(upbeat music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a Cube Conversation. >> Hi and welcome to The Cube Studios for another Cube Conversation where we go in depth with thought leaders driving innovation across the tech industry. I'm your host, Peter Burris. One of the most interesting trends in the technology industry today is the application of AI, machine learning, deep learning, and other classes of advanced technology, to solving the types of business problems that could not be addressed before. And the outcomes that are being generated by these new toolings are significant and impressive, but they are not evenly distributed across the industry. Some companies are doing it really well, most companies are not. So, the promise is there, we just have to turn that promise into something that's more reliable, more repeatable, and more certain. Now, to have that conversation about how we're going to do that, we've got Anant Chintamaneni who's the vice president and general manager at HP, for their BlueData group. Anant, welcome to The Cube. >> It's great to be here, Peter, thank you. >> So, Anant, let's start with this notion of successful applications of AI and ML technology. What are you seeing in the industry? Am I wrong in characterizing that we're seeing some success, but just uneven success? >> Yeah, I completely agree with you. As a trusted partner for a large number of enterprises out there, and we work with hundreds of customers, and we intersect with them at various phases of their journey, we're seeing a tremendous growth in the interest for AI, machine learning, and even, in some cases, deep learning. I mean we're talking about enterprises across financial services, retail, health care, manufacturing, in the auto industry especially, with autonomous cars. The evolution from collecting all that big data, unstructured data, doing analytics on it, the logical next step for them is to exploit that data further and get prescriptive and predictive analytics. So, absolutely, it's the next frontier for a lot of these organizations, and it's a boardroom mandate. >> And we're seeing it turn into specific operational capabilities that are absolutely essential to how the business makes money and how the business serves its customers, but I can tell you, and I want to test this with you: I hear, all the time, customers telling me that it's just too complex that all these use cases are driving off in bespoke workflows to actually achieve the use cases in a variety of different roles and responsibilities. That seems like it's a prescription that's going to only lead to periodic success. Have I got that right? >> Absolutely. I mean, if you look at what you can do with data and analytics, there's obviously different types of business users and business use cases. We're talking about in financial services or even retail, any of these large enterprises that have customer-facing operations, there's value to be generated at the time you intersect with the customer. There's value to be generated in identifying opportunities to upsell, cross-sell. There's also opportunities around just revenue generation, coming up with new business models. Let's face it: all these industries are being disrupted, and they're trying to come up with ways by which they can be more data-driven and create these new business models. The problem is that, when you have these different groups, there's a number of use cases, and there's a number of different ways to solve it. You have human beings involved there who have their tools of choice, who have their specific methodologies in trying to go after a specific problem, so there's no uniformity and no uniform platform either, so each of these silos of environment that are being created, so you have this trend where you have exponentially going instead of use cases, but then the workflows are not there for them to kind of scale these use cases in a consistent, repeatable fashion even if you're using different tools. >> And I think, what really, we're trying to suggest that enterprises do is allow problems to suggest their own sets of solutions using data science and related technologies, but come up with a way to ensure a uniformity of success. Now, to do that, it seems as though we need to start thinking about how we're going to operationalize those workflows that tie the data science work to the actual implementation and run times that lead to the business getting the outcomes that they want. >> Absolutely. I think, for the last several years, everybody was fascinated by creating the best Python-based machine learning model or now, more recently, doing modeling with autonomous machine learning type of techniques. And there's a lot of different ways to create these models that demonstrates some success in the lab, but ultimately, if you want to get business value from those models and all the hard work that you've done, it has to be injected into the business process, whether that's, like we talked about, the use cases, whether it's doing scoring at the edge to find a defect in a manufacturing process that is a multimillion-dollar cost, or if you are trying to run something on a nightly basis or on an hourly basis to identify fraud or security breaches. So, you're absolutely right that operationalization of machine learning is ultimately the key, and I think that's the progression that enterprises have to make, which is they made lots of investments in talent, in tools to create these models, but they have to figure out how to operationalize them, and so that's absolutely the next frontier. And I think, if you look at the New Age companies, they've got unified platforms where it's easy for their data scientists to come up with an idea, try out different tools, access the data, and then operationalize that model, so you have a feature or a capability in these New Age Internet properties available within days, sometimes even hours, and that's a capability that's missing in the enterprises. So, I think that discipline of operationalization, allowing users to work with their tools of choice, access their datasets, but all in the context of security and governance and trying to operationalize it, is absolutely where these enterprises need to go in order to get success and real business value. >> Well, you mentioned the edge. It seems as though another element must be that it also can target to the infrastructure where it naturally can run so that we're not trying to force-fit everything up into a cloud where we move all the data around. There are going to be circumstances where the nature of the data, the nature of the model, requires that it run proximate to some activity. Have I got that right? >> Absolutely. I think, if you look at when you operationalize a model and when you're talking about a manufacturing facility or even like a car, which is practically the edge, then you need to be able to take your model and operationalize it at the edge so you can do inferencing. You could give the signals that need to happen at that point in time. And, similarly, there are other more mundane type of operations that will happen where the data is actually present or being generated whether that's in the cloud or in the data center. >> So, Anant, we've talked about the need to operationalize. Just give us a very, very quick view of how that need translates into actual offers and services. >> Yeah, so we've been working with our customers to essentially give them a set of capabilities that allows them to have the necessary tools to capture the data, process the data. I mean this has been happening for the last several years with the whole big data management space, fast data management space. So, we give a set of tools to allow customers to do data engineering, we allow the data scientist which is the persona that is interested in creating the model, the right set of visual interfaces, and/or the ability to onboard their product of choice so that they can be more productive, they can share their models, they can version them, and then, eventually, a set of tools for the devops and the operations team to take those models and deploy them. And that comprehensive, end-to-end capability which is to build, which is to then deploy, monitor, and then have that closed-loop process, so again, being able to monitor that model, see how it's deviating, and go through that closed-loop cycle, is the set of capabilities that we're providing in an integrated product. >> Anant Chintamaneni, vice president at HPE, working with the BlueData team, thanks very much for being on The Cube. >> Thank you, Peter, thanks for the opportunity. >> And, once again, thanks for joining us for another Cube Conversation. I'm Peter Burris, see you next time. (upbeat music)

from our studios in the heart of Silicon Valley Palo Alto California this is a cute conversation hi and welcome to the cube Studios for another cube conversation where we go in-depth with thought leaders driving innovation across the tech industry I'm your host Peter Burris AI is on the forefront of every board in every enterprise on a global basis as well as machine learning deep learning and other advanced technologies that are intended to turn data into business action that differentiates the business leads to more revenue leads to more profitability but the challenge is is that all of these new use cases are not able to be addressed with the traditional ways that we've set up the workflows that we've set up to address them so as a consequence we're going to need greater opera's the operationalization of how we translate business problems into ml and related technology solutions big challenge we've got a great guest today to talk about it non-division diof is a distinguished technologist and lead data scientists at HPE in the blue data team nonde welcome to the cube thank you happy to be here so ananda let's start with this notion of a need for an architected approach to how we think about matching AI ml technology to operations so that we get more certain results better outcomes more understanding of where we're going and how the technology is working within the business absolutely yeah ai and doing AI in an enterprise is not new there have been enterprise-grade tools in the space before but most of them have a very prescribed way of doing things sometimes you use custom sequel to use that particular tool or the way you present data to that tool requires some level of pre-processing which makes you copy the data into the tool so you have already data fidelity maybe at risk and you have a data duplication happening and then the scale right when you talk about doing AI at the scale that is required now considering data is so big and there is a variety of data sets for the scale it can probably be done but there is a huge cost associated with that and you may still not meet the variety of use cases that you want to actually work on so the problem now is to make sure that you empower your users who are working in the space and augment them with the right set of technologies and the ability to bring data in a timely manner for them to work on these solutions so it sounds as though what we're trying to do is simplify the process of taking great ideas and turn it into great outcomes but you mentioned users I think it's got to start with or let me ask you if we have to start here that we've always thought about how is going to center in the data science or the data scientist as these solutions have start to become more popularized if diffused across the industry a lot more people are engaging are all roles being served as well as you need to be absolutely I think that's the biggest challenge right in the past you know when we talk about very prescribed solutions end to end was happening within those tools so the different user persona were probably part of that particular solution and also the way these models came into production which is really making it available for a consumer is read coding or redeveloping this in technologies that were production friendly which is you're rewriting that and sequel you're recording that and C so there is a lot of details that are lost in translation and the third big problem was really having visibility or having a say from a developer's point of view or a data scientist point of view in how these things are performing in production that how do you actually take it back take that feedback back into deciding you know is this model still good or how do you retrain so when you look at this lifecycle holistically this is an iterative process it is no longer you know workflow where you hand things off this is not a water flow methodology anymore this is a very very continuous and iterative process especially in the New Age data science the tools that are developing where you build the model that developer decides what the run time is and the run times are capable of serving those models as is you don't have to recode you don't have to lose things during translation so with this back to your question of how do you serve two different roles now all those personas and all those roles have to be part of the same project and they have to be part of the same experiment they're just serving different parts of the lifecycle and now you've whatever tooling you provide or whatever architecture technologies you provide have to look at it holistically there has to be continuous development there has to be collaboration there has to be central repositories that actually cater to those needs so each so the architected approach needs to be able to serve each of the roles but in a way that is collaborative and is ultimately put in service to the outcome and driving the use of the technology forward well that leads to another question should it should the should this architected approach be tied to one or another set of algorithms or one or another set of implementation infrastructure or does it have to be able to serve a wide array of Technology types yeah great question right this is a living ecosystem we can no longer build for you know you plant something for the next two years or the next three years technologies are coming every day and the reason is because the types of use cases are evolving and what you need to solve that use case is completely different when you look at two different use cases so whatever standards you come up with you know the consistency has to be across how a user is on-boarded into the system a consistency has to be about data access about security about how does one provision these environments but as far as what tool is used or how is that tool being applied to a specific problem there's a lot of variability in there and it has to cater your architecture has to make sure that this variability is addressed and it is growing so HPE spends a lot of time with customers and you're learning from your customer successes and how you turn that into tooling that leads to this type of operator operationalization but give us some visibility into some of those successes that really stand out for you that have been essential to how HP has participated in this journey to create better tools for better AI and m/l absolutely you know traditionally with blue data HPE now you know we've been exposed to a lot of big data processing technologies where the current landscape the data is different data is not always at rest data is not structured you know data is coming it could be a stream of data it could be a picture and in the use cases like we talked about you know it could be image recognition or a voice recognition where the type of data is very different right so back to how we've learnt from our customers like in my role I talked to you know tens of customers on a daily or weekly basis and each one of them are at a different level of maturity in their life cycle and these are some very established customers but you know the various groups that are adopting this new age technologies even within an organization there is a lot of variability so whatever we offered them we have to help support all of that particular user groups there are some who are coming from the classic or language background there are some that are coming from Python background some are doing things in Scala someone doing things in SPARC and there are some commercial tools that they're using like h2o driverless AI or data iku so what we have to look at is in this life cycle we have to make sure that all these communities are represented and/or addressed and if they build a model in a specific technology how do we consume that how do we take it in then how do we deploy that from an end to point of view it doesn't matter where a model gets built it does matter how end-users access it it doesn't matter how security is applied to it it does matter how scaling is applied to it so really there is a lot of consistency is required in the operationalization and also in how you onboard those different tools how do you make sure that consistency or methodology or standard practices are applied in this entire lifecycle and also monitoring that's a huge aspect right when you have deployed a model and it's in production monitoring means two different things to people where is it even available you know when you go to a website when you click on something is a website available very similarly when you go to an endpoint or you're scoring against a model is that model available do you have enough resources can it scale depending on how much requests come in that's one aspect of monitoring and the second aspect is really how was the model performing you know is that what is the accuracy what is the drift when is it time to retrain so you no longer have the luxury to look at these things in isolation right so it we want to make sure that all these things can be addressed in a manner knowing that this iteration sometimes can be a month sometimes it can be a day sometimes it's probably a few hours and that is why it can no longer be an isolated and even infrastructure point of view some of these workloads may need things like GPU and you may need it for a very short amount of time let how do you make sure that you give what is needed for that duration that is required and take it back and assign it to something else because these are very valuable resources so I want to build on if I may on that notion of onboarding the tools we're talking about use cases that enterprises are using today to create business value we're talking about HPE as an example delivering tooling that operationalize is how that's done today but the reality is we're gonna see the state of the art still evolve pretty dramatically over the next few years how is HPE going about ensuring that your approach and the approach you working with your customers does not get balkanized does not get you know sclerotic that it's capable of evolving and changing as folks learn new approaches to doing things absolutely you know it this has to start with having an open architecture you know you have to there has to be standards without which enterprises can't run but at the same time those standards shouldn't be so constricting that it doesn't allow you to expand into newer use cases right so what HP EML ops offers is really making sure that you can do what you do today in a best-practice manner or in the most efficient manner bringing time to value you know making sure that there is you know instant provisioning or access to beta or making sure that you don't duplicate data compute storage separation containerization you know these are some of the standard best practice technologies that are out there making sure that you adopt those and what these sets users for is to make sure that they can evolve with the later use cases you can never have you know you can never have things you know frozen in time you just want to make sure that you can evolve and this is what it sets them up for and you evolve with different use cases and different tools as they come along nada thanks very much has been a very it's been a great conversation we appreciate you being on the cube thank you Peter so my guest has been non Division I of the distinguished technologists and lead data scientists at HPE blue data and for all of you thanks for joining us again for another cube conversation on Peter burst see you next time you [Music]

(upbeat trumpet music) >> From our studios in the heart of Silicon Valley, Palo Alto, California. This is a CUBE Conversation. >> Welcome, everybody, I'm Dave Vellante and we're here in our Palo Alto studios and we're going to talk about big data. For the last ten years, we've seen organizations come to the realization that data can be used to drive competitive advantage and so they dramatically lowered the cost of collecting data. We certainly saw this with Hadoop, but you know what data is plentiful, insights aren't. Infrastructure around big data is very challenging. I'm here with Kumar Sreekanti, co-founder and CEO of BlueData, and a long time friend of mine. Kumar, it's great to see you again. Thanks so much for coming to theCUBE. >> Thank you, Dave, thank you. Good to see you as well. >> We've had a number of conversations over the years, the Hadoop days, on theCUBE, you and I go way back, but I said up front, big data sounded so alluring, but it's very, very complex to get started and we're going to get into that. I want to talk about BlueData. Recently sold to company to HPE, congratulations. >> Thank you, thank you. >> It's fantastic. Go back, why did you start BlueData? >> When I started BlueData, prior to that I was at VMware and I had a great opportunity to be in the driving seat, working with many talented individuals, as well as with many customers and CIOs. I saw while VMware solved the problem of single instance of virtual machines and transform the data center, I see the new wave of distributed systems, vis-a-vis first example of that is Hadoop, were quite rigid. They were running on bare metal and they were not flexible. They were having, customers, a lot of issues, the ones that you just talked about. There's a new stack coming up everyday. They're running on bare metal. I can't run the production and the DevOps on the same systems. Whereas the cloud was making progress so we felt that there is an opportunity to build a Vmware-like platform that focuses on big data applications. This was back in 2013, right. That was the early genesis. We saw that data is here and data is the new oil as many people have said and the organizations have to figure out a way to harness the power of that and they need an invisible infrastructure. They need very innovative platforms. >> You know, it's funny. We see data as even more valuable than oil because you can only once. (Kumar laughs) You can use data many, many times. >> That's a very good one. >> Companies are beginning to realize that and so talk about the journey of big data. You're a product guy. You've built a lot of products, highly technical. You know a lot of people in the valley. You've built great teams. What was the journey like with BlueData? >> You know, a lot of people would like it to be a straight line from the starting to that point. (Dave laughs) It is not, it's fascinating. At the same time, a stressful, up and downs journey, but very fulfilling. A, this is probably one of the best products that I've built in my career. B, it actually solves a real problem to the customers and in the process you actually find a lot of satisfaction not only building a great product. It actually building the value for the customers. Journey has been very good. We were very blessed with extremely good advisors from the right beginning. We were really fortunate to have good investors and I was very, as you said, my knowledge and my familiarity in the valley, I was able to build a good team. Overall, an extremely good journey. It's putting a bow on the top, as you pointed out, the exit, but it's a good journey. There's a lot of nuance I learned in the process. I'm happy to share as we go through. >> Let's double-click on the problem. We talked a little bit about it. You referenced it. Everyday there's a new open source project coming out. There's The Scoop and The Hive and a new open open source database coming out. Practitioners are challenged. They don't have the skillsets. The Ubers and the Facebooks, they could probably figure it out and have the engineers to do it, but the average enterprise may not. Clearly complexity is the problem, but double-click on that and talk a little bit about, from your perspective, what that challenge is. >> That's a very good point. I think when we started the company, we exactly noticed that. There are companies that have the muscle to hire the set of engineers and solve the problem, vertically specific to their application or their use case, but the average, which is Fortune 500 companies, do not have that kind of engineering man power. Then I also call this day two operations. When you actually go back to Vmware or Windows, as soon as you buy the piece of software, next day it's operational and you know how to use it, but with these new stacks, by the time stack is installed, you already have a newer version. It's actually solutions-led meaning that you want to have a solution understanding, but you want to make the infrastructure invisible meaning, I want to create a cluster or I want to funnel the data. I don't want to think about those things. I just wanted to directly worry about what is my solution and I want BlueData to worry about creating me a cluster, automating it. It's automation, automation, automation, orchestration, orchestration, orchestration. >> Okay, so that's the general way in which you solve this problem. Automate, you got to take the humans out of the equation. Talk specifically about the BlueData architecture. What's the secret sauce behind it? >> We were very fortunate to see containers as the new lightweight virtual machines. We have taken an approach. There are certain applications, particularly stateful, need a different handling than cloud-native non-stateful applications so what we said was, in fact our architecture predates Kubernetes, so we built a bottoms-up, pure white-paper architecture that is geared towards big data, AIML applications. Now, actually, even HPC is starting to move into that direction. >> Well, tell me actually, talk a little bit about that in terms of the evolution of the types of workloads that we've seen. You know, it started all out, Hadoop was batch, and then very quickly that changed. Talk about that spectrum. >> It's actually when we started, the highest ask from the customers were Hadoop and batch processing, but everybody knew that was the beginning and with the streaming and the new streaming technologies, it's near realtime analytics and moving to now AIML applications like H2O and Cafe and now I'm seeing the customer's asking and say, I would like to have a single platform that actually runs all these applications to me. The way we built it, going back to your previous question, the architecture is, our goal is for you to be able to create these clusters and not worry about the copying