from around the globe it's thecube with coverage of kubecon and cloudnativecon north america 2020 virtual brought to you by red hat the cloud native computing foundation and ecosystem partners hello and welcome back to the cube's coverage of kubecon plus cloud nativecon 2020 the virtual edition abinav joshi is here he's the senior product marketing manager for openshift at red hat and tom dean is the senior director of pro product management at cloudera gentlemen thanks for coming on thecube good to see you thank you very much for having us here hey guys i know you would be here it was great to have you and guys i know you're excited about the partnership and i definitely want to get in and talk about that but before we do i wonder if we could just set the tone you know what are you seeing in the market tom let's let's start with you i had a great deep dive a couple of weeks back with anupam singh and he brought me up to speed on what's new with cloudera but but one of the things we discussed was the accelerated importance of data putting data at the core of your digital business tom what are you seeing in the marketplace right now yeah absolutely so um overall we're still seeing a growing demand for uh storing and and processing massive massive amounts of data even in the past few months um where perhaps we see a little bit more variety is on by industry sector is on the propensity to adopt some of the latest and greatest uh technologies that are out there or that we we deliver to the market um so whether perhaps in the retail hospitality sector you may see a little bit more risk aversion around some of the latest tools then you you go to the healthcare industry as an example and you see we see a strong demand for our latest technologies uh with with everything that is that is going on um so overall um still a lot lots of demand around this space so abnormal i mean we just saw in ibm's earnings though the momentum of red hat you know growing in the mid teens and the explosion that we're seeing around containers and and obviously openshift is at the heart of that how the last nine months affected your customers priorities and what are you seeing yeah we've been a lot more busier like in the last few months because there's like a lot of use cases and if you look at the like a lot of the research and so on and we are seeing that from our customers as well that now the customers are actually speeding up the digital transformation right people say that okay kovac 19 has actually uh speeded up the digital transformation for a lot of our customers for the right reasons to be able to help the customers and so on so we are seeing a lot of attraction on like number of verticals and number of use cases beyond the traditional lab dev data analytics aiml messaging streaming edge and so on like lots of use cases in like a lot of different like industry verticals so there's a lot of momentum going on on openshift and the broader that portfolio as well yeah it's ironic the the timing of the pandemic but it sure underscores that this next 10 years is going to be a lot different than the last 10 years okay let's talk about some of the things that are new around data tom cloudera you guys have made a number of moves since acquiring hortonworks a little over two years ago what's new with uh with the cloudera data platform cdp sure so yes our latest therap uh platform is called cbp clara data platform last year we announced the public cloud version of cdp running on aws and then azure and what's new is just two months ago we announced the release of the version of this platform targeted at the data center and that's called cvp private cloud and really the focus of this platform this new version has been around solving some of the pain points that we see around agility or time to value and the ease of use of the platform and to give you some specific examples with our previous technology it could take a customer three months to provision a data warehouse if you include everything from obtaining the infrastructure to provisioning the warehouse loading the data setting security policies uh and fine-tuning the the software now with cbp private cloud we've been able to take those uh three months and turn it into three minutes so significant uh speed up in in that onboarding time and in time to valley and a key piece of this uh that enabled this this speed up was a revamping of the entire stack specifically the infrastructure and service services management layer and this is where the containerization of the platform comes in specifically kubernetes and red hat open shift that is a key piece of the puzzle that enables this uh order of magnitude uh improvement in time right uh now abner you think about uh red hat you think about cloudera of course hortonworks the stalwarts of of of open source you got kind of like birds of a feather how are red hat and cloudera partnering with each other you know what are the critical aspects of that relationship that people should be aware of yeah absolutely that's a very good question yeah so on the openshift side we've had a lot of momentum in the market and we have well over 2000 customers in terms of a lot of different verticals and the use cases that i talked about at the beginning of our conversation in terms of traditional and cloud native app dev databases data analytics like ai messaging and so on right and the value that you have with openshift and the containers kubernetes and devops like part of the solution being able to provide the agility flexibility scalability the cross cloud consistency like so all that that you see in a typical app dev world is directly applicable to fast track the data analytics and the ai projects as well and we've seen like a lot of customers and some of the ones that we can talk about in a public way like iix rbc bank hca healthcare boston children's bmw exxon mobil so all these organizations are being are able to leverage openshift to kind of speed up the ai projects and and help with the needs of the data engineers data scientists and uh and the app dev folks now from our perspective providing the best in class uh you say like experience for the customers at the platform level is key and we have to make sure that the tooling that the customers run on top of it uh gets the best in class the experience in terms of the day zero to day two uh management right and it's uh and and it's an ecosystem play for us and and and that's the way cloudera is the top isv in the space right when it comes to data analytics and ai and that was our key motivation to partner with cloudera in terms of bringing this joint solution to market and making sure that our customers are successful so the partnership is at all the different levels in the organization say both up and down as well as in the the engineering level the product management level the marketing level the sales level and at the support and services level as well so that way if you look at the customer journey in terms