(electronic music) >> From New York City, it's theCUBE. Covering, New Relic Futurestack 2019. Brought to you by New Relic. >> Hi, I'm Stu Minamin and we're here at New Relic's Futurestack 2019 in the middle of Manhattan. Right next door to Grand Central Station at the Grand Hyatt. Right next door to Grand Central Station at the Grand Hyatt. Happy to welcome to the program, first time guest, Nadya Duke Boone, who's the vice president and general manager of application monitoring here at New Relic. Thanks so much for joining us. >> You're welcome, it's great to be here. >> All right, so, a lot of announcements this morning. Of course, observability front and center Lou talking about how that fits into this space. You have handled really kind of the APM product inside New Relic, so I'm hoping you can help us understand kind of the journey that New Relic's going on. And I've heard in the marketplace, you know, there's AI ops, and there's observability in all of these things. And, you know, APM was the old world for the monolith. So, you know, how does New Relic help live across all of these environments that customers are living in today, and you know, undergoing so much change and new things? >> So as Lou talked about this morning, we think to be an observability platform like New Relic 1, you've got to be open, connected and programmable. That is, we think about that within the application monitoring space, um, we really think it comes down to the matter and issue of like, what are the questions you need to ask. And that really depends on like what stacks you need to see and what are the questions you need to ask. And so, I think it's a false dichotomy to say you need to like, pick a side in observability or monitoring. I think it's really a yes/and. You don't have to pick a side. And with New Relic, what we're able to do whether using our agents and all the rich data they give you or they're using our open platform, the important thing is that we're able to bring it all together in one place. So you can get all your questions answered. >> Yeah, I spent lots of time in my career trying to help break down silos. You know, the traditional infrastructure world, the networking and storage and compute teams. >> Sure >> You know, virtualization helped pull some things together. Software tends to be a unifying factor, but when I look at, you know, the people that own application and the developers. I mean, you've got monoliths, you've got this containerization in microservices coming. You've got the new serverless environments here. You've got a lot of fragmentation inside the customers. How does that impact your business today and are we going to see those, you know, pulled together over time? >> Yeah, what we hear from customers is that, you know, they're going to be running heterogenius environments for a long time. If you're over a year old company, you're not running a single tech stack. You've made choices for your business needs and you need to be able to see across your whole estate. And where New Relic's adding value for our customers, is by bringing this all together and connecting it. So, you can actually see, let's say from a lambda function and our lambda agents, all the way back through your Java monolith and down to the server whether it's running containers or on bare metal, you can see all the way down. And then you can connect it out to you front end as well. And I think it's that ability to see across, is where we're playing. >> All right, uh, can you bring us inside your customers? What are some of the challenges they're facing? And how do you help them along those transformations that they're undergoing? Cause, as you've said, they're going to have this heterogenius environment for quite a long time. >> Yeah, well I think one of the thing they're saying is that they're trying to move faster. And one of the ways they're moving faster is by changing the process by which they build software. So, you know, we've been talking about DevOps for years. We've been talking about Agile for much longer than years. Um, but those changes bring about new needs also, for observability. Cause now, you've got a team that maybe wants to see very deeply with, um, the things they're on call for. But software refuses to break neatly at team boundaries. It just won't, it's going to break wherever it wants to break. So you need to be able to quickly assess, across your whole enterprise what's going on and help those teams talk to you. So, that's definitely a problem we're solving for our customers now. And if I were to pick one more, that I'm hearing, um, well, I'll pick one from this morning and that's cost management, right. As people move to the Cloud, um, its so powerful and easy to be able to start up new services in the Cloud but then, do you know what you have, do you know what is costs, do you know how to optimize? Um, we announced 12 new applications this morning. One of them is addressing exactly that point. >> Yeah, um, okay, what are some of the challenges customers have really monitoring across these different environments? I think cost, it's, well, the promise of Cloud is to help me understand and control my cost quite a bit. But, you know, I understand my data center cost and, in general, much more than I do what I have in the Cloud. >> So, you mean, trying to understand in their software? >> So, I guess, just, if they have these different environments that need to span from a monitoring standpoint what are some of the challenges that customers have and the differences and how does New Relic pull those together for them? >> Well, I think some of it is bringing their teams together. If you've got folks that have a Dev accent and an Ops accent, they may have different points of view about monitoring right? And so, a Dev team might be saying lets go all in on this method or this tool. But an Ops team might be saying something else. And then as you introduce new technologies and maybe now people don't always want to run an agent. They want to have complete visibility over their software. And so, with New Relic, we're giving them those choices. We're giving them, like, hey, you can run an agent, you can, if you've already got stuff at Zipkin, cause maybe, internally, you've got like a great Zipkin champion. Like, great, we're going to be there with you on that too. So, we want to be able to help these teams come together. Um, rather than forcing them to sort of live in silos. >> All right, uh, Lou put a real emphasis talking about platform. And he said platform with a capital 'P'. >> Yeah >> Help us understand a little bit about that and the impact that's going to have for your customers. >> Yeah, absolutely, I think, you know, anyone can say I've got more than one product, therefore I have a platform I think. When we talk about a Platform, we think of software engineers, a Platform is something I can build on. So, I think a capital 'p' Platform is the ability to build apps, to be able to extend it, to be able to add data because you're open. Um, and then the power that we bring, you know, I got to put in my plug, is by connecting it all together. Um, but I think the power of the Platform, um, has been really showing off in the work that we've been doing with our customers to build these new applications. >> All right, um, you mentioned open, which was one of the three features of the Platform itself. Uh, there's open and with API'S and then there's open source can you help us tease through a little bit because there's the openness and then there's some open source pieces. How do those go together and um, I guess, more importantly, what does it mean for the customers? >> Mhmm, thanks for asking, cause I do think those words kind of got tumbled up. So, let's first, let me like tease it apart a little bit. So, first part of open, you sort of already mentioned this, is like, we're open to all data. So, metrics, vents, logs, traces, you can send that data. That's, that's the first thing. You don't have to be running a New Relic agent to use New Relic. The second part though, uh, is that we are actually building and contributing to the open source community software development kits and exporters to make it easy for our customers. And so, we've shipped, we're shipping Open Census and Drop Wizard and Micrometer and exporters and Prometheus scrapers so that these are open source tools that our customers can get, can extend if they need to, to get that data in. So, we're making it easy to get the open data in by providing these open source tools. Um, and we're in there with the communities contributing to the communities as well. And then, finally, you know, the last one is with our new programmable Platform, we are also all in on open source on that. So, we're contributing to open source for folks building on New Relic and our customers are telling us that they're excited to also be able to do that and to share and exchange with each other. >> There's value to the customer and I guess the question is, your relationship with your customer is going to change though. As they're building applications not just, you know, more than just a tool. And I've heard from many of the customers that use New Relic, is, they talk about the partnership. And it really is taking that partnership to the next level. What I say is, New Relic is not coming out and saying oh, we're an open source company and we're building our company around open source. So, you know, it seems that somewhat a maturation of the model but not open source being the be all and end all of New Relic's mission. >> Our mission is to help customers build more perfect software. I mean, that's why we come to work. Is to help them do that and we think this is the right step. Um, to be able to do that and our community around New Relic, as you said, is excited and dynamic. It's great to be here at Futurestack and hear them talking to each other and hear the buzz. I was at our customer advisory board meeting yesterday which is 11 execs from some of our biggest customers and they were talking about how excited they are to see how this is going to help them with their business cause they can connect, now their telemetry data to sort of higher order business problems. Um, and they're also excited to share. So, I think it's the right step for New Relic and our customers. >> There's a lot of startups out there that attack pieces of what New Relic's trying to deliver. Um, you know, how does New Relic look at the landscape out there and the challenge when you're trying to be a platform is, are you providing good enough solutions? Or, you know, are you providing, you know, best solutions across all of these environments? >> Yeah, I think any of our point solutions could go head to head with anything on the market. Um, you know, and the fact that the market is so dynamic is because it's a real problem space for people who are building software. So, folks are going to keep innovating and coming up with new ideas and my mission is to make sure that everyone writing software, is instrumenting it and able to observe it. So I think, I love that more and more folks are joining this conversation. I think it's a great time to be working on monitoring observability. >> Okay, uh, let's start at the top talking a little bit about observability, what should customers be looking at, should they be thinking about that? What feedback are you getting from some of your key customers? Uh, in the space in general and how New Relic's looking to address it? >> Yep, well I think comes down to, a little bit of what we talked about earlier, visibility and answerability and if I were talking to an exec or if I was talking to an engineer, and I was looking at their tools, you know, whatever level you're at and saying, what do you need to monitor how can you get that data in and can you answer the questions? Do you have the tools, the ability to query, to connect the data. Um, to see, hey there's an event that happened and how did my systems change? So I think a lot of it comes down to, is it visible, can I ask the questions? And then for every stack, and no matter what job I'm doing. >> All right, um, when we look at this broad term which gets overused some, but, digital transformation Um, the comment I've made is the long pole in the tent of going through that transformation, really is the application portfolio. You know, I can modernize my platform, I can go to Cloud, but, you know, changing my applications, especially the ones that run my business, is really tough you know. If I'm a company that's been around 15-20 years, you know, I probably have applications that are as old as the company, if not longer. >> Yep. >> Uh, just broadly, how are your customers doing, uh, are they being able to kind of, you know, move along that modernization journey of the application uh, better today than they might have a couple of years ago, or just kind of macro level? >> I think so, I think, you know, between what the Cloud vendors are doing and what we're doing, folks are getting both tools and they're also getting support. I think, you know, the community, the software engineering community is really leaning into this moment. And talking about how to do these types of trasnformations. So I think there's a lot of just, knowledge sharing going on, there's a lot of advice and consulting that you can get. And then I think the tools are lending themselves to being able to do, you know, some people move to the Cloud or lift and shift. Some people use it as an excuse to re-architect. A lot of folks pick and choose. Because not every apps work the same and some apps are, you know, are, um. For some given app, it might be a more relevant time to change it, a more relevant time to let it stay put and you can make those choices. And I think people are approaching it with a certain rational sense. >> Yeah, uh, one last question for you, New Relic's a leader in, according to, the analyst firms that look at the APM market. New Relic's doing a lot of the things that I hear from, you know, the startups getting lots of money thrown at them, so, how should customers think of New Relic today? >> I think, we're the best leading APM product on the market for a reason. And we can never rest our laurel. So I think customers should at us as a trusted partner. Who's going to continue to grow and meet them wherever they are. Our customers are going to Cloud, we want to be there first to meet them there and welcome them in the door. And that comes back to how do we help customers through digital transformation? We're a big software company. We get it, like, we are going through the same, we go through these same questions ourselves. Um, and we talk to our customers all the time. So I think for our customers, it's like, we're the platform and the right partner. Because we're never going to stop. >> Nadya, thank you so much for sharing the updates. Congratulations on the launch today and, uh, best of luck going forward. >> Thanks a bunch. >> All right, lots more here at New Relic Futurestack 2019, I'm Stu Minamin, thanks for watching theCUBE. (electronic music)

>>data by its very nature is distributed and siloed. But most data architectures today are highly centralized. Organizations are increasingly challenged to organize and manage data and turn that data into insights this idea of a single monolithic platform for data, it's giving way to new thinking. We're a decentralized approach with open cloud native principles and Federated governance will become an underpinning underpinning of digital transformations. Hi everybody, this is Day Volonte. Welcome back to HP discover 2021 the virtual version. You're watching the cubes continuous coverage of the event and we're here with Matt Mako is the field C T O for Israel software at H P E. And we're gonna talk about HP software strategy and esmeralda and specifically how to take a I analytics to scale and ensure the productivity of data teams. Matt, welcome to the cube. Good to see you. >>Good to see you again. Dave thanks for having me today. >>You're welcome. So talk a little bit about your role as CTO. Where do you spend your time? >>Yeah. So I spend about half of my time talking to customers and partners about where they are on their digital transformation journeys and where they struggle with this sort of last phase where we start talking about bringing those cloud principles and practices into the data world. How do I take those data warehouses, those data lakes, those distributed data systems into the enterprise and deploy them in a cloud like manner. And then the other half of my time is working with our product teams to feed that information back so that we can continually innovate to the next generation of our software platform. >>So when I remember I've been following HP and HP for a long, long time, the cube is documented. We go back to sort of when the company was breaking in two parts and at the time a lot of people were saying, oh HP is getting rid of the software business to get out of software. I said no, no, no hold on, they're really focusing and and the whole focus around hybrid cloud and and now as a service and so you're really retooling that business and sharpen your focus. So so tell us more about asthma, it's cool name. But what exactly is as moral software, >>I get this question all the time. So what is Israel? Israel is a software platform for modern data and analytics workloads using open source software components. And we came from some inorganic growth. We acquired a company called citing that brought us a zero trust approach to doing security with containers. We bought blue data who came to us with an orchestrator before kubernetes even existed in mainstream. They were orchestrating workloads using containers for some of these more difficult workloads, clustered applications, distributed applications like Hadoop. And then finally we acquired Map are which gave us this scale out, distributed file system and additional analytical capabilities. And so what we've done is we've taken those components and we've also gone out into the marketplace to see what open source projects exist, to allow us to bring those club principles and practices to these types of workloads so that we can take things like Hadoop and spark and Presto and deploy and orchestrate them using open source kubernetes, leveraging Gpu s while providing that zero trust approaches security. That's what Israel is all about. Is taking those cloud practices and principles but without locking you in again using those open source components where they exist and then committing and contributing back to the open source community where those projects don't exist. >>You know, it's interesting. Thank you for that history. And when I go back, I always been there since the early days of big data and Hadoop and so forth. The map are always had the best product. But but they can't get back then. It was like Kumbaya open source and they had this kind of proprietary system, but it worked and that's why it was the best product. And so at the same time they participated in open source projects because everybody that that's where the innovation is going. So you're making that really hard to use stuff easier to use with kubernetes orchestration. And then obviously I'm presuming with the open source chops, sort of leaning into the big trends that you're seeing in the marketplace. So my question is, what are those big trends that you're seeing when you speak to technology executives, which is a big part of what you do? >>Yeah. So the trends I think are a couple of fold and it's funny about Duke, I think the final nails in the coffin have been hammered in with the Hadoop space now. And so that that leading trend of of where organizations are going. We're seeing organizations wanting to go cloud first, but they really struggle with these data intensive workloads. Do I have to store my data in every cloud? Am I going to pay egress in every cloud? Well, what if my data scientists are most comfortable in AWS? But my data analysts are more comfortable in Azure. How do I provide that multi cloud experience for these data workloads? That's the number one question I get asked. And that's the probably the biggest struggle for these Chief Data Officers. Chief Digital Officer XYZ. How do I allow that innovation but maintaining control over my data compliance especially, we talk international standards like G. D. P. R. To restrict access to data, the ability to be forgotten in these multinational organizations. How do I sort of square all of those components and then how do I do that in a way that just doesn't lock me into another appliance or software vendors stack? I want to be able to work within the confines of the ecosystem. Use the tools that are out there but allow my organization to innovate in a very structured, compliant way. >>I mean I love this conversation. And just to me you hit on the key word which is organization. I want to I want to talk about what some of the barriers are. And again, you heard my wrap up front. I I really do think that we've created not only from a technology standpoint and yes, the tooling is important, but so is the organization. And as you said, you know, an analyst might want to work in one environment, a data scientist might want to work in another environment. The data may be very distributed. They maybe you might have situations where they're supporting the line of business. The line of business is trying to build new products. And if I have to go through this, hi this monolithic centralized organization, that's a barrier uh for me. And so we're seeing that change that kind of alluded to it upfront. But what do you see as the big, you know, barriers that are blocking this vision from becoming a reality? >>It very much is organization dave it's the technology is actually no longer the inhibitor here. We have enough technology, enough choices out there. That technology is no longer the issue. It's the organization's willingness to embrace some of those technologies and put just the right level of control around accessing that data because if you don't allow your data scientists and data analysts to innovate, they're going to do one of two things, they're either going to leave and then you have a huge problem keeping up with your competitors or they're gonna do it anyway, and they're gonna do it in a way that probably doesn't comply with the organizational standards. So the more progressive enterprises that I speak with have realized that they need to allow these various analytical users to choose the tools, they want to self provision those as they need to and get access to data in a secure and compliant way. And that means we need to bring the cloud to generally where the data is because it's a heck of a lot easier than trying to bring the data where the cloud is while conforming to those data principles. And that's, that's Hve strategy, you've heard it from our CEO for years now, everything needs to be delivered as a service. It's essential software that enables that capability, such as self service and secure data provisioning, etcetera. >>Again, I love this conversation because if you go back to the early days of the Duke, that was what was profound about. Do bring bring five megabytes of code, do a petabyte of data and it didn't happen. We shoved it all into a data lake and it became a data swamp. And so it's okay, you know, and that's okay. It's a one dato maybe maybe in data is is like data warehouses, data hubs data lake. So maybe this is now a four dot Oh, but we're getting there. Uh, so an open but open source one thing's for sure. It continues to gain momentum. It's where the innovation is. I wonder if you could comment on your thoughts on the role that open source software plays for large enterprises. Maybe some of the hurdles that are there, whether they're legal or licensing or or or just fears. How important is open source software today? >>I think the cloud native development, you know, following the 12 factor applications microservices based, pave the way over the last decade to make using open source technology tools and libraries mainstream, we have to tip our hats to red hat right for allowing organizations to embrace something. So core is an operating system within the enterprise. But what everyone realizes that its support, that's what has to come with that. So we can allow our data scientists to use open source libraries, packages and notebooks. But are we going to allow those to run in production? And so if the answer is no, then that if we can't get support, we're not going to allow that. So where HP es Merrill is taking the lead here is again embracing those open source capabilities, but if we deploy it, we're going to support it or we're going to work with the organization that has the committees to support it. You call HPD the same phone number you've been calling for years for tier 1 24 by seven support and we will support your kubernetes, your spark your presto your Hadoop ecosystem of components were that throat to choke and we'll provide all the way up to break fix support for some of these components and packages giving these large enterprises the confidence to move forward with open source but knowing that they have a trusted partner in which to do so >>and that's why we've seen such success with, say, for instance, managed services in the cloud or versus throwing out all the animals in the zoo and say, okay, figure it out yourself. But of course what we saw, which was kind of ironic was we, we saw people finally said, hey, we can do this in the cloud more easily. So that's where you're seeing a lot of data. A land. However, the definition of cloud or the notion of cloud is changing no longer. Is it just this remote set of services somewhere out there? In the cloud? Some data center somewhere. No, it's, it's moving on. Prem on prem is creating hybrid connections you're seeing, you know, co location facility is very proximate to the cloud. We're talking now about the edge, the near edge and the far edge deeply embedded, you know? And so that whole notion of cloud is, is changing. But I want to ask you, there's still a big push to cloud, everybody is a cloud first mantra. How do you see HP competing in this new landscape? >>I I think collaborating is probably a better word, although you could certainly argue if we're just leasing or renting hardware than it would be competition. But I think again, the workload is going to flow to where the data exists. So if the data is being generated at the edge and being pumped into the cloud, then cloud is prod, that's the production system. If the data is generated, the on system on premises systems, then that's where it's going to be executed, that's production. And so HBs approach is very much coexist, coexist model of if you need to do deaf tests in the cloud and bring it back on premises, fine or vice versa. The key here is not locking our customers and our prospective clients into any sort of proprietary stack, as we were talking about earlier, giving people the flexibility to move those workloads to where the data exists. That is going to allow us to continue to get share of wallet. Mindshare, continue to deploy those workloads and yes, there's going to be competition that comes along. Do you run this on a G C P or do you run it on a green lake on premises? Sure. We'll have those conversations. But again, if we're using open source software as the foundation for that, then actually where you run it is less relevant. >>So a lot of, there's a lot of choices out there when it comes to containers generally and kubernetes specifically, uh, you may have answered this, you get zero trust component, you've got the orchestrator, you've got the, the scale out, you know, peace. But I'm interested in hearing in your words why an enterprise would or should consider s morale instead of alternatives to kubernetes solutions? >>It's a fair question. And it comes up in almost every conversation. We already do kubernetes, so we have a kubernetes standard and that's largely true. And most of the enterprises I speak to their using one of the many on premises distributions of the cloud distributions and they're all fine. They're all fine for what they were built for. Israel was generally built for something a little different. Yes, everybody can run microservices based applications, devoPS based workloads, but where is Meryl is different is for those data intensive and clustered applications. Those sort of applications require a certain degree of network awareness, persistent storage etcetera, which requires either a significant amount of intelligence. Either you have to write in go lang or you have to write your own operators or Israel can be that easy button. We deploy those state full applications because we bring a persistent storage later that came from that bar we're really good at deploying those stable clustered applications and in fact we've open sourced that as a project cube director that came from Blue data and we're really good at securing these using spiffy inspire to ensure that there is that zero trust approach that came from side tail and we've wrapped all of that in kubernetes so now you can take the most difficult, gnarly, complex data intensive applications in your enterprise and deploy them using open source and if that means we have to coexist with an existing kubernetes distribution, that's fine. That's actually the most common scenario that I walk into is I start asking about what about these other applications you haven't done yet? The answer is usually we haven't gotten to him yet or we're thinking about it and that's when we talk about the capabilities of s role and I usually get the response, oh, a we didn't know you existed and be, well, let's talk about how exactly you do that. So again, it's more of a coexist model rather than a compete with model. Dave >>Well, that makes sense. I mean, I think again, a lot of people think, oh yeah, Kubernetes, no big deal, it's everywhere. But you're talking about a solution, I'm kind of taking a platform approach with capabilities, you've got to protect the data. A lot of times these microservices aren't some micro uh and things are happening really fast, You've got to be secure, you've got to be protected. And like you said, you've got a single phone number, you know, people say one throat to choke, Somebody said the other day said no, no single hand to shake, it's more of a partnership and I think that's a proposed for HPV met with your >>hair better. >>So you know, thinking about this whole, you know, we've gone through the pre big data days and the big data was all, you know, the hot buzz where people don't maybe necessarily use that term anymore, although the data is bigger and getting bigger, which is kind of ironic. Um where do you see this whole space going? We've talked about that sort of trends are breaking down the silos, decentralization. Maybe these hyper specialized roles that we've created maybe getting more embedded are lined with the line of business. How do you see it feels like the last, the next 10 years are going to be different than the last 10 years. How do you see it matt? >>I completely agree. I think we are entering this next era and I don't know if it's well defined, I don't know if I would go out on an edge to say exactly what the trend is going to be. But as you said earlier, data lakes really turned into data swamps. We ended up with lots of them in the enterprise and enterprises had to allow that to happen. They had to let each business unit or each group of users collect the data that they needed and I. T. Sort of had to deal with that down the road. And so I think the more progressive organizations are leading the way they are again taking those lessons from cloud and application developments, microservices and they're allowing a freedom of choice there, allowing data to move to where those applications are. And I think this decentralized approach is really going to be king. And you're gonna see traditional software packages, you're gonna see open source, you're going to see a mix of those. But what I think we'll probably be common throughout all of that is there's going to be this sense of automation, this sense that we can't just build an algorithm once released and then wish it luck that we've got to treat these these analytics and these these data systems as living things that there's life cycles that we have to support, which means we need to have devops for our data science. We need a ci cd for our data analytics. We need to provide engineering at scale like we do for software engineering. That's going to require automation and an organizational thinking process to allow that to actually occur. And so I think all of those things that sort of people process product, but it's all three of those things are going to have to come into play. But stealing those best ideas from cloud and application development, I think we're going to end up with probably something new over the next decade or so >>again, I'm loving this conversation so I'm gonna stick with it for a second. I it's hard to predict, but I'll some takeaways that I have matt from our conversation. I wonder if you could, you could comment. I think, you know, the future is more open source. You mentioned automation deV's are going to be key. I think governance as code, security designed in at the point of code creation is going to be critical. It's not no longer to be a bolt on and I don't think we're gonna throw away the data warehouse or the data hubs or the data lakes. I think they become a node. I like this idea and you know, jim octagon. But she has this idea of a global data mesh where these tools lakes, whatever their their node on the mesh, they're discoverable. They're shareable. They're they're governed uh in a way and that really I think the mistake a lot of people made early on in the big data movement, Oh we have data, we have to monetize our data as opposed to thinking about what products that I can I build that are based on data that then I can, you know, can lead to monetization. And I think and I think the other thing I would say is the business has gotten way too technical. All right. It's an alienated a lot of the business lines and I think we're seeing that change. Um and I think, you know, things like Edinburgh that simplify that are critical. So I'll give you the final thoughts based on my rent. >>I know you're ready to spot on. Dave. I think we we were in agreement about a lot of things. Governance is absolutely key. If you don't know where your data is, what it's used for and can apply policies to it, it doesn't matter what technology throw at it, you're going to end up in the same state that you're essentially in today with lots of swamps. Uh I did like that concept of of a note or a data mesh. It kind of goes back to the similar thing with a service smashed or a set of a P I is that you can use. I think we're going to have something similar with data that the trick is always how heavy is it? How easy is it to move about? And so I think there's always gonna be that latency issue. Maybe not within the data center, but across the land, latency is still going to be key, which means we need to have really good processes to be able to move data around. As you said, government determine who has access to what, when and under what conditions and then allow it to be free, allow people to bring their choice of tools, provision them how they need to while providing that audit compliance and control. And then again, as as you need to provision data across those notes for those use cases do so in a well measured and govern way. I think that's sort of where things are going. But we keep using that term governance. I think that's so key. And there's nothing better than using open source software because that provides traceability, the audit ability and this frankly openness that allows you to say, I don't like where this project is going. I want to go in a different direction and it gives those enterprises that control over these platforms that they've never had before. >>Matt. Thanks so much for the discussion. I really enjoyed it. Awesome perspectives. >>Well, thank you for having me. Dave are excellent conversation as always. Uh, thanks for having me again. >>All right. You're very welcome. And thank you for watching everybody. This is the cubes continuous coverage of HP discover 2021 of course, the virtual version next year. We're gonna be back live. My name is Dave a lot. Keep it right there. >>Yeah.

