>> Announcer: Live from San Jose, it's The Cube. Presenting Big Data, Silicon Valley. Brought to you by SiliconANGLE Media and its ecosystem partners. >> Welcome back to The Cube, we are live in San Jose at Forager Eatery, a really cool place down the street from the Strata Data Conference. This is our 10th big data event, we call this BigData SV, we've done five here, five in New York, and this is our day one of coverage, I'm Lisa Martin with my co-host George Gilbert, and we're joined by a Cube alumni, Jacque Istok, the head of data from Pivotal. Welcome back to the cube, Jacque. >> Thank you, it's great to be here. >> So, just recently you guys announced, Pivotal announced, the GA of your Kubernetes-based Pivotal container service, PKS following this initial beta that you guys released last year, tell us about that, what's the main idea behind PKS? >> So, as we were talking about earlier, we've had this opinionated platform as a service for the last couple of years, it's taken off, but it really requires a very specific methodology for deploying microservices and kind of next gen applications, and what we've seen with the ground swell behind Kubernetes is a very seamless way where we can not just do our opinionated applications, we can do any applications leveraging Kubernetes. In addition, it actually allows us to again, kind of have an opinionated way to work with stateful, stateful data, if you will. And so, what you'll see is two of the main things we have going on, again, if you look at both of those products they're all managed by a thing we call Bosch and Bosch allows for not just the ease of installation, but also the actual operation of the entire platform. And so, what we're seeing is the ability to do day two operations not just around just the apps, not just the platform, but also the data products that run within it. And you'll see later this year as we continue to evolve our data products running on top of either the PKS product or the PCF product. >> Quick question before you jump in George, so you talk about some of the technology benefits and reasoning for that, from a customer perspective, what are some of the key benefits that you've designed this for, or challenges to solve? >> I'd say the key benefits, one is convenience and ease of installation, and operationalization. Kubernetes seems to have basically become the standard for being able to deploy containers, whether its on Pram or off Pram, and having an enterprise solution to do that is something that customers are actually really looking towards, in fact, we had sold about a dozen of these products even before it was GA there was so much excitement around it. But, beyond that, I think we've been really focused on this idea of digital transformation. So Pivotal's whole talk track really is changing how companies build software. And I think the introduction of PKS really takes us to the next level, which is that there's no digital transformation without data, and basically Kubernetes and PKS allow us to implement that and perform for our customers. >> This is really a facilitator of a company's digital transformation journey. >> Correct. In a very easy and convenient way, and I think, you know, whether it's our generation, or, you know, what's going on in just technology, but everybody is so focused on convenience, push button, I just want it to work. I don't want to have to dig into the details. >> So this picks up on a theme we've been pounding on for a couple of years on our side, which is the infrastructure was too hard to stand up and operate >> Male Speaker: Yeah. >> But now that we're beginning to solve some of those problems, talk about some of the use case. Let's pick GE because that's a flagship customer, start with some of the big outcomes, some of the big business outcomes they're shooting for and then how some of the pivotal products map into that. >> Sure, so there's a lot of use cases. Obviously, GE is both a large organization, as well as an investor inside of Pivotal. A lot of different things we can talk about one that comes to mind out of the gate is we've got a data suite we sell in addition to PKS and PCF, and within that data suite there are a couple of products, green plum being one of them. Green plum is this open source MPP data platform. Probably one of the most successful implementations within GE is this ability to actually consolidate a bunch of different ERP data and have people be able to querey it, again, cheaply, easily, effectively and there are a lot of different ways you can implement a solution like that. I think what's attractive to these guys specifically around green plum is that it leverages, you know, standard ANSI SQL, it scales to pedobytes of data, we have this ability to do on pram and off pram I was actually at the Gartner Conference earlier this week and walking around the show it was actually somewhat eye opening to me to be able to see that if you look at just that one product, there really isn't a competitive product that was being showcased that was open source, multi cloud, analytical in nature, et cetera. And so I think, again, to get back to the GE scenario, what was attractive to them was everything they're doing on pram can move to the cloud, whether it's Google, Azure, Amazon they can literally run the exact same product and the exact same queries. If you extend it beyond that particular use case, there are other use cases that are more real time, and again, inside of the data suite, we've got another product called gem fire, which is an in-memory data grid that allows for this rapid ingest, so you can kind of think and imagine whether it's jet engines, or whether it's wind turbines data is constantly being generated, and our ability to take that data in real time, ingest it, actually perform analytics on it as it comes in, so, again, kind of a loose example would be if you know the heat tolerance of a wind turbine is between this temperature and this temperature, do something: send an alarm, shut it down, et cetera. If you can do that in real time, you can actually save millions of dollars by not letting that turbine fail. >> Okay, it sounds here like the gem fire product and the green plum DBMS are very complimentary. You know, one is speed, and one is sort of throughput. And we've seen almost like with Hadupen overreaction in turning a coherent platform into a bunch of building blocks. >> Male Speaker: Yes. >> And with green plum you have everything packaged together. Would it be proper to think of green plum as combining the best of the data link and the data warehouse where you've got the data scientists and data engineers with what would have been another product and the business analysts and the BI crowd satisfied with the same product, but what would have been another? >> Male Speaker: So, I'd say you're spot on. What is super interesting to me is, one, I've been doing data warehousing now for, I don't know, 20 years, and for the last five, I've kind of felt like data warehouse, just the term, was equivalent to the mainframe. So, I actually kind of relegated it the I'm not going to use that term anymore, but with the advent of the cloud and with other products that are out there we're seeing this resurgence where the data warehouse is cool again, and I think part of it is because we had this shift where we had really expensive products doing the classic EDW and it was too rigid, and it was too expensive, and Haduke sort of came on and everyone was like hey this is really easy, this is really cheap, we can store whatever we want, we can do any kind of analytics, and I think, I was saying