(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) >> Hey everyone, welcome to theCUBE's special program series Women of the Cloud, brought to you by AWS. I'm your host, Lisa Martin. Very pleased to welcome Angie Perez Thomas the area sales leader from AWS as my next guest. Angie, welcome to theCUBE. It's great to have you here. >> I'm super excited. Thank you so much, Lisa. >> Of course. Talk to me a little bit about you a little bit about your role in sales at AWS. >> Yeah, absolutely. So I'm a tenure Amazonian so I've been with AWS for about 10 years here. And as you mentioned, I'm the area sales leader and so my team supports new enterprise customers and executives who are just starting their journey into the cloud. >> Talk a little bit about some of your career paths. Did you have a linear path? You said tenure Amazonian, linear path maybe more Zig-zaggy. I'd love to get some of your recommendations for those who may be early in their tech careers looking to grow their careers. What are some of the experiences that you've had that you think are have shaped your career? >> Yeah, absolutely. So, you know, mine have, I've gone back and forth through different roles, both in leadership and as an IC and I'd probably say I've got three recommendations for those looking to grow their career in technology. So the first one is prioritize your time to actually think about what career experiences you want in in your fullness of your career. And so this actually may look like sitting down reserving time to actually deep think about what are those experiences you're looking to gain but also doing research on other careers of those who may inspire you and kind of collecting those ideas. My second recommendation is around documenting, writing down those career aspirations and actually putting it within and memorializing it within a document. So I've applied Amazon's working backwards methodology myself and applied that on my career and writing my own career press release. And so it's dated in 2029. It's got a headline and you know, it's a physical document of my own career aspirations. And third, I recommend sharing this documentation with others. You know, I really enjoy receiving and reading what others are wanting to do with their career aspirations and helping provide feedback and guidance. And so what we find is people genuinely want to help others. >> I agree. I love your recommendations for really being mindful, being thoughtful about what it is that you want to do doing that research, and then actually documenting it. I think it's so wonderful that you're taking Amazon's working backward approach from the press release going this is where I want to be in five years or in 10 years. And then putting that on paper. I still connect a lot with things like you that you put down on paper that you want to accomplish or something about writing it down that actually helps to you bring it to fruition. And then to your point is great about sharing it with others that can be mentors, that can be sponsors. I'm sure you've had some great mentors and sponsors along your career path that have probably helped you pretty successful. >> Yeah, absolutely. It's been really an effective tool for communicating with those who have helped me navigate as well. >> Talk a little bit about some of the successes now we'll switch gears but we'll continue on the success train. Some of the successes that you've had helping organizations really navigate, migrate to the cloud and and become successful businesses as a result. >> Yeah, no, absolutely. So across my tenure at AWS, you know I've truly enjoyed working with our customer executives and helping them deliver on their business outcomes. And so just recently I met with the COO of a real estate firm here in the Pacific Northwest and the COO has an initiative to identify and modify home titles and deeds with decades old discriminatory language and restrictions. So, although not invisible, due to the Fair Housing Act of 1968, racial covenants they're still present in millions of home titles across the United States today. And so partnering with AWS and using our cloud technology, you know, our teams together were able to build an application that was able to where homeowners are able to look up their titles you know, analyze it for discriminatory language and be able to submit it for modification. And so this, you know, today it can be done manually, but partnering with AWS, our teams were able to address modifying titles and deeds at scale. And so it's truly incredible what cloud computing has enabled just all of us to accomplish together. And so I kind of think of it like this our a catalyst for change is our customers and AWS and our partners is the how to accelerate that change. So it's really this partnership >> I love that accelerating change is so important across so many aspects of life, but the example that you gave is so, it's such an interesting use case. I wouldn't think that there is discriminatory language in deeds for houses, but the fact that it's probably a pervasive problem globally and the ability to help organizations to be able to change that for the better with cloud, with automation at scale is huge. I can imagine that's a use case that can be replicated surely across the states and more. >> Yeah, it's definitely gained interest across with different real estate forms across the United States. So we're really excited to be partnering and having impact on this change. >> And it's also an example of tech for good. I mean, we talk about that all the time but the fact that there's discriminatory language and housing deeds is still kind of blows my mind. But and we've seen so much in tech in terms of diversity and equity and inclusion but from a diversity perspective there's still a lot more to do. I'd love to get your opinion on what you think some of the the present day challenges are with respect to diversity in tech and maybe some of the things you think can be changed to for the better. >> Yeah, so you know, there's been a huge focus on, you know hiring for diverse talent in the tech industry for a number of years. And where I think we as an industry have an opportunity is to improve in investing and developing in this diverse talent and try to really think about how are we building up the skillsets to build today's and tomorrow's leaders. And so when I think about this it requires senior leaders to be really intentional about building a diverse ecosystem of talent and investing in this diverse talent. And let me clarify a little bit when I talk about investing in diverse talent, you know, this expands outside of just mentoring. This includes sponsoring, coaching, really providing opportunities where this talent has the ability to have a seat at the table. Getting into the room where it all happens. And so by doing so we're helping this talent build their skillsets to learn what questions are being asked within, the room? How are others communicating with each other? So that they can build the skillset so not only have a seat at the table but can be really leading with that seat at the table. And I would say last, we as companies we tend to or you know, we in the industry, we tend to just focus on developing those within our companies. And where I see a need is to really challenge the industry to reach outside of our own companies in diverse talent. And so developing just that ecosystem because not just thinking about the roles that are open today but really building the skillsets for the roles and and senior level positions that are going to be open tomorrow and making sure we're developing this talent to raise their hand and be the leading candidate for those opportunities. >> I love how you said kind of really a couple things that you know, with all the women in this program that I've spoken to is a common theme in terms of diversity and it's really about senior leaders making investments. And another thing that you said that's spot on is doing it with intention. There's so much to be gained by having an intention with diversity, thought diversity. To your point, going outside, it sounds to me like kind of let's go outside of our comfort zones to bring in different thoughts, different perspectives be able to grow them in their career because of course technologies and products and solutions can only get better the more diversity of thought we have. >> Yeah, no, absolutely. It's really being intentional. We as senior leaders, we have a law on our plate. And so yes this is an additional thing to be thinking about but it really has impact and change in driving the right things both for our customers and for the industry as well. And so it's an investment that's worth making. >> And speaking of that investment worth making I liked how you said, let's have some forethought about what are some of the roles that are going to be there in the future. How are some of the roles today going to be evolving? How do you see your role evolving in the next few years? How do you see cloud evolving and what excites you about that? >> Yeah, well, cloud has really been helping our customers move faster and adapt to just the ever changing landscape. I mean it's over the last couple years it's been very real for all of us to see. And so my role has moved from just being an advisor to a CIO to actually being an advisor to both the CEO and board of directors and when they come speak to us, cost or cloud is not just about cost savings, it truly is about helping a CEO deliver on their business outcomes. So I'll give an example. We're working with a growing community bank and their executive team has embarked on a transformation to becoming a digital first bank. And so when we think about the economic factors that they're working with them to come to mind. The first, their move towards online banking has it's accelerated with the pandemic really creating that customer experience of which when you think about local banks, you think about community where everybody knows your name over in the brick and mortar down the road. Well they have to bridge that community and trust into the digital world. And second, they needed to improve on operational efficiencies. And so they have to strategically think about what investments they're going to make to balance inflation while driving growth. And so where I've been finding both myself and my teams is having a seat at the table with these executives, helping them make these strategic business decisions. And we know we're successful when our customers are able to deliver on those business outcomes. They meet those objectives, they exceed those objectives. And then we know we've just exceeded customer expectation when our partnership actually shows up in their next earnings call. You know, it's really special. >> Oh, I bet it is. I mean, being able to be that influential in terms of an organization's success I love how you talked about kind of a career evolution that your career has evolved from now you're really with the board of directors having a seat at the table there. My last question for you is kind of on that front Angie is what are some of the changes in in the tech workforce that you've seen the last few years and what are some of the things that you're excited about that are down the road? >> Yeah, so a couple things where I've really seen change and evolution has been in the leadership level. We are needing to lead with empathy and really think about inclusion as a cornerstone skillset. So for our customers, our partners, our employees we've really moved into this hybrid environment. We're both leaders and team norms. We're challenged to change. We have to adapt. And so really having inclusion as that foundational skillset is a requirement for both today and tomorrow's leaders. What I'm really excited about is on the innovation front. Anyone can innovate now, you don't need to be a part of the R&D division of a company. We're seeing that cloud is providing tools all the way down to the elementary student level. So when you think about that, just think the imagination of our youth, brought to life with cloud technology. I mean, the future really is bright. >> It is. That horizon is endless. And I'm going to take some of your advice, Angie I loved that you talked about from your own perspective and your recommendations for the audience. Write that down, write your own press release in terms of what you want to see down the road. I'm going to take your advice, I'm going to do that. I thank you so much for joining me on the program. You've been so inspiring. Your career path has been impressive. What you're seeing in terms of innovation and cloud coming next is incredibly exciting. Thank you so much for your time, Angie. >> Thank you Lisa. >> For Angie Perez Thomas. I'm Lisa Martin. You're watching theCUBE's special program series Women of the Cloud, brought to you by AWS. We'll see you soon. (upbeat music)

(intro music) >> Good afternoon guys and gals. Welcome back to The Strip, Las Vegas. It's "theCUBE" live day four of our coverage of "AWS re:Invent". Lisa Martin, Dave Vellante. Dave, we've had some awesome conversations the last four days. I can't believe how many people are still here. The AWS ecosystem seems stronger than ever. >> Yeah, last year we really noted the ecosystem, you know, coming out of the isolation economy 'cause everybody had this old pent up demand to get together and the ecosystem, even last year, we were like, "Wow." This year's like 10x wow. >> It really is 10x wow, it feels that way. We're going to have a 10x wow conversation next. We're bringing back DataStax to "theCUBE". Please welcome Thomas Bean, it's CMO. Thomas welcome to "theCUBE". >> Thanks, thanks a lot, thanks for having me. >> Great to have you, talk to us about what's going on at DataStax, it's been a little while since we talked to you guys. >> Indeed, so DataStax, we are the realtime data company and we've always been involved in technology such as "Apache Cassandra". We actually created to support and take this, this great technology to the market. And now we're taking it, combining it with other technologies such as "Apache Pulse" for streaming to provide a realtime data cloud. Which helps our users, our customers build applications faster and help them scale without limits. So it's all about mobilizing all of this information that is going to drive the application going to create the awesome experience, when you have a customer waiting behind their mobile phone, when you need a decision to take place immediately to, that's the kind of data that we, that we provide in the cloud on any cloud, but especially with, with AWS and providing the performance that technologies like "Apache Cassandra" are known for but also with market leading unit economics. So really empowering customers to operate at speed and scale. >> Speaking of customers, nobody wants less data slower. And one of the things I think we learned in the in the pan, during the pandemic was that access to realtime data isn't nice to have anymore for any business. It is table stakes, it's competitive advantage. There's somebody right behind in the rear view mirror ready to take over. How has the business model of DataStax maybe evolved in the last couple of years with the fact that realtime data is so critical? >> Realtime data has been around for some time but it used to be really niches. You needed a lot of, a lot of people a lot of funding actually to, to implement these, these applications. So we've adapted to really democratize it, made super easy to access. Not only to start developing but also scaling. So this is why we've taken these great technologies made them serverless cloud native on the cloud so that developers could really start easily and scale. So that be on project products could be taken to the, to the market. And in terms of customers, the patterns is we've seen enterprise customers, you were talking about the pandemic, the Home Depot as an example was able to deliver curbside pickup delivery in 30 days because they were already using DataStax and could adapt their business model with a real time application that combines you were just driving by and you would get the delivery of what exactly you ordered without having to go into the the store. So they shifted their whole business model. But we also see a real strong trend about customer experiences and increasingly a lot of tech companies coming because scale means success to them and building on, on our, on our stack to, to build our applications. >> So Lisa, it's interesting. DataStax and "theCUBE" were started the same year, 2010, and that's when it was the beginning of the ascendancy of the big data era. But of course back then there was, I mean very little cloud. I mean most of it was on-prem. And so data stacks had, you know, had obviously you mentioned a number of things that you had to do to become cloud friendly. >> Thomas: Yes. >> You know, a lot of companies didn't make it, make it through. You guys just raised a bunch of dough as well last summer. And so that's been quite a transformation both architecturally, you know, bringing the customers through. I presume part of that was because you had such a great open source community, but also you have a unique value problem. Maybe you could sort of describe that a little. >> Absolutely, so the, I'll start with the open source community where we see a lot of traction at the, at the moment. We were always very involved with, with the "Apache Cassandra". But what we're seeing right now with "Apache Cassandra" is, is a lot of traction, gaining momentum. We actually, we, the open source community just won an award, did an AMA, had a, a vote from their readers about the top open source projects and "Apache Cassandra" and "Apache Pulse" are part of the top three, which is, which is great. We also run a, in collaboration with the Apache Project, the, a series of events around the, around the globe called "Cassandra Days" where we had tremendous attendance. We, some of them, we had to change venue twice because there were more people coming. A lot of students, a lot of the big users of Cassandra like Apple, Netflix who spoke at these, at these events. So we see this momentum actually picking up and that's why we're also super excited that the Linux Foundation is running the Cassandra Summit in in March in San Jose. Super happy to bring that even back with the rest of the, of the community and we have big announcements to come. "Apache Cassandra" will, will see its next version with major advances such as the support of asset transactions, which is going to make it even more suitable to more use cases. So we're bringing that scale to more applications. So a lot of momentum in terms of, in terms of the, the open source projects. And to your point about the value proposition we take this great momentum to which we contribute a lot. It's not only about taking, it's about giving as well. >> Dave: Big committers, I mean... >> Exactly big contributors. And we also have a lot of expertise, we worked with all of the members of the community, many of them being our customers. So going to the cloud, indeed there was architectural work making Cassandra cloud native putting it on Kubernetes, having the right APIs for developers to, to easily develop on top of it. But also becoming a cloud company, building customer success, our own platform engineering. We, it's interesting because actually we became like our partners in a community. We now operate Cassandra in the cloud so that all of our customers can benefit from all the power of Cassandra but really efficiently, super rapidly, and also with a, the leading unit economies as I mentioned. >> How will the, the asset compliance affect your, you know, new markets, new use cases, you know, expand your TAM, can you explain that? >> I think it will, more applications will be able to tap into the power of, of "NoSQL". Today we see a lot on the customer experience as IOT, gaming platform, a lot of SaaS companies. But now with the ability to have transactions at the database level, we can, beyond providing information, we can go even deeper into the logic of the, of the application. So it makes Cassandra and therefore Astra which is our cloud service an even more suitable database we can address, address more even in terms of the transaction that the application itself will, will support. >> What are some of the business benefits that Cassandra delivers to customers in terms of business outcomes helping businesses really transform? >> So Cassandra brings skill when you have millions of customers, when you have million of data points to go through to serve each of the customers. One of my favorite example is Priceline, who runs entirely on our cloud service. You may see one offer, but it's actually everything they know about you and everything they have to offer matched while you are refreshing your page. This is the kind of power that Cassandra provide. But the thing to say about "Apache Cassandra", it used to be also a database that was a bit hard to manage and hard to develop with. This is why as part of the cloud, we wanted to change these aspects, provide developers the API they like and need and what the application need. Making it super simple to operate and, and, and super affordable, also cost effective to, to run. So the the value to your point, it's time to market. You go faster, you don't have to worry when you choose the right database you're not going to, going to have to change horse in the middle of the river, like sixth month down the line. And you know, you have the guarantee that you're going to get the performance and also the best, the best TCO which matters a lot. I think your previous person talking was addressing it. That's also important especially in the, in a current context. >> As a managed service, you're saying, that's the enabler there, right? >> Thomas: Exactly. >> Dave: That is the model today. I mean, you have to really provide that for customers. They don't want to mess with, you know, all the plumbing, right? I mean... >> Absolutely, I don't think people want to manage databases anymore, we do that very well. We take SLAs and such and even at the developer level what they want is an API so they get all the power. All of of this powered by Cassandra, but now they get it as a, and it's as simple as using as, as an API. >> How about the ecosystem? You mentioned the show in in San Jose in March and the Linux Foundation is, is hosting that, is that correct? >> Yes, absolutely. >> And what is it, Cassandra? >> Cassandra Summit. >> Dave: Cassandra Summit >> Yep. >> What's the ecosystem like today in Cassandra, can you just sort of describe that? >> Around Cassandra, you have actually the big hyperscalers. You have also a few other companies that are supporting Cassandra like technologies. And what's interesting, and that's been a, a something we've worked on but also the "Apache Project" has worked on. Working on a lot of the adjacent technologies, the data pipelines, all of the DevOps solutions to make sure that you can actually put Cassandra as part of your way to build these products and, and build these, these applications. So the, the ecosystem keeps on, keeps on growing and actually the, the Cassandra community keeps on opening the database so that it's, it's really easy to have it connect to the rest of the, the rest environment. And we benefit from all of this in our Astra cloud service. >> So things like machine learning, governance tools that's what you would expect in the ecosystem forming around it, right? So we'll see that in March. >> Machine learning is especially a very interesting use case. We see more and more of it. We recently did a, a nice video with one of our customers called Unifour who does exactly this using also our abstract cloud service. What they provide is they analyze videos of sales calls and they help actually the sellers telling them, "Okay here's what happened here was the customer sentiment". Because they have proof that the better the sentiment is, the shorter the sell cycle is going to be. So they teach the, the sellers on how to say the right things, how to control the thing. This is machine learning applied on video. Cassandra provides I think 200 data points per second that feeds this machine learning. And we see more and more of these use cases, realtime use cases. It happens on the fly when you are on your phone, when you have a, a fraud maybe to detect and to prevent. So it is going to be more and more and we see more and more of these integration at the open source level with technologies like even "Feast" project like "Apache Feast". But also in the, in, in the partners that we're working with integrating our Cassandra and our cloud service with. >> Where are customer conversations these days, given that every company has to be a data company. They have to be able to, to democratize data, allow access to it deep into the, into the organizations. Not just IT or the data organization anymore. But are you finding that the conversations are rising up the, up the stack? Is this, is this a a C-suite priority? Is this a board level conversation? >> So that's an excellent question. We actually ran a survey this summer called "The State of the Database" where we, we asked these tech leaders, okay what's top of mind for you? And real time actually was, was really one of the top priorities. And they explained for the one that who call themselves digital leaders that for 71% of them they could correlate directly the use of realtime data, the quality of their experience or their decision making with revenue. And that's really where the discussion is. And I think it's something we can relate to as users. We don't want the, I mean if the Starbucks apps take seconds to to respond there will be a riot over there. So that's, that's something we can feel. But it really, now it's tangible in, in business terms and now then they take a look at their data strategy, are we equipped? Very often they will see, yeah, we have pockets of realtime data, but we're not really able to leverage it. >> Lisa: Yeah. >> For ML use cases, et cetera. So that's a big trend that we're seeing on one end. On the other end, what we're seeing, and it's one of the things we discussed a lot at the event is that yeah cost is important. Growth at all, at all cost does not exist. So we see a lot of push on moving a lot of the workloads to the cloud to make them scale but at the best the best cost. And we also see some organizations where like, okay let's not let a good crisis go to waste and let's accelerate our innovation not at all costs. So that we see also a lot of new projects being being pushed but reasonable, starting small and, and growing and all of this fueled by, by realtime data, so interesting. >> The other big topic amongst the, the customer community is security. >> Yep. >> I presume it's coming up a lot. What's the conversation like with DataStax? >> That's a topic we've been working on intensely since the creation of Astra less than two years ago. And we keep on reinforcing as any, any cloud provider not only our own abilities in terms of making sure that customers can manage their own keys, et cetera. But also integrating to the rest of the, of the ecosystem when some, a lot of our customers are running on AWS, how do we integrate with PrivateLink and such? We fit exactly into their security environment on AWS and they use exactly the same management tool. Because this is also what used to cost a lot in the cloud services. How much do you have to do to wire them and, and manage. And there are indeed compliance and governance challenges. So that's why making sure that it's fully connected that they have full transparency on what's happening is, is a big part of the evolution. It's always, security is always something you're working on but it's, it's a major topic for us. >> Yep, we talk about that on pretty much every event. Security, which we could dive into, but we're out of time. Last question for you. >> Thomas: Yes. >> We're talking before we went live, we're both big Formula One fans. Say DataStax has the opportunity to sponsor a team and you get the whole side pod to, to put like a phrase about DataStax on the side pod of this F1 car. (laughter) Like a billboard, what does it say? >> Billboard, because an F1 car goes pretty fast, it will be hard to, be hard to read but, "Twice the performance at half the cost, try Astra a cloud service." >> Drop the mike. Awesome, Thomas, thanks so much for joining us. >> Thank for having me. >> Pleasure having you guys on the program. For our guest, Thomas Bean and Dave Vellante, I'm Lisa Martin and you're watching "theCUBE" live from day four of our coverage. "theCUBE", the leader in live tech coverage. (outro music)

