(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)

(upbeat music) >> Welcome back to Supercloud 2, an open collaborative where we explore the future of cloud and data. Now, you might recall last August at the inaugural Supercloud event we validated the technical feasibility and tried to further define the essential technical characteristics, and of course the deployment models of so-called supercloud. That is, sets of services that leverage the underlying primitives of hyperscale clouds, but are creating new value on top of those clouds for organizations at scale. So we're talking about capabilities that fundamentally weren't practical or even possible prior to the ascendancy of the public clouds. And so today at Supercloud 2, we're digging further into the topic with input from real-world practitioners. And we're exploring the intersection of data and cloud, And importantly, the realities and challenges of deploying technology for a new business capability. I'm pleased to have with me in our studios, west of Boston, Veronika Durgin, who's the head of data at Saks. Veronika, welcome. Great to see you. Thanks for coming on. >> Thank you so much. Thank you for having me. So excited to be here. >> And so we have to say upfront, you're here, these are your opinions. You're not representing Saks in any way. So we appreciate you sharing your depth of knowledge with us. >> Thank you, Dave. Yeah, I've been doing data for a while. I try not to say how long anymore. It's been a while. But yeah, thank you for having me. >> Yeah, you're welcome. I mean, one of the highlights of this past year for me was hanging out at the airport with you after the Snowflake Summit. And we were just chatting about sort of data mesh, and you were saying, "Yeah, but." There was a yeah, but. You were saying there's some practical realities of actually implementing these things. So I want to get into some of that. And I guess starting from a perspective of how data has changed, you've seen a lot of the waves. I mean, even if we go back to pre-Hadoop, you know, that would shove everything into an Oracle database, or, you know, Hadoop was going to save our data lives. And the cloud came along and, you know, that was kind of a disruptive force. And, you know, now we see things like, whether it's Snowflake or Databricks or these other platforms on top of the clouds. How have you observed the change in data and the evolution over time? >> Yeah, so I started as a DBA in the data center, kind of like, you know, growing up trying to manage whatever, you know, physical limitations a server could give us. So we had to be very careful of what we put in our database because we were limited. We, you know, purchased that piece of hardware, and we had to use it for the next, I don't know, three to five years. So it was only, you know, we focused on only the most important critical things. We couldn't keep too much data. We had to be super efficient. We couldn't add additional functionality. And then Hadoop came along, which is like, great, we can dump all the data there, but then we couldn't get data out of it. So it was like, okay, great. Doesn't help either. And then the cloud came along, which was incredible. I was probably the most excited person. I'm lying, but I was super excited because I no longer had to worry about what I can actually put in my database. Now I have that, you know, scalability and flexibility with the cloud. So okay, great, that data's there, and I can also easily get it out of it, which is really incredible. >> Well, but so, I'm inferring from what you're saying with Hadoop, it was like, okay, no schema on write. And then you got to try to make sense out of it. But so what changed with the cloud? What was different? >> So I'll tell a funny story. I actually successfully avoided Hadoop. The only time- >> Congratulations. >> (laughs) I know, I'm like super proud of it. I don't know how that happened, but the only time I worked for a company that had Hadoop, all I remember is that they were running jobs that were taking over 24 hours to get data out of it. And they were realizing that, you know, dumping data without any structure into this massive thing that required, you know, really skilled engineers wasn't really helpful. So what changed, and I'm kind of thinking of like, kind of like how Snowflake started, right? They were marketing themselves as a data warehouse. For me, moving from SQL Server to Snowflake was a non-event. It was comfortable, I knew what it was, I knew how to get data out of it. And I think that's the important part, right? Cloud, this like, kind of like, vague, high-level thing, magical, but the reality is cloud is the same as what we had on prem. So it's comfortable there. It's not scary. You don't need super new additional skills to use it. >> But you're saying what's different is the scale. So you can throw resources at it. You don't have to worry about depreciating your hardware over three to five years. Hey, I have an asset that I have to take advantage of. Is that the big difference? >> Absolutely. Actually, from kind of like operational perspective, which it's funny. Like, I don't have to worry about it. I use what I need when I need it. And not to take this completely in the opposite direction, people stop thinking about using things in a very smart way, right? You like, scale and you walk away. And then, you know, the cool thing about cloud is it's scalable, but you also should not use it when you don't need it. >> So what about this idea of multicloud. You know, supercloud sort of tries to go beyond multicloud. it's like multicloud by accident. And now, you know, whether it's M&A or, you know, some Skunkworks is do, hey, I like Google's tools, so I'm going to use Google. And then people like you are called on to, hey, how do we clean up this mess? And you know, you and I, at the airport, we were talking about data mesh. And I love the concept. Like, doesn't matter if it's a data lake or a data warehouse or a data hub or an S3 bucket. It's just a node on the mesh. But then, of course, you've got to govern it. You've got to give people self-serve. But this multicloud is a reality. So from your perspective, from a practitioner's perspective, what are the advantages of multicloud? We talk about the disadvantages all the time. Kind of get that, but what are the advantages? >> So I think the first thing when I think multicloud, I actually think high-availability disaster recovery. And maybe it's just how I grew up in the data center, right? We were always worried that if something happened in one area, we want to make sure that we can bring business up very quickly. So to me that's kind of like where multicloud comes to mind because, you know, you put your data, your applications, let's pick on AWS for a second and, you know, US East in AWS, which is the busiest kind of like area that they have. If it goes down, for my business to continue, I would probably want to move it to, say, Azure, hypothetically speaking, again, or Google, whatever that is. So to me, and probably again based on my background, disaster recovery high availability comes to mind as multicloud first, but now the other part of it is that there are, you know, companies and tools and applications that are being built in, you know, pick your cloud. How do we talk to each other? And more importantly, how do we data share? You know, I work with data. You know, this is what I do. So if, you know, I want to get data from a company that's using, say, Google, how do we share it in a smooth way where it doesn't have to be this crazy, I don't know, SFTP file moving. So that's where I think supercloud comes to me in my mind, is like practical applications. How do we create that mesh, that network that we can easily share data with each other? >> So you kind of answered my next question, is do you see use cases going beyond H? I mean, the HADR was, remember, that was the original cloud use case. That and bursting, you know, for, you know, Thanksgiving or, you know, for Black Friday. So you see an opportunity to go beyond that with practical use cases. >> Absolutely. I think, you know, we're getting to a world where every company is a data company. We all collect a lot of data. We want to use it for whatever that is. It doesn't necessarily mean sell it, but use it to our competitive advantage. So how do we do it in a very smooth, easy way, which opens additional opportunities for companies? >> You mentioned data sharing. And that's obviously, you know, I met you at Snowflake Summit. That's a big thing of Snowflake's. And of course, you've got Databricks trying to do similar things with open technology. What do you see as the trade-offs there? Because Snowflake, you got to come into their party, you're in their world, and you're kind of locked into that world. Now they're trying to open up. You know, and of course, Databricks, they don't know our world is wide open. Well, we know what that means, you know. The governance. And so now you're seeing, you saw Amazon come out with data clean rooms, which was, you know, that was a good idea that Snowflake had several years before. It's good. It's good validation. So how do you think about the trade-offs between kind of openness and freedom versus control? Is the latter just far more important? >> I'll tell you it depends, right? It's kind of like- >> Could be insulting to that. >> Yeah, I know. It depends because I don't know the answer. It depends, I think, because on the use case and application, ultimately every company wants to make money. That's the beauty of our like, capitalistic economy, right? We're driven 'cause we want to make money. But from the use, you know, how do I sell a product to somebody who's in Google if I am in AWS, right? It's like, we're limiting ourselves if we just do one cloud. But again, it's difficult because at the same time, every cloud provider wants for you to be locked in their cloud, which is why probably, you know, whoever has now data sharing because they want you to stay within their ecosystem. But then again, like, companies are limited. You know, there are applications that are starting to be built on top of clouds. How do we ensure that, you know, I can use that application regardless what cloud, you know, my company is using or I just happen to like. >> You know, and it's true they want you to stay in their ecosystem 'cause they'll make more money. But as well, you think about Apple, right? Does Apple do it 'cause they can make more money? Yes, but it's also they have more control, right? Am I correct that technically it's going to be easier to govern that data if it's all the sort of same standard, right? >> Absolutely. 100%. I didn't answer that question. You have to govern and you have to control. And honestly, it's like it's not like a nice-to-have anymore. There are compliances. There are legal compliances around data. Everybody at some point wants to ensure that, you know, and as a person, quite honestly, you know, not to be, you know, I don't like when my data's used when I don't know how. Like, it's a little creepy, right? So we have to come up with standards around that. But then I also go back in the day. EDI, right? Electronic data interchange. That was figured out. There was standards. Companies were sending data to each other. It was pretty standard. So I don't know. Like, we'll get there. >> Yeah, so I was going to ask you, do you see a day where open standards actually emerge to enable that? And then isn't that the great disruptor to sort of kind of the proprietary stack? >> I think so. I think for us to smoothly exchange data across, you know, various systems, various applications, we'll have to agree to have standards. >> From a developer perspective, you know, back to the sort of supercloud concept, one of the the components of the essential characteristics is you've got this PaaS layer that provides consistency across clouds, and it has unique attributes specific to the purpose of that supercloud. So in the instance of Snowflake, it's data sharing. In the case of, you know, VMware, it might be, you know, infrastructure or self-serve infrastructure that's consistent. From a developer perspective, what do you hear from developers in terms of what they want? Are we close to getting that across clouds? >> I think developers always want freedom and ability to engineer. And oftentimes it's not, (laughs) you know, just as an engineer, I always want to build something, and it's not always for the, to use a specific, you know, it's something I want to do versus what is actually applicable. I think we'll land there, but not because we are, you know, out of the kindness of our own hearts. I think as a necessity we will have to agree to standards, and that that'll like, move the needle. Yeah. >> What are the limitations that you see of cloud and this notion of, you know, even cross cloud, right? I mean, this one cloud can't do it all. You know, but what do you see as the limitations of clouds? >> I mean, it's funny, I always think, you know, again, kind of probably my background, I grew up in the data center. We were physically limited by space, right? That there's like, you can only put, you know, so many servers in the rack and, you know, so many racks in the data center, and then you run out space. Earth has a limited space, right? And we have so many data centers, and everybody's collecting a lot of data that we actually want to use. We're not just collecting for the sake of collecting it anymore. We truly can't take advantage of it because servers have enough power, right, to crank through it. We will run enough space. So how do we balance that? How do we balance that data across all the various data centers? And I know I'm like, kind of maybe talking crazy, but until we figure out how to build a data center on the Moon, right, like, we will have to figure out how to take advantage of all the compute capacity that we have across the world. >> And where does latency fit in? I mean, is it as much of a problem as people sort of think it is? Maybe it depends too. It depends on the use case. But do multiple clouds help solve that problem? Because, you know, even AWS, $80 billion company, they're huge, but they're not everywhere. You know, they're doing local zones, they're doing outposts, which is, you know, less functional than their full cloud. So maybe I would choose to go to another cloud. And if I could have that common experience, that's an advantage, isn't it? >> 100%, absolutely. And potentially there's some maybe pricing tiers, right? So we're talking about latency. And again, it depends on your situation. You know, if you have some sort of medical equipment that is very latency sensitive, you want to make sure that data lives there. But versus, you know, I browse on a website. If the website takes a second versus two seconds to load, do I care? Not exactly. Like, I don't notice that. So we can reshuffle that in a smart way. And I keep thinking of ways. If we have ways for data where it kind of like, oh, you are stuck in traffic, go this way. You know, reshuffle you through that data center. You know, maybe your data will live there. So I think it's totally possible. I know, it's a little crazy. >> No, I like it, though. But remember when you first found ways, you're like, "Oh, this is awesome." And then now it's like- >> And it's like crowdsourcing, right? Like, it's smart. Like, okay, maybe, you know, going to pick on US East for Amazon for a little bit, their oldest, but also busiest data center that, you know, periodically goes down. >> But then you lose your competitive advantage 'cause now it's like traffic socialism. >> Yeah, I know. >> Right? It happened the other day where everybody's going this way up. There's all the Wazers taking. >> And also again, compliance, right? Every country is going down the path of where, you know, data needs to reside within that country. So it's not as like, socialist or democratic as we wish for it to be. >> Well, that's a great point. I mean, when you just think about the clouds, the limitation, now you go out to the edge. I mean, everybody talks about the edge in IoT. Do you actually think that there's like a whole new stove pipe that's going to get created. And does that concern you, or do you think it actually is going to be, you know, connective tissue with all these clouds? >> I honestly don't know. I live in a practical world of like, how does it help me right now? How does it, you know, help me in the next five years? And mind you, in five years, things can change a lot. Because if you think back five years ago, things weren't as they are right now. I mean, I really hope that somebody out there challenges things 'cause, you know, the whole cloud promise was crazy. It was insane. Like, who came up with it? Why would I do that, right? And now I can't imagine the world without it. >> Yeah, I mean a lot of it is same wine, new bottle. You know, but a lot of it is different, right? I mean, technology keeps moving us forward, doesn't it? >> Absolutely. >> Veronika, it was great to have you. Thank you so much for your perspectives. If there was one thing that the industry could do for your data life that would make your world better, what would it be? >> I think standards for like data sharing, data marketplace. I would love, love, love nothing else to have some agreed upon standards. >> I had one other question for you, actually. I forgot to ask you this. 'Cause you were saying every company's a data company. Every company's a software company. We're already seeing it, but how prevalent do you think it will be that companies, you've seen some of it in financial services, but companies begin to now take their own data, their own tooling, their own software, which they've developed internally, and point that to the outside world? Kind of do what AWS did. You know, working backwards from the customer and saying, "Hey, we did this for ourselves. We can now do this for the rest of the world." Do you see that as a real trend, or is that Dave's pie in the sky? >> I think it's a real trend. Every company's trying to reinvent themselves and come up with new products. And every company is a data company. Every company collects data, and they're trying to figure out what to do with it. And again, it's not necessarily to sell it. Like, you don't have to sell data to monetize it. You can use it with your partners. You can exchange data. You know, you can create products. Capital One I think created a product for Snowflake pricing. I don't recall, but it just, you know, they built it for themselves, and they decided to kind of like, monetize on it. And I'm absolutely 100% on board with that. I think it's an amazing idea. >> Yeah, Goldman is another example. Nasdaq is basically taking their exchange stack and selling it around the world. And the cloud is available to do that. You don't have to build your own data center. >> Absolutely. Or for good, right? Like, we're talking about, again, we live in a capitalist country, but use data for good. We're collecting data. We're, you know, analyzing it, we're aggregating it. How can we use it for greater good for the planet? >> Veronika, thanks so much for coming to our Marlborough studios. Always a pleasure talking to you. >> Thank you so much for having me. >> You're really welcome. All right, stay tuned for more great content. From Supercloud 2, this is Dave Vellante. We'll be right back. (upbeat music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR, This is "Breaking Analysis with Dave Vellante". >> Black Hat 22 was held in Las Vegas last week, the same time as theCUBE Supercloud event. Unlike AWS re:Inforce where words are carefully chosen to put a positive spin on security, Black Hat exposes all the warts of cyber and openly discusses its hard truths. It's a conference that's attended by technical experts who proudly share some of the vulnerabilities they've discovered, and, of course, by numerous vendors marketing their products and services. Hello, and welcome to this week's Wikibon CUBE Insights powered by ETR. In this "Breaking Analysis", we summarize what we learned from discussions with several people who attended Black Hat and our analysis from reviewing dozens of keynotes, articles, sessions, and data from a recent Black Hat Attendees Survey conducted by Black Hat and Informa, and we'll end with the discussion of what it all means for the challenges around securing the supercloud. Now, I personally did not attend, but as I said at the top, we reviewed a lot of content from the event which is renowned for its hundreds of sessions, breakouts, and strong technical content that is, as they say, unvarnished. Chris Krebs, the former director of Us cybersecurity and infrastructure security agency, CISA, he gave the keynote, and he spoke about the increasing complexity of tech stacks and the ripple effects that that has on organizational risk. Risk was a big theme at the event. Where re:Inforce tends to emphasize, again, the positive state of cybersecurity, it could be said that Black Hat, as the name implies, focuses on the other end of the spectrum. Risk, as a major theme of the event at the show, got a lot of attention. Now, there was a lot of talk, as always, about the expanded threat service, you hear that at any event that's focused on cybersecurity, and tons of emphasis on supply chain risk as a relatively new threat that's come to the CISO's minds. Now, there was also plenty of discussion about hybrid work and how remote work has dramatically increased business risk. According to data from in Intel 471's Mark Arena, the previously mentioned Black Hat Attendee Survey showed that compromise credentials posed the number one source of risk followed by infrastructure vulnerabilities and supply chain risks, so a couple of surveys here that we're citing, and we'll come back to that in a moment. At an MIT cybersecurity conference earlier last decade, theCUBE had a hypothetical conversation with former Boston Globe war correspondent, Charles Sennott, about the future of war and the role of cyber. We had similar discussions with Dr. Robert Gates on theCUBE at a ServiceNow event in 2016. At Black Hat, these discussions went well beyond the theoretical with actual data from the war in Ukraine. It's clear that modern wars are and will be supported by cyber, but the takeaways are that they will be highly situational, targeted, and unpredictable because in combat scenarios, anything can happen. People aren't necessarily at their keyboards. Now, the role of AI was certainly discussed as it is at every conference, and particularly cyber conferences. You know, it was somewhat dissed as over hyped, not surprisingly, but while AI is not a panacea to cyber exposure, automation and machine intelligence can definitely augment, what appear to be and have been stressed out, security teams can do this by recommending actions and taking other helpful types of data and presenting it in a curated form that can streamline the job of the SecOps team. Now, most cyber defenses are still going to be based on tried and true monitoring and telemetry data and log analysis and curating known signatures and analyzing consolidated data, but increasingly, AI will help with the unknowns, i.e. zero-day threats and threat actor behaviors after infiltration. Now, finally, while much lip service was given to collaboration and public-private partnerships, especially after Stuxsnet was revealed early last decade, the real truth is that threat intelligence in the private sector is still evolving. In particular, the industry, mid decade, really tried to commercially exploit proprietary intelligence and, you know, do private things like private reporting and monetize that, but attitudes toward collaboration are trending in a positive direction was one of the sort of outcomes that we heard at Black Hat. Public-private partnerships are being both mandated by government, and there seems to be a willingness to work together to fight an increasingly capable adversary. These things are definitely on the rise. Now, without this type of collaboration, securing the supercloud is going to become much more challenging and confined to narrow solutions. and we're going to talk about that little later in the segment. Okay, let's look at some of the attendees survey data from Black Hat. Just under 200 really serious security pros took the survey, so not enough to slice and dice by hair color, eye color, height, weight, and favorite movie genre, but enough to extract high level takeaways. You know, these strongly agree or disagree survey responses can sometimes give vanilla outputs, but let's look for the ones where very few respondents strongly agree or disagree with a statement or those that overwhelmingly strongly agree or somewhat agree. So it's clear from this that the respondents believe the following, one, your credentials are out there and available to criminals. Very few people thought that that was, you know, unavoidable. Second, remote work is here to stay, and third, nobody was willing to really jinx their firms and say that they strongly disagree that they'll have to respond to a major cybersecurity incident within the next 12 months. Now, as we've reported extensively, COVID has permanently changed the cybersecurity landscape and the CISO's priorities and playbook. Check out this data that queries respondents on the pandemic's impact on cybersecurity, new requirements to secure remote workers, more cloud, more threats from remote systems and remote users, and a shift away from perimeter defenses that are no longer as effective, e.g. firewall appliances. Note, however, the fifth response that's down there highlighted in green. It shows a meaningful drop in the percentage of remote workers that are disregarding corporate security policy, still too many, but 10 percentage points down from 2021 survey. Now, as we've said many times, bad user behavior will trump good security technology virtually every time. Consistent with the commentary from Mark Arena's Intel 471 threat report, fishing for credentials is the number one concern cited in the Black Hat Attendees Survey. This is a people and process problem more than a technology issue. Yes, using multifactor authentication, changing passwords, you know, using unique passwords, using password managers, et cetera, they're all great things, but if it's too hard for users to implement these things, they won't do it, they'll remain exposed, and their organizations will remain exposed. Number two in the graphic, sophisticated attacks that could expose vulnerabilities in the security infrastructure, again, consistent with the Intel 471 data, and three, supply chain risks, again, consistent with Mark Arena's commentary. Ask most CISOs their number one problem, and they'll tell you, "It's a lack of talent." That'll be on the top of their list. So it's no surprise that 63% of survey respondents believe they don't have the security staff necessary to defend against cyber threats. This speaks to the rise of managed security service providers that we've talked about previously on "Breaking Analysis". We've seen estimates that less than 50% of organizations in the US have a SOC, and we see those firms as ripe for MSSP support as well as larger firms augmenting staff with managed service providers. Now, after re:Invent, we put forth this conceptual model that discussed how the cloud was becoming the first line of defense for CISOs, and DevOps was being asked to do more, things like securing the runtime, the containers, the platform, et cetera, and audit