the data, single copy of the data. We built a technology called DataTap which we talked about in the past and that allows you to have a single copy of the data and multiple applications to be able to access that. >> Now, HPC, you mentioned HPC. It used to be, maybe still is, this sort of crazy crowd. (laughter) You know, they do things differently and everybody bandwidth, bandwidth, bandwidth and very high-end performance. How do you see that fitting in? Do you see that going mainstream? >> I'm glad you pointed out because I'm not saying everything is moving over, but I am starting to see, in fact, I was in a conversation this morning with an HPC team and an HPC customer. They are seeing the value of the scale of distributed systems. HPC tend to be scale up and single high bandwidth. They are seeing the value of how can I actually bring these two pieces together? I would say it's in infancy. Don't take me to say, look how long Hadoop take, 10 years so it's probably going to take a longer time, but I can see enterprises thinking of a single unified platform that's probably driven by Kubernetes and have these applications instantiated, orchestrated, and automated on that type. >> Now, how about the cloud? Where does that fit? We often say in theCUBE that it's not Moore's Law anymore. The innovation cocktail is data, all this data that we've collected, applying machine intelligence, and then scaling with the cloud. Obviously cloud is hugely important. It gobbled up the whole Hadoop business, but where do you see it fitting? >> Cloud is a big elephant in the room. We all have to acknowledge. I think it provides significant advantages. I always used to say this, and I may have said this in my previous CUBE interviews, cloud is all about the innovation. The reason cloud got so much traction, is because if you compare the amount of innovation to on-prem, they were at least five years ahead of that. Even the BlueData technology that we brought to the barer, EMR on Amazon was in front of the data, but it was only available Amazon. It's what we call an opinionated stack. That means you are forced to use what they give you as opposed to, I want to bring my own piece of software. We see cloud, as well as on-prem pretty much homogenous. In fact, BlueData software runs both on-prem, on the cloud, in a hybrid fashion. Same software and you can bring your stack on the top of the BlueData. >> Okay, so hybrid was the next piece of it. >> What we see is cloud has, at least from the angle from my exposure, cloud is very useful for certain applications, especially what I'm seeing is, if you are collecting the large amounts of data on the cloud, I would rather run a batch processing and curate the data and bring the very important amount of data back into the on-prem and run some realtime. It's just one example. I see a balance between the two. I also see a lot of organizations still collecting terabits of data on-prem and they're not going to take terabits of data overnight to the cloud. We are seeing all the customers asking, we would like to see a hybrid solution. >> The reason I like the acquisition by HPE because not only is it a company started by a friend and someone that I respect and knows how to build solid technology that can last, but it's software. HPE, as a company, my view needs more software content. (Kumar laughs) Software's eating the world as Marc Andressen says. It would be great to see that software live as an independent entity. I'm sure decisions are still being made, but how do you see that playing out? What are the initial discussions like? What can you share with us? >> That's a very, very, well put there. Currently, the goal from my boss and the teams there is, we want to keep the BlueData software independent. It runs on all x86 hardware platforms and we want to drive the roadmap driven by the customer needs on the software like we want to run more HPC applications. Our roadmap will be driven by the customer needs and the change in the stack on the top, not by necessarily the hardware. >> Well, that fits with HPE's culture of always trying to give optionality and we've had this conversation many, many times with senior-level people like Antonio. It's very important that there's no lock-in, open mindset, and certainly HPE lives up to that. Thanks so much for coming-- >> You're welcome. Back into theCUBE. >> I appreciate you having me here as well. >> Your career has been amazing as we go back a long time. Wow. From hardware, software, all these-- >> Great technologies. (laughter) >> Yeah, solving hard problems and we look forward to tracking your career going forward. >> Thank you, thank you. Thanks so much. >> And thank you for watching, everybody. This is Dave Vellante from our Palo Alto Studios. We'll see ya next time. (upbeat trumpet music)

>> From the theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> Well, it looks like hybrid cloud is finally here. We've seen a decade of posturing, marchitecture, slideware and narrow examples of hybrid cloud, but there's little question that the definition of cloud is expanding to include on-premises workloads in hybrid models. Now depending on which numbers you choose to represent IT spending, public cloud only accounts for actually less than 5% of the total pie. So the big question is, how will this now evolve? Customers want control, they want governance, they want security, flexibility and a feature-rich set of services to build their digital businesses. It's unlikely that they can buy all that, so they're going to have to build it with partners, specifically vendors, SI's, consultancies and their own developers. The tug of war to win the new cloud day has finally started in earnest between the hyperscalers and the largest enterprise tech companies in the world. Hello and welcome to this week's Wikibon CUBE insights, powered by ETR. In this Breaking Analysis, we'll walk you through how we see the battle for hybrid cloud, how we got here, where we are and where it's headed. First, I want to go back to 2009, in a blog post by a man named Chuck Hollis. Chuck Hollis, at the time, was a CTO and marketing guru inside of EMC who, remember, owned VMware. Chuck was kind of this hybrid, multi-tool player, pun intended. EMC at the time had a big stake, a lot at stake, as the ascendancy of AWS was threatening the historical models, which had defined enterprise IT. Now around that time, NIST published its first draft of a cloud computing definition which, as I recall, included language, something to the effect of accessing remote services over the public network, i.e., public IP networks. Now, NIST has essentially or since evolved that definition, but the original draft was very favorable to the public cloud. And the vendor community, the traditional vendor community, said hang on, we're in this game too. So that was 2009 when Chuck Hollis published this slide. He termed it Private Cloud, a term which he saw buried inside of a Gartner research post or research note that was not really fleshed out and defined. The idea was pretty compelling. The definition of cloud centered on control, where you, as the customer, had on-prem workloads that could span public and on-prem clouds, if you will, with federated security and a data plan that spanned the states. Essentially, you had an internal and an external cloud with a single point of control. This is basically what the hybrid cloud vision has become. An abstraction layer that spans on-prem and public clouds and we can extend that across clouds and out to the edge, where a customer has a single point of control and federated governance and security. Now we know this is still aspirational, but we're now seeing vendor offerings that put forth this promise and a roadmap to get there from different points of view, that we're going to talk about today. The NIST definition now reads cloud computing is a model for enabling ubiquitous, convenient on-demand network access to a shared pool of configurable computing resources, e.g., network server storage, applications and services, that can be rapidly provisioned and released with minimal management effort or service provider interaction. So there you have it, that is inclusive of on-prem, but it took the industry a decade plus to actually get where we are today. And they did so by essentially going to school with the public cloud offerings. Now in 2018, AWS announced Outposts and that was another wake up call to the on-prem community. Externally, they pointed to the validation that hybrid cloud was real. Hey, AWS is doing it so clearly they've capitulated, but most on-prem vendors at the time didn't have a coherent offering for hybrid, but the point is the on-prem vendors responded as they saw AWS moving past the demilitarized zone into enemy lines. And here's what the competitive landscape of hybrid offerings looks like today. All three US-based hyperscalers have an offering or multiple offerings in various forms, Outposts from Amazon and other services that they offer, Google Anthos and Azure Arc, they're all so prominent, but the real action today is coming from the on-prem vendors. Every major company has an offering. Now most of these stemmed from services-led and finance-led initiatives, but they're evolving to true Azure Service models. HPE GreenLake is prominent and the company's CEO, Antonio Neri, is putting the whole company behind Azure Service. HPE claims to be the first, it uses that in its marketing, with such an Azure Service offering, but actually Oracle was their first with Cloud@Customer. You know, possibly Microsoft could make a claim to being early as well, but it really doesn't matter. Let's see, Dell has responded with Apex and is going hard after this opportunity. Cisco has Cisco Plus and Lenovo has TruScale. IBM also has a long services and finance-led history and has announced pockets of Azure Service in areas like storage. And Pure Storage is an example that we chose of a segment player, of course within storage, that has a strong Azure Service offering, and there are others like that. So the landscape is getting very busy. And so, let's break this down a bit. AWS is bringing its programmable infrastructure model and its own hardware to what it calls the edge. And it looks at on-prem data centers as just another edge node. So that's how they're de-positioning the on-prem crowd, but the fact is, when you really look at what Outposts can do today, it's limited, but AWS will move quickly so expect a continued rapid evolution of their model and the services that are supported on Outposts. Azure gets its hardware from partners and has relationships with virtually everyone that matters. Anthos is, as well, a software layer and Google created Kubernetes as the great equalizer in cloud. And it was a nice open source gift to the industry and has obviously taken off. So the cloud guys have the advantage of owning a cloud. The pure on-prem players, they don't, but the on-prem crowd has rich stacks, much richer and more mature in a lot of areas, as it relates to supporting on-premises workloads and much more so than the cloud players, but they don't have mature cloud stacks. They're kind of just getting started with things like subscription billing and API-based microservices offerings. They got to figure out Salesforce compensation and just the overall Azure service mentality versus the historical product box mentality, and that takes time. And they're each coming at this from their respective different points of view and points of strength. HPE is doing a very good job of marketing and go-to market. It probably has the cleanest model, enabled by the company's split from HP, but it has some gaps that it's needed to fill and it's doing so through acquisitions. Ezmeral, for example, is it's new data play. It just bought Zerto to facilitate backup as a service. And it's expanded partnerships to fill gaps in the portfolio. Some partnerships, which they couldn't do before because it created conflicts inside of HPE or HP. Dell is all about the portfolio, the breadth of the portfolio, the go-to-market prowess and its supply chain advantage. It's very serious about Azure Service with Apex and it's driving hard to win that day. Cisco comes at this from a huge portfolio and of course, a point of strength and networking, which maybe is a bit tougher to offer as a service, but Cisco has a large and fast growing subscription business in collaborations, security and other areas, so it's cloud-like in that regard. And Oracle, of course, has the huge advantage of an extremely rich functional stack and it owns a cloud, which has dramatically improved in the past few years, but Oracle is narrow to the red stack, at least today. Oracle, if it wanted to, we think, could dominate the database cloud, it could be the database cloud, especially if it decided to open its cloud to competitive database offerings and run them in the Oracle cloud. Hmm. Wonder if Oracle will ever move in that direction. Now a big part of this shift is the appeal of OPEX versus CAPEX. Let's take a look at some ETR data that digs a bit deeper into this topic. This data is from an August ETR drill down, asking CIOs and IT buyers how their budgets are split between OPEX and CAPEX. The mid point of the yellow line shows where we are today, 57% OPEX, expecting to grow to 63% one year from now. That's not a huge difference, there's not a huge difference when you drill into global 2000, which kind of surprised me. I thought global 2000 would be heavier CAPEX, but they seem to be accelerating the shift to OPEX slightly faster than the overall base, but not really in a meaningful way. So I didn't really discern big differences there. Now, when you dig further into industries and look at subscription versus consumption models for OPEX, you see about 60/40 favoring subscription models, with most industry slowly moving toward consumption or usage based models over time. There are a couple of outliers, but generally speaking, that's the trend. What's perhaps more interesting is when you drill into subscription versus usage based models by product area, and that's what this chart shows. It shows by tech segment, the percent subscription, that's the blue, versus consumption or usage based, that's the gray bars, yellow being indifferent or maybe it's I don't know. What stands out are two areas that are more usage heavy, consumption heavy. That's database, data warehousing, and IS. So database is surely weighted by companies like Snowflake and offerings like Redshift and other cloud databases from Azure and Google and other managed services, but the IS piece, while not surprising, is, we think, relevant because most of the legacy vendor Azure Service offerings are borrowing from a SaaS-oriented subscription model with a hardware twist. In other words, as a customer, you're committing to a term and a minimum spend over the life of that term. You're locked in for a year or three years, whatever it is, to account for the hardware and headroom the vendor has to install because they want to allow you to increase your usage. So that's the usage based model. See, you're then paying by the drink for that consumption above that minimum threshold. So it's a hybrid subscription consumption model, which is actually quite interesting. And we've been saying, what would really be cool is if one of the on-prem penguins on the iceberg would actually jump in and offer a true consumption model right out of the box, as a disruptive move to the industry and to the cloud players, and take that risk. And I think that might happen once they feel comfortable with the financial model and they have nailed the product market fit, but right now, the model is what it is. And even AWS without post requires a threshold and a minimum commitment. So we'd love to see someone take that chance and offer true cloud consumption pricing to facilitate more experimentation and lower risk for the customer entry points. Now let's take a look at some of these players and see what kind of spending momentum they have. This is our popular XY chart-view that plots net score or spending velocity on the x-axis and market share or pervasiveness in the data set on the... Oh, sorry, net score or spending momentum on the y-axis and pervasiveness or market share on the x-axis. Now this is cut by cloud computing vendors, as defined by the customers responding. There were nearly 1500 respondents in the ETR survey, so a couple of points here. Note the red line is the elevated line. In other words, anything above that is considered really robust momentum. And no surprise, Azure, AWS and Google are above that line. Azure and AWS always battle it out for top share of voice in the x-axis in this survey. Now this, remember, is the July survey, but ETR, they gave me a sneak peek at the October results that they're going to be releasing in the coming week and Dell cloud and VMware cloud, which is VCF and maybe some other components, not VMware cloud and AWS, that's a separate beast, but those two are moving up in the y-axis. So they're demonstrating spending momentum. IBM is moving down and Oracle is at a respectable 20% on the y-axis. Now, interestingly, HPE and Lenovo don't show up in the cloud taxonomy, in that cloud cut, and neither does Cisco. I believe I'm correct in that this is an open-ended question, i.e., who are your cloud suppliers? So the customers are not resonating with that messaging yet, but I'm going to double check on that. Now to widen the aperture a bit, we said let's do a cut of the on-prem and cloud players within cloud accounts, so we can include HPE and Cisco and see how they're doing inside of cloud accounts. So that's what this chart does. It's a filter on 975 customers who identify themselves as cloud accounts. So here we were able to add in Cisco and HPE. Now, Lenovo still doesn't show up on the data. It shows up in laptops and desktops, but not as prominent in the enterprise, not prominent at all, but HPE Ezmeral did show up and it's moving forward in the October survey, again, part of the sneak peek. Ezmeral is HPE's data platform that they've introduced, combining the assets of MapR, BlueData and some other organic development. Now, as you can see, HPE and Cisco, they show up on the chart, as I said, and you can see the rope in the tug of war is starting to get a little bit more taut. The cloud guys have momentum and big account presence, but the on-prem folks also have big footprints, rich stacks and many have strong services arms, and a lot of customer affinity. So let's wrap with some comments about how this will shake out and what's some of the markers we can watch. Now, the first thing I'll say is we're starting to hear the right language come out of the vendor community. The idea that they're investing in a layer to abstract the underlying complexity of the clouds and on-prem infrastructure and turning the world into, essentially, a programmable interface to resources. The question is, what about giving access through that layer to underlying primitives in the public cloud? VMware has been very clear on this. They will facilitate that access. I believe Red Hat as well. So watch to the degree in which the large on-prem players are enabling that access for developers. We believe this is the right direction overall, but it's also very hard and it's going to require lots of resources and R & D. I would say at this point that each company has its respective strengths and weaknesses. I see HPE mostly focused today on making its on-prem offerings work like a cloud, whereas some of the others, VMware, Dell and Cisco, are stressing to a greater degree, in my view, enabling multi-cloud and edge connections, cross connections. Not that HPE isn't open to that when you ask them about it, but its marketing is more on-prem leaning, in my opinion. Now all of the traditional vendors, in my view, are still defensive about the cloud, although I would say much less so each day. Increasingly, they look at the public cloud as an opportunity to build value on top of that abstraction layer, if you will. As I said earlier, these on-prem guys, they all have ways to go. They're in the early stages of figuring out what a cloud operating model looks like, how it works, what services to offer, how to pay sellers and partners, but the public cloud vendors, they're miles ahead in that regard, but at the same time, they're navigating into on-prem territory. And they're very immature, in most cases. So how do they service all this stuff? How do they establish partnerships and so forth? And how do they build stacks on prem that are as rich as they are in the cloud? And what's their motivation to do that? Are they getting pulled, digging their heels in? Or are they really serious about it? Now, in some respects, Oracle is in the best position here in terms of hybrid maturity, but again, it's narrowly focused on the Red Stack. I would say the same for Pure Storage, more mature as a service, but narrowly focused, of course, on storage. Let's talk marketplace and ecosystems. One of the hallmarks of public clouds is optionality of tooling. Just all you do is go to the AWS Marketplace and you'll see what I mean. It's got this endless bevy of choices. It's got one of everything in there and you can buy directly from your AWS Console. So watch how the hybrid cloud plays out in terms of partner inclusion and ease of doing business, that's another sign of maturity. Let's talk developers and edge. This is by far the most important and biggest hole in the hybrid portfolios, outside the public cloud players. If you're going to build infrastructure as code, who do you expect to code it? How are the on-prem players cultivating developer communities? IBM paid 34 billion to buy its way in. Actually, in today's valuation terms, you might say that's looking like a good play, but still, that cash outlay is equal to one third of IBM's revenue. So big, big bet on OpenShift, but IBM's infrastructure strategy is fragmented and its cloud business, as IBM reports in its financial statements, is a services-heavy, kitchen sink set of offerings. It's very confusing. So they got to still do some clean up there, but they're serious about the architectural battle for hybrid cloud, as Arvind Krishna calls it. Now VMware, by cobbling together the misfit developer toys of the remnants from the EMC Federation, including Pivotal, is trying to get there. You know, but when you talk to customers, they're still not all in on VMware's developer affinity. Now Cisco has DevNet, but that's basically CCIE's and other trained networking engineers learning to code in languages like Python. It's not necessarily true devs, although they're upskilling. It's a start and they're investing, Cisco, that is, investing in the community, leveraging their champions, and I would say Dell could do the same with, for example, the numerous EMC storage admins that are out there. Now Oracle bought Sun to get Java, and that's a large community of developers, but even so, when you compare AWS and Microsoft ecosystems to the others, it's not even close in terms of developer affinity. So lots of work to be done there. One other point is Pure's acquisition of Portworx, again, while narrowly focused, is a good move and instructive of the changes going on in infrastructure. Now how does this all relate to the edge? Well, I'm not going to talk much about that today, but suffice to say, developers, in our view, will win the edge. And right now, they're coding in the cloud. Now they're often coding in the cloud and moving work on prem, wrapping them in containers, but watch how sticky that model is for the respective players. The other thing to watch is cadence of offerings. Another hallmark of cloud is a rapid expansion of features. The public cloud players don't appear to be slowing down and the on-prem folks seem to be accelerating. I've been watching HPE and GreenLake and their cadence of offerings, and watch how quickly the newbies of Azure Service can add functionality, I have no doubt Dell is going to be right there as well, as is Cisco and others. Also pay attention to financial metrics, watch how Azure Service impacts the income statements and how the companies deal with that because as you shift to deferred revenue models, it's going to hurt profitability. And I'm not worried about that at all because it won't hurt cashflow, or at least it shouldn't. As long as the companies communicate to Wall Street and they're transparent, i.e., they don't shift reporting definitions every year and a half or two years, but watch for metrics around retention and churn, RPO or Remaining Performance Obligations, billing versus bookings, increased average contract values, cohort selling, the impact on both gross margin and operating margin. These are the things you watch with SaaS companies and essentially, these big hardware players are becoming Azure Service slash SaaS companies. These are going to be the key indicators of success and the proof in the pudding of the transition to Azure Service. It should be positive for these companies, assuming they get the product market fit right, and can create a flywheel effect with their respective ecosystems and partner channels. Now I'm sure you can think of other important factors to watch, but I'm going to leave it here for now. Remember these episodes, they're all available as podcasts, wherever you listen. All you got to do is search Breaking Analysis podcast and please subscribe, check out ETR's website at etr.plus. We also publish a full report every week on wikibon.com and siliconangle.com. You can get in touch with me, email david.vellante@siliconangle.com or you can DM me @dvellante. You can comment on our LinkedIn posts. This is Dave Vellante for theCUBE Insights powered by ETR. Have a great week, everybody, stay safe, be well. And we'll see you next time. (soft music)