of selecting a solution uh putting it in place and then getting the value out of it so the partnership it actually spans across the entire spectrum yeah and tom you know i wonder if you could add anything there i mean it's not just about the public cloud with containers you're seeing obviously the acceleration of of cloud native principles on-prem in a hybrid you know across clouds it's sort of the linchpin containers really and kubernetes specifically linchpin to enable that what would you add to that discussion yeah as part of the partnership when we were looking for a vendor who could provide us that kubernetes layer we looked at our customer base and if you think about who clara is focused on we really go after that global the global 2000 firms out there these customers have very strict uh security requirements and they're often in these highly regulated uh industries and so when we looked at a customer's base uh we saw a lot of overlap and there was a natural good fit for us there but beyond that just our own technical evaluation of the solutions and also talking to uh to our own customers about who they do they see as a trusted platform that can provide enterprise grade uh features on on a kubernetes layer red hat had a clear leadership in in that front and that combined with our own uh long-standing relationship with our parent company ibm uh it made this partnership a natural good thing for us right and cloudera's always had a good relationship with ibm tom i want to stay with you if i can for a minute and talk about the specific joint solutions that you're providing with with red hat what are you guys bringing to customers in in terms of those solutions what's the business impact where's the value absolutely so the solution is called cbd or color data platform private cloud on red hat openshift and i'll describe three uh the three pillars that make up cbp uh first what we have is the five data analytic experiences and that is meant to cover the end to end data lifecycle in the first release we just came out two months ago we announced the availability of two of those five experiences we have data warehousing for bi analytics as well as machine learning and ai where we offer a collaborative data science data science tools for data scientists to come together do exploratory data analytics but also develop predictive models and push them to production going forward we'll be adding the remaining three uh experiences they include data engineering or transformations on uh on your data uh data flow for streaming analytics and ingest uh as well as operational database for uh real-time surveying of both structure and unstructured data so these five experiences have been re-banked right compared to our prior platform to target these specific use cases and simplify uh these data disciplines the second pillar that i'll talk about is the sdx or uh what what we call the shared data experience and what this is is the ability for these five experiences to have one global data set that they can all access with shared metadata security including fine grain permissions and a suite of governance tools that provide lineage provide auditing and business metadata so by having these shared data experiences our developers our users can build these multi-disciplinary workflows in a very straightforward way without having to create all this custom code and i can stitch you can stitch them together and the last pillar that i'll mention uh is the containerization of of the platform and because of containers because of kubernetes we're now able to offer that next level of agility isolation uh and infrastructure efficiency on the platform so give you a little bit more specific examples on the agility i mentioned going from three months to three minutes in terms of the speed up with i uh with uh containers we can now also give our users the ability to bring their own versions of their libraries and engines without colliding with another user who's sharing the platform that has been a big ask from our customers and last i'll mention infrastructure efficiency by re-architecting our services to running a microservices architecture we can now impact those servers in a much more efficient way we can also auto scale auto suspend bring all this as you mentioned bring all these cloud native concepts on premises and the end result of that is better infrastructure efficiency now our customers can do more with the same amount of hard work which overall uh reduces their their total spend on the solution so that's what we call cbp private cloud great thanks for that i mean wow we've seen really the evolution from the the wild west days of you know the early days of so-called big data ungoverned a lot of shadow data science uh maybe maybe not as efficient as as we'd like and but certainly today taking advantage of some of those capabilities dealing with the noisy neighbor problem enough i wonder if you could comment another question that i have is you know one of the things that jim whitehurst talked about when ibm acquired red hat was the scale that ibm could bring and what i always looked at in that context was ibm's deep expertise in vertical industries so i wonder what are some of the key industry verticals that you guys are targeting and succeeding in i mean yes there's the pandemic has some effects we talked about hospitality obviously airlines have to have to be careful and conserving cash but what are some of the interesting uh tailwinds that you're seeing by industry and some of the the more interesting and popular use cases yeah that's a very good question now in terms of the industry vertical so we are seeing the traction in like a number of verticals right and the top ones being the financial services like healthcare telco the automotive industry as well as the federal government are some of the key ones right and at the end of the day what what all the customers are looking at doing is be able to improve the experience of their customers with the digital services that they roll out right as part of the pandemic and so on as well and then being able to gain competitive edge right if you can have the services in your platform and make them kind of fresh and relevant and be able to update them on a regular basis that's kind of that's your differentiator these days right and then the next one is yeah if you do all this so you should be able to increase your revenue be able to save cost as well that's kind of a key one that you mentioned right that that a lot of the industries like the hospitality the airlines and so on are kind of working on saving cash right so if you can help them save the cost that's kind of key and then the last one is is being able to automate the business processes right because there's not like a lot of the manual processes so yeah if you can add in like a lot of automation that's all uh good for your business and then now if you look at the individual use cases in these different industry verticals what we're