>>Okay, we're back with the hybrid Cloud power panel. I'm Dave Ante, and with me our Eric Lockard, who's the corporate vice president of Microsoft Azure Specialized Thomas Corn's, the senior vice president of products at Nutanix. And Indu Carey, who's the Senior Vice President of engineering, NCI and nnc two at Nutanix. Gentlemen, welcome to the cube. Thanks for coming on. >>It's to be >>Here. Have us, >>Eric, let's, let's start with you. We hear so much about cloud first. What's driving the need for hybrid cloud for organizations today? I mean, I not just ev put everything in the public cloud. >>Yeah, well, I mean the public cloud has a bunch of inherent advantages, right? I mean it's, it has effectively infinite capacity, the ability to, you know, innovate without a lot of upfront costs, you know, regions all over the world. So there is a, a trend towards public cloud, but you know, not everything can go to the cloud, especially right away. There's lots of reasons. Customers want to have assets on premise, you know, data gravity, sovereignty and so on. And so really hybrid is the way to achieve the best of both worlds, really to kind of leverage the assets and investments that customers have on premise, but also take advantage of, of the cloud for bursting or regionality or expansion, especially coming outta the pandemic. We saw a lot of this from work from home and, and video conferencing and so on, driving a lot of cloud adoption. So hybrid is really the way that we see customers achieving the best of both worlds. >>Yeah, it makes sense. I wanna, Thomas, if you could talk a little bit, I don't wanna inundate people with the acronyms, but, but the Nutanix cloud clusters on Azure, what is that? What problems does it solve? Give us some color there please. >>Yeah, there, so, you know, cloud clusters on Azure, which we actually call NC two to make it simple and SONC two on Azure is really our solutions for hybrid cloud, right? And you about hybrid cloud, highly desirable customers want it. They, they know this is the right way to do it for them, given that they wanna have workloads on premises at the edge, any public clouds, but it's complicated. It's hard to do, right? And the first thing that you did with just silos, right? You have different infrastructure that you have to go and deal with. You have different teams, different technologies, different areas of expertise and dealing with different portals, networkings get complicated, security gets complicated. And so you heard me say this already, you know, hybrid can be complex. And so what we've done, we then c to Azure is we make that simple, right? We allow teams to go and basically have a solution that allows you to go and take any application running on premises and move it as is to any Azure region where Ncq is available. Once it's running there, you keep the same operating model, right? And that's, so that's actually super valuable to actually go and do this in a simple fashion, do it faster, and basically do hybrid in a more cost effective fashion, know for all your applications. And that's really what's really special about NC two Azure today. >>So Thomas, just a quick follow up on that. So you're, you're, if I understand you correctly, it's an identical experience. Did I get that right? >>This is, this is the key for us, right? Is when you think you're sending on premises, you are used to way of doing things of how you run your applications, how you operate, how you protect them. And what we do here is we extend the Nutanix operating model two workloads running in Azure using the same core stack that you're running on premises, right? So once you have a cluster deploying C to an Azure, it's gonna look like the same cluster that you might be running at the edge or in your own data center using the same tools you, using the same admin constructs to go protect the workloads, make them highly available, do disaster recovery or secure them. All of that becomes the same. But now you are in Azure, and this is what we've spent a lot of time working with Americanist teams on, is you actually have access now to all of those suites of Azure services in from those workloads. So now you get the best of both world, you know, and we bridge them together and you get seamless access of those services between what you get from Nutanix, what you get from Azure. >>Yeah. And as you alluded to, this is traditionally been non-trivial and people have been looking forward to this for, for quite some time. So Indu, I want to understand from an engineering perspective, your team had to work with the Microsoft team, and I'm sure there was this, this is not just a press releases or a PowerPoint, you had to do some some engineering work. So what specific engineering work did you guys do and what's unique about this relative to other solutions in the marketplace? >>So let me start with what's unique about this, and I think Thomas and Eric both did a really good job of describing that the best way to think about what we are delivering jointly with Microsoft is that it speeds of the journey to the public cloud. You know, one way to think about this is moving to the public cloud is sort of like remodeling your house. And when you start remodeling your house, you know, you find that you start with something and before you know it, you're trying to remodel the entire house. And that's a little bit like what journey to the public cloud sort of starts to look like when you start to refactor applications. Because it wasn't, most of the applications out there today weren't designed for the public cloud to begin with. NC two allows you to flip that on its head and say that take your application as is and then lift and shift it to the public cloud, at which point you start the refactor journey. >>And one of the things that you have done really well with the NC two on Azure is that NC two is not something that sits by Azure side. It's fully integrated into the Azure fabric, especially the software defined network and SDN piece. What that means is that, you know, you don't have to worry about connecting your NC two cluster to Azure to some sort of an net worth pipe. You have direct access to the Azure services from the same application that's now running on an NC two cluster. And that makes your refactoring journey so much easier. Your management plan looks the same, your high performance notes let the NVMe notes, they look the same. And really, I mean, other than the facts that you're doing something in the public cloud, all the nutanix's goodness that you're used to continue to receive that, there is a lot of secret sauce that we have had to develop as part of this journey. >>But if we had to pick one that really stands out, it is how do we take the complexity, the network complexity of a public cloud, in this case Azure, and make it as familiar to Nutanix's customers as the VPC construc, the virtual private cloud construc that allows them to really think of that on-prem networking and the public cloud networking in very similar terms. There's a lot more that's gone on behind the scenes. And by the way, I'll tell you a funny sort of anecdote. My dad used to say when I drew up that, you know, if you really want to grow up, you have to do two things. You have to like build a house and you have to marry your kid off to someone. And I would say our dad a third do a flow development with the public cloud provider of the partner. This has been just an absolute amazing journey with Eric and the Microsoft team, and you're very grateful for their >>Support. I, I need NC two for my house. I live in a house that was built in, it's 1687 and we connect all to new and it's, it is a bolt on, but, but, but, and so, but the secret sauce, I mean there's, there's a lot there, but is it a PAs layer? You didn't just wrap it in a container and shove it into the public cloud, You've done more than that. I'm inferring, >>You know, the, it's actually an infrastructure layer offering on top of fid. You can obviously run various types of platform services. So for example, down the road, if you have a containerized application, you'll actually be able to TA it from OnPrem and run it on C two. But the NC two offer itself, the NCAA offer itself is an infrastructure level offering. And the trick is that the storage that you're used to the high performance storage that you know, define tenants to begin with, the hypervisor that you're used to, the network constructs that you're used to light MI segmentation for security purposes, all of them are available to you on NC two in Azure, the same way that we're used to do on-prem. And furthermore, managing all of that through Prism, which is our management interface and management console also remains the same. That makes your security model easier, that makes your management challenge easier, that makes it much easier for an application person or the IT office to be able to report back to the board that they have started to execute on the cloud mandate and they've done that much faster than they'll be able to otherwise. >>Great. Thank you for helping us understand the plumbing. So now Thomas, maybe we can get to like the customers. What, what are you seeing, what are the use cases that are, that are gonna emerge for the solution? >>Yeah, I mean we've, you know, we've had a solution for a while, you know, this is now new on Azure's gonna extend the reach of the solution and get us closer to the type of use cases that are unique to Azure in terms of those solutions for analytics and so forth. But the kind of key use cases for us, the first one you know, talks about it is a migration. You know, we see customers on that cloud journey. They're looking to go and move applications wholesale from on premises to public cloud. You know, we make this very easy because in the end they take the same concept that are around the application and make them, we make them available Now in the Azure region, you can do this for any applications. There's no change to the application, no networking change. The same IP will work the same whether you're running on premises or in Azure. >>The app stays exactly the same, manage the same way, protected the same way. So that's a big one. And you know, the type of drivers point politically or maybe I wanna go do something different or I wanna go and shut down location on premises, I need to do that with a given timeline. I can now move first and then take care of optimizing the application to take advantage of all that Azure has to offer. So migration and doing that in a simple fashion, in a very fast manner is, is a key use case. Another one, and this is classic for leveraging public cloud force, which are doing on premises, is disaster recovery. And something that we refer to as elastic disaster recovery, being able to go and actually configure a secondary site to protect your on premises workloads. But I think that site sitting in Azure as a small site, just enough to hold the data that you're replicating and then use the fact that you cannot get access to resources on demand in Azure to scale out the environment, feed over workloads, run them with performance, potentially fill them back to on premises and then shrink back the environment in Azure to again, optimize cost and take advantage of elasticity that you get from public cloud models. >>And then the last one, building on top of that is just the fact that you cannot get bursting use cases and maybe running a large environment, typically desktop, you know, VDI environments that we see running on premises and I have, you know, a seasonal requirement to go and actually enable more workers to go get access the same solution. You could do this by sizing for the large burst capacity on premises wasting resources during the rest of the year. What we see customers do is optimize what they're running on premises and get access to resources on demand in Azure and basically move the workload and now basically get combined desktop running on premises desktops running on NC two on Azure, same desktop images, same management, same services, and do that as a burst use case during, say you're a retailer that has to go and take care of your holiday season. You know, great use case that we see over and over again for our customers, right? And pretty much complimenting the notion of, look, I wanna go to desktop as a service, but right now, now I don't want to refactor the entire application stack. I just won't be able to get access to resources on demand in the right place at the right time. >>Makes sense. I mean this is really all about supporting customers', digital transformations. We all talk about how that was accelerated during the pandemic and, but the cloud is a fundamental component of the digital transformations. And Eric, you, you guys have obviously made a commitment between Microsoft and and Nutanix to simplify hybrid cloud and that journey to the cloud. How should customers, you know, measure that? What does success look like? What's the ultimate vision here? >>Well, the ultimate vision is really twofold. I think the one is to, you know, first is really to ease a customer's journey to the cloud to allow them to take advantage of all the benefits to the cloud, but to do so without having to rewrite their applications or retrain their, their administrators and or, or to obviate their investment that they already have in platforms like, like Nutanix. And so the, the work that companies have done together here, you know, first and foremost is really to allow folks to come to the cloud in the way that they want to come to the cloud and take really the best of both worlds, right? Leverage, leverage their investment in the capabilities of the Nutanix platform, but do so in conjunction with the advantages and and capabilities of of Azure, you know. Second, it is really to extend some of the cloud capabilities down onto the on-premise infrastructure. And so with investments that we've done together with Azure arc for example, we're really extending the Azure control plane down onto on-premise Nutanix clusters and bringing the capabilities that that provides to the Nutanix customer as well as various Azure services like our data services and Azure SQL server. So it's really kind of coming at the problem from, from two directions. One is from kind of traditional on-prem up into the cloud, and then the second is kind of from the cloud leveraging the investment customers have in in on-premise hci. >>Got it. Thank you. Okay, last question. Maybe each of you could just give us one key takeaway for our audience today. Maybe we start with with with with Thomas and then Indu and then Eric you can bring us home. >>Sure. So the key takeaway is, you know, you takes cloud clusters on Azure is ngi, you know, this is something that we've had tremendous demand from our customers, both from the Microsoft side and the Nutanix side going, going back years literally, right? People have been wanting to go and see this, this is now live GA open for business and you know, we're ready to go and engage and ready to scale, right? This is our first step in a long journey in a very key partnership for us at Nutanix. >>Great Indu >>In our Dave. In a prior life about seven or eight, eight years ago, I was a part of a team that took a popular patch preparation software and moved it to the public cloud. And that was a journey that took us four years and probably several hundred million dollars. And if we had had NC two then it would've saved us half the money, but more importantly would've gotten there in one third the time. And that's really the value of this. >>Okay. Eric, bring us home please. >>Yeah, I'll just point out like this is not something that's just both on or something. We, we, we started yesterday. This is something the teams, both companies have been working on together for, for years really. And it's, it's a way of, of deeply integrating Nutanix into the Azure Cloud and with the ultimate goal of, of again, providing cloud capabilities to the Nutanix customer in a way that they can, you know, take advantage of the cloud and then compliment those applications over time with additional Azure services like storage, for example. So it really is a great on-ramp to the cloud for, for customers who have significant investments in, in Nutanix clusters on premise, >>Love the co-engineering and the ability to take advantage of those cloud native tools and capabilities, real customer value. Thanks gentlemen. Really appreciate your time. >>Thank >>You. Thank you. Thank you. >>Okay, keep it right there. You're watching. Accelerate hybrid cloud, that journey with Nutanix and Microsoft technology on the cube. You're leader in enterprise and emerging tech coverage >>Organizations are increasingly moving towards a hybrid cloud model that contains a mix of on premises public and private clouds. A recent study confirms 83% of businesses agree that hybrid multi-cloud is the ideal operating model. Despite its many benefits, deploying a hybrid cloud can be challenging, complex, slow and expensive require different skills and tool sets and separate siloed management interfaces. In fact, 87% of surveyed enterprises believe that multi-cloud success will require simplified management of mixed infrastructures >>With Nutanix and Microsoft. Your hybrid cloud gets the best of both worlds. The predictable costs, performance control and data sovereignty of a private cloud and the scalability, cloud services, ease of use and fractional economics of the public cloud. Whatever your use case, Nutanix cloud clusters simplifies IT. Operations is faster and lowers risk for migration projects, lowers cloud TCO and provides investment optimization and offers effortless, limitless scale and flexibility. Choose NC two to accelerate your business in the cloud and achieve true hybrid cloud success. Take a free self-guided 30 minute test drive of the solutions provisioning steps and use cases at nutanix.com/azure td. >>Okay, so we're just wrapping up accelerate hybrid cloud with Nutanix and Microsoft made possible by Nutanix where we just heard how Nutanix is partnering with cloud and software leader Microsoft to enable customers to execute on a true hybrid cloud vision with actionable solutions. We pushed and got the answer that with NC two on Azure, you get the same stack, the same performance, the same networking, the same automation, the same workflows across on-prem and Azure Estates. Realizing the goal of simplifying and extending on-prem workloads to any Azure region to move apps without complicated refactoring and to be able to tap the full complement of native services that are available on Azure. Remember, all these videos are available on demand@thecube.net and you can check out silicon angle.com for all the news related to this announcement and all things enterprise tech. Please go to nutanix.com as of course information about this announcement and the partnership, but there's also a ton of resources to better understand the Nutanix product portfolio. There are white papers, videos, and other valuable content, so check that out. This is Dave Ante for Lisa Martin with the Cube, your leader in enterprise and emerging tech coverage. Thanks for watching the program and we'll see you next time.

>>Okay, we're back with the hybrid Cloud power panel. I'm Dave Ante and with me our Eric Lockhart, who's the corporate vice president of Microsoft Azure, Specialized Thomas Corny, the senior vice president of products at Nutanix, and Indu Care, who's the Senior Vice President of engineering, NCI and nnc two at Nutanix. Gentlemen, welcome to the cube. Thanks for coming on. >>It's to >>Be here. Have us, >>Eric, let's, let's start with you. We hear so much about cloud first. What's driving the need for hybrid cloud for organizations today? I mean, I wanna just ev put everything in the public cloud. >>Yeah, well, I mean, the public cloud has a bunch of inherent advantages, right? I mean, it's, it has effectively infinite capacity, the ability to, you know, innovate without a lot of upfront costs, you know, regions all over the world. So there is a, a trend towards public cloud, but you know, not everything can go to the cloud, especially right away. There's lots of reasons. Customers want to have assets on premise, you know, data gravity, sovereignty and so on. And so really hybrid is the way to achieve the best of both worlds, really to kind of leverage the assets and investments that customers have on premise, but also take advantage of, of the cloud for bursting or regionality or expansion, especially coming outta the pandemic. We saw a lot of this from work from home and, and video conferencing and so on, driving a lot of cloud adoption. So hybrid is really the way that we see customers achieving the best of both worlds. >>Yeah, makes sense. I wanna, Thomas, if you could talk a little bit, I don't wanna inundate people with the acronyms, but, but the Nutanix Cloud clusters on Azure, what is that? What problems does it solve? Give us some color there, please. >>That is, so, you know, cloud clusters on Azure, which we actually call NC two to make it simple. And so NC two on Azure is really our solutions for hybrid cloud, right? And you think about the hybrid cloud, highly desirable customers want it. They, they know this is the right way to do for them, given that they wanna have workloads on premises at the edge, any public clouds. But it's complicated. It's hard to do, right? And the first thing that you deal with is just silos, right? You have different infrastructure that you have to go and deal with. You have different teams, different technologies, different areas of expertise and dealing with different portals. Networkings get complicated, security gets complicated. And so you heard me say this already, you know, hybrid can be complex. And so what we've done, we then c to Azure is we make that simple, right? We allow teams to go and basically have a solution that allows you to go and take any application running on premises and move it as is to any Azure region where ncq is available. Once it's running there, you keep the same operating model, right? And that's something actually super valuable to actually go and do this in a simple fashion, do it faster, and basically do, do hybrid in a more cost effective fashion, know for all your applications. And that's really what's really special about NC Azure today. >>So Thomas, just a quick follow up on that. So you're, you're, if I understand you correctly, it's an identical experience. Did I get that right? >>This is, this is the key for us, right? Is when you think you're sending on premises, you are used to way of doing things of how you run your applications, how you operate, how you protect them. And what we do here is we extend the Nutanix operating model two workloads running in Azure using the same core stack that you're running on premises, right? So once you have a cluster deploying C to an Azure, it's gonna look like the same cluster that you might be running at the edge or in your own data center, using the same tools, using, using the same admin constructs to go protect the workloads, make them highly available with disaster recovery or secure them. All of that becomes the same, but now you are in Azure, and this is what we've spent a lot of time working with Americanist teams on, is you actually have access now to all of those suites of Azure services in from those workloads. So now you get the best of both world, you know, and we bridge them together and you get seamless access of those services between what you get from Nutanix, what you get from Azure. >>Yeah. And as you alluded to, this is traditionally been non-trivial and people have been looking forward to this for, for quite some time. So Indu, I want to understand from an engineering perspective, your team had to work with the Microsoft team, and I'm sure there was this, this is not just a press releases or a PowerPoint, you had to do some some engineering work. So what specific engineering work did you guys do and what's unique about this relative to other solutions in the marketplace? >>So let me start with what's unique about this, and I think Thomas and Eric both did a really good job of describing that the best way to think about what we are delivering jointly with Microsoft is that it speeds up the journey to the public cloud. You know, one way to think about this is moving to the public cloud is sort of like remodeling your house. And when you start remodeling your house, you know, you find that you start with something and before you know it, you're trying to remodel the entire house. And that's a little bit like what journey to the public cloud sort of starts to look like when you start to refactor applications. Because it wasn't, most of the applications out there today weren't designed for the public cloud to begin with. NC two allows you to flip that on its head and say that take your application as is and then lift and shift it to the public cloud, at which point you start the refactor journey. >>And one of the things that you have done really well with the NC two on Azure is that NC two is not something that sits by Azure side. It's fully integrated into the Azure fabric, especially the software defined network and SDN piece. What that means is that, you know, you don't have to worry about connecting your NC two cluster to Azure to some sort of a net worth pipe. You have direct access to the Azure services from the same application that's now running on an C2 cluster. And that makes your refactoring journey so much easier. Your management claim looks the same, your high performance notes let the NVMe notes, they look the same. And really, I mean, other than the facts that you're doing something in the public cloud, all the Nutanix goodness that you're used to continue to receive that, there is a lot of secret sauce that we have had to develop as part of this journey. >>But if we had to pick one that really stands out, it is how do we take the complexity, the network complexity, offer public cloud, in this case Azure, and make it as familiar to Nutanix's customers as the VPC construc, the virtual private cloud construct that allows them to really think of their on-prem networking and the public cloud networking in very similar terms. There's a lot more that's gone on behind the scenes. And by the way, I'll tell you a funny sort of anecdote. My dad used to say when I drew up that, you know, if you really want to grow up, you have to do two things. You have to like build a house and you have to marry your kid off to someone. And I would say our dad a third do a code development with the public cloud provider of the partner. This has been just an absolute amazing journey with Eric and the Microsoft team, and you're very grateful for their support. >>I need NC two for my house. I live in a house that was built and it's 1687 and we connect old to new and it's, it is a bolt on, but, but, but, and so, but the secret sauce, I mean there's, there's a lot there, but is it a PAs layer? You didn't just wrap it in a container and shove it into the public cloud, You've done more than that. I'm inferring, >>You know, the, it's actually an infrastructure layer offering on top of fid. You can obviously run various types of platform services. So for example, down the road, if you have a containerized application, you'll actually be able to tat it from OnPrem and run it on C two. But the NC two offer itself, the NCAA often itself is an infrastructure level offering. And the trick is that the storage that you're used to the high performance storage that you know, define Nutanix to begin with, the hypervisor that you're used to, the network constructs that you're used to light MI segmentation for security purposes, all of them are available to you on NC two in Azure, the same way that we're used to do on-prem. And furthermore, managing all of that through Prism, which is our management interface and management console also remains the same. That makes your security model easier, that makes your management challenge easier, that makes it much easier for an accusation person or the IT office to be able to report back to the board that they have started to execute on the cloud mandate and they have done that much faster than they'll be able to otherwise. >>Great. Thank you for helping us understand the plumbing. So now Thomas, maybe we can get to like the customers. What, what are you seeing, what are the use cases that are, that are gonna emerge for this solution? >>Yeah, I mean we've, you know, we've had a solution for a while and you know, this is now new on Azure is gonna extend the reach of the solution and get us closer to the type of use cases that are unique to Azure in terms of those solutions for analytics and so forth. But the kind of key use cases for us, the first one you know, talks about it is a migration. You know, we see customers on the cloud journey, they're looking to go and move applications wholesale from on premises to public cloud. You know, we make this very easy because in the end they take the same culture that are around the application and make them, we make them available Now in the Azure region, you can do this for any applications. There's no change to the application, no networking change. The same IP will work the same whether you're running on premises or in Azure. >>The app stays exactly the same, manage the same way, protected the same way. So that's a big one. And you know, the type of drivers point to politically or maybe I wanna go do something different or I wanna go and shut down education on premises, I need to do that with a given timeline. I can now move first and then take care of optimizing the application to take advantage of all that Azure has to offer. So migration and doing that in a simple fashion, in a very fast manner is, is a key use case. Another one, and this is classic for leveraging public cloud force, which are doing on premises IT disaster recovery and something that we refer to as elastic disaster recovery, being able to go and actually configure a secondary site to protect your on premises workloads, but I that site sitting in Azure as a small site, just enough to hold the data that you're replicating and then use the fact that you cannot get access to resources on demand in Azure to scale out the environment, feed over workloads, run them with performance, potentially feed them back to on premises and then shrink back the environment in Azure to again, optimize cost and take advantage of elasticity that you get from public cloud models. >>Then the last one, building on top of that is just the fact that you cannot get boosting use cases and maybe running a large environment, typically desktop, you know, VDI environments that we see running on premises and I have, you know, a seasonal requirement to go and actually enable more workers to go get access the same solution. You could do this by sizing for the large burst capacity on premises wasting resources during the rest of the year. What we see customers do is optimize what they're running on premises and get access to resources on demand in Azure and basically move the workload and now basically get combined desktops running on premises desktops running on NC two on Azure, same desktop images, same management, same services, and do that as a burst use case during, say you're a retailer that has to go and take care of your holiday season. You know, great use case that we see over and over again for our customers, right? And pretty much complimenting the notion of, look, I wanna go to desktop as a service, but right now I don't want to refactor the entire application stack. I just wanna be able to get access to resources on demand in the right place at the right time. >>Makes sense. I mean this is really all about supporting customers', digital transformations. We all talk about how that was accelerated during the pandemic and, but the cloud is a fundamental component of the digital transformation generic. You, you guys have obviously made a commitment between Microsoft and and Nutanix to simplify hybrid cloud and that journey to the cloud. How should customers, you know, measure that? What does success look like? What's the ultimate vision here? >>Well, the ultimate vision is really twofold. I think the one is to, you know, first is really to ease a customer's journey to the cloud to allow them to take advantage of all the benefits to the cloud, but to do so without having to rewrite their applications or retrain their, their administrators and or or to obviate their investment that they already have and platforms like, like Nutanix. And so the, the work that companies have done together here, you know, first and foremost is really to allow folks to come to the cloud in the way that they want to come to the cloud and take really the best of both worlds, right? Leverage, leverage their investment in the capabilities of the Nutanix platform, but do so in conjunction with the advantages and and capabilities of, of Azure. You know, Second is really to extend some of the cloud capabilities down onto the on-premise infrastructure. And so with investments that we've done together with Azure arc for example, we're really extending the Azure control plane down onto on premise Nutanix clusters and bringing the capabilities that that provides to the, the Nutanix customer as well as various Azure services like our data services and Azure SQL server. So it's really kind of coming at the problem from, from two directions. One is from kind of traditional on-premise up into the cloud and then the second is kind of from the cloud leveraging the investment customers have in in on-premise hci. >>Got it. Thank you. Okay, last question. Maybe each of you can just give us one key takeaway for our audience today. Maybe we start with with with with Thomas and then Indu and then Eric you can bring us home. >>Sure. So the key takeaway is, you know, Nutanix Cloud clusters on Azure is now ga you know, this is something that we've had tremendous demand from our customers, both from the Microsoft side and the Nutanix side going, going back years literally, right? People have been wanting to go and see this, this is now live GA open for business and you know, we're ready to go and engage and ready to scale, right? This is our first step in a long journey in a very key partnership for us at Nutanix. >>Great Indu >>In our Dave. In a prior life about seven or eight, eight years ago, I was a part of a team that took a popular cat's preparation software and moved it to the public cloud. And that was a journey that took us four years and probably several hundred million. And if we had had NC two then it would've saved us half the money, but more importantly would've gotten there in one third the time. And that's really the value of this. >>Okay. Eric, bring us home please. >>Yeah, I'll just point out like this is not something that's just both on or something. We, we, we started yesterday. This is something the teams, both companies have been working on together for, for years, really. And it's, it's a way of, of deeply integrating Nutanix into the Azure Cloud and with the ultimate goal of, of again, providing cloud capabilities to the Nutanix customer in a way that they can, you know, take advantage of the cloud and then compliment those applications over time with additional Azure services like storage, for example. So it really is a great on-ramp to the cloud for, for customers who have significant investments in, in Nutanix clusters on premise, >>Love the co-engineering and the ability to take advantage of those cloud native tools and capabilities, real customer value. Thanks gentlemen. Really appreciate your time. >>Thank >>You. Thank you. >>Okay. Keep it right there. You're watching Accelerate Hybrid Cloud, that journey with Nutanix and Microsoft technology on the cube. You're a leader in enterprise and emerging tech coverage.