(bright 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. Where a decentralized approach, with open cloud native principles and federated governance, will become an underpinning of digital transformations. Hi everybody. This is Dave Volante. Welcome back to HPE Discover 2021, the virtual version. You're watching theCube's continuous coverage of the event and we're here with Matt Maccaux, who's a field CTO for Ezmeral Software at HPE. We're going to talk about HPE software strategy, and Ezmeral and specifically how to take AI analytics to scale and ensure the productivity of data teams. Matt, welcome to theCube. 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 a CTO. Where do you spend your time? >> 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? 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 HPE, for a long, long time, theCube has 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 their software business, they're getting out of software." I said, "No, no, no, hold on. They're really focusing", and the whole focus around hybrid cloud and now as a service, you've really retooling that business and sharpened your focus. So tell us more about Ezmeral, it's a cool name, but what exactly is Ezmeral software? >> I get this question all the time. So what is Ezmeral? Ezmeral is a software platform for modern data and analytics workloads, using open source software components. We came from some inorganic growth. We acquired a company called Cytec, that brought us a zero trust approach to doing security with containers. We bought BlueData 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. Then finally we acquired MapR, which gave us this scale out distributed file system and additional analytical capabilities. 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 cloud 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 approach to security, that's what Ezmeral 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 opensource community where those projects don't exist. >> You know, it's interesting, thank you for that history, and when I go back, I have been there since the early days of Big Data and Hadoop and so forth and MapR always had the best product, but they couldn't get it out. 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. So at the same time they participated in open source projects because everybody did, 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? >> So the trends are, I think, are a couplefold, and it's funny about Hadoop, but I think the final nails in the coffin have been hammered in with the Hadoop space now. So that leading trend, 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 probably the biggest struggle for these chief data officers, chief digital officers, is how do I allow that innovation but maintaining control over my data compliance especially when we talk international standards, like GDPR, to restrict access to data, the ability to be forgotten, in these multinational organizations how do I sort of square all of those components? Then how do I do that in a way that just doesn't lock me into another appliance or software vendor 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 you just, to me, you hit on the key word, which is organization. I want to talk about what some of the barriers are. And again, you heard my wrap up front. 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 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. 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 monolithic centralized organization, that's a barrier for me. And so we're seeing that change, that I kind of alluded to it up front, but what do you see as the big barriers that are blocking this vision from becoming a reality? >> It very much is organization, Dave. The technology's 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 going to do it anyway. And they're going to 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 HPE's strategy. You've heard it from our CEO for years now. Everything needs to be delivered as a service. It's Ezmeral Software that enables that capability, such as self-service and secure data provisioning, et cetera. >> Again, I love this conversation because if you go back to the early days of Hadoop, that was what was profound about a Hadoop. Bring five megabytes of code to a petabyte of data, and it didn't happen. We shoved it all into a data lake and it became a data swamp. And that's okay, it's a one dot oh, you know, maybe in data as is like data warehouses, data hubs, data lakes, maybe this is now a four dot oh, but we're getting there. 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 just fears, how important is open source software today? >> I think the cloud native developments, following the 12 factor applications, microservices based, paved 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 as an operating system within the enterprise. But what everyone realized is that it's 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? So if the answer is no, well? Then if we can't get support, we're not going to allow that. So where HPE Ezmeral 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 committers to support it. You call HPE, the same phone number you've been calling for years for tier one 24 by seven support, and we will support your Kubernetes, your Spark your Presto, your Hadoop ecosystem of components. We're 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, versus throwing out all the animals in the zoo and say, okay, figure it out yourself. But then, of course, what we saw, which was kind of ironic, was people finally said, "Hey, we can do this in the cloud more easily." So that's where you're seeing a lot of data 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 moving to on-prem, on-prem is creating hybrid connections. You're seeing co-location facilities very proximate to the cloud. We're talking now about the edge, the near edge, and the far edge, deeply embedded. So that whole notion of cloud is changing. But I want to ask you, there's still a big push to cloud, everybody has a cloud first mantra, how do you see HPE competing in this new landscape? >> I think collaborating is probably a better word, although you could certainly argue if we're just leasing or renting hardware, then it would be competition, but I think again... The workload is going to flow to where the data exists. So if the data's 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 via on-premises systems, then that's where it's going to be executed. That's production, and so HPE's approach is very much co-exist. It's a co-exist model of, if you need to do DevTests 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, mind share, continue to deploy those workloads. And yes, there's going to competition that comes along. Do you run this on a GCP or do you run it on a GreenLake 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 there's a lot of choices out there, when it comes to containers generally and Kubernetes specifically, and you may have answered this, you get the zero trust component, you've got the orchestrator, you've got the scale-out piece, but I'm interested in hearing in your words why an enterprise would or should consider Ezmeral instead of alternatives to Kubernetes solutions? >> It's a fair question, and it comes up in almost every conversation. "Oh, we already do Kubernetes, we have a Kubernetes standard", and that's largely true in most of the enterprises I speak to. They're using one of the many on-premises distributions to their cloud distributions, and they're all fine. They're all fine for what they were built for. Ezmeral was generally built for something a little different. Yes, everybody can run microservices based applications, DevOps based workloads, but where Ezmeral is different is for those data intensive, in clustered applications. Those sorts of applications require a certain degree of network awareness, persistent storage, et cetera, which requires either a significant amount of intelligence. Either you have to write in Golang, or you have to write your own operators, or Ezmeral can be that easy button. We deploy those stateful applications, because we bring a persistent storage layer, that came from MapR. We're really good at deploying those stateful clustered applications, and, in fact, we've opened sourced that as a project, KubeDirector, that came from BlueData, and we're really good at securing these, using SPIFFE and SPIRE, to ensure that there's that zero trust approach, that came from Scytale, 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 co-exist 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 them yet", or "We're thinking about it", and that's when we talk about the capabilities of Ezmeral and I usually get the response, "Oh. A, we didn't know you existed and B well, let's talk about how exactly you do that." So again, it's more of a co-exist model rather than a compete with model, Dave. >> Well, that makes sense. I mean, I think again, a lot of people, they go, "Oh yeah, Kubernetes, no big deal. It's everywhere." But you're talking about a solution, kind of taking a platform approach with capabilities. You got to protect the data. A lot of times, these microservices aren't so micro and things are happening really fast. You've got to be secure. You got to be protected. And like you said, you've got a single phone number. You know, people say one throat to choke. Somebody in the media the other day said, "No, no. Single hand to shake." It's more of a partnership. I think that's apropos for HPE, Matt, with your heritage. >> That one's better. >> So, you know, thinking about this whole, we've gone through the pre big data days and the big data was all the hot buzzword. People don't maybe necessarily use that term anymore, although the data is bigger and getting bigger, which is kind of ironic. Where do you see this whole space going? We've talked about that sort of trend toward breaking down the silos, decentralization, maybe these hyper specialized roles that we've created, maybe getting more embedded or aligned with the line of business. How do you see... It feels like 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 IT sort of had to deal with that down the road. I think that 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. They're allowing data to move, to where those applications are, and I think this decentralized approach is really going to be king. You're going to see traditional software packages. You're going to see open source. You're going to see a mix of those, but what I think will 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, release it and then wish it luck. That we've got to treat these analytics, and 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. I think all of those things. The sort of people, process, products. It's all three of those things that are going to have to come into play, but stealing those best ideas from cloud and application developments, 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 going to stick with it for a sec. It's hard to predict, but some takeaways that I have, Matt, from our conversation, I wonder if you could comment? I think the future is more open source. You mentioned automation, Devs 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 no longer going be a bolt on. I don't think we're going to throw away the data warehouse or the data hubs or the data lakes. I think they become a node. I like this idea, I don't know if you know Zhamak Dehghani? but she has this idea of a global data mesh where these tools, lakes, whatever, they're a node on the mesh. They're discoverable. They're shareable. They're governed in a way. I think the mistake a lot of people made early on in the big data movement is, "Oh, we got data. We have to monetize our data." As opposed to thinking about what products can I build that are based on data that then can lead to monetization? I think the other thing I would say is the business has gotten way too technical. (Dave chuckles) It's alienated a lot of the business lines. I think we're seeing that change, and I think things like Ezmeral that simplify that, are critical. So I'll give you the final thoughts, based on my rant. >> No, your rant is spot on Dave. I think we are 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 you throw at it, you're going to end up in the same state that you're essentially in today, with lots of swamps. I did like that concept of a node or a data mesh. It kind of goes back to the similar thing with a service mesh, or a set of APIs that you can use. I think we're going to have something similar with data. The trick is always, how heavy is it? How easy is it to move about? I think there's always going to be that latency issue, maybe not within the data center, but across the WAN. 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, govern it. 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 you need to provision data across those nodes for those use cases, do so in a well measured and governed 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, auditability and this, frankly, openness that allows you to say, "I don't like where this project's going. I want to go in a different direction." And it gives those enterprises a 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. Excellent conversation as always. Thanks for having me again. >> You're very welcome. And thank you for watching everybody. This is theCube's continuous coverage of HPE Discover 2021. Of course, the virtual version. Next year, we're going to be back live. My name is Dave Volante. Keep it right there. (upbeat music)

>> Okay. Now we're going to dig deeper into HPE Ezmeral and try to better understand how it's going to impact customers. And with me to do that are Robert Christiansen, who is the Vice President of Strategy in the office of the CTO and Kumar Sreekanti, who is the Chief Technology Officer and Head of Software, both of course, with Hewlett Packard Enterprise. Gentlemen, welcome to the program. Thanks for coming on. >> Good seeing you, Dave. Thanks for having us. >> It's always good to see you guys. >> Thanks for having us. >> So, Ezmeral, kind of an interesting name, catchy name, but Kumar, what exactly is HPE Ezmeral? >> It's indeed a catchy name. Our branding team has done fantastic job. I believe it's actually derived from Esmeralda, is the Spanish for emarald. Often it's supposed some very mythical bars, and they derived Ezmeral from there. And we all initially when we heard, it was interesting. So, Ezmeral was our effort to take all the software, the platform tools that HPE has and provide this modern operating platform to the customers and put it under one brand. So, it has a modern container platform, it does persistent storage with the data fabric and it doesn't include as many of our customers from that. So, think of it as a modern container platform for modernization and digitazation for the customers. >> Yeah, it's an interesting, you talk about platform, so it's not, you know, a lot of times people say product, but you're positioning it as a platform so that has a broader implication. >> That's very true. So, as the customers are thinking of this digitazation, modernization containers and Microsoft, as you know, there is, has become the stable all. So, it's actually a container orchestration platform with golfers open source going into this as well as the persistence already. >> So, by the way, Ezmeral, I think Emerald in Spanish, I think in the culture, it also has immunity powers as well. So immunity from lock-in, (Robert and Kumar laughing) and all those other terrible diseases, maybe it helps us with COVID too. Robert, when you talk to customers, what problems do you probe for that Ezmeral can do a good job solving? >> Yeah, that's a really great question because a lot of times they don't even know what it is that they're trying to solve for other than just a very narrow use case. But the idea here is to give them a platform by which they can bridge both the public and private environment for what they do, the application development, specifically in the data side. So, when yo're looking to bring containerization, which originally got started on the public cloud and it has moved its way, I should say it become popular in the public cloud and it moved its way on premises now, Ezmeral really opens the door to three fundamental things, but, you know, how do I maintain an open architecture like you're referring to, to some low or no lock-in of my applications. Number two, how do I gain a data fabric or a data consistency of accessing the data so I don't have to rewrite those applications when I do move them around. And then lastly, where everybody's heading, the real value is in the AI ML initiatives that companies are really bringing and that value of their data and locking that data at where the data is being generated and stored. And so the Ezmeral platform is those multiple pieces that Kumar was talking about stacked together to deliver the solutions for the client. >> So Kumar, how does it work? What's the sort of IP or the secret source behind it all? What makes HPE different? >> Yeah. Continuing on (indistinct) it's a modern glass form of optimizing the data and workloads. But I think I would say there are three unique characteristics of this platform. Number one is that it actually provides you both an ability to run statefull and stateless as workloads under the same platform. And number two is, as we were thinking about, unlike another Kubernete is open source, it actually add, use you all open-source Kurbenates as well as an orchestration behind them so you can actually, you can provide this hybrid thing that Robert was talking about. And then actually we built the workflows into it, for example, they'll actually announced along with it Ezmeral, ML expert on the customers can actually do the workflow management around specific data woakload. So, the magic is if you want to see the secrets out of all the efforts that has been going into some of the IP acquisitions that HPE has done over the years, we said we BlueData, MAPR, and the Nimble, all these pieces are coming together and providing a modern digitization platform for the customers. >> So these pieces, they all have a little bit of a machine intelligence in them, you have people, who used to think of AI as this sort of separate thing, I mean the same thing with containers, right? But now it's getting embedded into the stack. What is the role of machine intelligence or machine learning in Ezmeral? >> I would take a step back and say, you know, there's very well the customers, the amount of data that is being generated and 95% or 98% of the data is machine generated. And it does a series of a window gravity, and it is sitting at the edge and we were the only one that had edge to the cloud data fabric that's built to it. So, the number one is that we are bringing computer or a cloud to the data that taking the data to the cloud, right, if you will. It's a cloud like experience that provides the customer. AI is not much value to us if we don't harness the data. So, I said this in one of the blog was we have gone from collecting the data, to the finding the insights into the data, right. So, that people have used all sorts of analysis that we are to find data is the new oil. So, the AI and the data. And then now your applications have to be modernized and nobody wants write an application in a non microservices fashion because you wanted to build the modernization. So, if you bring these three things, I want to have a data gravity with lots of data, I have built an AI applications and I want to have those three things I think we bring to the customer. >> So, Robert let's stay on customers for a minute. I mean, I want to understand the business impact, the business case, I mean, why should all the cloud developers have all the fun, you've mentioned it, you're bridging the cloud and on-prem, they talk about when you talk to customers and what they are seeing is the business impact, what's the real drivers for that? >> That's a great question cause at the end of the day, I think the recent survey that was that cost and performance are still the number one requirement for this, just real close second is agility, the speed at which they want to move and so those two are the top of mind every time. But the thing we find Ezmeral, which is so impactful is that nobody brings together the Silicon, the hardware, the platform, and all of that stack together work and combine like Ezmeral does with the platforms that we have and specifically, we start getting 90, 92, 93% utilization out of AI ML workloads on very expensive hardware, it really, really is a competitive advantage over a public cloud offering, which does not offer those kinds of services and the cost models are so significantly different. So, we do that by collapsing the stack, we take out as much intellectual property, excuse me, as much software pieces that are necessary so we are closest to the Silicon, closest to the applications, bring it to the hardware itself, meaning that we can interleave the applications, meaning that you can get to true multitenancy on a particular platform that allows you to deliver a cost optimized solution. So, when you talk about the money side, absolutely, there's just nothing out there and then on the second side, which is agility. One of the things that we know is today is that applications need to be built in pipelines, right, this is something that's been established now for quite some time. Now, that's really making its way on premises and what Kumar was talking about with, how do we modernize? How do we do that? Well, there's going to be some that you want to break into microservices containers, and there's some that you don't. Now, the ones that they're going to do that they're going to get that speed and motion, et cetera, out of the gate and they can put that on premises, which is relatively new these days to the on-premises world. So, we think both won't be the advantage. >> Okay. I want to unpack that a little bit. So, the cost is clearly really 90 plus percent utilization. >> Yes. >> I mean, Kumar, you know, even pre virtualization, we know that it was like, even with virtualization, you never really got that high. I mean, people would talk about it, but are you really able to sustain that in real world workloads? >> Yeah. I think when you make your exchangeable cut up into smaller pieces, you can insert them into many areas. We have one customer was running 18 containers on a single server and each of those containers, as you know, early days of new data, you actually modernize what we consider week run containers or microbiome. So, if you actually build these microservices, and you all and you have versioning all correctly, you can pack these things extremely well. And we have seen this, again, it's not a guarantee, it all depends on your application and your, I mean, as an engineer, we want to always understand all of these caveats work, but it is a very modern utilization of the platform with the data and once you know where the data is, and then it becomes very easy to match those two. >> Now, the other piece of the value proposition that I heard Robert is it's basically an integrated stack. So I don't have to cobble together a bunch of open source components, there's legal implications, there's obviously performance implications. I would imagine that resonates and particularly with the enterprise buyer because they don't have the time to do all this integration. >> That's a very good point. So there is an interesting question that enterprises, they want to have an open source so there is no lock-in, but they also need help to implement and deploy and manage it because they don't have the expertise. And we all know that the IKEA desk has actually brought that API, the past layer standardization. So what we have done is we have given the open source and you arrive to the Kubernetes API, but at the same time orchestration, persistent stories, the data fabric, the AI algorithms, all of them are bolted into it and on the top of that, it's available both as a licensed software on-prem, and the same software runs on the GreenLake. So you can actually pay as you go and then we run it for them in a colo or, or in their own data center. >> Oh, good. That was one of my latter questions. So, I can get this as a service pay by the drink, essentially I don't have to install a bunch of stuff on-prem and pay it perpetualized... >> There is a lot of containers and is the reason and the lapse of service in the last discover and knowledge gone production. So both Ezmeral is available, you can run it on-prem, on the cloud as well, a congenital platform, or you can run instead on GreenLake. >> Robert, are there any specific use case patterns that you see emerging amongst customers? >> Yeah, absolutely. So there's a couple of them. So we have a, a really nice relationship that we see with any of the Splunk operators that were out there today, right? So Splunk containerized, their operator, that operator is the number one operator, for example, for Splunk in the IT operation side or notifications as well as on the security operations side. So we've found that that runs highly effective on top of Ezmeral, on top of our platforms so we just talked about, that Kumar just talked about, but I want to also give a little bit of backgrounds to that same operator platform. The way that the Ezmeral platform has done is that we've been able to make it highly active, active with HA availability at nine, it's going to be at five nines for that same Splunk operator on premises, on the Kubernetes open source, which is as far as I'm concerned, a very, very high end computer science work. You understand how difficult that is, that's number one. Number two is you'll see just a spark workloads as a whole. All right. Nobody handles spark workloads like we do. So we put a container around them and we put them inside the pipeline of moving people through that basic, ML AI pipeline of getting a model through its system, through its trained, and then actually deployed to our ML ops pipeline. This is a key fundamental for delivering value in the data space as well. And then lastly, this is, this is really important when you think about the data fabric that we offer, the data fabric itself doesn't necessarily have to be bolted with the container platform, the container, the actual data fabric itself, can be deployed underneath a number of our, you know, for competitive platforms who don't handle data well. We know that, we know that they don't handle it very well at all. And we get lots and lots of calls for people saying, "Hey, can you take your Ezmeral data fabric "and solve my large scale, "highly challenging data problems?" And we say, "yeah, "and then when you're ready for a real world, "full time enterprise ready container platform, "we'd be happy to prove that too." >> So you're saying you're, if I'm inferring correctly, you're one of the values as you're simplifying that whole data pipeline and the whole data science, science project pun intended, I guess. (Robert and Kumar laughing) >> That's true. >> Absolutely. >> So, where does a customer start? I mean, what, what are the engagements like? What's the starting point? >> It's means we're probably one of the most trusted and robust supplier for many, many years and we have a phenomenal workforce of both the (indistinct), world leading support organization, there are many places to start with. One is obviously all these salaries that are available on the GreenLake, as we just talked about, and they can start on a pay as you go basis. There are many customers that actually some of them are from the early days of BlueData and MAPR, and then already running and they actually improvise on when, as they move into their next version more of a message. You can start with simple as well as container platform or system with the store, a computer's operation and can implement as an analyst to start working. And then finally as a big company like HPE as an everybody's company, that finance it's services, it's very easy for the customers to be able to get that support on day to day operations. >> Thank you for watching everybody. It's Dave Vellante for theCUBE. Keep it right there for more great content from Ezmeral.