seeing that the use cases cannot vary from the industry to industry like if you look at the financial services the use cases like fraud detection being able to do the risk analysis and compliance being able to improve the customer support and so on are some of the key use cases the cyber security is coming up a lot as well because uh yeah nobody wants to be hacked and so and and so on yeah especially like in these times right and then moving on to healthcare and the life sciences right what we're seeing the use cases on being able to do the data-driven diagnostics and care and being able to do the discovery of drugs being able to say track kobit 19 and be able to tell that okay uh which of my like hospital is going to be full when and what kind of ppe am i going to need at my uh the the sites and so on so that way i can yeah and mobilize like as needed are some of the key ones that we are seeing on the healthcare side uh and then in terms of the automotive industry right that's where being able to speed up the autonomous driving initiatives uh being able to do uh the auto warranty pricing based on the history of the drivers and so on and then being able to save on the insurance cost is a big one that we are seeing as well for the insurance industries and then but more like manufacturing right being able to do the quality assurance uh at the shop floor being able to do the predictive maintenance on machinery and also be able to do the robotics process automation so like lots of use cases that customers are prioritizing but it's very verticalized it kind of varies from the vertical to a vertical but at the end of the day yeah it's all about like improving the customer experience the revenue saving cost and and being able to automate the business processes yeah that's great thank you for that i mean we we heard a lot about automation we were covering ansible fest i mean just think about fraud how much you know fraud detection has changed in the last 10 years it used to be you know so slow you'd have to go go through your financial statements to find fraud and now it's instantaneous cyber security is critical because the adversaries are very capable healthcare is a space where you know it's ripe for change and now of course with the pandemic things are changing very rapidly automotive another one an industry that really hasn't hadn't seen much disruption and now you're seeing with a number of things autonomous vehicles and you know basically software on wheels and insurance great example even manufacturing you're seeing you know a real sea change there so thank you for that description you know very often in the cube we like to look at joint engineering solutions that's a gauge of the substance of a partnership you know sometimes you see these barney deals you know there's a press release i love you you love me okay see you but but so i wonder if you guys could talk about specific engineering that you're doing tom maybe you could start sure yeah so on the on the engineering and product side um we've um for cbp private cloud we've we've changed our uh internal development and testing to run all on uh openshift uh internally uh and as part of that we we have a direct line to red hat engineering to help us solve any issues that that uh we run into so in the initial release we start with support of openshift43 we're just wrapping up uh testing of and we'll begin with openshift46 very soon on another aspect of their partnership is on being able to update our images to account for any security vulnerabilities that are coming up so with the guidance and help from red hat we've been we've standardized our docker images on ubi or the universal based image and that allows us to automatically get many of these security fixes uh into our into our software um the last point that i mentioned here is that it's not just about providing kubernetes uh red hat helps us with the end to end uh solution so there is also the for example bringing a docker registry into the picture or providing a secure vault for storing uh all the secrets so all these uh all these pieces combined make up the uh a strong complete solution actually the last thing i'll mention is is a support aspect which is critical to our customers in this model our customers can bring support tickets to cluberra but as soon as we determine that it may be an issue that uh related to red hat or openshift where we can use their help we have that direct line of communication uh and automated systems in the back end to resolve those support tickets uh quickly for our customers so those are some of the examples of what we're doing on the technical side great thank you uh enough we're out of time but i wonder if we could just close here i mean when we look at our survey data with our data partner etr we see containers container orchestration container management generally and again kubernetes specifically is the the number one area of investment for companies that has the most momentum in terms of where they're putting their efforts it's it's it's right up there and even ahead of ai and machine learning and even ahead of cloud which is obviously larger maybe more mature but i wonder if you can add anything and bring us home with this segment yeah absolutely and i think uh so uh one thing i want to add is like in terms of the engineering level right we also have like between cloudera and red hat the partnership and the sales and the go to market levels as well because once you build the uh the integration it yeah it has to be built out in the customer environments as well right so that's where we have the alignment um at the marketing level as well as the sales level so that way we can like jointly go in and do the customer workshops and make sure the solutions are getting deployed the right way right uh and also we have a partnership at the professional services level as well right where um the experts from both the orgs are kind of hand in hand to help the customers right and then at the end of the day if you need help with support and that's what tom talked about that we have the experts on the support side as well yeah and then so to wrap things up right uh so all the industry research and the customer conversation that we are having are kind of indicating that the organizations are actually increasing the focus on digital uh transformation with the data and ai being a key part of it and that's where this strategic partnership between cloudera and and red hat is going to play a big role to help our mutual customers uh through that our transition and be able to achieve the key goals that they set for their business great well guys thanks so much for taking us through the partnership and the integration work that you guys are doing with customers a great discussion really appreciate your time yeah thanks a lot dave really appreciate it really enjoyed the conversation all right keep it right there everybody you're watching thecube's coverage of cubecon plus cloud nativecon north america the virtual edition keep it right there we'll be right back