>>Hello, and welcome to this cube conversation. I'm Sean for, with the cube here in Palo Alto, California, actually coming out of the pandemic this year. Hopefully we'll be back to real life soon. Uh it's uh, in March, shouldn't it be? April spring, 2021. Got a great guest Chris Lynch, who is executive chairman, CEO of scale, who took over at the helm of this company about two and a half years ago, or so, um, lots of going on Chris. Great to see you, uh, remotely, uh, in Boston, we're here in Palo Alto. Great to see you. >>Great to see you as well, but hope to see you in person, this sprint. >>Yeah. I got to say people really missing real life. And I started to see events coming back to vaccines out there, but a lot going on. I mean, Dave and I Volante, I was just talking about how, um, you know, when we first met you and big data world was kicking ass and taking names a lot's changed at Duke went the way it went. Um, you know, Vertica coming, you led, did extremely well sold. HP continue to be a crown jewel for HPE. Now the world has changed in the data and with COVID more than ever, you starting to see more and more people really doubling down. You can see who the winners and losers are. You starting to see kind of the mega trend, and now you've got the edge and other things. So I want to get your take at scale, took advantage of that pivot. You've been in charge. Give us the update. What's the current strategy of that scale? >>Sure. Well, when I took the company over about two and a half years ago, it was very focused on accelerating the dupe instances. And, uh, as you mentioned earlier, the dupe is sort of plateaued, but the ability to take that semantic layer and deliver it in the cloud is actually even more relevant with the advent of snowflake and Databricks and the emergence of, uh, Google big query, um, and Azure as the analytic platforms, in addition to Amazon, which obviously was, was the first mover in the space. So I would say that while people present big day in as sort of a passing concept, I think it's been refined and matured and companies are now digitizing their environment to take advantage of being able to deliver all of this big data in a way that, um, they could get actionable insights, which I don't think has been the case through the early stages of the development of big data concepts. >>Yeah, Chris, we've always followed your career. You've been a strong operator, but also see things a little bit early, get on the wave, uh, and help helps companies turn around also on public, a great career. You've had, I got to ask you in your opinion and you, and you can make sense for customers and make sure customers see the value proposition. So I got to ask you in this new world of the semantic layer, you mentioned snowflake, Amazon and cloud scales. Huge. Why is the semantic layer important? What is it and why is it important for customers? What are they really buying with this? >>Well, they're buying a few things, the buying freedom and choice because we're multicloud, um, they're, they're buying the ability to evolve their environments versus your evolution versus revolution. When they think about how they move forward in the next generation of their enterprise architecture. And the reason that you need the semantic layer, particularly in the cloud is that we separate the source from the actual presentation of the data. So we allow data to stay where it is, but we create one logical view that was important for legacy data workloads, but it's even more important in a world of hybrid compute models in multi-vendor cloud models. So having one source of truth, consistency, consistent access, secure access, and actual insights to wall, and we deliver this with no code and we allow you to turbocharge the stacks of Azure and Amazon Redshift and Google big query while being able to use the data that you've created your enterprise. So, so there's a demand for big data and big data means being able to access all your data into one logical form, not pockets of data that are in the cloud that are behind the firewall that are constrained by, um, vendor lock-in, but open access to all of the data to make the best decisions. >>So if I'm an enterprise and I'm used to on-premise data warehouses and data management, you know, from whether it's playing with a dupe clusters or whatever, I see snowflake, I see the cloud scale. How do I get my teams kind of modernized if you had to kind of go in and say, cause most companies actually have a hard time doing that. They're like they got to turn their existing it into cloud powerhouses. That's what they want to do. So how do you get them there? What's the secret in your opinion, to take a team and a company that's used to doing it on prem, on premises to the cloud? >>Sure. It's a great question. So as I mentioned before, the difference between evolution and revolution today, without outscale to do what you're suggesting is a revolution. And you know, it's very difficult to perform heart surgery on the patient while he's running the Boston marathon. And that's the analog I would give you for trying to digitize your environment without this semantic layer that allows you to first create a logical layer, right? This information in a logical mapping so that you can gradually move data to the appropriate place. Without us. You're asked to go from, you know, one spot to another and do that while you're running your business. And that's what discourages companies or creates tremendous risk with digitizing your environment or moving to cloud. They have to be able to do it in a way that's non-disruptive to their business and seamless with respect to their current workflows. >>No, Chris, I got to ask you without, I know you probably not expecting this question, but um, most people don't know that you are also an investor before you as CEO, um, angel investor as well. You did an angel investment deal with a chemical data robot. We've had a good outcome. And so you've seen the wave, you've seen a kind of how the progress, you mentioned snowflake earlier. Um, as you look at those kinds of deals, as they've evolved, you know, you're seeing this acceleration with data science, what's your take on this because you know, those companies that have become successful or been acquired that you've invested in now, you're operating at scale as a company, you got to direct the company into the right direction. Where is that? Where are you taking this thing? >>Sure. It's a great, great question. So with respect to AI and ML and the investment that I made almost 10 years ago and data robot, um, I believe then, and I believe now more than ever that AI is going to be the next step function in industrial productivity. And I think it's going to change, you know, the composition of our lives. And, um, I think I have enough to have been around when the web was commercialized in the internet, the impact that's having had on the world. I think that impact pales in comparison to what AI, the application of AI to all walks of life has gone going to do. Um, I think that, um, within the next 24 months companies that don't have an AI strategy will be shorted on wall street. I think every phone, every, every vertical function in the marketplace is going to be impacted by AI. >>And, um, we're just seeing the infancy of mass adoption application when it comes to at scale. I think we're going to be right in the middle of that. We're about the democratization of those AI and machine learning models. One of the interesting things we developed it, this ML ops product, where we're able to allow you with your current BI tool, we're able to take machine learning models and just all the legacy BI data into those models, providing better models, more accurate, and precise models, and then re publish that data back out to the BI tool of your choice, whether it be Tableau, Microsoft power, BI Excel, we don't care. >>So I got to ask you, okay, the enterprises are easy targets, large enterprises, you know, virtualization of the, of this world that we're living with. COVID virtualization being more, you know, virtual events, virtual meetings, virtual remote, not, not true virtualization, as we know it, it virtualization, but like life of virtualization of life companies, small companies like the, even our size, the cube, we're getting more data. So you start to see people becoming more data full, not used to dealing with data city mission. They see opportunities to pivot, leverage the data and take advantage of the cloud scale. McKinsey, just put out a report that we covered. There's a trillion dollars of new Tam in innovation, new use cases around data. So a small company, the size of the cube Silicon angle could be out there and innovate and build a use case. This is a new dynamic. This is something that was seen, this mid-market opportunity where people are starting to realize they can have a competitive advantage and disrupt the big guys and the incumbents. How do you see this mid market opportunity and how does at-scale fit into that? >>So you're as usual you're spot on John. And I think the living breathing example of snowflake, they brought analytics to the masses and to small and medium enterprises that didn't necessarily have the technical resources to implement. And we're taking a page out of their book. We're beginning to deliver the end of this quarter, integrated solutions, that map SME data with public markets, data and models, all integrated in their favorite SAS applications to make it simple and easy for them to get EnLink insight and drive it into their business decisions. And we think we're very excited about it. And, you know, if, if we can be a fraction, um, if we can, if we get a fraction of the adoption that snowflake has will be very soon, we'll be very successful and very happy with the results this year. >>Great to see you, Chris, I want to ask you one final question. Um, as you look at companies coming out of the pandemic, um, growth strategies is going to be in play some projects going to be canceled. There's pretty obvious, uh, you know, evidence that, that has been exposed by working at remote and everyone working at home, you can start to see what worked, what wasn't working. So that's going to be clear. You're gonna start to see pattern of people doubling down on certain projects. Um, at scales, a company has a new trajectory for folks that kind of new the old company, or might not have the update. What is at scale all about what are what's the bumper sticker? What's the value proposition what's working that you're doubling down on. >>We want to deliver advanced multi-dimensional analytics to customers in the cloud. And we want to do that by delivering, not compromising on the complexity of analytics, um, and to do that, you have to deliver it, um, in a seamless and easy to use way. And we figure out a way to do that by delivering it through the applications that they know and love today, whether it be their Salesforce or QuickBooks or you name, the SAS picked that application, we're going to turbocharge them with big data and machine learning in a way that's going to enhance their operations without, uh, increase the complexity. So it's about delivering analytics in a way that customers can absorb big customers and small customers alike. >>While I got you here, one final final question, because you're such an expert at turnarounds, as well as growing companies that have a growth opportunity. There's three classes of companies that we see emerging from this new cloud scale model where data's involved or whatever new things out there, but mainly data and cloud scale. One is use companies that are either rejuvenating their business model or pivoting. Okay. So they're looking at cost optimization, things of that nature, uh, class number two innovation strategy, where they're using technology and data to build new use cases or changed existing use cases for kind of new capabilities and finally pioneers, pioneering new net, new paradigms or categories. So each one has its own kind of profile. All, all are winning with data as a former investor and now angel investor and someone who's seen turnarounds and growing companies that are on the innovation wave. What's your takeaway from this because it's pretty miraculous. If you think about what could happen in each one of those cases, there's an opportunity for all three categories with cloud and data. What's your personal take on that? >>So I think if you look at, um, ways we've seen in the past, you know, particularly the, you know, the internet, it created a level of disruption that croup that delivered basically a renewed, um, playing field so that the winners and losers really could be reset and be based on their ability to absorb and leverage the new technology. I think the same as an AI and ML. So I think it creates an opportunity for businesses that were laggerts to catch, operate, or even supersede the competitors. Um, I think it has that kind of an impact. So from my, my view, you're going to see as big data and analytics and artificial intelligence, you know, mature and coalesce, um, vertical integration. So you're going to see companies that are full stack businesses that are delivered through AI and cloud, um, that are completely new and created or read juvenile based on leveraging these new fundamentals. >>So I think you're going to see a set of new businesses and business models that are created by this ubiquitous access to analytics and data. And you're going to see some laggerts catch up that you're going to see some of the people that say, Hey, if it isn't broke, don't fix it. And they're going to go by the wayside and it's going to happen very, very quickly. When we started this business, John, the cycle of innovation was five it's now, you know, under a year, maybe, maybe even five months. So it's like the difference between college for some professional sports, same football game, the speed of the game is completely different. And the speed of the game is accelerating. >>That's why the startup actions hot, and that's why startups are going from zero to 60, if you will, uh, very quickly, um, highly accelerated great stuff. Chris Lynch veteran the industry executive chairman CEO of scale here on the cube conversation with John furrier, the host. Thank you for watching Chris. Great to see you. Thanks for coming on. >>Great to see you, John, take care. Hope to see you soon. >>Okay. Let's keep conversation. Thanks for watching.

>>Good morning. Good afternoon. And good evening. I am Kevin Duke with HPE GreenLake cloud services and welcome to the HPE GreenLake per private cloud session. I enjoined today by Raj mystery and Steve show Walter, who will walk us through today's presentation and demonstration. We'd like to keep this session interactive. So please submit your questions in the chat window. We have subject matter experts on the line to answer your questions. So with that, I'll hand over to Raj Kilz. Thanks Kevin. So cloud is now fast becoming a reality. We says HPE and what our customers say to is that it's not an expectation anymore. It's an absolute necessity. So the research and the stats that you see on the screen, just kind of like prove that over the last five to six years, organizations, enterprises are adopting cloud. Be it in the data center with the hyperscalers are a mixture of both. >>But the interesting thing that we see now is a moving investment to basically increase private cloud capability. Uh, and in that vein, what we've done with Greenlight cloud services is create a rich portfolio that delivers that cloud-like experience either at the edge in your data center, co-location the actually matches and actually embraces the work that you do with the hyperscalers. What we're doing here is we're providing self-service capability elasticity in the means and the way that you would use this and the way that you would flex things open down more importantly, all of this is one and operated for you, which comes true to what we say in terms of delivering that cloud experience within those locations, being at the edge, the data center or the Colwell, um, Greenlight for private cloud was initially launched, uh, in summer 2020, it was the first iteration of what we call the Greenlight platforms. >>What we're trying to do with this element of the Greenlight cloud services portfolio is four things which is eliminate the complexity of building things that live, breathe, behave, and act like in a cloud-like manner in the data center, because this is hard. Yeah, the visibility around the way that you would manage and understand and run and operate certain elements of that cloud, uh, the governance and the compliance pace, which is important, especially when it comes to things like applying policies, et cetera, that you would have. And then the skills gap that we do from our managed services perspective, that takes things off. So from an infrastructure standpoint, beginning of the left, well class HPE compute storage networks, which is embracing the virtualization and the software defined networking layer together with a pretty rich cloud automation and orchestration portal all wrapped up for you. Pre-built pre architected, removing complexity, increasing time to value, uh, and, and, uh, the, the actual delivery timescales, if we move to the actual experience, although this is actually, uh, embracing the way that you would access these, uh, solutions. >>So in, through GreenLake central, uh, that's where your other service experience begins with HPE is your entry point into the world of as a service from here on which you would actually access that service. So from a, from a private cloud standpoint, this is where you would initiate the cloud management portal. And then you would begin either working in that and the roles of either administrator, consumer, et cetera. Lastly, you know, pushing buttons and provisioning stuff is really easy, but a lot of our focuses is in the pulse provisioning processes, understanding is it turned on? Is it off? How much is it costing me? Am I getting the most efficiency out of it? Am I running out of capacity to deliver services to my users? All of this is finally wrapped up with the managed platform capability, which means you now have to understand and treat Hewlett-Packard enterprise as an MSP and a cloud provider within our data center. We take care of the infrastructure, the software, and the experience, your entry point is that the cloud management layer, that's how we get you going. >>Hey, Roger, I know we made some announcements earlier today about a new scalable form factor version of private cloud. I was wondering if maybe you could talk about how that extends the value proposition for customers >>Question Steve. Um, so what the scalable form factor is really is it's also looking at market feedback, understanding of what our customers are bringing this as a entry point into the smaller and the more medium enterprise who are looking to deliver private cloud capabilities, you know, making it easier for them to embrace it and then scale. The other differences is the way that we actually have flexibility in the way that that cloud solution now grows. So different options, uh, available to customers in terms of what they want to do. We typically talk as a team and to our customers about different roles. So we have a notion of the cloud administrator or the cloud operator. This is more of your classic kind of like administrative role. So this is where our customers would come in, right at the cloud management layer and begin configuring their EMR environment, networking services, et cetera, from here, onwards is the cloud consumer. >>So applications, line, application, developers, lines of business, et cetera, they're presented with a self-service catalog for them to come and provision stuff. It could be normal VMs. It could be kind of like applications depending on what the administrators or the operators of Charles and to present to them. Lastly, it's around, how do I understand what's going on in the environment? So the focus, as I mentioned beforehand, visibility to them understand what's happening to then optimize later. So addressing the needs of lines of, or it leaders and business leaders within our customer base, although this then begins from our central point of access, which is GreenLake central from here, services and solutions that customers subscribe to are presented, depending on who you are, your privileges, your role within the actual environment, you get different options. So a cloud administrator may see different things in central because they require administrative functions within the cloud environment. I consume it such as me may have limited views in central access a service, but I can basically only read our provision set, things that are done for me from a lines of business or from an it leadership perspective. It's about providing predictive billing visibility into cost, understanding from a planning standpoint and allowing people to optimize it, ease and speed. So central is where your journey begins. And then from within there, you launch the necessary service like you're subscribed to today's focus is the private cloud. >>So who is this a solution built for Raj? Uh, initially we started off at the large enterprise level, Kevin, but what we've done as we did as HPE has listened to our customers. So we've reintroduced on them. We're launching today, the scalable form factor to address the needs of a multitude of clients, both large and small and enable people to have different kind of like deployment types. So remote office branch office for the larger customers, and for those smaller enterprises wanting to begin their private cloud journey, a great way for them to do that with HPE. >>All right. Thank you. >>Um, how does the customer access the private cloud environment, uh, by agreeing like central Kevin? Great question. That's our entry point for any of the services? Uh, easier to see later on in the demo that Steve's going to walk through, uh, it's where customers come in and depending on role access, privilege, rights, et cetera, you are presented with your services. And from within that you access the service depending on the role that's been assigned to you. So state, why don't you show us a little bit about what a cloud administrator or a cloud operator can do within the environment? Sure. Happy to rush. As we talked to these personas are use cases. You know, our experience, as Raj mentioned, will always start in GreenLake central. So the role or persona I'm taking on here is that of an administrator of this private cloud environment. So again, I start off by logging into GreenLake central. Once this stood up, services stood up and available, uh, within my data center, I see the green Lake for private cloud tile, which gives me an overview of services I'm consuming. And some of the things that might be running in that environment, clicking on the tile, takes me to the cloud management platform dashboard. This is where I, as an administrator can configure and control lots of things in the environment on behalf of my end users. So a couple of examples of things I might want to do first off. Uh, there's an important >>Notion of grouping that we use for access control within the environment. So I may want to organize my users into groups to control what they can see, what they can do, what sort of policies I can apply to them next. I probably want to configure the underlying software defined network that Raj talked about. So again, we deliver a software defined networking capability from within the software defined network. This is where I can create things like underlying networks, underlying distributed V switches at an IP address pools. I can also configure and manage software defined routers, firewall rules, and some of those sorts of things within the environment, uh, in the IP address pools I have that I want to make available to some of those underlying networks I could manage from within here as well. We also feature software to find load balancing capabilities. So again, if I expect to my developers or my end users, to be able to provision resources that require some load balancing, I can create those load balancers define the types of load balancing I want to make available to those end users from within here as well. >>Finally, I can manage keys and certificates. So if I have things like key payers or SSL certificates, again, that I want to make available to my end users, um, I could manage all that from within here. And then one of the final things I might want to do is start to manage a, an automation library. So a library of virtual images, I don't want to make available for, for my end users because the private cloud solution is based on VMware. I might want to just pull in some existing VMware images. I have, I might want to create some new custom images, but really I have a central place to be able to manage that library of images and then, you know, decide who has access to which images and how I want to make those available to end users users to be able to provision and lifecycle manage. >>So that's a quick overview of some of the administrative capabilities, uh, Kevin, any questions at this point about that capability? I got one for you customers bring their own tooling to the private cloud. Oh yeah. So that's a great question. So, you know, almost every customer I talk to nowadays has made a large investment, typically in some sort of automation tooling. And one of the things that we want to provide is the ability to surface that tooling and kind of allow customers to be able to reuse that tooling within our private cloud environment. So within the private cloud platform, as an administrator, I can create all sorts of scripts and, you know, maybe some basic capabilities I wanted to find for scripts, but they also have the ability to integrate automation platforms. So we can see in this particular environment, I've, I've onboarded a, uh, a set of Ansible playbooks that exist in a get repo. Uh, I really just point the cloud management platform to that repo it scrapes all the playbooks that it finds there and those become available as tasks and workflows that I can use, uh, after I provisioned BM. So again, I can reuse that investment that I've made in automating things like application provisioning, application configuration for my end users within my environment. >>I've got another one for you. Uh, how do customers improve control and governance of their private cloud? Yeah. So there are a couple of different ways to do that. So, you know, we'll talk specifically. One of the capabilities I have within, uh, the cloud management platform is the ability to create, uh, policies. Policies are really a way I can provide my users with, you know, self-service access to kind of go do the things that they need to do, but provide some control around what they can do. So there's all sorts of policies I can create. So policies around things like if I've got certain group of users that I want to require to get provision approval, anytime they approve provision something, I wanted an administrator to approve it. I can also limit the things that maybe a group of consumers of consumers can consume within my environment. Maybe I want to define a certain host name rule. So rather than create your own host names, I have a rule I want applied. Um, if tagging and showback is important, I might want to force some tags within my environment, say, Hey, anybody who provisioned something needs to provide me a value for this tag. And then I can define how that applies within the environment. So hopefully that answers some questions and gives you a feel of how these cloud administrators would work within the environment, TB to be able to manage the overall environment itself. >>Perfect. Thanks Steve. So what we've just described in seeing is, is the ability for a cloud administrator to a do day one tasks, set things up, set some services off and more importantly, apply some rules, controls, and governance. So it keeps users safe and it keeps it happy. Really. So let's say I'm Raj, I'm the head of applications and I've got a team of developers. So I'm now going to come in as a consumer. Can you show me what I can do as a consumer pleaser? >>Sure. Raj. So again, just like with the administrative use case, we talked about as a, as a cloud consumer, my experience starts in GreenLake central. So once I'm logged into GreenLake central, if I've been provided access to the environment by my administrators, I see the green Lake for private cloud tile, and I click on it to get to the cloud management platform. Just like the administrator you use. Now, I probably see a lot less because I probably have a lot less capabilities here, but one of the first places I'd probably want to go is take a look at what instances have been provisioned and maybe provision an instance on my own. So, you know, instance provisioning is very simple. Really. It's just a few clicks and answer a few questions. Uh, so in this case, if I have access to multiple groups, so kind of that logical separation that I talked about, I'd first pick, you know, which group is this a part of? >>Uh, again, in my particular case, I can provide a freeform name because that's the policy that's been set up for me. Um, I've got a forced tag, right? So I have to provide