Live from San Diego, California it's theCUBE! covering KubeCon and CloudNativeCon brought to you by Red Hat a CloudNative computing foundation and its ecosystem partners. >> Welcome back, this is theCube's coverage of KubeCon, CloudNativeCon 2019 in San Diego I'm Stu Miniman with my co-host for the week, John Troyer, and happy to welcome to the program, Tom Phelan, who's an HPE Fellow and was the BlueData CTO >> That's correct. >> And is now part of Hewlett-Packard Enterprise. Tom, thanks so much for joining us. >> Thanks, Stu. >> All right, so we talked with a couple of your colleagues earlier this morning. >> Right. >> About the HPE container platform. We're going to dig in a little bit deeper later. >> So, set the table for us as to really the problem statement that HP is going to solve here. >> Sure, so Blue Data which is what technologies we're talking about, we addressed the issues of how to run applications well in containers in the enterprise. Okay, what this involves is how do you handle security how do you handle Day-2 operations of upgrade of the software how do you bring CI and CD actions to all your applications. This is what the HPE container platform is all about. So, the announcement this morning, which went out was HPE is announcing the general availability of the HPE container platform, an enterprise solution that will run not only CloudNative applications, are typically called microservices applications, but also Legacy applications on Kubernetes and it's supported in a hybrid environment. So not only the main public cloud providers, but also on premise. And a little bit of divergence for HPE, HPE is selling this product, licensing this product to work on heterogeneous hardware. So not only HPE hardware, but other competitors' hardware as well. >> It's good, one of the things I've been hearing really over the last year is when we talked about Kubernetes, it resonated, for the most part, with me. I'm an infrastructure guy by background. When I talk in the cloud environment, it's really talking more about the applications. >> Exactly. >> And that really, we know why does infrastructure exist? Infrastructure is just to run my applications, it's about my data, it's about my business processes >> Right. >> And it seems like that is a y'know really where you're attacking with this solution. >> Sure, this solution is a necessary portion of the automated infrastructure for providing solutions as a service. So, um, historically, BlueData has been specializing in artificial intelligence, machine learning, deep learning, big data, that's where our strong suit came from. So we, uh, developed a platform that would containerize those applications like TensorFlow, um, Hadoop, Spark, and the like, make it easy for data scientists to stand up some clusters, and then do the horizontal scalability, separate, compute, and storage so that you can scale your compute independent of your storage capacity. What we're now doing is part of the HPE container platform is taking that same knowledge, expanding it to other applications beyond AI, ML, and DL. >> So what are some of those Day-2 implications then uh what is something that folks run into that then now with an HPE container platform you think will eliminate those problems? >> Sure, it's a great question, so, even though, uh, we're talking about applications that are inherently scalable, so, AI and ML and DL, they are developed so they can be horizontal- horizontally scalable, they're not stateless in the true sense of the word. When we say a stateless application, that means that, uh, there is no state in the container itself that matters. So if you destroy the container, reinstate it, there's no loss of continuity. That's a true stateless or CloudNative application. Uh, AI and ML and DL applications tend to have configuration information and state information that's stored in what's known as the Root Storage of the compute node, okay, what's in slash, so you might see, um, per node configuration information in a configuration file in the Etsy directory. Okay, today, if you just take standard off the shelf Kubernetes, if you deploy, um, Hadoop for example, or TensorFlow, and you configure that, you lose that state when the container goes down. With the HPE container platform, we are, we have been moving forward with a, or driving, a open source project known as KubeDirector. A portion of KubeDirector, of the functionality is to preserve that, uh, Root Storage so that if a container goes down, we are allowed- we are enabled to bring a Nether Instance of that container and have it have the same Root Storage. So it'll look like a just a reboot to the node rather than a reinstall of that node. So that's a huge value when you're talking about these, um, machine learning and deep learning applications that have the state in root. >> All right, so, Tom, how does KubeDirector fit compared to compare contrast it, does it kind of sit aside something like Rook, which was talked about in the keynote, talking about being able to really have that, uh, that kind of universal backplate across all of my clusters >> Right, you're going to have to be >> Is that specific for AI and ML or is this >> I, well, that's a great question, so KubeDirector itself is a Kubernetes operator, okay, uh, and we have implemented that, the open-source communities joining in, so, but what it allows us, KubeDirector is, um, application agnostic, so, you could author a YAML file with some pertinent information about the application that you want to deploy on Kubernetes. You give that YAML file to the KubeDirector operator, it will then deploy the application on your Kubernetes cluster and then manage the Day-2 activities, so this is beyond Helm, or beyond KubeFlow, which are deployment engines. So this also has, well, what happens if I lose my container? How do I bring the services back up, and those services are dependent upon the type of application that's there. That's what KubeDirector does. So, KubeDirector allows a new application to be deployed and managed on Kubernetes without having to write a operator in Go Code. Makes it much easier to bring a new application to the platform. >> Gotcha, so Tom, kind of a two-part question, first part, so, uh, you were one of the co-founders of BlueData >> And now with HPE, there's, sometimes I think with technology, some of them are kind of invented in a lab, or in a graduate student's head, others come out of real world experience. And, uh, you're smiling 'cause I think BlueData was really built around, uh, y'know, at least your experience was building these BlueData apps. >> This is a hundred percent real world experience. So we were one of the real early pioneers of bringing, um, these applications into containers y'know, truth be told, when BlueData first started, we were using VMs. We were using OpenStack, and VM more. And we realized that we didn't need to pay that overhead it was possible to go ahead and get the same thing out of a container. So we did that, and we suffered all the slings and arrows of how to make the, um, security of the container, uh, to meet enterprise class standards. How do we automatically integrate with active directory and LDAP, and Kerberos, with a single sign on all those things that enterprises require for their infrastructure, we learned that the hard way through working with, y'know, international banking organizations, financial institutions, investment houses, medical companies, so our, our, all our customers were those high-demand enterprises. Now that we're apart of HP, we're taking all that knowledge that we acquired, bringing it to Kubernetes, exposing it through KubeDirector, where we can, and I agree there will be follow on open-source projects, releasing more of that technology to the open-source community. >> Mhm that was, that was actually part-two of my question is okay, what about, with now with HPE, the apps that are not AI, ML and you nailed it, right, >> Yeah. >> All those enterprise requirements. >> Same problems exist, right, there is secure data, you have secure data in a public cloud, you have it on premise, how do you handle data gravity issues so that you store, you run your compute close to your data where it's necessary you don't want to pay for moving data across the web like that. >> All right, so Tom, platforms are used for lots of different things, >> Yes. >> Bring us inside, what do you feel from your early customers, some of the key use cases that should be highlighted? >> Our key use cases were those customers who were very interested, they had internal developers. So they had a lot of expertise in house, maybe they had medical data scientists, or financial advisors. They wanted to build up sandboxes, so we helped them stand up, cookie-cutter sandboxes within a few moments, they could go ahead and play around with them, if they screwed them up, so what? Right, we tear them down and redo it within moments, they didn't need a lot of DevOps, heavy weight-lifting to reinstall bare-metal servers with these complex stacks of applications. The data scientist that I want to use this software which just came out of the open-source community last week, deployed in a container and I want to mess it up, I want to tighten, y'know, really push the edge on this and so we did that. We developed this sandboxing platform. Then they said, okay, now that you've tested this, I have it in queue A, I've done my CI/CD, I've done my testing, now I want to promote it into production. So we did that, we allowed the customer to deploy and define different quality of service depending on what tier their application was running in. If it was in testing dev, it got the lowest tier. If it was in CI/CD, it got a higher level of resource priority. Once it got promoted to production, it got guaranteed resource priority, the highest solution, so that you could always make sure that the customer who is using the production cluster got the highest level of access to the resources. So we built that out as a solution, KubeDirector now allows us to deploy that same sort of thing with the Kubernetes container orchestrator. >> Tom, you mentioned blue metal, uh, bare-metal, we've talked about VMs, we've been hearing a lot of multicloud stories here, already today, the first day of KubeCon, it seems like that's a reality out in the world, >> Can you talk about where are people putting applications and why? >> Well, clearly, uh, the best practices today are to deploy virtual machines and then put containers in virtual machines, and they do that for two very legitimate reasons. One is concern about the security, uh, plane for containers. So if you had a rogue actor, they could break out of the container, and if they're confined within the virtual machine, you can limit the impact of the damage. One very good, uh, reason for virtual machines, also there's a, uh, feeling that it's necessary to maintain, um, the container's state running in a virtual machine, and then be allowed to upgrade the the Prom Code, or the host software itself. So you want to be able to vMotion a virtual machine from one physical host to another, and then maintain the state of the containers. What KubeDirector brings and what BlueData and HP are stating is we believe we can provide both of those functionalities on containers on bare-metal. Okay, and we've spoken a bit about today already about how KubeDirector allows the Root File System to be preserved. That is a huge component of of why vMotion is used to move the container from one host to another. We believe that we can do that with a reboot. Also, um, HPE container platform runs all virtual machines as, um, reduced priority. So you're not, we're not giving root priority or privileged priority to those containers. So we minimize the attack plane of the software running in the container by running it as an unprivileged user and then tight control of the container capabilities that are configured for a given container. We believe it's just enough priority or just enough functionality which is granted to that container to run the application and nothing more. So we believe that we are limiting the attack plane of that through the, uh and that's why we believe we can validly state we can run these containers on bare-metal without, without the enterprise having to compromise in areas of security or persistence of the data. >> All right, so Tom, the announcement this week, uh is HP container platform available today? >> It will be a- we are announcing it. It's a limited availability to select customers It'll be generally available in Queue 1 of 2020. >> All right, and y'know, give us, y'know, we come back to KubeCon, which will actually be in Boston >> Yes. >> Next year in November >> When we're sitting down with you and you say hugely successful >> Right. >> Give us some of those KPIs as to y'know >> Sure. >> What are your teams looking at? >> So, we're going to look at how many new customers these are not the historic BlueData customers, how many new customers have we convinced that they can run their production work loads on Kubernetes And we're talking about I don't care how many POCs we do or how many testing dev things I want to know about production workloads that are the bread and butter for these enterprises that HP is helping run in the industry. And that will be not only, as we've talked about, CloudNative applications, but also the Legacy, J2EE applications that they're running today on Kubernetes. >> Yeah, I, uh, I don't know if you caught the keynote this morning, but Dan Kohn, y'know, runs the CNCF, uh, was talking about, y'know, a lot of the enterprises have been quitting them with second graders. Y'know, we need to get over the fact that y'know things are going to break and we're worried about making changes y'know the software world that y'know we've been talking about for a number of years, absolutely things will break, but software needs to be a resilient and distributed system, so, y'know, what advice do you give the enterprise out there to be able to dive in and participate? >> It's a great question, we get it all the time. The first thing is identify your most critical use case. Okay, that we can help you with and, and don't try to boil the ocean. Let's get the container platform in there, we will show you how you have success, with that one application and then once that's you'll build up confidence in the platform and then we can run the rest of your applications and production. >> Right, well Tom Phelan, thanks so much for the updates >> Thank you, Stu. >> Congratulations on the launch >> Thank you. >> with the HP container platform and we look forward to seeing the results in 2020. >> Well I hope you invite me back 'cause this was really fun and I'm glad to speak with you today. Thank you. >> All right, for John Troyer, I'm Stu Miniman, still watch more to go here at KubeCon, CloudNativeCon 2019. Thanks for watching theCUBE. (energetic music)

(upbeat music) >> Hi, and welcome to another Cube Conversation where we speak with thought leaders in depth about the topics that are most important to the overall technology community. I'm Peter Burris, your host. Every business inspires to be a digital business, which is every business, faces a significant challenge. They need to use their data in new and value creating ways. But some of that data is not lending itself to new applications, new uses because it's locked up in formats, in technologies and applications that don't lend themselves to change. That's one of the big challenges that every business faces. What can they do to help unlock, to help liberate their data from older formats and older approaches so they can create new sources of value with it. To have that conversation, we're joined by a great guest. Greg Tinker is the CTO and Founder of SereneIT. Greg, welcome back to theCube. >> Thank you Peter, very appreciate it buddy. >> It's been a long time. This is your first time here with SereneIT so why don't you tell us a little bit about SereneIT. >> Sure, so at a high level we are a technology partner. SereneIT focuses on the next generation model structures of engineering first. There's a lot of VARS, in simplest terms, I would say we're a value at a reseller, sure. But we capitalize and focus just on the VA. Anybody can bring VR. The legacy approach of just being a reseller is no longer valid in our industry. Complexities and trying to have a situation where you can liberate data, try to take it from a legacy entrenched model, process, procedure and go into a new modern IT software defined ecosystem is very complex. And our objective is to make the, enablement of IT serene or simple and that's where SereneIT comes from. >> You know, I love the name but if you go back 20 years as you said, the asset that IT was focused on and took care of was the hardware. >> That's right. >> And we bought the hardware from a reseller, they just made the installations, configurations and what not. But as you said, today we're focused on the data. That's the asset. >> That's correct. >> And just as we used to have challenges uplifting and all the things we had to do with hardware, we're having similar types of challenges when you think about how to apply data to new uses, sustain that asset feature of it but apply it in new ways to create new value. As you talk to customers, what is the problem that you find they're encountering as they try to think what to do with their high value traditional data? So there's actually, I'll call it three strategic problems. Becoming to where it to be a workload optimized model structures or your data driven intelligence, trying to pull something out of the data model, trying to pull something out of the data, make it tangible to the business. And then trying to figure out a way to make it easy to enable the users, that is the employees to do something with the data the have. Making it more of a cloud-centric approach. Everybody wants that easy button now. So at a high level, trying to make that a possibility is where we spend our time today. And give you a quick example of that would be legacy block storage. We do a lot in the storage world. And we focus on software defined storage apparatus or solutions. So a lot of our clients are kind of mired down with legacy block, via Fibre Channel basics that were great for their era. But today with cost being a big factor in trying to be able to leverage an ecosystem where I can take my data, wherever my data sits and leverage it on multiple different apparatuses, be it BlueData, be in Kubernetes, be it name your favorite Docker solution. Trying to be able to use that in an ecosystem in a software defined hyper cloud, doing that on a legacy block is very problematic. And that's where we help customers transition from that legacy mindset, legacy IT infrastructure into a more of a modern software defined data program. >> So what's talk about that. Because there's a more modern technology, but really what they're doing is they're saying, look I've got this data, using these protocols like Fibre Channel with these applications and it's doing its job. >> That's right. >> But I want to create options on how I might use that data in the future, options that aren't available to me or aren't available to my business if it stays locked inside Fibre Channel for example. >> That's correct. >> So what you're really doing, is you're giving them paths to new options with their data that can be sustained whatever the technology is. Have I got that right? >> In a nutshell, Frank I would agree with your sentiment on that, your comment is spot on. We take customers data, we look at the business as a whole. And we focus on, what is the core of the business? Be it, maybe it's a High-Performance Computing Cluster Maybe it's a Oracle, Cyrus, Informant name your favorite data base structure. Maybe it's MapR, maybe it's a Dupe. We look at the business and determine, how are we using that data? How much data do we need? What's my data working set size? Understanding that and then we actually would design a solution that will be a software defined ecosystem that we can move that data in. And nine times out of 10 we can do it on the fly. Rarely, rarely ever do we have an outage to do it. Or that might be a small few minute outage window when we do a cut over, where we keep everything in mirroring Lockstep . >> Well that's one of the beauties of software defined is that you have those kinds of flexibilities. >> That's right. >> But think of, so talk to me a little bit about the you are, the customer realizes they have a problem. They find you guys. >> Sure. >> So how do they find you? >> So we do a lot with large scale Fortune 50, Fortune 100, the large scale enterprise businesses. And we do that with our, we're known in the engineering world, big accounts, because of our backdrop in HP engineering. And so HP brings us a lot into these accounts to help them solve a big business problem. So that's how a lot of our customers are finding us today. We are reaching out with media, like theCube here to talk to clients about the fact that we do exist and that we exist to help them consume a more modern IT in footprint. To help them go from that legacy model into that more modern model. >> Okay, so the customer realizes they have a problem, HPE and others, help identify you guys, matches you together. You show up, how do you work with the customer? Is it your big brains and the customer passive? Or you're working side by side to help them accelerate their journey? >> We find it best that we do it in a cohesive manner. We sit down and have a long discussion with their, usually their Chief Executive officer, their CTO, Chief Technology Officer, we'll sit down and talk about the business constraints. And then we'll go down to the directors the guys on the front lines that see the problems on a day to day basis. And we look at where their constraints are. Is it performance, IOP driven. Nine times out of 10, those problems are no longer there. They were solved years ago. Today it's more about the legacy model of, let me log a ticket to stand up a new virtual machine to a SQL database to do this application. So I've logged the ticket, a week two later I finally get a virtual machine. And now I got to get five more teams engaged, I get it online. Total business takes about a month to get some new apparatus up. Where if we go into a software defined ecosystem where we have these playbooks and this model written for the business, we can do that in 10 minutes. Be it on Nutanix do it with SimpliVity, VMware models, we don't' differentiate that. We let the customer tell us which one they use. 'Cause everybody has their liking. Be it some are VMware shop, some are Hyper-V, some are KBM. We do all of them. >> But the point is you want to help them move form an old world that was focused on executing the tasks associate with bringing the system up to a new world that's focused on the resources being able to configure themselves, being able to bring to bring themselves up test themselves in a software defined manner introducing some of those DevOps processes. Whatever the technology is, they have the people and the process to execute the technology. >> That's exactly right, because the technology in a nutshell. If you look at just technology itself that's not the hard part. Not for us anyway, 'cause we're an engineering team that's what we do well. The data driven intelligence stuff and helping customers bring more value out of their data. We can help them with that and show them exactly how we would do it. Be it a different technologies and stuff and we'll get into that discussion later. But the biggest problem we see is the people and processes which you just mentioned. Pushing the button, achieve an objective. That is where the old way of being very ticket driven Siloed approach, really slow down the economics of business. Was a huge driving force of not achieving the ROI that you actually set out to do years ago. Where we have one client that has a little over 4,000 servers and how my team and I explain it to the clients. Come out to the Golden Gate bridge. January 1 you start painting. December 30th you're done painting and January 1 you start painting again. You never get done. It's always getting painted. Patching of these large scale enterprises is the exact same way. You can't patch all the servers on a Saturday. You can't patch three thousand machines, BIOS, firmware, the list goes on. What we do for them is we actually put in an apparatus engine, basically an automation engine and instead of an army of 10 people doing firmware or BIOS and all the stuff updates, we automated 100% of that entire process. That's what SereneIT does. Help a customer take a, could be a legacy model, bare metal machine and show them how we can automate the bare metal machine. We can do the exact same thing in any hypervisor on the planet today. >> So that it's done faster, simpler. The outcome is more predictable. The result is more measurable. >> Yes. >> That's really great stuff. Let's go back to this notion of data because we kind of started with this idea of data and having to evolve the formats increasing the flexibility of it's utilization. We talked about hypervisors and all that technology is kind of sucking it forward, bringing that data forward making it possible to do things with it, but still the data itself is a major challenge. How are you working with customers to get them to envision the new data world independent of some of these other technologies? >> Sure, okay. So yeah, we have clients right now, we have (mumbled) systems these are global file systems that have enormous amount of data in it, some of it is compiled code logics for drivers and firmware and Kernel code structure that are forthcoming technologies that aren't even released yet. We have clients that have data based structure with ascii text is very common road driven. We have customers that have flat ascii files that are just flat text files. So we help the customers grab data from that existing data footprint for new lines of business. Determine what are we touching, how are we touching and how often are we touching it and why are we touching it? Case in point, when you have a large manufacturer doing chip design and your looking at a global file system you're trying to give assertation data as to what drivers are our developers working on most frequently. In the medical community, we have a client we're working on at global scale, we're doing real time data analytics to figure out if we're doing SQL injection from a hacker. So we show them exactly how we can do this in an inline driver stack and show them how to do it with the technology reducing their actual CapEx expend. There's legacy tools out there that work great. You know one of these is like, I won't give names of product and stuff, but there's a lot of cool technologies that's been around for a long time. >> That works. >> That works. >> And it just needs a smart person, or a smart team to put it together so it can be applied. >> That's what we've been doing with our clients is trying to show them that we can take the data that you have, be it flat ascii files or binary data structures. And we can show them that we can give you data analytics and pull that back. We have another client in law industry that we manage worldwide and we do e-discovery. On trying to figure out phrases and things that are maybe concerning to them in a financial world that is the global market. And we're able to give them that data structure on their own intellectual property and we give that to them in real time. We give them a dashboard so they can log in to the dashboard and they can see real time data transparency at a moments notice, so they can tell what the market is doing in Britain or they can tell what the market is doing in Singapore or U.S. by just looking at a dashboard and we're pulling data back. And we're pulling it from outside of world data points, this could be Facebook. Real time feeds, news, media and we pull it from internal data feeds. Email transactions that are going from their financial, they have like CIO's the Chief Investment Officers. Most people think of that as an information officer, right? So we're able to pull data from that and show them that they have a great deal of intellectual property at their fingertips that honestly they've never used before and that's what we're helping customers do today. >> Greg Tinker, Founder, CTO SereneIT. Thanks so much for being on theCube. >> Thank you very much Peter. >> And once again want to thank you for listening to this Cube Conversation. Until next time. (upbeat music)