>> Live from Barcelona, Spain, it's theCUBE, covering KubeCon + CloudNativeCon Europe 2019. Brought to you by Red Hat, the Cloud Native Computing Foundation, and ecosystem partners. >> Welcome back to Barcelona, Spain, this is theCUBE's live coverage of KubeCon, CloudNativeCon, 2019. 7,700 people in attendance, including myself, Stu Miniman, and co-host Corey Quinn, and returning to the program, Abby Fuller, who is the principal container czarina (Abby laughs) at Amazon Web Services. Yeah, Abby, I could say it without laughing, but, uh-- >> I can't. >> I don't think you can. Yeah, so, you know, let's just, czarina? You know, how does one, you know, become a czarina in their career, Abby? Let's start there. >> You ask Deepak really nicely, and he'll change your title for you. Longer answer, I think I'm doing a similar version of what I've always done for Amazon. Which is, how can I get what customers are asking for, and their feedback, and what they're struggling with, they're working on, or enjoying? Taking that back to our internal product development process, and then doing the same thing back the other way. So if we're building something, how can I help educate customers on how to work with it, and how to use it, how to build with it? So, same thing, just funnier title. >> All right, well, Abby, you know, it's a big, cloud show, so of course we know Amazon will be here. Lot's of developers here at the show, lot's of activity. Yesterday AWS held a, kind of, pre-show workshop. Maybe start there, tell us a little bit about that. >> Yeah, so we had AWS Container Day, maybe five or six hundred people, we did it at the hotel that is allegedly across the street, but is really, like, twenty five minute walk away. We did some workshops, we did a Birds of a Feather session at night. We had a little, mini, product preview announcement, so that was pretty fun. Something called, Container Insights, from CloudWatch team. I think my favorite thing about KubeCon is my favorite thing about the Kubernetes community, right, which is that, everyone is so happy to be here. They're all so enthusiastic. I've never had that many questions at a Birds of a Feather session before. We sent a ton of Amazon people here, to, kind of, talk about EKS, and Kubernetes, and community work. And the energy at the KubeCon is always so impressive. >> Give us a little sampling, you know, there's passion, is there questions? Are they trying to understand the various pieces? Are they excited about some of the new features? What's some of the energy you're capturing? >> Yeah, you know, I think it's both. I think on the EKS side, there's always the balance, right, in the Kubernetes community between, how can I have more power and flexibility? And then, how can you carry pager for more of this? So I think it's always an interesting balance, between the folks that are like, hmm, do you think you could manage that for me as well? And the folks that are like, I want to be able to pass in control plain flags. So, there's always an interesting balance. A lot of questions about version upgrades. I think that one is always, always seems to be top of mind, 'cause the Kubernetes community moves so fast. So, compared to a lot of other products, and how quickly they can release new versions, Kubernetes moves so fast. So, if you don't have a good upgrade strategy, you're in trouble. So-- >> Well, to that point, yesterday during the talk, there was a slide that went up, that listed, over the trailing 12 months, that there were 1,900 and change major service and feature releases. And that's very much a two edged sword, sitting in the audience, 'cause on the one hand, yay, the pace of innovation continues to increase, and services are getting better all the time. On the other, it's one of those, hmm, at least four of those would have been critically important, but I may not know about them. And to that end, something that the container group seems to have done, that almost no one else has, has been to put up a public roadmap of what's coming down the pike. Which has been tremendously helpful for customers, as far as being able to plan things out. How did that come to be? >> A lot of talking. I think, ultimately, right, all teams at AWS work the same way. Which is, backwards from what the customer is asking for. So, we have a lot of customer meetings. We have a lot of customer conversations, we talk to a lot of people. I do a lot with that on social media, or at conferences, or with blogs, or with live streaming. But ultimately, at the root of it, we all follow the same process. And I think the roadmap is really an extension of that. It's, how could we get, both what we're working on, to customers a little bit faster, but also, how can you have a voice that we hear so much more loudly? So, right? That you can be the smallest start up, or the largest enterprise, and you can open a GitHub issue just the same. And say, hey, you know, I'd really like to see you do that. And, I think the other piece of it, is that everyone has an AWS story. Where they build something custom, to work around something, or to add a feature, and then six weeks later we're like, we shipped it! And that's awesome, it's a good problem to have, and being able to delete code is one of everyone's favorite problems, I think. It's my favorite problem. >> It's one of life's true joys. >> It is one of life's true joys. (Corey laughs) But, what I think is even better than that, is a little bit of a heads up. And I think that that really builds trust between us and the community, is, how can we let you know we're working on, so you can plan around it? Or, if you don't see something, let us know that we're not thinking about the things that you value. >> Well, So Abby, you know, we've been at the Amazon shows for a number of years-- >> Yeah. >> And that customer feedback loop is something that we hear a lot. >> Yeah. >> Are there any dynamics about, just being in a big, open source community here, is, you know, just listening, and feedback loops as part of that? So, how does that impact, you know, how you work on things? >> Yeah, so, when we do events like this, I try to talk to as many people as possible. I try to listen in to the conversations, when I can. People come by the booth, they come by the meeting rooms. And I think it's about taking that back from all the different sources that were at the conference, the reviews online, the blog posts that people write after this, coverage like theCUBE, taking that all back, and then let's go through it. And then, how many of these things do we know about? Have a lot of people asked us for this? Is this something new? If it is new, how can we go find other people to talk to, to see who else is having that problem, that maybe we just didn't know to ask about before? So it's all part of that same working backwards process, but feedback comes from so many different places, and I think