(gentle music) >> Okay, we're back with the hybrid cloud power panel. I'm Dave Vellante, and with me Eric Lockard who is the Corporate Vice President of Microsoft Azure Specialized. Thomas Cornely is the Senior Vice President of Products at Nutanix and Indu Keri, who's the Senior Vice President of Engineering, NCI and NC2 at Nutanix. Gentlemen, welcome to The Cube. Thanks for coming on. >> It's good to be here. >> Thanks for having us. >> Eric, let's, let's start with you. We hear so much about cloud first. What's driving the need for hybrid cloud for organizations today? I mean, I want to just put everything in the public cloud. >> Yeah, well, I mean the public cloud has a bunch of inherent advantages, right? I mean it's, it has effectively infinite capacity the ability to, you know, innovate without a lot of upfront costs, you know, regions all over the world. So there is a trend towards public cloud, but you know not everything can go to the cloud, especially right away. There's lots of reasons. Customers want to have assets on premise you know, data gravity, sovereignty and so on. And so really hybrid is the way to achieve the best of both worlds, really to kind of leverage the assets and investments that customers have on premise but also take advantage of the cloud for bursting, originality or expansion especially coming out of the pandemic. We saw a lot of this from work from home and and video conferencing and so on driving a lot of cloud adoption. So hybrid is really the way that we see customers achieving the best of both worlds. >> Yeah, makes sense. I want to, Thomas, if you could talk a little bit I don't want to inundate people with the acronyms, but the Nutanix Cloud clusters on Azure, what is that? What problems does it solve? Give us some color there, please. >> Yeah, so, you know, cloud clusters on Azure which we actually call NC2 to make it simple. And so NC2 on Azure is really our solutions for hybrid cloud, right? And you think about hybrid cloud highly desirable, customers want it. They, they know this is the right way to do it for them given that they want to have workloads on premises at the edge, any public clouds, but it's complicated. It's hard to do, right? And the first thing that you deal with is just silos, right? You have different infrastructure that you have to go and deal with. You have different teams, different technologies, different areas of expertise. And dealing with different portals, networking get complicated, security gets complicated. And so you heard me say this already, you know hybrid can be complex. And so what we've done we then NC2 Azure is we make that simple, right? We allow teams to go and basically have a solution that allows you to go and take any application running on premises and move it as-is to any Azure region where NC2 is available. Once it's running there you keep the same operating model, right? And that's, so that actually super valuable to actually go and do this in a simple fashion. Do it faster, and basically do hybrid in a more (indistinct) fashion know for all your applications. And that's what's really special about NC2 today. >> So Thomas, just a quick follow up on that. So you're, you're, if I understand you correctly it's an identical experience. Did I get that right? >> This is the key for us, right? When you think you're sitting on premises you are used to way of doing things of how you run your applications, how you operate, how you protect them. And what we do here is we extend the Nutanix operating model to workloads running in Azure using the same core stack that you're running on premises, right? So once you have a cluster, deploy in NC2 Azure, it's going to look like the same cluster that you might be running at the edge or in your own data center, using the same tools, using the same admin constructs to go protect the workloads make them highly available do disaster recovery or secure them. All of that becomes the same. But now you are in Azure, and this is what we've spent a lot of time working with Eric and his teams on is you actually have access now to all of those suites of Azure services (indistinct) from those workloads. So now you get the best of both world, you know and we bridge them together and you to get seamless access of those services between what you get from Nutanix, what you get from Azure. >> Yeah. And as you alluded to this is traditionally been non-trivial and people have been looking forward to this for quite some time. So Indu, I want to understand from an engineering perspective, your team had to work with the Microsoft team, and I'm sure there was this is not just a press release, this is, or a PowerPoint you had to do some some engineering work. So what specific engineering work did you guys do and what's unique about this relative to other solutions in the marketplace? >> So let me start with what's unique about this. And I think Thomas and Eric both did a really good job of describing that. The best way to think about what we are delivering jointly with Microsoft is that it speeds up the journey to the public cloud. You know, one way to think about this is moving to the public cloud is sort of like remodeling your house. And when you start remodeling your house, you know, you find that you start with something and before you know it, you're trying to remodel the entire house. And that's a little bit like what journey to the public cloud sort of starts to look like when you start to refactor applications. Because it wasn't, most of the applications out there today weren't designed for the public cloud to begin with. NC2 allows you to flip that on its head and say that take your application as-is and then lift and shift it to the public cloud at which point you start the refactor journey. And one of the things that you have done really well with the NC2 on Azure is that NC2 is not something that sits by Azure side. It's fully integrated into the Azure fabric especially the software-defined networking, SDN piece. What that means is that, you know you don't have to worry about connecting your NC2 cluster to Azure to some sort of a network pipe. You have direct access to the Azure services from the same application that's now running on an NC2 cluster. And that makes your refactor journey so much easier. Your management claim looks the same, your high performance notes let the NVMe notes they look the same. And really, I mean, other than the fact that you're doing something in the public cloud all the Nutanix goodness that you're used to continue to receive that. There is a lot of secret sauce that we have had to develop as part of this journey. But if we had to pick one that really stands out it is how do we take the complexity, the network complexity offer public cloud, in this case Azure and make it as familiar to Nutanix's customers as the VPC, the virtual private cloud (indistinct) that allows them to really think of their on-prem networking and the public cloud networking in very similar terms. There's a lot more that's done on behind the scenes. And by the way, I'll tell you a funny sort of anecdote. My dad used to say when I grew up that, you know if you really want to grow up, you have to do two things. You have to like build a house and you have to marry your kid off to someone. And I would say our dad a third, do a cloud development with the public cloud provider of the partner. This has been just an absolute amazing journey with Eric and the Microsoft team and we're very grateful for their support. >> I need NC2 for my house. I live in a house that was built and it's 1687 and we connect all the new and it is a bolt on, but the secret sauce, I mean there's, there's a lot there but is it a (indistinct) layer. You didn't just wrap it in a container and shove it into the public cloud. You've done more than that, I'm inferring. >> You know, the, it's actually an infrastructure layer offering on top of (indistinct). You can obviously run various types of platform services. So for example, down the road if you have a containerized application you'll actually be able to take it from on prem and run it on NC2. But the NC2 offer itself, the NC2 offering itself is an infrastructure level offering. And the trick is that the storage that you're used to the high performance storage that you know define Nutanix to begin with the hypervisor that you're used to the network constructs that you're used to light micro segmentation for security purposes, all of them are available to you on NC2 in Azure the same way that we're used to do on-prem. And furthermore, managing all of that through Prism, which is our management interface and management console also remains the same. That makes your security model easier that makes your management challenge easier that makes it much easier for an application person or the IT office to be able to report back to the board that they have started to execute on the cloud mandate and they've done that much faster than they would be able to otherwise. >> Great. Thank you for helping us understand the plumbing. So now Thomas, maybe we can get to like the customers. What, what are you seeing, what are the use cases that are that are going to emerge for this solution? >> Yeah, I mean we've, you know we've had a solution for a while and you know this is now new on Azure is going to extend the reach of the solution and get us closer to the type of use cases that are unique to Azure in terms of those solutions for analytics and so forth. But the kind of key use cases for us the first one you know, talks about it is a migration. You know, we see customers on that cloud journey. They're looking to go and move applications wholesale from on premises to public cloud. You know, we make this very easy because in the end they take the same culture that were around the application and we make them available now in the Azure region. You can do this for any applications. There's no change to the application, no networking change the same IP constraint will work the same whether you're running on premises or in Azure. The app stays exactly the same manage the same way, protected the same way. So that's a big one. And you know, the type of drivers for (indistinct) maybe I want to go do something different or I want to go and shut down the location on premises I need to do that with a given timeline. I can now move first and then take care of optimizing the application to take advantage of all that Azure has to offer. So migration and doing that in a simple fashion in a very fast manner is, is a key use case. Another one, and this is classic for leveraging public cloud force, which we're doing on premises IT disaster recovery and something that we refer to as Elastic disaster recovery, being able to go and actually configure a secondary site to protect your on premises workloads. But I think that site sitting in Azure as a small site just enough to hold the data that you're replicating and then use the fact that you cannot get access to resources on demand in Azure to scale out the environment feed over workloads, run them with performance potentially fill them back to on premises, and then shrink back the environment in Azure to again optimize cost and take advantage of the elasticity that you get from public cloud models. Then the last one, building on top of that is just the fact that you cannot get bursting use cases and maybe running a large environment, typically desktop, you know, VDI environments that we see running on premises and I have, you know, a seasonal requirement to go and actually enable more workers to go get access the same solution. You could do this by sizing for the large burst capacity on premises wasting resources during the rest of the year. What we see customers do is optimize what they're running on premises and get access to resources on demand in Azure and basically move the workloads and now basically get combined desktops running on premises desktops running on NC2 on Azure same desktop images, same management, same services and do that as a burst use case during say you're a retailer that has to go and take care of your holiday season. You know, great use case that we see over and over again for our customers, right? And pretty much complimenting the notion of, look I want to go to desktop as a service, but right now I don't want to refactor the entire application stack. I just want to be able to get access to resources on demand in the right place at the right time. >> Makes sense. I mean this is really all about supporting customer's, digital transformations. We all talk about how that was accelerated during the pandemic and but the cloud is a fundamental component of the digital transformations generic. You, you guys have obviously made a commitment between Microsoft and Nutanix to simplify hybrid cloud and that journey to the cloud. How should customers, you know, measure that? What does success look like? What's the ultimate vision here? >> Well, the ultimate vision is really twofold, I think. The one is to, you know first is really to ease a customer's journey to the cloud to allow them to take advantage of all the benefits to the cloud, but to do so without having to rewrite their applications or retrain their administrators and or to obviate their investment that they already have and platforms like Nutanix. And so the work that companies have done together here, you know, first and foremost is really to allow folks to come to the cloud in the way that they want to come to the cloud and take really the best of both worlds, right? Leverage their investment in the capabilities of the Nutanix platform, but do so in conjunction with the advantages and capabilities of Azure. You know, second is really to extend some of the cloud capabilities down onto the on-premise infrastructure. And so with investments that we've done together with Azure arc for example, we're really extending the Azure control plane down onto on-premise Nutanix clusters and bringing the capabilities that provides to the Nutanix customer as well as various Azure services like our data services and Azure SQL server. So it's really kind of coming at the problem from two directions. One is from kind of traditional on-premise up into the cloud, and then the second is kind of from the cloud leveraging the investment customers have in on-premise HCI. >> Got it. Thank you. Okay, last question. Maybe each of you could just give us one key takeaway for our audience today. Maybe we start with Thomas and then Indu and then Eric you can bring us home. >> Sure. So the key takeaway is, you know, cloud customers on Azure is now GA you know, this is something that we've had tremendous demand from our customers both from the Microsoft side and the Nutanix side going back years literally, right? People have been wanting to go and see this this is now live GA open for business and you know we're ready to go and engage and ready to scale, right? This is our first step in a long journey in a very key partnership for us at Nutanix. >> Great, Indu. >> In our day, in a prior life about seven or eight years ago, I was a part of a team that took a popular text preparation software and moved it to the public cloud. And that was a journey that took us four years and probably several hundred million dollars. And if we had NC2 then it would've saved us half the money, but more importantly would've gotten there in one third the time. And that's really the value of this. >> Okay. Eric, bring us home please. >> Yeah, I'll just point out that, this is not something that's just bought on or something we started yesterday. This is something the teams both companies have been working on together for years really. And it's a way of deeply integrating Nutanix into the Azure Cloud. And with the ultimate goal of again providing cloud capabilities to the Nutanix customer in a way that they can, you know take advantage of the cloud and then compliment those applications over time with additional Azure services like storage, for example. So it really is a great on-ramp to the cloud for customers who have significant investments in Nutanix clusters on premise. >> Love the co-engineering and the ability to take advantage of those cloud native tools and capabilities, real customer value. Thanks gentlemen. Really appreciate your time. >> Thank you. >> Thank you. >> Okay. Keep it right there. You're watching accelerate hybrid cloud, that journey with Nutanix and Microsoft technology on The Cube, your leader in enterprise and emerging tech coverage. (gentle music)

>> TheCUBE presents UI Path Forward Five brought to you by UI Path. >> Welcome back to UI Path Forward Five. You're watching The Cubes, Walter Wall coverage. This is day one, Dave Vellante, with my co-host Dave Nicholson. We're taking RPA to intelligence automation. We're going from point tools to platforms. Neeraj Mathur is here. He's the director of Intelligent Automation at VMware. Yes, VMware. We're not going to talk about vSphere or Aria, or maybe we are, (Neeraj chuckles) but he's joined by Thomas Stocker who's a principal product manager at UI Path. And we're going to talk about testing automation, automating the testing process. It's a new sort of big vector in the whole RPA automation space. Gentleman, welcome to theCUBE. Good to see you. >> Neeraj: Thank you very much. >> Thomas: Thank you. >> So Neeraj, as we were saying, Dave and I, you know, really like VMware was half our lives for a long time but we're going to flip it a little bit. >> Neeraj: Absolutely. >> And talk about sort of some of the inside baseball. Talk about your role and how you're applying automation at VMware. >> Absolutely. So, so as part of us really running the intelligent automation program at VMware, we have a quite matured COE for last, you know four to five years, we've been doing this automation across the enterprise. So what we have really done is, you know over 45 different business functions where we really automated quite a lot different processes and tasks on that. So as part of my role, I'm really responsible for making sure that we are, you know, bringing in the best practices, making sure that we are ready to scale across the enterprise but at the same time, how, you know, quickly we are able to deliver the value of this automation to our businesses as well. >> Thomas, as a product manager, you know the product, and the market inside and out, you know the competition, you know the pricing, you know how customers are using it, you know all the features. What's your area of - main area of focus? >> The main area of the UiPathT suite... >> For your role, I mean? >> For my role is the RPA testing. So meaning testing RPA workflows themselves. And the reason is RPA has matured over the last few years. We see that, and it has adopted a lot of best practices from the software development area. So what we see is RPA now becomes business critical. It's part of the main core business processes in corporation and testing it just makes sense. You have to continuously monitor and continuously test your automation to make sure it does not break in production. >> Okay. And you have a specific product for this? Is it a feature or it's a module? >> So RPA testing or the UiPath T Suite, as the name suggests it's a suite of products. It's actually part of the existing platform. So we use Orchestrator, which is the distribution engine. We use Studio, which is our idea to create automation. And on top of that, we build a new component, which is called the UiPath Test Manager. And this is a kind of analytics and management platform where you have an oversight on what happened, what went wrong, and what is the reason for automation to **bring. >> Okay. And so Neeraj, you're testing your robot code? >> Neeraj: Correct. >> Right. And you're looking for what? Governance, security, quality, efficiency, what are the things you're looking for? >> It's actually all of all of those but our main goal to really start this was two-front, right? So we were really looking at how do we, you know, deliver at a speed with the quality which we can really maintain and sustain for a longer period, right? So to improve our quality of delivery at a speed of delivery, which we can do it. So the way we look at testing automation is not just as an independent entity. We look at this as a pipeline of a continuous improvement for us, right? So how it is called industry as a CICD pipeline. So testing automation is one of the key component of that. But the way we were able to deliver on the speed is to really have that end to end automation done for us to also from developers to production and using that pipeline and our testing is one piece of that. And the way we were able to also improve on the quality of our delivery is to really have automated way of doing the code reviews, automated way of doing the testing using this platform as well. and then, you know, how you go through end to end for that purpose. >> Thomas, when I hear testing robots, (Thomas chuckles) I don't care if it's code or actual robots, it's terrifying. >> It's terrify, yeah. >> It's terrifying. Okay, great. You, you have some test suite that says look, Yeah, we've looked at >> The, why is that terrifying? >> What's, It's terrifying because if you have to let it interact with actual live systems in some way. Yeah. The only way to know if it's going to break something is either you let it loose or you have some sort of sandbox where, I mean, what do you do? Are you taking clones of environments and running actual tests against them? I mean, think it's >> Like testing disaster recovery in the old days. Imagine. >> So we are actually not running any testing in the production live environment, right? The way we build this actually to do a testing in the separate test environment on that as well by using very specific test data from business, which you know, we call that as a golden copy of that test data because we want to use that data for months and years to come. Okay. Right? Yeah. So not touching any production environmental Facebook. >> Yeah. All right. Cause you, you can imagine >> Absolutely >> It's like, oh yeah we've created a robotic changes baby diapers let's go ahead and test it on these babies. [Collective Laughter] Yeah >> I don't think so. No, no, But, but what's the, does it does it matter if there's a delta between the test data and the, the, the production data? How, how big is that delta? How do you manage that? >> It does matter. And that's where actually that whole, you know, angle of how much you can, can in real, in real life can test right? So there are cases where you would have, even in our cases where, you know, the production data might be slightly different than the test data itself. So the whole effort goes into making sure that the test data, which we are preparing here, is as close to the products and data itself, right? It may not be a hundred percent close but that's the sort of you know, boundary or risk you may have to take. >> Okay. So you're snapshotting, that moving it over, a little V motion? >> Neeraj: Yeah. >> Okay. So do you do this for citizen developers as well? Or is you guys pretty much center of excellence writing all the bots? >> No, right now we are doing only for the unattended, the COE driven bots only at this point of time, >> What are you, what are your thoughts on the future? Because I can see I can see some really sloppy citizen coders. >> Yeah. Yeah. So as part of our governance, which we are trying to build for our citizen developers as well, there there is a really similar consideration for that as well. But for us, we have really not gone that far to build that sort of automation right >> Now, narrowly, just if we talk about testing what's the business impact been on the testing? And I'm interested in overall, but the overall platform but specifically for the testing, when did that when did you start implementing that and, and what what has been the business benefit? >> So the benefit is really on the on the speed of the delivery, which means that we are able to actually deliver more projects and more automation as well. So since we adopted that, we have seen our you know, improvement, our speed is around 15%, right? So, so, you know, 15% better speed than previously. What we have also seen is, is that our success rate of our transactions in production environment has gone to 96% success rate, which is, again there is a direct implication on business, on, on that point of view that, you know, there's no more manual exception or manual interaction is required for those failure scenarios. >> So 15% better speed at what? At, at implementing the bots? At actually writing code? Or... >> End to end, Yes. So from building the code to test that code able to approve that and then deploy that into the production environment after testing it this is really has improved by 15%. >> Okay. And, and what, what what business processes outside of sort of testing have you sort of attacked with the platform? Can you talk to that? >> The business processes outside of testing? >> Dave: Yeah. You mean the one which we are not testing ourself? >> Yeah, no. So just the UI path platform, is it exclusively for, for testing? >> This testing is exclusively for the UI path bots which we have built, right? So we have some 400 plus automations of UI bots. So it's meant exclusively >> But are you using UI path in any other ways? >> No, not at this time. >> Okay, okay. Interesting. So you started with testing? >> No, we started by building the bots. So we already had roughly 400 bots in production. When we came with the testing automation, that's when we started looking at it. >> Dave: Okay. And then now building that whole testing-- >> Dave: What are those other bots doing? Let me ask it that way. >> Oh, there's quite a lot. I mean, we have many bots. >> Dave: Paint a picture if you want. Yeah. In, in finance, in auto management, HR, legal, IT, there's a lot of automations which are there. As I'm saying, there's more than 400 automations out there. Yeah. So so it's across the, you know, enterprise on that. >> Thomas. So, and you know, both of you have a have a view on this, but Thomas's views probably wider across other, other instances. What are the most common things that are revealed in tests that indicate something needs to be fixed? Yeah, so think of, think of a test, a test failure, an error. What are the, what are the most common things that happen? >> So when we started with building our product we conducted a, a survey among our customers. And without a surprise the main reason why automation breaks is change. >> David: Sure. >> And the problem here is RPA is a controlled process a controlled workflow but it runs in an uncontrollable environment. So typically RPA is developed by a C.O.E. Those are business and automation experts, but they operate in an environment that's driven by new patches new application changes ruled out by IT. And that's the main challenge here. You cannot control that. And so far, if you, if you do not proactively test what happens is you catch an issue in production when it already breaks, right? That's reactive, that's leads to maintenance to un-claim maintenance actually. And that was the goal right from the start from the taste suite to support our customers here and go over to proactive maintenance meaning testing before and finding those issues before the heat production. >> Yeah. Yeah, yeah. So I'm, I'm still not clear on, so you just gave a perfect example, changes in the environment. >> Yeah. >> So those changes are happening in the production environment. >> Thomas: Yeah. The robot that was happily doing its automation stuff before? >> Thomas: Yeah. Everyone was happy with it. Change happens. Robot breaks. >> Thomas: Yeah. >> Okay. You're saying you test before changes are implemented? To see if those changes will break the robot? >> Thomas: Yeah. >> Okay. How do you, how do you expose those changes that are in the, in a, that are going to be in a production environment to the robot? You must have a, Is is that part of the test environment? Does that mean that you have to have what fully running instances of like an ERP system? >> Thomas: Yeah. You know, a clone of an environment. How do you, how do you test that without having the live robot against the production environment? >> I think there's no big difference to standard software testing. Okay. The interesting thing is, the change actually happens earlier. You are affected on production side with it but the change happens on it side or on DevOps side. So you typically will test in a test environment that's similar to your production environment or probably in it in a pre-product environment. And the test itself is simply running your workflow that you want to test, but mark away any dependencies you don't want to invoke. You don't want to send a, a letter to a customer in a test environment, right? And then you verify that the result is what you actually expect, right? And as soon as this is not the case, you will be notified you will have a result, the fail result, and you can act before it breaks. So you can fix it, redeploy to production and you should be good now. >> But the, the main emphasis at VMware is testing your bots, correct? >> Neeraj: Testing your bots. Yes. Can I apply this to testing other software code? >> Yeah, yeah. You, you can, you can technically actually and Thomas can speak better than me on that to any software for that matter, but we have really not explored that aspect of it. >> David: You guys have pretty good coders, good engineers at VMware, but no, seriously Thomas what's that market looking like? Is that taking off? Are you, are you are you applying this capability or customers applying it for just more broadly testing software? >> Absolutely. So our goal was we want to test RPA and the application it relies on so that includes RPA testing as well as application testing. The main difference is typical functional application testing is a black box testing. So you don't know the inner implementation of of that application. And it works out pretty well. The big, the big opportunity that we have is not isolated Not isolated testing, isolated RPA but we talk about convergence of automation. So what we offer our customers is one automation platform. You create one, you create automation, not redundantly in different departments, but you create once probably for testing and then you reuse it for RPA. So that suddenly helps your, your test engineers to to move from a pure cost center to a value center. >> How, how unique is this capability in the industry relative to your competition and and what capabilities do you have that, that or, or or differentiators from the folks that we all know you're competing with? >> So the big advantage is the power of the entire platform that we have with UiPath. So we didn't start from scratch. We have that great automation layer. We have that great distribution layer. We have all that AI capabilities that so far were used for RPA. We can reuse them, repurpose them for testing. And that really differentiates us from the competition. >> Thomas, I I, I detect a hint of an accent. Is it, is it, is it German or >> It's actually Austrian. >> Austrian. Well, >> You know. Don't compare us with Germans. >> I understand. High German. Is that the proper, is that what's spoken in Austria? >> Yes, it is. >> So, so >> Point being? >> Point being exactly as I drift off point being generally German is considered to be a very very precise language with very specific words. It's very easy to be confused about between the difference the difference between two things automation testing and automating testing. >> Thomas: Yes. >> Because in this case, what you are testing are automations. >> Thomas: Yes. >> That's what you're talking about. >> Thomas: Yes. >> You're not talking about the automation of testing. Correct? >> Well, we talk about >> And that's got to be confusing when you go to translate that into >> Dave: But isn't it both? >> 50 other languages? >> Dave: It's both. >> Is it both? >> Thomas: It actually is both. >> Okay. >> And there's something we are exploring right now which is even, even the next step, the next layer which is autonomous testing. So, so far you had an expert an automation expert creating the automation once and it would be rerun over and over again. What we are now exploring is together with university to autonomously test, meaning a bot explores your application on the test and finds issues completely autonomously. >> Dave: So autonomous testing of automation? >> It's getting more and more complicated. >> It's more clear, it's getting clearer by the minute. >> Sorry for that. >> All right Neeraj, last question is: Where do you want to take this? What's your vision for, for VMware in the context of automation? >> Sure. So, so I think the first and the foremost thing for us is to really make it more mainstream for for our automation developer Excel, right? What I mean by that is, is to really, so so there is a shift now how we engage with our business users and SMEs. And I said previously they used to actually test it manually. Now the conversation changes that, hey can you tell us what test cases you want what you want us to test in an automated measure? Can you give us the test data for that so that we can keep on testing in a continuous manner for the months and years to come down? Right? The other part of the test it changes is that, hey it used to take eight weeks for us to build but now it's going to take nine weeks because we're going to spend an extra week just to automate that as well. But it's going to help you in the long run and that's the conversation. So to really make it as much more mainstream and then say that out of all these kinds of automation and bots which we are building, So we are not looking to have a test automation for every single bot which we are building. So we need to have a way to choose where their value is. Is it the quarter end processing one? Is it the most business critical one, or is it the one where we are expecting of frequent changes, right? That's where the value of the testing is. So really bring that as a part of our whole process and then, you know >> We're still fine too. That great. Guys, thanks so much. This has been really interesting conversation. I've been waiting to talk to a real life customer about testing and automation testing. Appreciate your time. >> Thank you very much. >> Thanks for everything. >> All right. Thank you for watching, keep it right there. Dave Nicholson and I will be back right after this short break. This is day one of theCUBE coverage of UI Path Forward Five. Be right back after this short break.