>> From the SiliconANGLE media office, in Boston, Massachusetts, it's theCUBE. Now, here's your host, Stu Miniman. >> Hi, I'm Stu Miniman, and welcome to a Cube Conversation here in our new Boston area studio, happy to welcome back to the program a VIP from our community, Patrick Osborne, who's the Vice President and General Manager for big data and secondary storage at Hewlett Packard Enterprise, Patrick, great to talk to you. >> Great to be back, thanks, Stu. >> All right, we're talking about the big thing, hundredth year of the NFL kicking off here. Maybe we're talking a little bit about the changing role of infrastructure and, we've been talking about it at the Wikibon team for a number of years, data is at the center of the universe today, when we talk about IT and businesses and what they're thinking about, and in some ways everything's changed, and in other ways it feels like I go to some of these shows and the people that have even more experience than me are like "Oh, geez, we've recreated the mainframe." So, we're fresh off of VMworld, you skipped the show this year, but I know HPE had a large presence at the show, and let me start there, I guess, we look at data centers and cloud, and the mission VMware has is how do they maintain relevant as customers are changing their applications? They just made billions of dollars of acquisitions to be more in the cloud native environment, so when you look at, HPE's very well known in the infrastructure space, had some changes as to what pieces are in the company versus partnered with the company, so when you talk to your customers and they're changing what I call the long pole in the tent of modernization, it's the applications. Where are they today, where are some of the areas that they're doing well, and where are the areas that it's challenging and struggling? >> Yeah, so I'd say from an HPE perspective, we've made a number of investments as well, over the last couple years, both inorganic and organic investments in the space, and I think that even though we've historically been known as an infrastructure company, we're very quickly pivoting towards being known as an enterprise workload company, and so for my perspective, the things that we're trying to do, especially in our division around AI and ML and analytics is being able to provide a platform for customers, especially application developers. I think when we talk about how the world is changing, the buyer personas people were selling to now have completely drastically changed, right? There's no more dedicated backup teams, there's rarely now dedicated storage teams, maybe only in very large organizations, and so now you're catering to a different set of folks, and for instance, over the last two or three years, we've seen the advent of folks like a chief data officer, the CDO, data scientists, data engineers, and so for us, we have a whole new buyer persona and user persona that we not only have to cater to in our UX design, but also present the value, which is a much different conversation than we've had in the past. >> Yeah, you know I actually had a number of conversations with customers at the VMworld show, and they talked about, organizationally they often still have hardware-defined roles, yet they live in a software-defined world. So, even groups that are like "I still have some storage headcount and some "networking headcount," but virtualization and cloud are slowly eating over pieces it, but there's still some turf battles, which I was sad to hear because, I've worked for the last couple of decades to try to eliminate silos and get people working together, so we know those organizational changes often take even longer than the long cycles of technology that we're trying to roll out here. You mentioned some of the big data pieces, and yeah, HPE's made a number of acquisitions. Most recently MapR. Wonder if you could help us connect the dots. When we covered heavily, the big data wave, and Dave Vellante would say, "Look, the people that "deploy these technologies, the end users will create "way more value than the distributions of Hadoop will." When we did our forecast, they were there, but the promise of big data was, data was going to go from that burden, how do I keep it, how do I maintain it, how do I back it up, to new value for the company, new revenue that we could have along that where, and whether or not that happened often mattered on deployment, but when you go into the AI space, like what you're doing with BlueData, is that a continuation of what we were seeing with the big data space, is there some new waves that are drastically changing the outcomes in what we're seeing, how does that all fit together? >> Yeah, so I mean I think it's definitely an extension of all these things are creative and incremental at the end of the day. I think some of the things around how people are operationalizing AI and ML are pretty unique, and so from our perspective, we made some investments around BlueData, and we've had some recent product announcements in that area around helping folks operationalize machine learning, which is, at this point it's becoming very real and people are putting it into a number of different use cases, and then to come along with that, the need to store data, right, so we talk about this often, which nobody talks about storage anymore, everyone talks about data, right? The need to store all this data that's coming in in a persistent data layer is super important, more important than it ever was, and it comes in multiple different forms and multiple different factors, and also protocols. So, to have a data platform that is very scalable, has enterprise resiliency to it, the ability to take data and manifest it in different ways, right, is important for that entire ecosystem, we felt that MapR was a great platform, they have a great data platform, that started with Hadoop, moved into supporting things like streams, Kafka and Spark, and then certainly now have been shifted into a Kubernetes and container deployment, and then mapping their file system and their database and streams to servicing AI and ML workloads, so it's kind of along the same vein and being able to live in that world that you're still separating compute and storage, and being able to scale those independently, but work together from a security perspective, I think it's really important. >> One of the boundaries that I've always been fascinated with is, some of the underlying components that we're changing, so when we rolled out virtualization, the whole storage and networking industry had to work to kind of put the pieces back together as we took advantage of that. You mentioned Kubernetes, at the KubeCon show, there's lots of that same plumbing things that need to be understand and work. But on the other hand, we've seen a massive transformation in the database market. 10 years ago, everybody had one database to rule them all, and now most companies we talk to, it's like "Oh, well I've got lots of little databases "and now pulling them together differently." But that boundary between what's happening at the infrastructure layer and what's happening at the application layer. On the one hand, they seem to be pulling apart, you know, I should just be able to use cloud or serverless and makes it easy, but on the same hot time, you're talking everybody's like "I've got the best infrastructure for your AI deployment," so can you talk a little bit about some of the hard challenges that HPE's looking at solving, what do you look to actually create, whether that be a box or a service or some offering, cause I know HPE has lots of different areas that you look at those solutions. >> Yeah, we're trying to, when we go and have a successful deployment at our customers, and we have deployments in most verticals, right, in the Fortune 500 Global 2000, whether it's financial services, automotive, manufacturing, you can name healthcare, right? I think what we've seen is that the successful deployments are the ones that bring together the application owner's line of business, even the data scientist engineers, along with the infrastructure folks, right? Think, sometimes they're at odds. And so when you can bring together a platform that at the end of the day is going to provide something, right, as a service, it's either an analytic sandbox, big data and analytics as a service, AI as a service, right, there is a set of folks that are trying to service a number of application developers and data scientists internally, that's a platform that can have a uniform data structure, where you can grab all this data and have access to it securely, and be able to deploy your workflow on top of that, in a virtualized, multitenant way, deployed in containers with the toolsets and the applications that they want to have access to, but not have to deal with the infrastructure, right? And then that can be the providence of the CIO and the data center team and the infrastructure folks working with those teams, and that's where we've seen the magic happen for successful deployments, and those are the ones that, they end up growing and scaling very quickly, and they can be deployed on-prem, they can be deployed, we have some of our pilots and POCs that start completely in the cloud and then come back on prem for different reasons, security, data locality, governance, what have you, but it provides the flexibility, but I think what we found is that, taking an outcome and a services-based approach that bring everyone to the table, that's where we see the projects really get a big business benefit for our customers. >> You know, I was having a conversation earlier today, and when we watch the adoption of virtualization, it's been almost 20 years now, since most people are doing it. When we'd reached about 10 years in, we felt that most people were doing it and were on their journey, but things like converged and hyperconverged infrastructure really helped accelerate us past that kind of early majority into the late majority because it was the simplicity of that offering. We wonder, are we reaching some of that same point when we look at cloud, and when I say cloud, not just public cloud, but what we're doing in private, where the hybrid, multicloud mixup that we have, because while cloud is definitely real and here to stay, I don't think anybody would really say that cloud, circa 2019, is easy. So, how does HPE and its partners, how do we make it even easier so that customers can move down that journey to modernize themselves even more, and get out of what we call that undifferentiated heavy lifting? >> Yeah, so definitely want to avoid the undifferentiated heavy lifting, because that's certainly a weight on many organizations, and so what we are trying to provide is a platform that increases customers' time to value, and by providing, by abstracting a lot of difficult things. I mean there's a lot of data gravity in this space, right, you're talking about, we have projects right now for autonomous cars where they ingest two, five, 10 petabytes a day, for example, and it's not, it's very difficult to migrate and move that data, right, so you want to be able to bring that data in, tap into it securely, there's a lot of networking that goes on that's very difficult from a security perspective as well as multitenancy and making sure that that model is set up correctly. So for us, it's all about providing a platform that can service multiple tenants and multiple organizations that are all using similar toolsets at the end of the day, but you can have your specific data scientists and data engineers operating on a platform that they don't have to worry about infrastructure. Right, cause at the end of the day, when we go visit those folks who own those applications, oftentimes they don't want to deal with, "I need to go request in a VM, "I need to go request a block of IP addresses, "I need some LUNs for my storage, "I need a server deployment to run bare metal," you know, some bare metal tooling. They really want to establish a service, just like we saw with virtualization, and so right now it's sort of the fight for, how can I make my infrastructure as invisible as possible and fight for the eyeballs of the developers? >> Great. Want to just give you the final word, Patrick, what's exciting you, kind of second half of the year, things you're looking forward to? >> Yeah, so the things that excite me is certainly customer acquisition, right, we've been marching along that very quickly with some of these new acquisitions and some of the net new development we've done within HPE, I think that the, we've got a lot of stuff cooking with Kubernetes in that area, and so we'll make some big announcements at KubeCon, and that's always very exciting to talk in these new ecosystems, and speaking of ecosystems, we're establishing, I think there's new ecosystems that are forming in the market, especially around AI and ML, it's still a very nascent market, and so we're bringing on new partners every week from an application development perspective, and so for me it's really exciting to talk with all these new apps, these new tool chains, new toolsets, libraries, algorithms, and I think it's really exciting to kind of move up stack and be in this very cool world of application development. >> I know when I see the market landscape of some of the AI space, you need to have a big monitor or be able to zoom in, cause there's a lot of players, there's a lot of pieces, we always worry about things like API sprawls and the like, but absolutely super exciting space, Patrick Osborne, thanks so much for giving us an update on what's happening, especially how AI is driving a lot of new innovation in the area. >> Yeah, very exciting, thanks for having me. >> All right, Patrick Osborne with HPE, and I'm Stu Miniman, thanks as always for joining theCUBE.

>> From the SiliconANGLE Media Office in Boston, Massachusetts, it's The Cube! Now, here's your host, Dave Velante. >> We're back with Patrick Osborne. All right Patrick, we've been talking about how customers want to be data-driven, they're doing digital transformation, they want to put data at the core of the enterprise. All sounds good. What's your strategy as HPE in terms of helping them get there? >> Yeah so, for our customers, this is a common theme, right. Some feel that they're going to be disrupted, they've been disrupted, right, and one of the key threads that runs through that is that they want to get more AI driven, right, so they want to use analytics as a way to provide new services around their products, get these services out faster, be able to use all that data they have in their enterprise. So for us, it's being able to have that conversation of whether the data sits out in the edge, right, you guys are very familiar with our Edge strategy, using Edgeline and Aruba, our core infrastructure in the data center, which we've had-- for a long history of helping customers with that, and then more recently around Hybrid Cloud, right, so most of the products, services, experiences we have there have Hybrid Cloud built in from the ground up. So for us, all those conversations have to do with data. >> So you recently made an acquisition of BlueData. >> Correct. >> What was that all about? Was that your AI play? Was it a software-as-a-service play? Explain that. >> Yeah, certainly a little bit of both. BlueData's a fantastic platform and it allows you to virtualize, containerize the application, so we know that in the market, you've got mode one applications, you've got mode two applications, a lot of mode one apps, you know, business applications, mission critical applications, have been virtualized. But what we also see is that a lot of the new product development is around these mode two applications that are using things like big data, whether it's a dupe in H-D-F-S, fast data - some of the streaming services - and now you're doing things around A-I inferencing, modeling, all using essentially containers as a way to do that, fuel that application development. So when we saw BlueData, it's essentially a platform to be able to virtualize all of these container-based applications. And as customers, big data and analytics and A-I platforms and their pipeline gets bigger and more complicated, it allows us to, A) manage that, increase their time to value, unlock a lot of the resources on the data scientists side, right, who have the responsibility of managing all of those applications, and it's a really great platform, great people, right, so they have a team of data scientists to be able to help customers implement this, not only within the product but within their own enterprise. And then we've got some really, really big logos that we're going to build off of for the BlueData ecosystem. >> So things are moving very fast. You've got all this data, you're applying machine intelligence and quickly moving from a world that was all batch to one that's real time, and we blinked and real time flew by. Now you've got this machine intelligence world where systems are acting, they're sensing, they're hearing, they're smelling... >> Yup. >> And so, is that what you're seeing with customers? Are they trying to build these sort of new systems that will act on their behalf? >> Absolutely. We see it on our own. If you take a look at some of our strategy, as HPE, especially within our storage and big data division, one of the big things that we're doing is introducing all of our products with the capability of A-I ops, right. And so all of our products that go out the door, using platforms like Infosite, will have this A-I ops capability to, not only just start with support automation, predictive analytics, and now you've got predictive A-I driving the actual management experience of these products. And it lets our customers ultimately unlock those resources that were doing mundane, repeatable tasks to focus on where they're going to add value. >> So I got to ask you, you guys do-- obviously a lot of your revenue comes from indirect channels. So you've seen the cloud, the cloud is not about selling boxes anymore, now you're seeing all this machine intelligence and automation. What does all this mean for partners? Where's the opportunity for those guys? >> Yeah so, I would say that when we go and make an acquisition like Blue Data, it's a great, like we said before, it's a great product, it's a good platform, right, they've got great engineers and great people within the organization and certainly some big logos. The reason why they got those logos was partly based on the product, but it's also a very services led methodology. So for-- I'd say for our partner community, being able to do discovery services, to understand what they're requirements are, a lot of folks that use BlueData and these type of platforms are builders. They're building a platform that has services on it for their end user customers. So being able to gather those requirements, do implementation, certainly be able to take a lot of this dynamic application ecosystem that is either very new, it's nascent, when you take a look at A-I or it's even in the open source arena, being able to de-risk that for the customers, from a services perspective, is a huge opportunity. >> Okay, great, so that's exciting because it's new frontier for those folks. So think about HPE, the tech that you guys have, the partner opportunity that you just described, how are you going to change the life of a data scientist? And maybe we could add in some other personas as well. >> Yeah, so, as a lot of our customers and partners certainly know, data scientists don't grow on trees, right, and they're very important folks within these large organizations, right, so you want to unlock their capabilities. So for us at the end of the day, we're trying to have a platform and as a service experience around A-I that unlocks the value of these data scientists. So for example, if I have a production environment or a U-A-T or a test dev environment, I can very quickly spin up your entire toolchain as a data scientist, right, so your toolchain, your models, your H-D-F-S data lakes, you can tap into existing data lakes, all of my streaming data, Kafka, Spark, all this stuff, very dynamic ecosystem, complex application dependencies. What I can do is I can sandbox that, I can test it, I can iterate, and I can very, very quickly provide that type of environment for your developers and your data scientists (snaps) just like that. >> So, in addition to the data scientist, is the chief data officer somebody that you guys are interacting with? There's also the application developer. Are you trying to sort of effect this collaboration amongst those different roles and personas? >> Yeah I think one of the greatest things for the partners and we've seen this at HPE too, is that you're going to be calling on new buyer personas. Right, so in the case of infrastructure, working a lot with enterprise architects, data center manager, infrastructure manager, C-I-O. In the case of BlueData and some of these A-I and analytics projects, right, you're at the front of the budget cycle, right, so you're talking to line of business, application developers, data scientists, analytics team, and now the rise of the C-D-O, the chief data officer. So you not only get to establish value with the infrastructure team, who are going to have to support this, right, you're going to go make some new friends and be able to get on the front of the budget cycle with a whole new set of buyer personas, and I think that's very exciting for partners. >> So, we talk a lot about A-I and, sort of this machine learning environment, machine intelligence. Software-defined is a hot topic. It's kind of a buzzword but it has meaning. What does it mean to you? Where does it fit in this whole equation? >> It's very adjacent to the big data and A-I analytics conversation. I think that what we see in software-defined, it's heavy on scale, right, so now that you're into petabytes, tens of petabytes, hundreds of petabytes, scaling, scaling, scaling, you need some new architectures to be able to do that cost-effectively. And you think about automated cars for example. They're-- each car is spinning off terabytes of data a day, so think about how am I going to store that, it's a monumental task. You got scale on your mind, you also have automation, right, so not only the scale of being able to store that effectively from a price-point, to be able to automate that. So you want to keep your-- the folks who are managing that infrastructure, they're going to have to increase the amount of systems, capacity under management, and the only way you can achieve that is through automation. And so, we see some themes around that and software-defined is really, kind of stepping in in that angle where you've got N-V-M-E, S-S-Ds, can saturate-- two N-V-M-E can saturate a C-P-U at this point, right, and now you're moving to hundred gig fabrics, so this rack-scale architecture that you can provide and paint on different software-defined personalities onto it is something that customers are definitely leaning in towards right now. >> And what you've been describing-- you mentioned autonomous vehicles-- data's at the edge, it's at the core, it's everywhere, and so, easier to bring, maybe, let's call it, ten meg of code to a petabyte of data than the reverse. >> Yeah, and what we see too is customers want to-- they want to dip their toe in this water, right, starting with very large enterprises, and we're able to, as HPE, bring a vetted ecosystem, whether from a workload perspective, 'cause we always talk about follow the workload, in software-defined, if you need something like scale-out file for A-I workloads, or you need scale-out file for more of a high performance, capacity-driven architecture, you're looking for object storage, you're looking for hyper-converged secondary, right, we bring an ecosystem of partners running on our infrastructure that's scalable, automated, and customers can feel confident in. >> Awesome. Well thank you Patrick, love the story. >> Yeah, thank you so much. >> You're welcome. (upbeat music)

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