that, that ultimately is what makes it cool, right? It's because you get different feedback at a KubeCon than you will at a re:Invent, than you will on a Twitter, or that you will at a customer meeting. So, you need all of those sources to kind of figure out, what's more important? And, who is it important to? >> Yeah, one of the things that I find fascinating about the entire AWS Container story is, you almost get to decide your own level of involvement. You can run it all yourself, on top of EC2, you can wind up doing one of the manage serves with ECS, or EKS. And then there's Fargate, which I'm very bullish on for the future, if for no other reason that, if that takes over, suddenly we will never have to hear someone from Amazon mispronounce AMI, ever again. Which, I'll take my victories where I can find them. (Abby laughs) But, what are you seeing customers doing with Fargate? What's the paradigm look like, that's different than you might have expected at launch? >> Yeah, so, the way that I ultimately think about Fargate, right, is as a, it's a capacity provider for EC2. So, when you think about, kind of, the levels of control, right? You start at maybe the orchestrator level, so an ECS or an EKS. And if you're using ECS through Fargate, you're not interacting directly with EC2. So it's about, how can I control and define everything at just the container level, just at the task definition level, without having to think about the underlying EC2 instances? And they're still there, before someone tells me that serverless still has servers. But, you're not the one that's actively managing them. We're managing them on your behalf. All you care about is your workload itself. And then you can go a step deeper than that, and say, you know what, I want control over those EC2 instances. I want to manage them myself, maybe I want to do something in user data, or I want to be able to run DaemonSets myself, on the underlying infrastructure, and that's fine. So, I think it's ultimately about the level of control that you want. Fargate, to me, is interesting because it's like Lambda, in the sense that people have seemed very joyful about not having to manage EC2. Because ultimately, that's not what's providing them business value. That's not what let's them differentiate, and I think the way that Werner puts it is, you want everything that you write to be business logic. And I think with things like Lambda and Fargate, it gets you one step closer to that. That instead of having to manage infrastructure, to then manage your code, it's, just manage my code, please figure out the rest of it for me. >> This is borderline heresy in some circles, so don't, at me. (Abby laughs) But, what I'm wondering is, are things like containers, and functions as a service, aligned longer term, on the same axis? At some point, where it just becomes an implementation detail, and not a battle that needs to be fought. >> Yeah, the way that we think about it, right, is that, and I think the way that customers see it, is that serverless is ultimately a spectrum. There are many different flavors of it, depends on how you kind of want to work with it. But ultimately, I think, even longer term, maybe this is even more heretical, right? But, I want to not care. I don't want to have to care about the primitive that you're using. I don't want you to have to choose. And right now, I think you have to choose, regardless of the tool that you're using, you must choose very early. And to take advantage of a new tool, to go from containers, to Lambda, or whatever else you want to use, you have to re-write. Or you have to rebuild, or you have to re-wrap what you're doing. And I want to get to a point where you don't care. That I can use whatever combination of the below that I want to use, and that AWS will provide tools around that, that just says, you run this however you want. You mix and match whatever flavors you like, and we'll take care of it. >> Yeah, it's interesting, almost every time we've done one of these Kubernetes shows, we've had somebody from Amazon on, and even if we haven't had an AWS employee, almost every customer we have on is doing some, if not a lot of Amazon. There's some out there that look, and they're like, well, Amazon doesn't have the biggest booth, and Amazon has all of these different choices out there, so they must not be fully committed to, you know, capitol K, Kubernetes, and things like that. How can you help us understand what's going on? >> Yeah, so, I think Bob Wise, and his team spent a ton of time working on the community, and the whole team does, right? We're one of the biggest contributors to etcd, we're hosting Birds of a Feather. We've contributed back to a fair amount of community projects, and I think a lot of them are, in fact, around how to just make Kubernetes work better on AWS. And that might be something that we built because, EKS. Or, it might be something like Cluster Autoscaler, right? Which, ultimately, people would like to work better with Auto Scaling groups. So, I think we have the community involvement, but, I think it's about having a quiet community involvement, right? That, it's about chopping wood, and carrying water, and being present, and committing, and showing up, and having experts, and answering questions, and being present in things like SIG groups, than it is, necessarily, having the biggest booth. >> Yeah, I mean, from my perspective, at conferences, across the board, community involvement can never be measured by who spends enough money on the conference to have a booth large enough to play ice hockey in. That doesn't really seem to be as good of a barometer. Things like the roadmap, tend to be a spectacular, I guess, expression of how that engagement is starting to look. And I really am enthusiastic to see what's been done so far, and I'm looking forward to seeing more of it. >> Well thank you, I'm really proud of the roadmap. It's been so interesting to see customers take a, kind of, a new level of transparency, for us, product roadmap wise. And then, I love seeing people go through, and start adding more. So, I feel like the roadmap started to feel successful to me when customers started opening a ton of issues, and saying, hey, have you thought about this? Our new thing is, we've been posting requests for comments, or design docs on there, and saying, you know, we're thinking about building this, and here's what we were thinking about building. Did the way that we built this solve the problem that you're trying to solve? 