was kind of that last line of defense. So a couple things we picked up from Black Hat which are consistent with this shift and some that are somewhat new, first, is getting visibility across the expanded threat surface was a big theme at Black Hat. This makes it even harder to identify risk, of course, this being the expanded threat surface. It's one thing to know that there's a vulnerability somewhere. It's another thing to determine the severity of the risk, but understanding how easy or difficult it is to exploit that vulnerability and how to prioritize action around that. Vulnerability is increasingly complex for CISOs as the security landscape gets complexified. So what's happening is the SOC, if there even is one at the organization, is becoming federated. No longer can there be one ivory tower that's the magic god room of data and threat detection and analysis. Rather, the SOC is becoming distributed following the data, and as we just mentioned, the SOC is being augmented by the cloud provider and the managed service providers, the MSSPs. So there's a lot of critical security data that is decentralized and this will necessitate a new cyber data model where data can be synchronized and shared across a federation of SOCs, if you will, or mini SOCs or SOC capabilities that live in and/or embedded in an organization's ecosystem. Now, to this point about cloud being the first line of defense, let's turn to a story from ETR that came out of our colleague Eric Bradley's insight in a one-on-one he did with a senior IR person at a manufacturing firm. In a piece that ETR published called "Saved by Zscaler", check out this comment. Quote, "As the last layer, we are filtering all the outgoing internet traffic through Zscaler. And when an attacker is already on your network, and they're trying to communicate with the outside to exchange encryption keys, Zscaler is already blocking the traffic. It happened to us. It happened and we were saved by Zscaler." So that's pretty cool. So not only is the cloud the first line of defense, as we sort of depicted in that previous graphic, here's an example where it's also the last line of defense. Now, let's end on what this all means to securing the supercloud. At our Supercloud 22 event last week in our Palo Alto CUBE Studios, we had a session on this topic on supercloud, securing the supercloud. Security, in our view, is going to be one of the most important and difficult challenges for the idea of supercloud to become real. We reviewed in last week's "Breaking Analysis" a detailed discussion with Snowflake co-founder and president of products, Benoit Dageville, how his company approaches security in their data cloud, what we call a superdata cloud. Snowflake doesn't use the term supercloud. They use the term datacloud, but what if you don't have the focus, the engineering depth, and the bank roll that Snowflake has? Does that mean superclouds will only be developed by those companies with deep pockets and enormous resources? Well, that's certainly possible, but on the securing the supercloud panel, we had three technical experts, Gee Rittenhouse of Skyhigh Security, Piyush Sharrma who's the founder of Accurics who sold to Tenable, and Tony Kueh, who's the former Head of Product at VMware. Now, John Furrier asked each of them, "What is missing? What's it going to take to secure the supercloud? What has to happen?" Here's what they said. Play the clip. >> This is the final question. We have one minute left. I wish we had more time. This is a great panel. We'll bring you guys back for sure after the event. What one thing needs to happen to unify or get through the other side of this fragmentation and then the challenges for supercloud? Because remember, the enterprise equation is solve complexity with more complexity. Well, that's not what the market wants. They want simplicity. They want SaaS. They want ease of use. They want infrastructure risk code. What has to happen? What do you think, each of you? >> So I can start, and extending to the previous conversation, I think we need a consortium. We need a framework that defines that if you really want to operate on supercloud, these are the 10 things that you must follow. It doesn't matter whether you take AWS, Slash, or TCP or you have all, and you will have the on-prem also, which means that it has to follow a pattern, and that pattern is what is required for supercloud, in my opinion. Otherwise, security is going everywhere. They're like they have to fix everything, find everything, and so on and so forth. It's not going to be possible. So they need a framework. They need a consortium, and this consortium needs to be, I think, needs to led by the cloud providers because they're the ones who have these foundational infrastructure elements, and the security vendor should contribute on providing more severe detections or severe findings. So that's, in my opinion, should be the model. >> Great, well, thank you, Gee. >> Yeah, I would think it's more along the lines of a business model. We've seen in cloud that the scale matters, and once you're big, you get bigger. We haven't seen that coalesce around either a vendor, a business model, or whatnot to bring all of this and connect it all together yet. So that value proposition in the industry, I think, is missing, but there's elements of it already available. >> I think there needs to be a mindset. If you look, again, history repeating itself. The internet sort of came together around set of IETF, RSC standards. Everybody embraced and extended it, right? But still, there was, at least, a baseline, and I think at that time, the largest and most innovative vendors understood that they couldn't do it by themselves, right? And so I think what we need is a mindset where these big guys, like Google, let's take an example. They're not going to win at all, but they can have a substantial share. So how do they collaborate with the ecosystem around a set of standards so that they can bring their differentiation and then embrace everybody together. >> Okay, so Gee's point about a business model is, you know, business model being missing, it's broadly true, but perhaps Snowflake serves as a business model where they've just gone out and and done it, setting or trying to set a de facto standard by which data can be shared and monetized. They're certainly setting that standard and mandating that standard within the Snowflake ecosystem with its proprietary framework. You know, perhaps that is one answer, but Tony lays out a scenario where there's a collaboration mindset around a set of standards with an ecosystem. You know, intriguing is this idea of a consortium or a framework that Piyush was talking about, and that speaks to the collaboration or lack thereof that we spoke of earlier, and his and Tony's proposal that the cloud providers should lead with the security vendor ecosystem playing a supporting role is pretty compelling, but can you see AWS and Azure and Google in a kumbaya moment getting together to make that happen? It seems unlikely, but maybe a better partnership between the US government and big tech could be a starting point. Okay, that's it for today. I want to thank the many people who attended Black Hat, reported on it, wrote about it, gave talks, did videos, and some that spoke to me that had attended the event, Becky Bracken, who is the EIC at Dark Reading. They do a phenomenal job and the entire team at Dark Reading, the news desk there, Mark Arena, whom I mentioned, Garrett O'Hara, Nash Borges, Kelly Jackson, sorry, Kelly Jackson Higgins, Roya Gordon, Robert Lipovsky, Chris Krebs, and many others, thanks for the great, great commentary and the content that you put out there, and thanks to Alex Myerson, who's on production, and Alex manages the podcasts for us. Ken Schiffman is also in our Marlborough studio as well, outside of Boston. Kristen Martin and Cheryl Knight, they help get the word out on social media and in our newsletters, and Rob Hoff is our Editor-in-Chief at SiliconANGLE and does some great editing and helps with the titles of "Breaking Analysis" quite often. Remember these episodes, they're all available as podcasts, wherever you listen, just search for "Breaking Analysis Podcasts". I publish each on wikibon.com and siliconangle.com, and you could email me, get in touch with me at david.vellante@siliconangle.com or you can DM me @dvellante or comment on my LinkedIn posts, and please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, and we'll see you next time on "Breaking Analysis". (upbeat music)

(upbeat music) >> The cybersecurity landscape has changed dramatically over the past 24 to 36 months. Rapid cloud migration has created a new layer of security defense, sure, but that doesn't mean CISOs can relax. In many respects, it further complicates, or at least changes, the CISO's scope of responsibilities. In particular, the threat surface has expanded. And that creates more seams, and CISOs have to make sure their teams pick up where the hyperscaler clouds leave off. Application developers have become a critical execution point for cyber assurance. "Shift left" is the kind of new buzz phrase for devs, but organizations still have to "shield right," meaning the operational teams must continue to partner with SecOps to make sure infrastructure is resilient. So it's no wonder that in ETR's latest survey of nearly 1500 CIOs and IT buyers, that business technology executives cite security as their number one priority, well ahead of other critical technology initiatives including collaboration software, cloud computing, and analytics rounding out the top four. But budgets are under pressure and CISOs have to prioritize. It's not like they have an open checkbook. They have to contend with other key initiatives like those just mentioned, to secure the funding. And what about zero trust? Can you go out and buy zero trust or is it a framework, a mindset in a series of best practices applied to create a security consciousness throughout the organization? Can you implement zero trust? In other words, if a machine or human is not explicitly allowed access, then access is denied. Can you implement that policy without constricting organizational agility? The question is, what's the most practical way to apply that premise? And what role does infrastructure play as the enforcer? How does automation play in the equation? The fact is, that today's approach to cyber resilience can't be an "either/or," it has to be an "and" conversation. Meaning, you have to ensure data protection while at the same time advancing the mission of the organization with as little friction as possible. And don't even talk to me about the edge. That's really going to keep you up at night. Hello and welcome to this special CUBE presentation, "A Blueprint for Trusted Infrastructure," made possible by Dell Technologies. In this program, we explore the critical role that trusted infrastructure plays in cybersecurity strategies, how organizations should think about the infrastructure side of the cybersecurity equation, and how Dell specifically approaches securing infrastructure for your business. We'll dig into what it means to transform and evolve toward a modern security infrastructure that's both trusted and agile. First up are Pete Gerr and Steve Kenniston, they're both senior cyber security consultants at Dell Technologies. And they're going to talk about the company's philosophy and approach to trusted infrastructure. And then we're going to speak to Parasar Kodati, who's a senior consultant for storage at Dell Technologies to understand where and how storage plays in this trusted infrastructure world. And then finally, Rob Emsley who heads product marketing for data protection and cyber security. We're going to going to take a deeper dive with Rob into data protection and explain how it has become a critical component of a comprehensive cyber security strategy. Okay, let's get started. Pete Gerr, Steve Kenniston, welcome to theCUBE. Thanks for coming into the Marlborough studios today. >> Great to be here, Dave. Thanks. >> Thanks, Dave. Good to see you. >> Great to see you guys. Pete, start by talking about the security landscape. You heard my little wrap up front. What are you seeing? >> I thought you wrapped it up really well. And you touched on all the key points, right? Technology is ubiquitous today. It's everywhere. It's no longer confined to a monolithic data center. It lives at the edge. It lives in front of us. It lives in our pockets and smartphones. Along with that is data. And as you said, organizations are managing sometimes 10 to 20 times the amount of data that they were just five years ago. And along with that, cyber crime has become a very profitable enterprise. In fact, it's been more than 10 years since the NSA chief actually called cyber crime the biggest transfer of wealth in history. That was 10 years ago. And we've seen nothing but accelerating cyber crime and really sophistication of how those attacks are perpetrated. And so the new security landscape is really more of an evolution. We're finally seeing security catch up with all of the technology adoption, all the build out, the work from home and work from anywhere that we've seen over the last couple of years. We're finally seeing organizations, and really it goes beyond the IT directors, it's a board level discussion today. Security's become a board level discussion. >> Yeah, I think that's true as well. It's like it used to be that security was, "Okay, the SecOps team. You're responsible for security." Now you've got, the developers are involved, the business lines are involved, it's part of onboarding for most companies. You know, Steve, this concept of zero trust. It was kind of a buzzword before the pandemic. And I feel like I've often said it's now become a mandate. But it's still fuzzy to a lot of people. How do you guys think about zero trust? What does it mean to you? How does it fit? >> Yeah. Again, I thought your opening was fantastic. And this whole lead in to, what is zero trust? It had been a buzzword for a long time. And now, ever since the federal government came out with their implementation or desire to drive zero trust, a lot more people are taking it a lot more seriously, 'cause I don't think they've seen the government do this. But ultimately, it's just like you said, right? If you don't have trust to those particular devices, applications, or data, you can't get at it. The question is, and you phrase it perfectly, can you implement that as well as allow the business to be as agile as it needs to be in order to be competitive? 'Cause we're seeing, with your whole notion around DevOps and the ability to kind of build, make, deploy, build, make, deploy, right? They still need that functionality but it also needs to be trusted. It needs to be secure and things can't get away from you. >> Yeah. So it's interesting. I've attended every Reinforce since 2019, and the narrative there is, "Hey, everything in the cloud is great. And this narrative around, 'Oh, security is a big problem.' doesn't help the industry." The fact is that the big hyperscalers, they're not strapped for talent, but CISOs are. They don't have the capabilities to really apply all these best practices. They're playing Whac-A-Mole. So they look to companies like yours, to take your R&D and bake it into security products and solutions. So what are the critical aspects of the so-called Dell Trusted Infrastructure that we should be thinking about? >> Yeah, well, Dell Trusted Infrastructure, for us, is a way for us to describe the the work that we do through design, development, and even delivery of our IT system. So Dell Trusted Infrastructure includes our storage, it includes our servers, our networking, our data protection, our hyper-converged, everything that infrastructure always has been. It's just that today customers consume that infrastructure at the edge, as a service, in a multi-cloud environment. I mean, I view the cloud as really a way for organizations to become more agile and to become more flexible, and also to control costs. I don't think organizations move to the cloud, or move to a multi-cloud environment, to enhance security. So I don't see cloud computing as a panacea for security, I see it as another attack surface. And another aspect in front that organizations and security organizations and departments have to manage. It's part of their infrastructure today, whether it's in their data center, in a cloud, or at the edge. >> I mean, I think that's a huge point. Because a lot of people think, "Oh, my data's in the cloud. I'm good." It's like Steve, we've talked about, "Oh, why do I have to back up my data? It's in the cloud?" Well, you might have to recover it someday. So I don't know if you have anything to add to that or any additional thoughts on it? >> No, I mean, I think like what Pete was saying, when it comes to all these new vectors for attack surfaces, you know, people did choose the cloud in order to be more agile, more flexible. And all that did was open up to the CISOs who need to pay attention to now, okay, "Where can I possibly be attacked? I need to be thinking about is that secure?" And part of that is Dell now also understands and thinks about, as we're building solutions, is it a trusted development life cycle? So we have our own trusted development life cycle. How many times in the past did you used to hear about vendors saying you got to patch your software because of this? We think about what changes to our software and what implementations and what enhancements we deliver can actually cause from a security perspective, and make sure we don't give up or have security become a hole just in order to implement a feature. We got to think about those things. And as Pete alluded to, our secure supply chain. So all the way through, knowing what you're going to get when you actually receive it is going to be secure and not be tampered with, becomes vitally important. And then Pete and I were talking earlier, when you have tens of thousands of devices that need to be delivered, whether it be storage or laptops or PCs, or whatever it is, you want to be know that those devices can be trusted. >> Okay, guys, maybe Pete, you could talk about how Dell thinks about its framework and its philosophy of cyber security, and then specifically what Dell's advantages are relative to the competition. >> Yeah, definitely, Dave. Thank you. So we've talked a lot about Dell as a technology provider. But one thing Dell also is is a partner in this larger ecosystem. We realize that security, whether it's a zero trust paradigm or any other kind of security environment, is an ecosystem with a lot of different vendors. So we look at three areas. One is protecting data in systems. We know that it starts with and ends with data. That helps organizations combat threats across their entire infrastructure. And what it means is Dell's embedding security features consistently across our portfolios of storage, servers, networking. The second is enhancing cyber resiliency. Over the last decade, a lot of the funding and spending has been in protecting or trying to prevent cyber threats, not necessarily in responding to and recovering from threats. We call that resiliency. Organizations need to build resiliency across their organization, so not only can they withstand a threat, but they can respond, recover, and continue with their operations. And the third is overcoming security complexity. Security is hard. It's more difficult because of the things we've talked about, about distributed data, distributed technology, and attack surfaces everywhere. And so we're enabling organizations to scale confidently, to continue their business, but know that all the IT decisions that they're making have these intrinsic security features and are built and delivered in a consistent, secure way. >> So those are kind of the three pillars. Maybe we could end on what you guys see as the key differentiators that people should know about that Dell brings to the table. Maybe each of you could take a shot at that. >> Yeah, I think, first of all, from a holistic portfolio perspective, right? The secure supply chain and the secure development life cycle permeate through everything Dell does when building things. So we build things with security in mind, all the way from, as Pete mentioned, from creation to delivery, we want to make sure you have that secure device or asset. That permeates everything from servers, networking, storage, data protection, through hyperconverged, through everything. That to me is really a key asset. Because that means you understand when you receive something it's a trusted piece of your infrastructure. I think the other core component to think about, and Pete mentioned, as Dell being a partner for making sure you can deliver these things, is that even though that's part of our framework, these pillars are our framework of how we want to deliver security, it's also important to understand that we are partners and that you don't need to rip and replace. But as you start to put in new components, you can be assured that the components that you're replacing as you're evolving, as you're growing, as you're moving to the cloud, as you're moving to more on-prem type services or whatever, that your environment is secure. I think those are two key things. >> Got it. Okay. Pete, bring us home. >> Yeah, I think one of the big advantages of Dell is our scope and our scale, right? We're a large technology vendor that's been around for decades, and we develop and sell almost every piece of technology. We also know that organizations might make different decisions. And so we have a large services organization with a lot of experienced services people that can help customers along their security journey, depending on whatever type of infrastructure or solutions that they're looking at. The other thing we do is make it very easy to consume our technology, whether that's traditional on premise, in a multi-cloud environment, or as a service. And so the best-of-breed technology can be consumed in any variety of fashion, and know that you're getting that consistent, secure infrastructure that Dell provides. >> Well, and Dell's got probably the top supply chain, not only in the tech business, but probably any business. And so you can actually take your dog food, or your champagne, sorry, (laughter) allow other people to share best practices with your customers. All right, guys, thanks so much for coming up. I appreciate it. >> Great. Thank you. >> Thanks, Dave. >> Okay, keep it right there. After this short break, we'll be back to drill into the storage domain. You're watching "A Blueprint for Trusted Infrastructure" on theCUBE, the leader in enterprise and emerging tech coverage. Be right back. (upbeat music)

(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)

>> Announcer: From theCUBE studios in Palo Alto and Boston, it's theCUBE, covering IBM Think brought to you by IBM. >> Hi, there, this is Dave Vellante. We're back at the IBM Think 2020 Digital Event Experience are socially responsible and distant. I'm here in the studios in Marlborough, our team in Palo Alto. We've been going wall to wall coverage of IBM Think, Kit Chee here is the Vice President, and general manager of Cloud and Enterprise sales at Intel. Kit, thanks for coming on. Good to see you. >> Thank you, Dave. Thank you for having me on. >> You're welcome, and Evaristus Mainsah, Mainsah is here. Mainsah, he is the general manager of the IBM Cloud Pack Ecosystem for the IBM Cloud. Evaristus, it's good to see you again. Thank you very much, I appreciate your time. >> Thank you, Dave. Thank you very much. Thanks for having me. >> You're welcome, so Kit, let me start with you. How are you guys doing? You know, there's this pandemic, never seen it before. How're things where you are? >> Yeah, so we were quite fortunate. Intel's had an epidemic leadership team. For about 15 years now, we have a team consisting of medical safety and operational professionals, and this same team has, who has navigated as across several other health issues like bad flu, Ebola, Zika and each one and one virus then navigating us at this point with this pandemic. Obviously, our top priority as it would be for IBM is protecting the health and well being of employees while keeping the business running for our customers. The company has taken the following measures to take care of it direct and indirect workforce, Dave and to ensure business continuity throughout the developing situation. They're from areas like work from home policies, keeping hourly workers home and reimbursing for daycare, elderly care, helping with WiFi policies. So that's been what we've been up to Intel's manufacturing and supply chain operations around the world world are working hard to meet demand and we are collaborating with supply pains of our customers and partners globally as well. And more recently, we have about $16 Million to support communities, from frontline health care workers and technology initiatives like online education, telemedicine and compute need to research. So that's what we've been up to date. Pretty much, you know, busy. >> You know, every society that come to you, I have to say my entire career have been in the technology business and you know, sometimes you hear negative toward the big tech but, but I got to say, just as Kit was saying, big tech has really stepped up in this crisis. IBM has been no different and, you know, tech for good and I was actually I'm really proud. How are you doing in New York City? >> Evaristus: No, thank you, Dave, for that, you know, we are, we're doing great and, and our focus has been absolutely the same, so obviously, because we provide services to clients. At a time like this, your clients need you even more, but we need to focus on our employees to make sure that their health and their safety and their well being is protected. And so we've taken this really seriously, and actually, we have two ways of doing this. One of them is just on to purpose as a, as a company, on our clients, but the other is trying to activate the ecosystem because problems of this magnitude require you to work across a broad ecosystem to, to bring forth in a solution that are long lasting, for example, we have a call for code, which where we go out and we ask developers to use their skills and open source technologies to help solve some technical problems. This year, the focus was per AVADA initiatives around computing resources, how you track the Coronavirus and other services that are provided free of charge to our clients. Let me give you a bit more color, so, so IBM recently formed the high performance computing consortium made up of the feYderal government industry and academic leaders focus on providing high performance computing to solve the COVID-19 problem. So we're currently we have 33 members, now we have 27 active products, deploying something like 400 teraflops as our petaflop 400 petaflops of compute to solve the problem. >> Well, it certainly is challenging times, but at the same time, you're both in the, in the sweet spot, which is Cloud. I've talked to a number of CIOs who have said, you know, this is really, we had a cloud strategy before but we're really accelerating our cloud strategy now and, and we see this as sort of a permanent effect. I mean, Kit, you guys, big, big on ecosystem, you, you want frankly, a level playing field, the more optionality that you can give to customers, you know, the better and Cloud is really been exploding and you guys are