>> Live from New York. It's theCUBE. Covering theCUBE New York City, 2018. Brought to you by SiliconANGLE Media, and it's ecosystem partners. >> I'm John Furrier with Peter Burris, our next two guests are Jim Franklin with Dell EMC Director of Product Management Anant Chintamaneni, who is the Vice President of Products at BlueData. Welcome to theCUBE, good to see you. >> Thanks, John. >> Thank you. >> Thanks for coming on. >> I've been following BlueData since the founding. Great company, and the founders are great. Great teams, so thanks for coming on and sharing what's going on, I appreciate it. >> It's a pleasure, thanks for the opportunity. >> So Jim, talk about the Dell relationship with BlueData. What are you guys doing? You have the Dell-ready solutions. How is that related now, because you've seen this industry with us over the years morph. It's really now about, the set-up days are over, it's about proof points. >> That's right. >> AI and machine learning are driving the signal, which is saying, 'We need results'. There's action on the developer's side, there's action on the deployment, people want ROI, that's the main focus. >> That's right. That's right, and we've seen this journey happen from the new batch processing days, and we're seeing that customer base mature and come along, so the reason why we partnered with BlueData is, you have to have those softwares, you have to have the contenders. They have to have the algorithms, and things like that, in order to make this real. So it's been a great partnership with BlueData, it's dated back actually a little farther back than some may realize, all the way to 2015, believe it or not, when we used to incorporate BlueData with Isilon. So it's been actually a pretty positive partnership. >> Now we've talked with you guys in the past, you guys were on the cutting edge, this was back when Docker containers were fashionable, but now containers have become so proliferated out there, it's not just Docker, containerization has been the wave. Now, Kubernetes on top of it is really bringing in the orchestration. This is really making the storage and the network so much more valuable with workloads, whether respective workloads, and AI is a part of that. How do you guys navigate those waters now? What's the BlueData update, how are you guys taking advantage of that big wave? >> I think, great observation, re-embrace Docker containers, even before actually Docker was even formed as a company by that time, and Kubernetes was just getting launched, so we saw the value of Docker containers very early on, in terms of being able to obviously provide the agility, elasticity, but also, from a packaging of applications perspective, as we all know it's a very dynamic environment, and today, I think we are very happy to know that, with Kubernetes being a household name now, especially a tech company, so the way we're navigating this is, we have a turnkey product, which has containerization, and then now we are taking our value proposition of big data and AI and lifecycle management and bringing it to Kubernetes with an open source project that we launched called Cube Director under our umbrella. So, we're all about bringing stateful applications like Hadoop, AI, ML to the community and to our customer base, which is some of the largest financial services in health care customers. >> So the container revolution has certainly groped developers, and developers have always had a history of chasing after the next cool technology, and for good reason, it's not like just chasing after... Developers tend not to just chase after the shiny thing, they chased after the most productive thing, and they start using it, and they start learning about it, and they make themselves valuable, and they build more valuable applications as a result. But there's this interesting meshing of creators, makers, in the software world, between the development community and the data science community. How are data scientists, who you must be spending a fair amount of time with, starting to adopt containers, what are they looking at? Are they even aware of this, as you try to help these communities come together? >> We absolutely talk to the data scientists and they're the drivers of determining what applications they want to consume for the different news cases. But, at the end of the day, the person who has to deliver these applications, you know data scientists care about time to value, getting the environment quickly all prepared so they can access the right data sets. So, in many ways, most of our customers, many of them are unaware that there's actually containers under the hood. >> So this is the data scientists. >> The data scientists, but the actual administrators and the system administrators were making these tools available, are using containers as a way to accelerate the way they package the software, which has a whole bunch of dependent libraries, and there's a lot of complexity our there. So they're simplifying all that and providing the environment as quickly as possible. >> And in so doing, making sure that whatever workloads are put together, can scaled, can be combined differently and recombined differently, based on requirements of the data scientists. So the data scientist sees the tool... >> Yeah. >> The tool is manifest as, in concert with some of these new container related technologies, and then the whole CICD process supports the data scientist >> The other thing to think about though, is that this also allows freedom of choice, and we were discussing off camera before, these developers want to pick out what they want to pick out what they want to work with, they don't want to have to be locked in. So with containers, you can also speed that deployment but give them freedom to choose the tools that make them best productive. That'll make them much happier, and probably much more efficient. >> So there's a separation under the data science tools, and the developer tools, but they end up all supporting the same basic objective. So how does the infrastructure play in this, because the challenge of big data for the last five years as John and I both know, is that a lot of people conflated. The outcome of data science, the outcome of big data, with the process of standing up clusters, and lining up Hadoop, and if they failed on the infrastructure, they said it was a failure overall. So how you making the infrastructure really simple, and line up with this time of value? >> Well, the reality is, we all need food and water. IT still needs server and storage in order to work. But at the end of the day, the abstraction has to be there just like VMware in the early days, clouds, containers with BlueData is just another way to create a layer of abstraction. But this one is in the context of what the data scientist is trying to get done, and that's the key to why we partnered with BlueData and why we delivered big data as a service. >> So at that point, what's the update from Dell EMC and Dell, in particular, Analytics? Obviously you guys work with a lot of customers, have challenges, how are you solving those problems? What are those problems? Because we know there's some AI rumors, big Dell event coming up, there's rumors of a lot of AI involved, I'm speculating there's going to be probably a new kind of hardware device and software. What's the state of the analytics today? >> I think a lot of the customers we talked about, they were born in that batch processing, that Hadoop space we just talked about. I think they largely got that right, they've largely got that figured out, but now we're seeing proliferation of AI tools, proliferation of sandbox environments, and you're psyched to see a little bit of silo behavior happening, so what we're trying to do is that IT shop is trying to dispatch those environments, dispatch with some speed, with some agility. They want to have it at the right economic model as well, so we're trying to strike a better balance, say 'Hey, I've invested in all this infrastructure already, I need to modernize it, and that I also need to offer it up in a way that data scientists can consume it'. Oh, by the way, we're starting to see them start to hire more and more of these data scientists. Well, you don't want your data scientists, this very expensive, intelligent resource, sitting there doing data mining, data cleansing, detail offloads, we want them actually doing modeling and analytics. So we find that a lot of times right now as you're doing an operational change, the operational mindset as you're starting to hire these very expensive people to do this very good work, at the corest of the data, but they need to get productive in the way that you hired them to be productive. >> So what is this ready solution, can you just explain what that is? Is it a program, is it a hardware, is it a solution? What is the ready solution? >> Generally speaking, what we do as a division is we look for value workloads, just generally speaking, not necessarily in batch processing, or AI, or applications, and we try and create an environment that solves that customer challenge, typically they're very complex, SAP, Oracle Database, it's AI, my goodness. Very difficult. >> Variety of tools, using hives, no sequel, all this stuff's going on. >> Cassandra, you've got Tensorflow, so we try fit together a set of knowledge experts, that's the key, the intellectual property of our engineers, and their deep knowledge expertise in a certain area. So for AI, we have a sight of them back at the shop, they're in the lab, and this is what they do, and they're serving up these models, they're putting data through its paces, they're doing the work of a data scientist. They are data scientists. >> And so this is where BlueData comes in. You guys are part of this abstraction layer in the ready solutions. Offering? Is that how it works? >> Yeah, we are the software that enables the self-service experience, the multitenancy, that the consumers of the ready solution would want in terms of being able to onboard multiple different groups of users, lines of business, so you could have a user that wants to run basic spark, cluster, spark jobs, or you could have another user group that's using Tensorflow, or accelerated by a special type of CPU or GPU, and so you can have them all on the same infrastructure. >> One of the things Peter and I were talking about, Dave Vellante, who was here, he's at another event right now getting some content but, one of the things we observed was, we saw this awhile ago so it's not new to us but certainly we're seeing the impact at this event. Hadoop World, there's now called Strata Data NYC, is that we hear words like Kubernetes, and Multi Cloud, and Istio for the first time. At this event. This is the impact of the Cloud. The Cloud has essentially leveled the Hadoop World, certainly there's some Hadoop activity going on there, people have clusters, there's standing up infrastructure for analytical infrastructures that do analytics, obviously AI drives that, but now you have the Cloud being a power base. Changing that analytics infrastructure. How has it impacted you guys? BlueData, how are you guys impacted by the Cloud? Tailwind for you guys? Helpful? Good? >> You described it well, it is a tailwind. This space is about the data, not where the data lives necessarily, but the robustness of the data. So whether that's in the Cloud, whether that's on Premise, whether that's on Premise in your own private Cloud, I think anywhere where there's data that can be gathered, modeled, and new insights being pulled out of, this is wonderful, so as we ditched data, whether it's born in the Cloud or born on Premise, this is actually an accelerant to the solutions that we built together. >> As BlueData, we're all in on the Cloud, we support all the three major Cloud providers that was the big announcement that we made this week, we're generally available for AWS, GCP, and Azure, and, in particular, we start with customers who weren't born in the Cloud, so we're talking about some of the large financial services >> We had Barclays UK here who we nominated, they won the Cloud Era Data Impact Award, and what they're actually going through right now, is they started on Prem, they have these really packaged certified technology stacks, whether they are Cloud Era Hadoop, whether they are Anaconda for data science, and what they're trying to do right now is, they're obviously getting value from that on Premise with BlueData, and now they want to leverage the Cloud. They want to be able to extend into the Cloud. So, we as a company have made our product a hybrid Cloud-ready platform, so it can span on Prem as well as multiple Clouds, and you have the ability to move the workloads from one to the other, depending on data gravity, SLA considerations. >> Compliancy. >> I think it's one more thing, I want to test this with you guys, John, and that is, analytics is, I don't want to call it inert, or passive, but analytics has always been about getting the right data to human beings so they can make decisions, and now we're seeing, because of AI, the distinction that we draw between analytics and AI is, AI is about taking action on the data, it's about having a consequential action, as a result of the data, so in many respects, NCL, Kubernetes, a lot of these are not only do some interesting things for the infrastructure associated with big data, but they also facilitate the incorporation of new causes of applications, that act on behalf of the brand. >> Here's the other thing I'll add to it, there's a time element here. It used to be we were passive, and it was in the past, and you're trying to project forward, that's no longer the case. You can do it right now. Exactly. >> In many respects, the history of the computing industry can be drawn in this way, you focused on the past, and then with spreadsheets in the 80s and personal computing, you focused on getting everybody to agree on the future, and now, it's about getting action to happen right now. >> At the moment it happens. >> And that's why there's so much action. We're passed the set-up phase, and I think this is why we're hearing, seeing machine learning being so popular because it's like, people want to take action there's a demand, that's a signal that it's time to show where the ROI is and get action done. Clearly we see that. >> We're capitalists, right? We're all trying to figure out how to make money in these spaces. >> Certainly there's a lot of movement, and Cloud has proven that spinning up an instance concept has been a great thing, and certainly analytics. It's okay to have these workloads, but how do you tie it together? So, I want to ask you, because you guys have been involved in containers, Cloud has certainly been a tailwind, we agree with you 100 percent on that. What is the relevance of Kubernetes and Istio? You're starting to see these new trends. Kubernetes, Istio, Cupflow. Higher level microservices with all kinds of stateful and stateless dynamics. I call it API 2.0, it's a whole other generation of abstractions that are going on, that are creating some goodness for people. What is the impact, in your opinion, of Kubernetes and this new revolution? >> I think the impact of Kubernetes is, I just gave a talk here yesterday, called Hadoop-la About Kubernetes. We were thinking very deeply about this. We're thinking deeply about this. So I think Kubernetes, if you look at the genesis, it's all about stateless applications, and I think as new applications are being written folks are thinking about writing them in a manner that are decomposed, stateless, microservices, things like Cupflow. When you write it like that, Kubernetes fits in very well, and you get all the benefits of auto-scaling, and so control a pattern, and ultimately Kubernetes is this finite state machine-type model where you describe what the state should be, and it will work and crank towards making it towards that state. I think it's a little bit harder for stateful applications, and I think that's where we believe that the Kubernetes community has to do a lot more work, and folks like BlueData are going to contribute to that work which is, how do you bring stateful applications like Hadoop where there's a lot of interdependent services, they're not necessarily microservices, they're actually almost close to monolithic applications. So I think new applications, new AI ML tooling that's going to come out, they're going to be very conscious of how they're running in a Cloud world today that folks weren't aware of seven or eight years ago, so it's really going to make a huge difference. And I think things like Istio are going to make a huge difference because you can start in the cloud and maybe now expand on to Prem. So there's going to be some interesting dynamics. >> Without hopping management frameworks, absolutely. >> And this is really critical, you just nailed it. Stateful is where ML will shine, if you can then cross the chasma to the on Premise where the workloads can have state sharing. >> Right. >> Scales beautifully. It's a whole other level. >> Right. You're going to the data into the action, or the activity, you're going to have to move the processing to the data, and you want to have nonetheless, a common, seamless management development framework so that you have the choices about where you do those things. >> Absolutely. >> Great stuff. We can do a whole Cube segment just on that. We love talking about these new dynamics going on. We'll see you in CF CupCon coming up in Seattle. Great to have you guys on. Thanks, and congratulations on the relationship between BlueData and Dell EMC and Ready Solutions. This is Cube, with the Ready Solutions here. New York City, talking about big data and the impact, the future of AI, all things stateful, stateless, Cloud and all. It's theCUBE bringing you all the action. Stay with us for more after this short break.

Live from New York It's theCUBE! Covering theCUBE New York City 2018 Brought to you by Silicon Angle Media and it's ecosystem partners >> Hey welcome back and we're live in theCUBE in New York city. It's our 2nd day of two days of coverage CUBE NYC. The hashtag CUBENYC Formerly Big data NYC renamed because it's about big data, it's about the server, it's about Cooper _________'s multi-cloud data. It's all about data, and that's the fundamental change in the industry. Our next guest is Yaron Haviv, who's the CTO of Iguazio, key alumni, always coming out with some good commentary smart analysis. Kind of a guest host as well as an industry participant supplier. Welcome back to theCUBE. Good to see you. >> Thank you John. >> Love having you on theCUBE because you always bring some good insight and we appreciate that. Thank you so much. First, before we get into some of the comments because I really want to delve into comments that David Richards said a few years ago, CEO of RenDisco. He said, "Cloud's going to kill Hadoop". And people were looking at him like, "Oh my God, who is this heretic? He's crazy. What is he talking about?" But you might not need Hadoop, if you can run server less Spark, Tensorflow.... You talk about this off camera. Is Hadoop going to be the open stack of the big data world? >> I don't think cloud necessary killed Hadoop, although it is working on that, you know because you go to Amazon and you know, you can consume a bunch of services and you don't really need to think about Hadoop. I think cloud native serve is starting to kill Hadoop, cause Hadoop is three layers, you know, it's a file system, it's DFS, and then you have server scheduling Yarn, then you have applications starting with map produce and then you evolve into things like Spark. Okay, so, file system I don't really need in the cloud. I use Asfree, I can use a database as a service, as you know, pretty efficient way of storing data. For scheduling, Kubernetes is a much more generic way of scheduling workloads and not confined to Spark and specific workloads. I can run with Dancerflow, I can run with data science tools, etc., just containerize. So essentially, why would I need Hadoop? If I can take the traditional tools people are now evolving in and using like Jupiter Notebooks, Spark, Dancerflow, you know, those packages with Kubernetes on top of a database as a service and some object store, I have a much easier stack to work with. And I could mobilize that whether it's in the cloud, you know on different vendors. >> Scale is important too. How do you scale it? >> Of course, you have independent scaling between data and computation, unlike Hadoop. So I can just go to Google, and use Vquery, or use, you know, DynamoDB on Amazon or Redchick, or whatever and automatically scale it down and then, you know >> That's a unique position, so essentially, Hadoop versus Kubernetes is a top-line story. And wouldn't that be ironic for Google, because Google essentially created Map Produce and Coudera ran with it and went public, but when we're talking about 2008 timeframe, 2009 timeframe, back when ventures with cloud were just emerging in the mainstream. So wouldn't it be ironic Kubernetes, which is being driven by Google, ends up taking over Hadoop? In terms of running things on Kubernetes and cloud eight on Visa Vis on premise with Hadoop. >> The poster is tend to give this comment about Google, but essentially Yahoo started Hadoop. Google started the technology  and couple of years after Hadoop started, with Google they essentially moved to a different architecture, with something called Percolator. So Google's not too associated with Hadoop. They're not really using this approach for a long time. >> Well they wrote the map-produced paper and the internal conversations we report on theCUBE about Google was, they just let that go. And Yahoo grabbed it. (cross-conversation) >> The companies that had the most experience were the first to leave. And I think it may respect what you're saying. As the marketplace realizes the outcomes of the dubious associate with, they will find other ways of achieving those outcomes. It might be more depth. >> There's also a fundamental shift in the consumption where Hadoop was about a ranking pages in a batch form. You know, just collecting logs and ranking pages, okay. The chances that people have today revolve around applying AI to business application. It needs to be a lot more concurring, transactional, real-time ish, you know? It's nothing to do with Hadoop, okay? So that's why you'll see more and more workers, mobilizing different black server functions, into service pre-canned services, etc. And Kubernetes playing a good role here is providing the trend. Transport for migrating workloads across cloud providers, because I can use GKE, the Google Kubenetes, or Amazon Kubernetes, or Azure Kubernetes, and I could write a similar application and deploy it on any cloud, or on Clam on my own private cluster. It makes the infrastructure agnostic really application focused. >> Question about Kubernetes we heard on theCUBE earlier, the VP of Project BlueData said that Kubernetes ecosystem and community needs to do a better job with Stapla, they nailed Stapflalis, Stafle application support is something that they need help on. Do you agree with that comment, and then if so, what alternatives do you have for customers who care about Stafe? >> They should use our product (laughing) >> (mumbling) Is Kubernetes struggling there? And if so, talk about your product >> So, I think that our challenge is rounded that there are many solutions in that. I think that they are attacking it from a different approach Many of them are essentially providing some block storage to different containers on really cloud 90. What you want to be able is to have multiple containers access the same data. That means either sharing through file systems, for objects or through databases because one container is generating, for example, ingestion or __________. Another container is manipulating that same data. A third container may look for something in the data, and generate a trigger or an action. So you need shared access to data from those containers. >> The rest of the data synchronizes all three of those things. >> Yes because the data is the form of state. The form of state cannot be associated with the same container, which is what most of where I am very active and sincere in those committees, and you have all the storage guys in the committees, and they think the block story just drag solution. Cause they still think like virtual machines, okay? But the general idea is that if you think about Kubernetes is like the new OS, where you have many processes, they're just scattered around. In OS, the way for us to share state between processes an OS, is whether through files, or through databases, in those form. And that's really what >> Threads and databases as a positive engagement. >> So essentially I gave maybe two years ago, a session at KubeCon in Europe about what we're doing on storing state. It's really high-performance access from those container processes to our database. Impersonate objects, files, streams or time series data, etc And then essentially, all those workloads just mount on top of and we can all share stape. We can even control the access for each >> Do you think you nailed the stape problem? >> Yes, by the way, we have a managed service. Anyone could go today to our cloud, to our website, that's in our cloud. It gets it's own Kubernetes cluster, a provision within less than 10 minutes, five to 10 minutes. With all of those services pre-integrated with Spark, Presto, ______________, real-time, these services functions. All that pre-configured on it's own time. I figured all of these- >> 100% compatible with Kubernetes, it's a good investment >> Well we're just expanding it to Kubernetes stripes, now it's working on them, Amazon Kubernetes, EKS I think, we're working on AKS and GK. We partner with Azure and Google. And we're also building an ad solution that is essentially exactly the same stock. Can run on an edge appliance in a factory. You can essentially mobilize data and functions back and forth. So you can go and develop your work loads, your application in the cloud, test it under simulation, push a single button and teleport the artifacts into the edge factory. >> So is it like a real-time Kubernetes? >> Yes, it's a real-time Kubernetes. >> If you _______like the things we're doing, it's all real-time. >> Talk about real-time in the database world because you mentioned time-series databases. You give objects store versus blog. Talk about time series. You're talking about data that is very relevant in the moment. And also understanding time series data. And then, it's important post-event, if you will, meaning How do you store it? Do you care? I mean, it's important to manage the time series. At the same time, it might not be as valuable as other data, or valuable at certain points and time, which changes it's relationship to how it's stored and how it's used. Talk about the dynamic of time series.. >> Figured it out in the last six or 12 months that since real-time is about time series. Everything you think about real-time censored data, even video is a time-series of frames, okay And what everyone wants to do is just huge amount of time series. They want to cross-correlate it, because for example, you think about stock tickers you know, the stock has an impact from news feeds or Twitter feeds, or of a company or a segment. So essentially, what they need to do is something called multi-volume analysis of multiple time series to be able to extract some meaning, and then decide if you want to sell or buy a stock, as in vacation example. And there is a huge gap in the solution in that market, because most of the time series databases were designed for operational databases, you know, things that monitor apps. Nothing that injects millions of data points per second, and cross-correlates and run real-time AI analytics. Ah, so we've essentially extended because we have a programmable database essentially under the hoop. We've extended it to support time series data with about 50 to 1 compression ratio, compared to some other solutions. You know we've break with the customer, we've done sizing, they told them us they need half a pitabyte. After a small sizing exercise, about 10 to 20 terabytes of storage for the same data they stored in Kassandra for 500 terabytes. No huge ingestion rates, and what's very important, we can do an in-flight with all those cross-correlations, so, that's something that's working very well for us. >> This could help on smart mobility. Kenex 5G comes on, certainly. Intelligent edge. >> So the customers we have, these cases that we applied right now is in financial services, two or three main applications. One is tick data and analytics, everyone wants to be smarter learning on how to buy and sell stocks or manage risk, the second one is infrastructure, monitoring, critical infrastructure, monitoring is SLA monitoring is be able to monitor network devices, latencies, applications, you now, transaction rate, or that, be able to predict potential failures or escalation We have similar applications; we have about three Telco customers using it for real-time time. Series analytics are metric data, cybersecurity attacks, congestion avoidance, SLA management, and also automotive. Fleet management, file linking, they are also essentially feeding huge data sets of time series analytics. They're running cross-correlation and AI logic, so now they can generate triggers. Now apply to Hadoop. What does Hadoop have anything to do with those kinds of applications? They cannot feed huge amounts of datasets, they cannot react in real-time, doesn't store time-series efficiently. >> Hapoop (laughing) >> You said that. >> Yeah. That's good. >> One, I know we don't have a lot of time left. We're running out of time, but I want to make sure we get this out here. How are you engaging with customers? You guys got great technical support. We can vouch for the tech chops that you guys have. We seen the solution. If it's compatible to Kubernetes, certainly this is an alternative to have really great analytical infrastructure. Cloud native, goodness of your building, You do PFC's, they go to your website, and how do you engage, how do you get deals? How do people work with you? >> So because now we have a cloud service, so also we engage through the cloud. Mainly, we're going after customers and leads, or from webinars and activities on the internet, and we sort of follow-up with those customers, we know >> Direct sales? >> Direct sales, but through lead generation mechanism. Marketplace activity, Amazon, Azure, >> Partnerships with Azure and Google now. And Azure joint selling activities. They can actually resale and get compensated. Our solution is an edge for Azure. Working on similar solution for Google. Very focused on retailers. That's the current market focus of since you think about stores that have a single supermarket will have more than a 1,000 cameras. Okay, just because they're monitoring shelves in real-time, think about Amazon go, kind of replication. Real-time inventory management. You cannot push a 1,000 camera feeds into the cloud. In order to analyze it then decide on inventory level. Proactive action, so, those are the kind of applications. >> So bigger deals, you've had some big deals. >> Yes, we're really not a raspberry pie-kind of solution. That's where the bigger customers >> Got it. Yaron, thank you so much. The CTO of Iguazio Check him out. It's actually been great commentary. The Hadoop versus Kubernetes narrative. Love to explore that further with you. Stay with us for more coverage after this short break. We're live in day 2 of CUBE NYC. Par Strata, Hadoop Strata, Hadoop World. CUBE Hadoop World, whatever you want to call it. It's all because of the data. We'll bring it to ya. Stay with us for more after this short break. (upbeat music)