'Cause ultimately, you can build the coolest thing in the world, and if it doesn't solve problems for your customers, what's the point? >> Yeah, and Abby, I'll reiterate that the roadmap was something that, you know, the ecosystem, the community, was very excited about. What other things did you want to share before we wrap? You know, things at the show, or related to the container space that, you know, you're hearing your customers talking, and asking a lot about. >> Yeah, so I've heard great things about all the sessions. I think that I'm a little biased, 'cause I was on the program committee. So, obviously the selection was universally excellent. Yeah, I think, what I like the most, I think, about events like this, is that everyone seems to have a different way of solving things. They're all asking for something new. They're all talking about a different project. They're all in different SIG groups. They're all making different feature requests. They're all using different tools. I think that that's really powerful, and I think was what's made Kubernetes so amazing, is that, the whole community feels like this. This is a huge turn out for a conference, and everyone feels very, like, actively engaged. And I like seeing us, kind of, push the boundaries, right? Between, how much can I pass off to something like EKS? And then, how much can I keep customizing, but on only the things that matter to me? >> I guess, as you're talking about roadmap, and plans for the future, if I were to build an environment on AWS, going back, let's say a decade-ish, I would have built something in a single AWS account, using EC2 classic, and maybe simple DB, as a data store. Which, generally, is in no way aligned with best practices today, and migrating off of those types of architectures, for some customers, has been painful. Is there any way to, I guess, loosen the abstraction, for lack of a better term? Of, what, the things we can do, and build in a forward looking way today, that will make migrating to whatever best practices emerge from the customer learnings, or the rest, in the future, not be the equivalent of an entire migration? >> Yeah, so, I think what you're asking, right, is, how can I make, kind of, adopting new technologies, or migrating, a little bit easier? >> Yeah. Or even, adopting new patterns. >> That's a really interesting one. Yeah. I think where I see this space kind of going, and where I think it gets interesting to me, is thinks like App Mesh. So, I can have many different kinds of compute inside of a mesh, through App Mesh, right? So I can have an application running on EC2, I can have a container running with EKS, or ECS, I can have Kubernetes on EC2. In the fullness of time, I'd love to see things like Lambda functions inside an App Mesh. What I like about that, is that, how that can make the migration process easier. Because if I can have many types of primitives in the same mesh, I can mix and match, or I can drain traffic off from one to the other, and I can experiment a little bit more without having to re-write, 'cause I can try it out. It can be part of the same mesh, and if I want to move, I can just move more stuff over. So, I think that's interesting, and I think, as for, kind of, the best practices, and stuff like that, we evolve hand in hand with our customers. As our customers are figuring out new technologies that they want to use, or new ways of building things, we want to be right there with them. And I think the AWS way is about, how can we help customers build whatever way they want to do, but help them be secure, reliable and scalable. >> Yeah. What I'm hearing from that, as a take away, is, if I'm not playing around with service mesh's, or app mesh's now, it's probably time to fix that, and learn how they work. >> I think it's a new technology. I think it's an interesting one, I'm excited to see where it goes, but, watching it, kind of, grow along with Kubernetes, has been really interesting. >> All right, well Abby Fuller, thanks so much for joining again on theCUBE. >> Thanks for having me. >> For Corey Quinn, I'm Stu Miniman, you're watching KubeCon, CloudNativeCon 2019, in Barcelona, Spain, thanks for watching theCUBE. (futuristic music)

>> Announcer: Live! From Las Vegas. It's theCUBE. Covering HPE Discover 2017. Brought to you by Hewlett Packard Enterprise. >> Welcome back everyone. We are here live in Las Vegas for SiliconANGLE's Cube exclusive coverage of three days of wall to wall interviews here at HPE Discover 2017. I'm John Furrier, your host with Dave Vellante, cohost. And our next two guest is Rod Bagg, VP of Analytics, Customer Support, Data Center, Infrastructure, HPE, formerly Nimble now HPE. and Paul Sabin, Senior Network and Infrastructure Manager at Baker Botts LLP. Guys, thanks for joining on theCUBE. >> Male Voices: Thanks for having us. >> So we talked before we came on camera about all the great stories Nimble obviously part of the fold here at HP Enterprise. Your customer stories. Let's get right into it. Tell your story about how Nimble put you out of a job. That's my favorite one. Go. >> Okay, so when I started or when we bought Nimble Storage, I was the senior storage engineer. So we purchased it, we brought it in-house. It was up within, within an hour, I was already starting carve out LUNs. At that point, I'm using the restful APIs to carve out the rest of the 200 LUNS that we needed. Presenting it to the hosts. And by the end of it, it ran itself. Between InfoSight and the fact that the product just is so easily automated, I kid you not, true story, at the end of the year when we were doing our self evaluations, my evaluation said, and congratulations, you don't need me anymore. My position is obsolete. And the management came back and said, Paul, you're absolutely right. We agree that we don't need this position anymore so we're going to promote you to the senior network infrastructure team. (John laughs) So I manage that now. >> So you got promoted. But this is a trend in automation. This is the DevOps, this is the programmable infrastructure world we're moving into with hybrid. >> Exactly. Rod, this is big deal. >> Yeah, yeah exactly. InfoSight as we see it plays a big role in that. Really the product is simple and being able to automate that. But InfoSight giving our customers sort of visibility at a very deep level into how the systems are performing. And what we do on the backend to drive availability really takes a lot of pain off of our customers. Not sure that we put everybody out of work but we certainly make life easier. So that they can focus on the business aspect. >> And you automate those tasks the way that really should be automated and that's a cool thing. >> Yup. >> Take a minute. I'll like you to take a minute just to explain what the product is and what you guys are doing. Just so we can get that out there as context. And then jump into some more stories. >> Yeah so from an InfoSight perspective? >> John: Yeah. >> So InfoSight is our predictive cloud analytics platform that uses machine learning to predict and prevent problems from occurring to our customers. So we're not disrupting their business. And so we collect somewhere in the order of, about maybe 25 million pieces of information from every array and the virtual environment. Everyday from every single array. All of that gets into a galactic database, where we have a team of data scientists working with our support engineers and our