen the same scale and it's more than 5G. It includes things like... Even in the public cloud, we have much more efficient, better hardware, which can do like deep learning networks much more efficiently. So machine learning, a lot of natural language programming, being able to handle unstructured data. So in my opinion, it's quite phenomenal to see how technology has actually come to rescue and as, you know, billions of us have gone online over the last two years. >> Yeah, so, Doug, so Sanjeev's point, he's saying, basically, you ain't seen nothing yet. What are your thoughts here, your final thoughts. >> Well, yeah, I mean, there's some incredible technologies coming including 5G, but you know, it's only going to pave the cow path if the underlying app, if the underlying process is clunky. You have to modernize, take advantage of, you know, serverless scalability, autonomous optimization, advanced data science. There's lots of cutting edge capabilities out there today, but you know, lifting and shifting you got to get your hands dirty and actually modernize on that data front. I mentioned my research this year, I'm doing a lot of in depth looks at some of the analytical data platforms. You know, these lake houses we've had some conversations about that and helping companies to harness their data, to have a more personalized and predictive and proactive experience. So, you know, we're talking about the Snowflakes and Databricks and Googles and Teradata and Vertica and Yellowbrick and that's the research I'm focusing on this year. >> Yeah, your point about paving the cow path is right on, especially over the pandemic, a lot of the processes were unknown. But you saw this with RPA, paving the cow path only got you so far. And so, you know, great points there. Tony, you get the last word, bring us home. >> Well, I'll put it this way. I think there's a lot of hope in terms of that the new generation of developers that are coming in are a lot more savvy about things like data. And I think also the new generation of people in the business are realizing that we need to have data as a core competence. So I do have optimism there that the fact is, I think there is a much greater consciousness within both the business side and the technical. In the technology side, the organization of the importance of data and how to approach that. And so I'd like to just end on that note. >> Yeah, excellent. And I think you're right. Putting data at the core is critical data mesh I think very well describes the problem and (mumbles) credit lays out a solution, just the technology's not there yet, nor are the standards. Anyway, I want to thank the panelists here. Amazing. You guys are always so much fun to work with and love to have you back in the future. And thank you for joining today's broadcast brought to you by Couchbase. By the way, check out Couchbase on the road this summer at their application modernization summits, they're making up for two years of shut in and coming to you. So you got to go to couchbase.com/roadshow to find a city near you where you can meet face to face. In a moment. Ravi Mayuram, the chief technology officer of Couchbase will join me. You're watching theCUBE, the leader in high tech enterprise coverage. (bright music)