(calm electronic music) >> Okay, welcome back everyone to theCUBE's coverage here live in New York City for AWS Summit 2022. I'm John Furrier, host of theCUBE, but a great conversation here as the day winds down. First of all, 10,000 plus people, this is a big event, just New York City. So sign of the times that some headwinds are happening? I don't think so, not in the cloud enterprise innovation game. Lot going on, this innovation conversation we're going to have now is about the confluence of cloud scale integration data and the future of how FinTech and other markets are going to change with technology. We got Chris Thomas, the CTO of Slalom, and Rob Krugman, chief digital officer at Broadridge. Gentlemen, thanks for coming on theCUBE. >> Thanks for having us. >> So we had a talk before we came on camera about your firm, what you guys do, take a quick minute to just give the scope and size of your firm and what you guys work on. >> Yeah, so Broadridge is a global financial FinTech company. We work on, part of our business is capital markets and wealth, and that's about a third of our business, about $7 trillion a day clearing through our platforms. And then the other side of our business is communications where we help all different types of organizations communicate with their shareholders, communicate with their customers across a variety of different digital channels and capabilities. >> Yeah, and Slalom, give a quick one minute on Slalom. I know you guys, but for the folks that don't know you. >> Yeah, no problem. So Slalom is a modern consulting firm focused on strategy, technology, and business transformation. And me personally, I'm part of the element lab, which is focused on forward thinking technology and disruptive technology in the next five to 10 years. >> Awesome, and that's the scope of this conversation. The next five to 10 years, you guys are working on a project together, you're kind of customer partners. You're building something. What are you guys working on? I can't wait to jump into it, explain. >> Sure, so similar to Chris, at Broadridge, we've created innovation capability, innovation incubation capability, and one of the first areas we're experimenting in is digital assets. So what we're looking to do is we're looking at a variety of different areas where we think consolidation network effects that we could bring can add a significant amount of value. And so the area we're working on is this concept of a wallet of wallets. How do we actually consolidate assets that are held across a variety of different wallets, maybe traditional locations- >> Digital wallets. >> Digital wallets, but maybe even traditional accounts, bring that together and then give control back to the consumer of who they want to share that information with, how they want their transactions to be able to control. So the idea of, people talk about Web 3 being the internet of value. I often think about it as the internet of control. How do you return control back to the individual so that they can make decisions about how and who has access to their information and assets? >> It's interesting, I totally like the value angle, but your point is what's the chicken and the egg here, the cart before the horse, you can look at it both ways and say, okay, control is going to drive the value. This is an interesting nuance, right? >> Yes, absolutely. >> So in this architectural world, they thought about the data plane and the control plane. Everyone's trying to go old school, middleware thinking. Let's own the data plane, we'll win everything. Not going to happen if it goes decentralized, right, Chris? >> Yeah, yeah. I mean, we're building a decentralized application, but it really is built on top of AWS. We have a serverless architecture that scales as our business scales built on top of things like S3, Lambda, DynamoDB, and of course using those security principles like Cognito and AWS Gateway, API Gateway. So we're really building an architecture of Web 3 on top of the Web 2 basics in the cloud. >> I mean, all evolutions are abstractions on top of each other, IG, DNS, Key, it goes the whole nine yards. In digital, at least, that's the way. Question about serverless real quick. I saw that Redshift just launched general availability of serverless in Redshift? >> Yes. >> You're starting to see the serverless now part of almost all the services in AWS. Is that enabling that abstraction, because most people don't see it that way. They go, oh, well, Amazon's not Web 3. They got databases, you could use that stuff. So how do you connect the dots and cross the bridge to the future with the idea that I might not think Web 2 or cloud is Web 3? >> I'll jump in quick. I mean, I think it's the decentralize. If you think about decentralization. serverless and decentralization, you could argue are the same way of, they're saying the same thing in different ways. One is thinking about it from a technology perspective. One is thinking about it from an ecosystem perspective and how things come together. You need serverless components that can talk to each other and communicate with each other to actually really reach the promise of what Web 3 is supposed to be. >> So digital bits or digital assets, I call it digital bits, 'cause I think zero ones. If you digitize everything and everything has value or now control drives the value. I could be a soccer team. I have apparel, I have value in my logos, I have photos, I have CUBE videos. I mean some say that this should be an NFT. Yeah, right, maybe, but digital assets have to be protected, but owned. So ownership drives it too, right? >> Absolutely. >> So how does that fit in, how do you explain that? 'Cause I'm trying to tie the dots here, connect the dots and tie it together. What do I get if I go down this road that you guys are building? >> So I think one of the challenges of digital assets right now is that it's a closed community. And I think the people that play in it, they're really into it. And so you look at things like NFTs and you look at some of the other activities that are happening and there are certain naysayers that look at it and say, this stuff is not based upon value. It's a bunch of artwork, it can't be worth this. Well, how about we do a time out there and we actually look at the underlying technology that's supporting this, the blockchain, and the potential ramifications of that across the entire financial ecosystem, and frankly, all different types of ecosystems of having this immutable record, where information gets stored and gets sent and the ability to go back to it at all times, that's where the real power is. So I think we're starting to see. We've hit a bit of a hiccup, if you will, in the cryptocurrencies. They're going to continue to be there. They won't all be there. A lot of them will probably disappear, but they'll be a finite number. >> What percentage of stuff do you think is vapor BS? If you had to pick an order of magnitude number. >> (laughs) I would say at least 75% of it. (John laughs) >> I mean, there's quite a few projects that are failing right now, but it's interesting in that in the crypto markets, they're failing gracefully. Because it's on the blockchain and it's all very transparent. Things are checked, you know immediately which companies are insolvent and which opportunities are still working. So it's very, very interesting in my opinion. >> Well, and I think the ones that don't have valid premises are the ones that are failing. Like Terra and some of these other ones, if you actually really looked at it, the entire industry knew these things were no good. But then you look at stable coins. And you look at what's going on with CBDCs. These are backed by real underlying assets that people can be comfortable with. And there's not a question of, is this going to happen? The question is, how quickly is it going to happen and how quickly are we going to be using digital currencies? >> It's interesting, we always talk about software, software as money now, money is software and gold and oil's moving over to that crypto. How do you guys see software? 'Cause we were just arguing in the queue, Dave Vellante and I, before you guys came on that the software industry pretty much does not exist anymore, it's open source. So everything's open source as an industry, but the value is integration, innovation. So it's not just software, it's the free. So you got to, it's integration. So how do you guys see this software driving crypto? Because it is software defined money at the end of the day. It's a token. >> No, I think that's absolutely one of the strengths of the crypto markets and the Web 3 market is it's governed by software. And because of that, you can build a trust framework. Everybody knows it's on the public blockchain. Everybody's aware of the software that's driving the rules and the rules of engagement in this blockchain. And it creates that trust network that says, hey, I can transact with you even though I don't know anything about you and I don't need a middleman to tell me I can trust you. Because this software drives that trust framework. >> Lot of disruption, lot of companies go out of business as a middleman in these markets. >> Listen, the intermediaries either have to disrupt themselves or they will be disrupted. I think that's what we're going to learn here. And it's going to start in financial services, but it's going to go to a lot of different places. I think the interesting thing that's happening now is for the first time, you're starting to see the regulators start to get involved. Which is actually a really good thing for the market. Because to Chris's point, transparency is here, how do you actually present that transparency and that trust back to consumers so they feel comfortable once that problem is solved. And I think everyone in the industry welcomes it. All of a sudden you have this ecosystem that people can play in, they can build and they can start to actually create real value. >> Every structural change that I've been involved in my 30 plus year career has been around inflection points. There was always some sort of underbelly. So I'm not going to judge crypto. It's been in the market for a while, but it's a good sign there's innovation happening. So as now, clarity comes into what's real. I think you guys are talking a conversation I think is refreshing because you're saying, okay, cloud is real, Lambda, serverless, all these tools. So Web 3 is certainly real because it's a future architecture, but it's attracting the young, it's a cultural shift. And it's also cooler than boring Web 2 and cloud. So I think the cultural shift, the fact that it's got data involved, there's some disruption around middleman and intermediaries, makes it very attractive to tech geeks. You look at, I read a stat, I heard a stat from a friend in the Bay Area that 30% of Cal computer science students are dropping out and jumping into crypto. So it's attracting the technical nerds, alpha geeks. It's a cultural revolution and there's some cool stuff going on from a business model standpoint. >> There's one thing missing. The thing that's missing, it's what we're trying to work on, I think is experience. I think if you're being honest about the entire marketplace, what you would agree is that this stuff is not easy to use today, and that's got to be satisfied. You need to do something that if it's the 85 year old grandma that wants to actually participate in these markets that not only can they feel comfortable, but they actually know how to do it. You can't use these crazy tools where you use these terms. And I think the industry, as it grows up, will satisfy a lot of those issues. >> And I think this is why I want to tie back and get your reaction to this. I think that's why you guys talking about building on top of AWS is refreshing, 'cause it's not dogmatic. Well, we can't use Amazon, it's not really Web 3. Well, a database could be used when you need it. You don't need to write everything through the blockchain. Databases are a very valuable capability, you get serverless. So all these things now can work together. So what do you guys see for companies that want to be Web 3 for all the good reasons and how do they leverage cloud specifically to get there? What are some things that you guys have learned that you can point to and share, you want to start? >> Well, I think not everything has to be open and public to everybody. You're going to want to have some things that are secret. You're going to want to encrypt some things. You're going to want to put some things within your own walls. And that's where AWS really excels. I think you can have the best of both worlds. So that's my perspective on it. >> The only thing I would add to it, so my view is it's 2022. I actually was joking earlier. I think I was at the first re:Invent. And I remember walking in and this was a new industry. >> It was tiny. >> This is foundational. Like cloud is not a, I don't view like, we shouldn't be having that conversation anymore. Of course you should build this stuff on top of the cloud. Of course you should build it on top of AWS. It just makes sense. And we should, instead of worrying about those challenges, what we should be worrying about are how do we make these applications easier to use? How do we actually- >> Energy efficient. >> How do we enable the promise of what these things are going to bring, and actually make it real, because if it happens, think about traditional assets. There's projects going on globally that are looking at how do you take equity securities and actually move them to the blockchain. When that stuff happens, boom. >> And I like what you guys are doing, I saw the news out through this crypto winter, some major wallet exchanges that have been advertising are hurting. Take me through what you guys are thinking, what the vision is around the wallet of wallets. Is it to provide an experience for the user or the market industry itself? What's the target, is it both? Share the design goals for the wallet of wallets. >> My favorite thing about innovation and innovation labs is that we can experiment. So I'll go in saying we don't know what the final answer is going to be, but this is the premise that we have. In this disparate decentralized ecosystem, you need some mechanism to be able to control what's actually happening at the consumer level. So I think the key target is how do you create an experience where the consumer feels like they're in control of that value? How do they actually control the underlying assets? And then how does it actually get delivered to them? Is it something that comes from their bank, from their broker? Is it coming from an independent organization? How do they manage all of that information? And I think the last part of it are the assets. It's easy to think about cryptos and NFTs, but thinking about traditional assets, thinking about identity information and healthcare records, all of that stuff is going to become part of this ecosystem. And imagine being able to go someplace and saying, oh, you need my information. Well, I'm going to give it to you off my phone and I'm going to give it to you for the next 24 hours so you can use it, but after that you have no access to it. Or you're my financial advisor, here's a view of what I actually have, my underlying assets. What do you recommend I do? So I think we're going to see an evolution in the market. >> Like a data clean room. >> Yeah, but that you control. >> Yes! (laughs) >> Yes! >> I think about it very similarly as well. As my journey into the crypto market has gone through different pathways, different avenues. And I've come to a place where I'm really managing eight different wallets and it's difficult to figure exactly where all my assets are and having a tool like this will allow me to visualize and aggregate those assets and maybe even recombine them in unique ways, I think is hugely valuable. >> My biggest fear is losing my key. >> Well, and that's an experience problem that has to be solved, but let me give you, my favorite use case in this space is, 'cause NFTs, right? People are like, what does NFTs really mean? Title insurance, right? Anyone buy a house or refinance your mortgage? You go through this crazy process that costs seven or eight thousand dollars every single time you close on something to get title insurance so they could validate it. What if that title was actually sitting on the chain, you got an NFT that you put in your wallet and when it goes time to sell your house or to refinance, everything's there. Okay, I'm the owner of the house. I don't know, JP Morgan Chase has the actual mortgage. There's another lien, there's some taxes. >> It's like a link tree in the wallet. (laughs) >> Yeah, think about it, you got a smart contract. Boom, closing happens immediately. >> I think that's one of the most important things. I think people look at NFTs and they think, oh, this is art. And that's sort of how it started in the art and collectable space, but it's actually quickly moving towards utilities and tokenization and passes. And that's where I think the value is. >> And ownership and the token. >> Identity and ownership, especially. >> And the digital rights ownership and the economics behind it really have a lot of scale 'cause I appreciate the FinTech angle you are coming from because I can now see what's going on here with you. It's like, okay, we got to start somewhere. Let's start with the experience. The wallet's a tough nut to crack, 'cause that requires defacto participation in the industry as a defacto standard. So how are you guys doing there? Can you give an update and then how can people get, what's the project called and how do people get involved? >> Yeah, so we're still in the innovation, incubation stages. So we're not launching it yet. But what I will tell you is what a lot of our focus is, how do we make these transactional things that you do? How do we make it easy to pull all your assets together? How do we make it easy to move things from one location to the other location in ways that you're not using a weird cryptographic numeric value for your wallet, but you actually can use real nomenclature that you can renumber and it's easy to understand. Our expectation is that sometime in the fall, we'll actually be in a position to launch this. What we're going to do over the summer is we're going to start allowing people to play with it, get their feedback, and we're going to iterate. >> So sandbox in when, November? >> I think launch in the fall, sometime in the fall. >> Oh, this fall. >> But over the summer, what we're expecting is some type of friends and family type release where we can start to realize what people are doing and then fix the challenges, see if we're on the right track and make the appropriate corrections. >> So right now you guys are just together on this? >> Yep. >> The opening up friends and family or community is going to be controlled. >> It is, yeah. >> Yeah, as a group, I think one thing that's really important to highlight is that we're an innovation lab. We're working with Broadridge's innovation lab, that partnership across innovation labs has allowed us to move very, very quickly to build this. Actually, if you think about it, we were talking about this not too long ago and we're almost close to having an internal launch. So I think it's very rapid development. We follow a lot of the- >> There's buy-in across the board. >> Exactly, exactly, and we saw lot of very- >> So who's going to run this? A Dow, or your companies, is it going to be a separate company? >> So to be honest, we're not entirely sure yet. It's a new product that we're going to be creating. What we actually do with it. Our thought is within an innovation environment, there's three things you could do with something. You can make it a product within the existing infrastructure, you can create a new business unit or you can spin it off as something new. I do think this becomes a product within the organization based upon it's so aligned to what we do today, but we'll see. >> But you guys are financing it? >> Yes. >> As collective companies? >> Yeah, right. >> Got it, okay, cool. Well, let us know how we can help. If you guys want to do a remote in to theCUBE. I would love the mission you guys are on. I think this is the kind of work that every company should be doing in the new R and D. You got to jump in the deep end and swim as fast as possible. But I think you can do it. I think that is refreshing and that's smart. >> And you have to do it quick because this market, I think the one thing we would probably agree on is that it's moving faster than we could, every week there's something else that happens. >> Okay, so now you guys were at Consensus down in Austin when the winter hit and you've been in the business for a long time, you got to know the industries. You see where it's going. What was the big thing you guys learned, any scar tissue from the early data coming in from the collaboration? Was there some aha moments, was there some oh shoot moments? Oh, wow, I didn't think that was going to happen. Share some anecdotal stories from the experience. Good, bad, and if you want to be bold say ugly, too. >> Well, I think the first thing I want to say about the timing, it is the crypto winter, but I actually think now's a really great time to build something because everybody's continuing to build. Folks are focused on the future and that's what we are as well. In terms of some of the challenges, well, the Web 3 space is so new. And there's not a way to just go online and copy somebody else's work and rinse and repeat. We had to figure a lot of things on our own. We had to try different technologies, see which worked better and make sure that it was functioning the way we wanted it to function. Really, so it was not easy. >> They oversold that product out, that's good, like this team. >> But think about it, so the joke is that when winter is when real work happens. If you look at the companies that have not been affected by this it's the infrastructure companies and what it reminds me of, it's a little bit different, but 2001, we had the dot com bust. The entire industry blew up, but what came out of that? >> Everything that exists. >> Amazon, lots of companies grew up out of that environment. >> Everything that was promoted actually happened. >> Yes, but you know what didn't happen- >> Food delivery. >> But you know what's interesting that didn't happen- >> (laughs) Pet food, the soccer never happened. >> The whole Super Bowl, yes. (John laughs) In financial services we built on top of legacy. I think what Web 3 is doing, it's getting rid of that legacy infrastructure. And the banks are going to be involved. There's going to be new players and stuff. But what I'm seeing now is a doubling down of the infrastructure investment of saying okay, how do we actually make this stuff real so we can actually show the promise? >> One of the things I just shared, Rob, you'd appreciate this, is that the digital advertising market's changing because now banner ads and the old techniques are based on Web 2 infrastructure, basically DNS as we know it. And token problems are everywhere. Sites and silos are built because LinkedIn doesn't share information. And the sites want first party data. It's a hoarding exercise, so those practices are going to get decimated. So in comes token economics, that's going to get decimated. So you're already seeing the decline of media. And advertising, cookies are going away. >> I think it's going to change, it's going to be a flip, because I think right now you're not in control. Other people are in control. And I think with tokenomics and some of the other things that are going to happen, it gives back control to the individual. Think about it, right now you get advertising. Now you didn't say I wanted this advertising. Imagine the value of advertising when you say, you know what, I am interested in getting information about this particular type of product. The lead generation, the value of that advertising is significantly higher. >> Organic notifications. >> Yeah. >> Well, gentlemen, I'd love to follow up with you. I'm definitely going to ping in. Now I'm going to put CUBE coin back on the table. For our audience CUBE coin's coming. Really appreciate it, thanks for sharing your insights. Great conversation. >> Excellent, thank you for having us. >> Excellent, thank you so much. >> theCUBE's coverage here from New York City. I'm John Furrier, we'll be back with more live coverage to close out the day. Stay with us, we'll be right back. >> Excellent. (calm electronic music)