powering, you know, all the world's Clouds. >> We are, Dave and honestly, that's a huge responsibility that we undertake. Before the pandemic, we saw the market through the lens of four key mega trends and the experiences we are all having currently now deepens our belief in the importance of addressing these mega trends, but specifically, we see marketplace needs around key areas of cloudification of everything below point, the amount of online activities that have spiked just in the last 60 days. It's a testimony of that. Pervasive AI is the second big area that we have seen and we are now resolute on investments in that area, 5G network transformation and the edge build out. Applications run the business and we know enterprise IT faces challenges when deploying applications that require data movement between Clouds and Cloud native technologies like containers and Kubernetes will be key enablers in delivering end to end data analytics, AI, machine learning and other critical workloads and Cloud environments at the edge. Pairing Intel's data centric portfolio, including Intel's obtain SSPs with Red Hat, Openshift, and IBM Cloud Paks, enterprise can now break through storage bottlenecks and have unconstrained data availability in the hybrid and multicloud environments, so we're pretty happy with the progress we're making that together with IBM. >> Yeah, Evaristus, I mean, you guys are making some big bets. I've, you know, written and discussed in my breaking analysis, I think a lot of people misunderstand IBM Cloud, Ginni Rometty arm and a bow said, hey, you know, we're after only 20% of the workloads are in cloud, we're going after the really difficult to move workloads and the hybrid workloads, that's really the fourth foundation that Arvin you know, talks about, that you and IBM has built, you know, your mainframes, you have middleware services, and in hybrid Cloud is really that fourth sort of platform that you're building out, but you're making some bets in AI. You got other services in the Cloud like, like blockchain, you know, quantum, we've been having really interesting discussions around quantum, so I wonder if you can talk a little bit about sort of where you're allocating resources, some of the big bets that, that you're making for the next decade. >> Well, thank you very much, Dave, for that. I think what we're seeing with clients is that there's increasing focus on and, and really an acceptance, that the best way to take advantage of the Cloud is through a hybrid cloud strategy, infused with data, so it's not just the Cloud itself, but actually what you need to do to data in order to make sure that you can really, truly transform yourself digitally, to enable you to, to improve your operations, and in use your data to improve the way that you work and improve the way that you serve your clients. And what we see is and you see studies out there that say that if you adopt a hybrid cloud strategy, instead of 2.5 times more effective than a public cloud only strategy, and Why is that? Well, you get thi6ngs such as you know, the opportunity to move your application, the extent to which you move your applications to the Cloud. You get things such as you know, reduction in, in, in risk, you, you get a more flexible architecture, especially if you focus on open certification, reduction and certification reduction, some of the tools that you use, and so we see clients looking at that. The other thing that's really important, especially in this moment is business agility, and resilience. Our business agility says that if my customers used to come in, now, they can't come in anymore, because we need them to stay at home, we still need to figure out a way to serve them and we write our applications quickly enough in order to serve this new client, service client in a new way. And well, if your applications haven't been modernized, even if you've moved to the Cloud, you don't have the opportunity to do that and so many clients that have made that transformation, figure out they're much more agile, they can move more easily in this environment, and we're seeing the whole for clients saying yes, I do need to move to the Cloud, but I need somebody to help improve my business agility, so that I can transform, I can change with the needs of my clients, and with the demands of competition and this leads you then to, you know, what sort of platform do you need to enable you to do this, it's something that's open, so that you can write that application once you can run it anywhere, which is why I think the IBM position with our ecosystem and Red Hat with this open container Kubernetes environment that allows you to write application once and deploy it anywhere, is really important for clients in this environment, especially, and the Cloud Paks which is developed, which I, you know, General Manager of the Cloud Pak Ecosystem, the logic of the Cloud Paks is exactly that you'll want plans and want to modernize one, write the applications that are cloud native so that they can react more quickly to market conditions, they can react more quickly to what the clients need and they, but if they do so, they're not unlocked in a specific infrastructure that keeps them away from some of the technologies that may be available in other Clouds. So we have talked about it blockchain, we've got, you know, Watson AI, AI technologies, which is available on our Cloud. We've got the weather, company assets, those are key asset for, for many, many clients, because weather influences more than we realize, so, but if you are locked in a Cloud that didn't give you access to any of those, because you hadn't written on the same platform, you know, that's not something that you you want to support. So Red Hat's platform, which is our platform, which is open, allows you to write your application once and deploy it anyways, particularly our customers in this particular environment together with the data pieces that come on top of that, so that you can scale, scale, because, you know, you've got six people, but you need 600 of them. How do you scale them or they can use data and AI in it? >> Okay, this must be music to your ears, this whole notion of you know, multicloud because, you know, Intel's pervasive and so, because the more Clouds that are out there, the better for you, better for your customers, as I said before, the more optionality. Can you6 talk a little bit about the rela6tionship today between IBM and Intel because it's obviously evolved over the years, PC, servers, you know, other collaboration, nearly the Cloud is, you know, the latest 6and probably the most rel6evant, you know, part of your, your collaboration, but, but talk more about what that's like you guys are doing together that's, that'6s interesting and relevant. >> You know, IBM and Intel have had a very rich history of collaboration starting with the invention of the PC. So for those of us who may take a PC for granted, that was an invention over 40 years ago, between the two companies, all the way to optimizing leadership, IBM software like BB2 to run the best on Intel's data center products today, right? But what's more germane today is the Red Hat piece of the study and how that plays into a partnership with IBM going forward, Intel was one of Red Hat's earliest investors back in 1998, again, something that most people may not realize that we were in early investment with Red Hat. And we've been a longtime pioneer of open source. In fact, Levin Shenoy, Intel's Executive Vice President of Data Platforms Group was part of COBOL Commies pick up a Red Hat summit just last week, you should definitely go listen to that session, but in summary, together Intel and Red Hat have made commercial open source viable and enterprise and worldwide competing globally. Basically, now we've65 used by nearly every vertical and horizontal industr6y. We are bringing our customers choice, scalability and speed of innovation for key technologies today, such as security, Telco, NFV, and containers, or even at ease and most recently Red Hat Openshift. We're very excited to see IBM Cloud Packs, for example, standardized on top of Openshift as that builds the foundation for IBM chapter two, and allows for Intel's value to scale to the Cloud packs and ultimately IBM customers. Intel began partnering with IBM on what is now called Pax over two years ago and we 6are committed to that success and scaling that, try ecosystem, hardware partners, ISVs and our channel. >> Yeah, so theCUBE by the way, covered Red Hat summit last week, Steve Minima and I did a detailed analysis. It was awesome, like if we do say so ourselves, but awesome in the sense of, it allowed us to really sort of unpack what's going on at Red Hat and what's happening at IBM. Evaristus, so I want to come back to you on this Cloud Pack, you got, it's, it's the kind of brand that you guys have, you got Cloud Packs all over the place, you got Cloud Packs for applications, data, integration, automation, multicloud management, what do we need to know about Cloud pack? What are the relevant components there? >> Evaristus: I think the key components is so this is think of this as you know, software that is designed that is Cloud native is designed for specific core use cases and it's built on Red Hat Enterprise Linux with Red Hat Openshift container Kubernetes environment, and then on top of that, so you get a set of common services that look right across all of them and then on top of that, you've got specific both open source and IBM software that deals with specific plant situations. So if you're dealing with applications, for example, the open source and IBM software would be the run times that you need to write and, and to blow applications to have setups. If you're dealing with data, then you've got Cloud Pack to data. The foundation is still Red Hat Enterprise Linux sitting on top of with Red Hat Openshift container Kubernetes environment sitting on top of that providing you with a set of common services and then you'll get a combination of IBM zone open, so IBM software as well as open source will have third party software that sits on top of that, as well as all of our AI infrastructure that sits on top of that and machine learning, to enable you to do everything that you need to do, data to get insights updates, you've got automation to speed up and to enable us to do work more efficiently, more effectively, to make your smart workers better, to make management easier, to help management manage work and processes, and then you've got multicloud management that allows you to see from a single pane, all of your applications that you've deployed in the different Cloud, because the idea here, of course, is that not all sitting in the same Cloud. Some of it is on prem, some of it is in other Cloud, and you want to be able to see and deploy applications across all of those. And then you've got the Cloud Pack to security, which has a combination of third party offerings, as well as ISV offerings, as well as AI offerings. Again, the structure is the same, REL, Red Hat Openshift and then you've got the software that enables you to manage all aspects of security and to deal with incidents when, when they arise. So that gives you data applications and then there's integration, as every time you start writing an application, you need to integrate, you need to access data security from someplace, you need to bring two pipes together for them to communicate and we use a Cloud Pack for integration to allow us to do that. You can open up API's and expose those API so others writing application and gain access to those API's. And again, this idea of resilience, this idea of agility, so you can make changes and you can adapt data things about it. So that's what the Cloud Pack provides for you and Intel has been an absolutely fantastic partner for us. One of the things that we do with Intel, of course, is to, to work on the reference architectures to help our certification program for our hardware OEMs so that we can scale that process, get many more OEMs adopt and be ready for the Cloud Packs and then we work with them on some of the ISV partners and then right up front. >> Got it, let's talk about the edge. Kity, you mentioned 5G. I mean it's a really exciting time, (laughs) You got windmills, you got autonomous vehicles, you got factories, you got to ship, you know, shipping containers. I mean, everything's getting instrumented, data everywhere and so I'm interested in, let's start with Intel's point of view on the edge, how that's going to evolve, you know what it means to Cloud. >> You know, Dave, it's, its definitely the future and we're excited to partner with IBM here. In addition to enterprise edge, the communication service providers think of the Telcos and take advantage of running standardized open software at the Telco edge, enabling a range of new workloads via scalable services, something that, you know, didn't happen in the past, right? Earlier this year, Intel announced a new C on second generation, scalable, atom based processes targeting the 5G radio access network, so this is a new area for us, in terms of investments going to 5G ran by deploying these new technologies, with Cloud native platforms like Red Hat Openshift and IBM Cloud Packs, comm service providers can now make full use of their network investments and bring new services such as Artificial Intelligence, augmented reality, virtual reality and gaming to the market. We've only touched the surface as it comes to 5G and Telco but IBM Red Hat and Intel compute together that I would say, you know, this space is super, super interesting, as more developed with just getting started. >> Evaristus, what do you think this means for Cloud and how that will evolve? Is this sort of a new Cloud that will form at the edge? Obviously, a lot of data is going to stay at the edge, probably new architectures are going to emerge and again, to me, it's all about data, you can create more data, push more data back to the Cloud, so you can model it. Some of the data is going to have to be done in real time at the edge, but it just really extends the network to new horizons. >> Evaristus: It does exactly that, Dave and we think of it and which is why I thought it will impact the same, right? You wouldn't be surprised to see that the platform is based on open containers and that Kubernetes is container environment provided by Red Hat and so whether your data ends up living at the edge or your data lives in a private data center, or it lives in some public Cloud, and how it flows between all of them. We want to make it easy for our clients to be able to do that. So this is very exciting for us. We just announced IBM Edge Application Manager that allows you to basically deploy and manage applications at endpoints of all these devices. So we're not talking about 2030, we're talking about thousands or hundreds of thousands. And in fact, we're working with, we're getting divided Intel's device onboarding, which will enable us to use that because you can get that and you can onboard devices very, very easily at scale, which if you get that combined with IBM Edge Application Manager, then it helps you onboard the devices and it helps you divide both central devices. So we think this is really important. We see lots of work that moving on the edge devices, many of these devices and endpoints now have sufficient compute to be able to run them, but right now, if they are IoT devices, the data has been transferred to hundreds of miles away to some data center to be processed and enormous pass and then only 1% of that actually is useful, right? 99% of it gets thrown away. Some of that actually has data residency requirements, so you may not be able to move the data to process, so why wouldn't you just process the data where the data is created around your analytics where the data is spread, or you have situations that are disconnected as well. So you can't actually do that. You don't want to stop this still in the supermarket, because there's, you lost connectivity with your data center and so the importance of being able to work offline and IBM Edge Application Manager actually allows you so it's tournament so you can do all of this without using lots of people because it's a process that is all sort or automated, but you can work whether you're connected or you're disconnected, and then you get replication when you get really, really powerful for. >> All right, I think the developer model is going to be really interesting here. There's so many new use cases and applications. Of course, Intel's always had a very strong developer ecosystem. You know, IBM understands the importance of developers. Guys, we've got to wrap up, but I wonder if you could each, maybe start with Kit. Give us your sense as to where you want to see this, this partnership go, what can we expect over the next, you know, two to five years and beyond? >> I think it's just the area of, you know, 5G, and how that plays out in terms of edge build out that we just touched on. I think that's a really interesting space, what Evaristus has said is spot on, you know, the processing, and the analytics at the edge is still fairly nascent today and that's growing. So that's one area, building out the Cloud for the different enterprise applications is the other one and obviously, it's going to be a hybrid world. It's not just a public Cloud world on prem world. So the whole hybrid build out What I call hybrid to DoD zero, it's a policy and so the, the work that both of us need to do IBM and Intel will be critical to ensure that, you know, enterprise IT, it has solutions across the hybrid sector. >> Great. Evaristus, give us the last word, bring us home. >> Evaristus: And I would agree with that as well, Kit. I will say this work that you do around the Intel's market ready solutions, right, where we can bring our ecosystem together to do even more on Edge, some of these use cases, this work that we're doing around blockchain, which I think you know, again, another important piece of work and, and I think what we really need to do is to focus on helping clients because many of them are working through those early cases right now, identify use cases that work and without commitment to open standards, using exactly the same standard across like what you've got on your open retail initiative, which we're going to do, I think is going to be really important to help you out scale, but I wanted to just add one more thing, Dave, if you if you permit me. >> Yeah. >> Evaristus: In this COVID era, one of the things that we've been able to do for customers, which has been really helpful, is providing free technology for 90 days to enable them to work in an offline situation to work away from the office. One example, for example, is the just the ability to transfer files and bandwidth, new bandwidth is an issue because the parents and the kids are all working from home, we have a protocol, IBM Aspera, which will make available customers for 90 days at no cost. You don't need to give us your credit card, just log on and use it to improve the way that you work. So your bandwidth feels as if you are in the office. We have what's an assistant that is now helping clients in more than 18 countries that keep the same thing, basically providing COVID information. So those are all available. There's a slew of offerings that we have. We just want listeners to know that they can go on the IBM website and they can gain those offerings they can deploy and use them now. >> That's huge. I knew about the 90 day program, I didn't realize a sparrow was part of that and that's really important because you're like, Okay, how am I going to get this file there? And so thank you for, for sharing that and guys, great conversation. You know, hopefully next year, we could be face to face even if we still have to be socially distant, but it was really a pleasure having you on. Thanks so much. Stay safe, and good stuff. I appreciate it. >> Evaristus: Thank you very much, Dave. Thank you, Kit. Thank you. >> Thank you, thank you. >> All right, and thank you for watching everybody. This is Dave Volante for theCUBE, our wall to wall coverage of the IBM Think 2020 Digital Event Experience. We'll be right back right after this short break. (upbeat music)

(energetic funk music) >> Hi, I'm Stu Miniman, and welcome to a special CUBE Conversation. I'm coming to you remote from our Boston-area studio in Marlborough, Massachusetts, and really happy to welcome to the program, actually, from down the road from where I'm sitting, but testing our coast-to-coast live-to-air remote capabilities, of course, everybody is working from home or things like that, Rob Strechay, CUBE alumni and friend of the program. Rob, it's great to see you. >> Yeah, thanks for having me on. Really glad to help you guys out and get on here and talk about what's goin' on, 'cause this remote thing is definitely going to be the wave for the next month or two, at least. >> Yeah, so, Rob, you spend a lot of your time talking to companies about strategic planning and the first thing for you is, in the industry, sometimes we talk about these black swan events, the things that, you know, we had our plans in place, a lot of companies either had their 2020 sales kickoffs or were getting ready for them, and all of a sudden, basically, everything that you were planning for, let's stop and re-evaluate, because coronavirus stuff is hitting, economic conditions globally are being impacted. What's the first thing that you tell people when you're advising when something completely unexpected and far-reaching, that we might not have full information on, hits? >> Yeah, I think there is, it's a great way to target and say, "Okay, where can I trim the fat, "but at the same time, where do I not "want to over-rotate or panic?" And I think that's a big piece of it, is that you don't want to go and panic too much and say, "Hey, we have to throw everything out." I think there's an opportunity, and there's definitely opportunities, but if you're looking at the different verticals that are being hit by this, if you look at things like healthcare, we have HIMSS that's supposed to be going on this week that was canceled, and all of the medical professionals and IT professionals at those hospitals are pretty much on lockdown. So if you're selling into that vertical, maybe then it is time to panic a little bit, or find another vertical, or understand how you go cross-vertical, in a way. So you have to evaluate what's going on, but don't panic, don't over-react to these types of events. >> So, Rob, you've worked with a lot of companies that provide disaster recovery. In the IT space, it's, "How do I deal with a failure?" One of the things that I know a lot of companies look at is when things go wrong, when there is some natural disaster or, like what we're having today, is, "Do I jump in and say, 'Hey, we can offer things'?" You see a lot of the companies that are providing remote services, take your Google, Microsoft, Zooms, are, "Hey, here's a free tier that you're able to use," but how much do you jump on this as a marketer, or how much do you just say, "Hey, we're here for you, "if there's anything we can do to help there," but you don't want to be seen as ambulance chasing or trying to profit off of some widespread disasters? >> Yeah, I think that's exactly it. The ambulance chasing part, you have to use a little common sense when you're going into these, and I think that goes a long way. You don't want to be seen as ambulance chasing, and, for instance, some of the small- to medium-size companies I've been watching in the tech and talking to some of their teams, they're putting out information, saying, "Hey, we're still up." If they're shipping hardware, "Hey, we're "still within our lead times. "We've built out enough capacity prior to this "and we'll be able to ship within 14 days of an order." So, reassuring their customers that they can get the kit out to them. At the same time, they're saying, "Hey, here is "what we're seeing from our customers, "that, if you're having trouble even once we do "ship it, you don't have somebody on site "to take it in, so we can offer services "to help you with that. So, helping them do staff augmentation or do things in a different manner, I see that as not the ambulance chasing aspect of it. I think if you're marketing into it, it's a little tough when you say, "Hey, well, "I'm the best remote desktop thing going, "and everybody can work from home," and trying to say, "And by the way, you have to buy into this "particular tier to get your entire company going." I think, again, you can look at, how do you share, maybe, the pain or share a loss leader going in, and look to build that. If you have confidence in your product, you'll get them on board, and they'll continue to do this, and they'll continue to move forward with it, because, like you said, I don't think anybody was necessarily prepared for a quarantine of an entire country, like Italy, or something of that nature. >> Yeah, the remote work is definitely a very hot topic. That doesn't necessarily mean that today is the day to start the 5,000-person virtual desktop project. >> Exactly. >> Because we know how long these things have a lead time. Rob, I want to ask you, actually, when you talk to customers, you've spent a lot of time in your career talking to customers, one of the buzzwords we've had in the industry is digital transformation. One of the big outcomes of digital transformation is to be able to react and move fast and be more agile. So I just wanted to get your take on what you're hearing from customers, where, of course, it's a spectrum, but what they're doing, and is this something that they should they should put on hold, is it something is going to help them prepare for things that they weren't necessarily thinking about? >> I think it's the latter, right? I think you're really, I would push in on digital transformation at this point in time, because you're not going to know what's going to break until you get into these situations, and I would say that we've seen a couple in the financial industry as we've gone through the volatility in the markets where they've pushed in on digital transformation, or there are some startups that have really pushed in on doing things in a new way from the traditional financial services companies, and they've found out, hey, stuff is breaking, and they're going to pay some fines to the SEC. And some of the traditional ones that have their digital transformation projects, they've bumped into this same exact thing, where they were having outages. So it's not just the new startups, it's some of the older, more established players that are finding out that, hey, you don't know until you get into that war, you don't know until you engage that enemy, per se, as that black swan event, what's going to break. So push in, I would almost double down on it, and say, "Listen, this is going to be "the way that helps us smooth these out. "As we can distribute things out, "we don't have, necessarily, one data center "where everybody has to go to, "and now that entire county is locked down, "or there's the National Guard surrounding it "and you can't get to it." >> Yeah, Rob, I'm wondering if you have any commentary on just the general dispersion of the workforce. You've worked for a variety of sizes of company, you've been a remote worker, you've worked for companies where you're far separated from the headquarters. Any kind of tips or recommendations from your background that you'd have for people and