product engineers to build wellness rules. We have about eight hundred health checks that are really looking out at every part of the infrastructure for our customers and really avoiding issues for them. >> So you take the data across your entire install base. >> Rod: Yup. >> I'm sure you take care of the data so it's not all-- >> Rod: Oh yeah, it's all secure. >> Secure and nanomized. And then use that as predictive to prescribe or both or how are you-- >> Yeah both. So our real goal there is that if we know of an issue, that's either we found in our labs or maybe one customer has experienced it. Really, we're doing everything we possibly can to analyze that issue across the entire install base. So we're learning from peers. >> Male Voice: Yup. >> And applying those learnings across the install base and preventing other customers from hitting that issue. >> The system is autodidactic in this sense. It learns and then applies, is that right? >> Yeah. So we do machine learning. Semi-supervised in a lot of cases. So where we've seen and issue and we can train the models. And then it will look out for those sort of issue across the entire install. >> John: I like the notion of wellness. >> Yup. >> Brings some of the people we relate to. We also heard terms like self-driving storage. >> Yup. >> Layoff testers. >> Yeah. >> But this is again, the trend that really is needed. Share other stories that you have because this is really where IT is going as it moves to a different kind of application and consumption model for you guys. >> Right so, well, kind of touching about what he was talking about, when you're as a storage guy, what's the number one thing that us storage guys have to do, is we have to prove that it's not the storage that's the problem. So usually, what happened was, in the old world, I would produce some statistics of, okay, and here's the IOPS that we're producing and here's the latency during this time. So based on this, it wasn't me, I don't know who it was. I'm just going to tell you it's not me. In the new world-- [John] That was the finger pointing world. >> Yes it was! >> The other guy got it. >> But with InfoSight, it's like hey, I can tell you but you're also welcome to go here as well. But let me show you VMM site where it's going to show you, not only what was happening at the storage. But let me take you all the way down to the host and then the VM and we're going to find this problem. And yeah, turns out sometimes it's going to be the VM that's all of a sudden taking whatever reason adding a huge amount of latency. And that, is something that, there's no more finger pointing in it anymore. All of a sudden, we're in the same team, it's like this kumbaya thing. >> That's awesome. It's good for the cohesiveness as a team. But also it's time savers too. When you reduce the steps to do things, you get your weekends back as you guys say before you came on camera. Tell the story about how you had to do all this work on the provisioning on the replication side, >> Sure. When we deployed the arrays, we decided it was business decision to go ahead and put the production arrays into our production data center and then we would do the DR at a later time. So I've got all of my data live, on production. And they say, okay, we're adding our Nimble storage at our DR site. Paul, how much replication bandwidth do we need? And so, same story. In the old world, you go and you pull your statistics from your replication technology, you put it in excel spreadsheet, you figure out, okay, here's my peaks and I just want to say, if we fall behind just a little bit, this is what we can do. And so usually what happens is, I say, guys, in my best guess, based on what I can see from my limited scope because my eyes are bleeding at this point. >> From the spreadsheet. You're in a spreadsheet right now. >> Paul: Yes, exactly. >> You're in spreadsheet hell. >> I'm in spreadsheet hell. And so what I do is, after about a weekend's worth of work, I put in this recommendation and I usually fluff it because I could be wrong in my statistics and so this is what I end up creating. >> You don't want to be under. You want to be over. >> Exactly, I'm always trying to do that. So the firm, I'm, hopefully this is, nobody's watching at the office, but sometimes they maybe overpaying for something because I just don't want to make that chance. In the new world, this is actually the coolest thing ever. So I'm on InfoSight and I go to this little dropdown, it's like the tool planner, okay, what's that? Where it's going to tell you what you need for bandwidth based on your actual real data. So then I'm pulling, like okay, based on this time, what is the replication if I want to do it every hour. And what if I want to do it every two hours? So then I just take that and I turn it into this report that I got to present to the executive team and they're like, oh my goodness, you have certainly stepped up. How many weekends did you use on this one? And you know, I'm not going to tell them it took me five minutes in InfoSight (John laughs) to be able to create this report. >> Now that they. >> But now they know. >> Cat's out, but you already got promoted. >> Oh that's true. >> Hey Rod, can you talk about the decision to acquire Nimble. What was the genesis. Obviously there's a portfolio component, tuck-ins, fill in some gaps. But there's this other sort of IP piece. Maybe take us back. >> Yeah, so certainly, there was the portfolio fit with the storage platform. So that was obviously a big part of it. I think the other obviously big part was InfoSight. So the idea that what we're doing there with our customers and approving the availability of the systems and the operational performance of the system and keeping a close eye on that to make sure it's optimized. So all that value prop around InfoSight was a big part of the decision I think. We are working on extending InfoSight into the HP product line. Starting with 3PAR so we are working already with that engineering team. To be able to bring some of these features out as quickly as we can into the 3PAR world as well. >> So what is that, from an engineering standpoint, is that sort of the requirement there is to point InfoSight at the data, the 3PAR data? >> Yeah exactly. So 3PAR does collect a lot of data already. >> Yeah sure do. >> So really, we're just pulling that data into our pipelines and so on within InfoSight and taking advantage of some of the machine learning and algorithms and so on that we already do. Things like DMVision, would be possible and so on in that environment as well if you're a 3PAR customer. >> It's interesting. Back in, maybe 10 years ago, 3PAR was sort of the gold standard of what we used to call the hero report. >> Rod: That's right, yup, yeah. People love that. >> Thin provisioning. What impact it was. >> Rod: Yup. How much you save, et cetera. And then that predated the whole big data analytics years right? >> Rod: Yeah, exactly. >> So when Nimble started, they