a tag or a label that tells me what, uh, what, what area this is a part of. And as I continue to drill down, now I get to a point where I can select my image based on the images that have been made, made available to me. Um, I can choose a size of a VM. So we have sort of some pre-provision sizes that might administrators have made available to me. And in some cases I can customize some things within those sizes, or maybe I can't, again, just depending on how this was created, select the network that I want to connect to, uh, and provide a few other options. One, the things I do want to talk about is this notion of tagging tagging is very important from a showback perspective. >>And we'll talk about when we get to cost analysis, how we can use any tags that get applied here to be able to do some show back reporting later. So if I want to provide a tag for an owner to make sure I can always write a report that says, show me everything that Steve has consumed. I've got the ability to provide those tags here. And again, through a policy, I can make those tags required. A couple of other choices. I have any of that available automation that maybe my administrators have made available. I can run here. I can select some scaling of my application, maybe go ahead and auto select the backup schedule, manage some lifecycle actions if maybe this VM only needs to run during weekdays. And I don't need it on the weekends. I can have it automatically shut down and start up. >>And at the end, just click on complete. Uh, and my VM is often being built. And then, you know, once my VMs are up and running, I've got access to be able to manage those VMs on a running basis. So, you know, if I have a VM that's running and I want to be able to manage it very simple again, from within the cloud management platform to go take a look at maybe how this VM is performing, maybe I want to log into the console. Maybe I want to take a look at the log stash that, you know, the log log error messages that this VM has created, or maybe I just want to stop it, start, it can create an image from it, or maybe, you know, after I've provisioned, it runs some of those workflows on it as a, as a end-user, I've got the capability to kind of fully manage and fully control those VMs once I have them up and running. >>So that's a quick overview of that cloud consumer use case. Uh, Kevin, do you have any questions right now about that use case? Yeah, I do, uh, cloud consumers today want more than a VM, so how can a private cloud deliver more value for cloud consumers? Yeah, so that's a great question. So we talked a little bit about the cloud management platforms, ability to integrate with existing automation, for things like, uh, application installation and configuration. Uh, but one thing I didn't talk about is kind of an alternate way. We can use that and that's through this notion of blueprints. So within the cloud management platform, I, as a developer or as an administrator can set up blueprints, which are really, uh, very complex applications. These could be multi-node multi-tiered applications where each tier may have a different application installed. They may be load balanced, all those sorts of things, and I can stitch all those together and make them available as a catalog item. >>It's just kind of one simple catalog item for an end user to consume. So they don't have to understand all the complexity or all the multiple nodes or all the workflows required on the backend to provide that service. I've already done all that hard work. I advertise it to them and they don't have to know, again, in this particular case, I've got a web tier made up of a couple of VMs, a database tier made up of a couple of VMs. Uh, there's some automation running, maybe through those Ansible playbooks, uh, in, in the backend to make all those things happen really, as an end user, I just say, Hey, I want one of these applications. I may need to answer a few questions, uh, depending on how the application or their blueprint is built. And then I could push that out as an application. And again, I don't have to understand all the complexities that make up that multi-node multi-tiered application on in the background >>Stay. That's really cool. So like phase as good as it can be. So, right. So we've pushed some buttons, we've set some stuff up, we've provisioned some stuff. So right at the beginning, you know, we spoke about the post provisioning stuff. So how do we actually manage the costs and also look at their usage within their environment, which is also important to our customers. >>Yeah. So it's a great question, Raj. So, you know, obviously customers want to understand what their overall green link consumption is, what their bill is, how all those things relate together. And then they probably want to do much more detailed cost analysis as well. So the good news here, we provide all this tooling and all this is available right through GreenLake central. So a couple of the tiles that you'll see in GreenLake central tie into the private cloud solution, just like they would any other GreenLake solution. So if I want to see overall what I've consumed, uh, within my private cloud, as a GreenLake resource, I can drill down to understand, Hey, what was actually metered as what I consumed, how did that relate to my GreenLake rate card? You know, how did that, how did that create the number that appeared on my GreenLake bill for this particular service at the end of the month, I've also got the tools to do capacity planning, again, just like every other green Lake environment. >>Uh, we want to be able to show kind of that capacity planning view so customers can understand kind of what they're consuming, uh, what direction that's trending. And when we need to add some, we may need to add some more additional capacity. So again, when a customer needs more, it's already there and ready to go, they just start to consume it and pay for it as a part of their green Lake bill. So Greenlight customers have a dedicated account team that kind of works with them to keep an eye on that capacity. And again, make sure we're working with customers to make the right decisions about when is the right time to add additional capacity to the environment. And then finally, you know, our customers also get access to consumption analytics for much more detailed cost reporting. So within consumption analytics, I can take advantage of those tags that I talked about previously. >>So here's a report I created where I want to see my private cloud consumption and use really broken down by cost center. And by the VMs that my users within each of those cost centers is consuming. So I wrote a report to do some showback costing based on those tags. So in this particular case, I can tell, for example, the colo engineer cost center that Hey you over the last month, you've consumed 32, uh, elements within the private cloud environment. You know, your total cost for that was $860. And I can give them the ability to, you know, if they want drill down on this. So, you know, now they'll see every individual VM that was provisioned, uh, where it ran when it ran. And in this particular case, I've broken down the cost between compute and storage, because I really wanted to see those separately as separate line items, but, you know, really give customers the ability to do whatever showback or chargeback reporting makes sense within their organization, based on the tags. >>They want to apply it and how they want to be able to show and consume those costs. So, Kevin, any questions about, uh, sort of this cost analysis use case? Yep. Is there a way to proactively monitor consumption of the private cloud environment? Yeah, so we actually provide a couple of different ways to do that. Uh, one right within consumption analytics that we talked about, one of the capabilities I have is, is the ability to set a budget. So in this particular case, I've set a budget again, kind of by that cost center that I can take a look at, Hey, you know, what are all these cost centers consuming within this private cloud environment? Uh, and how does that relate to, you know, what maybe, uh, an amount that I've given them to be able to use? So I can take a look at it and see, Hey, in the current period, you know, I've got one, a cost center that's over budget two that are under budget and take a look at their historical use as well. >>Going back to the cloud management platform. I also have more of a hard way to be able to set those consumption boundaries, uh, by using a policy. So again, if I want to create a policy that says, Hey, you know, Steve can only have 20 VMs. Uh, once he's provisioned those 20 VMs, he can't have any more, um, you know, he's got to come back and ask for more. And again, you know, when I create this policy, I could apply it to a group or an individual user just kind of based on how I want to put those guard rails around that environment and then sort of do that around that environment. So there's kind of a way to do this in more of a soft way based on cost to understand budgets and get notifications. When I get close to my budget limits or more of a hard way to actually, you know, be able to limit resources that customers can consume within the environment itself. So with that, Raj, I'll throw it back to you. >>Thanks, Dave, >>Just to wrap up really, you know, Steve and Kevin, thank you for the great demonstration and the chat, really, um, a few things for the audience and our customers, uh, to understand what we're now doing with Greenlight for private cloud and other platform solutions is helping you to get started really, really quickly, allowing you to begin your journey with us at the right level. And then you can scale depending on how you are actually managing your transformation, be it from an infrastructure standpoint application standpoint, or you are looking to basically just modernize the way that you deliver services back out to your internal users. The other side of it is, is the important fact that we now act and behave very much like a cloud. So because we run those environments for you, we eliminate the complexity of feeding them all, Trang, all the infrastructure, the configuration, and the updates of the software layer. It leaves you free to basically deliver the services like Steve has just shown the other side of it. Final point, is this all usage based? Uh, so again, lowering kind of like the initial investment risk for you guys, allowing you to, uh, benefit from the way that we've actually integrated the solutions and technologies. So you can just embrace them and take advantage of. >>Excellent. Thank you, Raj. So I would like to thank you all for today. Thank >>You, Raj and Steve, for a brilliant demonstration. If you would like more information or like to speak to someone directly, then please fill out the poll by clicking on the poll option at the top of the chat box. So in closing, if you are interested in HPE GreenLake for private cloud, then please start a trial. It's easy. Thank you. Thank you all and goodbye for now..

>>Well, welcome to the special cube conversation. I'm Jennifer with the cube, your host, we're here with Jeremy and Iisha Sharma director of product management for trim. We're going to talk about data, data lakes, the future of data, and how it works with cloud and in the new applications. Iisha thanks for joining me. >>Thank you for having me, John, >>You guys are a cutting-edge startup. You've got a lot of good action going on. You're kind of on the new, the new guard as Andy Jassy at AWS always talks about this. The old guard incumbents you guys are on the, on the new breed, you guys are doing the new stuff around data lakes and also making data accessible for customers. Uh, what, what is that all about? Take us through what is Dremio. >>So Dremio is the data Lake service that essentially allows you to very simply run SQL queries on directly on your data Lake storage, without having to make any of those copies that everybody's going on about all the time. So you're really able to get that fast time to value without having to have this long process of let's put in a request to my data team, let's make all of those copies and then finally get this very reduced scope of, of your data and still have to go back to your data team every time you need it, you need a change to that. So dreamy is bringing you that fast time to value with that. No copy data strategy, and really providing you the flexibility to keep your data in your data Lake storage, as the single source of truth. >>You know, the past 10 years, we've watched with cube coverage since we've been doing this program and in the community following from the early days of Hadoop to now, we've seen the trials and tribulations of ETL data warehousing. We've seen the starts and stops, and we've seen that the most successful formula has been store everything. Um, and then, you know, then the ease of use became a challenge. I don't want to have to hire really high powered engineers to manage certain kinds of clusters. I just got cloud now comes into the mix. I got on-premise storage, but the notion of a data Lake became hugely popular because it became a phrase meant store everything, and it meant different things to different peoples. And since then, teams of people have been hired to be the data teams. So it's kind of new. So I got to ask you, what is the challenge of these data teams? What do they look like? What's the psychology going on with some of the people on these teams? What problems are they solving what's going on? Because you know, they becoming data full >>To take >>Us through what's going on with data teams, >>To your point, the volumes, the variety of data, Eastern growing exponentially every day, there's really no end to it, right? And companies are looking to get their hands on as much data as they possibly can. So that means data teams in a position to how do I provide access to as many users as easily as possible that self service experience or data, um, and data democratization as much of a great concept as it is in theory, it comes with its own challenges in terms of all of those copies that ended up being created to provide the quote unquote self service experience. And then with all of these copies comes the cost to store all of them. And you've just added a tremendous amount of complexity and delayed your time to value significantly. >>You mentioned self-service is one of those things that seems like a moving train. Everyone I talked to is like, Oh, self-service is the Holy grail we've got to get to self-service almost. And then you get to some self serves, then you gotta, you gotta re rethink it cause more stuff's changing. So I have to ask in that capacity, you've got data architects and you've got analysts, the customer of the data. How's the, what's the relationship between those two is who gives and who gets, who drives it, who leans in to the analyst, feed the requirements into the architect, set up the boundaries. How is that relationship? Can you take us through how you guys view the relationship between the data analyst and architect? I mean data architect and the data analysts. >>Sure. So you have the data architect, the data team that's actually responsible for providing data access at the end of the day, right? They're the people that have the data democratization requirement on them. And so they've created these copies, tremendous amount of copies. A lot of the times the data Lake storage is, is that source of truth. But, um, you're copying your data into a data warehouse. And then what they end up doing is your, your end user, your analyst, they want, they all want different types of data. They want different views of this data. So there's a tremendous amount of personalized copies that the architects end up creating. And then on top of it, there's performance. We need to get everything back in a timely manner. Otherwise what's the point, right? Real time analytics. So there's all these performance related copies, whether that be additive tables or, you know, VI extract cues, all of that fun stuff. >>And so the architect is the one that's responsible for creating all of those. That's what they have to do to provide access to the analyst. And then, like I'm saying, when we need an update to that data set, when I discover that I have a new data set, that I need to join with an existing one, I have the analyst go to the data architect and say, Hey, by the way, I need this new data set. Can you make this usable for me? Or can you provide me access? And so then we did protect has to process that request now. And so again, coming back to all these copies that have been created, um, the data architect goes through a tremendous amount of work and almost, um, has, has to do this over and over again to actually make the data available to the analyst. But it's a cycle that goes on between the two. >>Yeah. It's interesting dynamic. It's a power dynamic, but also trying to get to the innovation. I've got to ask you, some people are saying that data copies are the major obstacle for democratization. How do you respond to that? What's your view? >>They absolutely are. Data copies are the complete opposite of data democratization. There's no aspect of self-service there, which is exactly what you're looking to do with data democratization. Um, because of those copies, how do you manage those? How do you govern those? How, uh, like I was saying, when somebody needs a new data set or an update to one, they have to go back to that data team. And there goes that self-service actually Dana coffees create a bottleneck because it all comes back to that data team that has to continue to get through those requests that are coming in from their analysts. So, uh, data copies and data democratization is completely automated. >>You know, I remember talking to David latte in a cube event two years ago, he said infrastructure as code was the big DevOps movement. And we felt that data ops would be something similar where data as code, where you didn't have to think about it. So you're kind of getting to this idea of, you know, copies are bad because it doesn't, it holds back that self-service this modern error is looking for more of programmability with data. Kind of what you're teasing out here is that's the modern architecture. Is that how you see it? How do, how do you see, uh, a, uh, a modern data architecture? >>Yeah, so the modern data or the data architecture has evolved significantly in the last several years, right? We started with traditional data warehouses and the traditional data Lake with Duke where the storage and compute were totally tightly coupled. And then we moved on to cloud data warehouses, where there was a separation of compute and storage, and that provided a little more flexibility there. But then with the modern data architecture now with cloud data lakes, you have this aspect of separating, not only storage and compute, but also compute data. So that creates a separate tier for data altogether. What does that look like? So you have your data and your feeling storage as three ATLs, whatever it may be. And on top of that. So of course it's an open format, right? And so on top of that, thanks to technology. It's like Apache iceberg and Delta Lake. There's this ability to give your files, your data, a table structure. And so that starts to bring the capabilities that a data warehouse was providing the data. Thanks to these. You have the ability to do transactions, record level mutations, burgeoning things that were missing completely from a data Lake architecture before. And so, um, introducing that, that data to your, having that separation of compute and data really, really accelerate the ability to get that time to value because you're keeping your data in the data Lake storage at the end of the day. >>And it's interesting, you see all the hot companies tend to be, have that kind of mindset and architecture, and it's creating new opportunities as a ton of white space. So I have to kind of ask you guys, how does Dremio fit into this because you guys are playing in this kind of the new wave here with data it's growing extremely, it's moving fast. You got, again, edge is developing more. Data's coming in at the edge. You've got hybrid testing multi-cloud environments on the horizon. I mean this ultimate multicloud, but I mean, data in real time across multiple clouds is the next kind of area people are focused on. What does, what's the role of GMU and all this to take, take us through that. >>Yeah. So Dremio provides, again, like I said, this data Lake service, and we're all referring to just storage or Hadoop. When we say data Lake, we're talking about an entire solution. Um, so you keep your data, you keep your data in your data, Lake orange. And then on top of that, with the integrations that Dremio has with Apache iceberg and Delta, like we do provide that data here that I was talking about. And so you've given your data, this table structure, and now you can operate on it like you would in a data warehouse. So there's really no need to move your data from a data Lake data warehouse, again, keeping that data Lake as that source of truth. And then on top of that, um, when we talk about copies, personalized copies, performance related copies, you, you really, like I was saying, you've created so much complexity with Jeremy of you don't do that when it comes to personalized copies, we've got the semantic layer and that's a very key aspect of Dremio where you can provide as many views of, of data that you want without having to make any copies. So it really accelerates that, that data democratization story, and then when it, >>So it's the no cop, my strategy trim, you guys are on it, but you're about no copy keeps semantic layer, have that be horizontal across whatever environment and just applications have, can applications tap into this, or how do you guys integrate into apps if I'm an app developer, for instance, how does that work? >>Of course. So that's, that's one of the most important use cases in the sense that when there's an application or even when it's a, you know, a BI client or some other tool that's tapping into the data in S3 or ATLs, a lot of people see performance degradation. Typically with the Dremio, that's not the case we've got, Aeroflight integrated into Tremino, it's a key component as well. And that puts so much, uh, it, so put so much ease in terms of running dashboards off of that, running your analytics apps off of that, because that replay can deliver 20 times the performance that PIO DBC could. So coming back to the no data strategy or note copy data strategy, there's no those local copies anymore that you needed to make. >>So one of the things I got to ask you is, cause this comes up all the time. So she had less pass re-invent. I notice again, Amazon was, I was banging on this hard Azure as well on their side too. Their whole thing is we want to take the AI environment and make it so that people can normal people can use it and deploy machine learning. The same thing kind of comes down into this layer where you're talking about is this democratization is a huge trend because you don't have to be super peaked, you know, math, PhD, data scientist, or ETL, or data Wrangler. You just want to actually code the data or play party with the data in any way you want to do with it. So, so the question I have is is that that's certainly a great trend and no one debates that, but the reality is people are storing data, like almost hoarding it, just throw it in a data Lake and we'll deal with them later. How does you guys solve that problem? Because once that starts happening, do you have to hire someone super smart to dig that out or rearchitected or because that seems to be kind of the pattern, right? You know, throw everything into data Lake, uh, and we'll deal with it later >>Called the data swamp. And it's like, no one knows what's going on. >>Of course though, you don't actually want to throw everything into a data Lake. There still needs to be a certain amount of structure that all of this lands in. You want it to live in one place, but have still a little bit of structure so that, um, Dremio and other are, are much more enabled to query that with fantastic performance. So there's, there's still some amount of structure that needs to happen at a data Lake level, but from, uh, that semantic layer that we have with during the, you you're, you're creating structure for your end user, >>How would you advise, how would you advise someone who wants to hedge their future and not take on too much technical debt, but says, Hey, you know, I do have the store. Is there a best practice on kind of some guard rails around getting going, how do you, how do you advise your customers who want to get it going? >>So how we advise our customers is again, plugin put your, put your data in that data Lake. A lot of them already have three TLS in place. And getting started with Bermeo is really easy. I would say I did it for the first time and it took a matter of minutes if not less. And so what you're doing with Dremio is connecting data directly to that data source and then creating a semantic layer on top. So you bring together a bunch of data. That's sitting in your data Lake, you know, if that sales data and Sophia, and we give you a really streamlined way to say together, the, you know, last, however, we go back in time, create a view on top of all of that. If you have that structured in folders as great, we will provide you a way to create one view on top of all of that, as opposed to having a view for every day or whatnot. And so again, that semantic layer really comes in handy when you're trying to, as the architect provide access to this data Lake. And then as the user who just, just interacts with the data as, as the views are provided to them, there's really, uh, there's a whole lot of transparency there, and it's really easy to get up and running with drumming. >>I'm looking forward to it. I got to finally ask the question is how do I get started? How do people engage with you guys? Is it, is it a freemium? Is it a cloud service? What's the requirements? What are some of the ways that people can engage and work with you guys? >>Yeah, so we get started, uh, on our website at dot com. And speaking of self-service, we've got a virtual lab at dremio.com/labs that you can get started with that gives you a product tour and even gives you a getting started, walk through the tissue through your first query so that you can see how well it works. And in addition to that, we've got a free trial of Dremio available on AWS marketplace. >>Awesome. Net marketplace is a good place to download stuff. So can I ask you a personal question, Isha? Um, you're the director of product management. You get to see inside the kitchen where everyone's making the, making the product. You also got the customer relationships out there looking at product market fit, as it evolves, customer's requirements evolve. What's some of the cool things that you've seen in this space. That's just interesting to you that either you kind of expected or maybe some surprises, what's the coolest thing you've seen come out of this new data environment we're living in. >>I think just the ability to the way things have evolved, right? It used to be data Lake or data warehouse, and you pick one, you probably have both, but you're not like reaching either to their highest potential. Now you've got, this is coming together of both of them. I think it's been fantastic to see how you've got technology is like a iceberg and Delta Lake and bringing those two things together. And you know, you're in your data Lake and it's great in terms of cost and storage and all of that. But now you're able to have so much flexibility in terms of some of those data warehouse capabilities. And on top of that with technologies like Dremio, and just in general, this open format concept, you're, you're never locked in with a particular vendor with a particular format. You're not locking yourself out of a technology that you don't even know exists yet. And thinking in the past, you were always going to end up there. You always ended up putting your data in something where it was going to be difficult to change it, to get it out. But now you have so much flexibility with the open architecture that's coming. What's the DNA like of the >>Culture at Treme. And obviously you've got a cutting edge. We're in a big, hot wave data. You're enabling a lot of value. Uh, what's the, what's it like there at Jemena? What do you guys strive for? What's the purpose? What's the, what's the DNA of the culture. >>There's a lot of excitement in terms of getting customers to this flexibility, to get them out of things they're locked into really in providing them with accessibility to their data, right? This data access data democratization concept to make that actually happen so that, you know, time to value is a key thing. You want to derive insights out of your, out of your data. And everybody, I drove you in super excited and charging towards that, >>Unlocking that value. That's awesome. Aisha, thank you for coming on the cube conversation. Great to see you. Thanks for coming on. Appreciate it. He's just Sharma director of product management. Dremio here inside the cube. I'm John for your host. Thanks for watching.