today? >> Yeah, I think, again, for the people who haven't done it before, it is an adjustment. You actually find that you work more hours being at home than you would in a natural, an office. I think that also, how do you keep your sanity when you're really distant from people, and how do you keep that connection and that culture? I definitely think that these solutions, like Teams and Slack and what have you, and Zoom and Webex, have come a long way to help connect people, and I think it's really leveraging those tools around you to have that connection. And I think that we've seen some of the announcements of people about putting out guidelines of, "Hey, here's how we have a remote workforce." And I've seen, actually, more, and it's been a trend out on the West Coast for a little while, where their engineering teams, the dev teams, are very diverse and very disparate because you can't find everybody in the Valley anymore. So how, maybe some of the people are in Washington, maybe they're in Oregon, and California, you have some on the East Coast, or even over in the Ukraine, for instance. Trying to create events to bring everybody together, doing more outreach as an executive to the entire company, becomes critical because sharing of that information is what people want to understand. They want to feel connected back into what's going on at what they perceive as corporate. >> Yeah, so some great commentary there, Rob. Yeah, absolutely, we've seen plenty of the software companies out there that have not only tooling, but best practices on how to do this, as well as, through social media, I've seen a lot of blog posts, things on Twitter and the like, yeah, and some things that you don't think about as much. I'm not regularly a remote worker, but, right, make sure you take time to eat, make sure you've blocked out the hours that you're going to have meals with family. And something I always noticed is, "Oh my gosh, I might spend an entire day "like this on conference calls (Rob laughs) "or sitting and working," as opposed to, if you're in the office, you get up, you walk around, you talk to some people, and it's like, you need to make sure you stretch (laughs) a little bit-- >> Yeah, absolutely. >> Because otherwise, you can end up sitting for eight hours, and that's really not good. >> Rob: (laughs) Yeah, definitely need those mental breaks. >> Yeah, all right, Rob, I want to give you the final word. What's, let's kind of put beyond some of the things that are right in front of us right now, give you kind of an open technology space. What's interesting you out in the market here in the early parts of 2020? >> Yeah, I think there's a lot of very interesting things going on with AI, and I think people are finally starting to get past the hype of "AI this, AI that" and trying to look at what the use cases are behind AI, and how that's really going to help reinvent some of the technology that we have used. I kind of always say that everything old is new again. But I think there's going to be some great new tech coming out that will help enable these types of digital transformations, and I see a lot of new companies approaching AI and not just saying, "Hey, I'm an AI company," but, "Here's the use case that I'm really fulfilling." And I think that's showing some of the maturity, I think that's going to help as this artificial intelligence or machine learning really starts to push in and help people become more operationally efficient, so maybe then we can start to realize some more of cloud, and more of this, "Hey, I had this data center, "now I am moving everything to the cloud," versus, "Well, I'm going to move it, "but I'm going to lift and shift, "and I still have the operational legacy." >> Yeah, absolutely. If I can do a little compare and contrast, back in the big-data world, everybody used to always complain that we had the best minds in our business working on how we could optimize people clicking on an ad, (Rob laughs) and when I look at AI, there's a lot of tech for good out there, there's amazing outcomes, there's things that it can really be transformational. All right, Rob, I know that you've been doing a little bit more writing, you're posting on LinkedIn some of your strategy. If people want to learn more and keep an eye on what you're doing, what would you recommend? >> Yeah, I would say go onto my Twitter feed, @RealStrech, and-or go to my LinkedIn. Feel free to connect with me there. It's Rob Strechay, you can find me there pretty easily. There's not many Strechays in the world. So feel free to connect with me and view my articles there, and really, this has been a lot of fun. >> All right, well, always good to get two boys from Parsippany, New Jersey, to get together, talk about technology, and share it with the community. Rob, great to catch up with you. >> Thanks, Stu, take care. >> All right, I'm Stu Miniman, everybody, and thank you so much for watching theCUBE. (energetic funk music)

>> From the SiliconAngle media office, in Boston, Massachusetts, it's theCUBE. Now, here's your host, Dave Vellante. >> Hello everyone, and welcome to this Cube Conversation. You know, the auto industry was a, if not the dominant force in the 20th century economy, and clearly, you see it in the headlines today. I mean all you got to do is look at Tesla. The stock is absolutely on fire, Tesla's market value is actually greater than that of Ford and GM combined. Even though its revenues are about one 12th of those two combined. The macro discussion today is really heating up around ESG, which stands for environmental social governance. So, electric vehicles are really picking up momentum, and maybe that's the tailwind for Tesla, but consumers are pragmatic, the electric is still more expensive than internal combustion-powered vehicles, so we'll see how that plays out. One of the things we talk about a lot on theCUBE is the software content in automobiles. In many ways, these vehicles are code on wheels, so that's part of the hype factor, too. But you know, I've always argued that the incumbent auto makers are actually in a pretty reasonable position to compete. While autonomous vehicles, they may disrupt the incumbents, and even though right now Silicon Valley is ahead of Detroit and Japan and Germany and Korea, there's an ecosystem that is evolving to support traditional auto makers. Now, one of those players is Tactile Mobility. The vast majority of data created around autonomous vehicles today is visual-based with LIDAR as a key enabler. But a human driver, you think about it, they don't just rely on sight, they're able to feel the road, the bumps, the curves, and the impacts of things like weather. In fact, it's estimated that more than 20% of vehicle crashes in the US each year are weather-related. And intelligent cars, they really still can't predict road conditions ahead. Tactile offers software that uses sensors that already live in the vehicles to predict and feel road conditions like black ice and potholes to improve safety. And with me to talk about these trends and his company is Amit Nisenbaum, who's the CEO of Tactile Mobility, Amit, thanks so much for coming on theCUBE. >> Thank you Dave very much for having me. >> Yeah, so really, it was a great opportunity, when I heard you were in town, invited you out, and really appreciate you coming out to our Marlborough studios, but let me start with, why your founders launched Tactile Mobility. >> Well, Dave, it's a very interesting story, I think, for our company, as well for other entrepreneurs to learn from it, because actually, the company's been around for about eight years, and it all started from a conundrum from a question that was posed to our founder, Boaz Mizrachi, which was about how do you take a vehicle from point A to point B at a set speed, with minimum gas consumption, using only the software and data coming off the vehicle sensors that are run of the mill sensors? And that question started this whole company, he believed that it's only an optimization question, meaning all of the data is out there, meaning data about the conditions of the road, the grates, the curvatures, the conditions and the health of the vehicle, meaning engine efficiency, tire health, et cetera et cetera. And what he found out was that actually neither this nor that has existed. So it was way more complicated than a mere optimization question, it's about how do you generate that data about the vehicle and the road? And he launched the company in order to go after those two data sets. He was able to solve that, or to address that question, and to take a vehicle and to show that you can take a vehicle from point A to point B at a set speed while minimizing fuel consumption, up to 10%. By the time that he has done that, gas prices dropped, and the question was what's next, and fortunately enough, the industry and the hype around autonomous vehicles has come around, and that has been the next frontier for our company, and that's what we been focusing on since then, but not only on that but on also other aspects, which I'll be happy to speak about. >> That is an awesome story of a pivot, you see this all the time with startups, it's kind of survive until you can thrive, and then something happens that's a tailwind, great technology that the visionary can see how to reapply it, and a little bit of luck involved, maybe, okay, so you-- >> Stamina. >> Stamina, right, you got to have a strong heart and stomach to be a startup. Okay, and you joined just a couple years ago, what attracted you to Tactile? >> Well I've been in this industry, actually in the cross section of the two industries of automotive and energy for about 12 years now, starting from a company called Better Place that you might have heard of, I was one of the first 10 employees there, and those two industries have been near and dear to my heart ever since. I like big questions, I like big challenges, I like big plays that have the potential to make a real difference, so the fact that the Tactile Mobility, at the time it was called MobiWize, it was in this industry was a big plus, but also the fact that the offering is not really the vanilla flavor offering, everybody's doing LIDAR and radar and cameras, all of a sudden there is someone else that is saying "Wait a minute, there is that "neglected segment, that additional set "of sensors, the sense of tactility that all of us "are using when we're driving, "and computers will need that as well. "How about that, this is something "that nobody pays attention to." And that really caught my attention. >> So I kind of hinted at this in my little narrative up front, the hype was all around autonomous, but let's face it, level five autonomous, it's, we're talking at least 2030, maybe further, but everybody drives some form of autonomous vehicle today, if you purchase a new vehicle, and that's really the space that you play in, so what are the big trends that you see, and what's the problem that you're solving? >> Yeah, so first of all, you're absolutely right, when people speak about autonomous vehicles, they imagine themself a car, a vehicle with big red button and that's it, that's what is called level five. However, there are four levels below that that lead to that, and today most of the vehicles leaving the assembly line are either level two or level three. That's why we're also saying that we're in the business of smart and autonomous vehicles, and the challenges there, if you're looking at the vehicles themself, are challenges of how do we make those vehicles both safer, as well as more enjoyable to ride? And the ability to address both of those together is actually not as simple as one might think, so that's what we're focusing on, and that's the trend, the trend of no compromises, that you go both for safety, as well as a user experience, that's on the vehicle side. Having said that, being a data company that has a proprietary software stack, that allows it to generate that data, the tactile data, the data about the dynamic between the vehicle and the road, allows us also to take that data to the cloud, and in the cloud to split that dynamic into two separate models. One we model independently the vehicle, the vehicle health, and the other one is we're turning each one of the vehicles to become like a probe that feels the road conditions and maps the location of bumps, cracks, oil spills, black ice, et cetera et cetera, and by that we are able to crowd source the data and create new layer of the map, road conditions there. Going back to the question that was posed about how do you take that vehicle from point A to point B, in minimum fuel, here you go, we have those two types of data, and now we can use it in other verticals as well. >> Well that's very interesting, so a lot of people say "Oh, autonomous vehicles, it's all about real time, "you can't do anything in the cloud," and you actually, you're refuting that, because you're building essentially a map of what's happening on the roads, whether it's a pothole or a bump or a curve, et cetera. And so essentially you're doing that in the cloud, modeling that in the cloud and then what, bringing it down in real time, right? >> Yeah, so first of all, the first use case is indeed to bring it back to the vehicles and so the vehicle, and the vehicles around it, will know what's ahead of them. Use cases, there are about preconditioning vehicle systems, for instance, you're approaching a pothole, probably you want, you meaning the vehicle, would like to tune the suspension to become harder or softer. You're approaching black ice, probably you want, you, the vehicle, would like to slow down, so that's one use case, but there are other use cases. Other use cases around, for instance, road authorities and municipalities, we do have customers around the globe, road authorities and municipalities, that are subscribed to our data services, the road condition data services, that allow them to better plan maintenance, as well as dispatch crews to locations of hazards in real time. >> Yeah, so I remember when I was a kid, we had a CB, that's how you communicated what was ahead. "Hey, watch out, there's a pothole up ahead." >> Great technology. >> Now we're doing that, and now does that essentially require some kind of peer to peer network, or? >> So we're agnostic of the technology, we're the data layer behind all of that. These days, everything, or most of the use cases, are still running on vehicle to cloud to vehicle, or to anybody else, but there are companies that are working on vehicle to vehicle. >> So you mentioned a stack, what does your stack look like, can you describe that a little bit? >> Two parts, one is embedded software, that sits on one of the vehicle computers, one of the ECUs, and the other one is the cloud component, the component, the embedded software that sits on one of the vehicle ECUs usually either the gateway, or one of the vehicle dynamics ECUs, or maybe ADAS ECU, et cetera, it takes in real time, mounds of data for multiple existing nonvisual sensors, such as wheel speed from all four wheels, wheel angle, position of the gas pedal, torque of the brake pedal and much much more, ingest all of that, create a unified signal that describes in real time the dynamic between the vehicle and the road, that signal is very very noisy, so we apply signal processing methodologies to clean it, and then we apply on top of it algorithms and AI and all of that in real time, in order to derive insights about the vehicle road dynamics. You probably ask yourself, "Give me a concrete example" or something like that, 'cause it's kind of amorphous. The killer app these days with OEMs, vehicle manufacturers, is what is called available grip level. It's basically a signal to the vehicle computer about how drastically can the vehicle accelerate, decelerate, or change direction, all different types of acceleration, before it will start to skid. Think about it as the performance envelope of the vehicle. Nobody but us can model this using software only in any condition, and this type of data has multiple use cases in the vehicle, happy to tell you more about those, question is if we have time. >> We do, but I want to make a point. The software only, the thing, if I understand it correctly, the OEM doesn't have to change any hardware that, you're using the existing sensors of the vehicle, of which there are certainly dozens if not hundreds, to actually take advantage of this, right, you don't have to do any kind of hardware changes, is that correct? >> We're a data and data analytics and AI company. >> Yeah, so if you wanted to add some color and double click on some examples, that would be great. >> Sure, so going back to the available grip level type of data, of insight, I call it, think about adaptive cruise control, the function that allows a vehicle to drive at a set speed, however, to avoid colliding into the front vehicle. So today, it seems like all of the data is there for ACC, adaptive cruise control, to be effective, you know the distance from the vehicle, probably using a radar, you know the relative velocity between the two vehicles, so you have all of the information, however you don't know, you, again, the vehicle computer, how hard the vehicle can brake given how slippery the road is, given how healthy or worn out the tires are, et cetera et cetera. That means that the vehicle computer needs to err on the safe side and keep the large distance in order to allow safe braking. What's wrong with that? Going back to the question about the trend before, first of all it's not natural to the driver. We keep a certain distance for a certain reason, and when the distance is too large, it just doesn't feel natural to us. That's one thing. However, on the other side, it's also not safe, how is that? You keep too large of a distance, someone at the end will cut you in. And ironically, you kept a large distance to stay safe, all of a sudden you're worse off. So being able to allow the vehicle to know really, what is the tight distance, safe distance to stay from the vehicle, allows that vehicle to be more enjoyable to ride, as well as safe. >> So take that example, because today, I can sort of personalize that adaptive cruise control and say "Okay, I want one bar, two bar, three bar," but that's it, and I sometimes say "Whoa, is three bar right, is two bar right?" And you're right, sometimes I go "Eh, it's too far, "I think I'll cut it down to two bar or one bar." You're saying with your software, the system is intelligent enough to optimize that, to keep me safe, but also keep me having comfortable driving. >> Absolutely true, actually those three bars is kind of a psychological exercise, right? Because the shortest bar is that large distance. When they tell you two bars or three bars, it's kind of like "Do you want to keep a large, "very large, or extra large distance," right? Because they will never allow you to keep shorter distance shorter than what is really really the bare minimum in order to brake at the worst case scenario. >> Even if it's safe. And that's really where your software comes in, okay. Now Porsche is an investor in the company, presumably it's a customer, right? >> No, they actually said publicly that they're a customer as well. >> Okay, great, so talk about how customers are using this, and what the adoption cycle looks like, and maybe give us some examples of how it's being applied. >> So customers, you mean OEMs, car manufacturers. So the way that they use it, I just described it now, the adoption cycle, we in this industry unfortunately cycles are long. We work years to create relationships with the car manufacturers to allow them to learn about our capabilities, to validate the integrity of our software. They also most commonly run RFPs or RFQs in order to choose the right technology, and I'm glad to say that we're winning again and again and again, and then there is the integration cycle, which by itself is a few years in length. So the cycle altogether is long, however, we found that our approach is quite effective, and the approach, not necessarily the technology, yes, but also the way that we approach those OEMs. We are quite, if I may say, humble. We know that we're not the car engineers, the typical car engineers. We actually know very little about cars, what we know, we know data very well and we know AI very well. And when we come to them, we say "We're not trying to replace your engineers, "we're not trying to do what you do, "we're trying to tackle the same problems "that you weren't able to tackle before "from a very different angle," and that works very well. >> So, you talked about the integration cycle of a couple, or maybe even longer, how long is the design cycle for these things, is it also years, or? >> So, the design cycle from our perspective is much much more agile, actually we are working in the Agile framework in terms of the development of the software itself, but you're asking about the design, much faster, but when I said a few years, a couple of years, I meant per OEM to design together, to allow them to feel that we're designing, meaning customizing the software to their needs, as well as implementing it, that's the length. >> But what they get is a competitive advantage, so Porsche as a leader, obviously, and an early adopter, is going to be able to now commercialize this technology, and of course it'll be embedded, but now it'll be a feature that the car salesperson will highlight, and maybe they market it, maybe they don't, but that gives them a competitive differentiation, right? So are you seeing that other OEMs are starting to really get this, and sort of leaning in, or what's your experience? >> Yes, it's the typical technology adoption curve, there are the early adopters, and there are the mainstream and the late adopters, I'm glad to say that these days we're not only working with the early adopters, but also more with the mainstream. I encourage you to stay tuned, I believe that in the coming month or two, we'll have a big announcement about another major OEM that has chosen us commercially for mass production, and we are in quite advanced stages with OEMs both in Europe and North America, starting also to spin out to Asia. >> And is the business model, is it a subscription model, is it a one time payment from the OEM, how's it work? >> That's another thing that made me excited about the company, going back to your question from before, it's quite diverse, I would say. For the OEMs, that's software that we embed in their vehicle, it's software licensing. However, the data that we generate and then upload to the cloud and repurpose it with the OEMs themself, but also as I said before, road authorities, municipalities, fleet managers, insurance companies, I didn't have a chance to touch on all of the verticals. That's a subscription model, so the two models working together, it's actually quite an attractive, valuable position for us and for our investors. >> So there's software license, and then there's data as a service. And so there's also adjacent industries that you can go after, you just mentioned a couple, so when you think about the total available market, which obviously, any CEO is going to do, TAM expansion is part of your job, but so what's that vision, what does that look like? >> So in terms of the size itself, it's measured in the trillions, it's very very big. In terms of the different verticals, the ones that I tapped on are the first ones, but even within those, these days we're really trying to stay razor focused on the OEMs and road authorities and municipalities. We have fleets and fleet managers that are coming to us with requests for the data that we call vehicle DNA, that's the data about the vehicle health, et cetera, and that's the third vertical that we're starting to address these days, but we're only 25 people, growing to 40, we're trying to be very very agile, that's from one end, and from the other end, now that we showed our value to the car manufacturers, we're going for the force multipliers, meaning partnerships with the channels, with the T1s, the suppliers to the OEMs themself. >> And let's see, you've been around eight years, you've been there two years, right, and then I think you did a raise of roughly, what, nine million to date? >> In October 2019, we announced the latest round of nine million dollars from Porsche, as well as some other investors, yes. >> Great, okay, so I mean not a ton of money, but you guys are small, and so, little bit more on the companies, 20, going to 40, you're well capitalized, but today, you see people raising 250 million, what do you sense as your capital needs, I mean you're obviously actively raising money, and doing what a CEO does, but can you share with us your milestones for the next 12, 18 months? >> First of all, we were fortunate, and fortune has something to do with it, I think that being disciplined is another thing, to have revenue already. So our capital needs, we're still not profitable, and we're growing fast, so we need to raise in order to support that growth, but we're quite diligent about that. Also, true, companies have raised tens and hundreds of millions of dollars. First of all, not all companies in this industry are created equal, we're not a hardware company, we're a software and data. We're also not trying to do a fully integrated offering like, let's say Zuks or something like that, which requires way way more money. And actually, I'm quite glad that we're raising as we need, but not more than that, because what you raise, you need to return tenfold, so we have enough in order to support the growth of the company in years to come. >> Well the OEM model is very sales efficient as well, so it's not like in software companies today, are hiring people to do inside sales, outside sales, enterprise sales, and so it's a different business. Well Amit, first of all, congratulations, a really interesting story, really appreciate you coming out to our studios here in Marlborough and sharing your story, and best of luck to you. >> Thank you very much, Dave, it's been a pleasure coming here, and I'm glad that you invited me. >> Great, and thank you everybody for watching, this is Dave Vellante with theCUBE, we'll see you next time. (techno music)