>>EDR NDR, what are the differences, which one's better? Are they better together? Today's security stack contains a lot of different tools and types of data and fortunate, as you know, this creates data silos, which leads to vis visibility gaps. EDR is endpoint detection and response. It's designed to monitor and mitigate endpoint attacks, which are typically focused on computers and servers, NDR network detection, and response. On the other hand, monitors network traffic to gain visibility into potential or active cyber threats, delivering real time visibility across the broader network. One of the biggest advantages that NDR has over EDR is that bad actors can hide or manipulate endpoint data, pretty easily network data. On the other hand, much harder to manipulate because attackers and malware can avoid detection at the endpoint. NDR, as you're gonna hear is the only real source for reliable, accurate, and comprehensive data. >>All endpoints use the network to communicate, which makes your network data, the ultimate source of truth. My name is Lisa Martin, and today on the special cube presentation, Tom Binkowski senior director of product marketing at net scout, and I are gonna explore the trends and the vital reasons why relying upon EDR is not quite enough. We're also gonna share with you the growing importance of advanced NDR. Welcome to the series, the growing importance of advanced NDR in the first segment, Tom's gonna talk with me about the trends that are driving enterprise security teams to implement multiple cyber security solutions that enable greater visibility, greater protection. We're also gonna explore Gartner's concept of the security operations center, SOC visibility triad, and the three main data sources for visibility, SIM EDR and NDR in segment two, Tom. And I will talk about the role of NDR and how it overcomes the challenges of EDR as Tom's gonna discuss, as you'll hear EDR is absolutely needed, but as he will explain it, can't be solely relied upon for comprehensive cybersecurity. And then finally, we'll come back for a third and final segment to discuss why not all NDR is created equal. Tom's gonna unpack the features and the capabilities that are most important when choosing an NDR solution. Let's do this. Here comes our first segment. >>Hey, everyone kicking things off. This is segment one. I'm Lisa Martin with Tom Binowski, senior director of product marketing at nets scout. Welcome to the growing importance of advanced NDR. Tom, great to have you on the program, >>Glad to be here. >>So we're gonna be talking about the trends that are driving enterprise security teams to implement multiple cyber security solutions that really enable greater visibility and protection. And there are a number of factors that continue to expand the ECAC service for enterprise networks. I always like to think of them as kind of the spreading amorphously you shared had shared some stats with me previously, Tom, some cloud adoption stats for 2022 94% of all enterprises today use a cloud service and more than 60% of all corporate data is store in the cloud. So, Tom, what are some of the key trends that nets scout is seeing in the market with respect to this? >>Yeah, so just to continue that, you know, those stats that, that migration of workloads to the cloud is a major trend that we're seeing in that was exasperated by the pandemic, right along with working from home. Those two things are probably the most dramatic changes that we we see out there today. But along with that is also this growing sophistication of the network, you know, today, you know, your network environment, isn't a simple hub and spoke or something like that. It is a very sophisticated combination of, you know, high speed backbones, potentially up to a hundred gigabits combination with partner networks. You have, like we said, workloads up in, in private clouds, pub public clouds. So you have this hybrid cloud environment. So, and then you have applications that are multi-tiered, there are pieces and parts. And in all of that, some on your premise, some up in a private cloud, some on a public cloud, some actually pulling data off when you a customer network or potentially even a, a partner network. So really, really sophisticated environment today. And that's requiring this need for very comprehensive network visibility, not only for, for cybersecurity purposes, but also just to make sure that those applications and networks are performing as you have designed them. >>So when it comes to gaining visibility into cyber threats, I, you talked about the, the sophistication and it sounds like even the complexity of these networks, Gartner introduced the concept of the security operations, visibility triad, or the SOC visibility triad break that down for us. It consists of three main data sources, but to break those three main data sources down for us. >>Sure. So Gartner came out a few years ago where they were trying to, you know, summarize where do security operations team get visibility into threats and they put together a triad and the three sides of the trier consists of one, the SIM security information event manager, two, the endpoint or, or data that you get from EDR systems, endpoint detection, response systems. And the third side is the network or the data you get from network detection, response systems. And, you know, they didn't necessarily say one is better than the other. They're basically said that you need all three in order to have comprehensive visibility for cybersecurity purposes. >>So talk, so all, all three perspectives are needed. Talk about what each provides, what are the different perspectives on threat detection and remediation? >>Yeah. So let's start with the SIM, you know, that is a device that is gathering alerts or logs from all kinds of different devices all over your network. Be it routers servers, you know, firewalls IDs, or even from endpoint detection and network detection devices too. So it is, it is the aggregator or consumer of all those alerts. The SIM is trying to correlate those alerts across all those different data sources and, and trying to the best it can to bubble up potentially the highest priority alerts or drawing correlations and, and, and, and giving you some guidance on, Hey, here's something that we think is, is really of importance or high priority. Here's some information that we have across these disparate data sources. Now go investigate the disadvantage of the SIM is that's all it gives you is just these logs or, or, or information. It doesn't give you any further context. >>Like what happened, what is really happening at the end point? Can I get visibility into the, into the files that were potentially manipulated or the, the registry setting or what, what happened on the network? And I get visibility into the packet date or things like that. It that's, so that's where it ends. And, and that's where the, so there other two sides of the equation come in, the endpoint will give you that deeper visibility, endpoint detection response. It will look for known and or unknown threats, you know, at that endpoint, it'll give you all kinds of additional information that is occurring in endpoint, whether it be a registry setting in memory on the file, et cetera. But you know, one of, some of its disadvantages, it's really difficult because really difficult to deploy pervasive because it requires an agent and, you know, not all devices can accept an agent, but what it miss, what is lacking is the context on the network. >>So if I was an analyst and I started pursuing from my SIM, I went down to the end point and, and said, I wanna investigate this further. And I hit a, I hit a dead end from some sort, or I realize that the device that's potentially I should be alerted to, or should be concerned about is an IOT device that doesn't even have an agent on it. My next source of visibility is on the network and that's where NDR comes in. It, it sees what's traversing. The entire network provides you visibility into that from both a metadata and even a ultimately a packer perspective. And maybe, you know, could be deployed a little bit more strategically, but you know, it doesn't have the perspective of the endpoint. So you can see how each of these sort of compliments each other. And that's why, you know, Gartner said that, that you need 'em all, then they all play a role. They all have their pros and cons or advantage and disadvantages, but, you know, bringing them and using 'em together is, is the key. >>I wanna kinda dig into some of the, the EDR gaps and challenges, as you talked about as, as the things evolve and change the network, environment's becoming far more sophisticated and as well as threat actors are, and malware is. So can you crack that open more on some of the challenges that EDR is presenting? What are some of those gaps and how can organizations use other, other, other data sources to solve them? >>Yeah, sure. So, you know, again, just be clear that EDR is absolutely required, right? We, we need that, but as sort of these network environments get more complex, are you getting all kinds of new devices being put on the network that devices being brought into the network that may be, you didn't know of B Y O D devices you have, I T devices, you know, popping up potentially by the thousands in, in, in some cases when new applications or world that maybe can't accept an and endpoint detection or an EDR agent, you may have environments like ICS and skate environments that just, you can't put an endpoint agent there. However, those devices can be compromised, right? You have different environments up in the cloud or SaaS environments again, where you may not be able to deploy an endpoint agent and all that together leaves visibility gaps or gaps in, in, in the security operation triad. Right. And that is basically open door for exploitation >>Open door. Go ahead. Sorry. >>Yeah. And then, then you just have the malware and the, and the attackers getting more sophisticated. They, they have malware that can detect an EDR agent running or some anti malware agent running on device. And they'll simply avoid that and move on to the next one, or they know how to hide their tracks, you know, whether it be deleting files, registry, settings, things like that. You know, so it's, that's another challenge that, that, that just an agent faces. Another one is there are certain applications like my SQL that are, you know, have ministry administrative rights into certain parts of the windows operate system that EDR doesn't have visibility into another area that maybe EDR may not have visibility is, is, is in, you know, malware that tries to compromise, you know, hardware, especially like bios or something like that. So there's a number of challenges as sort of the whole network environment and sophistication of bad actors and malware increases. >>Ultimately, I think one of the things that, that we've learned, and, and we've heard from you in this segment, is that doing business in, in today's digital economy, demands, agility, table stakes, right? Absolutely essential corporate digital infrastructures have changed a lot in response to the dynamic environment, but its businesses are racing to the clouds. Dave Alane likes to call it the forced March to the cloud, expanding activities across this globally distributed digital ecosystem. They also sounds like need to reinvent cybersecurity to defend this continuously expanding threat surface. And for that comprehensive network, visibility is, as I think you were saying is really, really fundamental and more advanced network detection is, and responses required. Is that right? >>That's correct. You know, you know, we, we at ESCO, this is, this is where we come from. Our perspective is the network. It has been over for over 30 years. And, and we, as well as others believe that that network visibility, comprehensive network visibility is fundamental for cyber security as well as network performance and application analysis. So it, it, it's sort of a core competency or need for, for modern businesses today. >>Excellent. And hold that thought, Tom, cause in a moment, you and I are gonna be back to talk about the role of NDR and how it overcomes the challenges of EDR. You're watching the cube, the leader in enterprise tech coverage. Hey everyone, welcome back. This is segment two kicking things off I'm Lisa Martin with Tom Binkowski, senior director of product marketing at nets scout, Tom, great to have you back on the program. >>Good to be here. >>We're gonna be talking about the growing importance of advanced NDR in this series. In this segment specifically, Tom's gonna be talking about the role of NDR and how it overcomes the challenges of EDR. So Tom, one of the things that we talked about previously is one of the biggest advantages that NDR has over EDR is that bad actors can hide or manipulate endpoint data pretty easily, whereas network data, much harder to manipulate. So my question, Tom, for you is, is NDR the only real source for reliable, accurate, comprehensive data. >>I'm sure that's arguable, right? Depending on who you are as a vendor, but you know, it's, it's our, our answer is yes, NDR solutions also bring an analyst down to the packet level. And there's a saying, you know, the, the packet is the ultimate source or source of truth. A bad actor cannot manipulate a packet. Once it's on the wire, they could certainly manipulate it from their end point and then blast it out. But once it hits the wire, that's it they've lost control of it. And once it's captured by a network detection or, or network monitoring device, they can't manipulate it. They can't go into that packet store and, and manipulate those packets. So the ultimate source of truth is, is lies within that packet somewhere. >>Got you. Okay. So as you said in segment one EDR absolutely necessary, right. But you did point out it can't organizations can't solely rely on it for comprehensive cybersecurity. So Tom, talk about the benefits of, of this complimenting, this combination of EDR and NDR and, and how can that deliver more comprehensive cybersecurity for organizations? >>Yeah, so, so one of the things we talked about in the prior segment was where EDR, maybe can't be deployed and it's either on different types of devices like IOT devices, or even different environments. They have a tough time maybe in some of these public cloud environments, but that's where NDR can, can step in, especially in these public cloud environments. So I think there's a misconception out there that's difficult to get packet level or network visibility and public clouds like AWS or Azure or Google and so on. And that's absolutely not true. They have all kinds of virtual tapping capabilities that an NDR solution or network based monitoring solution could take advantage of. And one of the things that we know we spoke about before some of that growing trends of migrating workloads to the cloud, that's, what's driving that those virtual networks or virtual taps is providing visibility into the performance and security of those workloads. >>As they're migrated to public clouds, NDR can also be deployed more strategically, you know, prior segment talking about how the, in order to gain pervasive visibility with EDR, you have to deploy an agent everywhere agents can't be deployed everywhere. So what you can do with NDR is there's a lot fewer places in a network where you can strategically deploy a network based monitoring device to give you visibility into not only that north south traffic. So what's coming in and out of your network, but also the, the, the, the east west traffic too west traversing, you know, within your network environment between different points of your op your, your multi-tiered application, things like that. So that's where, you know, NDR has a, a, a little bit more advantage. So fewer points of points in the network, if you will, than everywhere on every single endpoint. And then, you know, NDR is out there continuously gathering network data. It's both either before, during, and even after a threat or an attack is, is detected. And it provides you with this network context of, of, you know, what's happening on the wire. And it does that through providing you access to, you know, layer two through layer seven metadata, or even ultimately packets, you know, the bottom line is simply that, you know, NDR is providing, as we said before, that that network context that is potentially missing or is missing in EDR. >>Can you talk a little bit about XDR that kind of sounds like a superhero name to me, but this is extended detection and response, and this is an evolution of EDR talk to us about XDR and maybe EDR NDR XDR is really delivering that comprehensive cybersecurity strategy for organizations. >>Yeah. So, you know, it's, it's interesting. I think there's a lot of confusion out there in the industry. What is, what is XDR, what is XDR versus an advanced SIM, et cetera. So in some cases, there are some folks that don't think it's just an evolution of EDR. You know, to me, XDR is taking, look at these, all these disparate data sources. So going back to our, when our first segment, we talked about the, the, the security operations center triad, and it has data from different perspectives, as we were saying, right? And XCR, to me is the, is, is trying to bring them all together. All these disparate data source sets or sources bring them together, conduct some level of analysis on that data for the analyst and potentially, you know, float to the top. The most, you know, important events are events that we, that you know, that the system deems high priority or most risky and so on. But as I, as I'm describing this, I know there are many advanced Sims out there trying to do this today too. Or they do do this today. So this there's this little area of confusion around, you know, what exactly is XDR, but really it is just trying to pull together these different sources of information and trying to help that analyst figure out, you know, what, where's the high priority event that's they should be looking at, >>Right? Getting those high priority events elevated to the top as soon as possible. One of the things that I wanted to ask you about was something that occurred in March of this year, just a couple of months ago, when the white house released a statement from president Biden regarding the nation's cyber security, it included recommendations for private companies. I think a lot of you are familiar with this, but the first set of recommendations were best practices that all organizations should already be following, right? Multifactor authentication, patching against known vulnerabilities, educating employees on the phishing attempts on how to be effective against them. And the next statement in the president's release, focus on data safety practices, also stuff that probably a lot of corporations doing encryption maintaining offline backups, but where the statement focused on proactive measures companies should take to modernize and improve their cybersecurity posture. It was vague. It was deploy modern security tools on your computers and devices to continuously look for and mitigate threats. So my question to you is how do, how do you advise organizations do that? Deploy modern security tools look for and mitigate threats, and where do the data sources, the SOC tri that we talked about NDR XDR EDR, where did they help fit into helping organizations take something that's a bit nebulous and really figure out how to become much more secure? >>Yeah, it was, it was definitely a little vague there with that, with that sentence. And also if you, if you, I think if, if you look at the sentence, deploy modern security tools on your computers and devices, right. It's missing the network as we've been talking about there, there's, there's a key, key point of, of reference that's missing from that, from that sentence. Right. But I think what they mean by deploying monitor security tools is, is really taking advantage of all these, these ways to gain visibility into, you know, the threats like we've been talking about, you're deploying advanced Sims that are pulling logs from all kinds of different security devices or, and, or servers cetera. You're, you're deploying advanced endpoint detection systems, advanced NDR systems. And so on, you're trying to use, you're trying to utilize XDR new technology to pull data from all those different sources and analyze it further. And then, you know, the other one we, we haven't even mentioned yet. It was the, so the security operation and automation, right. Response it's now, now what do we do? We've detected something, but now help me automate the response to that. And so I think that's what they mean by leveraging modern, you know, security tools and so on >>When you're in customer conversations, I imagine they're coming to, to Netscale looking for advice like what we just talked through the vagueness in that statement and the different tools that organizations can use. So when you're talking to customers and they're talking about, we need to gain visibility across our entire network, across all of our devices, from your perspective from net Scout's perspective, what does that visibility actually look like and deliver across an organization that does it well? >>Yeah, we, I mean, I think the simple way to put it is you need visibility. That is both broad and deep. And what I mean by broad is that you need visibility across your network, no matter where that network may reside, no matter what protocols it's running, what, you know, technologies is it, is it virtualized or, or legacy running in a hundred gigabits? Is it in a private cloud, a public cloud, a combination of both. So that broadness, meaning wherever that network is or whatever it's running, that's, that's what you need visibility into. It has to be able to support that environment. Absolutely. And the, the, absolutely when I, we talk about being deep it's, it has to get down to a packet level. It can't be, you know, as high as say, just looking at net flow records or something like that, that they are valuable, they have their role. However, you know, when we talk about getting deep, it has to ultimately get down to the packet level and that's, and we've said this in this time that it's ultimately that source of truth. So that, that's what that's, I think that's what we need. >>Got it. That that depth is incredibly important. Thanks so much, Tom, for talking about this in a moment, you and I are gonna be back, we're gonna be talking about why not all NDR is created equally, and Tom's gonna actually share with you some of the features and capabilities that you should be looking for when you're choosing an NDR solution. You're watching the cube, the leader in enterprise tech coverage, >>And we're clear. >>All right. >>10 45. Perfect. You guys are >>Okay. Good >>Cruising. Well, >>Welcome back everyone. This is segment three. I'm Lisa Martin with Tom gin. Kowski senior director of product marketing at nets scout. Welcome back to the growing importance of advanced NDR in this segment, Tom and I are gonna be talking about the fact that not all NDR is created equally. He's gonna impact the features, the capabilities that are most important when organizations are choosing an NDR solution. Tom, it's great to have you back on the program. >>Great, great to be here. >>So we've, we've covered a lot of content in the first two segments, but as we, as we see enterprises expanding their it infrastructure, enabling the remote workforce, which is here to stay leveraging the crowd cloud, driving innovation, the need for cybersecurity approaches and strategies that are far more robust and deep is really essential. But in response to those challenges, more and more enterprises are relying on NDR solutions that fill some of the gaps that we talked about with some of the existing tool sets in the last segment, we talked about some of the gaps in EDR solutions, how NDR resolves those. But we also know that not all NDR tools are created equally. So what, in your perspective, Tom are some of the absolutely fundamental components of NDR tools that organizations need to have for those tools to really be robust. >>Yeah. So we, we, we touched upon this a little bit in the previous segment when we talked about first and foremost, your NDR solution is providing you comprehensive network visibility that must support whatever your network environment is. And it should be in a single tool. It shouldn't have a one vendor per providing you, you know, network visibility in the cloud and another vendor providing network visibility in a local network. It should be a single NDR solution that provides you visibility across your entire network. So we also talked about it, not only does it need to be broadened like that, but also has to be deep too, eventually down to a packet level. So those are, those are sort of fundamental table stakes, but the NDR solution also must give you the ability to access a robust source of layer two or layer three metadata, and then ultimately give you access to, to packets. And then last but not least that solution must integrate into your existing cybersecurity stack. So in the prior segments, we talked a lot about, you know, the, the SIM, so that, that, that NDR solution must have the ability to integrate into that SIM or into your XDR system or even into your source system. >>Let's kind of double click on. Now, the evolution of NDR can explain some of the differences between the previous generations and advanced NDR. >>Yeah. So let's, let's start with what we consider the most fundamental difference. And that is solution must be packet based. There are other ways to get network visibility. One is using net flow and there are some NDR solutions that rely upon net flow for their source of, of, of visibility. But that's too shallow. You ultimately, you need to get deeper. You need to get down to a pack level and that's again where some, so, you know, you, you want to make sure that your NDR or advanced NDR solution is packet based. Number two, you wanna make sure that when you're pulling packets off the wire, you can do it at scale, that full line rate and in any environment, as we, as we spoke about previously, whether it be your local environment or a public cloud environment, number three, you wanna be able to do this when your traffic is encrypted. As we know a lot of, lot of not of network traffic is encrypted today. So you have the ability to have to have the ability to decrypt that traffic and then analyze it with your NDR system. >>Another, another, another one number four is, okay, I'm not just pulling packets off the wire, throwing full packets into a data storage someplace. That's gonna, you know, fill up a disc in a matter of seconds, right? You want the ability to extract a meaningful set of metadata from layer two to layer seven, the OSI model look at key metrics and conducting initial set of analysis, have the ability to index and compress that data, that metadata as well as packets on these local storage devices on, you know, so having the ability to do this packet capture at scale is really important, storing that packets and metadata locally versus up in a cloud to, you know, help with some compliance and, and confidentiality issues. And then, you know, last final least when we talk about integration into that security stack, it's multiple levels of integration. Sure. We wanna send alerts up into that SIM, but we also want the ability to, you know, work with that XDR system to, or that, that source system to drill back down into that metadata packets for further analysis. And then last but not least that piece of integration should be that there's a robust set of information that these NDR systems are pulling off the wire many times in more advanced mature organizations, you know, security teams, data scientists, et cetera. They just want access to that raw data, let them do their own analysis outside, say the user interface with the boundaries of a, of a vendor's user interface. Right? So have the ability to export that data too is really important and advance in the systems. >>Got it. So, so essentially that the, the, the breadth, the visibility across the entire infrastructure, the depth you mentioned going down to a packet level, the scale, the metadata encryption, is that what net scout means when you talk about visibility without borders? >>Yeah, exactly. You know, we, we have been doing this for over 30 years, pulling packets off of wire, converting them using patent technology to a robust set of metadata, you know, at, at full line rates up to a hundred in any network environment, any protocols, et cetera. So that, that's what we mean by that breadth. And in depth of visibility, >>Can you talk a little bit about smart detection if we say, okay, advanced NDR needs to deliver this threat intelligence, but it also needs to enable smart detection. What does net scout mean by that? >>So what you wanna make sure you have multiple methods of detection, not just a methods. So, you know, not just doing behavioral analysis or not just detecting threats based on known indicators or compromise, what you wanna wanna have multiple ways of detecting threats. It could be using statistical behavioral analysis. It could be using curated threat intelligence. It could be using, you know, open source signature engine, like from Sara COTA or other threat analytics, but to, but you also wanna make sure that you're doing this both in real time and have the ability to do it historically. So after a, a threat has been detected, for example, with another, with another product, say an EDR device, you now want the ability to drill into the data from the network that had occurred in, in, you know, prior to this. So historically you want the ability to comb through a historical set of metadata or packets with new threat intelligence that you've you've gathered today. I wanna be able to go back in time and look through with a whole new perspective, looking for something that I didn't know about, but you know, 30 days ago. So that's, that's what we, what we mean by smart detection. >>So really what organizations need is these tools that deliver a far more comprehensive approach. I wanna get into a little bit more on in integration. You talked about that in previous segments, but can you, can you give us an example of, of what you guys mean by smart integration? Is that, what does that deliver for organizations specifically? >>Yeah, we really it's three things. One will say the integration to the SIM to the security operations center and so on. So when, when an ed, when an NDR device detects something, have it send an alert to the SIM using, you know, open standards or, or, or like syslog standards, et cetera, the other direction is from the SIM or from the so, so one, you know, that SIM that, so is receiving information from many different devices that are, or detecting threats. The analyst now wants the ability to one determine if that's a true threat or not a false positive, if it is a true threat, you know, what help me with the remediation effort. So, you know, an example could be an alert comes into a SIM slash. So, and part of the playbook is to go out and grab the metadata packets associated with this alert sometime before and sometime after when that alert came in. >>So that could be part of the automation coming from the SIM slash. So, and then last one, not least is we alluded to this before is having the ability to export that robust set of layer two through layer seven metadata and or packets to a third party data lake, if you will, and where analysts more sophisticated analysts, data scientists, and so on, can do their own correlation, enrich it with their own data, combined it with other data sets and so on, do their own analysis. So it's that three layers of, of integration, if you will, that really what should be an advanced NDR system? >>All right, Tom, take this home for me. How does nets scout deliver advanced NDRs for organizations? >>We do that via solution. We call Omni the security. This is Netscout's portfolio of, of multiple different cyber security products. It all starts with the packets. You know, our core competency for the last 30 years has been to pull packets off the wire at scale, using patented technologies, for example, adapt service intelligence technologies to convert those broad packets into robust set of layer seven layer two through seven metadata. We refer to that data as smart data with that data in hand, you now have the ability to conduct multiple types of threat detection using statistical behavioral, you know, curative threat intelligence, or even open source. So rules engine, you have the ability to detect threats both in real time, as well as historically, but then a solution goes beyond just detecting threats or investigating threats has the ability to influence the blocking of threats too. So we have integrations with different firewall vendors like Palo Alto, for example, where they could take the results of our investigation and then, you know, create policies, blocking policies into firewall. >>In addition to that, we have our own Omni a E D product or our Arbor edge defense. That's, that's a product that sits in front of the firewall and protects the firewall from different types of attacks. We have integration that where you can, you can also influence policies being blocked in the a E and in last but not least, our, our solution integrates this sort of three methods of integration. As we mentioned before, with an existing security system, sending alerts to it, allowing for automation and investigation from it, and having the ability to export our data for, you know, custom analysis, you know, all of this makes that security stack that we've been talking about better, all those different tools that we have. That's that operations triads that we talked about or visibility triad, we talked about, you know, our data makes that entire triad just better and makes the overall security staff better and makes overall security just, just better too. So that, that that's our solution on the security. >>Got it. On the security. And what you've talked about did a great job. The last three segments talking about the differences between the different technologies, data sources, why the complimentary and collaborative nature of them working together is so important for that comprehensive cybersecurity. So Tom, thank you so much for sharing such great and thoughtful information and insight for the audience. >>Oh, you're welcome. Thank you. >>My pleasure. We wanna thank you for watching the program today. Remember that all these videos are available@thecube.net, and you can check out today's news on Silicon angle.com and of course, net scout.com. We also wanna thank net scout for making this program possible and sponsoring the cube. I'm Lisa Martin for Tomski. Thanks for watching and bye for now.