could have started with that premise. Right around that time. >> Yeah, yup. >> I remember when I first saw it, I was like wow this is magic. >> Yeah exactly. That was the premise, was to really apply data science to all of that data that was coming in. Really transform the support experience for Nimble. And I think that's the other big element for HP as well. There's lots of that we do in our support organization that, to be honest, it's quite enviable, by a lot of storage and high tech vendors. >> You guys took a different approach. I think what's really notable for me, which I'm impressed with is, everyone talks about this but very few put into action, is making the user experience center, >> Rod: Yeah exactly. >> Of the value. I mean all of the things you talk about, the benefits, is really centered around your experience right. Saving you time, making your life easier, shifting the automation, that could be automated with the right things. And moving into higher value things. So Paul, what's your thoughts on this as it goes forward. This world is evolving. We're hearing the message here, simplifying, hybrid IT, you got cloud right on the doorstep, multiple clouds are going to be the endgame, we'll know all this, so all said and done. Whole new infrastructure is going to be out there. What's your view of how that user experience for the practitioners will evolve. What's your vision. How do you see it playing out. >> Rod: Be out of a job again. (Paul laughs) >> No, true story. The firm decided that they were going to bring us some people to help us look into what cloud we should, or how we should utilize the cloud because even from us, we're trying to keep ourselves agile as a law firm. Because if we can provide our services in a better, more meaningful and faster way, that gives us a competitive edge. So we brought in this team and they went over all of our IOPS and at the time it was under the different storage system so it took at least 20, 30 hours of my time to get all these numbers that they wanted. And then they created this report for us. Which I thought was really meaningful and valuable. The last line was, you should do cloud work, cloud makes sense. So that was it. Solid advice you know. Money well spent. (laughs) >> And that's what Meg's basically saying in the key note. The right mix of cloud versus on-prem. Certainly law firms have proprietary information and they want it secure. I guess my question really is, fundamentally is, a provocative one, I'd love to get your thoughts on. Serious question, you can laugh at at it a little bit but with AI bots coming, you can almost see these kinds of legal tasks being automated away. So, you might be, next promotion is taking over the firm. That's where big data can in. So how are you guys looking at that as a firm because I'm sure the lawyers are saying, hey you know what, I can shift my value to higher yield activities >> Paul: Exactly. >> Where that makes sense. You guys talk about that at all? >> We do. And I actually use the example of NASA. I really love NASA, I'm a huge fan. And NASA decide, they declared, we're going to go to Mars. We're going to do this. How are we going to do this? We have to let go of our operational stuff. We have to let go, I mean we can launch the shuttle all day long, we're comfortable with that. We can go into the space station, we're comfortable with that. But now, we've got to go new. And the way we have to do that is, we have to drop this stuff. Let's let other people do this. Let's let the InfoSight team start handling a lot of that work for me. And now, I'm asking my team, guys, I want you to start dreaming. Get out of the operational work. Start dreaming out loud. Let's figure out ways we can deliver value to our attorneys. >> Exactly. >> To free them. And let's let them just, again, take that same freedom, with the business intelligence and the machine learning, you're right that they're document management, which is their bread and butter, is their document production. Even that's getting scrutinized or transformed through this machine learning. And so, you could take this as a, as a way of saying no, there goes my job. Or you can say no, now I've got the opportunity to do something even better and cooler and really bring the value. >> And stretching. That's the whole stretch goal. Having that moonshot, in this case Mars. >> Paul: Mars right. >> It's the stretch and leverage right. >> Paul: Yes. >> That's the concept. How do you apply that to storage because now HP's got the composability, they got synergy. >> Paul: Yeah, yup. >> They have all kinds of. Now glue layer's kind of developing. We heard Antonio Neri in the press and analyst queue. We heard Meg Whitman talk about, you know, most her acquisitions have been in software, except for maybe one or two, over the past couple years, have been software. >> Paul: Yup. >> So, hardware, software kind of blending. >> Yeah. I think so, from the storage perspective certainly, I think that's happening. I think from the InfoSight perspective, where we see that going, is again, today when we put a lot of effort into our recommendation models. And that's an area that's very much in the deep data sciences realm. So when we come up with those recommendations, >> John: Umhmm. >> you know, we do things where we can prevent people from hitting issues and not just sort of happen automatically but some of these things are, something needs changing in their environment. So maybe, maybe there's a QoS policy that should be applied on the array to optimize performance because of some peak workload during Christmas, something of that nature. So that's still a last mile problem for us because you've got a human at the other end that's got to go in there and fix it and hopefully do it right and not ignore it and everything else. >> I can see the headline now, storage wellness coming to HP. >> Rod: Yeah exactly. >> But this is really interesting, comes with self-healing right. >> So that's where we want to go with that. That is really the thing we're working towards in the vision is, how do go and do that, change those QoS policies for the customer where we could inject, let's say, a change control within their change management system. They can go hit a button which we orchestrate that change for them. It's all documented and well controlled. >> It's not just storing the data, it's being data driven for the data being stored in the self crafting storage. >> Rod: Exactly, yeah, exactly. >> Rod, Paul thanks so much for sharing the stories and congratulations on the promotion. >> Thank you. >> And congratulations on InfoSight. You guys got great story there. >> But I never get promoted. (everyone laughs) >> Come in theCUBE, >> great story right. >> get promoted. >> Birds of a feather. >> Appreciate it. >> Thanks for having us. More live coverage here from theCUBE. Here at HP Discover 2017 after this short break. I'm John Furrier with Dave Vellante. We'll be right back. (lively music)