>> Narrator: Live from Midtown Manhattan, it's theCUBE, covering BigData New York City 2017 Brought to you by SiliconANGLE Media and its ecosystem sponsors. >> Hey, welcome back everyone, live in New York, it's theCUBE coverage, day three of three days of wall-to-wall coverage of BigData at NYC, in conjunction with Strata Data right around the corner, separate event than ours, we've been covering. It's our eighth year. We're here expanding on our segment we just had with Matt from Deli EMC on, really on the front lines consultant, we've got Jason from BlueData, and Casey from BlueTalon, two separate companies but the blue in the name, team blue. And of course, Matt from Dell EMC, guys, welcome back to theCUBE and let's talk about the partnerships. I know you guys have a partnership, Dell EMC leads the front lines mostly with the customer base you guys come in with the secret sauce to help that solution which I want to get to in a minute, but the big theme here this week is partnerships. And before we get into the relationship that you guys have, I want you to talk about the changes in the ecosystem, because we're seeing a couple key things. Open source, one, and it's winning, continues to grow, but the Linux Foundation pointed out the open source that we cover that exponential growth is going to be in open-source software. You can see from 4 lines of code to billions in the next 10 years. So more onboarding, so clear development path. Ecosystems have work. Now they're coming into the enterprise with suppliers, whether it's consulting, it's front-end, or full stack developers coming together. How do you see ecosystems playing in both the supplier side and also the customer side? >> So we see from the supplier side, right, and from the customer side as well, and it kind of drives both of those conversations together is that you had the early days of I don't want vendor lock-in, right, I want to have a disparate virtual cornucopia of tools in the marketplace, and then they were, each individual shop was trying to develop those and implement those on their own. And what you're now seeing is that companies still want that diversity in the tools that they utilize, and that they work with, but they don't want that, the complication of having to deliver all those tools themselves, and so they're looking more for partners that can actually bring an ecosystem to the table where it's a loose coupling of events, but that one person actually has the forefront, has the customer's best interest in mind, and actually being able to drive through those pieces. And that's what we see from a partnership, why we're driving towards partnerships, 'cause we can be a point solution, we can solve a lot of pieces, but by bringing us as a part of an ecosystem and with a partner that can actually help deliver the customer and business value to the customer, that's where we're starting to see the traction and the movement and the wins for us as an organization. >> BlueData, you guys have had very big successes, big data as a service, docker containers, this is the programmer's nirvana. Infrastructure plus code, that's the DevOps ethos going mainstream. Your thoughts on partnering, 'cause you can't do it alone. >> Yeah, I mean, for us, speaking of DevOps, and we see our software platform provides a solution for bringing a DevOps approach to data science and big data analytics. And it's much more streamlined approached, an elastic and agile approach to big data analytics and data science, but to your point, we're partnered with Dell EMC because they bring together an entire solution that delivers an elastic platform for secure multi-tenant environments for data science teams and analytics teams for a variety of different open source tool sets. So there is a large ecosystem of open source tools out there from Hadoop to Spark to Kafka to a variety of different data science, machine learning and deep learning tool sets out there, and we provide through our platform the ability to dockerize all of those environments, make them available through self-service to the data science community so they can get up and running quickly and start building their models and running their algorithms. And for us, it's on any infrastructure. So, we work closely with Dell EMC to run it on Isilon and their infrastructure, Dell-powered servers, but also you can run it in a hybrid cloud architecture. So you could run it on Azure and now GCP, and AWS. >> So this is the agility piece for the developer. They get a lot of agility, they get their security. Dell EMC has all the infrastructure side, so you got to partner together. Matt, pull this together. The customer doesn't want, they want a single pane of glass, or however you want to look at it, they don't want to deal with the nuances. You guys got to bring it all together. They want it to work. Now the theme I hear at BigData New York is integration is everything, right, so, if it doesn't integrate, the plumbings not working. How important is it for the customer to have this smooth, seamless experience? >> It's critical for them to, they have to be able to believe that it's going to be a seamless experience, and these are just two partners in the ecosystem. When we talk to enterprise customers, they have other vendors. They have half a dozen or a dozen other vendors solving big data problems, right? The Hadoop analytic tools, on and on and on. And when they choose a partner like us, they want to see that we are bringing other partners to the table that are going to complement or enhance capabilities that they have, but they want to see two key things. And we need to see the same things as well when we look at our partnerships. We want to see APIs, we want to see open APIs that are well-documented so that we know these tools can play with each other, and two, these have to be organizations we can work with. At the end of the day, a customer does business with Dell EMC because they know we're going to stand behind whatever we put in front of them. >> John: They get a track record too, you're pretty solid. >> Yep, it is-- >> But I want to push on the ecosystem, not you guys, it's critical, but I mean one thing that I've seen over my 30 years in the enterprise is ecosystems, you see bullshit and you see real deal, right, so. A lot of customers are scared, now with all this FUD and new technology, it's hard to squint through what the BS is in an ecosystem. So how do you do ecosystems right in this new market? 'Cause like you said, it's not API, that's kind of technical, but philosophy-wise you can't do the barney deals, you got Pat Gelsinger standing up on stage at VMworld, basically flew down to stand in front of all the customers of VMworld's customers and said, we're not doing a barney deal. Now, he didn't say barney deals, that's our old term. He said, it's not an optical deal we're doing with VMware. We got your back. He didn't say that, but that's my interpretation, that's what he basically said. The CEO of AWS said that. That's a partner, you know what I'm saying? So, some deals are okay we got a deal on paper, what's the difference, how do you run an ecosystem, in your opinion? >> Yeah, it's not trivial. It's not an easy thing. It takes an executive, at that level, it takes a couple of executives coming together-- >> John: From the top, obviously. >> Committing, it's not just money, it's reputation, right? If you're at that level, it's about reputation which then trickles down to the company's reputation, and so within the ecosystem, we want to sort of crawl, walk, run. Let's do some projects-- >> So you're saying reputation in communities is the number one thing. >> I think so, people are not going to go, so you will always have the bleeding edge. Someone's going to go play with a tool, they're going to see if it works-- >> Wow, reputation's everything. >> Yeah. If it fails, they're going to tell, what is the saying, if something fails, if something bad happens you tell twelve people-- >> All right, so give them a compliment. What's BlueTalon do great for you guys? Explain their talent in the ecosystem. >> So BlueTalon's talent in the ecosystem, other than being just great people, we love Carey, is that they-- >> I'll get you to say something bad about him soon, but give him the compliment first. >> They have simplified the complexity of doing security, policy and role-based security for big data. So regardless of where your data lives, regardless of if it's Hadoop, Spark, Flink, Mongo, AWS, you define a policy once. And so if I am in front of the chief governance officer, my infrastructure doesn't have a value problem to them, but theirs does, right? The legal team, when we have to do proposals, this is what gets us through the legal and compliance for GDPR in this, it's that centralized control that is so critical to the capability we provide for big data. If you sprawl your data everywhere, and we know data sprawls everywhere-- >> So you can rely on them, these guys. >> Absolutely. >> All right, BlueData, give them a compliment, where do they fit? >> So they have solved the problem of deploying containers, big data environments, in any cloud. And the notion of ephemeral clusters for big data workloads is actually really, really hard to solve. We've seen a lot of organizations attempt to do this, we see frameworks out there, like Kupernetes, that people are trying to build on. These guys have fixed it. We have gone through the most rigorous security audits at the biggest banks in the world, and they have signed off because of the network segmentation and the data segmentation, it just works. >> I think I'm running a presidential debate, now you got to say something nice about him. No, I mean, Dell EMC we know what these guys do. But for you guys, I mean, how big is BlueTalon, company-wise? I mean, you guys are not small but you're not massive either. >> We're not small, but we're not massive, right. So, we're probably around 40 resources global, and so from our perspective, we're-- >> John: That's a great deal, working with a big gorilla in Dell EMC, they got a lot of market share, big muscle? >> Exactly, and so for us, like we talked about earlier, right, the big thing for us is ecosystem functions. We do what we do really well, right, we build software that does control unified access across multiple platforms as well as multiple distributions whether it be private cloud, on-prem, or public cloud, and for us, again, it's great that we have the software, it's great that we can do those things, but if we can't actually help customers use that software to deliver value, it's useless. >> Do you guys go to the market together, do you just hold hands in front of the customer, bundle products? >> No, we go to market together, so we actually, we work, a lot of our team in enablement is not enabling our customers, it is enabling Dell EMC on the use of our software and how to do that. So we actually work with Dell EMC to train and work-- >> So you're a tight partner. There's certification involved, close relationships, you're not mailing it in. >> And then we're also involved with the customer side as well, so it's not like we go, okay great, now it's sold, we throw up our hands and walk away. >> John: Well, they're counting on you that. >> They're counting on us for the specific pieces, but we're also working with Dell EMC so that we can get that breadth right in their reach, so that they can actually go confidently to their customers and actually understand where we fit and when we don't fit. Because we're not everything to everybody, right, and so they have to understand those pieces to be able to know when that works right and how the best practices are. And so again, we're 40 people, they're, I forget, there were 80,000 at one point? Maybe even more than that? But even in the services arm, there's several thousands of people in the-- >> What's the whole point of ecosystems you're getting at here? Point at the critical thing. You've got a big piece of the puzzle, it's not just they're bundling you in. You're an active part of that, and it's an integration world right, so he needs to rely on you to integrate with his systems. >> Yeah, we have to integrate with the other parts of the ecosystem too, so it really is a three-way integration on this perspective where they do what they do really well, we do what we do and they're complementary to each other, but without the services and the glue from Dell EMC-- >> So when you bring Dell EMC into the deals too? >> We do, so we bring Dell EMC into deals, and Dell EMC sells us through a reseller agreement with them so we actually help jointly either bring them to a deal we've already found, we'll bring services to them, or we'll actually go out and do joint development of customers. So we actually come out and help with the sales process and cycles to actually understand is there a fit or is there not a fit? So, it's not a one-size-fits-all, it's not just a, yes we got something on paper that we can sell you and we'll sell you every once in a while, it really is a way to develop an ecosystem to deliver value to the customer. >> All right, so let's talk about the customer mindset real quick. When you, are they, how far along on them, I really don't know much 'cause I'm really starting to probe in this area, how savvy are they to the partnership levels? I mean, you disclose it, you're transparent about it, but I mean, are customers getting that the partnering is very key? I mean, are they drilling, asking tough questions, are you kind of getting them educated one way, are they savvy about it? They may have been doing partners in house, but remember the enterprise had a generation of down-to-the-bone cutting, outsource everything, consolidation, and then you know, go back around 2010, the uplift on reinvestment hit, so we're kind of in this renaissance right now. So, thoughts? >> The partnership is actually the secret sauce that's part of our sales cycle. When we talk about big data outcomes and enabling self-service, customers assume oh, okay, you guys built some software, you've got some hardware, and then when we double-click into how we make this capable, we say oh, well we partner with BlueTalon and BlueData, and this other, and they go, wait a minute, that's not your software? No, no, we didn't build that. We have scoured the market and we've found partners that we work with and we trust, and all of a sudden you can see their shoulders relax and they realize that we're not just there to sell them more kit. We're actually there to help them solve their problems. And it is a game changer, because they deal with vendors every day. Software Vendor X, Software Vendor Y, Hardware Vendor Z, and so to have a company that they have good relationships with already bring more capabilities to them, the guard comes down and they say okay, let's talk about how we can make this work. >> All right, so let's get to the meat of the partnership, which I want to get to 'cause I think that's fundamental. Thanks for sharing perspective on the community piece. We're being on it, we've been doing, we're a community brand ourselves. We're not a close guard, we're not about restricting and censoring people at events, that's not what we're about. So you guys know that, so appreciate you commenting on the community there. The Elastic Data Platform you guys are talking about, it's a partnership deal. You provide an EPIC software, you guys providing some great security in there. What is it about, what's the benefit? So it's you're leading them to product, take a minute to explain the product and then the roles. >> Yeah, so the Elastic Data Platform is a capability, a set of capabilities that is meant to help our enterprise customers get to that next level of self-service. Data science as a service, and do that on any cloud with any tools in a security-controlled manner. That's what Elastic Data Platform is. And it's meant to plug in to the customer's existing investments and their existing tools and augment that, and through our services arm, we tie these technologies together using their open APIs, that's why that's so critical for us, and we bring that value back to our customers. >> And you guys are providing the EPIC software? What is EPIC software? I mean, I love epic software, that's an epic, I hope it's not an epic fail, so an epic name, but epic-- >> Elastic Private Instant Clusters, it's actually an acronym for what it stands for, that is what it provides for our customers. >> John: So you're saying that EPIC stands for-- >> Elastic Private Instant Clusters. So it can run in a private cloud environment on your on-prem infrastructure, but as I said before, it can run in a hybrid architecture on the public cloud as well. But yeah, I mean, we're working closely with the Dell EMC team, they're an investor, we work closely with their services organization, with their server organization, the storage organization, but they really are the glue that brings it all together. From services to software to hardware, and provides the complete solution to the customers. So, as I think Matt-- >> John: Multi-tenancy is a huge deal, multi-tenancy's a huge deal. >> Absolutely, yeah. Also the ability to have logical isolation between each of those different tenants for different data science teams, different analyst teams, you know, that's particularly at large financial services organizations like Barclays, you spoke yesterday, Matt alluded to earlier. They talked about the need to support a variety of different business units who each have their own unique use cases, whether it's batch processing with Hadoop or real-time streaming and fast data with Spark, Kafka, and NoSQL Database, or whether it's deep learning, machine learning. Each of those different tenants has different needs, and so you can spin up containers using our solution for each of those tenants. >> John: Yeah, that's been a big theme this week too, and so many little things, this one relates to this one, is the elastic nature of how people want to manage the provisioning of more resource. So, here's what we see. They're using collective intelligence, data, hey, they're data science guys, they figured it out! Whatever the usage is, they can do a virtual layer if you will, and then based upon the use they can then double down. So let the users drive this real collaborative, that seems to the a big theme, so this helps there. The other theme has been the centralized, this is the GDPR hanging over one's head, but the, even though that's more of threat and it's a gun to the head, it's the hammer or the guillotine, however you look at it, there's more of enablement around centralization, so it's not just the threat of that, it's other things that are benefiting. >> Right, it's more than just the threat of the GDPR and being compliant with those perspectives, right? The other big portion of this is, if you want to do, you do want to provide self-service. So the key to self-service is that's great, I can create an environment, but if it takes me a long time to get data to that environment to actually be able to utilize it or protect the data that's in that environment by having to rewrite policies from a different place, then you don't get the benefit right, the acceleration of the self-service. So having centralized policies of distributed enforcements gives you that elastic ability, right? Again, we can deploy the central engines again on-premises, but you can protect data that's in the cloud or protect data that's in a private cloud, so as companies move data for their different workloads, we can put the same protections with them and it goes immediately with them, so you don't have to manage it in multiple places. It's not like, oh, did I remember to put that rule over in this system? Oh, no I didn't, oh and guess what just happened to me? You know, I did get smacked with a big fine because I didn't, I wasn't compliant. So compliance-- >> How about Audit, too? I mean, are you checking the Audit side too? >> Yeah, so Audit's a great portion of that, and we do Audit for a couple of reasons. One is to make sure that you are compliant, but two is to make sure you actually have the right policies defined. Are people accessing the data the way you expect them to access that data? So that's another big portion of us and what we do from an audit perspective is that data usage lineage, and we actually tell you what the customer, what the user was trying to do. So if a customer's trying to access the data you see a large group trying to access a certain set of data but they're being denied access to it, now you can look and say, is that truly correct? Do I want them not being-- >> John: Well, Equifax, that thing was being phished out over months and months and months. Not just four, that thing has been phished over 10 times. In fact, state-sponsored actors were franchises of that organization. So, they were in the VPN, so it's not even, so you, so this is where the issues, okay, let's just say that happened again. You would have flagged it. >> We flag it. >> You would have seen the pattern access and said, okay, a lot of people cleaning us out. >> Yep, while it's happening. Right, so you get to see that usage, the lineage of the usage of the data, right, so you get to see that pattern as well. Not only who's trying to access, all right, 'cause protecting the perimeter is, as we all know, is no longer viable. So we actually get to watch the usage of the, the usage pattern so you can detect an anomaly in that type of system, as well as you can quickly change policies to shut down that gap, and then watch to see what happens, see who's continuing to try to hit it. >> Well, it's been a great conversation. Love that you guys are on and great to see the Elastic Data Platform come together through the partnerships, again. As you know, we're really passionate about highlighting and understanding more about the community dynamic as it becomes more than just socialization, it's a business model to the enterprise, as it was in open source. We'll be covering that. So I'd like to go around the panel here just to end this segment. Share something that someone might not know what's going on in industry that you want to point out, that's an observation, an anecdote that hasn't been covered, hasn't been serviced, it could be a haymaker, it could be something anecdotal, personal observation. In the big data world, BigData NYC this week or beyond, what should people know about that may or may not be covered out there that's happened that they should know about? >> Well, I think this one's, people pretty much should know about this one, right, but four or five years ago Hadoop was going to replace everything in the world. And two, three years ago the RDBMS's groups were like, Hadoop will never make it out of the science fair project. Right, we're in a world now where that's no longer true. It's somewhere in between. Hadoop is going to remain, and they're going to be continued, and the RDBMS is also going to continue. So you need to look at ecosystems that can actually allow you to cover both sides of that coin, which we're talking about here, is those types of tools are going to continue together forward. So you have to look at your entire ecosystem and move away from siloed functions to how you actually look at an entire data protection in data usage on environment. >> Matt? >> I would say that the technology adoption in the enterprise is outstripping the organization's ability to keep up with it. So as we deploy new technologies, tools, and techniques to do all sorts of really amazing things, we see the organization lagging in its ability to keep up. And so policies and procedures, operating models, whatever you want to call that, put it under the data governance umbrella, I suppose. If those don't keep up, you're going to end up with just an organization that is mismatched with the technology that is put into place, and ultimately you can end up in a massive compliance problem. Now, that's worst case. But even in best case, you're going to have a really inefficient use of your resources. My favorite question to ask organizations, so let's say you could put a timer on one of the data science sandboxes. So what happens when the timer goes off and the data science is not done? And you've got a line of people waiting for resources, what do you do? What is, how does the organization respond to that? It's a really simple question, but the answer's going to be very nuanced. So if that's the policy, that's the operating model stuff that we're talking about that we've got to think about when we enable self-service and self-security, those things have to come hand-in-hand. >> That's the operational thinking that needs to come through. >> Okay, Jason? >> Yeah, I think even for us, I mean this has been happening for some time now, but I think there still is this notion that the traditional way to deploy Hadoop and other big data workloads on prem is bare metal, and that's the way it's always been done. Or, you can run it in the cloud. But I think what we're seeing now, what we've seen evolve over the past couple of years is you can run your on-prem workloads using docker containers in a containerized environment. You can have this cloud-like experience on-prem but you can also provide the ability to be able to move those workloads, whether they're on-prem or in the cloud. So you can have this hybrid approach and multi-cloud approach. So I think that's fundamentally changing, it's a new dynamic, a new paradigm for big data, either on-prem or in the cloud. It doesn't have to be on bare metal anymore. And we get the same, we've been able to get-- >> It's on-prem, people want on-prem, that's where the action is, and cloud no doubt, but right now it's the transition. Hybrid cloud's definitely going to be there. I guess my observation is the tool shed problem. You know, I said earlier all day, you don't want to have a tool shed full of tools you don't use anymore or buy a hammer that wants to turn into a lawn mower 'cause the vendor changed, pivoted. You got to be careful what you buy, the tools, so don't think like a tool. Think like a platform. And I think having a platform mentality, understanding the system, or operating environment as you were getting to, I think really is a fundamental exercise that most decision makers think about. 