(gentle music) >> We're back at the Seaport in Boston. I'm Dave Vellante with my co-host, Paul Gillin. Tracie Zenti is here. She's the Director of Global Partner Management at Microsoft, and Tom Anderson is the Vice President of Ansible at Red Hat. Guys, welcome to theCube. >> Hi, thank you. >> Yep. >> Ansible on Azure, we're going to talk about that. Why do I need Ansible? Why do I need that kind of automation in Azure? What's the problem you're solving there? >> Yeah, so automation itself is connecting customers' infrastructure to their end resources, so whether that infrastructure's in the cloud, whether it's in the data center, or whether it's at the edge. Ansible is the common automation platform that allows customers to reuse automation across all of those platforms. >> And so, Tracie, I mean, Microsoft does everything. Why do you need Red Hat to do Ansible? >> We want that automation, right? We want our customers to have that ease of use so they can be innovative and bring their workloads to Azure. So that's exactly why we want Ansible. >> Yeah, so kind of loaded questions here, right, as we were sort of talking offline. The nature of partnerships is changing. It's about co-creating, adding value together, getting those effects of momentum, but maybe talk about how the relationship started and how it's evolving and I'd love to have your perspective on the evolving nature of ecosystems. >> Yeah, I think the partnership with Red Hat has been strong for a number of years. I think my predecessor was in the role for five years. There was a person in there for a couple years before that. So I think seven or eight years, we've been working together and co-engineering. Red Hat enterprised Linux. It's co-engineered. Ansible was co-engineered. We work together, right? So we want it to run perfectly on our platform. We want it to be a good customer experience. I think the evolution that we're seeing is in how customers buy, right? They want us to be one company, right? They want it to be easy. They want be able to buy their software where they run it on the cloud. They don't want to have to call Red Hat to buy and then call us to buy and then deploy. And we can do all that now with Ansible's the first one we're doing this together and we'll grow that on our marketplace so that it's easy to buy, easy to deploy, easy to keep track of. >> This is not just Ansible in the marketplace. This is actually a fully managed service. >> That's right. >> What is the value you've added on top of that? >> So it runs in the customer account, but it acts kind of like SaaS. So Red Hat gets to manage it, right? And it's in their own tenant. So they get in the customer's own tenant, right? So with a service principle, Red Hat's able to do that management. Tom, do you want to add anything to that? >> Yeah, the customers don't have to worry about managing Ansible. They just worry about using Ansible to automate their infrastructure. So it's a kind of a win-win situation for us and for our customers. We manage the infrastructure for them and the customer's resources themselves and they get to just focus on automating their business. >> Now, if they want to do cross-cloud automation or automation to their hybrid cloud, will you support that as well? >> 100%. >> Absolutely. >> Yeah. >> We're totally fine with that, right? I mean, it's unrealistic to think customers run everything in one place. That isn't enterprise. That's not reality. So yeah, I'm fine with that. >> Well, that's not every cloud provider. >> No (laughing) that's true. >> You guys over here, at Amazon, you can't even say multicloud or you'll get thrown off the stage. >> Of course we'd love it to all run on Azure, but we want our customers to be happy and have choice, yeah. >> You guys have all, I mean, you've been around a long time. So you had a huge on-prem state, brought that to the cloud, and Azure Stack, I mean, it's been around forever and it's evolved. So you've always believed in, whatever you call it, Hybrid IT, and of course, you guys, that's your call of mission. >> Yeah, exactly. >> So how do you each see hybrid? Where's the points of agreement? It sounds like there's more overlap than gaps, but maybe you could talk about your perspective. >> Yeah, I don't think there are any points of disagreement. I think for us, it's meeting our customers where their center of gravity is, where they see their center of management gravity. If it's on Azure, great. If it's on their data center, that's okay, too. So they can manage to or from. So if Azure is their center of gravity, they can use automation, Ansible automation, to manage all the things on Azure, things on other cloud providers, things in their data center, all the way out to their edge. So they have the choice of what makes the most sense to them. >> And Azure Arc is obviously, that's how Azure Stack is evolving, right? >> Yeah, and we have Azure Arc integration with Ansible. >> Yeah. >> So yeah, absolutely. And I mean, we also have Rell on our marketplace, right? So you can buy the basement and you could buy the roof and everything in between. So we're growing the estate on marketplace as well to all the other products that we have in common. So absolutely. >> How much of an opportunity, just go if we go inside? Give us a little peak inside Microsoft. How much of an opportunity does Microsoft think about multi-cloud specifically? I'm not crazy about the term multicloud, 'cause to me, multicloud, runs an Azure, runs an AWS, runs on Google, maybe runs somewhere else. But multicloud meaning that common experience, your version of hybrid, if you will. How serious is Microsoft about that as a business opportunity? A lot of people would say, well, Microsoft really doesn't want. They want everything in their cloud. But I'd love to hear from you if that is good. >> Well, we have Azure Red Hat OpenShift, which is a Microsoft branded version of OpenShift. We have Ansible now on our marketplace. We also, of course, we have AKS. So I mean, container strategy runs anywhere. But we also obviously have services that enhance all these things. So I think, our marketplace is a third party marketplace. It is designed to let customers buy and run easily on Azure and we'd want to make that experience good. So I don't know that it's... I can't speak to our strategy on multicloud, but what I can speak to is when businesses need to do innovation, we want it to be easy to do that, right? We want it to be easy to buy, defined, buy, deploy, manage, and that's what we're trying to accomplish. >> Fair to say, you're not trying to stop it. >> No, yeah, yeah. >> Whether or not it evolves into something that you heavily lean into or see. >> When we were talking before the cameras turned on, you said that you think marketplaces are the future. Why do you say that? And how will marketplaces be differentiated from each other in the future? >> Well, our marketplace is really, first of all, I think, as you said off camera, they're now. You can buy now, right? There's nothing that stops you. But to me, it's an extension of consumerization of IT. I've been in IT and manageability for about 23 years and full automation is what we and IT used to always talk about, that single pane of glass. How do you keep track of everything? How do you make it easy? How do you support? And IT is always eeking out that last little bit of funding to do innovation, right? So what we can do with consumerization of IT is make it easier to innovate. Make it cheaper to innovate, right? So I think marketplaces do that, right? They've got gold images you can deploy. You're also able to deploy custom images. So I think the future is as particularly with ours, like we support, I don't remember the exact number, but over a hundred countries of tax calculation. We've got like 17 currencies. So as we progress and customers can run from anywhere in the world and buy from anywhere in the world and make it simple to do those things that used to take maybe two months to spin up services for innovation and Ansible helps with that, that's going to help enterprises innovate faster. And I think that's what marketplaces are really going to bring to the forefront is that innovation. >> Tom, why did Ansible, I'm going to say one, I mean, you're never done. But it was unclear a few years ago, which automation platform was going to win in the marketplace and clearly, Ansible has taken a leading position. Why? What were the factors that led to that? >> Honestly, it was the strength of the community, right? And Red Hat leaning into that community to support that community. When you look out at the upstream community for Ansible and the number of participants, active participants that are contributing to the community just increases its value to everybody. So the number of integrations, the number of things that you can automate with Ansible is in the thousands and thousands, and that's not because a group of Red Hat engineers wrote it. That's because our community partners, like Microsoft wrote the user integrations for Ansible. F5 does theirs. Customers take those and expand on them. So the number of use cases that we can address through the community and through our partners is immense. >> But that doesn't just happen. I mean, what have you done to cultivate that community? >> Well, it's in Red Hat's DNA, right? To be the catalyst in a community, to bring partners and users together, to share their knowledge and their expertise and their skills, and to make the code open. So anybody can go grab Ansible from upstream and start doing stuff with it, if they want. If they want to mature on it and management for it and support all the other things that Red Hat provides, then they come to us for a subscription. So it's really been about sort of catalyzing and supporting that community, and Red Hat is a good steward of these upstream communities. >> Is Azure putting Ansible to use actually within your own platform as opposed to being a managed service? Are you adopting Ansible for automation of the Azure Platform? >> I'll let you answer that. >> So two years ago, Microsoft presented at AnsibleFest, our fall conference, Budd Warrack, I'm butchering his last name, but he came on and told how the networking team at Microsoft supports about 35,000 access points across hundreds of buildings, all the Microsoft campuses using Ansible to do that. Fantastic story if you want to go on YouTube and look up that use case. So Microsoft is an avid user of the Ansible technology in their environment. >> Azure is kind of this really, I mean, incredible strategic platform for Microsoft. I wonder if you could talk about Azure as a honeypot for partners. I mean, it seems, I mean, the momentum is unbelievable. I mean, I pay attention to their earnings calls every quarter of Azure growth, even though I don't know what the exact number is, 'cause they won't give it to me but they give me the growth rates and it's actually accelerating. >> No lie. (Tracie laughing) >> I've got my number. It's in the tens of billions. I mean, I'm north of 35 billion, but growing at the high 30%. I mean, it's remarkable. So talk about the importance of that to the ecosystem as a honey pot. >> Paul Satia said it right. Many times partners are essential to our strategy. But if you think about it, software solves problems. We have software that solves problems. They have software that solves problems, right? So when IT and customers are thinking of solving a problem, they're thinking software, right? And we want that software to run on Azure. So partners have to be essential to our strategy. Absolutely. It's again, we're one team to the customer. They want to see that as working together seamlessly. They don't want it to be hardware Azure plus software. So that's absolutely critical to our success. >> And if I could add for us, the partners are super important. So some of our launch partners are like F5 and CyberArk who have certified Ansible content for Ansible on Azure. We have service provider partners like Accenture and Kindra that are launching with us and providing our joint customers with help to get up to speed. So it really is a partner play. >> Absolutely. >> Where are you guys taking this? Where do you want to see it go? What are some of the things that observers should pay attention to as marketers of success and evolution? >> Well, certainly for us, it's obviously customer adoption, but it is providing them with patterns. So out of the box patterns that makes it easy for them to get up and running and solve the use cases and problems that they run into most frequently. Problems ain't the right word. Challenges or opportunities on Azure to be able to automate the things. So we're really leaning into the different use cases, whether it's edge, whether it's cloud, whether it's cloud to edge, all of those things. We want to provide users with out of the box Ansible content that allows 'em to just get up and automating super fast, and doing that on Azure makes it way easier for us because we don't have to focus on the install and the setting up and configuring it. It's all just part of the experience >> And Tracie, for Microsoft, it's world domination with a smile. (all laughing) >> Of course. No, of course not. No, I think it's to continue to grow the co-engineering we do across all of the Red Hat products. I can't even tell you the number of things we work on together, but to look forward strategically at what opportunities we have across our products and theirs to integrate like Arc and Ansible, and then making it all easy to buy, making it available so that customers have choice and they can buy how they want to and simplify. So we're just going to continue to do that and we're at that infancy right now and as we grow, it'll just get easier and easier with more and more products. >> Well, bringing the edge into the equation is going to be really interesting. Microsoft with its gaming, vector is amazing, and recent, awesome acquisitions. All the gamers are excited about that and that's a huge edge play. >> You'll have to bring my son on for that interview. >> Yeah. >> My son will interview. >> He knows more than all of us, I'm sure. What about Ansible? What's ahead for Ansible? >> Edge, so part of the Red Hat play at the Edge. We've getting a lot of customer pull for both industrial Edge use cases in the energy sector. We've had a joint customer with Azure that has a combined Edge platform. Certainly, the cloud stuff that we're announcing today is a huge growth area. And then just general enterprise automation. There's lots of room to run there for Ansible. >> And lots of industries, right? >> Yeah. >> Telco, manufacturing. >> Retail. >> Retail. >> Yeah. >> Yeah. There's so many places to go, yeah, that need the help. >> The market's just, how you going to count it anymore? It's just enormous. >> Yeah. >> It's the entire GDP the world. But guys, thanks for coming to theCUBE. >> Yeah. >> Great story. Congratulations on the partnership and the announcements and look forward to speaking with you in the future. >> Yeah, thanks for having us. >> Thanks for having us. >> You're very welcome. And keep it right there. This is Dave Vellante for Paul Gillin. This is theCUBE's coverage of Red Hat Summit 2022. We'll be right back at Seaport in Boston. (gentle music)

(upbeat music) >> My name's Courtney Pallota, I'm the Vice President of Marketing at ChaosSearch. We've partnered with theCUBE team to take every one of those assets, tailor them to meet whatever our needs were, and get them out and shared far and wide. And theCUBE team has been tremendously helpful in partnering with us to make that a success. >> theCUBE has been fantastic with us. They are thought leaders in this space. And we have a unique product, a unique vision, and they have an insight into where the market's going. They've had conference with us with data mesh, and how do we fit into that new realm of data access. And with our unique vision, with our unique platform, and with theCUBE, we've uniquely come out into the market. >> What's my overall experience with theCUBE? Would I do it again, would I recommended it to others? I said, I recommend theCUBE to everyone. In fact, I was at IBM, and some of the IBM executives didn't want to go on theCUBE because it's a live interview. Live interviews can be traumatic. But the fact of the matter is, one, yeah, they're tough questions, but they're in line, they're what clients are looking for. So yes, you have to be on ball. I mean, you're always on your toes, but you get your message out so crisply. So I recommend it to everyone. I've gotten a lot of other executives to participate, and they've all had a great example. You have to be ready. I mean, you can't go on theCUBE and not be ready, but now you can get your message out. And it has such a good distribution. I can't think of a better platform. So I recommended it to everyone. If I say ChaosSearch in one word, I'd say digital transformation, with a hyphen.

[Thomas Hazel] - Hello, this is Thomas Hazel, founder CTO here at ChaosSearch. And tonight I'm going to demonstrate a new feature we are offering this quarter called JSON Flex. If you're familiar with JSON datasets, they're wonderful ways to represent information. You know, they're multidimensional, they have ability to set up arrays as attributes but those arrays are really problematic when you need to expand them or flatten them to do any type of elastic search or relational access, particularly when you're trying to do aggregations. And so the common process is to exclude those arrays or pick and choose that information. But with this new Chaos Flex capability, our system uniquely can index that data horizontally in a very small and efficient representation. And then with our Chaos Refinery, expand each attribute as you wish vertically, so you can do all the basic and natural constructs you would have done if you had, you know, a more straightforward, two dimensional, three dimensional type representation. So without further ado, I'mma get into this presentation of JSON Flex. Now, in this case, I've already set up the service to point to a particular S3 account that has CloudTrail data, one that is pretty problematic when it comes down to flattening data. And again, if you know CloudTrail, one row can become 10,000 as data gets flattened. So without further ado, let me jump right in. When you first log into the ChaosSearch service, you'll see a tab called 'Storage'. This is the S3 account, and I have variety of buckets. I have the refinery, it's a data refinery. This is where we create views or lenses into these index streams that you can do analysis that publishes it in elastic API as an index pattern or relational table in SQL Now a particular bucket I have here is a whole bunch of demonstration datasets that we have to show off our capabilities and our offering. In this bucket, I have CloudTrail data and I'm going to create what we call a 'object group'. An object group is a entry point, a filter of which files I want to index that data. Now, it can be statically there or a live streaming. These object groups had the ability to say, what type of data do you want to index on? Now through our wizard, you can type in, you know, prefix in this case, I want to type in CloudTrail, and you see here, I have a whole bunch of CloudTrail. I'mma choose one file to make it quick and easy. But this particular CloudTrail data will expand and we can show the capability of this horizontal to vertical expansion. So I walked through the wizard, as you can see here, we discovered JSON, it's a gzip file. Leave flattening unlimited 'cause we want to be able to expand infinitely. But this case, instead of doing default virtual, I'm going to horizontally represent this information. And this uniquely compresses the data in a way that can be stored efficiently on disc but then expanded in our data refinery on Pond Query or search requests. So I'mma create this object group. Now I'm going to call this, you know, 'JSON Flex test' and I could set up live indexing, SQS pops up but I'mma skip that and skip Retention and just create it. Once this object group is created, you kind of think of it as a virtual bucket, 'cause it does filter the data as you can see here. When I look at the view, I just see CloudTrail, but within the console, I can say start indexing. Now this is static data there could be a live stream and we set up workers to index this data. Whether it's one file, a million files or one terabyte, or one petabyte, we index the data. We discover all the schema, and as you see here, we discovered 104 columns. Now what's interesting is that we represent this expansion in a horizontal way. You know, if you know CloudTrail records zero, record one, record two. This can expand pretty dramatically if you fully flatten it but this case we horizontally representing it as the index. So when I go into the data refinery, I can create a view. Now, if you know the data refinery of ChaosSearch, you can bring multiple data streams together. You can do transformations virtually, you can do correlations, but in this case, I'm just going to take this one particular index stream, we call 'JSON Flex' and walk through a wizard, we try to simplify everything and select a particular attribute to expand. Now, again, we represent this in one row but if you had arrays and do all the permutations, it could go one to 100 to 10,000. We had one JSON audit that went from one row to 1 million rows. Now, clearly you don't want to create all those permutations, when you're tryna put into a database. With our unique index technology, you can do it virtually and sort horizontally. So let me just select 'Virtual' and walk through the wizard. Now, as I mentioned, we do all these different transformations changed schema, we're going to skip all that and select the order time, records event and say, 'create this'. I'm going to say, you know, 'JSON Flex View', I can set up caching, do a variety of things, I'm going to skip that. And once I create this, it's now available in the elastic API as an index pattern, as well as SQL via our Presto API dialect. And you can use Looker, Tableau, et cetera. But in this case, we go to this 'Analytics tab' and we built in the Kibana, open search tooling that is Apache Tonetto. And I click on discovery here and I'm going to select that particular view. Again, it looks like, oops, it looks like an index pattern, and I'mma choose, let's see here, let's choose 15 years from past and present and make sure I find where actually was timed. And what you'll see here is, you know, sure. It's just one particular data set has a variety of columns, but you see here is unlike that record zero, records one, now it's expanded. And so it has been expanded like a vertical flattening that you would traditionally do if you wanted to do anything that was an elastic or a relational construct, you know, that fit into a table format. Now the 'vantage of JSON Flex, you don't have that stored as a blob and use these proprietary JSON API's. You can use your native elastic API or your native SQL tooling to get access to it naturally without that expense of that explosion or without the complexity of ETLing it, and picking and choosing before you actually put into the database. That completes the demonstration of ChaosSearch new JSON Flex capability. If you're interested, come to ChaosSearch.io and set up a free trial. Thank you.

>> Welcome to theCUBE, I am Dave Vellante. And today we're going to explore the ebb and flow of data as it travels into the cloud and the data lake. The concept of data lakes was alluring when it was first coined last decade by CTO James Dixon. Rather than be limited to highly structured and curated data that lives in a relational database in the form of an expensive and rigid data warehouse or a data mart. A data lake is formed by flowing data from a variety of sources into a scalable repository, like, say an S3 bucket that anyone can access, dive into, they can extract water, A.K.A data, from that lake and analyze data that's much more fine-grained and less expensive to store at scale. The problem became that organizations started to dump everything into their data lakes with no schema on our right, no metadata, no context, just shoving it into the data lake and figure out what's valuable at some point down the road. Kind of reminds you of your attic, right? Except this is an attic in the cloud. So it's too big to clean out over a weekend. Well look, it's 2021 and we should be solving this problem by now. A lot of folks are working on this, but often the solutions add other complexities for technology pros. So to understand this better, we're going to enlist the help of ChaosSearch CEO Ed Walsh, and Thomas Hazel, the CTO and Founder of ChaosSearch. We're also going to speak with Kevin Miller who's the Vice President and General Manager of S3 at Amazon web services. And of course they manage the largest and deepest data lakes on the planet. And we'll hear from a customer to get their perspective on this problem and how to go about solving it, but let's get started. Ed, Thomas, great to see you. Thanks for coming on theCUBE. >> Likewise. >> Face to face, it's really good to be here. >> It is nice face to face. >> It's great. >> So, Ed, let me start with you. We've been talking about data lakes in the cloud forever. Why is it still so difficult to extract value from those data lakes? >> Good question. I mean, data analytics at scale has always been a challenge, right? So, we're making some incremental changes. As you mentioned that we need to see some step function changes. But in fact, it's the reason ChaosSearch was really founded. But if you look at it, the same challenge around data warehouse or a data lake. Really it's not just to flowing the data in, it's how to get insights out. So it kind of falls into a couple of areas, but the business side will always complain and it's kind of uniform across everything in data lakes, everything in data warehousing. They'll say, "Hey, listen, I typically have to deal with a centralized team to do that data prep because it's data scientists and DBAs". Most of the time, they're a centralized group. Sometimes they're are business units, but most of the time, because they're scarce resources together. And then it takes a lot of time. It's arduous, it's complicated, it's a rigid process of the deal of the team, hard to add new data, but also it's hard to, it's very hard to share data and there's no way to governance without locking it down. And of course they would be more self-serve. So there's, you hear from the business side constantly now underneath is like, there's some real technology issues that we haven't really changed the way we're doing data prep since the two thousands, right? So if you look at it, it's, it falls two big areas. It's one, how to do data prep. How do you take, a request comes in from a business unit. I want to do X, Y, Z with this data. I want to use this type of tool sets to do the following. Someone has to be smart, how to put that data in the right schema, you mentioned. You have to put it in the right format, that the tool sets can analyze that data before you do anything. And then second thing, I'll come back to that 'cause that's the biggest challenge. But the second challenge is how these different data lakes and data warehouses are now persisting data and the complexity of managing that data and also the cost of computing it. And I'll go through that. But basically the biggest thing is actually getting it from raw data so the rigidness and complexity that the business sides are using it is literally someone has to do this ETL process, extract, transform, load. They're actually taking data, a request comes in, I need so much data in this type of way to put together. They're literally physically duplicating data and putting it together on a schema. They're stitching together almost a data puddle for all these different requests. And what happens is anytime they have to do that, someone has to do it. And it's, very skilled resources are scanned in the enterprise, right? So it's a DBS and data scientists. And then when they want new data, you give them a set of data set. They're always saying, what can I add to this data? Now that I've seen the reports. I want to add this data more fresh. And the same process has to happen. This takes about 60% to 80% of the data scientists in DPA's to do this work. It's kind of well-documented. And this is what actually stops the process. That's what is rigid. They have to be rigid because there's a process around that. That's the biggest challenge of doing this. And it takes an enterprise, weeks or months. I always say three weeks or three months. And no one challenges beyond that. It also takes the same skill set of people that you want to drive digital transformation, data warehousing initiatives, motorization, being data driven or all these data scientists and DBS they don't have enough of. So this is not only hurting you getting insights out of your day like in the warehouses. It's also, this resource constraint is hurting you actually getting. >> So that smallest atomic unit is that team, that's super specialized team, right? >> Right. >> Yeah. Okay. So you guys talk about activating the data lake. >> Yep. >> For analytics. What's unique about that? What problems are you all solving? You know, when you guys crew created this magic sauce. >> No, and basically, there's a lot of things. I highlighted the biggest one is how to do the data prep, but also you're persisting and using the data. But in the end, it's like, there's a lot of challenges at how to get analytics at scale. And this is really where Thomas and I founded the team to go after this, but I'll try to say it simply. What we're doing, I'll try to compare and contrast what we do compared to what you do with maybe an elastic cluster or a BI cluster. And if you look at it, what we do is we simply put your data in S3, don't move it, don't transform it. In fact, we're against data movement. What we do is we literally point and set that data and we index that data and make it available in a data representation that you can give virtual views to end-users. And those virtual views are available immediately over petabytes of data. And it actually gets presented to the end-user as an open API. So if you're elastic search user, you can use all your elastic search tools on this view. If you're a SQL user, Tableau, Looker, all the different tools, same thing with machine learning next year. So what we do is we take it, make it very simple. Simply put it there. It's already there already. Point us at it. We do the hard of indexing and making available. And then you publish in the open API as your users can use exactly what they do today. So that's, dramatically I'll give you a before and after. So let's say you're doing elastic search. You're doing logging analytics at scale, they're lending their data in S3. And then they're ETL physically duplicating and moving data. And typically deleting a lot of data to get in a format that elastic search can use. They're persisting it up in a data layer called leucine. It's physically sitting in memories, CPU, SSDs, and it's not one of them, it's a bunch of those. They in the cloud, you have to set them up because they're persisting ECC. They stand up same by 24, not a very cost-effective way to the cloud computing. What we do in comparison to that is literally pointing it at the same S3. In fact, you can run a complete parallel, the data necessary it's being ETL out. When just one more use case read only, or allow you to get that data and make this virtual views. So we run a complete parallel, but what happens is we just give a virtual view to the end users. We don't need this persistence layer, this extra cost layer, this extra time, cost and complexity of doing that. So what happens is when you look at what happens in elastic, they have a constraint, a trade-off of how much you can keep and how much you can afford to keep. And also it becomes unstable at time because you have to build out a schema. It's on a server, the more the schema scales out, guess what? you have to add more servers, very expensive. They're up seven by 24. And also they become brutalized. You lose one node, the whole thing has to be put together. We have none of that cost and complexity. We literally go from to keep whatever you want, whatever you want to keep an S3 is single persistence, very cost effective. And what we are able to do is, costs, we save 50 to 80%. Why? We don't go with the old paradigm of sit it up on servers, spin them up for persistence and keep them up 7 by 24. We're literally asking their cluster, what do you want to cut? We bring up the right compute resources. And then we release those sources after the query done. So we can do some queries that they can't imagine at scale, but we're able to do the exact same query at 50 to 80% savings. And they don't have to do any tutorial of moving that data or managing that layer of persistence, which is not only expensive, it becomes brittle. And then it becomes, I'll be quick. Once you go to BI, it's the same challenge, but the BI systems, the requests are constant coming at from a business unit down to the centralized data team. Give me this flavor of data. I want to use this piece of, you know, this analytic tool in that desk set. So they have to do all this pipeline. They're constantly saying, okay, I'll give you this data, this data, I'm duplicating that data, I'm moving it and stitching it together. And then the minute you want more data, they do the same process all over. We completely eliminate that. >> And those requests are queue up. Thomas, it had me, you don't have to move the data. That's kind of the exciting piece here, isn't it? >> Absolutely no. I think, you know, the data lake philosophy has always been solid, right? The problem is we had that Hadoop hang over, right? Where let's say we were using that platform, little too many variety of ways. And so, I always believed in data lake philosophy when James came and coined that I'm like, that's it. However, HTFS, that wasn't really a service. Cloud object storage is a service that the elasticity, the security, the durability, all that benefits are really why we founded on-cloud storage as a first move. >> So it was talking Thomas about, you know, being able to shut off essentially the compute so you don't have to keep paying for it, but there's other vendors out there and stuff like that. Something similar as separating, compute from storage that they're famous for that. And you have Databricks out there doing their lake house thing. Do you compete with those? How do you participate and how do you differentiate? >> Well, you know you've heard this term data lakes, warehouse, now lake house. And so what everybody wants is simple in, easy in, however, the problem with data lakes was complexity of out. Driving value. And I said, what if, what if you have the easy in and the value out? So if you look at, say snowflake as a warehousing solution, you have to all that prep and data movement to get into that system. And that it's rigid static. Now, Databricks, now that lake house has exact same thing. Now, should they have a data lake philosophy, but their data ingestion is not data lake philosophy. So I said, what if we had that simple in with a unique architecture and indexed technology, make it virtually accessible, publishable dynamically at petabyte scale. And so our service connects to the customer's cloud storage. Data stream the data in, set up what we call a live indexing stream, and then go to our data refinery and publish views that can be consumed the elastic API, use cabana Grafana, or say SQL tables look or say Tableau. And so we're getting the benefits of both sides, use scheme on read-write performance with scheme write-read performance. And if you can do that, that's the true promise of a data lake, you know, again, nothing against Hadoop, but scheme on read with all that complexity of software was a little data swamping. >> Well, you've got to start it, okay. So we got to give them a good prompt, but everybody I talked to has got this big bunch of spark clusters, now saying, all right, this doesn't scale, we're stuck. And so, you know, I'm a big fan of Jamag Dagani and our concept of the data lake and it's early days. But if you fast forward to the end of the decade, you know, what do you see as being the sort of critical components of this notion of, people call it data mesh, but to get the analytics stack, you're a visionary Thomas, how do you see this thing playing out over the next decade? >> I love her thought leadership, to be honest, our core principles were her core principles now, 5, 6, 7 years ago. And so this idea of, decentralize that data as a product, self-serve and, and federated computer governance, I mean, all that was our core principle. The trick is how do you enable that mesh philosophy? I can say we're a mesh ready, meaning that, we can participate in a way that very few products can participate. If there's gates data into your system, the CTL, the schema management, my argument with the data meshes like producers and consumers have the same rights. I want the consumer, people that choose how they want to consume that data. As well as the producer, publishing it. I can say our data refinery is that answer. You know, shoot, I'd love to open up a standard, right? Where we can really talk about the producers and consumers and the rights each others have. But I think she's right on the philosophy. I think as products mature in this cloud, in this data lake capabilities, the trick is those gates. If you have to structure up front, if you set those pipelines, the chance of you getting your data into a mesh is the weeks and months that Ed was mentioning. >> Well, I think you're right. I think the problem with data mesh today is the lack of standards you've got. You know, when you draw the conceptual diagrams, you've got a lot of lollipops, which are APIs, but they're all unique primitives. So there aren't standards, by which to your point, the consumer can take the data the way he or she wants it and build their own data products without having to tap people on the shoulder to say, how can I use this?, where does the data live? And being able to add their own data. >> You're exactly right. So I'm an organization, I'm generating data, when the courageously stream it into a lake. And then the service, a ChaosSearch service, is the data is discoverable and configurable by the consumer. Let's say you want to go to the corner store. I want to make a certain meal tonight. I want to pick and choose what I want, how I want it. Imagine if the data mesh truly can have that producer of information, you know, all the things you can buy a grocery store and what you want to make for dinner. And if you'd static, if you call up your producer to do the change, was it really a data mesh enabled service? I would argue not. >> Ed, bring us home. >> Well, maybe one more thing with this. >> Please, yeah. 'Cause some of this is we're talking 2031, but largely these principles are what we have in production today, right? So even the self service where you can actually have a business context on top of a data lake, we do that today, we talked about, we get rid of the physical ETL, which is 80% of the work, but the last 20% it's done by this refinery where you can do virtual views, the right or back and do all the transformation need and make it available. But also that's available to, you can actually give that as a role-based access service to your end-users, actually analysts. And you don't want to be a data scientist or DBA. In the hands of a data scientist the DBA is powerful, but the fact of matter, you don't have to affect all of our employees, regardless of seniority, if they're in finance or in sales, they actually go through and learn how to do this. So you don't have to be it. So part of that, and they can come up with their own view, which that's one of the things about data lakes. The business unit wants to do themselves, but more importantly, because they have that context of what they're trying to do instead of queuing up the very specific request that takes weeks, they're able to do it themselves. >> And if I have to put it on different data stores and ETL that I can do things in real time or near real time. And that's game changing and something we haven't been able to do ever. >> And then maybe just to wrap it up, listen, you know 8 years ago, Thomas and his group of founders, came up with the concept. How do you actually get after analytics at scale and solve the real problems? And it's not one thing, it's not just getting S3. It's all these different things. And what we have in market today is the ability to literally just simply stream it to S3, by the way, simply do, what we do is automate the process of getting the data in a representation that you can now share an augment. And then we publish open API. So can actually use a tool as you want, first use case log analytics, hey, it's easy to just stream your logs in. And we give you elastic search type of services. Same thing that with CQL, you'll see mainstream machine learning next year. So listen, I think we have the data lake, you know, 3.0 now, and we're just stretching our legs right now to have fun. >> Well, and you have to say it log analytics. But if I really do believe in this concept of building data products and data services, because I want to sell them, I want to monetize them and being able to do that quickly and easily, so I can consume them as the future. So guys, thanks so much for coming on the program. Really appreciate it.