>>From around the globe. It's the cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS, and our community partners. >>Hi everyone. Welcome back to the cubes. Virtual coverage of AWS reinvent 2020 virtual. We are the cube virtual I'm John ferry, your host with my coach, Dave Alante for keynote analysis from Swami's machine learning, all things, data huge. Instead of announcements, the first ever machine learning keynote at a re-invent Dave. Great to see you. Thanks Johnny. And from Boston, I'm here in Palo Alto. We're doing the cube remote cube virtual. Great to see you. >>Yeah, good to be here, John, as always. Wall-to-wall love it. So, so, John, um, how about I give you my, my key highlights from the, uh, from the keynote today, I had, I had four kind of curated takeaways. So the first is that AWS is, is really trying to simplify machine learning and use machine intelligence into all applications. And if you think about it, it's good news for organizations because they're not the become machine learning experts have invent machine learning. They can buy it from Amazon. I think the second is they're trying to simplify the data pipeline. The data pipeline today is characterized by a series of hyper specialized individuals. It engineers, data scientists, quality engineers, analysts, developers. These are folks that are largely live in their own swim lane. Uh, and while they collaborate, uh, there's still a fairly linear and complicated data pipeline, uh, that, that a business person or a data product builder has to go through Amazon making some moves to the front of simplify that they're expanding data access to the line of business. I think that's a key point. Is there, there increasingly as people build data products and data services that can monetize, you know, for their business, either cut costs or generate revenue, they can expand that into line of business where there's there's domain context. And I think the last thing is this theme that we talked about the other day, John of extending Amazon, AWS to the edge that we saw that as well in a number of machine learning tools that, uh, Swami talked about. >>Yeah, it was great by the way, we're live here, uh, in Palo Alto in Boston covering the analysis, tons of content on the cube, check out the cube.net and also check out at reinvent. There's a cube section as there's some links to so on demand videos with all the content we've had. Dave, I got to say one of the things that's apparent to me, and this came out of my one-on-one with Andy Jassy and Andy Jassy talked about in his keynote is he kind of teased out this idea of training versus a more value add machine learning. And you saw that today in today's announcement. To me, the big revelation was that the training aspect of machine learning, um, is what can be automated away. And it's under a lot of controversy around it. Recently, a Google paper came out and the person was essentially kind of, kind of let go for this. >>But the idea of doing these training algorithms, some are saying is causes more harm to the environment than it does good because of all the compute power it takes. So you start to see the positioning of training, which can be automated away and served up with, you know, high powered ships and that's, they consider that undifferentiated heavy lifting. In my opinion, they didn't say that, but that's clearly what I see coming out of this announcement. The other thing that I saw Dave that's notable is you saw them clearly taking a three lane approach to this machine, learning the advanced builders, the advanced coders and the developers, and then database and data analysts, three swim lanes of personas of target audience. Clearly that is in line with SageMaker and the embedded stuff. So two big revelations, more horsepower required to process training and modeling. Okay. And to the expansion of the personas that are going to be using machine learning. So clearly this is a, to me, a big trend wave that we're seeing that validates some of the startups and I'll see their SageMaker and some of their products. >>Well, as I was saying at the top, I think Amazon's really trying, working hard on simplifying the whole process. And you mentioned training and, and a lot of times people are starting from scratch when they have to train models and retrain models. And so what they're doing is they're trying to create reusable components, uh, and allow people to, as you pointed out to automate and streamline some of that heavy lifting, uh, and as well, they talked a lot about, uh, doing, doing AI inferencing at the edge. And you're seeing, you know, they, they, uh, Swami talked about several foundational premises and the first being a foundation of frameworks. And you think about that at the, at the lowest level of their S their ML stack. They've got, you know, GPU's different processors, inferential, all these alternative processes, processors, not just the, the Xav six. And so these are very expensive resources and Swami talked a lot about, uh, and his colleagues talked a lot about, well, a lot of times the alternative processor is sitting there, you know, waiting, waiting, waiting. And so they're really trying to drive efficiency and speed. They talked a lot about compressing the time that it takes to, to run these, these models, uh, from, from sometimes weeks down to days, sometimes days down to hours and minutes. >>Yeah. Let's, let's unpack these four areas. Let's stay on the firm foundation because that's their core competency infrastructure as a service. Clearly they're laying that down. You put the processors, but what's interesting is the TensorFlow 92% of tensor flows on Amazon. The other thing is that pie torch surprisingly is back up there, um, with massive adoption and the numbers on pie torch literally is on fire. I was coming in and joke on Twitter. Um, we, a PI torch is telling because that means that TensorFlow is originally part of Google is getting, is getting a little bit diluted with other frameworks, and then you've got MX net, some other things out there. So the fact that you've got PI torch 91% and then TensorFlow 92% on 80 bucks is a huge validation. That means that the majority of most machine learning development and deep learning is happening on AWS. Um, >>Yeah, cloud-based, by the way, just to clarify, that's the 90% of cloud-based cloud, uh, TensorFlow runs on and 91% of cloud-based PI torch runs on ADM is amazingly massive numbers. >>Yeah. And I think that the, the processor has to show that it's not trivial to do the machine learning, but, you know, that's where the infrared internship came in. That's kind of where they want to go lay down that foundation. And they had Tanium, they had trainee, um, they had, um, infrared chow was the chip. And then, you know, just true, you know, distributed training training on SageMaker. So you got the chip and then you've got Sage makers, the middleware games, almost like a machine learning stack. That's what they're putting out there >>And how bad a Gowdy, which was, which is, which is a patrol also for training, which is an Intel based chip. Uh, so that was kind of interesting. So a lot of new chips and, and specialized just, we've been talking about this for awhile, particularly as you get to the edge and do AI inferencing, you need, uh, you know, a different approach than we're used to with the general purpose microbes. >>So what gets your take on tenant? Number two? So tenant number one, clearly infrastructure, a lot of announcements we'll go through those, review them at the end, but tenant number two, that Swami put out there was creating the shortest path to success for builders or machine learning builders. And I think here you lays out the complexity, Dave butts, mostly around methodology, and, you know, the value activities required to execute. And again, this points to the complexity problem that they have. What's your take on this? >>Yeah. Well you think about, again, I'm talking about the pipeline, you collect data, you just data, you prepare that data, you analyze that data. You, you, you make sure that it's it's high quality and then you start the training and then you're iterating. And so they really trying to automate as much as possible and simplify as much as possible. What I really liked about that segment of foundation, number two, if you will, is the example, the customer example of the speaker from the NFL, you know, talked about, uh, you know, the AWS stats that we see in the commercials, uh, next gen stats. Uh, and, and she talked about the ways in which they've, well, we all know they've, they've rearchitected helmets. Uh, they've been, it's really a very much database. It was interesting to see they had the spectrum of the helmets that were, you know, the safest, most safe to the least safe and how they've migrated everybody in the NFL to those that they, she started a 24%. >>It was interesting how she wanted a 24% reduction in reported concussions. You know, you got to give the benefit of the doubt and assume some of that's through, through the data. But you know, some of that could be like, you know, Julian Edelman popping up off the ground. When, you know, we had a concussion, he doesn't want to come out of the game with the new protocol, but no doubt, they're collecting more data on this stuff, and it's not just head injuries. And she talked about ankle injuries, knee injuries. So all this comes from training models and reducing the time it takes to actually go from raw data to insights. >>Yeah. I mean, I think the NFL is a great example. You and I both know how hard it is to get the NFL to come on and do an interview. They're very coy. They don't really put their name on anything much because of the value of the NFL, this a meaningful partnership. You had the, the person onstage virtually really going into some real detail around the depth of the partnership. So to me, it's real, first of all, I love stat cast 11, anything to do with what they do with the stats is phenomenal at this point. So the real world example, Dave, that you starting to see sports as one metaphor, healthcare, and others are going to see those coming in to me, totally a tale sign that Amazon's continued to lead. The thing that got my attention was is that it is an IOT problem, and there's no reason why they shouldn't get to it. I mean, some say that, Oh, concussion, NFL is just covering their butt. They don't have to, this is actually really working. So you got the tech, why not use it? And they are. So that, to me, that's impressive. And I think that's, again, a digital transformation sign that, that, you know, in the NFL is doing it. It's real. Um, because it's just easier. >>I think, look, I think, I think it's easy to criticize the NFL, but the re the reality is, is there anything old days? It was like, Hey, you get your bell rung and get back out there. That's just the way it was a football players, you know, but Ted Johnson was one of the first and, you know, bill Bellacheck was, was, you know, the guy who sent him back out there with a concussion, but, but he was very much outspoken. You've got to give the NFL credit. Uh, it didn't just ignore the problem. Yeah. Maybe it, it took a little while, but you know, these things take some time because, you know, it's generally was generally accepted, you know, back in the day that, okay, Hey, you'd get right back out there, but, but the NFL has made big investments there. And you can say, you got to give him, give him props for that. And especially given that they're collecting all this data. That to me is the most interesting angle here is letting the data inform the actions. >>And next step, after the NFL, they had this data prep data Wrangler news, that they're now integrating snowflakes, Databricks, Mongo DB, into SageMaker, which is a theme there of Redshift S3 and Lake formation into not the other way around. So again, you've been following this pretty closely, uh, specifically the snowflake recent IPO and their success. Um, this is an ecosystem play for Amazon. What does it mean? >>Well, a couple of things, as we, as you well know, John, when you first called me up, I was in Dallas and I flew into New York and an ice storm to get to the one of the early Duke worlds. You know, and back then it was all batch. The big data was this big batch job. And today you want to combine that batch. There's still a lot of need for batch, but when people want real time inferencing and AWS is bringing that together and they're bringing in multiple data sources, you mentioned Databricks and snowflake Mongo. These are three platforms that are doing very well in the market and holding a lot of data in AWS and saying, okay, Hey, we want to be the brain in the middle. You can import data from any of those sources. And I'm sure they're going to add more over time. Uh, and so they talked about 300 pre-configured data transformations, uh, that now come with stage maker of SageMaker studio with essentially, I've talked about this a lot. It's essentially abstracting away the, it complexity, the whole it operations piece. I mean, it's the same old theme that AWS is just pointing. It's its platform and its cloud at non undifferentiated, heavy lifting. And it's moving it up the stack now into the data life cycle and data pipeline, which is one of the biggest blockers to monetizing data. >>Expand on that more. What does that actually mean? I'm an it person translate that into it. Speak. Yeah. >>So today, if you're, if you're a business person and you want, you want the answers, right, and you want say to adjust a new data source, so let's say you want to build a new, new product. Um, let me give an example. Let's say you're like a Spotify, make it up. And, and you do music today, but let's say you want to add, you know, movies, or you want to add podcasts and you want to start monetizing that you want to, you want to identify, who's watching what you want to create new metadata. Well, you need new data sources. So what you do as a business person that wants to create that new data product, let's say for podcasts, you have to knock on the door, get to the front of the data pipeline line and say, okay, Hey, can you please add this data source? >>And then everybody else down the line has to get in line and Hey, this becomes a new data source. And it's this linear process where very specialized individuals have to do their part. And then at the other end, you know, it comes to self-serve capability that somebody can use to either build dashboards or build a data product. In a lot of that middle part is our operational details around deploying infrastructure, deploying, you know, training machine learning models that a lot of Python coding. Yeah. There's SQL queries that have to be done. So a lot of very highly specialized activities, what Amazon is doing, my takeaway is they're really streamlining a lot of those activities, removing what they always call the non undifferentiated, heavy lifting abstracting away that it complexity to me, this is a real positive sign, because it's all about the technology serving the business, as opposed to historically, it's the business begging the technology department to please help me. The technology department obviously evolving from, you know, the, the glass house, if you will, to this new data, data pipeline data, life cycle. >>Yeah. I mean, it's classic agility to take down those. I mean, it's undifferentiated, I guess, but if it actually works, just create a differentiated product. So, but it's just log it's that it's, you can debate that kind of aspect of it, but I hear what you're saying, just get rid of it and make it simpler. Um, the impact of machine learning is Dave is one came out clear on this, uh, SageMaker clarify announcement, which is a bias decision algorithm. They had an expert, uh, nationally CFUs presented essentially how they're dealing with the, the, the bias piece of it. I thought that was very interesting. What'd you think? >>Well, so humans are biased and so humans build models or models are inherently biased. And so I thought it was, you know, this is a huge problem to big problems in artificial intelligence. One is the inherent bias in the models. And the second is the lack of transparency that, you know, they call it the black box problem, like, okay, I know there was an answer there, but how did it get to that answer and how do I trace it back? Uh, and so Amazon is really trying to attack those, uh, with, with, with clarify. I wasn't sure if it was clarity or clarified, I think it's clarity clarify, um, a lot of entirely certain how it works. So we really have to dig more into that, but it's essentially identifying situations where there is bias flagging those, and then, you know, I believe making recommendations as to how it can be stamped. >>Nope. Yeah. And also some other news deep profiling for debugger. So you could make a debugger, which is a deep profile on neural network training, um, which is very cool again on that same theme of profiling. The other thing that I found >>That remind me, John, if I may interrupt there reminded me of like grammar corrections and, you know, when you're typing, it's like, you know, bug code corrections and automated debugging, try this. >>It wasn't like a better debugger come on. We, first of all, it should be bug free code, but, um, you know, there's always biases of the data is critical. Um, the other news I thought was interesting and then Amazon's claiming this is the first SageMaker pipelines for purpose-built CIC D uh, for machine learning, bringing machine learning into a developer construct. And I think this started bringing in this idea of the edge manager where you have, you know, and they call it the about machine, uh, uh, SageMaker store storing your functions of this idea of managing and monitoring machine learning modules effectively is on the edge. And, and through the development process is interesting and really targeting that developer, Dave, >>Yeah, applying CIC D to the machine learning and machine intelligence has always been very challenging because again, there's so many piece parts. And so, you know, I said it the other day, it's like a lot of the innovations that Amazon comes out with are things that have problems that have come up given the pace of innovation that they're putting forth. And, and it's like the customers drinking from a fire hose. We've talked about this at previous reinvents and the, and the customers keep up with the pace of Amazon. So I see this as Amazon trying to reduce friction, you know, across its entire stack. Most, for example, >>Let me lay it out. A slide ahead, build machine learning, gurus developers, and then database and data analysts, clearly database developers and data analysts are on their radar. This is not the first time we've heard that. But we, as the kind of it is the first time we're starting to see products materialized where you have machine learning for databases, data warehouse, and data lakes, and then BI tools. So again, three different segments, the databases, the data warehouse and data lakes, and then the BI tools, three areas of machine learning, innovation, where you're seeing some product news, your, your take on this natural evolution. >>Well, well, it's what I'm saying up front is that the good news for, for, for our customers is you don't have to be a Google or Amazon or Facebook to be a super expert at AI. Uh, companies like Amazon are going to be providing products that you can then apply to your business. And, and it's allowed you to infuse AI across your entire application portfolio. Amazon Redshift ML was another, um, example of them, abstracting complexity. They're taking, they're taking S3 Redshift and SageMaker complexity and abstracting that and presenting it to the data analysts. So that, that, that individual can worry about, you know, again, getting to the insights, it's injecting ML into the database much in the same way, frankly, the big query has done that. And so that's a huge, huge positive. When you talk to customers, they, they love the fact that when, when ML can be embedded into the, into the database and it simplifies, uh, that, that all that, uh, uh, uh, complexity, they absolutely love it because they can focus on more important things. >>Clearly I'm this tenant, and this is part of the keynote. They were laying out all their announcements, quick excitement and ML insights out of the box, quick, quick site cue available in preview all the announcements. And then they moved on to the next, the fourth tenant day solving real problems end to end, kind of reminds me of the theme we heard at Dell technology worlds last year end to end it. So we are starting to see the, the, the land grab my opinion, Amazon really going after, beyond I, as in pass, they talked about contact content, contact centers, Kendra, uh, lookout for metrics, and that'll maintain men. Then Matt would came on, talk about all the massive disruption on the, in the industries. And he said, literally machine learning will disrupt every industry. They spent a lot of time on that and they went into the computer vision at the edge, which I'm a big fan of. I just loved that product. Clearly, every innovation, I mean, every vertical Dave is up for grabs. That's the key. Dr. Matt would message. >>Yeah. I mean, I totally agree. I mean, I see that machine intelligence as a top layer of, you know, the S the stack. And as I said, it's going to be infused into all areas. It's not some kind of separate thing, you know, like, Coobernetti's, we think it's some separate thing. It's not, it's going to be embedded everywhere. And I really like Amazon's edge strategy. It's this, you, you are the first to sort of write about it and your keynote preview, Andy Jassy said, we see, we see, we want to bring AWS to the edge. And we see data center as just another edge node. And so what they're doing is they're bringing SDKs. They've got a package of sensors. They're bringing appliances. I've said many, many times the developers are going to be, you know, the linchpin to the edge. And so Amazon is bringing its entire, you know, data plane is control plane, it's API APIs to the edge and giving builders or slash developers, the ability to innovate. And I really liked the strategy versus, Hey, here's a box it's, it's got an x86 processor inside on a, throw it over the edge, give it a cool name that has edge in it. And here you go, >>That sounds call it hyper edge. You know, I mean, the thing that's true is the data aspect at the edge. I mean, everything's got a database data warehouse and data lakes are involved in everything. And then, and some sort of BI or tools to get the data and work with the data or the data analyst, data feeds, machine learning, critical piece to all this, Dave, I mean, this is like databases used to be boring, like boring field. Like, you know, if you were a database, I have a degree in a database design, one of my degrees who do science degrees back then no one really cared. If you were a database person. Now it's like, man data, everything. This is a whole new field. This is an opportunity. But also, I mean, are there enough people out there to do all this? >>Well, it's a great point. And I think this is why Amazon is trying to extract some of the abstract. Some of the complexity I sat in on a private session around databases today and listened to a number of customers. And I will say this, you know, some of it I think was NDA. So I can't, I can't say too much, but I will say this Amazon's philosophy of the database. And you address this in your conversation with Andy Jassy across its entire portfolio is to have really, really fine grain access to the deep level API APIs across all their services. And he said, he said this to you. We don't necessarily want to be the abstraction layer per se, because when the market changes, that's harder for us to change. We want to have that fine-grained access. And so you're seeing that with database, whether it's, you know, no sequel, sequel, you know, the, the Aurora the different flavors of Aurora dynamo, DV, uh, red shift, uh, you know, already S on and on and on. There's just a number of data stores. And you're seeing, for instance, Oracle take a completely different approach. Yes, they have my SQL cause they know got that with the sun acquisition. But, but this is they're really about put, is putting as much capability into a single database as possible. Oh, you only need one database only different philosophy. >>Yeah. And then obviously a health Lake. And then that was pretty much the end of the, the announcements big impact to health care. Again, the theme of horizontal data, vertical specialization with data science and software playing out in real time. >>Yeah. Well, so I have asked this question many times in the cube, when is it that machines will be able to make better diagnoses than doctors and you know, that day is coming. If it's not here, uh, you know, I think helped like is really interesting. I've got an interview later on with one of the practitioners in that space. And so, you know, healthcare is something that is an industry that's ripe for disruption. It really hasn't been disruption disrupted. It's a very high, high risk obviously industry. Uh, but look at healthcare as we all know, it's too expensive. It's too slow. It's too cumbersome. It's too long sometimes to get to a diagnosis or be seen, Amazon's trying to attack with its partners, all of those problems. >>Well, Dave, let's, let's summarize our take on Amazon keynote with machine learning, I'll say pretty historic in the sense that there was so much content in first keynote last year with Andy Jassy, he spent like 75 minutes. He told me on machine learning, they had to kind of create their own category Swami, who we interviewed many times on the cube was awesome. But a lot of still a lot more stuff, more, 215 announcements this year, machine learning more capabilities than ever before. Um, moving faster, solving real problems, targeting the builders, um, fraud platform set of things is the Amazon cadence. What's your analysis of the keynote? >>Well, so I think a couple of things, one is, you know, we've said for a while now that the new innovation cocktail is cloud plus data, plus AI, it's really data machine intelligence or AI applied to that data. And the scale at cloud Amazon Naylor obviously has nailed the cloud infrastructure. It's got the data. That's why database is so important and it's gotta be a leader in machine intelligence. And you're seeing this in the, in the spending data, you know, with our partner ETR, you see that, uh, that AI and ML in terms of spending momentum is, is at the highest or, or at the highest, along with automation, uh, and containers. And so in. Why is that? It's because everybody is trying to infuse AI into their application portfolios. They're trying to automate as much as possible. They're trying to get insights that, that the systems can take action on. >>And, and, and actually it's really augmented intelligence in a big way, but, but really driving insights, speeding that time to insight and Amazon, they have to be a leader there that it's Amazon it's, it's, it's Google, it's the Facebook's, it's obviously Microsoft, you know, IBM's Tron trying to get in there. They were kind of first with, with Watson, but with they're far behind, I think, uh, the, the hyper hyper scale guys. Uh, but, but I guess like the key point is you're going to be buying this. Most companies are going to be buying this, not building it. And that's good news for organizations. >>Yeah. I mean, you get 80% there with the product. Why not go that way? The alternative is try to find some machine learning people to build it. They're hard to find. Um, so the seeing the scale of kind of replicating machine learning expertise with SageMaker, then ultimately into databases and tools, and then ultimately built into applications. I think, you know, this is the thing that I think they, my opinion is that Amazon continues to move up the stack, uh, with their capabilities. And I think machine learning is interesting because it's a whole new set of it's kind of its own little monster building block. That's just not one thing it's going to be super important. I think it's going to have an impact on the startup scene and innovation is going, gonna have an impact on incumbent companies that are currently leaders that are under threat from new entrance entering the business. >>So I think it's going to be a very entrepreneurial opportunity. And I think it's going to be interesting to see is how machine learning plays that role. Is it a defining feature that's core to the intellectual property, or is it enabling new intellectual property? So to me, I just don't see how that's going to fall yet. I would bet that today intellectual property will be built on top of Amazon's machine learning, where the new algorithms and the new things will be built separately. If you compete head to head with that scale, you could be on the wrong side of history. Again, this is a bet that the startups and the venture capitals will have to make is who's going to end up being on the right wave here. Because if you make the wrong design choice, you can have a very complex environment with IOT or whatever your app serving. If you can narrow it down and get a wedge in the marketplace, if you're a company, um, I think that's going to be an advantage. This could be great just to see how the impact of the ecosystem this will be. >>Well, I think something you said just now it gives a clue. You talked about, you know, the, the difficulty of finding the skills. And I think that's a big part of what Amazon and others who were innovating in machine learning are trying to do is the gap between those that are qualified to actually do this stuff. The data scientists, the quality engineers, the data engineers, et cetera. And so companies, you know, the last 10 years went out and tried to hire these people. They couldn't find them, they tried to train them. So it's taking too long. And now that I think they're looking toward machine intelligence to really solve that problem, because that scales, as we, as we know, outsourcing to services companies and just, you know, hardcore heavy lifting, does it doesn't scale that well, >>Well, you know what, give me some machine learning, give it to me faster. I want to take the 80% there and allow us to build certainly on the media cloud and the cube virtual that we're doing. Again, every vertical is going to impact a Dave. Great to see you, uh, great stuff. So far week two. So, you know, we're cube live, we're live covering the keynotes tomorrow. We'll be covering the keynotes for the public sector day. That should be chock-full action. That environment is going to impact the most by COVID a lot of innovation, a lot of coverage. I'm John Ferrari. And with Dave Alante, thanks for watching.