before, the love affair with piecing all of that together is kind of over and I also think, it's funny, it was really hard for organizations to successfully stand up a Haduke platform, and I think the metric we hear is fifty percent of them fail, right, so part of that, I believe is because there just aren't enough people to be able to do what needed to be done. So, interestingly enough, because of those failures, because the Haduke ecosystem didn't quite integrate into the classic enterprise, products like green plum are suddenly very popular. I was just seeing our downloads for the open source part of green plum, and we're literally, at this juncture seeing 1500 distinct customers leveraging the open source product, so I feel like we're on kind of this upswing of getting everybody to understand that you don't have to go to Haduke to be able to do structured to unstructured data at scale. You can actually use some of these other products. >> Female Speaker: Sorry George, quickly, being in the industry for 20 years, we talk about, you know, culture a lot, and we say cultural shift. People started embracing Haduke, we can dump everything that data lake turned into swamps. I'm curious though, what is that, maybe it's not a cultural shift, maybe it's a cultural roller coaster, like, mainframes are cool again. Give us your perspective on how you've helped companies like GE sort of as technology waves come really kind of help design and maybe drive a culture that embraces the velocity of this change. >> Sure, so one of the things we do a lot is help our customers better leverage technology, and really kind of train it. So, we have a couple different aspects to pivotal. One of them is our labs aspect, and effectively that is our ability to teach people how to better build applications, how to better do data science, how to better do data engineering. Now, when we come in, we have an opinionated way to do all those things, and when a customer embraces it it actually opens up a lot of doors. So we're somewhat technology agnostic, which aids in your question, right, so we can come in, we're not trying to push a specific technology, we're trying to push a methodology and an end goal and solution. And I think, you know, often times of course that end goal and solution is best met by our products, but to your point about the roller coaster, it seems as though as we have evolved there is a notion that data will, from an organization, will all come together in a common object store, and then the ability to quickly be able to spin up an analytical or a programmmatic interface within that data is super important and that's where we're kind of leaning, and that's where I think this idea of convenience being able to push button instantiate a green plum cluster, push button instantiate a gem fire grid so that you can do analytics or you can take actions on it is so super important. >> Male Speaker: You said something that sounds really important which is we want to get it sounded like you were alluding to a single source of truth, and then you spin up whatever compute, you bring it to the data. But there's an emerging, still early school of thought which is maybe the single source of truth should be a hub centered around real time streams. >> Male Speaker: Sure. Yeah. >> How does Pivotal play in that role? >> So, there are a lot of products that can help facilitate that including our own. I would say that there is a broad ecosystem that kind of says, if I was going to start an organization today there are a number of vertical products I would need in order to be successful with data. One of the would be just a standard relational database. And if I pause there for a second, if you look at it, there is definitely a move toward building microservices so that you can glue all those pieces together. Those microservices require smaller, simpler relational type databases, or you know, SQL type databases on the front end, but they become simpler and simpler where I think if I was Oracle or some of the more stalwart on the relational side, it's not about how many widgets you can put into the database, it's really about it's simplicity and performance. From there, having some kind of message queue or system to be able to take the changes and the updates of the data down the line so that, not so much IT providing it to an end user, but more self service, being able to subscribe to the data that I care about. And again, going back to the simplicity, me as an end user being able to take control of my destiny and use whatever product or technology makes the most sense to me and if I sort of dovetail on the side of that, we've focused so much this year on convenience and flexibility that I think it is now at a spot where all of the innovations that we're doing in the Amazon marketplace on green plum, all of those innovations are actually leading us to the same types of innovations in data deployments on top of Kubernetes. And so two of them that come to mind, I felt like, I was in front of a group last week and we were presenting some of the things we had done, and one of them was self-healing of green plum and so it's often been said that these big analytical solutions are really hard to operate and through our innovations we're able to have, if a segment goes down or a host goes down, or network problems, through the implementation the system will actually self heal itself, so all of a sudden the operational needs become quite a bit less. In addition, we've also created this automatic snapshotting capability which allows, I think our last benchmark we did about a pedobyte of data in less than three minutes, so suddenly you've got this operational stalwart, almost a database as a service without really being a service really just this living breathing thing. And that kind of dovetails back to where we're trying to make all of our products perform in a way that customers can just use them and not worry about the nuts and bolts of it. >> Female Speaker: So last question, we've got about 30 seconds left. You mentioned a lot of technologies but you mentioned methodology. Is that approach from Pivotal one of the defining competitive advantages that you deliver to the market? >> Male Speaker: It is 100 per cent one of our defining our defining things. Our methodology is what is enabling our customers to be successful and it actually allows me to say we've partnered with postcrestkampf and green plum summit this year is next month in April and the theme of that is hashtag data tells the story. And so, from our standpoint, green plum is continuing to take off, gem fire is continuing to take off, Kubernetes is continuing to take off, PCF is continuing to take off, but we believe that digital transformation doesn't happen without data. We think data tells a story. I'm here to encourage everyone to come to green plum summit, I'm also here to encourage everyone to share their stories with us on twitter, hashtag data tells a story, so that we can continue to broaden this ecosystem. >> Female Speaker: Hahtag data tells a story. Jacque, thanks so much for carving out some time this week to come back to the cube and share what's new and differentiating at Pivotal. >> Thank you. >> We want to thank you for watching The Cube. I'm Lisa Martin with my co-host George Gilbert. We are live at Big Data SV, our tenth big data event come down here, see us, we're in San Jose at Forrager eatery, we've got a great party tonight and also tomorrow morning at eight am we've got a breakfast briefing you wont' want to miss. Stick around, we'll be back with our next guest after a short break.