>> From the SiliconANGLE media office in Boston, Massachusetts, it's theCUBE. Now here's your host, Stu Miniman. (techy music) >> Hi, I'm Stu Miniman, and welcome to a special CUBE conversation here in our Boston area studio. Happy to welcome to the program first time guest, Patrick Morley, who's the CEO of Carbon Black. Of course, recently announced acquisition by VMware of $2.1 billion. Patrick, thanks so much for joining us. >> Stu, thanks for having me. >> All right. So, you know, we love digging into tech. There is no hotter space than security, you know? All the cybers are, you know, really exciting stuff, and even your company's Waltham-based. >> That's right. >> So actually a little closer to Boston than we are here in Marlborough, Massachusetts. When we had a green screen we used to kind of fake it with the skyline, but you know, the Boston area people know more than just Massachusetts Tech, but you know, a lot of, you know, great technology in Boston of course, you know? A lot of good technologies, a lot of good schools that have driven things. You have been CEO since 2007 and have seen quite a bit. You know, merger, Bit9 and Carbon Black many years ago, IPO, you know, not that long ago in the past, and now acquisition, as we said, for $2.1 billion. So, you know, give us a little bit of step back as to, you know, the journey, how we got here, and you know, what's it like to be kind of at the helm with your crew through, you know, all of those changes? >> Yep, well certainly very, very proud and very thankful to all of the customers that have been with us for many, many, many years. And as you said when you first started here, Boston is an awesome place for cybersecurity. I think I fits a bit of the personality on the East Coast, and if you just look at Boston in general there's a lot of great cybersecurity talent, a lot of great cybersecurity companies. And I'm extremely proud and grateful to all of my employees in Massachusetts who have built Carbon Black over the last number of years. And of course we have offices elsewhere across the globe, but Boston is, and Massachusetts is, where the companies roots really come from. And as you said, 2007 is when I joined the company. Obviously cyber was a very different world back then, and it's amazing if you just kind of roll back. In 2007, the idea of a CISO, of a chief information security officer, was still very new, and most companies we dealt with back then did not have a CISO, they had a network administrator or somebody, so that's all changed. If you look at security as a board-level issue, in 2007 there were certainly some areas of some sectors like the government where it had a lot of importance, but outside of that it did not have the same visibility as a strategic issue as it does now, it's been amazing. >> So much, you know, my background is networking and virtualization. I've spent a lot of time, you know, since 2007 looking at all the cloud world, and as I said, back in the early 2000s security was top of mind but often bottom of budget. You know, the network people, you know, back in the day it was like, "Can't you just lock the door," or you know, "Make sure the rack is secure," and you know, "Well we'll run things over Optical," and therefore we'll know if somebody splices into it from a networking standpoint. Today, as you stated, clearly it's a board-level discussion, CISOs, you know, rising power in the organization, and often dictating a lot of how the stack is built out there. >> Absolutely. >> So wow, bring us a little bit, you know, your portfolio. You know, security is not a thing. You know, any customer I talk to, they're like, you know, there is no such thing as a silver bullet in security. Most customers I talk to really think of security as a programmatic effort, so help us understand a little bit, you know, where Carbon Black fits today, and then we'll get into, you know, your, you know, broadened scope once you're going to be under VMware. >> Yeah, so the core founding idea behind Carbon Black was a simple one, which was that fundamentally the adversary was in a position where they eventually would figure out a way to get in, and if you fundamentally believe that then you do everything you can to stop the adversary, but you say, "I need telemetry. "I need data in order to understand what's happening across my environment in order to be able to see and stop the adversary." And so we began a journey to essentially be able to collect and analyze all the data that an adversary, that an attacker would touch in order to run their program, and you know, we always have equated it to essentially a movie camera that allows you to rewind the tape, and with all that data that we collect we can run tremendous analytics against that in order to be able to see and stop the adversary and understand what's happening across the environment. We essentially created a market that's now called EDR endpoint detection and response, and it's that simple idea of being able to understand and have situational analysis, situational visibility across the whole enterprise. We did that initially on-premise, so we did all that analytics, and each one of our customers' back-ends in their data center, and two years ago we began a journey to say, "Look, we want to do two things." One is we want to leverage that data to be able to provide more security capabilities across a platform, so let's revolutionize, continue to revolutionize cybersecurity by offering a cloud-based platform, we're going to move all of that analytics up into the cloud, all those capabilities up into the cloud, and offer a multi-tenant, cloud native SaaS platform, and over the last two years we've done that with multiple services now up on that cloud, with thousands of customers who are using it, and the benefits of the cloud are pretty straightforward, and they've revolutionized other industries, they're revolutionizing cyber right now. Certainly you can analyze data at a scale that's just not possible when that data's locked up in multiple customers, so that's one big change. Obviously-- >> Yeah, I just, to want to help unpack and tease out that data piece, because you know, we always hear out there it almost, you know, is a bit trite, you know, the importance of data. Data's the new oil, it's the rocket ship, but you know, the value of that data, how much of that is Carbon Black leveraging the data, how much can the customer themselves take advantage of that data, or you know, this isn't in a vacuum. There are other security products, other pieces of, you know, that vendor's stack that might be able to leverage that data. >> Yeah, well Carbon Black's cloud native platform, security platform, is built on a totally, it's totally open, so from an API basis, so you should, you should think about, our customers certainly think about it this way, as one, we're leveraging that data, we analyze a trillion security events a day, one trillion, just immense, and the benefit of that is if we see something across the globe that has a high risk score, that's known malware, that might be a new form of attack, that might be a living-off-the-land attack, all of our customers get the benefit of that analytic. So Carbon Black, we certainly leverage it, but in addition, the way we've built the platform, customers can get access to all the data from their enterprise, and they can correlate that data with other aspects of their security or their IT infrastructure in order to build a more holistic view across the entire enterprise, and we also have third party partners out there, managed security service providers and others, who also have access to that data for their customer set to be able to run analytics on it. So when we think about data, as you said, you know, as the oil of the new world, we need to leverage that data, but we also need, in this new world order, to give our partners and our customers the capabilities to do what they want with that data as well for their own data. >> Yeah, love that, especially if you're talking in that cloud native world it can't just be something that's locked up and only used in one environment. You know, we track the observability companies out there, you know, they have similar type of messaging. Of course data protection, you know, once there is that, you know, breach, you know, how do I recover from this information? So that ripple effect, and love, you know, openness, APIs, making sure that can be shared. You know, maybe not something that traditionally I'd heard from VMware when you talk about the openness and where they're doing maybe. I think there are a couple things you want to talk about Carbon Black, but why not get to the VMware piece, too? >> Yeah, I was just going to, on the cloud side, you know, the power of the cloud, obviously it's revolutionized other industries, and certainly one of it is the ability to provide analytics at scale. The other piece, which I already mentioned, is the network effect on my ability to see something somewhere across the globe and help millions of other customers across the globe when I see something, and the other piece is just my ability to deploy quickly and my ability to innovate quickly, because rather than having to deliver new software into each enterprise I can do that on my cloud native platform. So I think it positions the defender, the security teams around the globe where they can be more on the offensive than they've ever been before because suddenly I don't have to spend my time worrying about deployment mechanics or other pieces. I can focus on what I really want to do, which is I want to secure my environment, I want to be able to understand what the adversary might be doing. So we're real excited about what we've done over the last two years with our cloud platform. >> Okay, so the deal hasn't closed yet but it's announced that you will be leading up the cloud security group at VMware. Give us a little bit, you know, directionally, where's that heading, what will that mean? Of course we've tracked, you know, where VMware touches a lot of that environment, you know, with my background in networking I talked to the Nicira team before, and then through what's become a very successful NSX, Sanjay Poonen with the AirWatch acquisition and where they've gone. Of course I would expect that's the closest piece that you started out with the endpoint protection with that team, with the Workforce ONE. So explain kind of the security portfolio, and interesting, cloud security is the discussion because that's the newer piece of the Carbon Black portfolio. Help us understand how the whole, all the pieces fit together. >> Yeah, so first I'll just reiterate what you said, which is the transaction's not yet closed, so everything I'm talking about is pre-closed, and obviously post-close we'll have additional commentary about what everything will look like. But absolutely we are very, my team, my customers, we announced the transaction a little over a month ago. Everyone was really, really excited, and I think fundamentally they're excited because organizations understand what Carbon Black delivers today, and what we deliver are great security products, and increasingly the majority of those products are in the cloud. And VMware has a tremendous reputation in the industry for the technical capabilities, for the value that they provide to customers, and just for the breadth of the portfolio that they have. You mentioned a few of them, right? And many organizations and people think about VMware from a virtualization standpoint. But increasingly over the last few years they've dramatically expanded their portfolio, network virtualization, and the NSX, the Workspace ONE as well, which was based on the AirWatch acquisition they did. Those are big businesses today, and they're helping organizations transform their infrastructure, the way they manage devices, et cetera. And so Carbon Black, on the security side, we've been partnered with VMware for the last couple of years. We've had an opportunity to get to know each other quite well. We've had an opportunity to integrate in two key spots. One, we've integrated with their App D capabilities, which you can think about essentially as helping to protect and provide telemetry for what's happening inside of the virtualized environment. And then secondarily, we've also partnered with Workspace ONE as well, again more on the device side. Those are two natural points where security, building security intrinsically into that compute stack, we've seen with customer reaction, has a fundamental impact on being able to have security right there rather than having to bolt it on afterwards. >> Yeah, you walk into an interesting configuration. First of all, you know, as you said VMware not thought of as a security company per se, lots of products that absolutely fit in the security space and are there. When you look out, of course VMware, you know, primarily owned by Dell, there's Secureworks, there's RSA, those are well known security brands. You know, how, give us how you think of how all those pieces go together and kind of the trajectory of where things are headed. >> Yeah, well goal number one, once we close the transaction, goal number one is to do two things. One, we're going to continue to drive forward with the cloud roadmap that we have. It's an aggressive road map we've been innovating aggressively over the last couple of years and we're going to continue to do that within VMware. The second piece is obviously to maximize the opportunity to build security into the compute stack of VMware, so that when customers think about security they don't have to think about it as a separate piece, but it's already there at their fingertips. And then as you mentioned, so those are two big goals right there, and as you mentioned obviously Dell has a large portfolio. There's other security products within the Dell portfolio, and you know, when we think about that obviously over time we're already partnered with some of those. Secureworks, for example, has been a very close and valuable part of Carbon Black's for many years. You'll see us continue to partner. There's other parts of the Dell family where we have partnered in the past, not tightly, but I think we'll have the opportunity to do more as part of the Dell family. All of this means for customers more value, because rather than having to go and figure it out themselves we're going to be delivering it in conjunction with the solutions they're already using. >> All right, Patrick, I want to help you, have you address a schism I see in the marketplace when it comes to the messaging around security. When peers of mine went to the RSA conference this year they came back almost unanimously with two words, doom and gloom. >> (laughing) Right. >> In Boston this year Amazon held the inaugural re:Inforce, positioned itself as the, you know, cloud security conference for the industry. We covered that, you know, both of those shows with theCUBE, and Stephen Schmidt from AWS said the state of cloud security is strong. VMware, very much we hear from Pat, you know, we need to do over, security's broken. Friends of mine in the security industry, and Carbon Black's been around since 2002, is you know, come on, you know, it's not just another acquisition, it's going to be a point product. You know, yes we have work to do as a whole, but you know, saying we need a do over or it's broken is a between hyperbolic from my peers in the industry, so what is the state of the industry, is there traditional storage and cloud storage is all rainbows and unicorns, or you know, where do you see it today? Of course we know as an industry there's always work to do, but you know, how do you round that circle? >> Yeah, I would take it, and you're right, by the way, I hear all the same commentary, and I think we have to take a step back and just look at industry, the industry in general, look at security in general. We started the interview talking about well, what was the world like in security in 2007? Security has gone from, "Hey, it's a niche area over here "and we know it's important but don't talk to us," to super strategic, again, at a board level, at a company level, and so that rapid growth has driven a lot of funding into the environment, a lot of vendors, there's over 5,000 security vendors out there today, all competing. I don't know how CISOs and CIOs and practitioners really figure out who does what, it's very challenging, and at the same time you've got the adversary, this third party continuing to advance their attack types using new techniques. You've got ransomware, which is a huge industry now, driving billions of dollars, so you have all of that happening, and so in hyper growth environments like that you get a lot of vendors. The average enterprise security team has 75 different products, and so, and they have to stitch that together, so the fundamentals of what, the way you described it I think are accurate on both sides. One, security's broken, it is broken. We've got too many vendors and we're bolting it on, we got to fix that. VMware is in a position, partnered with Carbon Black, to do that I think really well. The second piece is that the cloud does allow us, I'm not sure about rainbows, but the cloud does allow us to change security fundamentally because of some of the characteristics that I described earlier, and if you take Carbon Black plus VMware, plus what VMware is doing to deliver across any cloud, any device, any application, I think we're in a really interesting spot to help customers get more value from their compute stack and from security. >> You know, one of the things that VMware has always done well is they play in multiple environments. Back in the early days of server ritualization, didn't matter what hardware, they would get that across. Their cloud strategy went through quite a few iterations, you know, Sanjay Poonen came on our program and said, you know, "vCloud Air, we failed. "We got it wrong, we did it," but today every cloud show I go to there's a VMware piece of that. They're partnering with AWS, with Azure, with Google, with Alibaba, with Oracle even-- (chuckling) And IBM recently. But still one of the critiques I have for VMware is VMware does good at managing their house, but security, customers, as you said, they've got 75 tools and they're going to have their VMware state, and they're going to have their native cloud pieces, and they're going to have their non-VMware environment. So how can, you know, once you're under VMware, you know, participate in that environment? Will you primarily be VMware environment and the VMware cloud environment, or will it be a broader cloud security strategy? >> Yeah, well I think certainly VMware has done an amazing job over the last few years of really pushing this any-cloud model, right? "Hey, no matter where your workloads "are going to be in a hybrid cloud environment," you know, "we're going to be there to help you," and more effectively, more efficiently, faster, better performance, strong ROI. And so if you look at Carbon Black's roots, and I mentioned this earlier, one of our core beliefs is that one vendor can't do it all. You have to build on an open, extensible API-based platform, and that's what we've done since the beginning of the company, and so you will not see Carbon Black change our philosophy. You know, we will continue to be very, very open, and I think, by the way, that reflects very much VMware's strategy as of late, which is an open strategy where they're playing with lots of providers in the marketplace. Again, the benefit of Carbon Black plus VMware on that platform is that for VMware infrastructure, their products, I think you're going to see out of the box security capabilities that are going to give advantage to customers, from ease of use, from the way that that security works, et cetera, and then we will continue to partner with other vendors out there across the market. >> All right, Patrick, we know, you mentioned how many different tools customers have to deal with. There are more new threats coming out, you know, every day. There's no way that a person or a team can keep up with all of this, so you know, is AI the answer? How are these technologies going to be able to allow our systems to be able to protect us better and update, you know, we haven't talked abut AI yet. I know it does fit in-- >> We have to talk about AI. (chuckling) >> So just to understand how you know, the systems and the software and the solutions are going to help enable teams to be able to keep up with, you know, the rapidly expanding and changing landscape in security. >> Yeah, AI is a tool, we use it, and just as I've mentioned cloud, right, along with the ability to analyze trillions of events on a daily basis, things like AI can play a very significant role in helping me to understand what's happening across very large corpuses of data, and so we use a lot of it, and that allows us to understand when there's an anomaly somewhere across the globe on some system, some endpoint or device, anywhere across the globe and then leverage that to help our other customers. So AI role is playing an important part. It will continue to play an important part. But AI leverages the data that we collect, so if you go back to where Carbon Black is today with all that data that we're analyzing, one of the really interesting things is VMware today has 70 million VMs. 60 million of those are on-prem, 10 million of them are on the cloud. Part of the benefit that Carbon Black gets from VMware is we're going to get all this additional telemetry that we're going to be able to, again, consume, leverage AI capabilities to help with the analysis of that, and again, provide more customer back to the value on seeing and stopping the adversary. That also extends to what VMware's doing on the device side with Workspace ONE, et cetera, so there's a lot of opportunity over the coming quarters and years to provide more value for customers in understanding what's happening across their environment because of all of the touchpoints we're going to have as part of the VMware compute stack. >> All right, Patrick, final thing, what does this mean for your customers? You know, I think back to, you know, not that long ago you did an IPO, you know? What would that mean for the growth, the investment into technology and growing the team. Now, you know, in industry parlance, you know, you had another exit and you will be part of VMware, so we might not get as much visibility into the specific revenues and the hiring that you're doing there, but what will this ultimately mean for Carbon Black's current and potential future customers? >> Yeah, so we have over 5,000 global customers out there today, and first and foremost it's going to mean more investment from a product roadmap standpoint. If you look at 2019, this year, the number one area of investment for Carbon Black was in R&D, and as we move forward, again post-close, our customers are going to see continued investment in the platform, in our cloud security platform, in order to ensure we continue to bring more capabilities to market. And then, as I said earlier, in conjunction with that do everything we can to integrate in with the VMware product portfolio, again, so that security's not bolted on but it's intrinsic to the compute stack, and so I think that's the biggest thing. I have had the opportunity to go out and speak to many customers over the last four weeks. Customer and partner reaction has been outstanding. They get it, they understand it, they understand that there's a better way and that's what we're going to be doing as part of VMware. >> Yeah, any surprising nuggets in the last month talking to the customers and partners more that you've learned? >> This is going to sound self-serving, but it's the truth. I will tell you that the VMware reputation out there is outstanding. I mean, and I had been surprised at how little I have to do to tell them why this makes so much sense. They get it, the majority of our customers get it. They understand the possibilities of what we can provide, and there's a level of excitement out there, again with our customers and partners. It's just, it's awesome. >> All right, Patrick Morley, CEO of Carbon Black. Thank you so much for joining us on theCUBE. >> Stu, thanks. >> All right, lots of coverage, of course, through 2019 and gearing up for 2020 where we'll all have perfect hindsight, I'm sure. Check out thecube.net for the events we've been at, search where we're going to be, and please reach out if you have any questions. I'm Stu Miniman, and as always, thank you for watching theCUBE. (techy music)

>> From the Silicon Angle Media office in Boston, Massachusetts, It's theCUBE. (upbeat music) Now, here's your host, Stu Miniman. >> Hi. I'm Stu Miniman, and this is theCUBE's Boston area studio; our actually brand-new studio, and I'm really excited to have I believe is a first-time guest, a long-time caller, you know, a long time listener >> Yeah, yep. first time caller, good buddy of mine Dave Graham, who is the director, is a director of emerging technologies: messaging at Dell Technologies. Disclaimer, Dave and I worked together at a company some of you might have heard on the past, it was EMC Corporation, which was a local company. Dave and I both left EMC, and Dave went back, after Dell had bought EMC. So Dave, thanks so much for joining, it is your first time on theCUBE, yes? >> It is the first time on theCUBE. >> Yeah, so. >> Lets do some, Some of the first times that I actually interacted with, with this team here, you and I were bloggers and doing lots of stuff back in the industry, so it's great to be able to talk to you on-camera. >> Yeah, same here. >> All right, so Dave, I mentioned you were a returning former EMC-er, now Dell tech person, and you spent some time at Juniper, at some startups, but give our audience a little bit about your background and your passions. >> Oh, so background-wise, yep, so started my career in technology, if you will, at EMC, worked, started in inside sales of all places. Worked my way into a consulting/engineer type position within ECS, which was, obviously a pretty hard-core product inside of EMC now, or Dell Technologies now. Left, went to a startup, everybody's got to do a start up at some point in their life, right? Take the risk, make the leap, that was awesome, was actually one of those Cloud brokers that's out there, like Nasuni, company called Sertis. Had a little bit of trouble about eight months in, so it kind of fell apart. >> Yeah, the company did, not you. >> The company did! (men laughing) I was fine, you know, but the, yeah, the company had some problems, but ended up leaving there, going to Symantec of all places, so I worked on the Veritas side, kind of the enterprise side, which just recently got bought out by Avago, evidently just. >> Broadcom >> Broadcom, Broadcom, art of the grand whole Avago. >> Dave, Dave, you know we're getting up there in years and our tech, when we keep talking about something 'cause I was just reading about, right, Broadcom, which was of course Avago bought Broadcom in the second largest tech acquisition in history, but when they acquired Broadcom, they took on the name because most people know Broadcom, not as many people know Avago, even those of us with backgrounds in the chip semiconductor and all those pieces. I mean you got Brocade in there, you've got some of the software companies that they've bought over the time, so some of those go together. But yeah, Veritas and Symantec, those of us especially with some storage and networking background know those brands well. >> Absolutely, PLX's being the PCI switched as well, it's actually Broadcom, those things. So yeah, went from Symantec after a short period of time there, went to Juniper Networks, ran part of their Center of Excellence, kind of a data center overlay team, the only non-networking guy in a networking company, it felt like. Can't say that I learned a ton about the networking side, but definitely saw a huge expansion in the data center space with Juniper, which was awesome to see. And then the opportunity came to come back to Dell Technologies. Kind of a everything old becoming new again, right? Going and revisiting a whole bunch of folks that I had worked with 13, you know, 10 years ago. >> Dave, it's interesting, you know, I think about, talk about somebody like Broadcom, and Avago, and things like that. I remember reading blog posts of yours, that you'd get down to some of that nitty-level, you and I would be ones that would be the talk about the product, all right now pull the board out, let me look at all the components, let me understand, you know, the spacing, and the cooling, and all the things there, but you know here it's 2019, Dave. Don't you know software is eating the world? So, tell us a little bit about what you're working on these days, because