>>Hi, everyone. And thanks for joining us today for our session on the 2020 Data Drivers Award winners. I'm excited to be here today with you. I'm a lease. Bergeron, vice president, product marketing for snowflake. Thes rewards are intended to recognize companies and individuals for using snowflakes, data cloud to drive innovation and impact in their organizations. Before we start our conversations, I want to quickly congratulate all of our award winners. First in the business awards are data driver of the year is Cisco. Our machine learning master is you Nipper, Our data sharing leader is Rakuten. Our data application of the year is observed and our data for good award goes to door dash for the individual and team awards. We first have the cost. Jane, Chief Digital officer of Paccar. We have a militiamen, director of cybersecurity and data science winning our data science Manager of the Year award at Comcast for a date. A pioneer of the year. We have Faisal KP, who's our senior manager of enterprise data Services at Pizza Hut. And lastly, we have our best data team going to McKesson, led by Jimmy Herff Data and Analytics platform leader Huge congratulations to all of these winners. It was very difficult to pick them amongst amazing set of nominations. So now let's dive into our conversations. We'll start with the data driver of the year. Representing Cisco today is Robbie. I'm a month do director data platform, data and analytics. >>Let me welcome everybody to the wonderful. Within a few years before Cisco used to be a company, you know, in making the decisions partly with the data and partly with the cuts. Because, you know, the data is told in multiple places the trading is not done right and things like that. So we, you know, really understood it. You know what was a challenge in the organism? By then we defined the data strategy on we put in a few plants in place, and it is working very well. But what is more important is basically how we provide the data towards data scientists and the data community in Cisco. I'm making them available in a highly available scalable on the elastic platforms. That's where you know, snowflake came into picture really very well for arrest, along with the other data strategies that we have had in place more importantly, data. Democratization was a key. You know, you along with the simplification, something technologies involved in the past. Our clients need to be worrying, laudable the technologies involved, you know, for example, we used to manage her before we make it. Snowflake Andi Snowflake, in a solve all of these problems for us with the ease on it. Really helping enabling a data data given ordinances in our >>system. In the data sharing leaders category, Rockhampton was our winner. We have mark staying trigger VP of analytics here to share their story. I >>wanna thank Snowflake for the award, and it's an honor to be a today. The ease of use of snowflake has allowed projects to move forward innovation to move forward in a way that it simply couldn't have done on old Duke systems or or or other platforms. And I think the truth the same is true for us on a lot of the similar topics, but also in the data sharing space, data sharing is a part off innovation. Like I think, most of the tech companies we work with certainly are business partners, merchants, but also with a range of other service providers and other technology vendors, um on other companies that we strategically share data with 2 May benefit of their service or thio to allow data modeling or advanced data collaboration or strategic business deals using the data and evaluated with the data on. But I think if you look Greece snowflake, you would see a lot of time and effort money going to just establishing that data connection that often involved substantial investments in technology data pipelines, risk evaluation, hashing, encrypt encryption. Security on what we found with snowflakes sharing functionality is that we can not eliminate those concerns, but that the technology just supports the ability to share data securely easily, quickly in a way that we could never do >>previously. Now we have a really inspiring winner of the data for good award door dash with their Project Dash Initiative here to speak about their work is act shot near Engineering manager >>Thank you sports to snowflake for recognizing us for this initiative. Eso For those of you who don't know, Dash, the logistics technology platform company that connects people with the best in their cities and Project Dash, our flagship social impact program, uses the door dash logistics platform to tackle the challenges like hunger and food waste. It was launched in 2018 on over the first two years in partnership with food recovery organizations, we powered the delivery off over £2 million of surplus food from businesses to hunger relief agencies across the U. S. And Canada. Andi simply do Toko with tremendous need has a much we were ableto power. The delivery often estimated 5.8 million meals to food insecure communities and frontline workers across 48 states on the 3.5 million off. These meals have been delivered since much. We do all of our analysis for our business functions from like product development to skills and social impact in snowflake On the numbers I just provided here actually have come from Snowflake on. We have used it to provide various forms of reporting, tow our government and non profit partners on this snowflake. We can help them understand the impact, analyzed friends and ensure complaints in cases where we are supporting efforts for agencies like FEMA, our USDA onda. Lastly, our team is really excited to be recognized by snowflake for using data for good. It has reminded us to continue doubling down on our commitment to using our product and expertise to partner with communities we operated. Thank you again. >>The winner of the machine Learning Master's word is unit for Energy. Viola Sarcoma Data Innovation leader is here on behalf of unit for >>Hello, everyone, Thanks for having me here. It's really a pleasure. And we were really proud to get this award. It means a lot for you. Nipper. It's huge recognition for our effort since last couple of years assed part of our journey and also a celebration off our success now for you. Newport. It would not be possible to start looking at Advanced Analytics techniques, not having a solid data foundation in place. And that's where we invested a lot in our cloud data platform in the cloud back by snowflake. Having this platform allowed us to employ advanced analytics techniques, combining data from Markit from fundamental data, different other sources of data like weather and extracting new friends, new signals that basically help us to partly or even in some cases fully automate some trading strategy. And we believe this will be really fundamental for for the future off raiding in our company and we will definitely invest in this area in the future. >>Our data application of the year is observed. Observers recognizes the most innovative, data driven application built on Snowflake and representing observed today is their CEO, Jeremy Burton. >>Let me just echo the thanks from the other folks on the coal. I mean snowflakes, separation of storage. Compute. I can't overstate what a really big deal it is. Um, it means that we can ingest in store data. Really? For the price of Amazon s three on board, we're in a category where vendors of historically charged for volume of data ingested. So you can imagine this really represents huge savings. Um, in addition, and maybe on a more technical note, snowflakes, elastic architectures really enables us to direct queries appropriately, based on the complexity of the query. So small queries or simple queries weaken director extra small warehouses and complex queries. We can direct, you know, for Excel. Or I think even a six x l is either there are on its way. The key thing there is that users they're not sitting around waiting for results to appear regardless of the query complexity. So I mean, really? The separation storage compute on the elastic architectures is a really big deal for us. >>Turning to the data Pioneer of the Year Award, I'm excited to be here with Faisal KP, senior manager of Enterprise Data Services from Pizza Hut. >>First of all, thank you, Snowflake, for giving this wonderful person. I think it means a lot for us in terms of validating what we're doing. I think we were one of the earlier adopters of Snowflake. We saw the vision of snowflake, you know, stories. Russell's computer separation on all the goodies, right? Right from back in 2017, I believe what snowflake enabled us is to actually get the scale with very little manpower, which is needed to man the entire system. So on the Super Bowl day, we have, you know, the entire crew literally a boardroom where the right from the CME, most of the CEOs to all the folks will be sitting and watching what is happening in the system. And we have to do a lot of real time analytics during that time. So with snowflake, you know, way used the elasticity of the platform we use, you know, platform you know their solutions, like snow pipe to basically automate the data ingestion coming through various channels, from the commas, from the stores, everything simultaneously. So as soon as the program is done, you know, we can scale scale down to our normal volume, which means we can, you know, way can save a lot. Of course. So definitely it snowflake has been game changer for us in terms of how we provide real time analytics. Our systems are used by thousands off restaurants throughout the country and, you know, by hundreds of franchisees. So the scale is something we have achieved with a lot of ability and success. >>In the category of data science Manager of the Year Award, we have a mission Min, director of cybersecurity and data science at Comcast. >>So thank you for having me and thank you for this wonderful award. So one of the biggest challenges you see in this other security spaces the tremendous amount of data that we have to compute every day to find the gold haystack. So one of the big challenges we overcame with by uniting snowflake was how do we go from like my other counterparts on the panel have said Theo operational overhead of maintaining a large data store and moved to more of results driven and data focused environment. And, you know, part of that journey was really the tremendous leadership. Comcast saying, You know, we want Thio through our day to day lives by relying less on operational work and Maura on answering questions. And so you know, over the last year we've really put Snowflake at the center of our ecosystem, knowing that it's elastic platform and its ability scale infinitely have given us the ability to dream big and use it to drop five cybersecurity. And while it's traditionally used for cybersecurity, we're starting to see the benefits right away and the beauty of the snowflake. Ecos, Miss. We're now able to enable folks that not traditionally have big data skills, but they have standards, sequel skills, and they could still work in the snowflake platform. So, you know, the transition to cloud has been very powerful for us as an organization. But I think the end story, the real takeaways, by moving our secretary operation to the cloud, we're now been able to enable more people and get the results they were looking for. You know, as other people have said fast, people hate to wait. So the scale of snowflake really shines. >>Yeah. Now, let's hear from our data Executive of the year. The Cost. Jane. Chief Digital Officer Packer. >>Thank you very much, Snowflake, for this really incredible recognition and honor of the work we're doing it back. Are we began. The first step in this process was for us to develop an enterprise Great data platform in the cloud capable off managing every aspect of data at scale. This this platform includes snowflake as our analytics data warehouse amongst many other technologies that we used for ingestion of data, data processing, uh, data governance, transactional, uh, needs and others. So this platform, once developed, has really helped us leverage data across the broad pack. Our systems and applications globally very efficiently and is enabling pack are, as a result to enhance every aspect. Selfish business with data. >>Ah, big congratulations again to all of the winners of the 2020 Data Drivers Awards. Thanks so much for joining us for a great conversation. And we hope that you enjoy the rest of the data cloud summit

>>from around the globe. It's the Cube with digital coverage of smart data. Marketplace is brought to You by Io Tahoe Digital transformation is really gone from buzzword to a mandate. Additional businesses, a data business. And for the last several months, we've been working with Iot Tahoe on an ongoing content. Serious, serious, focused on smart data and automation to drive better insights and outcomes, essentially putting data to work. And today we're gonna do a deeper dive on automating data Discovery. And one of the thought leaders in this space is a J ahora who is the CEO of Iot. Tahoe's once again joining Me A J Good to see you. Thanks for coming on. >>A great to be here, David. Thank you. >>So let's start by talking about some of the business realities. And what are the economics that air? That air driving, automated data Discovery? Why is that so important? >>Yeah, and on this one, David, it's It's a number of competing factors we've got. The reality is data which may be sensitive, so this control on three other elements are wanting to drive value from that data. So innovation, you can't really drive a lot of value without exchanging data. So the ability to exchange data and to manage those costs, overheads and data discovery is at the roots of managing that in an automated way to classify that data in sets and policies to put that automation in place. >>Yeah. Okay, look, we have a picture of this. We could bring it up, guys, because I want oh, A j help the audience. Understand? Unaware data Discovery fits in here. This is as we talked about this, a complicated situation for a lot of customers. They got a variety of different tools, and you really laid it out nicely here in this diagram. So take us through. Sort of where that he spits. >>Yeah. I mean, where at the right hand side, This exchange. You know, we're really now in a data driven economy that is, everything's connected through AP, eyes that we consume on mine free mobile relapse. And what's not a parent is the chain of activities and tasks that have to go into serving that data two and eight p. I. At the outset, there may be many legacy systems, technologies, platforms on premise and cloud hybrids. You name it. Andi across those silos. Getting to a unified view is the heavy lifting. I think we've seen Cem some great impacts that be I titles such as Power Bi I tableau looker on DSO on in Clear. Who had Andi there in our ecosystem on visualising Data and CEO's managers, people that are working in companies day to day get a lot of value from saying What's the was the real time activity? What was the trend over this month? First his last month. The tools to enable that you know, we here, Um, a lot of good things are work that we're doing with snowflake mongo db on the public cloud platforms gcpd as your, um, about enabling building those pay planes to feed into those analytics. But what often gets hidden is have you sauce that data that could be locked into a mainframe, a data warehouse? I ot data on DPA, though, that all of that together that is the reality of that is it's it's, um, it's a lot of heavy lifting It z hands on what that, um, can be time consuming on the issue There is that data may have value. It might have potential to have an impact on the on the top line for a business on outcomes for consumers. But you never any sure unless you you've done the investigation discovered it unified that Onda and be able to serve that through to other technologies. >>Guys have. You would bring that picture back up again because A. J, you made a point, and I wanna land on that for a second. There's a lot of manual curating. Ah, an example would be the data catalogue if they decide to complain all the time that they're manually wrangling data. So you're trying to inject automation in the cycle, and then the other piece that I want you to addresses the importance of AP eyes. You really can't do this without an architecture that allows you to connect things together. That sort of enables some of the automation. >>Yeah, I mean, I don't take that in two parts. They would be the AP eyes so virtual machines connected by AP eyes, um, business rules and business logic driven by AP eyes applications. So everything across the stack from infrastructure down to the network um, hardware is all connected through AP eyes and the work of serving data three to an MP I Building these pipelines is is often, um, miscalculated. Just how much manual effort that takes and that manual ever. We've got a nice list here of what we automate down at the bottom. Those tasks of indexing, labeling, mapping across different legacy systems. Um, all of that takes away from the job of a data scientist today to engineer it, looking to produce value monetize data on day two to help their business day to conceive us. >>Yes. So it's that top layer that the business sees, of course, is a lot of work that has to go went into achieving that. I want to talk about some of the key tech trends that you're seeing and one of the things that we talked about a lot of metadata at the importance of metadata. It can't be understated. What are some of the big trends that you're seeing metadata and others? >>Yeah, I'll summarize. It is five. There's trains now, look, a metadata more holistically across the enterprise, and that really makes sense from trying. Teoh look across different data silos on apply, um, a policy to manage that data. So that's the control piece. That's that lever the other side's on. Sometimes competing with that control around sense of data around managing the costs of data is innovation innovation, being able to speculate on experiment and trying things out where you don't really know what the outcome is. If you're a data scientist and engineer, you've got a hypothesis. And now, before you got that tension between control over data on innovation and driving value from it. So enterprise wide manage data management is really helping to enough. Where might that latent value be across that sets of data? The other piece is adaptive data governance. Those controls that that that stick from the data policemen on day to steer its where they're trying to protect the organization, protect the brand, protect consumers data is necessary. But in different use cases, you might want to nuance and apply a different policy to govern that data run of into the context where you may have data that is less sensitive. Um, that can me used for innovation. Andi. Adapting the style of governance to fit the context is another trend that we're seeing coming up here. A few others is where we're sitting quite extensively and working with automating data discovery. We're now breaking that down into what can we direct? What do we know is a business outcome is a known up front objective on direct that data discovery to towards that. And that means applying around with Dems run technology and our tools towards solving a known problem. The other one is autonomous data discovery. And that means, you know, trying to allow background processes do winds down what changes are happening with data over time flagging those anomalies. And the reason that's important is when you look over a length of time to see different spikes, different trends and activity that's really giving a day drops team the ability to to manage and calibrate how they're applying policies and controls today. There, in the last two David that we're seeing is this huge drive towards self service so reimagining how to play policy data governance into the hands off, um, a day to consumer inside a business or indeed, the consumer themselves. The South service, um, if their banking customer or healthcare customer and the policies and the controls and rules, making sure that those are all in place to adaptive Lee, um, serve those data marketplaces that, um when they're involved in creating, >>I want to ask you about the autonomous data discovering the adaptive data. Governance is the is the problem where addressing their one of quality. In other words, machines air better than humans are doing this. Is that one of scale that humans just don't don't scale that well, is it? Is it both? Can you add some color to that >>yet? Honestly, it's the same equation that existed 10 years ago, 20 years ago. It's It's being exacerbated, but it's that equation is how do I control both things that I need to protect? How do we enable innovation where it is going to deliver business value? Had to exchange data between a customer, somebody in my supply chains safely. And all of that was managing the fourth that leg, which is cost overheads. You know, there's no no can checkbook here. I've got a figure out. If only see io and CDO how I do all of this within a fixed budget so that those aspects have always been there. Now, with more choices. Infrastructure in the cloud, um, NPR driven applications own promise. And that is expanding the choices that a a business has and how they put mandated what it's also then creating a layer off management and data governance that really has to now, uh, manage those full wrath space control, innovation, exchange of data on the cost overhead. >>That that top layer of the first slide that we showed was all about business value. So I wonder if we could drill into the business impact a little bit. What do your customers seeing you know, specifically in terms of the impact of all this automation on their business? >>Yeah, so we've had some great results. I think view the biggest Have Bean helping customers move away from manually curating their data in their metadata. It used to be a time where for data quality initiatives or data governance initiative that be teams of people manually feeding a data Cavallo. And it's great to have the inventory of classified data to be out to understand single version of the trees. But in a having 10 15 people manually process that keep it up to date when it's moving feet. The reality of it is what's what's true about data today? and another few sources in a few months. Time to your business on start collaborating with new partners. Suddenly the landscape has changed. The amount of work is gonna But the, um, what we're finding is through automating creating that data discovery feeding a dent convoke that's releasing a lot more time for our CAS. Mr Spend on innovating and managing their data. A couple of others is around cell service data and medics moving the the choices of what data might have business value into the hands of business users and and data consumers to They're faster cycle times around generating insights. Um, we really helping that by automating the creation of those those data sets that are needed for that. And in the last piece, I'd have to say where we're seeing impacts. A more recently is in the exchange of data. There are a number of marketplaces out there who are now being compelled to become more digital to rewire their business processes. Andi. Everything from an r p a initiative. Teoh automation involving digital transformation is having, um, see iose Chief data officers Andi Enterprise architects rethink how do they how they re worthy pipelines? But they dated to feed that additional transformation. >>Yeah, to me, it comes down to monetization. Of course, that's for for profit in industry, from if nonprofits, for sure, the cost cutting or, in the case of healthcare, which we'll talk about in a moment. I mean, it's patient outcomes. But you know, the the job of ah, chief data officer has gone from your data quality and governance and compliance to really figuring out how data and be monetized, not necessarily selling the data, but how it contributes for the monetization of the company and then really understanding specifically for that organization how to apply that. And that is a big challenge. We chatted about it 10 years ago in the early days of a Duke. And then, you know, 1% of the companies had enough engineers to figure it out. But now the tooling is available, the technology is there and the the practices air there, and that really to me, is the bottom line. A. J is it says to show me the money. >>Absolutely. It's is definitely then six sing links is focusing in on the saying over here, that customer Onda, where we're helping there is dio go together. Those disparities siloed source of data to understand what are the needs of the patient of the broker of the if it's insurance? Ah, one of the needs of the supply chain manager If its manufacturing onda providing that 3 60 view of data, um is helping to see helping that individual unlock the value for the business. Eso data is providing the lens, provided you know which data it is that can God assist in doing that? >>And you know, you mentioned r p A. Before an r p A customer tell me she was a six Sigma expert and she told me we would never try to apply six segment to a business process. But with our P A. We can do so very cheaply. Well, what that means is lower costs means better employee satisfaction and, really importantly, better customer satisfaction and better customer outcomes. Let's talk about health care for a minute because it's a really important industry. It's one that is ripe for disruption on has really been up until recently, pretty slow. Teoh adopt ah, lot of the major technologies that have been made available, but come, what are you seeing in terms of this theme, we're using a putting data to work in health care. Specific. >>Yeah, I mean, healthcare's Havlat thrown at it. There's been a lot of change in terms of legislation recently. Um, particularly in the U. S. Market on in other economies, um, healthcare ease on a path to becoming more digital on. Part of that is around transparency of price, saying to be operating effectively as a health care marketplace, being out to have that price transparency, um, around what an elective procedure is going to cost before taking that that's that forward. It's super important to have an informed decision around there. So we look at the US, for example. We've seen that health care costs annually have risen to $4 trillion. But even with all of that on cost, we have health care consumers who are reluctant sometimes to take up health care if they even if they have symptoms on a lot of that is driven through, not knowing what they're opening themselves up to. Andi and I think David, if you are, I want to book, travel, holiday, maybe, or trip. We want to know what what we're in for what we're paying for outfront, but sometimes in how okay, that choice, the option might be their plan, but the cost that comes with it isn't so recent legislation in the US Is it certainly helpful to bring for that tryst price, transparency, the underlying issue there? There is the disparity. Different formats, types of data that being used from payers, patients, employers, different healthcare departments try and make that make that work. And when we're helping on that aspect in particular related to track price transparency is to help make that date of machine readable. So sometimes with with data, the beneficiary might be on a person. I've been a lot of cases now we're seeing the ability to have different systems, interact and exchange data in order to process the workflow. To generate online at lists of pricing from a provider that's been negotiated with a payer is, um, is really a neighboring factor. >>So, guys, I wonder if you bring up the next slide, which is kind of the Nirvana. So if you if you saw the previous slide that the middle there was all different shapes and presumably to disparage data, this is that this is the outcome that you want to get. Everything fits together nicely and you've got this open exchange. It's not opaque as it is today. It's not bubble gum band aids and duct tape, but but but described this sort of outcome the trying to achieve and maybe a little bit about what gonna take to get there. >>Yeah, that's a combination of a number of things. It's making sure that the data is machine readable. Um, making it available to AP eyes that could be our ph toes. We're working with technology companies that employ R P. A full health care. I'm specifically to manage that patient and pay a data. Teoh, bring that together in our data Discovery. What we're able to do is to classify that data on having made available to eight downstream tour technology or person to imply that that workflow to to the data. So this looks like nirvana. It looks like utopia. But it's, you know, the end objective of a journey that we can see in different economies there at different stages of maturity, in turning healthcare into a digital service, even so that you could consume it from when you live from home when telling medicine. Intellicast >>Yes, so And this is not just health care but you wanna achieve that self service doing data marketplace in virtually any industry you working with TCS, Tata Consultancy Services Toe Achieve this You know, if you are a company like Iota has toe have partnerships with organizations that have deep industry expertise Talk about your relationship with TCS and what you guys are doing specifically in this regard. >>Yeah, we've been working with TCS now for room for a long while. Andi will be announcing some of those initiatives here where we're now working together to reach their customers where they've got a a brilliant framework of business for that zero when there re imagining with their clients. Um, how their business cause can operate with ai with automation on, become more agile in digital. Um, our technology, the dreams of patients that we have in our portfolio being out to apply that at scale on the global scale across industries such as banking, insurance and health care is is really allowing us to see a bigger impact on consumer outcomes. Patient outcomes And the feedback from TCS is that we're really helping in those initiatives remove that friction. They talk a lot about data. Friction. Um, I think that's a polite term for the the image that we just saw with the disparity technologies that the legacy that has built up. So if we want to create a transformation, Um, having a partnership with TCS across Industries is giving us that that reach and that impacts on many different people's day to day jobs and knives. >>Let's talk a little bit about the cloud. It's It's a topic that we've hit on quite a bit here in this in this content Siri's. But But you know, the cloud companies, the big hyper scale should put everything into the cloud, right? But but customers are more circumspect than that. But at the same time, machine intelligence M. L. A. The cloud is a place to do a lot of that. That's where a lot of the innovation occurs. And so what are your thoughts on getting to the cloud? Ah, putting dated to work, if you will, with machine learning stuff you're doing with aws. What? You're fit there? >>Yeah, we we and David. We work with all of the cloud platforms. Mike stuffed as your G, c p IBM. Um, but we're expanding our partnership now with AWS Onda we really opening up the ability to work with their Greenfield accounts, where a lot of that data that technology is in their own data centers at the customer, and that's across banking, health care, manufacturing and insurance. And for good reason. A lot of companies have taken the time to see what works well for them, with the technologies that the cloud providers ah, are offered a offering in a lot of cases testing services or analytics using the cloud to move workloads to the cloud to drive Data Analytics is is a real game changer. So there's good reason to maintain a lot of systems on premise. If that makes sense from a cost from a liability point of view on the number of clients that we work with, that do have and we will keep their mainframe systems within kobo is is no surprise to us, but equally they want to tap into technologies that AWS have such a sage maker. The issue is as a chief data officer, I don't have the budget to me, everything to the cloud day one, I might want to show some results. First upfront to my business users Um, Onda worked closely with my chief marketing officer to look at what's happening in terms of customer trains and customer behavior. What are the customer outcomes? Patient outcomes and partner at comes I can achieve through analytics data signs. So I, working with AWS and with clients to manage that hybrid topology of some of that data being, uh, in the cloud being put to work with AWS age maker on night, I hope being used to identify where is the data that needs to bay amalgamated and curated to provide the data set for machine learning advanced and medics to have an impact for the business. >>So what are the critical attributes of what you're looking at to help customers decide what what to move and what to keep, if you will. >>Well, what one of the quickest outcomes that we help custom achieve is to buy that business blustery. You know that the items of data that means something to them across those different silos and pour all of that together into a unified view once they've got that for a data engineer working with a a business manager to think through how we want to create this application. There was the turn model, the loyalty or the propensity model that we want to put in place here. Um, how do we use predictive and medics to understand what needs are for a patient, that sort of innovation is what we're looking applying the tools such a sagemaker, uh, night to be west. So they do the the computation and to build those models to deliver the outcome is is across that value chain, and it goes back to the first picture that we put up. David, you know the outcome Is that a P I On the back of it, you've got the machine learning model that's been developed in That's always such as data breaks. But with Jupiter notebook, that data has to be sourced from somewhere. Somebody has to say that yet you've got permission to do what you're trying to do without falling foul of any compliance around data. Um, it'll goes back to discovering that data, classifying it, indexing it in an automated way to cut those timelines down two hours and days. >>Yeah, it's the it's the innovation part of your data portfolio, if you will, that you're gonna put into the cloud. Apply tools like sage maker and others. You told the jury. Whatever your favorite tool is, you don't care. The customer's gonna choose that and hear the cloud vendors. Maybe they want you to use their tool, but they're making their marketplaces available to everybody. But it's it's that innovation piece, the ones that you where you want to apply that self service data marketplace to and really drive. As I said before monetization. All right, give us your final thoughts. A. J bring us home. >>So final thoughts on this David is that at the moment we're seeing, um, a lot of value in helping customers discover that day the using automation automatically curating a data catalogue, and that unified view is then being put to work through our A B. I's having an open architecture to plug in whatever tool technology our clients have decided to use, and that open architecture is really feeding into the reality of what see Iose in Chief Data Officers of Managing, which is a hybrid on premise cloud approach. Do you suppose to breed Andi but business users wanting to use a particular technology to get their business outcome having the flexibility to do that no matter where you're dating. Sitting on Premise on Cloud is where self service comes in that self service. You of what data I can plug together, Dr Exchange. Monetizing that data is where we're starting to see some real traction. Um, with customers now accelerating becoming more digital, uh, to serve their own customers, >>we really have seen a cultural mind shift going from sort of complacency. And obviously, cove, it has accelerated this. But the combination of that cultural shift the cloud machine intelligence tools give give me a lot of hope that the promises of big data will ultimately be lived up to ah, in this next next 10 years. So a J ahora thanks so much for coming back on the Cube. You're you're a great guest. And ah, appreciate your insights. >>Appreciate, David. See you next time. >>All right? And keep it right there. Very right back. Right after this short break