'Cause again, your relationship with the Elastic Data Platform proves that this operating environment's evolving, it's not about the tool. The tool has to be enabled, and if the tool is enabled into the platform it should have a data model that falls into place, no one should have to think about it, you get the compliance, you get the docker container, so don't buy too many tools. If you do, make sure they're clean and in a clean tool shed! You got a lawnmower, I guess that's the platform. Bad analogy, but you know, I think tools has been the rage in this market, and now I think platforming it is something that we're seeing more of. So guys, thanks so much, appreciate it. Elastic Data Platform by Dell EMC, with the EPIC Platform from BlueData, and BlueTalon providing the data governance and compliance, great stuff, I'm certain the GDPR, BlueTalon, you guys got a bright future, congratulations. All right, more CUBE coverage after this short break, live from New York, it's theCUBE. 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>> Narrator: Live from Midtown Manhattan, it's theCUBE. Covering Big Data New York City 2017. Brought to you by SiliconANGLE Media and its ecosystem sponsors. >> Hello everyone. Welcome back to our CUBE coverage here in New York City, live in Manhattan for theCUBE's coverage of Big Data NYC, our event we've had five years in a row. Eight years covering Big Data, Hadoop World originally in 2010, then it moved to Hadoop Strata Conference, Strata Hadoop, now called Strata Data. In conjunction with that event we have our Big Data NYC event. SiliconANGLE Media's CUBE. I'm John Furrier, your cohost, with Jim Kobielus, analyst at wikibon.com for Big Data. Our next guest is Gus Horn who is the global Big Data analytics and CTO ambassador for NetApp, machine learning, AI, guru, gives talks all around the world. Great to have you, thanks for coming in and spending the time with us. >> Thanks, John, appreciate it. >> So we were talking before the camera came on, you're doing a lot of jet setting really around Evangelize But also educating a lot of folks on the impact of machine learning and AI in particular. Obviously AI we love, we love the hype. It motivates young kids getting into software development, computer science, makes it kind of real for them. But still, a lot more ways to go in terms of what AI really is. And that's good, but what is really going on with AI? Machine learning is where the rubber hits the road. That seems to be the hot air, that's your wheelhouse. Give us the update, where is AI now? Obviously machine learning is super important, it's one of the hot topics here in New York City. >> Well, I think it's super important globally, and it's going to be disruptive. So before we were talking, I said how this is going to be a disruptive technology for all of society. But regardless of that, what machine learning is bringing is a methodology to deal with this influx of IOT data, whether it's autonomous vehicles, active safety in cars, or even looking at predictive analytics for complex manufacturing processes like an automotive assembly line. Can I predict when a welding machine is going to break and can I take care of it during a scheduled maintenance cycle so I don't take the whole line down? Because the impacts are really cascading and dramatic when you have a failure that you couldn't predict. And what we're finding is that Hadoop and the Big Data space is uniquely positioned to help solve these problems, both from quality control and process management and how you can get better uptime, better quality, and then we take it full circle and how can I build an environment to help automotive manufacturers to do test and DEV and retest and retraining and learning of the AI modules and the AI engines that have to exist in these autonomous vehicles. And the only way you can do that is with data, and managing data like a data steward, which is what we do at NetApp. So for us, it's not just about the solution, but the underlying architecture is going to be absolutely critical in setting up the agility you'll need in this environment, and the flexibility you need. Because the other thing that's happening in the space right now is that technology's evolving very quickly. You see this with the DGX from NVIDIA, you see P100 cards from NVIDIA. So I have an architecture that we have in Germany right now where we have multiple NVIDIA cards in our Hadoop cluster that we've architected. But I don't make NVIDIA cards. I don't make servers. I make really good storage. And I have an ecosystem that helps manage where that data is when it needs to be there, and especially when it doesn't need to there so we can get new data. >> Yeah, Gus, we were talking also before camera, the folks watching that you were involved with AI going way back to in your days at MIT, and that's super important. Because a lot of people, the pattern that we're seeing across all the events that we go to, and we'll be at the NetApp event next week, Insight, in Vegas, but the pattern is pretty clear. You have one camp, oh, AI is just the same thing that was going on in the late '70s, '80s, and '90s, but it now has a new dynamic with the cloud. So a lot of people are saying okay, there's been some concepts that have been developed in AI, in computer science, but now with the evolution of hyperconvergence infrastructure, with cloud computing, with now a new architecture, it seems to be turbocharging and accelerating. So I'd like to get your thoughts on why is it so hot now? Obviously machine learning, everyone should be on that, no doubt, but you got the dynamic of the cloud. And NetApp's in the storage business, so that's stores data, I get that. What's the dynamic with the cloud? Because that seems to be the accelerant right now with open source and in with AI. >> Yeah, I think you got to stay focused. The cloud is going to be playing an integral role in everything. And what we do at NetApp as a data steward, and what George Kurian said, our CEO, that data is the currency of today actually, right? It's really fundamentally what drives business value, it's the data. But there's one little slight attribute change that I'd like to add to that, and that it's a perishable commodity. It has a certain value at T-sub zero when you first get it. And especially true when you're trying to do machine learning and you're trying to learn new events and new things, but it rapidly degrades and becomes less valuable. You still need to keep it because it's historical and if we forget historical data, we're doomed to repeat mistakes. So you need to keep it and you have to be a good steward. And that's where we come into play with our technologies. Because we have a portfolio of different kinds of products and management capabilities that move the data where it needs to be, whether you're in the cloud, whether you're near the cloud, like in an Equinox colo, or even on prem. And the key attribute there, and especially in automotive they want to keep the data forever because of liability, because of intellectual property and privacy concerns. >> Hold on, one quick question on that. 'Cause I think you bring up a good point. The perishability's interesting because realtime, we see this now, bashing in realtime is the buzzword in the industry, but you're talking about something that's really important. That the value of the data when you get it fast, in context, is super important. But then the historical piece where you store it also plays into the machine learning dynamics of how deep learning and machine learning has to use the historical perspective. So in a way, it's perishable in the realtime piece in the moment. If you're a self-driving car you want the data in milliseconds 'cause it's important, but then again, the historical data will then come back. Is that kind of where you're getting at with that? >> Yeah, because the way that these systems operate is the paradigm is like deep learning. You want them to learn the way a human learns, right? The only reason we walk on our feet is 'cause we fell down a lot. But we remember falling down, we remember how we got up and could walk. So if you don't have the historical context, you're just always falling down, right? So you have to have that to build up the proper machine learning neural network, the kind of connections you need to do the right things. And then as you get new data and varieties of data, and I'll stick with automotive, because it can almost be thought of as an intractable amount of data. Because most people will keep cars for measured in decades. The quality of the car is incredible now, and they're all just loaded with sensors, right? High definition cameras, radars, GPS tracking. And you want to make sure you get improvements there because you have liability issues coming as well with these same technologies, so. >> Yeah, so we talk about the perishability of the data, that's a given. What is less perishable, it seems to me and Wikibon, is that what you derive from the data, the correlations, the patterns, the predictive models, the meat of machine learning and deep learning, AI in general, is less perishable in the sense that it has a validity over time. What are your thoughts at NetApp about how those data derived assets should be stored, should be managed for backup and recovery and protected? To what extent do those requirements need to be reflected in your storage retention policies if you're an enterprise doing this? >> That's a great question. So I think what we find is that that first landing zone, and everybody talks about that being the cloud. And for me it's a cloudy day, although in New York today it's not. There are lots of clouds and there are lots of other things that come with that data like GDPR and privacy, and what are you allowed to store, what are you allowed to keep? And how do you distinguish one from the other? That's one part. But then you're going to have to ETL it, you're going to have to transform that data. Because like everything, there's a lot of noise. And the noise is really fundamentally not that important. It's those anomalies within the stream of noise that you need to capture. And then use that as your training data, right? So that you learn from it. So there's a lot of processing, I think, that's going to have to happen in the cloud regardless of what cloud, and it has to be kind of ubiquitous in every cloud. And then from there you decide, how am I going to curate the data and move it? And then how am I going to monetize the data? Because that's another part of the equation, and what can I monetize? >> Well that's a question that we hear a lot on theCUBE. On day one we were ripping at some of the concepts that we see, and certainly we talk to enterprise customers. Whether it's a CIO, CVO, chief data officer, chief security officer. There's a huge application development going on in the enterprise right now. You see the opensource booming. This huge security practice is being built up and then it's got this governance with the data. Overlay that with IOT, it's kind of an architectural, I don't want to say reset, but a retrenching for a lot of enterprises. So the question I have for you guys as a critical part of the infrastructure of storage, storage isn't going away, there's no doubt about that, but now the architecture's changing. How are you guys advising your customers? What's your position on when you come into CXO and you give a talk and I said, hey, Gus, the house is on fire, we got so much going on. Bottom line me, what's the architecture? What's best for me, but don't lose the headroom. I need to have some headroom to grow, that's where I see some machine learning, what do I do? >> I think you have to embrace the cloud, and that's one of the key attributes that NetApp brings to the table. We have our core software, our ONTAP software, is in the cloud now. And for us, we want to make sure we make it very easy for our customers to both be in the cloud, be very protected in the cloud with encryption and protection of the data, and also get the scale and all of the benefits of the cloud. But on top of that, we want to make it easy for them to move it wherever they want it to be as well. So for us it's all about the data mobility and the fact that we want to become that data steward, that data engine that helps them drive to where they get the best business value. >> So it's going to be on prem, on cloud. 'Cause I know just for the record, you guys if not the earliest, one of the earliest in with AWS, when it wasn't fashionable. I interviewed you guys on that many years ago. >> And let me ask a related question. What is NetApp's position, or your personal thinking, on what data should be persisted closer to the edge in the new generation of IOT devices? So IOT, edge devices, they do inference, they do actuation and sensing, but they also do persistence. Now should any data be persisted there longterm as part of your overall storage strategy, if you're an enterprise? >> It could be. The question is durability, and what's the impact if for some reason that edge was damaged, destroyed or the data lost. So a lot of times when we start talking about opensource, one of the key attributes we always have to take into account is data durability. And traditionally it's been done through replication. To me that's a very inefficient way to do it, but you have to protect the data. Because it's like if you've got 20 bucks in your wallet, you don't want to lose it, right? You might split it into two 10s, but you still have 20, right? You want that durability and if it has that intrinsic value, you've got to take care of it and be a good steward. So if it's in the edge, it doesn't mean that's the only place it's going to be. It might be in the edge because you need it there. Maybe you need what I call reflexive actions. This is like when a car is well, you have deep learning and machine learning and vision and GPS tracking and all these things there, and how it can stay in the lane and drive, but the sensors themself that are coming from Delphi and Bosch and ZF and all of these companies, they also have to have this capability of being what I call a reflex, right? The reason we can blink and not get a stone in our eye is not because it went to our cerebral cortex. Because it went to the nerve stem and it triggered the blink. >> Yeah, it's cache. And you have to do the same thing in a lot of these environments. So autonomous vehicles is one. It could be using facial recognition for restricting access to a gate. And all the sudden this guy's on a blacklist, and you've stopped the gate. >> Before we get into some of the product questions I have for you, Hadoop in-place analytics, as well as some of the regulations around GDPR, to end the trend segment here is what's your thoughts on decentralization? You see a lot of decentralized apps coming out, you see blockchain getting a lot of traction. Obviously that's a tell sign, certainly in the headroom category of what may be coming down. Not really on the agenda for most enterprises today, but it does kind of indicate that the wave is coming for a lot more decentralization on top of distributed computing and storage. So how do you look at that, as someone who's out on the cutting edge? >> For me it's just yet another industry trend where you have to embrace it. I'm constantly astonished at the people who are trying to push back from things that are coming. To think that they're going to stop the train that's going to run 'em over. And the key is how can we make even those trends better, more reliable, and do the right thing for them? Because if we're the trusted advisor for our customers, regardless of whether or not I'm going to sell a lot of storage to them, I'm going to be the person they're going to trust to give 'em good advice as things change, 'cause that's the one thing that's absolutely coming is change. And oftentimes when you lock yourself into these quote, commodity approaches with a lot of internal storage and a lot of these things, the counterpart to that is that you've also locked yourself in probably for two to four years now, in a technology that you can't be agile with. And this is one of the key attributes for the in-place analytics that we do with our ONTAP product and we also have our E series product that's been around for six plus years in the space, is the defacto performance leader in the space, even. And by decoupling that storage, in some cases very little but it's still connected to the data node, and in other cases where it's shared like an NFS share, that decoupling has enormous benefits from an agility perspective. And that's the key. >> That kind of ties up with the blockchain thing as kind of a tell sign, but you mentioned the in-place analytics. That decoupling gives you a lot more cohesiveness, if you will, in each area. But tying 'em together's critical. How do you guys do that? What's the key feature? Because that's compelling for someone, they want agility. Certainly DevOps' infrastructure code, that's going mainstream, you're seeing that now. That's clearly cloud operation, whatever you want to call it, on prem, off prem. Cloud ops is here. This is a key part of it, what's the unique features of why that works so well? >> Well, some of the unique features we have, so if we look at your portfolio products, so I'll stick with the ONTAP product. One of the key things we have there is the ability to have incredible speed with our AFF product, but we can also Dedoop it, we can clone it, and snapshot it, snapshotting it into, for example, NPS or NetApp Private Storage, which is in Equinox. And now all the sudden I can now choose to go to Amazon, or I can go to Azure, I can go to Google, I can go to SoftLayer. It gives me options as a customer to use whoever has got the best computational engine. Versus I'm stuck there. I can now do what's right for my business. And I also have a DR strategy that's quite elegant. But there's one really unique attribute too, and that's the cloning. So a lot of my big customers have 1000 plus node traditional Hadoop clusters, but it's nearly impossible for them to set up a test DEV environment with production data without having an enormous cost. But if I put it in my ONTAP, I can clone that. I can make hundreds of clones very efficiently. >> That gets the cost of ownership down, but more importantly gets the speed to getting Sandboxes up and running. >> And the Sandboxes are using true production data so that you don't have to worry about oh, I didn't have it in my test set, and now I have a bug. >> A lot of guys are losing budget because they just can't prove it and they can't get it working, it's too clunky. All right, cool, I want to get one more thing in before we run out of time. The role of machine learning we talked about, that's super important. Algorithms are going to be here, it's going to be a big part of it, but as you look at that policy, where the foundational policy governance thing is huge. So you're seeing GDPR, I want to get your comments on the impact of GDPR. But in addition to GDPR, there's going to be another Equifax coming, they're out there, right? It's inevitable. So as someone who's got code out there, writing algorithms, using machine learning, I don't want to rewrite my code based upon some new policy that might come in tomorrow. So GDPR is one we're seeing that you guys are heavily involved in. But there might be another policy I might want to change, but I don't want to rewrite my software. How should a CXO think about that dynamic? Not rewriting code if a new governance policy comes in, and then the GDPR's obvious. >> I don't think you can be so rigid to say that you don't want to rewrite code, but you want to build on what you have. So how can I expand what I already have as a product, let's say, to accommodate these changes? Because again, it's one of those trains. You're not going to stop it. So GDPR, again, it's one of these disruptive regulations that's coming out of EMEA. But what we forget is that it has far reaching implications even in the United States. Because of their ability to reach into basically the company's pocket and fine them for violations. >> So what's the impact of the Big Data system on GDPR? >> It can potentially be huge. The key attribute there is you have to start when you're building your data lakes, when you're building these things, you always have to make sure that you're taking into account anonymizing personal identifying information or obfuscating it in some way, but it's like with everything, you're only as strong as your weakest link. And this is again where NetApp plays a really powerful role because in our storage products, we actually can encrypt the data at rest, at wire speed. So it's part of that chain. So you have to make sure that all of the parts are doing that because if you have data at rest in a drive, let's say, that's inside your server, it doesn't take a lot to beat the heck out of it and find the data that's in there if it's not encrypted. >> Let me ask you a quick question before we wrap up. So how does NetApp incorporate ML or AI into these kinds of protections that you offer to customers? >> Well for us it's, again, we're only as successful as our customers are, and what NetApp does as a company, we'll just call us the data stewards, that's part of the puzzle, but we have to build a team to be successful. So when I travel around the world, the only reason a customer is successful is because they did it with a team. Nobody does it on an island, nobody does it by themself, although a lot of times they think they can. So it's not just us, it's our server vendors that work with us, it's the other layers that go on top of it, companies like Zaloni or BlueData and BlueTalon, people we've partnered with that are providing solutions to help drive this for our customers. >> Gus, great to have you on theCUBE. Looking forward to next week. I know you're super busy at NetApp InSight. I know you got like five major talks you're doing but if we can get some time I think you'd be great. My final question, a personal one. We were talking that you're a search and rescue in Tahoe in case there's an avalanche, a lost skier. A lot of enterprises feel lost right now. So you kind of come in a lot and the avalanche is coming, the waves or whatever are coming, so you probably seen situations. You don't need to name names, but talk about what should someone do if they're lost? You come in, you can do a lot of consulting. What's the best advice you could give someone? A lot of CXOs and CEOs, their heads are spinning right now. There's so much on the table, so much to do, they got to prioritize. >> It's a great question. And here's the one thing is don't try to boil the ocean. You got to be hyper-focused. If you're not seeing a return on investment within 90 days of setting up your data lake, something's going wrong. Either the scope of what you're trying to do is too large, or you haven't identified the use case that will give you an immediate ROI. There should be no hesitation to going down this path, but you got to do it in a manner where you're tackling the biggest problems that have the best hit value for you. Whether it's ETLing goes into your plan of record systems, your enterprise data warehouses, you got to get started, but you want to make sure you have measurable, tangible success within 90 days. And if you don't, you have to reset and say okay, why is that not happening? Am I reinventing the wheel because my consultant said I have to write all this SCOOP and Flume code and get the data in? Or maybe I should have chosen another company to be a partner that's done this 1000 times. And it's not a science experiment. We got to move away from science experiment to solving business problems. >> Well science experiments and boiling of the ocean is don't try to overreach, build a foundational building block. >> The successful guys are the ones who are very disciplined and they want to see results. >> Some call it baby steps, some call it building blocks, but ultimately the foundation right now is critical. >> Gus: Yeah. >> All right, Gus, thanks for coming on theCUBE. Great day, great to chat with you. Great conversation about machine learning impact to organizations. theCUBE bringing you the data here live in Manhattan. I'm John Furrier, Jim Kobielus with Wikibon. More after this short break. We'll be right back. (digital music) (synthesizer music)