the high-level things definitely don't bring to mind the low-level board pieces that we used to talk about many years ago. >> Exactly, yeah, it's no longer, you know, thermals and processing power as much, right? Still aspects of that, but a lot of what we're focused on now, or what I'm focused on now is within what we call the emerging technology space. Or horizon 2, horizon 3, I guess. >> Sounds like something some analyst firm came up with, Dave. (Dave laughing) >> Yeah, like Industry 4.0, 5.0 type stuff. It's all exciting stuff, but you know when you look at technologies like five, 5G, fifth generation wireless, you know both millimeter waves, sub six gigahertz, AI, you know, everything old becoming new again, right? Stuff from the fifties, and sixties that's now starting to permeate everything that we do, you're not opening your mouth and breathing unless you're talking about AI at some point, >> Yeah, and you bring up a great point. So, we've spent some time with the Dell team understanding AI, but help connect for our audience that when you talk high AI we're talking about, we're talking about data at the center of everything, and it's those applications, are you working on some of those solutions, or is it the infrastructure that's going to enable that, and what needs to be done at that level for things to work right? >> I think it's all of the above. The beauty of kind of Dell Technologies that you sit across, both infrastructure and software. You look at the efforts and the energies, stuff like VMware buying, BitFusion, right, as a mechanism trying to assuage some of that low-level hardware stuff. Start to tap into what the infrastructure guys have always been doing. When you bring that kind of capability up the stack, now you can start to develop within the software mindset, how, how you're going to access this. Infrastructure still plays a huge part of it, you got to run it on something, right? You can't really do serverless AI at this point, am I allowed to say that? (man laughing) >> Well, you could say that, I might disagree with you, because absolutely >> Eh, that's fine. there's AI that's running on it. Don't you know, Dave, I actually did my serverless 101 article that I had, I actually had Ashley Gorakhpurwalla, who is the General Manager of Dell servers, holding the t-shirt that "there is no serverless, it's just, you know, a function that you only pay the piece that you need when you need and everything there." But the point of the humor that I was having there is even the largest server manufacturer in the world knows that underneath that serverless discussion, absolutely, there is still infrastructure that plays there, just today it tends to primarily be in AWS with all of their services, but that proliferation, serverless, we're just letting the developers be developers and not have to think about that stuff, and I mean, Dave, the stuff we've had background, you know, we want to get rid of silos and make things simpler, I mean, it's the things we've been talking about for decades, it's just, for me it was interesting to look at, it is very much a developer application driven piece, top-down as opposed to so many of the virtualization and infrastructure as a service is more of a bottom-up, let me try to change this construct so that we can then provide what you need above it, it's just a slightly different way of looking at things. >> Yeah, and I think we're really trying to push for that stuff, so you know you can bundle together hardware that makes it, makes the development platform easy to do, right? But the efforts and energy of our partnerships, Dell has engaged in a lot of partnerships within the industry, NVIDIA, Intel, AMD, Graphcore, you name it, right? We're out in that space working along with those folks, but a lot of that is driven by software. It's, you write to a library, like Kudu, or, you know pyEight, you know, PyTorch, you're using these type of elements and you're moving towards that, but then it has to run on something, right? So we want to be in that both-end space, right? We want to enable that kind of flexibility capability, and obviously not prevent it, but we want to also expose that platform to as many people within the industry as possible so they can kind of start to develop on it. You're becoming a platform company, really, when it comes down to it. >> I don't want to get down the semantical arguments of AI, if you will, but what are you hearing from customers, and what's some kind of driving some of the discussions lately that's the reality of AI as opposed to some of just the buzzy hype that everybody talks about? >> Well I still think there's some ambiguity in market around AI versus automation even, so what people that come and ask us are well, "you know, I believe in this thing called artificial intelligence, and I want to do X, Y, and Z." And these particular workloads could be better handled by a simple, not to distill it down to the barest minimum, but like cron jobs, something that's, go back in the history, look at the things that matter, that you could do very very simply that don't require a large amount of library, or sort of an understanding of more advanced-type algorithms or developments that way. In the reverse, you still have that capability now, where everything that we're doing within industry, you use chat-bots. Some of the intelligence that goes into those, people are starting to recognize, this is a better way that I could serve my customers. Really, it's that business out kind of viewpoint. How do I access these customers, where they may not have the knowledge set here, but they're coming to us and saying, "it's more than just, you know, a call, an IVR system," you know, like an electronic IVR system, right? Like I come in and it's just quick response stuff. I need some context, I need to be able to do this, and transform my data into something that's useful for my customers. >> Yeah, no, this is such a great point, Dave. The thing I've asked many times, is, my entire career we've talked about intelligence and we've talked about automation, what's different about it today? And the reality is, is it used to be all right. I was scripting things, or I would have some Bash processes, or I would put these things together. The order of magnitude and scale of what we're talking about today, I couldn't do it manually if I wanted to. And that automation is really, can be really cool these days, and it's not as, to set all of those up, there is more intelligence built into it, so whether it's AI or just machine learning kind of underneath it, that spectrum that we talk about it, there's some real-use cases, a real lot of things that are happening there, and it definitely is, order of magnitudes more improved than what we were talking about say, back when we were both at EMC and the latest generation of Symmetrix was much more intelligent than the last generation, but if you look at that 10 years later, boy, it's, it is night and day, and how could we ever have used those terms before, compared to where we are today. >> Yeah it's, it's, somebody probably at some point coined the term, "exponential". Like, things become exponential as you start to look at it. Yeah, the development in the last 10 years, both in computing horsepower, and GPU/GPGPU horsepower, you know, the innovation around, you know FPGAs are back in a big way now, right? All that brainpower that used to be in these systems now, you now can benefit even more from the flexibility of the systems in order to get specific workloads done. It's not for everybody, we all know that, but it's there. >> I'm glad you brought up FPGAs because those of us that are hardware geeks, I mean, some reason I studied mechanical engineering, not realizing that software would be a software world that we live in. I did a video with Amy Lewis and she's like, "what was your software-defined moments?" I'm like, "gosh, I'm the frog sitting in the pot, and, would love to, if I can't network-diagram it, or put these things together, networking guy, it's my background! So, the software world, but it is a real renaissance in hardware these days. Everything from the FPGAs you mentioned, you look at NVIDIA and all of their partners, and the competitors there. Anything you geeking out on the hardware side? >> I, yeah, a lot of the stuff, I mean, the era of GPU showed up in a big way, all right? We have NVIDIA to thank for that whole, I mean, the kudos to them for developing a software ecosystem alongside a hardware. I think that's really what sold that and made that work. >> Well, you know, you have to be able to solve that Bitcoin mining problem, so. >> Well, you know, depending on which cryptocurrency you did, EMD kind of snuck in there with their stuff and they did some of that stuff better. But you have that kind of competing architecture stuff, which is always good, competition you want. I think now that what we're seeing is that specific workloads now benefit from different styles of compute. And so you have the companies like Graphcore, or the chip that was just launched out of China this past week that's configurable to any type of network, enteral network underneath the covers. You see that kind of evolution in capability now, where general purpose is good, but now you start to go into reconfigurable elements so, I'll, FPGAs are some of these more advanced chips. The neuromorphic hardware, which is always, given my background in psychology, is always interesting to me, so anything that is biomorphic or neuromorphic to me is pinging around up here like, "oh, you're going to emulate the brain?" And Intel's done stuff, BraincChip's done stuff, Netspace, it's amazing. I just, the workloads that are coming along the way, I think are starting to demand different types or more effectiveness within that hardware now, so you're starting to see a lot of interesting developments, IPUs, TPUs, Teslas getting into the inferencing bit now, with their own hardware, so you see a lot of effort and energy being poured in there. Again, there's not going to be one ring to rule them all, to cop Tolkien there for a moment, but there's going to be, I think you're going to start to see the disparation of workloads into those specific hardware platforms. Again, software, it's going to start to drive the applications for how you see these things going, and it's going to be the people that can service the most amount of platforms, or the most amount of capability from a single platform even, I think are the people who are going to come out ahead. And whether it'll be us or any of our August competitors, it remains to be seen, but we want to be in that space we want to be playing hard in that space as well. >> All right Dave, last thing I want to ask you about is just career. So, it's interesting, at Vmworld, I kind of look at it in like, "wow, I'm actually, I'm sitting at a panel for Opening Acts, which is done by the VMunderground people the Sunday, day before VMworld really starts, talking about jobs and there's actually three panels, you know, careers, and financial, and some of those things, >> I'm going to be there, so come on by, >> Maybe I should join startin' at 1 o'clock Monday evening, I'm actually participating in a career cafe, talking about people and everything like that, so all that stuff's online if you want to check it out, but you know, right, you said psychology is what you studied but you worked in engineering, you were a systems engineer, and now you do messaging. The hardcore techies, there's always that boundary between the techies and the marketings, but I think it's obvious to our audience when they hear you geeking out on the TPUs and all the things there that you are not just, you're quite knowledgeable when it comes about the technology, and the good technical marketers I find tend to come from that kind of background, but give us a little bit, looking back at where you've been and where you're going, and some of those dynamics. >> Yeah, I was blessed from a really young age with a father who really loved technology. We were building PCs, like back in the eighties, right, when that was a thing, you know, "I built my AMD 386 DX box" >> Have you watched the AMC show, "Halt and Catch Fire," when that was on? >> Yeah, yeah, yeah, so there was that kind of, always interesting to me, and I, with the way my mind works, I can't code to save my life, that's my brother's gift, not mine. But being able to kind of assemble things in my head was kind of always something that stuck in the back. So going through college, I worked as a lab resident as well, working in computer labs and doing that stuff. It's just been, it's been a passion, right? I had the education, was very, you know, that was my family, was very hard on the education stuff. You're going to do this. But being able to follow that passion, a lot of things fell into place with that, it's been a huge blessing. But even in grad school when I was getting my Masters in clinical counseling, I ran my own consulting business as well, just buying and selling hardware. And a lot of what I've done is just I read and ask a ton of questions. I'm out on Twitter, I'm not the brightest bulb in the, of the bunch, but I've learned to ask a lot of questions and the amount of community support in that has gotten me a lot of where I am as well. But yeah, being able to come out on this side, marketing is, like you're saying, it's kind of an anathema to the technical guys, "oh those are the guys that kind of shine the, shine the turd, so to speak," right? But being able to come in and being able to kind of influence the way and make sure that we're technically sound in what we're saying, but you have to translate some of the harder stuff, the more hardcore engineering terms into layman's terms, because not everybody's going to approach that. A CIO with a double E, or an MS in electrical engineering are going on down that road are very few and far between. A lot of these folks have grown up or developed their careers in understanding things, but being able to kind of go in and translate through that, it's been a huge blessing, it's nice. But always following the areas where, networking for me was never a strong point, but jumping in, going, "hey, I'm here to learn," and being willing to learn has been one of the biggest, biggest things I think that's kind of reinforced that career process. >> Yeah, definitely Dave, that intellectual curiosity is something that serves anyone in the tech industry quite well, 'cause, you know, nobody is going to be an expert on everything, and I've spoken to some of the brightest people in the industry, and even they realize nobody can keep up with all of it, so that being able to ask questions, participate, and Dave, thank you so much for helping me, come have this conversation, great as always to have a chat. >> Ah, great to be here Stu, thanks. >> Alright, so be sure to check out the theCUBE.net, which is where all of our content always is, what shows we will be at, all the history of where we've been. This studio is actually in Marlborough, Massachusetts, so not too far outside of Boston, right on the 495 loop, we're going to be doing lot more videos here, myself and Dave Vellante are located here, we have a good team here, so look for more content out of here, and of course our big studio out of Palo Alto, California. So if we can be of help, please feel free to reach out, I'm Stu Miniman, and as always, thanks for watching theCUBE. (upbeat electronic music)

>> Live from Miami Beach, Florida, It's theCUBE covering VeeamON 2019. Brought to you by Veeam. >> Welcome back to Miami, everybody. This is Dave Vellante with Peter Burris. We're here at day one at VeeamON 2019. This is CUBE's 3rd year of doing VeeamON. We started in New Orleans, it was a great show. Last year was Chicago, and here, Miami at the Fontainbleau hotel. Marc Crespi is here, he's the vice president of sales engineering for the Americas at ExaGrid Systems Cube. Hello Marc, good to see you again. >> Good to see you. >> Thanks for coming on. So, give us the update. What's happening with ExaGrid? You guys got new headquarters in Marlborough. Marlborough's happening these days, right? We got the new shopping spa, and the mayor's going crazy, so give us the update on ExaGrid. >> Yes, so we just moved into a beautiful new headquarters in Marlborough and share it with some great other companies. The company continues to grow rapidly, double digit growth year over year, one of the few companies in this category that's growing that quickly. So everything's great. >> What's driving the growth? >> Well, customers are looking to fix the economics of backup. They've been spending too much money on it for a lot of years, so they look at products now, they want them to be simple, easy to use, and very cost-effective and we drive that trend very hard. >> Yeah I mean that doesn't really describe- what you just described, simple, easy to use, and cost-effective really doesn't describe backup for the past 20 years. So what are you doing specifically to make it simple, cost-effective, and easy to use? >> Well, first of all, by working with companies like Veeam. Veeam is a very easy-to-use product, it's very intuitive and then our product integrates very well with it so the products work together very well and makes just a very simple solution. >> What do you see as other big trends in backup? showed a slide today, 15 billion dollars. A big chunk of that, maybe close to half of it was backup and recovery, there's all kind of other stuff: data management, analytics, etc, etc, etc. What do you see, obviously cloud, you talked about the big superpowers, what are the big trends that are driving your business and more importantly, your customers transformation? >> Well, customers are looking to reduce the amount of data that they actually have to move. So, incremental technology's a really big- themes of pioneer in that, obviously doing incremental backups and that saves time and effort, saves space, along with data deduplication, it really makes for cost-effective storage solution. >> Talk a little bit more about why you're growing, how you sort of uniquely compete in the marketplace with some of the big whales. >> Sure, so our most unique feature is our architecture, and it has both technical aspects and economic aspects. Because we're a scale-out architecture, meaning that with every capacity increase of your data, we're not just adding storage, we're adding CompuPower network memory, etc. so that we keep the backup times very, very, very low. That also makes for a very cost-effective architecture because what we've done is you can scale out pretty much infinitely and we've also eliminated the concept of the end of a life of products. So we never force our customers into mandatory refreshes so their economics are very predictable over a long period of time. >> What do you see as the biggest use cases today that are driving your business? I mean, obviously, backup and recovery, I talked earlier about some of these emerging data management, cloud obviously, is this big, Edge, you seeing much going on there. What are some of those workloads and use cases that you see? >> I think probably one of the biggest use cases these days is what I would call instant recoveries, meaning that rather than doing a traditional restore, which could take a long number of minutes to hours. Customers will actually run production workloads off of the backup target as a way to get users back productive more quickly than would've been done in the past. >> Yeah, and that's key because you see in RPO and RTO's sort of companies putting more and more pressure on the IT groups to shrink those times, presuming you're seeing that in conjunction with digital, digital business, digital transformation. You talked about architecture before. What about your architecture and maybe with your partnership with Veeam allows customers to shrink those RPO and RTO times? >> I think the other aspect of our architecture that's very unique is what we called adaptive deduplication. One of the things we looked at when we architected the product was deduplication is obviously a very effective technology, but what are potential cons. Things that would make it less effective in backup. And one of the things we realized was if you put deduplication in the middle of the backup window and due to deduplication while the backups are running, then you could interfere with the speed of disk. So we do something called adaptive deduplication which means that we allow the object from the backup software to land and then we deduplicate and replicate them in parallel, but we make sure that we're not throttling the backups. So, we provide disk speeds even though we use deduplication. >> Okay. So, that's an example of one of the things you're doing to sort of improve it. How about Veeam integration? Is there anything specific there that you're doing that we should know about? >> Well, part of it is because of adaptive deduplication and because we maintain complete copies of backups. We uniquely support instant Veeam recovery like no other vendor can. Furthermore, we run what's called the Veeam data remover which is actually Veeam technology runs inside of our appliance and sets up a optimized communication protocol with the Veeam software that allows us to do a number of great things. >> Wait, double click on on that. So, is it an efficient protocol or is there other sort of accelerators that you've got in there? >> The protocol is optimized, and then we do some other acceleration around how you do synthetic folds and things of that sort that are unique to the data mover. >> And you have news with Veeam this week, do you not? >> Yes, we do. We're announcing something called ExaGrid backup with Veemam and what it is in a nutshell is the ability for a customer to purchase both technologies from their preferred reseller by just ordering one part number. So it dramatically simplifies the acquisition of the two technologies and allows customers to simplify the buying process. >> So Veeam, I know, is all channel sales. How about you guys? How do you go to market? >> We also are, yes. >> So, talk more about your go-to market. What do you have? Like, an overlay sales force that it helps facilitate? You got partners? Maybe you can talk more about your ecosystem. >> Well, we have a worldwide sales force and our sales people, the people that do the selling, work directly with our partners, so we don't have a specialized channel workforce, but we have a specialized channel strategy, and our entire sales team is very well trained on the channel, how to work with the channel, and make them happy and successful. >> So, backup for a long time time was kind of an afterthought. It was non-differentiated. You just did what you needed to make sure the devices could be recovered. >> Yeah, you bolted it on. >> You bolted it on. >> Right. >> Increasingly, it's becoming recognized as a central capability to any digital business, because if your data goes away or your data's no longer available, your digital business is gone. >> Right. >> That suggests we're going to get a greater degree of differentiation in the types of devices, in the types of systems, etc, that are going to become part of a backup solution. First of all, do you agree with that? And then secondly, go back to the use cases, where do you guys see yourselves fitting into that increasingly federated backup capability? >> Well, I certainly do agree with it. I mean, it's always been a necessity, but now even with things like Ransomware and the cryptoviruses, and things of that sort, it's even more important than it's ever been. It's no longer just data loss, etc. So, we fit into that trend and we'll continue to fit into that trend by continuing to drive the economics through the floor. Customers want that level of protection, it's a little bit like insurance. You need the protection, but you don't want to pay a dollar more than you have to, right? So you want to put it on an economic diet, and the way our technology evolves, we come out with denser, faster systems at a lower cost per terabyte just about every year. And we'll continue to do that. >> So do you anticipate then that there's going to be specialized use cases or are you just going after taking costs out of the equation? >> It's not so specialized because it's very horizontal. Everybody does it and everybody backs up all their data. So, we don't specialize in any one area of the data center like database or anything of that sort. We go wherever the customer needs us to go inside their data center. >> It's in the data center, sorry David, it's in the data center. >> In the data center, we also have a cloud offering, we have partners that will offer disaster recovery as a service, so they'll have data centers that manage on behalf of the customers, and we also have an offering that goes into Amazon web services. And, shortly, we'll be coming out with one for Azure. >> And that is what? A software based offering that uses the cloud as a target? >> Correct, it's a virtual appliance that you can replicate into the cloud. >> All right. We don't have much time left tonight, we have a really important topic to cover, which is, we talked about last year, but I want to bring it up again, which is sports. >> Yup. Why don't we talk Boston sports, we could talk about Warriors. I got a question for you, but- >> I'll watch >> I asked you last year, and I think it was May, we were in Chicago, I said "Would you have traded Tom Brady?" At a time when the sentiment was, he was done. And you said "No way, absolutely not." You, Peter McKay, and Patrick Osmond all said emphatically no, you made the right call. So good job. >> Thank you. >> Your thoughts? >> Would never trade him. He can play until he's 100 for all I care. As long as he keeps performing at such a high level, why would you lose him? >> And then, of course, the Red Sox, 108 wins, that was an amazing gift that they gave us. So, I don't know if you're a baseball fan. >> I am. >> All right, I got to ask you, Peter. Are the Warriors the greatest basketball team in the history of basketball? >> Well, let's see... >> Brendan says yes. >> They are the best basketball team at a time of the most competitive NBA. Some of the rules have changed, but the athletes are better, they're more conditioned, they are more knowledgeable by how to play this game, and they are the best team in basketball without Kevin Durant and without Boogie Cousins. >> Yeah. >> So ... hard to argue. >> They're sweeping Portland without Durant which is pretty amazing. So Brendan, for years, has been trying to tell me that. You know, Brendan is our local basketball genius so, I don't know. >> Now, would the Warriors have beaten say a Bill Russell Celtics team with the Celtics- Bill Russell Celtics team rules? Maybe not. >> Yeah, I don't know. I would say I'm starting to come around to Brendan's way of thinking. But, Marc, we'll give you the last word here. VeeamON 2019, great venue here in Miami, very hip, hip company, hip venue, ExaGrid growing, double digit growth rate, so congratulations on that. Your final thoughts? >> Just great to be here, I always like coming to Veeam events, they're always very well attended, I get to meet a lot of customers and really enjoy it. >> Marc Crespi, thanks very much for coming to theCUBE. It's great to see you again. >> Thank you. >> All right, keep it right there everybody. Peter and I will be back with our next guest right after this short break. This is VeeamON 2019 and you're watching theCUBE.