>>from around the globe. It's the Cube with coverage of Susic on digital brought to you by Susan. >>Welcome back. I'm stew minimum in coming to you from our Boston area studio. And this is the Cube's coverage of Silicon Digital 20. Happy to welcome to the program. Two of the keynote president presenters. First of all, we have Dr Mr Giacomo. He is the president of engineering and innovation and joining him, his presenter on the keynote stage, Daniel Nelson, who is the Vice president of Product solutions. Both of you with Souza. Gentlemen, thanks so much for joining us. >>Thank you. Thank you for having us. >>All right, So? So, Dr T let let's start out. You know, innovation, open source. Give us a little bit of the message for our audience that Daniel are talking about on stage. You know how you know we've been watching for decades the growth in the proliferation of open source and communities. So give us the update there, >>Andi. It's not stopping. It's actually growing even more and more and more and more innovations coming from open source. The way we look at it is that our customers that they have their business problems have their business reality. Andi s So we we have to curate and prepare and filter all the open source innovation that they can benefit from because that takes time to understand that. Match your needs and fix your problems. So it's Susa. We've always done that since 27 per sales. So working in the open source projects innovating they are, but with customers in mind. And what is pretty clear in 2020 is that large enterprises, small startups. Everybody's doing software. Everybody's doing, I t. And they all have the same type of needs in a way. They need to simplify their landscape because they've been accumulating investments all the way. Our infrastructure Joseph well, different solutions, different platforms from different bundles. They need to simplify that and modernize and the need to accelerate their business, to stay relevant and competitive in their own industries. And that's what we're focusing on. >>Yeah, it's interesting. I completely agree. When you say simplify thing, you know, Daniel, I I go back in the communities about 20 years, and in those days, you know, we were talking about the operating clinic was helping to, you know, go past the proprietary UNIX platforms. Microsoft, the enemy. And you were talking about, you know, operating system server storage, the application that it was a relatively simple environment and inherited today's, you know, multi cloud ai in your based architecture, you know, applications going through this radical transformation growth, though, give us a little bit of insight as to, you know, the impact this is having on ecosystems. And of course, you know, Susie's now has a broad portfolio that at all >>it's a great question, and I totally get where you're coming from. Like if you look 20 years ago, the landscape is completely different that the technologies were using or you're completely different. The problems were trying to solve with technology are more and more sophisticated, you know, at the same time that you know, there's kind of nothing new under the sun, every company, every technology, you know, every you know, modality goes through. This expansion of capabilities and the collapse around simplification is the capabilities become more more complex, manageable. And so there's this continuous tension between capabilities, ease of use, consume ability. What we see with open source is that that that that's kind of dynamic that still exist, but it's more online of like. Developers want easy to use technologies, but they want the cutting edge. They want the latest things. They want those things within their packets. And then if you look at operations groups or or or people that are trying to consume that technology, they want that technology to be consumable simple. It works well with others. People tend to pick and choose and have one pane of glass field operate within that. And that's where we see this dynamic. And that's kind of what the Susan portfolio was built. It's like, How do we take, you know, the thousands and thousands of developers that are working on these really critical projects, whether it's Linux is like you mentioned or kubernetes or for cloud foundry? And how do we make that then more consumable to the thousands of companies that are trying to do it, who may even be new to open source or may not contribute directly but have all the benefits that are coming to it. And that's where Susan fits and worse. Susan, who's fits historically and where we see us continuing to fit long term, is taking older is Legos. Put it together for companies that want that and then allow them a lot of autonomy and choice and how these technologies are consumed. >>One of the themes that I heard you both talked about in the keynote it was simplifying modernized. Telerate really reminded me of the imperatives of the CIO. You know, there's always run the business they need to help grow the business. And if they have the opportunity, they want to transform the business. I think you know, you said run improve in scale scale. Absolutely. You know, a critical thing that we talk about these days when I think back to the Cloud Foundry summit. You know, on the keynote stage, it was in the old way. If I could do faster, better, cheaper. Ah, you could use two of them today. We know faster, faster, faster is what you want. So >>it was a >>little bit of insight as to who you know, you talked about, you know, cloud foundry and kubernetes application modernization. You know, what are the imperatives that you're hearing from customers? And how are we with all of these tools out there? Hoping, You know, I t not just be responsive to the business, but it actually be a driver for the transformation of the business. >>It's a great question. And so when I talk to customers and Dr T feel free to chime in, you talked. You know, as many or more customers than then Ideo. You know they do have these these what are historically competing imperatives. But what we see with the adoption of some of these technologies that that faster is cheaper, faster is safer, you know, creating more opportunities to grow and to innovate better is the business. It's not risk injection when we change something, it's actually risk mitigation when we get good and changing. And so it's kind of that that that modality of moving from, um, you know, a a simplify model or very kind of like a manufacturing model of software so much more organic, much more permissive, much more being able to learn with an ecosystem style. And so that's how we see companies start to change the way they're adopting the technology. What's interesting about them is that same level of adoption that seemed thought of adoption is also how open source is is developed open source is developed organically is developed with many eyes. Make shallow bots is developed by like, Let me try this and see what happens right and be able to do that in smaller and smaller recommends. Just like we look at red Green deployments or being able to do micro services or binary or any of those things. It's like let's not do one greatly or what we're used to in waterfall, cause that's actually really risk. Let's do many, many, many steps forward and be able to transform an iterative Lee and be able to go faster iterative Lee and make that just part of what the business is good at. And so you're exactly right, like those are the three imperatives of the CIO. What I see with customers is the more that they are aligning those three areas together and not making them separate. But we have to be better at being faster and being transformed. And those are the companies that are really using I t. As a competitive advantage within the rich. >>Yeah, because most of the time they're different starting points. They have a history. They have different business strategy and things they've done in the past, you need to be able to accommodate all of that and the faster micro service, that native developments for sure, for the new APS. But they're also coming from somewhere on diff. You don't take care of that. You get are you can just accelerates if you simplify your existing because otherwise you spend your time making sure that your existing it's still running. So you have to combine all of that together. And, yeah, do you mentioned about funding and communities? And that's really I love those topics because, I mean, everybody knows about humanities. Now. It's picking up in terms of adoption in terms of innovation, technology building ai ml framework on top of it now, what's very interesting as where is that cloud? Foundry was designed for fast software development until native from the beginning, that 12 factor app on several like 45 years ago. Right? What we see now is we can extract the value that cloud foundry brings to speed up and accelerate your stuff by the Romans hikers, and we can combine that very nicely on very smoothly, simple in a simple way, with all the benefits you get from kubernetes and not from one communities from your communities running in your public clouds because you have records. They are. You have services that you want to consume from one public clouds. We have a great silicon fireside chat with open shot from Microsoft Azure actually discussing those topics. You might have also communities clusters at the edge that you want to run in your factory or close to your data and workloads in the field. So those things and then you mentioned that as well, taking care of the I T ops, simplify, modernize and accelerate for the I T ops and also accelerates forward their local themselves. We're benefiting from a combination of open source technologies, and today there's not one open source technology that can do that. You need to bundle, combine them, get our best, make sure that they are. They are integrated, that they are certified to get out of their stable together, that the security aspects, all the technology around them are integrating the services as well. >>Well, I'm really glad you brought up, you know, some of those communities that are out there, you know, we've been saying for a couple of years on the Cube. You know, Kubernetes is getting baked in everywhere. You know, Cisco's got partnerships with all the cloud providers, and you're not fighting them over whether to use a solution that you have versus theirs. I worry a little bit about how do I manage all of those environments. You end up with kubernetes sprawl just like we have with every other technology out there. Help us understand what differentiates Tuesday's, you know, offerings in this space. And how do you fit in with you know, the rest of that very dynamic and defer. >>So let me start with the aspect of combining things together on and Danielle. Maybe you can take the management piece. So the way we are making sure that Sousa, that we don't also just miles into a so this time off tools we have a stack, and we're very happy if people use it. But the reality is that there are customers that they have. Some investments have different needs. They use different technologies from the past. But we want to try different technologies, so you have to make sure that's for communities. Like for any other part of the stack. The I T stock of the stack. Your pieces are model around that you can accommodate different. Different elements are typically at Susa. We support different types off hyper visors. Well, that's focused on one. But we can support KPMG's and I probably this way, all of the of the Nutanix, hyper visor, netapp, hyper visors and everything. Same thing with the OS. There's not only one, we know that people are running, and that's exactly the same. Which humanities? And there's no one, probably that I've seen in our customer base that will just need one vendor for communities because they have a hybrid needs and strategy, and they will benefit from the native communities they found on a ks e ks decay. I remember clouds, you name them Andi have vendors in Europe as well. Doing that so far for us, it's very important that we bring us Sutro. Custom. Males can be combined with what they have, what they want, even if it's from the circle competition. And so this is a cloud. Foundry is running on a case. You can find it on the marketplace of public clouds. It could run on any any any communities. He doesn't have to be sitting on it. But then you end up with a lot of sales, right? How do we deal with that? >>So it's a great question, and I'll actually even broaden that out because it's not like we're only running kubernetes. Yes, we've got lots of clusters. We've got lots of of containers. We've got lots of applications that are moving there, but it's not like all the V M's disappear. It's not like all the beige boxes, like in the data center, like suddenly don't exist. You know, we we we all bring all the sense and decisions in the past word with us wherever we go. And so for us, it's not just that lens of how do we manage the most modern, the most cutting edge? That's definitely a part of it. But how do you do that? Within the context of all the other things you have to do within your business? How do I manage virtual virtual machines? How do I manage bare metal? How do I manage all those? And so for us, it's about creating a presentation layer on top of that where you can look at your clusters. Look at your V EMS. Look at all your deployments and be able to understand what's actually happening with the fire. We don't take a prescriptive approach. We don't say you have to use one technology. You have to use that. What we want to do is to be adaptive to the customer's needs. And so you've got these things here, some of our offerings. You've got some legacy offerings to Let's show you bring those together. Let's show you how you modernize your viewpoints, how you simplify your operational framework and how you end up accelerating what you can do with the staff that you've got in place. >>Yeah, I'm just on the management piece. Is there any recommendation from your team? You know, last year at Microsoft ignite, there was the launch of Azure are on. And, you know, we're starting to see a lot of solutions come out. There are concerns. Is that any of us that live through the multi vendor management days, um, you know, don't have good memories from those. It is a different discussion if we're just talking about kind of managing multiple kubernetes. But how do we learn from the past and you know, What do you recommend for people in this, you know, multi cloud era. >>So my suggestion to customers is you always start with what are your needs? What is strategic problems you're trying to solve, and then choose a vendor that is going to help you solve those strategic problems? So is it going to take a product centric view Isn't gonna tell you use this technology and this technology and this technology, what is going to take the view of, like, this is the problem you're gonna solve? Let me be your advisor within that and choose people that you're going to trust within that, um, that being said, you wanna have relationships with customers that have been there for a while that have done this that have a breath of experience in solving enterprise problems because everything that we're talking about is mostly around the new things. But keep in mind that there are there are nuances about the enterprise. There are things that are that are intrinsically bound within the enterprise that it takes a vendor with a lot of enterprise experience to be able to meet customers where they are. I think you've seen that you know in some of the some of the real growth opportunities with them hyper scaler that they've kind of moved into being more enterprise view of things, kind of moving away from just an individual bill perspective, enterprise problems. You're seeing that more and more. I think vendors and customers need to choose companies that meet them where they are that enable their decisions. Don't prescribe there. >>Okay, go ahead. >>Yes, Sorry. Yeah. I also wanted to add that I would recommend people to look at open source based solutions because that will prevent them to be in a difficult situation, potentially in the three years from now. So there are open source solutions that can do that on book. A viable, sustainable, healthy, open source solutions that are not just one vendor but multi vendor as well, because that leaves those open options open for you in the future as well. So if you need to move for another vendor or if you need to implement with an additional technology, you've made a new investment or you go to a new public clouds. If you based Duke Tracy's on open source, you have a little chance but later left >>I think that's a great point. Dr. T and I would you know, glom onto that by saying customers need to bring a new perspective on how they adjudicate these solutions, like it's really important to look at the health of the open source community. Just because it's open source doesn't mean that there's a secret army of gnomes that, you know in the middle of the night going fixed box, like there needs to be a healthy community around that. And that is not just individual contributors. That is also what are the companies that are invested in this, where they dedicating resources like That's another level. So what level of sophistication that a lot of customers need to bring into their own vendor selection? >>Excellent. Uh, you know, speaking about communities in open source. Want to make sure you have time share a little bit about the AI platform discussed in your >>Yeah, it's very, very interesting. And something I'm super excited about it, Sousa. And it's kind of this this, uh, we're starting to see ai done in these really interesting problems to solve and like, I'll just give you one example is that we're working with um uh, Formula One team around using AI to help them actually manage in car mechanics and actually manage some of the things that they're doing to get super high performance out of their vehicles. And that is such an interesting problem to solve. And it's such a natural artificial intelligence problem that even when you're talking about cars instead of servers or you're talking about race tracks, you know instead of data centers, you still got a lot of the same problems. And so you need an easy to use AI stack. You need it to be high performance. You needed to be real time. You need to be able to decisions made really quickly, easy, the same kinds of problems. But we're starting to see them in all these really interesting wheels in areas, which is one of the coolest things that I've seen in my career. Especially is in terms of I T. Is that I t is really everywhere. It's not. Just grab your sweater and go to the data center because it's 43 degrees in there. You know, it's also getting on the racetrack. It's also go to the airfield. It's also go to the grocery store and look at some of the problems being being being addressed himself there. And that is super fascinating. One of the things that I'm super excited up in our industry in total. >>Alright, well, really good to discussion here, Daniel. Dr B. Thank you so much for sharing everything from your keynote and been a pleasure washing. >>Thank you. >>Alright, Back with lots more coverage from Susan Con Digital 20. I'm stew minimum. And as always, Thank you for watching. >>Yeah, yeah, yeah.

>>Bach from Las Vegas. It's the cube covering AWS executive summit brought to you by extension. >>Welcome back everyone to the cubes live coverage of the Accenture executive summit here at the Venetian in Las Vegas. We are part of AWS reinvent. I'm your host, Rebecca Knight. We are joined by two guests for this segment. We have Prisaad Sanker and he is a senior managing director global ICI lead. Thank you so much for coming on the show. Personal and Larry soccer, global managing director ICI offerings. Thank you so much for coming on Larry. So present to minister with you this week marks the one year anniversary of intelligent cloud infrastructure, a group that you lead at Accenture. Tell us a little bit more about, about, first of all why this group was formed and the journey you've had this year, the highest, the highs and lows. >>Sure, sure. So first, first of all, thank you for having us. Um, so as you mentioned, December 1st will be one year of having formed this group. And the reason we did that was because all of our clients are going through a journey of digital transformation. And it's very important for us to be able to support that journey. So there are different elements that we have to bring together around cloud as well as infrastructure. So we brought together this group, which was actually in different parts of Accenture as one particular group, and we call it intelligent cloud. And infrastructure consists of 30,000 people pretty much in every part of the world supporting all different industries. And this is a way for us to bring together not just cloud computing, but also areas like networking, workplace, digital, other digital businesses that we need to be able to support in order to be able to help our clients through their journey of transformation. >>So this, this group was formed at a time of tremendous change and upheaval and the landscape. Talk to us a little bit about, we hear so much about digital transformation, our company's ready. What's the, let us into the client mindset. >>Yeah. So what happens is, you know, different industries obviously are progressing at different speeds. All of our clients are always worried about being disrupted within their industries, either by an existing competitor all by a completely new competitor that doesn't exist. You know, all the stories about, you know, the big companies that existed and almost vanished overnight. So that's something that keeps CEOs and CIO is awake at night just worrying about that. And so digital transformation is very important for them to be relevant to their client. It's all about bringing new products to their clients and also the speed with which they can actually do that. It's no longer enough to be a fast follower. You have to be an innovator. And cloud is the way that this innovation will happen for our clients. And so it's very important for us to be able to bring our group together. We are able to support that journey for our clients. Leary >>want to bring you into this conversation a little bit. It'd be what will be required for enterprises to make this big transition. I mean, he was talking about how you need to be an innovator. You can't just be a fast follower. >> Well, I mean a lot of times I look at it just given the size, the scale of most of our clients who are really up market, most of them don't have the option to just do a rip and replace and just reinvent themselves completely. So it really is how do I very rapidly modernize and transform my business to take advantage of it? And it really needs to start with your application landscape and end data. So how do I start to look at all the possibilities of the AWS is and start to re-imagine, reinvent Duke, use cloud native technologies. Also a significant amount of their estates are already running in legacy environments. >>We get the mainframe or other environments. How do you digitally decouple those so that you can extract value out of that? And ultimately those decisions of apps and data that are going to drive cloud deployments and architectures and data gravity really becomes the key decision factor to decide where do I place this day? And it was a great example today if you saw Jesse's keynote, he announced Achla where they're actually starting to look at how do I move compute and the processing closer to the actual datasets. So actually inverting the problem and moving closer to the data. And then we see that trend starting to proliferate on the other part of the keynote that was very interesting was the five G announcement. And first you heard about AWS pushing into local zones where they were getting much distributing it out closer to them, reduce latency and really starting to push out. >>So ultimately we seen the whole landscape being transformed by data, these new application architectures and where that data resides and out to traditional world that we've known of hybrid with public and private is really transforming with the Amazon outputs, with the BMCs and stuff like that into much more one about shared and dedicated infrastructure. Then the big, the next real big thing that starts to happen then is this whole explosion of IOT. So as price performance goes down with Moore's law, we can start to see a lot more cost effective IOT solutions. And all of a sudden a world that was very centralized, you know, running up in the, in the world of the Amazons had the public cloud is not going to be much more distributed to a lot more of that compute over time gets moved out there. So we've seen a very rapidly evolving landscape. Apps and data are ultimately driving our cloud clients cloud and infrastructure investments. And they're really just trying to figure out how they can rapidly transform their environments to take advantage of this new landscape. >> So both of you are describing this exceedingly complex environment that is changing dizzying speed. I mean, just even this morning, but the Andy Jassy on stage for three hours with all of the new products and services that AWS has coming out with. What is AWS? What is ICI and Accenture doing to help clients navigate this, this, this, this landscape? Prisaad you know, our >>team is, it's not just enough for infrastructure and cloud to be a horizontal function as it used to be. We feel that, you know, one of the things that Accenture really brings to the table is our industry differentiation. Spent a lot of time analyzing the industries that our clients are in. So we've actually changed the team of ICI to be three different things. The first is to be industry led, so it's no longer good enough to be a horizontal function. We have to understand the needs of each industry and really look at how cloud and infrastructure will support that industry. The second is all about intelligence. And Larry just talked about the proliferation of data, but it's also bringing artificial intelligence, making networks much more smart, you know, really infusing intelligence into everything we do. And the third is the concept of being invisible because our clients are expecting infrastructure to just be there all the time. >>They don't really have to understand how it works, but it has to be there. It's just like going to into room and turning on a switch and you expect electricity to be there. So infrastructure has to be very much like, because it has to be ubiquitous, it has to be just available all the time. So those are the things that we are trying to bring to our clients to make it very specific for and very industry specific for for our clients. And this goes into areas like cloud computing. It goes into 5g edge is going to be a big part of what is going to happen in various industries. And as Larry talked about, IOT devices are going to be just proliferating. It's going to be billions of IOT devices. There's trillions of dollars being spent. In fact, I think the spend on IOT is probably bigger than any other area that I have seen probably in my working lifetime. So it's going to be an exciting time to come for us. >>I mean, we tend to think about artificial intelligence as this futuristic Jetsons kind of thing, but really it's, it's here. And now, Larry, can you talk a little bit about how companies are using AI and having an impact already on their businesses? I mean obviously you see a lot of AI being used for different use cases. We saw some great examples today in Jesse's keynote and we're seeing a use for video analytics for example. And AI to try to figure out predictive maintenance type activity. So there's obviously a lot of business use cases. I think what's interesting from our perspective as well is a lot of the operational use cases. So if you take a look at it with all these new innovations, the rapid pace of change that we're seeing with cloud infrastructure, that application landscape, we've started to rely pretty heavily first on analytics to how do we, how do we figure out what's going on, how do we operate efficiently, how do we make sure we don't put the businesses grist, you know, really pivoting from reactive to proactive and predictive operations. >>We've obviously automated everything as much as we can. I've see AI actually playing a very interesting role in how we optimize these environments over time. So as you get a much more complex environment, much more dynamic, and with containers, Coobernetti's, serverless compute dynamic networks that Prisaad was talking about with software defined networking, AI is going to be the only way we can tune and optimize that over time. So you've obviously got all the business use cases that we see in healthcare that we see in mining, predictive operations and stuff like that. But how we actually use AI internally is going to be critical to how we actually be able to manage cloud and infrastructure and really optimize it over time. >>W what is the client? What's, what's on your minds of your customers right now? We know that only 20% of companies out there have really adopted the cloud. Two thirds have really yet to capture the benefits of the cloud. What are you hearing from them? What are they saying to you? What are their pain points? >>So I think, you know, all of our clients realize that ultimately the cloud is going to be where they will be at. You know, data centers are existing today, but at some point, you know, everybody's going to move to the cloud. Most of our clients have taken the easier workloads and you know, the easy part has already been done. That's the first 20% but 80% of the work still remains. And that's the more complicated work that has to come. So they're looking to us to give them the right solutions. And then there's a variety of other factors to be considered. For example, they have to look at security issues. They have to understand that, you know, there are data privacy aspects to be considered. So really it's a question of matching the right private and public options. And as Larry also mentioned, probably only 30 40% of the data will actually sit in the central cloud. Most of the other data is actually gonna move out to the edge with IOT devices and so on. So data gravity, where does your dataset, where does your compute sit? And Andy talked about it as well today in his keynote address. These are all things that are going to keep evolving and I think that's going to really change the landscape. >>I think they, I think they all see the power of cloud. I mean, which in my mind it's really around the innovation cycles. You know, what you look at the pace that they're innovating with with RDS and Redshift. So they all see that power. I think the biggest thing, they struggle with our skills. And culture because how do you upskill, retrain the organization, everything from the new technologies, how to architect in the new world where it's very ephemeral, dynamic, a serverless world. How do you start to adopt those technologies? How do you operationalize it? How do you go beyond just agile and really do true dev sec ops where you're integrating security and operations built in from the ground floor. And a lot of times he's a cultural change is one of the things we see in cloud and infrastructure operations for example, is how do you take develop operators who used to be eyes on glass, looking at console's turn them into developers where they, you know, they're writing the next analytic algorithms to get to predictive there they're automating automation scripts to improve operations and ultimately tuning the AI engines that optimize it. >>And I think that skills and culture barrier is probably the hardest thing for them to overcome. And how do you just, you can't just go to the cloud, you've got to behave differently. It really have to transform how you use it, how you operate and really transform the organization and culture. >>So these change management challenges, where do you even start? Because as you said, the adopting the technology is almost the easy part, or at least the most straightforward, but really getting everyone on board and really changing people's mindsets and mentalities and dispositions and the way that they collaborate with each other and collaborate cross-functionally. So what have you learned within ICI to, to help companies? And what's your advice? >>I think, I think there are three aspects that you have to get right. In fact, I was talking to one of the CEOs of a very large client of ours, and I think you have to get three things right and you've got to get them aligned and moving at the same time. The first obviously is the technology. So you have to understand what makes sense for you, for your industry. Make the right bets because if you make a wrong decision, then you know you're going to set yourself back. So getting the technology right obviously is important. The second is operating model, making sure that you get that the right operating model in place and kicked off right, right upfront. And the third, like Larry said, is transforming your workforce. So making sure that people are, you know, have all the right skill sets when you actually have the operating model and the technology ready. So it's very important to bring all those three aspects together and a company like Accenture, with our background around consulting, around change management, around technology, we're uniquely positioned understanding our client's industries and really bringing all of those three aspects together so that we're able to position our clients to take that journey forward. >>Larry, in terms of next year's Excenture executive summit, look into your crystal ball. You've already talked about a lot of emerging technologies, IOT, edge computing have talked a lot about AI. Of course. What do you think are going to be the hot topics? Looking ahead this this year in ice with an ICI, you >>touched on earlier, I think everyone's going to be talking about data gravity. As you get these bigger and bigger data sets, it becomes, you know, the network's always going to be the bottleneck. So even with Moore's law, stretching from 18 months to 24 the amount of data we produce, particularly with IOT and edge, is really going to transform things. And even though we've got massive network upgrades like 5g coming along, it will never be enough. I mean, that comes along every 12 years. We're seeing a doubling of price performance who competed? I think data gravity, you can start to see a very different landscape where it used to be public and private and now edge is really going to be obliterated to much more seamless architecture. Then there was a lot of the keynote today, and if you start to take a look at local zones and some of the announcements today, they were ready. Amazon was heading there with green Greengrass so you can have much more seamlessness. And how do I get compute closer to the processing? You're gonna be talking a lot about clustering, clustering, compute around datasets versus the other way around. So I think we're gonna see, and I think that's going to happen pretty fast. Usually a lot of this stuff we've been talking about IOT for years. I do think we're on the tipping point. I think we're about to see exponential growth just as price performance >>comes together. Some of the technologies had gotten gotten there, but, but I think that the whole focus on data and data gravity is what you're going to hear a lot about next year. I can't wait to hear the AWS reinvent band. Do a little pink Floyd or something like that for data gravity. We'll Larry and Prisaad. Thank you so much for coming on the cube. It was a pleasure having you on. Thanks for Brooke. I'm Rebecca night's stay tuned for more of the cubes live coverage of the Accenture executive summit.