>>Hi, Brian, this is Dave Volante. Welcome to this cube conversation with Thomas Hazel was the founder and CTO of chaos surgeon. I'm also joined by ed Walsh. Who's the CEO Thomas. Good to see you. >>Great to be here. >>Explain Jason. First of all, what >>Jason, Jason has a powerful data representation, a data source. Uh, but let's just say that we try to drive value out of it. It gets complicated. Uh, I can search. We activate customers, data lakes. So, you know, customers stream their Jason data to this, uh, cloud stores that we activate. Now, the trick is the complexity of a Jason data structure. You can do all these complexity of representation. Now here's the problem putting that representation into a elastic search database or relational databases, very problematic. So what people choose to do is they pick and choose what they want and or they just stored as a blob. And so I said, what if, what if we create a new index technology that could store it as a full representation, but dynamically in a, we call our data refinery published access to all the permutations that you may want, where if you do a full on flatten, your flattening of its Jason, one row theoretically could be put into a million rows and relational data sort of explode, >>But then it gets really expensive. But so, but everybody says they have Jason support, every database vendor that I talked to, it's a big announcement. We now support Jason. What's the deal. >>Exactly. So you take your relational database with all those relational constructs and you have a proprietary Jason API to pick and choose. So instead of picking, choosing upfront, now you're picking, choosing in the backend where you really want us the power of the relational analysis of that Jaison data. And that's where chaos comes in, where we expand those data streams we do in a relational way. So all that tooling you've been built to know and love. Now you can access to it. So if you're doing proprietary APIs or Jason data, you're not using Looker, you're not using Tableau. You're doing some type of proprietary, probably emailing now on the backend. >>Okay. So you say all the tools that you've trained, everybody on you can't really use them. You got to build some custom stuff and okay, so, so, so maybe bring that home then in terms of what what's the money, why do the suits care about this stuff? >>The reason this is so important is think about anything, cloud native Kubernetes, your different applications. What you're doing in Mongo is all Jason is it's very powerful but painful, but if you're not keeping the data, what people are doing a data scientist is, or they're just doing leveling, they're saying I'm going to only keep the first four things. So think about it's Kubernetes, it's your app logs. They're trying to figure out for black Friday, what happens? It's Lilly saying, Hey, every minute they'll cut a new log. You're able to say, listen, these are the users that were in that system for an hour. And here's a different things. They do. The fact of the matter is if you cut it off, you lose all that fidelity, all that data. So it's really important that to have. So if you're trying to figure out either what happened for security, what happened for on a performance, or if you're trying to figure out, Hey, I'm VP of product or growth, how do I cross sell things? >>You need to know what everyone's doing. If you're not handling Jason natively, like we're doing either your, it keeps on expanding on black Friday. All of a sudden the logs get huge. And the next day it's not, but it's really powerful data that you need to harness for business values. It's, what's going to drive growth. It's what's going to do the digital transformation. So without the technology, you're kind of blind. And to be honest, you don't know. Cause a data scientist is kind of deleted the data on you. So this is big for the business and digital transformation, but also it was such a pain. The data scientists in DBS were forced to just basically make it simple. So it didn't blow up their system. We allow them to keep it simple, but yes, >>Both power. It reminds me if you like, go on vacation, you got your video camera. Somebody breaks into your house. You go back to Lucas and see who and that the data's gone. The video's gone because it didn't, you didn't, you weren't able to save it cause it's too >>Expensive. Well, it's funny. This is the first day source. That's driving the design of the database because of all the value we should be designed the database around the information. It stores not the structure and how it's been organized. And so our viewpoint is you get to choose your structure yet contain all that content. So if a vendor >>It says to kind of, I'm a customer then says, Hey, we got Jason support. What questions should I ask to really peel the onion? >>Well, particularly relational. Is it a relational access to that data? Now you could say, oh, I've ETL does Jason into it. But chances are the explosion of Jason permutations of one row to a million. They're probably not doing the full representation. So from our viewpoint is either you're doing a blob type access to proprietary Jason APIs or you're picking and choosing those, the choices say that is the market thought. However, what if you could take all the vegetation and design your schema based on how you want to consume it versus how you could store it. And that's a big difference with, >>So I should be asking how, how do I consume this data? Are you ETL? Bring it in how much data explosion is going to occur. Once I do this, and you're saying for chaos, search the answer to those questions. >>The answer is, again, our philosophy simply stream your data into your cloud object, storage, your data lake and with our index technology and our data refinery. You get to create views, dynamic the incident, whether it's a terabyte or petabyte, and describe how you want your data because consumed in a relational way or an elastic search way, both are consumable through our data refinery, which is >>For us. The refinery gives you the view. So what happens if someone wants a different view, I want to actually unpack different columns or different matrices. You able to do that in a virtual view, it's available immediately over petabytes of data. You don't have that episode where you come back, look at the video camera. There's no data there left. So that's, >>We do appreciate the time and the explanation on really understanding Jason. Thank you. All right. And thank you for watching this cube conversation. This is Dave Volante. We'll see you next time.

>>Welcome to the cube. I'm Dave Volante. Today, we're going to explore the ebb and flow of data as it travels into the cloud. In the data lake, the concept of data lakes was a Loring when it was first coined last decade by CTO James Dickson, rather than be limited to highly structured and curated data that lives in a relational database in the form of an expensive and rigid data warehouse or a data Mart, a data lake is formed by flowing data from a variety of sources into a scalable repository, like say an S3 bucket that anyone can access, dive into. They can extract water. It can a data from that lake and analyze data. That's much more fine-grained and less expensive to store at scale. The problem became that organizations started to dump everything into their data lakes with no schema on it, right? No metadata, no context to shove it into the data lake and figure out what's valuable. >>At some point down the road kind of reminds you of your attic, right? Except this is an attic in the cloud. So it's too big to clean out over a weekend. We'll look it's 2021 and we should be solving this problem by now, a lot of folks are working on this, but often the solutions at other complexities for technology pros. So to understand this better, we're going to enlist the help of chaos search CEO and Walsh and Thomas Hazel, the CTO and founder of chaos search. We're also going to speak with Kevin Miller. Who's the vice president and general manager of S3 at Amazon web services. And of course they manage the largest and deepest data lakes on the planet. And we'll hear from a customer to get their perspective on this problem and how to go about solving it, but let's get started. Ed Thomas. Great to see you. Thanks for coming on the cube. Likewise face. It's really good to be in this nice face. Great. So let me start with you. We've been talking about data lakes in the cloud forever. Why is it still so difficult to extract value from those data? >>Good question. I mean, a data analytics at scale is always been a challenge, right? So, and it's, uh, we're making some incremental changes. As you mentioned that we need to seem some step function changes, but, uh, in fact, it's the reason, uh, search was really founded. But if you look at it the same challenge around data warehouse or a data lake, really, it's not just a flowing the data in is how to get insights out. So it kind of falls into a couple of areas, but the business side will always complain and it's kind of uniform across everything in data lakes, everything that we're offering, they'll say, Hey, listen, I typically have to deal with a centralized team to do that data prep because it's data scientist and DBS. Most of the time they're a centralized group, sometimes are business units, but most of the time, because they're scarce resources together. >>And then it takes a lot of time. It's arduous, it's complicated. It's a rigid process of the deal of the team, hard to add new data. But also it's hard to, you know, it's very hard to share data and there's no way to governance without locking it down. And of course they would be more self-service. So there's you hear from the business side constantly now underneath is like, there's some real technology issues that we haven't really changed the way we're doing data prep since the two thousands. Right? So if you look at it, it's, it falls, uh, two big areas. It's one. How do data prep, how do you take a request comes in from a business unit. I want to do X, Y, Z with this data. I want to use this type of tool sets to do the following. Someone has to be smart, how to put that data in the right schema. >>You mentioned you have to put it in the right format, that the tool sets can analyze that data before you do anything. And then secondly, I'll come back to that because that's a biggest challenge. But the second challenge is how these different data lakes and data we're also going to persisting data and the complexity of managing that data and also the cost of computing. And I'll go through that. But basically the biggest thing is actually getting it from raw data so that the rigidness and complexity that the business sides are using it is literally someone has to do this ETL process extract, transform load. They're actually taking data request comes in. I need so much data in this type of way to put together their Lilly, physically duplicating data and putting it together and schema they're stitching together almost a data puddle for all these different requests. >>And what happens is anytime they have to do that, someone has to do it. And it's very skilled. Resources are scant in the enterprise, right? So it's a DBS and data scientists. And then when they want new data, you give them a set of data set. They're always saying, what can I add this data? Now that I've seen the reports, I want to add this data more fresh. And the same process has to happen. This takes about 60 to 80% of the data scientists in DPA's to do this work. It's kind of well-documented. Uh, and this is what actually stops the process. That's what is rigid. They have to be rigid because there's a process around that. Uh, that's the biggest challenge to doing this. And it takes in the enterprise, uh, weeks or months. I always say three weeks to three months. And no one challenges beyond that. It also takes the same skill set of people that you want to drive. Digital transformation, data, warehousing initiatives, uh, monitorization being, data driven, or all these data scientists and DBS. They don't have enough of, so this is not only hurting you getting insights out of your dead like that, or else it's also this resource constraints hurting you actually getting smaller. >>The Tomic unit is that team that's super specialized team. Right. Right. Yeah. Okay. So you guys talk about activating the data lake. Yep, sure. Analytics, what what's unique about that? What problems are you all solving? You know, when you guys crew created this, this, this magic sauce. >>No, and it basically, there's a lot of things I highlighted the biggest one is how to do the data prep, but also you're persisting and using the data. But in the end, it's like, there's a lot of challenges that how to get analytics at scale. And this is really where Thomas founded the team to go after this. But, um, I'll try to say it simply, what are we doing? I'll try to compare and stress what we do compared to what you do with maybe an elastic cluster or a BI cluster. Um, and if you look at it, what we do is we simply put your data in S3, don't move it, don't transform it. In fact, we're not we're against data movement. What we do is we literally pointed at that data and we index that data and make it available in a data representation that you can give virtual views to end users. >>And those virtual views are available immediately over petabytes of data. And it re it actually gets presented to the end user as an open API. So if you're elastic search user, you can use all your lesser search tools on this view. If you're a SQL user, Tableau, Looker, all the different tools, same thing with machine learning next year. So what we do is we take it, make it very simple. Simply put it there. It's already there already. Point is at it. We do the hard of indexing and making available. And then you publish in the open API as your users can use exactly what they do today. So that's dramatically. I'll give you a before and after. So let's say you're doing elastic search. You're doing logging analytics at scale, they're lending their data in S3. And then they're,, they're physically duplicating a moving data and typically deleting a lot of data to get in a format that elastic search can use. >>They're persisting it up in a data layer called leucine. It's physically sitting in memories, CPU, uh, uh, SSDs. And it's not one of them. It's a bunch of those. They in the cloud, you have to set them up because they're persisting ECC. They stand up semi by 24, not a very cost-effective way to the cloud, uh, cloud computing. What we do in comparison to that is literally pointing it at the same S3. In fact, you can run a complete parallel, the data necessary. It's being ETL. That we're just one more use case read only, or allow you to get that data and make this virtual views. So we run a complete parallel, but what happens is we just give a virtual view to the end users. We don't need this persistence layer, this extra cost layer, this extra, um, uh, time cost and complexity of doing that. >>So what happens is when you look at what happens in elastic, they have a constraint, a trade-off of how much you can keep and how much you can afford to keep. And also it becomes unstable at time because you have to build out a schema. It's on a server, the more the schema scales out, guess what you have to add more servers, very expensive. They're up seven by 24. And also they become brittle. As you lose one node. The whole thing has to be put together. We have none of that cost and complexity. We literally go from to keep whatever you want, whatever you want to keep an S3, a single persistence, very cost effective. And what we do is, um, costs. We save 50 to 80% why we don't go with the old paradigm of sit it up on servers, spin them up for persistence and keep them up. >>Somebody 24, we're literally asking her cluster, what do you want to cut? We bring up the right compute resources. And then we release those sources after the query done. So we can do some queries that they can't imagine at scale, but we're able to do the exact same query at 50 to 80% savings. And they don't have to do any of the toil of moving that data or managing that layer of persistence, which is not only expensive. It becomes brittle. And then it becomes an I'll be quick. Once you go to BI, it's the same challenge, but the BI systems, the requests are constant coming at from a business unit down to the centralized data team. Give me this flavor of debt. I want to use this piece of, you know, this analytic tool in that desk set. So they have to do all this pipeline. They're constantly saying, okay, I'll give you this data, this data I'm duplicating that data. I'm moving in stitching together. And then the minute you want more data, they do the same process all over. We completely eliminate that. >>The questions queue up, Thomas, it had me, you don't have to move the data. That's, that's kind of the >>Writing piece here. Isn't it? I absolutely, no. I think, you know, the daylight philosophy has always been solid, right? The problem is we had that who do hang over, right? Where let's say we were using that platform, little, too many variety of ways. And so I always believed in daily philosophy when James came and coined that I'm like, that's it. However, HTFS that wasn't really a service cloud. Oddish storage is a service that the, the last society, the security and the durability, all that benefits are really why we founded, uh, Oncotype storage as a first move. >>So it was talking Thomas about, you know, being able to shut off essentially the compute and you have to keep paying for it, but there's other vendors out there and stuff like that. Something similar as separating, compute from storage that they're famous for that. And, and, and yet Databricks out there doing their lake house thing. Do you compete with those? How do you participate and how do you differentiate? >>I know you've heard this term data lakes, warehouse now, lake house. And so what everybody wants is simple in easy N however, the problem with data lakes was complexity of out driving value. And I said, what if, what if you have the easy end and the value out? So if you look at, uh, say snowflake as a, as a warehousing solution, you have to all that prep and data movement to get into that system. And that it's rigid static. Now, Databricks, now that lake house has exact same thing. Now, should they have a data lake philosophy, but their data ingestion is not daily philosophy. So I said, what if we had that simple in with a unique architecture, indexed technology, make it virtually accessible publishable dynamically at petabyte scale. And so our service connects to the customer's cloud storage data, stream the data in set up what we call a live indexing stream, and then go to our data refinery and publish views that can be consumed the lasted API, use cabana Grafana, or say SQL tables look or say Tableau. And so we're getting the benefits of both sides, you know, schema on read, write performance with scheme on, right. Reperformance. And if you can do that, that's the true promise of a data lake, you know, again, nothing against Hadoop, but a schema on read with all that complexity of, uh, software was, uh, what was a little data, swamp >>Got to start it. Okay. So we got to give a good prompt, but everybody I talked to has got this big bunch of spark clusters now saying, all right, this, this doesn't scale we're stuck. And so, you know, I'm a big fan of and our concept of the data lake and it's it's early days. But if you fast forward to the end of the decade, you know, what do you see as being the sort of critical components of this notion of, you know, people call it data mesh, but you've got the analytics stack. Uh, you, you, you're a visionary Thomas, how do you see this thing playing out over the next? >>I love for thought leadership, to be honest, our core principles were her core principles now, you know, 5, 6, 7 years ago. And so this idea of, you know, de centralize that data as a product, you know, self-serve and, and federated, computer, uh, governance, I mean, all that, it was our core principle. The trick is how do you enable that mesh philosophy? We, I could say we're a mesh ready, meaning that, you know, we can participate in a way that very few products can participate. If there's gates data into your system, the CTLA, the schema management, my argument with the data meshes like producers and consumers have the same rights. I want the consumer people that choose how they want to consume that data, as well as the producer publishing it. I can say our data refinery is that answer. You know, shoot, I love to open up a standard, right, where we can really talk about the producers and consumers and the rights each others have. But I think she's right on the philosophy. I think as products mature in this cloud, in this data lake capabilities, the trick is those gates. If you have the structure up front, it gets at those pipelines. You know, the chance of you getting your data into a mesh is the weeks and months that it was mentioning. >>Well, I think you're right. I think the problem with, with data mesh today is the lack of standards you've got. You know, when you draw the conceptual diagrams, you've got a lot of lollipops, which are API APIs, but they're all, you know, unique primitives. So there aren't standards by which to your point, the consumer can take the data the way he or she wants it and build their own data products without having to tap people on the shoulder to say, how can I use this? Where's the data live and, and, and, and, and being able to add their own >>You're exactly right. So I'm an organization I'm generally data will be courageous, a stream it to a lake. And then the service, uh, Ks search service is the data's con uh, discoverable and configurable by the consumer. Let's say you want to go to the corner store? You know, I want to make a certain meal tonight. I want to pick and choose what I want, how I want it. Imagine if the data mesh truly can have that producer of information, you, all the things you can buy a grocery store and what you want to make for dinner. And if you'd static, if you call up your producer to do the change, was it really a data mesh enabled service? I would argue not that >>Bring us home >>Well. Uh, and, um, maybe one more thing with this, cause some of this is we talking 20, 31, but largely these principles are what we have in production today, right? So even the self service where you can actually have business context on top of a debt, like we do that today, we talked about, we get rid of the physical ETL, which is 80% of the work, but the last 20% it's done by this refinery where you can do virtual views, the right our back and do all the transformation need and make it available. But also that's available to, you can actually give that as a role-based access service to your end users actually analysts, and you don't want to be a data scientist or DBA in the hands of a data science. The DBA is powerful, but the fact of matter, you don't have to affect all of our employees, regardless of seniority. If they're in finance or in sales, they actually go through and learn how to do this. So you don't have to be it. So part of that, and they can come up with their own view, which that's one of the things about debt lakes, the business unit wants to do themselves, but more importantly, because they have that context of what they're trying to do instead of queuing up the very specific request that takes weeks, they're able to do it themselves and to find out that >>Different data stores and ETL that I can do things in real time or near real time. And that's that's game changing and something we haven't been able to do, um, ever. Hmm. >>And then maybe just to wrap it up, listen, um, you know, eight years ago is a group of founders came up with the concept. How do you actually get after analytics at scale and solve the real problems? And it's not one thing it's not just getting S3, it's all these different things. And what we have in market today is the ability to literally just simply stream it to S3 by the way, simply do what we do is automate the process of getting the data in a representation that you can now share an augment. And then we publish open API. So can actually use a tool as you want first use case log analytics, Hey, it's easy to just stream your logs in and we give you elastic search puppet services, same thing that with CQL, you'll see mainstream machine learning next year. So listen, I think we have the data lake, you know, 3.0 now, and we're just stretching our legs run off >>Well, and you have to say it log analytics. But if I really do believe in this concept of building data products and data services, because I want to sell them, I want to monetize them and being able to do that quickly and easily, so that can consume them as the future. So guys, thanks so much for coming on the program. Really appreciate it. All right. In a moment, Kevin Miller of Amazon web services joins me. You're watching the cube, your leader in high tech coverage.