(orchestral music) >> Hi, I'm Peter Burris and welcome to another digital community event. As we do with all digital community events, we're gonna start off by having a series of conversations with real thought leaders about a topic that's pressing to today's enterprises as they try to achieve new classes of business outcomes with technology. At the end of that series of conversations, we're gonna go into a crowd chat and give you an opportunity to voice your opinions and ask your questions. So stay with us throughout. So, what are we going to be talking about today? We're going to be talking about the challenge that businesses face as they try to apply AI, ML, and new classes of analytics to their very challenging, very difficult, but nonetheless very value-producing outcomes associated with data. The challenge that all these businesses have is that often, you spend too much time in the infrastructure and not enough time solving the problem. And so what's required is new classes of technology and new classes of partnerships and business arrangements that allow for us to mask the underlying infrastructure complexity from data science practitioners, so that they can focus more time and attention on building out the outcomes that the business wants and a sustained business capability so that we can continue to do so. Once again, at the end of this series of conversations, stay with us, so that we can have that crowd chat and you can, again, ask your questions, provide your insights, and participate with the community to help all of us move faster in this crucial direction for better AI, better ML and better analytics. So, the first conversation we're going to have is with Anant Chintamaneni. Anant's the Vice President of Products at BlueData. Anant, welcome to theCUBE. >> Hi Peter, it's great to be here. I think the topic that you just outlined is a very fascinating and interesting one. Over the last 10 years, data and analytics have been used to create transformative experiences and drive a lot of business growth. You look at companies like Uber, AirBnB, and you know, Spotify, practically, every industry's being disrupted. And the reason why they're able to do this is because data is in their DNA; it's their key asset and they've leveraged it in every aspect of their product development to deliver amazing experiences and drive business growth. And the reason why they're able to do this is they've been able to leverage open-source technologies, data science techniques, and big data, fast data, all types of data to extract that business value and inject analytics into every part of their business process. Enterprises of all sizes want to take advantage of that same assets that the new digital companies are taking and drive digital transformation and innovation, in their organizations. But there's a number of challenges. First and foremost, if you look at the enterprises where data was not necessarily in their DNA and to inject that into their DNA, it is a big challenge. The executives, the executive branch, definitely wants to understand where they want to apply AI, how to kind of identify which huge cases to go after. There is some recognition coming in. They want faster time-to-value and they're willing to invest in that. >> And they want to focus more on the actual outcomes they seek as opposed to the technology selection that's required to achieve those outcomes. >> Absolutely. I think it's, you know, a boardroom mandate for them to drive new business outcomes, new business models, but I think there is still some level of misalignment between the executive branch and the data worker community which they're trying to upgrade with the new-age data scientists, the AI developer and then you have IT in the middle who has to basically bridge the gap and enable the digital transformation journey and provide the infrastructure, provide the capabilities. >> So we've got a situation where people readily acknowledge the potential of some of these new AI, ML, big data related technologies, but we've got a mismatch between the executives that are trying to do evidence-based management, drive new models, the IT organization who's struggling to deal with data-first technologies, and data scientists who are few and far between, and leave quickly if they don't get the tooling that they need. So, what's the way forward, that's the problem. How do we move forward? >> Yeah, so I think, you know, I think we have to double-click into some of the problems. So the data scientists, they want to build a tool chain that leverages the best in-class, open source technologies to solve the problem at hand and they don't want, they want to be able to compile these tool chains, they want to be able to apply and create new algorithms and operationalize and do it in a very iterative cycle. It's a continuous development, continuous improvement process which is at odds with what IT can deliver, which is they have to deliver data that is dispersed all over the place to these data scientists. They need to be able to provide infrastructure, which today, they're not, there's an impotence mismatch. It takes them months, if not years, to be able to make those available, make that infrastructure available. And last but not the least, security and control. It's just fundamentally not the way they've worked where they can make data and new tool chains available very quickly to the data scientists. And the executives, it's all about faster time-to-value so there's a little bit of an expectation mismatch as well there and so those are some of the fundamental problems. There's also reproducibility, like, once you've created an analytics model, to be able to reproduce that at scale, to be then able to govern that and make sure that it's producing the right results is fundamentally a challenge. >> Audibility of that process. >> Absolutely, audibility. And, in general, being able to apply this sort of model for many different business problems so you can drive outcomes in different parts of your business. So there's a huge number of problems here. And so what I believe, and what we've seen with some of these larger companies, the new digital companies that are driving business valley ways, they have invested in a unified platform where they've made the infrastructure invisible by leveraging cloud technologies or containers and essentially, made it such that the data scientists don't have to worry about the infrastructure, they can be a lot more agile, they can quickly create the tool chains that work for the specific business problem at hand, scale it up and down as needed, be able to access data where it lies, whether it's on-prem, whether it's in the cloud or whether it's a hybrid model. And so that's something that's required from a unified platform where you can do your rapid prototyping, you can do your development and ultimately, the business outcome and the value comes when you operationalize it and inject it into your business processes. So, I think fundamentally, this start, this kind of a unified platform, is critical. Which, I think, a lot of the new age companies have, but is missing with a lot of the enterprises. >> So, a big challenge for the enterprise over the next few years is to bring these three groups together; the business, data science world and infrastructure world or others to help with those problems and apply it successfully to some of the new business challenges that we have. >> Yeah, and I would add one last point is that we are on this continuous journey, as I mentioned, this is a world of open source technologies that are coming out from a lot of the large organizations out there. Whether it's your Googles and your Facebooks. And so there is an evolution in these technologies much like we've evolved from big data and data management to capture the data. The next sort of phase is around data exploitation with artificial intelligence and machine learning type techniques. And so, it's extremely important that this platform enables these organizations to future proof themselves. So as new technologies come in, they can leverage them >> Great point. >> for delivering exponential business value. >> Deliver value now, but show a path to delivery value in the future as all of these technologies and practices evolve. >> Absolutely. >> Excellent, all right, Anant Chintamaneni, thanks very much for giving us some insight into the nature of the problems that enterprises face and some of the way forward. We're gonna be right back, and we're gonna talk about how to actually do this in a second. (light techno music) >> Introducing, BlueData EPIC. The leading container-based software platform for distributed AI, machine learning, deep learning and analytics environments. Whether on-prem, in the cloud or in a hybrid model. Data scientists need to build models utilizing various stacks of AI, ML and DL applications and libraries. However, installing and validating these environments is time consuming and prone to errors. BlueData provides the ability to spin up these environments on demand. The BlueData EPIC app store includes, best of breed, ready to run docker based application images. Like TensorFlow and H2O driverless AI. Teams can also add their own images, to provide the latest tools that data scientists prefer. And ensure compliance with enterprise standards. They can use the quick launch button. which provides pre configured templates with the appropriate application image and resources. For example, they can instantly launch a new Sandbox environment using the template for TensorFlow with a Jupyter Notebook. Within just a few minutes, it'll be automatically configured with GPUs and easy access to their data. Users can launch experiments and make GPUs automatically available for analysis. In this case, the H2O environment was set up with one GPU. With BlueData EPIC, users can also deploy end points with the appropriate run time. And the inference run times can use CPUs or GPUs. With a container based BlueData Platform, you can deploy fully configured distributed environments within a matter of minutes. Whether on-prem, in the public cloud, or in a hybrid a architecture. BlueData was recently acquired by Hewlett Packward Enterprise. And now, HPE and BlueData are joining forces to help you on your AI journey. (light techno music) To learn more, visit www.BlueData.com >> And we're back. I'm Peter Burris and we're continuing to have this conversation about how businesses are turning experience with the problems of advance analytics and the solutions that they seek into actual systems that deliver continuous on going value and achieve the business capabilities required to make possible these advanced outcomes associated with analytics, AI and ML. And to do that, we've got two great guests with us. We've got Kumar Sreekanti, who is the co-founder and CEO of BlueData. Kumar, welcome back to theCUBE. >> Thank you, it is nice to be here, back again. >> And Kumar, you're being joined by a customer. Ramesh Thyagarajan, is the executive director of the Advisory Board Company which is part of Optum now. Ramesh, welcome to theCUBE. >> Great to be here. >> Alright, so Kumar let's start with you. I mentioned up front, this notion of turning technology and understanding into actual business capabilities to deliver outcomes. What has been BlueData's journey along, to make that happen? >> Yeah, it all started six years ago, Peter. It was a bold vision and a big idea and no pun intended on big data which was an emerging market then. And as everybody knows, the data was enormous and there was a lot of innovation around the periphery. but nobody was paying attention to how to make the big data consumable in enterprise. And I saw an enormous opportunity to make this data more consumable in the enterprise and to give a cloud-like experience with the agility and elasticity. So, our vision was to build a software infrastructure platform like VMware, specially focused on data intensity distributed applications and this platform will allow enterprises to build cloud like experiences both on enterprise as well as on hybrid clouds. So that it pays the journey for their cloud experience. So I was very fortunate to put together a team and I found good partners like Intel. So that actually is the genesis for the BlueData. So, if you look back into the last six years, big data itself has went through a lot of evolution and so the marketplace and the enterprises have gone from offline analytics to AI, ML based work loads that are actually giving them predictive and descriptive analytics. What BlueData has done is by making the infrastructure invisible, by making the tool set completely available as the tool set itself is evolving and in the process, we actually created so many game changing software technologies. For example, we are the first end-to-end content-arised enterprise solution that gives you distributed applications. And we built a technology called DataTap, that provides computed data operation so that you don't have to actually copy the data, which is a boom for enterprises. We also actually built multitenancy so those enterprises can run multiple work loads on the same data and Ramesh will tell you in a second here, in the healthcare enterprise, the multitenancy is such a very important element. And finally, we also actually contributed to many open source technologies including, we have a project called KubeDirector which is actually is our own Kubernetes and how to run stateful workloads on Kubernetes. which we have actually very happy to see that people like, customers like Ramesh are using the BlueData. >> Sounds like quite a journey and obviously you've intercepted companies like the advisory board company. So Ramesh, a lot of enterprises have mastered or you know, gotten, understood how to create data lakes with a dupe but then found that they still weren't able to connect to some of the outcomes that they saw. Is that the experience that you had. >> Right, to be precise, that is one of the kind of problems we have. It's not just the data lake that we need to be able to do the workflows or other things, but we also, being a traditional company, being in the business for a long time, we have a lot of data assets that are not part of this data lake. We're finding it hard to, how do we get the data, getting them and putting them in a data lake is a duplication of work. We were looking for some kind of solutions that will help us to gather the benefits of leaving the data alone but still be able to get into it. >> This is where (mumbles). >> This is where we were looking for things and then I was lucky and fortunate to run into Kumar and his crew in one of the Hadoop conferences and then they demonstrated the way it can be done so immediately hit upon, it's a big hit with us and then we went back and then did a POC, very quickly adapt to the technology and that is also one of the benefits of corrupting this technology is the level of contrary memorization they are doing, it is helping me to address many needs. My data analyst, the data engineers and the data scientists so I'm able to serve all of them which otherwise wouldn't be possible for me with just this plain very (mumbles). >> So it sounds as though the partnership with BlueData has allowed you to focus on activities and problems and challenges above the technology so that you can actually start bringing data science, business objectives and infrastructure people together. Have I got that right? >> Absolutely. So BlueData is helping me to tie them all together and provide an excess value to my business. We being in the healthcare, the importance is we need to be able to look at the large data sets for a period of time in order to figure out how a patient's health journey is happening. That is very important so that we can figure out the ways and means in which we can lower the cost of health care and also provide insights to the physician, they can help get people better at health. >> So we're getting great outcomes today especially around, as you said that patient journey where all the constituents can get access to those insights without necessarily having to learn a whole bunch of new infrastructure stuff but presumably you need more. We're talking about a new world that you mentioned before upfront, talking about a new world, AI, ML, a lot of changes. A lot of our enterprise customers are telling us it's especially important that they find companies that not only deliver something today but demonstrate a commitment to sustain that value delivery process especially as the whole analytics world evolves. Are you experiencing that as well? >> Yes, we are experiencing and one of the great advantage of the platform, BlueData platform that gave me this ability to, I had the new functionality, be it the TensorFlow, be it the H2O, be it the heart studio, anything that I needed, I call them, they give me the images that are plug-and-play, just put them and all the prompting is practically transparent to nobody need to know how it is achieved. Now, in order to get to the next level of the predictive and prescriptive analytics, it is not just you having the data, you need to be able to have your curated data asset set process on top of a platform that will help you to get the data scientists to make you. One of the biggest challenges that are scientist is not able to get their hands on data. BlueData platform gives me the ability to do it and ensure all the security meets and all the compliances with the various other regulated compliances we need to make. >> Kamar, congratulations. >> Thank you. >> Sounds like you have a happy customer. >> Thank you. >> One of the challenges that every entrepreneur faces is how did you scale the business. So talk to us about where you are in the decisions that you made recently to achieve that. >> As an entrepreneur, when you start a company, odds are against you, right? You're always worried about it, right. You make so many sacrifices, yourself and your team and all that but the the customer is the king. The most important thing for us to find satisfied customers like Rameshan so we were very happy and BlueData was very successful in finding that customer because i think as you pointed out, as Ramesh pointed out, we provide that clean solution for the customer but as you go through this journey as a co-founder and CEO, you always worry about how do you scale to the next level. So we had partnerships with many companies including HPE and we found when this opportunity came in front of me with myself and my board, we saw this opportunity of combining the forces of BlueData satisfied customers and innovative technology and the team with the HPs brand name, their world-class service, their investment in R&D and they have a very long, large list of enterprise customers. We think putting these two things together provides that next journey in the BlueData's innovation and BlueData's customers. >> Excellent, so once again Kumar Sreekanti, co-founder and CEO of BlueData and Ramesh Thyagarajan who is the executive director of the advisory board company and part of Optum, I want to thank both of you for being on theCUBE. >> Thank you >> Thank you, great to be here. >> Now let's hear a little bit more about how this notion of bringing BlueData and HPE together is generating new classes of value that are making things happen today but are also gonna make things happen for customers in the future and to do that we've got Dave Velante who's with Silicon Angle Wiki Bond joined by Patrick Osbourne who's with HPE in our Marlborough studio so Dave over to you. >> Thanks Peter. We're here with Patrick Osbourne, the vice president and general manager of big data and analytics at Hewlett Packard Enterprise. Patrick, thanks for coming on. >> Thanks for having us. >> So we heard from Kumar, let's hear from you. Why did HPE purchase, acquire BlueData? >> So if you think about it from three angles. Platform, people and customers, right. Great platform, built for scale addressing a number of these new workloads and big data analytics and certainly AI, the people that they have are amazing, right, great engineering team, awesome customer success team, team of data scientists, right. So you know, all the folks that have some really, really great knowledge in this space so they're gonna be a great addition to HPE and also on the customer side, great logos, major fortune five customers in the financial services vertical, healthcare, pharma, manufacturing so a huge opportunity for us to scale that within HP context. >> Okay, so talk about how it fits into your strategy, specifically what are you gonna do with it? What are the priorities, can you share some roadmap? >> Yeah, so you take a look at HPE strategy. We talk about hybrid cloud and specifically edge to core to cloud and the common theme that runs through that is data, data-driven enterprises. So for us we see BlueData, Epic platform as a way to you know, help our customers quickly deploy these new mode to applications that are fueling their digital transformation. So we have some great plans. We're gonna certainly invest in all the functions, right. So we're gonna do a force multiplier on not only on product engineering and product delivery but also go to market and customer success. We're gonna come out in our business day one with some really good reference architectures, with some of our partners like Cloud Era, H2O, we've got some very scalable building block architectures to marry up the BlueData platform with our Apollo systems for those of you have seen that in the market, we've got our Elastic platform for analytics for customers who run these workloads, now you'd be able to virtualize those in containers and we'll have you know, we're gonna be building out a big services practice in this area. So a lot of customers often talk to us about, we don't have the people to do this, right. So we're gonna bring those people to you as HPE through Point Next, advisory services, implementation, ongoing help with customers. So it's going to be a really fantastic start. >> Apollo, as you mentioned Apollo. I think of Apollo sometimes as HPC high performance computing and we've had a lot of discussion about how that's sort of seeping in to mainstream, is that what you're seeing? >> Yeah absolutely, I mean we know that a lot of our customers have traditional workloads, you know, they're on the path to almost completely virtualizing those, right, but where a lot of the innovation is going on right now is in this mode two world, right. So your big data and analytics pipeline is getting longer, you're introducing new experiences on top of your product and that's fueling you know, essentially commercial HPC and now that folks are using techniques like AI and modeling inference to make those services more scalable, more automated, we're starting to bringing these more of these platforms, these scalable architectures like Apollo. >> So it sounds like your roadmap has a lot of integration plans across the HPE portfolio. We certainly saw that with Nimble, but BlueData was working with a lot of different companies, its software, is the plan to remain open or is this an HPE thing? >> Yeah, we absolutely want to be open. So we know that we have lots of customers that choose, so the HP is all about hybrid cloud, right and that has a couple different implications. We want to talk about your choice of on-prem versus off-prem so BlueData has a great capability to run some of these workloads. It essentially allows you to do separation of compute and storage, right in the world of AI and analytics we can run it off-prem as well in the public cloud but then we also have choice for customers, you know, any customer's private cloud. So that means they want to run on other infrastructure besides HPE, we're gonna support that, we have existing customers that do that. We're also gonna provide infrastructure that marries the software and the hardware together with frameworks like Info Site that we feel will be a you know, much better experience for the customers but we'll absolutely be open and absolutely have choice. >> All right, what about the business impact to take the customer perspective, what can they expect? >> So I think from a customer perspective, we're really just looking to accelerate deployment of AI in the enterprise, right and that has a lot of implications for us. We're gonna have very scalable infrastructure for them, we're gonna be really focused on this very dynamic AI and ML application ecosystems through partnerships and support within the BlueData platform. We want to provide a SAS experience, right. So whether that's GPUs or accelerators as a service, analytics as a service, we really want to fuel innovation as a service. We want to empower those data scientists there, those are they're really hard to find you know, they're really hard to retain within your organization so we want to unlock all that capability and really just we want to focus on innovation of the customers. >> Yeah, and they spend a lot of time wrangling data so you're really going to simplify that with the cloud (mumbles). Patrick thank you, I appreciate it. >> Thank you very much. >> Alright Peter, back to you in Palo Alto. >> And welcome back, I'm Peter Burris and we've been talking a lot in the industry about how new tooling, new processes can achieve new classes of analytics, AI and ML outcomes within a business but if you don't get the people side of that right, you're not going to achieve the full range of benefits that you might get out of your investments. Now to talk a little bit about how important the data science practitioner is in this equation, we've got two great guests with us. Nanda Vijaydev is the chief data scientists of BlueData. Welcome to theCUBE. >> Thank you Peter, happy to be here. >> Ingrid Burton is the CMO and business leader at H2O.AI, Ingrid, welcome to the CUBE. >> Thank you so much for having us. >> So Nanda Vijaydev, let's start with you. Again, having a nice platform, very, very important but how does that turn into making the data science practitioner's life easier so they can deliver more business value. >> Yeah thank you, it's a great question. I think end of the day for a data scientist, what's most important is, did you understand the question that somebody asked you and what is expected of you when you deliver something and then you go about finding, what do I need for them, I need data, I need systems and you know, I need to work with people, the experts in the process to make sure that the hypothesis I'm doing is structured in a nice way where it is testable, it's modular and I have you know, a way for them to go back to show my results and keep doing this in an iterative manner. That's the biggest thing because the satisfaction for a data scientist is when you actually take this and make use of it, put it in production, right. To make this whole thing easier, we definitely need some way of bringing it all together. That's really where, especially compared to the traditional data science where everything was monolithic, it was one system, there was a very set way of doing things but now it is not so you know, with the growing types of data, with the growing types of computation algorithms that's available, there's a lot of opportunity and at the same time there is a lot of uncertainty. So it's really about putting that structure and it's really making sure you get the best of everything and still deliver the results, that is the focus that all data scientists strive for. >> And especially you wanted, the data scientists wants to operate in the world of uncertainty related to the business question and reducing uncertainty and not deal with the underlying some uncertainty associated with the infrastructure. >> Absolutely, absolutely you know, as a data scientist a lot of time used to spend in the past about where is the data, then the question was, what data do you want and give it to you because the data always came in a nice structured, row-column format, it had already lost a lot of context of what we had to look for. So it is really not about you know, getting the you know, it's really not about going back to systems that are pre-built or pre-processed, it's getting access to that real, raw data. It's getting access to the information as it came so you can actually make the best judgment of how to go forward with it. >> So you describe the world with business, technology and data science practitioners are working together but let's face it, there's an enormous amount of change in the industry and quite frankly, a deficit of expertise and I think that requires new types of partnerships, new types of collaboration, a real (mumbles) approach and Ingrid, I want to talk about what H2O.AI is doing as a partner of BlueData, HPE to ensure that you're complementing these skills in pursuit or in service to the customer's objectives. >> Absolutely, thank you for that. So as Nanda described, you know, data scientists want to get to answers and what we do at H2O.AI is we provide the algorithms, the platforms for data scientist to be successful. So when they want to try and solve a problem, they need to work with their business leaders, they need to work with IT and they actually don't want to do all the heavy lifting, they want to solve that problem. So what we do is we do automatic machine learning platforms, we do that with optimizing algorithms and doing all the kind of, a lot of the heavy lifting that novice data scientists need and help expert data scientists as well. I talk about it as algorithms to answers and actually solving business problems with predictions and that's what machine learning is really all about but really what we're seeing in the industry right now and BlueData is a great example of kind of taking away some of the hard stuff away from a data scientist and making them successful. So working with BlueData and HPE, making us together really solve the problems that businesses are looking for, it's really transformative and we've been through like the digital transformation journey, all of us have been through that. We are now what I would term an AI transformation of sorts and businesses are going to the next step. They had their data, they got their data, infrastructure is kind of seamlessly working together, the clusters and containerization that's very important. Now what we're trying to do is get to the answers and using automatic machine learning platforms is probably the best way forward. >> That's still hard stuff but we're trying to get rid of data science practitioners, focusing on hard stuff that doesn't directly deliver value. >> It doesn't deliver anything for them, right. They shouldn't have to worry about the infrastructure, they should worry about getting the answers to the business problems they've been asked to solve. >> So let's talk a little bit about some of the new business problems that are going to be able to be solved by these kinds of partnerships between BlueData and H2O.AI. Start, Nanda, what do you, what gets you excited when we think about the new types of business problems that customers are gonna be able to solve. >> Yeah, I think it is really you know, the question that comes to you is not filtered through someone else's lens, right. Someone is trying an optimization problem, someone is trying to do a new product discovery so all this is based on a combination of both data-driven and evidence-based, right. For us as a data scientist, what excites me is that I have the flexibility now that I can choose the best of the breed technologies. I should not be restricted to what is given to me by an IT organization or something like that but at the same time, in an organization, for things to work, there has to be some level of control. So it is really having this type of environments or having some platforms where some, there is a team that can work on the control aspect but as a data scientist, I don't have to worry about it. I have my flexibility of tools of choice that I can use. At the same time, when you talk about data, security is a big deal in companies and a lot of times data scientists don't get access to data because of the layers and layers of security that they have to go through, right. So the excitement of the opportunity for me is if someone else takes care of the problem you know, just tell me where is the source of data that I can go to, don't filter the data for me you know, don't already structure the data for me but just tell me it's an approved source, right then it gives me more flexibility to actually go and take that information and build. So the having those controls taken care of well before I get into the picture as a data scientist, it makes it extremely easy for us to focus on you know, to her point, focus on the problem, right, focus on accessing the best of the breed technology and you know, give back and have that interaction with the business users on an ongoing basis. >> So especially focus on, so speed to value so that you're not messing around with a bunch of underlying infrastructure, governance remaining in place so that you know what are the appropriate limits of using the data with security that is embedded within that entire model without removing fidelity out of the quality of data. >> Absolutely. >> Would you agree with those? >> I totally agree with all the points that she brought up and we have joint customers in the market today, they're solving very complex problems. We have customers in financial services, joint customers there. We have customers in healthcare that are really trying to solve today's business problems and these are everything from, how do I give new credit to somebody? How do I know what next product to give them? How do I know what customer recommendations can I make next? Why did that customer churn? How do I reach new people? How do I do drug discovery? How do I give a patient a better prescription? How do I pinpoint disease than when I couldn't have seen it before? Now we have all that data that's available and it's very rich and data is a team sport. It takes data scientists, it takes business leaders and it takes IT to make it all work together and together the two companies are really working to solve problems that our customers are facing, working with our customers because they have the intellectual knowledge of what their problems are. We are providing the tools to help them solve those problems. >> Fantastic conversation about what is necessary to ensure that the data science practitioner remains at the center and is the ultimate test of whether or not these systems and these capabilities are working for business. Nanda Vijaydev, chief data scientist of BlueData, Ingrid Burton CMO and business leader, H2O.AI, thank you very much for being on theCUBE. >> Thank you. >> Thank you so much. >> So let's now spend some time talking about how ultimately, all of this comes together and what you're going to do as you participate in the crowd chat. To do that let me throw it back to Dave Velante in our Marlborough studios. >> We're back with Patrick Osbourne, alright Patrick, let's wrap up here and summarize. We heard how you're gonna help data science teams, right. >> Yup, speed, agility, time to value. >> Alright and I know a bunch of folks at BlueData, the engineering team is very, very strong so you picked up a good asset there. >> Yeah, it means amazing technology, the founders have a long lineage of software development and adoption in the market so we're just gonna, we're gonna invested them and let them loose. >> And then we heard they're sort of better together story from you, you got a roadmap, you're making some investments here, as I heard. >> Yeah, I mean so if we're really focused on hybrid cloud and we want to have all these as a services experience, whether it's through Green Lake or providing innovation, AI, GPUs as a service is something that we're gonna be you know, continuing to provide our customers as we move along. >> Okay and then we heard the data science angle and the data science community and the partner angle, that's exciting. >> Yeah, I mean, I think it's two approaches as well too. We have data scientists, right. So we're gonna bring that capability to bear whether it's through the product experience or through a professional services organization and then number two, you know, this is a very dynamic ecosystem from an application standpoint. There's commercial applications, there's certainly open source and we're gonna bring a fully vetted, full stack experience for our customers that they can feel confident in this you know, it's a very dynamic space. >> Excellent, well thank you very much. >> Thank you. Alright, now it's your turn. Go into the crowd chat and start talking. Ask questions, we're gonna have polls, we've got experts in there so let's crouch chat.