>>from San Francisco. It's the queue every day to thank you. Brought to you by day to like you. Hey, >>welcome back already, Jeffrey. Here with the Cube were a day to IQ's headquarters in downtown San Francisco. They used to be metal sphere, which is what you might know them as. And they've rebranded earlier this year. And they're really talking about helping Enterprises in their journey to cloud native. And we're really excited to have really one of the product guys he's been here and seeing this journey and how through with the customers and helping the company transforming his Chandler hosing tonight. He's the s VP of engineering and product. Chandler, great to see you. Thanks. So, first off, give everyone kind of a background on on the day to like you. I think a lot of people knew mesosphere. You guys around making noise? What kind of changed in the marketplace to to do a rebranding? >>Sure. Yeah, we've been obviously, Mason's here in the past and may so so I think a lot of people watching the cube knows No, no one knows about Mace ose as as we were going along our journey as a company. We noticed that a lot of people are also asking for carbonates. Eso We've actually been working with kubernetes since I don't know 16 4017 something that for a while now and as Maur Maur as communities ecosystem starting involving mature more. We also want to jump in and take advantage of that. And we started building some products that were specific to kubernetes and eso. We thought, Look, you know, it's a little bit confusing for people May, SOS and Kubernetes and at times those two technologies were seen almost as competitive, even though we didn't always see it that way. The market saw it that way, so we said, Look, this is going too confusing for customers being called Mesa Sphere. Let's let's rebrand around Maur what we really do. And we felt like what we do is not just focus around one specific technology. We felt like we helped customers with more than that more than just may so support more than just community support, Andi said. Look, let's let's get us a name that shows what we actually do for customers, and that's really helping them take their workloads and put them on on Not just, you know, um, a source platform, but actually take their workloads, bring them into production and enterprise way. That's really ready for day two. And that's that's why we called it data. >>And let's unpack the day to, cause I think some people are really familiar with the concept of day two. And for some people, they probably never heard it. But it's a pretty interesting concept, and I think it packs a lot of meaning in it. A number of letters. I think you >>can kind of just think about it if you were writing software, right? I mean, Day zero is okay. We're gonna design it. We're gonna start playing with some ideas. We're gonna pull into different technologies. We're gonna do a POC. We're gonna build our skateboards. So to say, that's kind of your day. Zero. What do we want? Okay, we're gonna build a Data Analytics pipeline. We want spark. We're going to store data. Cassandra, we're gonna use cough. Go to pass it around. We're gonna run our containers on top of communities. That's just kind of your day. Zero idea. You get it working, you slap it on a cluster. Things are good right? Day one might be okay. Let's actually do a beta put in production in some kind of way. You start getting customers using it. But now, in Day two, after all that's done, you're like, Wait a second. Things were going wrong. Where's our monitoring? We didn't set that up. Where's our logging? Oh, I don't know. Like, >>who do we >>call this? Our container Run time, we think has above. Who do we call like? Oh, I don't know What support contract that we cut, Right? So that's the things that we want to help customers with. We want to help them in the whole journey, getting to Day two. But once they're there, we want them to be ready for day two, right? And that's what we do. >>I love it because one of my favorite quotes I've used it 1000 times. I'll do 2001 right? Is that open source is free like a puppy. Exactly for you. When you leave you guys, you're not writing a check necessarily to the to the shelter, But there's a whole lot of other check. You got a right and take care of. And I think that's such a key piece. Thio Enterprise, right. They need somebody to call when that thing breaks. >>Yeah. I mean, I haven't come from enterprise company. I was actually a customer basis Fear before I joined. Yeah, that's exactly why we're customers that we wanted. Not only that, insurance policy, but someone that partner with us as we start figuring this out, you know? I mean, just picking. You know what container run time do I want to use with communities? That one decision could take months if you're not familiar with it. And you you put a couple of your best architects on it. Go research container. You go research, cryo go research doctor. Tell me what's what's the best one we should use with kubernetes. Whereas if you're going, if you have a partnership with a company like day two, you can say, Look, I trust these. You know this company, they they're they're experts of this and they see a lot of this. Let's go with their recommendation. It's >>okay. So you got you got your white board. You've got a whole bunch of open source things going on, right? And you've got a whole bunch of initiatives and the pressure's coming down from from on high to get going, you've got containers, Asian and Cloud native and hybrid Cloud all the stuff. And then you've got some port CEO on his team trying to figure it out. You guys have a whole plethora of service is around some of these products. So as you try it and then you got the journey right and you don't start from from a standing start. You gotta go. You gotta go. So how do you map out the combination of how people progress through their journey? What are the different types of systems that they want to put in place and into, prioritize and have some type of a logical successful implementation and roll out of these things from day zero day 132? No, it's >>a great question. I think that's actually how we formed our product. Strategy is we've been doing this for a while now and we've we've gone. We've gone on this journey with really big advanced customers like ride sharing companies and large telcos customers like that. We've also gone on this journey with smaller, less sophisticated customers like, you know, industrial customers from the Midwest. Right? And those are two very, very different customers. But what's similar is they're both going on the same journey we feel like, but they're just at different places. So we wanted to build products, find the customer where they're at in their journey, and the way we see it really is just at the very beginning. It's just training, right? So we have, ah, bunch of support. We're sorry. Service is around training. Help you understand? Not just kubernetes, but the whole cloud native ecosystem. So what is all this stuff? How does it work? How does it fit together? How do I just deploy simple app to right? That's the beginning of it. We also have some products in that area as well, to help people scale their training across the whole whole organization. So that's really exciting for us once once, once that customer has their training down there like Okay, look, get I need a cluster now, like I need a destroyer of sorts and criminals itself is great, but it needs a lot of pieces to actually get it ready for prime time. And that's where we build a product called Convoy Say Okay, here is your enterprise great. Ready to go kubernetes destro right out of the box. And that product is really it's what you could use to just fiddle around with communities. It's also what you put into production right on the game. That's that's been scale tested, security tests and mixed workload tested. It's everything. So that's that's kind of our communities. Destro. So you've gotten your training. You have your destro and now you're like, OK, I actually wanna want to run some applesauce. >>Let me hold there. Is it Is it open corps? Or, you know, there's a lot of conversation in the way the boys actually >>the way we built convoy. It's a great question. The way we build convoys said, Okay, we don't We want to pick the best of breed from each of these. Have you seen the cloud native ecosystem kind of like >>by charter, high charter, whatever it is, where they have all the logos and all the different spiral thing. So it's crazy. Got thousands of logos, right? And >>we said, Look, we're gonna navigate this for you. What's the best container run time to pick. And it's It's almost as if we were gonna build this for ourselves using all open source technology. So convoys completely opens. Okay, um, there's some special sauce that we put in on how to bring these things together. Install it. But all the actual components itself is open source. Okay, so that's so if you're a customer, you're like, OK, I want open source. I don't want to be tied to any specific vendor. I want to run on Lee open. So >>yeah, I was just thinking in terms of you know, how Duke is a reference right. And you had, you know, the Horton worst cloud there and map our strategies, which were radically different in the way they actually packaged told a dupe under the covers. Yeah, >>you can think of it similar. How Cloudera per ship, Possibly where they had cdh. And they brought in a lot of open source. But they also had a lot of proprietary components to see th and what we've tried to get away from it is tying someone in tow. Us. I know that sounds counterintuitive from a business perspective, but we don't want customers to feel like if I go with D to like you. I always have to go with me to like you. I have to drink the Kool Aid, and I'm never gonna be able to get off. >>Kind of not. Doesn't really go with the open source. Exactly this stuff. It's not >>right for our customers, right? A lot of our customers want that optionality, and they don't want to feel locked in. And so when we built convoy, he said, Look, you know, if we were to start our own company, not not an infrastructure coming that we are right now, but just a software company build any kind of ab How would we approach it? And that was one of the problems we saw for We don't wanna feel like we're tied into any. >>Right. Okay, so you got to get the training, you got the products. What's >>next? What's next is if you think about the journey, you're like, OK, a lot. What we've found and this may or may not be totally true is one of the first things people like to run on committees is actually they're builds. So see, I see. And we said, How can we help with this. We looked around the market and there's a lot of great see, I see products out there right now. There's get lab, which is great partner of ours. It's a great product. There's there's your older products. Like Jenkins. There's a bunch of sass products, Travis. See all these things. But what we we wanted to do if we were customers of our own products is something that was native to Kubernetes. And so we started looking at projects like tectonic and proud. Some of these projects, right? And we said, How can we do the same thing we did with convoy where we bring these projects together and make it easy for someone to adopt these kubernetes native. See, I see tools. And we did some stuff there that we think is pretty innovative as well. And that's what that's the product we call dispatch. >>Okay. What do you got? More than just products. You've got profession service. That's right. So now >>you need help setting all this up. How do you actually bring your legacy applications to this new platform? How do you get your legacy builds onto these new build systems That that's where our service is coming the plate and kind of steer you through this whole journey. Lastly, what we next in the journey, though? Those service's compliment Really? Well, with with the kind of the rest of the product suite, right? And we didn't just stop with C i c. He said, what is the next type of work that we want to run here? Okay, so there we looked at things like red hat operators. Right? And we said, Look, red hats doing really cool thing here with this operator framework, how can we simplify it? We learn we've done a lot of this before with D. C. O s, where we built what we called the DCS sdk to help people bring advanced complex workloads onto that platform. And we saw a lot of similarities with operators to our d c West sdk. We said, How can we bring some of our understanding and knowledge to that world? And we built this open source product called kudo. Okay, people are free to go check that out. And that's how we bring more advanced workload. So if you think about the journey back to the journey again, you got some training you have your have your cluster, you put your builds on it. Now you want to run some advance work logs? That's where Kudo comes. >>Okay? And then finally, at the end of the trail is 1 800 I need help. Well, almost into the trail. We're not there yet. There was one thing they're still moving with one more step right on >>the very last one. Actually, we said, Okay, what's next in this journey? And that's running multiple clusters of the same. Okay, so that's kind of the scale. That's the end of the journey from for us, for our proxy as it stands right now. And that's where you build a product called Commander. And that's really helping us launch and manage multiple >>companies clusters at the same time. >>So it's so great that you have the perspective of a customer and you bring that directly in two. You know what you want because you just have gone through this this journey. But I'm just curious, you know, if you put your old hat on, you know, kind of c i o your customer. You know, you just talked about the cake chart with Lord knows how many logos? How do you help people even just begin to think about about the choices and about the crazy rapid change in what? That I mean? Kubernetes wasn't a thing four years ago to help them stay on top of it to help them, you know, both kind of have a night to the vision, you know, make sure you're delivering today on not just get completely distracted by every bright, shiny object that happens to come along. Yeah, no, >>I think it's really challenging for the buyers. You know, I think there's a, especially as the industry continues to make sure there's a new concept that gets thrown at all times. Service Manager. You know, some new, cool way to do monitoring or logging right? And you almost feel like a dinosaur. If you're not right on top of these things to go to a conference in, are you using? You know, you know B P f. Yet what is that? You didn't feel right? Exactly. I think I think most importantly, what customers want is the ability what, the ability to move their technology and their platforms as their business has the need. If the need isn't there for the business, and the technology is running well. There shouldn't be a reason to move to a new platform. Our new set of technologies, in fact, with dese us with Mason charities. To us, we have a lot of happy customers that are gonna be moving crib. Amazing if they wanted to anytime soon. Do you see What's that? Something's that criminal is currently doesn't do. It may never do because the community is just not focused on it that DCS is solving. And those customers just want to see that will continue to support them in the journey that they're on with their their business. And I think that's what's most important is just really understanding our customer's understanding their business, understand where they wanna go. What are their goals, So to say, for their technology platforms and and making sure you were always one step ahead >>of them, that's a >>good place to be one step ahead of demand. All right, well, thanks for for taking a few minutes and sharing the story. Appreciate it. Okay. Thank you. All right. Thanks. Chandler. I'm Jeff. You're watching >>the Cube. Where? Day two. I >>Q in downtown San Francisco. Thanks for watching. We'll see you next time

>>live from Miami, Florida It's the Q covering IBM is data in a I forum brought to you by IBM. >>We're back in Miami and you're watching the cubes coverage of the IBM data and a I forum. Tony hi. Amiss here is a distinguished engineer for Ditch the Digital and Cloud Business Analytics at IBM. Tony, first of all, congratulations on being a distinguished engineer. That doesn't happen often. Thank you for coming on the Cube. Thank you. So your area focus is on the B I and the Enterprise performance management space. >>Um, and >>if I understand it correctly, a big mission of yours is to try to modernize those make himself service, making cloud ready. How's that going? >>It's going really well. I mean, you know, we use things like B. I and enterprise performance management. When you really boil it down, there's that's analysis of data on what do we do with the data this useful that makes a difference in the world, and then this planning and forecasting and budgeting, which everyone has to do whether you are, you know, a single household or whether you're an Amazon or Boeing, which are also some of our clients. So it's interesting that we're going from really enterprise use cases, democratizing it all the way down to single user on the cloud credit card swipe 70 bucks a month >>so that was used to be used to work for Lotus. But Cognos is one of IBM's largest acquisitions in the software space ever. Steve Mills on his team architected complete transformation of IBM is business and really got heavily into it. I think I think it was a $5 billion acquisition. Don't hold me to that, but massive one of the time and it's really paid dividends now when all this sort of 2000 ten's came in and said, Oh, how Duke's gonna kill all the traditional b I traditional btw that didn't happen, that these traditional platforms were a fundamental component of people's data strategies, so that created the imperative to modernize and made sure that there could be things like self service and cloud ready, didn't it? >>Yeah, that's absolutely true. I mean, the work clothes that we run a really sticky were close right when you're doing your reporting, your consolidation or you're planning of your yearly cycle, your budget cycle on these technologies, you don't rip them out so easily. So yes, of course, there's competitive disruption in the space. And of course, cloud creates on opportunity for work loads to be wrong, Cheaper without your own I t people. And, of course, the era of digital software. I find it myself. I tried myself by it without ever talking to a sales person creates a democratization process for these really powerful tools that's never been invented before in that space. >>Now, when I started in the business a long, long time ago, it was called GSS decision support systems, and they at the time they promised a 360 degree view with business That never really happened. You saw a whole new raft of players come in, and then the whole B I and Enterprise Data Warehouse was gonna deliver on that promise. That kind of didn't happen, either. Sarbanes Oxley brought a big wave of of imperative around these systems because compliance became huge. So that was a real tailwind for it. Then her duke was gonna solve all these problems that really didn't happen. And now you've got a I, and it feels like the combination of those systems of record those data warehouse systems, the traditional business intelligence systems and all this new emerging tech together are actually going to be a game changer. I wonder if you could comment on >>well so they can be a game changer, but you're touching on a couple of subjects here that are connected. Right? Number one is obviously the mass of data, right? Cause data has accelerated at a phenomenal pace on then you're talking about how do I then visualize or use that data in a useful manner? And that really drives the use case for a I right? Because A I in and of itself, for augmented intelligence as we as we talk about, is only useful almost when it's invisible to the user cause the user needs to feel like it's doing something for them that super intuitive, a bit like the sort of transition between the electric car on the normal car. That only really happens when the electric car can do what the normal car can do. So with things like Imagine, you bring a you know, how do cluster into a B. I solution and you're looking at that data Well. If I can correlate, for example, time profit cost. Then I can create KP eyes automatically. I can create visualizations. I know which ones you like to see from that. Or I could give you related ones that I can even automatically create dashboards. I've got the intelligence about the data and the knowledge to know what? How you might what? Visualize adversity. You have to manually construct everything >>and a I is also going to when you when you spring. These disparage data sets together, isn't a I also going to give you an indication of the confidence level in those various data set. So, for example, you know, you're you're B I data set might be part of the General ledger. You know of the income statement and and be corporate fact very high confidence level. More sometimes you mention to do some of the unstructured data. Maybe not as high a confidence level. How our customers dealing with that and applying that first of all, is that a sort of accurate premise? And how is that manifesting itself in terms of business? Oh, >>yeah. So it is an accurate premise because in the world in the world of data. There's the known knowns on the unknown knowns, right? No, no's are what you know about your data. What's interesting about really good B I solutions and planning solutions, especially when they're brought together, right, Because planning and analysis naturally go hand in hand from, you know, one user 70 bucks a month to the Enterprise client. So it's things like, What are your key drivers? So this is gonna be the drivers that you know what drives your profit. But when you've got massive amounts of data and you got a I around that, especially if it's a I that's gone ontology around your particular industry, it can start telling you about drivers that you don't know about. And that's really the next step is tell me what are the drivers around things that I don't know. So when I'm exploring the data, I'd like to see a key driver that I never even knew existed. >>So when I talk to customers, I'm doing this for a while. One of the concerns they had a criticisms they had of the traditional systems was just the process is too hard. I got to go toe like a few guys I could go to I gotta line up, you know, submit a request. By the time I get it back, I'm on to something else. I want self serve beyond just reporting. Um, how is a I and IBM changing that dynamic? Can you put thes tools in the hands of users? >>Right. So this is about democratizing the cleverness, right? So if you're a big, broad organization, you can afford to hire a bunch of people to do that stuff. But if you're a startup or an SNB, and that's where the big market opportunity is for us, you know, abilities like and this it would be we're building this into the software already today is I'll bring a spreadsheet. Long spreadsheets. By definition, they're not rows and columns, right? Anyone could take a Roan Collin spreadsheet and turn into a set of data because it looks like a database. But when you've got different tabs on different sets of data that may or may not be obviously relatable to each other, that ai ai ability to be on introspect a spreadsheet and turn into from a planning point of view, cubes, dimensions and rules which turn your spreadsheet now to a three dimensional in memory cube or a planning application. You know, the our ability to go way, way further than you could ever do with that planning process over thousands of people is all possible now because we don't have taken all the hard work, all the lifting workout, >>so that three dimensional in memory Cuba like the sound of that. So there's a performance implication. Absolutely. On end is what else? Accessibility Maw wraps more users. Is that >>well, it's the ability to be out of process water. What if things on huge amounts of data? Imagine you're bowing, right? Howdy, pastors. Boeing How? I don't know. Three trillion. I'm just guessing, right? If you've got three trillion and you need to figure out based on the lady's hurricane report how many parts you need to go ship toe? Where that hurricane reports report is you need to do a water scenario on massive amounts of data in a second or two. So you know that capability requires an old lap solution. However, the rest of the planet other than old people bless him who are very special. People don't know what a laugh is from a pop tart, so democratizing it right to the person who says, I've got a set of data on as I still need to do what if analysis on things and probably at large data cause even if you're a small company with massive amounts of data coming through, people click. String me through your website just for example. You know what if I What if analysis on putting a 5% discount on this product based on previous sales have that going to affect me from a future sales again? I think it's the democratizing as the well is the ability to hit scale. >>You talk about Cloud and analytics, how they've they've come together, what specifically IBM has done to modernize that platform. And I'm interested in what customers are saying. What's the adoption like? >>So So I manage the Global Cloud team. We have night on 1000 clients that are using cloud the cloud implementations of our software growing actually so actually Maur on two and 1/2 1000. If you include the multi tenant version, there's two steps in this process, right when you've got an enterprise software solution, your clients have a certain expectation that your software runs on cloud just the way as it does on premise, which means in practical terms, you have to build a single tenant will manage cloud instance. And that's just the first step, right? Because getting clients to see the value of running the workload on cloud where they don't need people to install it, configure it, update it, troubleshoot it on all that other sort of I t. Stuff that subtracts you from doing running your business value. We duel that for you. But the future really is in multi tenant on how we can get vast, vast scale and also greatly lower costs. But the adoptions been great. Clients love >>it. Can you share any kind of indication? Or is that all confidential or what kind of metrics do you look at it? >>So obviously we look, we look a growth. We look a user adoption, and we look at how busy the service. I mean, let me give you the best way I can give you is a is a number of servers, volume numbers, right. So we have 8000 virtual machines running on soft layer or IBM cloud for our clients business Analytics is actually the largest client for IBM Cloud running those workloads for our clients. So it's, you know, that the adoption has been really super hard on the growth continues. Interestingly enough, I'll give you another factoid. So we just launched last October. Cognos Alex. Multi tenant. So it is truly multi infrastructure. You try, you buy, you give you credit card and away you go. And you would think, because we don't have software sellers out there selling it per se that it might not adopt as much as people are out there selling software. Okay, well, in one year, it's growing 10% month on month cigarette Ally's 10% month on month, and we're nearly 1400 users now without huge amounts of effort on our part. So clearly this market interest in running those softwares and then they're not want Tuesdays easer. Six people pretending some of people have 150 people pretending on a multi tenant software. So I believe that the future is dedicated is the first step to grow confidence that my own premise investments will lift and shift the cloud, but multi tenant will take us a lot >>for him. So that's a proof point of existing customer saying okay, I want to modernize. I'm buying in. Take 1/2 step of the man dedicated. And then obviously multi tenant for scale. And just way more cost efficient. Yes, very much. All right. Um, last question. Show us a little leg. What? What can you tell us about the road map? What gets you excited about the future? >>So I think the future historically, Planning Analytics and Carlos analytics have been separate products, right? And when they came together under the B I logo in about about a year ago, we've been spending a lot of our time bringing them together because, you know, you can fight in the B I space and you can fight in the planning space. And there's a lot of competitors here, not so many here. But when you bring the two things together, the connected value chain is where we really gonna win. But it's not only just doing is the connected value chain it and it could be being being vice because I'm the the former Lotus guy who believes in democratization of technology. Right? But the market showing us when we create a piece of software that starts at 15 bucks for a single user. For the same power mind you write little less less of the capabilities and 70 bucks for a single user. For all of it, people buy it. So I'm in. >>Tony, thanks so much for coming on. The kid was great to have you. Brilliant. Thank you. Keep it right there, everybody. We'll be back with our next guest. You watching the Cube live from the IBM data and a I form in Miami. We'll be right back.