>>From the Bellagio hotel in Las Vegas. It's the cube covering UI path forward for brought to you by UI path. >>Hey, welcome back to Las Vegas. Lisa Martin, with Dave Volante, the cube is here, live at UI path forward for very excited to be here in person. Next topic, the smart factory, a couple of guests here to unpack that for us, Jason Brixton joins us the smart factory lead at Deloitte and Thomas Hanson, the CRO of UI path gentlemen, and welcome to the program. Thank you. Thank you for having us great to have you great to be in person. Let's talk about smart track factory factory Ford auto. What is it from Deloitte perspective and then UI path. >>So if you think about smart factory, it's really that transition from the old kind of analog manufacturing environment to the digital, digital operating type environment that we see today. So technology has really changed in the last three or four years. And as a result of that elevation of technology, we're able to do a lot more on the manufacturing floor than we ever could. So what used to be more analog or hybrid with a little bit of technology is now starting to shift really to end to end integrated manufacturing operations that are based on digital platforms and we're loving it. It's a great place to be >>Great. Tell us what's your perspective? >>Well, first of all, it's great to be here. Thank you for the invite. It's so nice to be away from soon calls or, or other type of, uh, of calls, right. And be in person. Uh, look, we have an amazing partnership with the lights. Um, we have worked together for years. We've done more than 400 joint engagements with the large companies across the world. And in that process, we've really gone deeper from a vertical and industry perspective and smart factory is really the starting point of going super specific and figuring out what does automation or how does automation rather play into, um, to a, to a smart factory, like a beautiful trombone, that music from a beautiful trombone. >>So years ago, we wrote a piece talking about the cloud as an opportunity and how to take advantage of it. And one of the, the premise of the piece was you've got to build ecosystems and maybe it's within an industry or within a practice and build data in different disciplines because the power of many versus the capabilities of one, this smart factory initiative that you guys have going, it feels like an ecosystem play. Can you describe that ecosystem? Who's involved? I know SAP in for AWS, but, but tell us more about the ECOS. >>Yeah, sure. So your, your hunch, there is a great one, right? We, we learned early on that trying to do this as Deloitte or Deloitte plus one just, wasn't going to get it done, right? You really needed to harness the power of the many. And so at the, at the core of what we're doing at the smart factory at Wichita, that you alluded to is about bringing an ecosystem to life. So we have 21 partners that are going to be participating out of the gate with the smart factory. Wichitan the intent is to show a seamless solution and actual end-to-end production facility that showcases 21 amazing technologies and partners. And we're just really thrilled about what we're able to show our clients. So, >>Yep. So Koch industries owns Inforce. So obviously that's the Wichita connection, is that right? So they got to be involved in this. I mean, they were amazing company, but what can you tell us about, uh, their, their involvement? >>Yep. So Coke, obviously the in for connection, uh, Dragos, which is another in four company as a founder within, uh, within the ecosystem, which is fantastic. There they play at the core. They're also an incredibly important client, right? So the Coke business on the whole is critical to how we think about manufacturing across a whole range of industries from discreet production to scale process. Um, they're fantastic partners and we've had a great time working with them. And you guys are just, >>It's about to launch through soft launch. Can you tell us more where you are in the progression? >>Sure. So soft launch started two days ago. Oh wow. So the building, we have the keys, uh, we are doing some visits with a handful of friends and family, that ecosystem partners that you mentioned, there'll be coming out, uh, to see it and to provide some feedback. And then we go live in earnest in January >>At Thomas where's UI path fit. >>Well, we fit in essay as a key part in this initiative. Um, look, we, as a company, we are part the preferred partner. First, we do all our business together with partners and we have right about almost 5,000 partners now, globally. And then there's a few, then there's a few in that 5,000 that are unique that really stand out. And Deloitte of course is one of those very, very special partners that we work with globally, but also locally here in the us, across all the states across all the industries. So we're thrilled to be part of this automation plays a key key part of smart factory. When you think about it, the evolution of work there's so much boring, mundane work on there. Humankind is better served, spending their time and effort on the non mundane on the innovative on the creative. And that's what we try to ensure that the humans in the loop so to speak are focused on the innovative work, the graded work, and we have software robots, RPA automation handle all that boring and mundane work, >>Right? Letting the folks focus on the value, add to themselves a value add to the organization, more strategic investments. Thomas question for you is in terms of you talked about this being horizontal across industries, but I'm curious about what some of the feedback is from some of your customers, 8,000 customers. Now you've got a very large what, 726 million ARR, huge lot of customers over a hundred million ARR. What's been the feedback from some of those guys. >>Well, so first of all, uh, personally, I I've been in enterprise software for more than 20 years. And what I've experienced over the years are most large scale enterprise software projects tends to be multi-year in nature, be rather complex. And the failure rate can be rather high. Then in comes RPA and automation, which is a complete different kettle of fish in the sense that from conceptualization of identifying a process, to getting it built, getting it tested, getting it into production, you're talking days and weeks only. So the path to seeing value is so fast. What I've learned yesterday and today from the 1516 customer meetings I've had so far is the same unique trend or learning across all industries and also from various parts of the world. And that is very fast realization of value, perhaps starting initially with 5, 10 20 processes and then scaling super fast because the find that return on investment incredibly quickly with our solution. So that's what unifies it across geographies and across industries. >>What'd you think about the smart factory? And one of the things we've learned during COVID is there's so much unknown. So sometimes these processes aren't linear like a trombone, you know, going back and forth in and out, but is there unknown in the smart factory processes or is it pretty well known? And you can do the process mining on that known base. What's the dynamic >>Back there. So there's a few different dimensions to it. So yes, it is well known because it's a controlled environment, but one of the things that we're doing is we're actually actively introducing a lot of unknown factors to try to let the bots and the process mining kick into effect. Right? So we're artificially, let's just say injecting opportunity for us to do that. The other thing that we're doing is, and what's really unique about the smart factory at Wichita is it's one of four across the globe for Deloitte. And so we're bringing data in from the other three sites, which is data, that'll be less controlled. We're going to do process mining on that. Just try to take advantage of some of the, some of the capabilities associated with the solutions. >>Okay. So, so w when you think about process mining, do you start there, or do you start with, I sometimes call it paving the cow path, you know, taking what you've known, that linear process that, that hit that as the quick win, and then worry about the process money, or do you step back and say, wait a minute, we have to rethink the entire factory experience. Where do you start? >>I think it depends in the case of the smart factory with that, we've got a few different places, so we're using it to do ingestion of orders. So that's obviously a very controlled environment. We're then using it to do a lot of work around inventory management and optimization as well as month end close plays, which will be a lot more we're learning as we go. Right. So I think on the spectrum, it could be on either end my personal belief. If you look at it more long-term or actually out in the real world is that this is all about learning new things. It's about generating insights from data that frankly, you don't want human beings to have to go do that. And so having the ability to take advantage of an intelligent automation solution, as powerful as UI path is really a great advantage. >>One of the things that's misunderstood, I think about UI path is they look at what happened post let's say 2015, 2016, and say, oh, just like, just like every other Silicon valley company, double, double, triple, triple. And that's not how you guys started. You sort of let things bake for the better part of a decade and then got product market fit and then exploded. Um, and so that's, that to me was a key to your success in scaling this. I feel like you guys are building a new offering here. This is not just doing a one-off the product market fit. It's not like a point product. It's a, it's a big thing. So can you talk about the go to market, your product market fit? You're testing it out now, your goals, are you trying to scale this up? What, what are some of the things that you can share about your aspirations? So >>The partnership from a UI path perspective to Deloitte is a critical partnership. One of the select few on a global level, uh, we have enjoyed tremendous, uh, amount of engagements together. I mentioned early on 400, and I believe we, we now have together right about 1000 developers trained within your organization on your iPod, right? That's right. Yep. So we have a strong base that, of course we want to build full and hopefully put a syrup behind the thousand to 10,000. And over time, we want to make sure that it's globally inclusive, that we can serve all the marketers across the world where we have giant presence. And there's a select number of verticals and industries where we really have had success together that we of course want to go and specifically shoe in on what would have caused now be manufacturing together. And of course, a classic vertical we've been very strong in together as BFSI bank and financial services industry. So those are good areas. >>Well, Jason, you're building a business out of this, right? I mean, you've got a business plan around it and you're going to scale this thing. >>Oh, absolutely. Yeah. That's 100% the case. So we have smart factory at Wichita. That is part of our positioning in the marketplace. What we found is that telling people about tech and about solutions is one thing, showing it to them in a production environment is altogether different, right? Giving clients the opportunity to explore the art of the possible in a real setting like that is incredibly impactful. And so you talked about go to market, we see this relationship with the ecosystem and what we're trying to do in Wichita, that's sort of the epicenter of building an entire business, which ultimately will have huge global potential. >>We talk about speed for a minute. And the growth trajectory that UI path Thomas has been on for the last five years or so. I think I was reading, I think it was analysis that Dave wrote that in 2016 revenue was 1,000,020, 20, 15, 20, 20 600 million. So massive growth very quickly. My question, Jason is for you in terms of the speed. Ha how quickly are you looking to see the smart factory for Dato really impacting organizations around the globe because these guys are on a fast bulleted. >>Yeah. So I wish we had those growth rates. I will say though, selling and delivering these solutions holistically to manufacturers takes more time. So we think of our cycle as be measured, certainly in many months, certainly not years. We are starting to see an acceleration of that entire sales cycle and delivery cycle, just because of things like the pandemic driving organizations to just need to move faster. Frankly, if you're not moving towards digital manufacturing operations right now, you're probably behind. And so we're seeing that urgency from the market start to pick up, but we don't have that kind of growth rate, unfortunately. >>Well, what's it. What's interesting about Deloitte to me is you guys here, I think of you as a virtual company. I mean, I know you got a lot of bodies out there, but it's not like you've got a lot of physical locations. Right. And so now, but now you're just, you're investing in a physical plant essentially, >>Which is extremely exciting. We, we keep telling ourselves when we talk to folks, they own lots of buildings. So just because we're excited about our building doesn't mean they are, but you're exactly right, right. We're obviously a global services and products company. So this is one of a handful of buildings that are going to start to represent us as an organization. And we're really excited about what should we watch? >>It's kind of milestones for progress success. What are the markers that we should be paying attention to is independence. >>I think specifically on this, um, rapid experiment together, I think one of the key learnings we can take away that we can apply to other companies in the manufacturing industry specifically look from a UI perspective. We work with many large scale manufacturers around the world, but we've seen amazing fast progress with Bridgestone. For example, we implemented a smaller set of, uh, uh, bots that help them reduce their paperwork by 85% onto their branches with a Turkish e-commerce retailer called Archer. Lik I think I get the pronunciation correctly. They put 85 processes in place with our bots and are now to date transacting or running. I think it's 3 million e-commerce transactions with our processes. So the impact we can have in manufacturing together with the learnings from this, my factory, I think is just so exciting. Really? >>Yeah. The impact, the potential there is, is unlimited. Guys. Thank you for joining David, me talking to us about smart factory Ford auto, what it means for both businesses, how the partnership is evolving. It sounds like music from a beautiful trombone. Thank you so much for joining Dave and me today. Thank you For Dave Volante. I'm Lisa Martin. The Cubas live in Las Vegas at the Bellagio at UI path forward for we'll be right back.

(upbeat music) >> Welcome to AnsibleFest, 2021, the virtual version. This is The Cube and my name is Dave Volante. We're going to dig into automation and its continuing evolution. Tom Anderson is here. He's the vice president of Red Hat Ansible, the automation platform. And Richard Henshall is also here, Senior Manager of Ansible Product Management, of course, at Red Hat. Guys, welcome to the cube. Good to see you. >> Thanks for having us. >> Thank you for having us Dave. You're welcome, so Rich with this latest release of the Ansible Automation Platform, AAP, we'll get the acronyms out of the way. The focus seems to be an expanding the reach of automation and its potential use cases. I mean, I'll say automation everywhere, not to be confused with the RPA vendor, but the point is, you're trying to make it easier to automate things like provisioning, configuration management, application deployment, throw in orchestration and all these other IT processes. Now, you've talked about this theme in previous releases of AAP. So what's new in this release? What can customers do now that they couldn't do before? >> Yeah, it's a good question thank you. So, we look at this in two dimensions. So, the first dimension we have is like where automation can happen, right? So, you know, we always have traditional data center, clouds being been very prevalent for us for the last, you know, sort of five, 10 years in most people's view. But now we have the Edge, right? So now we have Edge computing, which is sometimes a lot more of the same, but also it comes with a different dynamic of how it has to be sort of used and utilized by different use cases, different industry segments. But then, while you expand the use cases to make sure that people can do automation where they need to do it and make sure if we don't close to the Edge or close to the data center, based on where the technology needs to be run, you also have to think about who's now using automation. So, the second dimension is making sure that different users can take access. You mentioned like application deployment, or infrastructure, or network configuration. We expand the number of different users we have that are starting to take advantage of Ansible. So how do we get more developers? How do we get into the developer workflow, into the development workflow, for how Ansible is created, as well as how we help with the operational, the posts deployment stage that people do operating automation, as well as then the running of Ansible Automation Platform itself. >> Excellent, okay. So, in thinking about some of those various roles or personas, I mean, I think about product leads. I would see developers, obviously you're going to be in there. Managers I would think want that view. You know the thrust seems to be, you're trying to continue to enhance the experience, for these personas and others, I suppose, with new tooling. Maybe you could add some color to that and what's happening in the market Tom if you take this and Rich chime in, what's happening in the market that makes this so important? Who are the key roles and personas that you're targeting? >> Yeah. So, there's a couple of things happening here. I mean, traditionally the people that had been using Ansible to automate their subsystems were the domain expert for that subsystem, right? I'm the storage operations team. I'm the network operations team. I'm using this tool to automate the tasks that I do day to day to operate my piece of the sub system. Now, what they're being asked to do is to expose that subsystem to other constituencies in the organization, right? So they had not, they're not waiting for a call to come in to say, can I have a network segment? Can I have this storage allocated to me? Can I deploy these servers so I can start testing or building or deploying my application. Those subsystems need to be exposed to those different audiences. And so the type of automation that is required is different. Now, we need to expose those subsystems in a way that makes those domain owners comfortable. So they're okay with another audience having access to their subsystem. But at the same time, they're able to ensure the governance and compliance around that, and then give that third-party that developer, that QE person, that man, that business, that line of business manager, whoever it might be, that's accessing that resource, a interface that is friendly and easy enough for them to do. It's kind of the democratization. I know it's a cliche, but the democratization of automated automation within organizations, giving them roles, specific experiences, of how they can access these different subsystems and speed their access to these systems and deploy applications. >> So if we could stay on that for a second, cause that's a complicated situation. You're now opening this up. You Richard mentioned the Edge. So you got to make sure that the person that's getting access has access, but then you also have to make sure that that individual can't screw it up, do things that you don't want that individual to do. And it's probably a whole other set of compliance issues and policy things that you have to bake in. Is that, am I getting that right? >> Yeah. And then that's the aspect of it. When you start to think, you know, Tom listed off there, you know, 10, you can just keep adding different sort of personas that individuals that work in roles, identify with as themselves. I'm a network person, I'm a storage person. To us they're all just Ansible users, right? There may be using a slightly different way, maybe using it slightly different places, but they're just an Ansible user, right? And so as you have, like those people that just like become organically, you've now got thousands potentially of Ansible users inside a large enterprise organization, or if you know, a couple of hundred if your smaller. But you're then go, well, what do I do with Ansible, right? And so at that point, you then start to say, now we try to look at it as what's their use of Ansible itself, because it's not just a command line tool. It's got a management interface, it's got analytics, we've got content management, we've got operational runtime, we've got responsiveness to, you know, disaster recovery scenarios for when, you know, when you need to be able to do certain actions, you may use it in different ways at different places. So we start, try and break out, what is the person doing with Ansible Automation Platform at this part of their workflow? Are they creating content, right? Are they consuming content, or are they operating that automation content for those other constituent users that Tom referred to. >> Yeah, that's really helpful because there's context, there are different roles, different personas need different contexts, you know, trying to do different things. Sometimes somebody just wants to see the analytics to make sure it's, you know, hey, everything's green, Oh, we got a yellow, versus, hey actually want to make some changes and I'm authorized to do so. Let's shift gears a little bit and talk about containers. I want to understand how containers are driving change for customers. Maybe what new tools you're providing to support this space? What about the Edge? Yeah, how real is that in terms of tangible pockets or patterns that you can identify that require new types of capabilities that you're delivering? Maybe you can help us unpack that a little bit. >> Okay so, I think there's two ways to look at containers, right? So the first is how are we utilizing the container technology itself, right? So containers are a package, right? So the amount of work we've been doing as Ansible's become more successful in the last couple of years, separating content out with Ansible collections. The ability to bring back manage, control a containerized runtime of Ansible so that you can lifecycle it, you can deploy it, it becomes portable. Edge is important there. How do I make sure I have the same automation running in the data center as the same automation running out on the Edge, if I'm looking at something that needs to be identical. The portability that the packaging of the container gives us, is a fantastic advantage, given you need to bring together just that automation you want. Smaller footprint, more refined footprint, lifecycle manage footprint. But at the same time, containers are also a very useful way of scaling the operation, right? And so as red hat puts things like Open Shift out in all these different locations, how can we leverage those platforms, to push the runtime of Ansible, the execution component, the execution plane of Ansible. How into anywhere that's hospitable for it to run? And as you move out towards Edge, as you move further away from the data center, you need a more ubiquitous sort of like run-time plane that you can put these things on. So they can just spin up when as, and when you need to. Potentially even at the end, actually being on the device, because at the same time with Edge, you also have different limits around how Edge works. It's not just about, hey I'm wifi points in an NFL stadium, actually, you're talking about I'm at the end of a 2000 mile, you know, piece of cable on an oil pipeline or potentially I'm a refinery out in the Gulf of Mexico. You know, you've got a very different dynamic to how you interact with that end point, than you do when it's a nice big controlled network, you know, powered location, which is well-governed and well-orchestrated. >> That's good. Thank you Rich. So Tom, think about automation, you know, back in the day, seems like a long time ago, but it really wasn't, automation used to scare some IT folks, because you know, sometimes it created unintended consequences or maybe it was a cultural thing and that you didn't want to automate themselves out of a job, but regardless. The cloud has changed that mindset, you know, showing us what's possible. You guys obviously had a big role in that, and the pandemic and digital initiatives, they really have made I call it the automation mandate. It was like the fourth March to digital, at least that's how I see it. I wonder if you could talk about, how you see your users approaching automation in as it relates to their business goals. Do you think automation is still being treated sometimes with trepidation or as a side project for some organizations or is it really continuing to evolve as a mainstream business imperative? >> Yes, so Dave we see it continuing to evolve as a strategic imperative for our customers. I mean, you'll, hear some of the keynote folks that are speaking here today. I've done an interview or doing an interview with Joe Mills from Discover, talking about extreme automation throughout Discovers organization. You'll hear representatives from JPMC talk about 22,000 JPMC employees contributing automation content in their environment, across 20 or 22 countries. I mean, just think about that scale, and the number of people that are involved in automation now and their tasks. So I think it's, I think we are, we have moved beyond or are moving beyond that idea that automation is just there to replace people's jobs. And it's much more about automation replacing the mundane, increasing consistency, increasing security, increasing agility, and giving people an opportunity to do more and more interesting stuff. So that's what we hear from our customers, this idea of them building. And it's not just the technology piece, but it's the cultural piece inside organizations where they're building these guilds or communities of practice, bringing people together to share best practices and experience with automation, so that they can feel comfortable learning from others and sharing with others and driving the organization forward. So we see a lot of that, and you'll hear a lot of that, at some of the Ansible Fest sessions this week. >> Well, I mean though I think that's a really important point. The last point you made about the skills, because I think you're right. I think we have moved beyond it's just job replacement. I don't know anybody who loves provisioning LUNs and say, oh, I'm the best in the world at that. It's just kind of something that was maybe important 10, 15, 20 years ago, but today, he should let the machines do that. So that's the whole skills transformation, is obviously a big part of digital transformation. Isn't it? >> It absolutely is. And frankly, we still hear, it's an impediment, that skills shortages are still an impediment to our customer success. They are still skilling up. I mean, honestly, that's one of the differentiators, for Ansible, as a language, a human readable language, that is easy to learn, easy to use, easy to share across an organization. So that's why you see job boards, and whatnot with so many opportunities that require or, or ask for Ansible skills out there. It's just a, it's become sort of a ubiquitous automation language in organizations, because it can be shared across lots of different roles. You don't have to be a Ruby software developer or a Python software developer to create automation with Ansible. You can be Tom Anderson or Rich Henshall. You don't have to, you don't have to be the, you know, the, the sharpest software developer in the world to take advantage of it. So anyway, that's one of the things that kind of overcoming some of the skills apprehension and bringing people into this, into the kind of new environment, of thinking about automation as code, not software code, but thinking of it like code. >> Got it. Guys we've got to leave it there, but Rich, how about you bring us home. We'll give you the last word. >> I mean, I think, you know what Tom just said there I think, about the skills side of things, is I think that the part that made it resonates the most. I mean I was a customer before I joined Red Hat, and trying to get large numbers of people, onto a same path, to try and achieve that outbound objective, that an organization has. The objective of an organization is not to automate, it's to achieve what is needed by what the automation facilitates. So how do we get those different groups to go from, Hey, this is about me, to this is actually about what we're trying to achieve as a business what we're trying to facilitate as a business, and how do we get those people easier access, a reduced barrier of entry to the skills they need to help make that successful, that compliments what they do, in their primary role, with a really strong secondary skill set that helps them do all the bits and pieces they need to do to make that job work. >> That's great, I mean you guys have done a great job, I mean it wasn't clear, you know, decade ago, or maybe half a decade ago, who was going to win this battle. Ansible clearly has market momentum and has become the leader. So guys congratulations on that and good job. Keep it going. I really appreciate your time. >> Thank you. >> Thank you. Thanks. >> Okay. This is the cubes, continuous coverage of Ansible Fest, 2021. Keep it right there for more content that educates and inspires. 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