>> From the SiliconANGLE Media office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Dave Vellante. (upbeat music) >> Hi, everybody, welcome to this CUBE Conversation. My name is Dave Vellante and we're here in theCUBE Studios in Marlborough, Massachusetts. We're gonna talk about storage and some of the trends that are going on in storage, and things have changed quite dramatically. It's not just about what media you're using today, you've got a lot of other considerations. Cloud, on-prem, in comes the edge, and it really drives new considerations for customers. Sundip Arora is here. He's the director of North America Storage and Big Data Solutions at Hewlett Packard Enterprise. He's gonna talk to me about some of these trends, the customer point of view, and what HPE is doing to solve some of these problems. Sundip, thanks very much for coming on theCUBE. >> Dave, thanks for having me, I'm super excited. >> So you heard my little narrative upfront about some of the big picture trends, what do you see as some of the tectonic shifts in the storage marketplace? >> Yeah, Dave. So listen, we've traveled around the continent here and I spend a lot of time with customers in North America, and what I hear from customers is their center of universe revolves around being able to map with their cloud journey and what does that mean for their data. Now, I look at our cloud operating model and I map that to HPE's own point of view. Our point of view is bringing the intelligent data platform to our customers. And when we talk about mapping the cloud operating model to our customer, what does that really mean for us? When I talk to customers, they tell me three things. It means that you have extreme cost efficiency, you've got super ease of use, and you've got resource optimization, how to utilize them in the best manner. >> So, let me ask you on that. Big Data is in your title, and one of the things that we observed early on in the big data days was it was about bringing five megabytes of code to a petabyte of data. Well, that sounded great and it was great, but it also caused problems because you're pushing, now, storage is everywhere. I mentioned the edge. So, I'm sure you're seeing that with customers. There is no more perimeter. Storage is just everywhere, wherever you want it to be. So when you talk about the cloud operating model, are you talking about bringing that experience to your data wherever that data lives? >> That's a great question. It used to be that you had an accounting system and that had a database, and that was delivering you a ton of data that you could analyze and store and read and write. And now, you've got data that's being produced at the edge, you've got point of sales systems, you've got autonomous vehicles, you've got data that's being produced on the cloud itself, and you've got data that's being produced at the core. So, what we are talking about is not just the automation of bringing that data in, but also how that data is being utilized. And to us, the way we map that challenge is through intelligence. >> Let's break down those three things: cost efficiency, ease of use, and resource optimization. Let's start with cost-efficiency. So, obviously, there's TCO. There's also the way in which I consume. The people, I presume, are looking for a different pricing model. Are you hearing that? >> Yeah, absolutely. So, as part of the cost of running their business and being able to operate like a cloud, everybody's looking at a variety of different procurement and utilization models. One of the ways HPE provides utilization model that can map to their cloud journey, a public cloud journey, is through GreenLake. The ability to use and consume data on demand, consume compute on demand, across the entire portfolio of products HPE has, essentially is what a GreenLake journey looks like. >> Let's go into ease of use. So, what do you mean by that? I mean, people, they think cloud, they think swipe the credit card and start deploying machines. What do you mean by ease of use? >> For us, ease of use translates back to how do you map to a simpler operating and support model. For us, the support model is the key for customers to be able to realize the benefits of going to the cloud. To get to a simpler support model, we use AIOps. And for us, AIOps means using a product called InfoSight. InfoSight is a product that uses deep learning and machine learning algorithms to look at a wide net of call-home data from physical resources out there and then be able to take that data and make it actionable. And the action behind that is predictiveness, the prescriptiveness of creating automated support tickets, and closing automated support tickets without anybody ever having to pick up a phone and call IT support. That InfoSight model now is being expanded across the board to all HPE products. It started with Nimble. Now InfoSight is available on 3PAR it's available on Synergy, and a recent announcement said it's also on ProLiants. And we expect that InfoSight becomes the glue, the automation AI glue, that goes across the entire portfolio of HPE products. >> So this is a great example of applying AI to data, so it's like call home taking to a whole level, isn't it? >> Yeah, it absolutely is. And in fact, what it does is it uses the call-home data that we've had for a long time with products like 3PAR, which essentially was amazing data but not being actioned on in an automated fashion. It takes that data and now, it creates an automation task around it. And many times, that automation task leads to much simpler support experience. >> Okay, the third item you mentioned was resource optimization. Let's drill down into that. I infer from that there are performance implications, there's maybe governance compliance, physical placement, can you elaborate, add some color to that? >> I think it's all of the above that you just talked about. It's definitely about applying the right performance level to the right set of applications. We call this application-aware storage. The ability to be able to understand which application is creating the data allows us to understand how that data needs to be accessed which in turn means we know where it needs to reside. One of the things that HPE is doing in the storage domain is creating a common storage fabric with the cloud. We call that the fabric for the cloud. The idea there is that we have a single layer between the on-premises and off-premises resources that allows us to move data as needed depending on the application needs and depending on the user needs. >> Okay, so that brings me to multi-cloud. It's the hot buzzword now. Some people don't like it but it's reality. And so, you've got data on-prem, you want to look like the cloud operating model, you got data in the cloud, the edge confuses things even more. And so, what is your perspective on multi-cloud, and then I have a follow-up for you. >> For us, multicloud means the ability to be able to run your business whether it's on-premises or off-premises based on the needs or the requirements of the application and the business user. We don't want to force a model down our customer's throat. We want them to have optimization across both models. The way we do that is using a couple of different products. We've got a product known as Cloud Bank, which maps to StoreOnce. StoreOnce is our purpose-built backup appliance where our customer can store a copy, a backup copy of the data on-premises, and then a backup copy of that on a public cloud like Azure, AWS, or Google. Similarly, we've got products with Nimble and 3PAR that allows to have tight integration with both public and private cloud domains. And in the future, the idea is to bring all of that together where the automation and the orchestration allows customers not to worry about what product they're using but more about what are the requirements of the application. >> Okay, because sometimes you gonna wanna bring data back, whether it's, pick and, yeah, I wanna put it in the cloud for bursting, I wanna bring it back for more control, whatever it is, when it comes back, I wanna have that cloud operating model, that's where the AIOps fits in that you were just describing. >> Yeah, absolutely. >> Okay, and so, let's get into, more specifically, what HPE is doing. You've referenced some of the things that you and your partners are doing, but what specifically are you doing from the standpoint of products, you mentioned what I call data plan and control plan. What do you have there that we can actually buy and employ? >> What we have, as I talked about earlier from an AIOps point of view, is our product called InfoSight, and InfoSight is available to all customers that today use 3PAR, Nimble, or ProLiant servers. As long as you have a valid support contract, it comes available to them. >> So I remember when HPE acquired Nimble, you said one of the things you're gonna do is take that technology and push it across the portfolio, so that's something that you've really done in a pretty short timeframe. >> We have, and what it does, it gives us the opportunity now not just to look at call-home data from storage, but then also look at call-home data from the compute side. And then what we can do is correlate the data coming back to have better predictability and outcomes on your data center operations as opposed to doing it at the layer of infrastructure. >> And you also said about the vision of this orchestration layer, can you talk more about that? Are we talking about across all clouds, whether it's on-prem or at the edge or the public cloud? >> Yeah, we are. We're talking about making it as simple as possible where the customers are not necessarily picking and choosing. It allows them to have a strategy that allows them to go across the data center, whether it's a public cloud, building their own private infrastructure, or running on a traditional on-premises SAN structure. So this vision for us, Cloud Fabric vision for us, allows for customers to do that. >> And what about software-defined storage? Where does that fit into this whole equation? >> I'm glad you mentioned that because that was the third tenet of what HPE truly brings to our customers. Software-defined is something that allows us to maximize the utilization of the existing resources that our customers have. So, what we've done is we've partnered with a great deal of really strong software-defined vendors, such us Commvault, Cohesity, Qumulo, Datera. We work very closely with the likes of Veeam, Zerto. And the goal there is to provide our customers with a whole range of options to drive building a software-defined infrastructure built off the Apollo Series of products. Apollo servers, our storage products for us, are extremely dense storage products that allow for both cost and resource optimization. >> What's the nature of these technology partners, partnerships? Are you doing engineering integration or is it just kind of going to market together? >> We bundle our partners into three main categories. We've got a set of complete partners. These complete partners are relationship where we do joint reference architecture. We create joint pricing list and we bring them in to the family. We've got a set of partners that's part of the Pathfinder program. The Pathfinder program are partners that we've made strategic, HPE has made strategic investments in. And then the third set is partners that we resell through HPE. So, depending on which partner it is, they fall into a different bucket, and we have all sets of resources, including engineering collaboration to make sure that the customer's buying a solution as opposed to a product. >> That's great, Sundip, thank you. Thank you for watching. But before we go, how do people learn more? >> The way you learn more is make sure you contact your partner and make sure you come to Discover. So, we'll hopefully see you at the Discover. (upbeat music)

from the silicon angle media office in Boston Massachusetts it's the queue now here's your host David on tape hi everybody this is Dave a latte from our studios in Marlborough Massachusetts you're watching the cube I'm here with Terry Richardson who's the vice president of North America alliances and channels for Hewlett Packard Enterprise we're gonna talk about a program that HPE has called HPE complete Terry good to see you thanks for having me Dave really good to be here you're welcome HPE complete what's that all about HP complete is our way to extend our portfolio to customers and partners so we essentially work with leading technology Alliance vendors and ISPs to certify their solutions with HPE infrastructure and we and we go kind of two steps further once that certification is complete we add those offerings to our price book so they become available to customers and partners you know it allows us to sell complete solutions to customers but we also we also take advantage of the opportunity to develop joint go to market programs with these vendors because that kind of last mile execution is what really matters how often do you have something that's available in a price book but kind of gets no attention so the complete program is really end to end and it allows us to as I said develop solutions for customers that that the components may not exist in the HP completely eportfolio so by leveraging complete we have I think the industry's most complete portfolio of offerings across the infrastructure this is really important because you have some companies some vendors will say okay we're gonna create a stack and be our stack and sort of a closed stack and I'm sure there's some advantage to that level of integration but one of them is not horizontal you know scale and and penetration in various markets so it allows you to say oK we've got some white space here and some gaps we're gonna fill that through partnerships and as you said there go to market implications as well so so I would think that partners actually really love that because they love choice they love flexibility right and and I think we we allow for it to be easier for partners if they take advantage of these technology offerings that are all available through HPE right because one of the challenges a traditional business partner has is how many different vendors can they contract with how many vendor certification programs can they go through how many different you know kind of hoops can they can they jump through this way they can kind of do everything through HPE and there's certainly some financial benefits for you know when they do so and so does that mean there's a SKU for all of these offerings kind of gets queued up in the HPE price book and and they contribute towards the partners attainments of their annual revenue commitment HPE that allows them to achieve their medallion status whether it's platinum gold or or silver so let's say a name so we're some of the partners that you work on so I don't have time to probably name them all but I'll give you a couple of examples we'll just talk about in the infrastructure category specific to storage so HPE portfolio is pretty complete led by you know kind of three part technology and nimble and the associated info site software as well as our you know backup data protection products but we have some gaps right we have gaps in file and objects so companies like accumulo Scalla t really start to fill in the in the blanks there we looked at the trend towards Software Defined and it brought us to a company called doTERRA that that we have for software-defined storage kind of you know continuing to extend our reach into kind of virtualized backup would be technologies with a company like like Veeam so you know just and then you know kind of the exploding secondary data market has led us to partner collaboratively with Co he city so our value prop to these vendors is they can tap into HP's global channel and kind of get you know much broader scale than they could as smaller companies and they also get a selling force at HPE that's motivated to deliver the solutions that our customers and partners need and and dragging those technologies along so it really becomes a win-win yeah from your standpoint you don't have to go out and you'll make acquisitions but you're not to buy every company who's known there and try to figure out how to stitch it together you can do some integration I mean cumulus unev Isilon they're doing very well in the market I know Jerome Ellicott known him for years great skill any CEO from skaila Tico he cities like killing it with modern data protection you got V was really well established you know almost a billion-dollar company now maybe even they'd be surpassed that and in de Tierra when you start talking about Software Defined and we talked about the edge in one of our earlier conversations starts to play kind of an interesting you know great potential technology and so love to see you guys partnering and stitching together and I'm sure like you say there are many more we just don't have time we don't have time to cover them all but those are examples and I think there's a real not only do the partners get to deal with a HPE broader portfolio but in in their go to market execution when they're in partnership with those vendors they tend to be hungrier selling organizations kind of the proverbial hunter analogy because they're focused on kind of rapid growth and market adoption to take advantage of a window in time so partners appreciate the fact that not only will HPE be hunting but a lot of these vendors are hunting too and the partners of the beneficiary what do they have to go through to complete HPE guns there's a pretty thorough certification process that our team goes through to to certify those offerings working on our infrastructure product platform so it's not just signed the contract in your in its as it's a series of technical testing that that goes on so then we put our name and brand behind something we have high confidence that it'll deliver the intent of results to customers and partners so there may be some engineering changes this imaginary work to take advantage of the capabilities in many of these cases it may be HPE compute infrastructure so taking advantage of all the capabilities of our compute flatform and ultimately management there's some integration work that gets done critical for partners simplifies their portfolio by working with HPE Terri thanks so much thank you Dave appreciate the explanation of HPE complete alright and thank you everybody for watching we'll see you next time you're watching the cube [Music]

>> From the Silicon Angle Media office in Boston, Massachusetts. It's the cue now here's your host, Dave Alon. >> Hi, >> everybody. Welcome to this Cube conversation. My name is Dave Lan Ting here on Marlborough Studios with Beth failing. Who's the president and GM of the Delhi Emcee Data protection division. Good to see you About. >> Good to be here days. >> So the reason why we're here today is this is the third year you've released the Global Data Protection Index. We love data. We love to dig into the data. So tell us about the survey. What? It's about the size of the survey. Who? You? You're responding, so >> Yeah, absolutely. That survey talked to twenty two hundred decision makers globally and asked them questions to understand where they are in their data protection, implementations and strategy and how much data loss or data disruption impacted their business over the past twelve months. >> So when you do, these survey's over three years like you have here, you get a time. Siri's things start to, you know, pattern start to emerge. What were the key findings this time? >> There are a couple of really interesting findings that stood out one as we talk to the customers about where they were on their I T maturity journey, we found that the number of adopters people who were fully immersed in data protection when from nine percent to fifty seven percent. So it's a really big jump. Another thing we saw was the data they were protecting grew by five times over five hundred percent. So even though we know data is growing dramatically, it still is striking just how much it's growing. >> I've said many times that the industry, our industries, marks to the cadence of Moore's long You could come draw that out Logue Logue graph paper. But the Kurdish is shifting, its becoming more exponential, certainly non linear, so that that data growth is even surprising to me on but relates to the cost of downtime and the impact of disruption. There's a data in here wanted to share that with us, >> and it's pretty striking. The number of customers that were not able to recover their data after disruption grew from fourteen to twenty seven percent, and the level of cost is growing as well. The average impact of a data disruption event is half a million dollars, but if you're not able to recover your data, understandably, it's almost twice that. >> So you know it's complexity is growing, and to me, this really talks to digital transformation >> of >> the way in which people are using data and differentiating from what they've done in the past. It dramatically increases their risk because the data value is so high. >> And the study shows that companies that have gone through a digital transformation and clearly leveraging the data as an asset are too times more profitable than companies that have not data matters. More and more people are realizing that the flip side of that coin is then the cost and the impact. Your business, if you do have a disruption or a data loss, is that much more significant. >> Historically, we've had these silos of applications that have infrastructure that's hardened and fossilized around them, and increasingly, we're sharing more data across those applications. You know, Cloud, which we'LL talk about, is is really accelerating some of those transformations and so you have more and more complexity. We live in a multi vendor world because people want best of breed. They want horses for courses, but it adds a layer of complexity to the process. What did the survey tell you? >> And first off, the average is three data protection vendors per respondent. That's consistent with where we were two years ago. But what we see also Mohr dramatically is that the likelihood of not being able to recover your data after a winsome or attack if you're using multiple vendors is two times is high. So as the threats are maturing, the need for us to be able to protect ourselves and our company's from those threats needs to mature as well. And the data seems to show that having three vendors may not be the best way to be responding to this increasingly risky world. >> So that's interesting that you talk about now. Some of the challenges that were brought forth in the study always wanna ask that in the study like this, there were three big ones that stood out. Cost is always top of mind. The right technical fit on DH, then gpr Compliance is another factor. What's the data show in terms of those challenges? >> So the top three you really hit them I won was the ballooning cost and complexity. Another was the need. Thio adhere to compliance, and then the third was the need to ensure that you have data protection that covers the emerging technologies, the emerging strategies. >> So we talked about multi vendor adds complexity as cost a cz risk and just talk about the challenges. What is delle AMC doing to address these challenges? What gives you confidence that you can earn the right to stay at the table? >> Yeah, eso were first are very proud of the legacy of data protection experience that we have and what we've learned in what we helped our customers do as part of that legacy. We've protected tens of thousands of customers around the globe for for decades. But what we're doing now is modernizing our capabilities, insuring that we're protecting the multi cloud environments, the new, the new types of applications, making SNU simple products like the idea so that customers can take that confidence they have in us and bring it forward with them into the next decade. >> I'm interested in how people are leveraging the club for data protection and also what Delhi emcee strategy is there because, you know, own a public cloud your relationships with with public cloud providers. But what is your strategy there and our people reverse? How are people using the cloud for data protection. And what is your strategy? There >> are strategies to provide the best global multi cloud data protection that anybody delivers in the world. And when we do that would providing all the use cases that customers are using for the data protection. One interesting fact from the survey. It was those customers who have adopted a cloud technology. Ninety eight percent of them are leveraging that technology for data protection. In those use cases, they're evolving beyond just backup. Beyonce cuse me beyond just long term retention archive to include backup replication, data protection for the cloud workloads. We're really doing a lot to make sure we keeping up with that very dynamic market. >> The people want to get more out of their their backup in data protection than just insurance. We've talked about this a lot, just in terms of leveraging analytics and ransomware etcetera. D are bringing that together on so forth. But I want to continue on the discussion of cloud because I talked about you have some relationship specifically and mentioned it, but VM wear and eight of us every relationship. But you have to have a portfolio you can't just put all your legs in one cloud basket. What's your strategy >> and the importance of enabling customers to leverage a W eso, Google, IBM or Azure? For a PJ colleagues, Alibaba is very essential for us, and we think it's even more important that you have a standard data protection strategy. When you're leveraging multiple cloud vendors and distributing your day birth date over more and more locations, it's even more important that you have avenged. You can count on and trust to bring our there together to a single data. Protections to allergy. >> One of things I like about service like this, especially over time. You can get a sense of the maturity model, you know, however you define it. Laggards, evaluators, adopters and leaders is always your consistent on how you ask that question. You can get a time Siri's and see how things are shifting. So there's, ah, question a slide in the study that talks about that. What did you find in terms of the adoption? >> And I hinted at this at the beginning, but I find this to be one of the most striking findings from the survey. The number of respondents that fell into the category of laggards not really putting a lot of thought at all into data protection shrank from thirty eight percent to two percent. So that's massive in two years. And on the flip side of that, the number of vendors who the number of professionals who were now considered a doctor's had gone from nine percent to fifty seven percent. So we really are seeing a massive shift in the number of companies that are now focused on data protection as a core part of their strategy. >> In my view, that's because of the digital transformation that's going on is more than just the buzzword. Every CEO is trying to get digital, right? Yeah. So just to summarize. So data is growing in this non running a fashion that we talked about that's driving up costs and cumbersome costs of disruption. Cost of downtime is growing. Even the best of breed leaders are struggling to keep up. The pace of innovation is so fast. If you're not figuring out how to monetize your data in some way, shape or form, and I don't mean selling your data, we're talking about how levitate it contributes to the monetization business. Cutting costs are increasing revenue and in some way, shape or form. If you're not doing that, then you're in trouble. I'm gonna come back and ask you again. What gives you confidence? That Delhi M. C. Is going to be the preferred supplier we heard about multiple vendors is problematic. So how are you gonna win in this game? >> One thing is making sure that we're building our business strategy on wheel data like this survey. So we're staying on top of what's happening in our customers world, and we're modernizing our products in a portfolio to meet those needs in the second is building on the legacy of the I T. Infrastructure that we've protected for many, many decades. We have the trust, We have the architecture, we have the performance. We have the best day cost to protect. And now we're bringing in modern, simple multi cloud data protection. We're on this and we're going to win. >> So surveys like this are they're big, they're expensive. Can we assume you're going to continue to fund this? Absolutely. So how do we get more information of this? I say the survey's done by it into independent firm Is that seventy website somewhere We're going to get more if >> you just go out to Delhi m si dot com and you will find the information. >> Great. I bet thanks for coming in. And sharing the results of the survey is always a pleasure. We're going to see you at Del Technologies World. >> Just a few weeks. >> Yeah. End of April early. May Look forward to that. >> Yeah. Today. Thanks for having me in >> your welcome. All right. Thanks for watching everybody. This is David. Lot day. We'LL see you next time.