>>live from Las Vegas. It's the Cube covering Splunk dot com. 19. Brought to you by spunk >>Welcome back, everyone to keep live coverage here in Las Vegas for Splunk dot com. 10th anniversary. 10 years of doing their big customer shows. Cubes. Seventh year of covering Splunk I'm John Ferrier, Host Cube. Our next guest is Cube. Alumni seem Haji, senior director and head of platform on industry for Splunk Knows the business way last topped. 2014 Great to see you. >>Good to see you again, John. You've been busy. I have. It's been a busy time. It's Plunk. >>You have been in the data business. We've been following your career for the years. Data stacks now Splunk on other endeavors. But you've been in the data, even swim in the data business. You've seen clouds scale, you understand. Open source. You understand kind of big dynamics. Splunk has a full enabling data platform. Started out with logs keeps moving along the by companies that interview. But this'll platform concept of enabling value valued customers has been a big part of the success that it continues to yield success every year. When people say no, what is successful data playful because everyone wants to own the data layer because we just want to get value on the data. So what as a product market, our product person, what is the date of platform? >>So it's really a question and, you know, you gonna hit the nail on the head when you said we've been talking about the data platform for several years, like decades. Almost so if you think about, you know, data platform, like, way back when and I'm dating myself. When I graduated from college, you know, people were looking for insights right there. Like give me a report, give me a dashboard way. Went into data, databases of data, warehouses. Enabling this you actually think about the data platform or data to everything. Platform is, as we explore. Call it. It has five critical elements in my in my mind. You know, the first is how do you get all of your information? Like the data that's coming in from networks, logs, applications, people, you and I generate a ton of data. How do we get this all together into a single place so you can get insights on it? 1 may think that it's pretty easy, but the truth is, we've been struggling as an industry with for decades. So it's fun to think what super unique is you can actually bring in any of the data. And some of the challenges that customers have had in the past is way forced them to structure this state of before they can ask questions of it. What's wrong? It's free form. You can bring it in any information and then structured when you're ready to ask that question. So you know a data platform. Number one is flexibility in the way you bring your data second. And you know this being the business is getting real time insights, alerts on your phone, real time decision ing and then you have, you know, operating in different ways on cloud on premises, hybrid environments. That's the third. And I think the fourth and the fifth are probably the most important, and into related is allowing like a good data platform caters to everyone in the or so from your most non technical business user to the most technical data admin I t. Guy security analysts with giving them the same information but allowing them to view it in many different ways and ask different questions of it. So we call this, you know, explained is from a product marketing in a business standpoint way Refer to it as many lenses on your same data. Good data platforms do that while allowing an empowering different users. So those are the five in my >>love kicking out on platform converses. Second, we could talk for now, but I know you got busy. I want to ask you all successful platforms in this modern era of rocket texture. When you get cloud scale, massive data volumes coming in need key building blocks. Take me through your view on why Splunk been successful plateau because you got a naval value from the dorm room to the boardroom. So we've gotta have that use case breath what you do. What key building blocks of this point. Data platform. >>Great question. And, you know, we've we've kind of figured this out is a cz. Well, a cz have been working on building out these building blocks at a most critical customers, right? Did you think about it? You start with the core, the index, if you will. And that's your place to bring you know, slung started with all your logs together and it's your single go to place then, as you think about it, with working with customers, they need massive date engines. So what we just announced today the general availability of data stream processor and data fabric search. It allows you to have those two massive engines from How do I bring my streaming data in to have Can I do massive scale processing? Thea other elements around a machine learning right. So in a world where we're moving to automation, that's super critical to the success. And then you have consuming the way you consume insights or uses consuming sites. If you think about you and I and this amount of time we spend on our phone, how do we make it easy for people to act on their information to those your core platform building blocks give index. You have your date engines, you have a I am l. You have your business analytics and then you have your portfolios on top, which is use case specific, if you will. For I t for security and then for de mops. >>That's awesome. And let's get into the news you were your product. Kino today? Yes, they was opening day. But I want to read the headline from Lung press release and commentary. Don't get your reaction to it. Splunk Enterprising X Man's data access with data fabric search and data stream processor powers Uses with context and collaboration keywords context in their collaboration. House search is a hard problem. Discovery. We've seen carnage and people trying things. You guys do a lot of data. Lot of diverse date has been a big team here, right? Your customers have grown with more data coming in. Why these two features important. What's the keys? Behind the fabric search on the data processor is that the real time is the date acceleration. What are some of the key value points? What people know about the fabric surge processor. >>So actually, let me start with the data stream processor. You know, with DSP, what we're really doing is looking at streaming data. So when you think about the real time customers I ot sensor data, anything that's coming on the wire data stream processor lets you bring that in display. Now, the uniqueness of data stream processor is you wanted Thio, you didn't have to bring it in. Splunk. You can actually like process that live on the wire and it works just as well. Not do fabric search. It's, you know, you alluded to this earlier. It's how do you search across your massive data leaks warehouses that exist without having to bring it all in one place. So in the product, he notes Demo. Today we showed a really cool demo of a business and bliss user, really solving a business problem while searching across S three Duke and data that's sitting in instruct and then with the fabric search, you can also do massive, like federated, like global size searches on the context and collaboration. That's really once you have all this data in Splunk, how do you How do you like your users? Consume it right? And that's the mobile connected experiences A cz well, a cz Phantom and Victor Rapps like really activating this data in automating it. >>I want to get your thoughts on something that we've been seeing on the Q. And I've been kind of promoting for about a year now, and it really came back for you. Go back to the early days of duping big data. And, you know, you know, those days getting diverse data is hard. And so because it's a different formats on the database scheme is Andorran structured to find that databases in a way hamper hinder that capability. We've been saying that diverse data gives a better machine, makes machine learning better. Machine learning is a day I provides business benefits. This flywheel is really important. And can you give an example of where that's playing out and spunk? Because that seems to be the magic right now. Is that getting the data together, knowing what day it is? No blind spots. As much as that is, it's possible. But getting that flag will doing better. Better diverse data, better machine learning better. Ay, I better I better business value. I >>think it comes down to the word divers, right? So when you're looking at data coming in from many different sources, you also get a holistic perspective on what's going on in your business. You get the insight on what your customers may be doing in engaging with your business. You get insight on how your infrastructure is performing and the way you can optimize people to the business from you know you need to. The ops and operations is to like how customers are working and interacting with your business. The other piece is when you think about machine learning in the I A. CZ, you automate this. It's a lot easier when you have the holistic context, right? So, you know, diverse data means more context. More context means better insight into what you're trying to get to. It's just gonna rounds out. The perspective I often refer to it is it's adding a new dimension to something you already know >>and opens up a whole nother conscious around. What is the practitioners? Role? Not just a database administrator is setting up databases because you're getting at, you know, context is important. What's the data about the data? What dough I keep what should be addressable foran application. Is this relevant content for this some day, it is more valuable than others at any given time, so address ability becomes a big thing. What's your vision around this idea of data address ability for applications? >>So, you know, just going back to what you said about the administrators and the doers we call them the doers there. The innovators right there. The bill, people building the cool stuff. And so when you actually can bring these elements in for them, you really are giving them the ability to innovate and do better and have that accessibility into the information and really kind of like, you know, like Bill the best that they could write. So, you know, we've been saying Turn data into doing and it really is true. Like these are again the architects of what's happening and they're the people, like taking all this diverse data, taking the machine, learning, taking the technology of the building blocks and then turning it into, like, hold doing that we d'oh! >>It's interesting with markets change him. It actually changed the role of the database person makes them broader, more powerful. >>Yes, and because you know they're the ones fueling the business. >>Thanks for coming. I really appreciate the insight. I wish we had more time on a personal question. What's exciting You in the industry these days? Actually, you're exploring. Companies continue to grow from start up the i p o massive growth now to a whole nother level of market leadership to defend that you put some good products out there. What? What are you getting excited about these days from tech standpoint? >>You know, I think it's we're finally getting it. We're finally getting what you know. Being a data to everything. Platform is, for example, right after the keynote. I had more than a few people come up to me and say, Well, you know, that made sense, right? Like when we think about Splunk is the data to everything platform on what data platforms are meant to dio and how they should operate. So I think the industry is finally getting their What's exciting me next is if you look behind us and all the industry traction that we're seeing. So you know, taking technology and data beyond. And really enabling businesses from financial service is to healthcare to manufacturers to do more. You know, the businesses that traditionally, like, maybe have not been adopting technology as fast as software companies. And now we're seeing that, and that's super exciting. >>You know, I always get into these kind of philosophical debates with people. Either on the Cube are are off the Cube, where you know what is a platform success look like, you know, I always say, I want to get your reaction to this. I always say, if it's got applications or things being enabled value on a healthy ecosystem, so do you agree with that statement? And if so, what's the proof points for Splunk on those two things? What is defining that? What a successful platform looks like? >>You know that I do agree with you. And when I think about a successful platform, it's if I look around this room and just see how you know, like New York Presbyterian as using Splunk Thio like we heard from Dell today an intel. So when you see the spectrum of customers using Splunk across a variety of successes, it's that super exciting to me that tells me that you know what it is everything when you say date it. Everything >>all right? We got a fun job these days. >>D'oh to be here. So it's great. >>Great to see you. Thanks for coming back on the Cube. I'm looking forward to catching up. I'm John Kerry here on the Cube. Let's see what she's awesome. Cube alumni from 2014. Now it's blonde leading the product efforts and marketing. I'm John. Where were you watching the Q. Be right back after this short break

>> from Austin, Texas. It's Theo Cube, covering pure storage. Accelerate 2019. Brought to you by pure storage. >> Welcome back to the Cube. Lisa Martin Day Volante is my co host were a pure accelerate 2019 in Austin, Texas. A couple of guests joining us. Next. Please welcome Barack elected director product management for slunk. Welcome back to the Cube. Thank you. And guess who's back. Von Stewart. V. P. A. Technology from pure Avon. Welcome back. >> Hey, thanks for having us guys really excited about this topic. >> We are too. All right, so But we'll start with you. Since you're so excited in your nice orange pocket square is peeking out of your jacket there. Talk about the Splunk, your relationship. Long relationship, new offerings, joint value. What's going on? >> Great set up. So Splunk impure have had a long relationship around accelerating customers analytics The speed at which they can get their questions answered the rate at which they could ingest data right to build just more sources. Look at more data, get faster time to take action. However, I shouldn't be leading this conversation because Split Split has released a new architecture, a significant evolution if you will from the traditional Splunk architectural was built off of Daz and a shared nothing architecture. Leveraging replicas, right? Very similar what you'd have with, like, say, in H D. F s Work it load or H c. I. For those who aren't in the analytic space, they've released the new architecture that's disaggregated based off of cashing and an object store construct called Smart Store, which Broth is the product manager for? >> All right, tell us about that. >> So we release a smart for the future as part of spunk Enterprise. $7 to about a near back back in September Timeframe. Really Genesis or Strong Smart Strong goes back to the key customer problem that we were looking to solve. So one of our customers, they're already ingesting a large volume of data, but the need to retain the data for twice, then one of Peter and in today's architecture, what it required was them to kind of lean nearly scale on the amount of hardware. What we realized it. Sooner or later, all customers are going to run into this issue. But if they want in just more data or reading the data for longer periods, of time, they're going to run into this cost ceiling sooner or later on. The challenge is that into this architecture, today's distributes killer dark picture that we have today, which of all, about 10 years back, with the evolution of the Duke in this particular architecture, the computer and story Jacqui located. And because computer storage acqua located, it allows us to process large volumes of data. But if you look at the demand today, we can see that the demand for storage or placing the demand for computer So these are, too to directly opposite trans that we're seeing in the market space. If you need to basically provide performance at scale, there needs to be a better model. They need a better solution than what we had right now. So that's the reason we basically brought Smart store on denounced availability last September. What's Marceau brings to the table is that a D couples computer and storage, So now you can scale storage independent of computers, so if you need more storage or if you need to read in for longer periods of time, you can just kill independent on the storage and with level age, remote object stores like Bill Flash bid to provide that data depository. But most of your active data said still decides locally on the indexers. So what we did was basically broke the paradigm off computer storage location, and we had a small twist. He said that now the computer stories can be the couple, but you bring comfort and stories closer together only on demand. So that means that when you were running a radio, you know, we're running a search, and whenever the data is being looked for that only when we bring the data together. The other key thing that we do is we have an active data set way ensure that the smart store has ah, very powerful cash manager that allows that ensures that the active data set is always very similar to the time when your laptop, the night when your laptop has active data sets always in the cash always on memory. So very similar to that smarts for cash allows you to have active data set always locally on the index. Start your search performance is not impact. >> Yes, this problem of scaling compute and storage independently. You mentioned H. D. F s you saw it early on there. The hyper converged guys have been trying to solve this problem. Um, some of the database guys like snowflakes have solved it in the cloud. But if I understand correctly, you're doing this on Prem. >> So we're doing this board an on Prem as well as in Cloud. So this smart so feature is already available on tramp were also already using a host all off our spun cloud deployments as well. It's available for customers who want obviously deploy spunk on AWS as well. >> Okay, where do you guys fit in? So we >> fit in with customers anywhere from on the hate say this way. But on the small side, at the hundreds of terabytes up into the tens and hundreds of petabytes side. And that's really just kind of shows the pervasiveness of Splunk both through mid market, all the way up through the through the enterprise, every industry and every vertical. So where we come in relative to smart store is we were a coat co developer, a launch partner. And because our object offering Flash Blade is a high performance object store, we are a little bit different than the rest of the Splunk s story partner ecosystem who have invested in slow more of an archive mode of s tree right, we have always been designed and kind of betting on the future would be based on high performance, large scale object. And so we believe smart store is is a ah, perfect example, if you will, of a modern analytics platform. When you look at the architecture with smart store as brush here with you, you want to suffice a majority of your queries out of cash because the performance difference between reading out a cash that let's say, that's NAND based or envy. Emmy based or obtain, if you will. When you fall, you have to go read a data data out of the Objects store, right. You could have a significant performance. Trade off wean mix significantly minimized that performance drop because you're going to a very high bandwith flash blade. We've done comparison test with other other smart store search results have been published in other vendors, white papers and we show Flash blade. When we run the same benchmark is 80 times faster and so what you can now have without architecture is confidence that should you find yourself in a compliance or regulatory issue, something like Maybe GDP are where you've got 72 hours to notify everyone who's been impacted by a breach. Maybe you've got a cybersecurity case where the average time to find that you've been penetrated occurs 206 days after the event. And now you gotta go dig through your old data illegal discovery, you know, questions around, you know, customer purchases, purchases or credit card payments. Any time where you've got to go back in the history, we're gonna deliver those results and order of magnitude faster than any other object store in the market today. That translates from ours. Today's days, two weeks, and we think that falls into our advantage. Almost two >> orders of magnitude. >> Can this be Flash Player >> at 80%? Sorry, Katie. Time 80 x. Yes, that's what I heard. >> Do you display? Consider what flashlight is doing here. An accelerant of spunk, workloads and customer environment. >> Definitely, because the forward with the smart, strong cash way allow high performance at scale for data that's recites locally in the cash. But now, by using a high performance object store like your flash played. Customers can expect the same high performing board when data is in the cash as well as invented sin. Remorseful >> sparks it. Interesting animal. Um, yeah, you have a point before we >> subjects. Well, I don't want to cut you off. It's OK. So I would say commenting on the performance is just part of the equation when you look at that, UM, common operational activities that a splitting, not a storage team. But a Splunk team has to incur right patch management, whether it's at the Splunk software, maybe the operating system, like linen store windows, that spunk is running on, or any of the other components on side on that platform. Patch Management data Re balancing cause it's unequal. Equally distributed, um, hardware refreshes expansion of the cluster. Maybe you need more computer storage. Those operations in terms of time, whether on smart store versus the classic model, are anywhere from 100 to 1000 times faster with smart store so you could have a deployment that, for example, it takes you two weeks to upgrade all the notes, and it gets done in four hours when it's on Smart store. That is material in terms of your operational costs. >> So I was gonna say, Splunk, we've been watching Splunk for a long time. There's our 10th year of doing the Cube, not our 10th anniversary of our 10th year. I think it will be our ninth year of doing dot com. And so we've seen Splunk emerged very cool company like like pure hip hip vibe to it. And back in the day, we talked about big data. Splunk never used that term, really not widely in its marketing. But then when we started to talk about who's gonna own the big data, that space was a cloud era was gonna be mad. We came back. We said, It's gonna be spunk and that's what's happened. Spunk has become a workload, a variety of workloads that has now permeated the organization, started with log files and security kind of kind of cumbersome. But now it's like everywhere. So I wonder if you could talk to the sort of explosion of Splunk in the workloads and what kind of opportunity this provides for you guys. >> So a very good question here, Right? So what we have seen is that spunk has become the de facto platform for all of one structure data as customers start to realize the value of putting their trying to Splunk on the watch. Your spunk is that this is like a huge differentiate of us. Monk is the read only skim on reed which allows you to basically put all of the data without any structure and ask questions on the flight that allows you to kind of do investigations in real time, be more reactive. What's being proactive? We be more proactive. Was being reactive scaleable platform the skills of large data volumes, highly available platform. All of that are the reason why you're seeing an increase that option. We see the same thing with all other customers as well. They start off with one data source with one use case and then very soon they realize the power of Splunk and they start to add additional use cases in just more and more data sources. >> But this no >> scheme on writer you call scheme on Reed has been so problematic for so many big data practitioners because it just became the state of swamp. >> That didn't >> happen with Splunk. Was that because you had very defined use cases obviously security being one or was it with their architectural considerations as well? >> They just architecture, consideration for security and 90 with the initial use cases, with the fact that the scheme on Reid basically gives open subject possibilities for you. Because there's no structure to the data, you can ask questions on the fly on. You can use that to investigate, to troubleshoot and allies and take remedial actions on what's happening. And now, with our new acquisitions, we have added additional capabilities where we can talk, orchestrate the whole Anto and flow with Phantom, right? So a lot of these acquisitions also helping unable the market. >> So we've been talking about TAM expansion all week. We definitely hit it with Charlie pretty hard. I have. You know, I think it's a really important topic. One of things we haven't hit on is tam expansion through partnerships and that flywheel effect. So how do you see the partners ship with Splunk Just in terms of supporting that tam expansion the next 10 years? >> So, uh, analytics, particularly log and Alex have really taken off for us in the last year. As we put more focus on it, we want to double down on our investments as we go through the end of this year and in the next year with with a focus on Splunk um, a zealous other alliances. We think we are in a unique position because the rollout of smart store right customers are always on a different scale in terms of when they want to adopt a new architecture right. It is a significant decision that they have to make. And so we believe between the combination of flash array for the hot tear and flash played for the cold is a nice way for customers with classic Splunk architecture to modernize their platform. Leverage the benefits of data reduction to drive down some of the cost leverage. The benefits of Flash to increase the rate at which they can ask questions and get answers is a nice stepping stone. And when customers are ready because Flash Blade is one of the few storage platforms in the market at this scale out band with optimized for both NFS and object, they can go through a rolling nondestructive upgrade to smart store, have you no investment protection, and if they can't repurpose that flash rate, they can use peers of service to have the flesh raise the hot today and drop it back off just when they're done within tomorrow. >> And what about C for, you know, big workloads, like like big data workloads. I mean, is that a good fit here? You really need to be more performance oriented. >> So flash Blade is is high bandwith optimization, which really is designed for workload. Like Splunk. Where when you have to do a sparse search, right, we'll find that needle in the haystack question, right? Were you breached? Where were you? Briefed. How were you breached? Go read as much data as possible. You've gotta in just all that data, back to the service as fast as you can. And with beast Cloud blocked, Teresi is really optimized it a tear to form of NAND for that secondary. Maybe transactional data base or virtual machines. >> All right, I want more, and then I'm gonna shut up sick. The signal FX acquisition was very interesting to me for a lot of reasons. One was the cloud. The SAS portion of Splunk was late to that game, but now you're sort of making that transition. You saw Tableau you saw Adobe like rip the band Aid Off and it was somewhat painful. But spunk is it. So I wonder. Any advice that you spend Splunk would have toe von as pure as they make that transition to that sass model. >> So I think definitely, I think it's going to be a challenging one, but I think it's a much needed one in there in the environment that we are in. The key thing is to always because two more focus and I'm sure that you're already our customer focus. But the key is key thing is to make sure that any service is up all the time on make sure that you can provide that up time, which is going to be crucial for beating your customers. Elise. >> That's good. That's good guidance. >> You >> just wanted to cover that for you favor of keeping you date. >> So you gave us some of those really impressive stats In terms of performance. >> They're almost too good to be true. >> Well, what's customer feedback? Let's talk about the real world when you're talking to customers about those numbers. What's the reaction? >> So I don't wanna speak for Broth, so I will say in our engagements within their customer base, while we here, particularly from customers of scale. So the larger the environment, the more aggressive they are to say they will adopt smart store right and on a more aggressive scale than the smaller environments. And it's because the benefits of operating and maintaining the indexer cluster are are so great that they'll actually turn to the stores team and say, This is the new architecture I want. This is a new storage platform and again. So when we're talking about patch management, cluster expansion Harbor Refresh. I mean, you're talking for a large sum. Large installs weeks, not two or 3 10 weeks, 12 weeks on end so it can be. You can reduce that down to a couple of days. It changes your your operational paradigm, your staffing. And so it has got high impact. >> So one of the message that we're hearing from customers is that it's far so they get a significant reduction in the infrastructure spent it almost dropped by 2/3. That's really significant file off our large customers for spending a ton of money on infrastructure, so just dropping that by 2/3 is a significant driver to kind of move too smart. Store this in addition to all the other benefits that get smart store with operational simplicity and the ability that it provides. You >> also have customers because of smart store. They can now actually bursts on demand. And so >> you can think of this and kind of two paradigms, right. Instead of >> having to try to avoid some of the operational pain, right, pre purchase and pre provisional large infrastructure and hope you fill it up. They could do it more of a right sides and kind of grow in increments on demand, whether it's storage or compute. That's something that's net new with smart store um, they can also, if they have ah, significant event occur. They can fire up additional indexer notes and search clusters that can either be bare metal v ems or containers. Right Try to, you know, push the flash, too. It's Max. Once they found the answers that they need gotten through. Whatever the urgent issues, they just deep provisionals assets on demand and return back down to a steady state. So it's very flexible, you know, kind of cloud native, agile platform >> on several guys. I wish we had more time. But thank you so much fun. And Deron, for joining David me on the Cube today and sharing all of the innovation that continues to come from this partnership. >> Great to see you appreciate it >> for Dave Volante. I'm Lisa Martin, and you're watching the Cube?