 Live from New York, it's theCUBE. Covering theCUBE, New York City, 2018. Brought to you by SiliconANGLE Media and its ecosystem partners. Okay, welcome back everyone. We're here live in New York City for two days of live coverage of CUBE NYC. This is our event. It's our ninth year covering the big data ecosystem going back nine years. But Hadoop evolves very rapidly in the past couple of years into all things data. You know, from data science, practitioners, all the way to hardcore IT developers in DevOps and Cloud. I'm John Furrier, Dave Vellante. Our next guest is Josh Isdha, who's the head of data at Pivotal. Certainly Pivotal's known in the cloud world with Cloud Foundry and a variety of works that they're doing with cloud native microservices of variety of DevOps. However, big data practice over there for you guys with a great large organization. Welcome back, but good to see you likewise. Thanks for having me. So you guys have been doing extremely well. Give us a update of what's going on with Pivotal data because, you know, at VMworld recently, just last week or two weeks ago, we saw PKS, Pivotal Kubernetes, really a center part of VMware strategy. But that's not just VMware, it's cross-multi-cloud. So when you start talking about multi-cloud, you can ignore the adoption of cloud and the role of data in those clouds. So whether it's on-premises, enterprises, leveraging data, there's a cloud dynamic, but also data is fundamental. How are you guys handling the data? What's the data strategy? What's the data posture? What's the data framework for you guys? Sure, so it's a great question. You know, let me start with, I actually feel like the market has coalesced around the product that we shepherd on analytics very nicely. We've seen huge adoption of the open source, huge adoption of our marketplace offerings, with specifically on the analytics side, with Greenplum, we've really focused on a number of things the last couple of years. So one, you know, I think having been in this industry for the last 10 years, we have effectively seen a shift from the old commercial, you know, proprietary engines that used to do analytics onto open platforms. And as many folks out there know, about two years ago Pivotal actually announced open sourcing all of the software that we shepherd. So Greenplum being one of those. As, and part of that transition was really to kind of make use of this idea that users are looking for an easier experience and easier access to data in general. And when you look at what Greenplum had to offer, effectively it was a sequel, an anti-sequel engine that was paralyzable. And, you know, over the course of 10 years, we've built a huge amount of energy around analytics within that ecosystem. And I think both Hadoop and Greenplum, you know, kind of tried to solve a similar problem but from two different angles. So Hadoop took a very interesting way of taking your data and spreading it out across, you know, commodity servers, but doing it in a very generic, non-structured way. And Greenplum effectively took Postgres and did the same thing, commodity servers all nine yards and spread it across many, many Postgreses. So a lot of times we'll refer to our MPP platform as a massively parallel Postgres platform because that's kind of what it is. Now, to bring it all back to reality, one of the things we've been focusing on is up-leveling the version of Postgres that we are based on. And I think all of the MPP players that have existed over the last 10 years were forked off of Postgres at some point in time. We forked at 8.2. We actually just began work in the open-source community on 9.3, and obviously a lot of changes between 8.3, 9, 1, 9, 2, et cetera, but we feel really good about that trajectory. And that's actually enabling us to more easily democratize data to kind of anybody. So the other thing we've been focused on is really harnessing interoperability and connectivity between the most popular open-source scale-out technologies. So think Spark, think Kafka, think HDFS, Hive, all of those things very easily and very seamlessly interact with the Green Plum platform so that Green Plum now becomes an enabler and a very compliant enabler in that. So on the 9.3, just to get my facts right. So you said you started work on that? Is it complete or just give a timetable on that? So great question. In 2005, we forked from 8.2, two years ago, we began the port from 8.2 to 8.3 that took, actually, honestly, it took almost two years. We learned a lot during that particular effort. And so the port from 8.2 to 8.3 was literally like a third of the time, 8.3 to 8.4, even less. And so we just committed 9.2 into the open-source community, 9.3 just began. Don't hold me to the dates and whatnot, but we're looking to, for Green Plum 6 to be released early next year, that would be based off of Postgres 9.3, so kind of the work effort that's being started right now. So what does this all mean for customers? What do I get? So great question. So again, I think we get ribbed a little bit on certain social media on 8.2 was literally 15-year-old technology, right? So totally get that. I think what people oftentimes forget is that along the way, we've added back features into that forked version in order to keep pace with some of the competitors. What this allows us to do is actually focus our efforts on AI, on machine learning, on analytics, and the database technology itself can be leveraged from the open-source community. And there's literally decades of experience and ingenuity that's been put into the Postgres open-source addition. So what do I get as a user? You know, as a, when I look at the different users that we have, if you're a developer, and again from a pivotal standpoint, we have a lot of DevOps developers who are leveraging our platforms. This allows me to take the same amount of effort and energy and ingenuity that I have leveraging applications in Postgres and translate them to green form. So I can get the power of parallel Postgres without having to actually engineer it. It also means all of the Postgres compatible utilities that are out there, anything from front ends to ETL engines to what not, all work out of the box with green form. Talk about the impact of Cloud because obviously that makes a lot of sense. You're leveling up at 9.3. By the way, it's pretty common to do the front end work take a couple of years foundationally and then accelerate back end releases, point releases. So I don't think people are going to really hold you that. As long as you keep the release to 9.3 on schedule. To some degree, in that window you got. So that's key. Now Cloud has been a big driver of analytics. So okay, database, I get that, go to open source, leverage a database, do what you got to do. But now pressure to use the data. The whole setup phase of data is over. Set up in clusters, we've seen evolve from data lakes. Now we're seeing, okay, now I have data buses, data planes, there's different words for it, but ultimately horizontally scalable data. Yep, totally. This is what people want to know. What's the usage? Where's the beef AI points of the demand side? Sure, the user side. So again, I think that the clouds, specifically like Amazon and Azure, have really given users this ability to very easily spin up and try anything that they want. And that's been great for adoption of new technologies. It's also been interesting, the fragmentation of the use of those technologies and where the data resides. One of the really big advantages of Green Plum is the fact that we can run on all the clouds. So we're in the marketplace and you get the same experience on Amazon. Which marketplace, Amazon? Amazon, Azure, and we've got the compatibility with Google as well. So at the end of the day, it's open source software, it can run anywhere. What we've got our engineers doing is making sure that it runs well everywhere. And so for example, in Amazon, the same kind of experience you'd get basically instantiating their MPP platform, you get the exact same experience, really basically the exact same cost, but you get a lot more features. So for example, we have this ability to literally load terabytes and terabytes of data in an hour inside of an Amazon platform. We also have the ability to snapshot and back up and restore that literally in a matter of minutes while queries are running, et cetera, et cetera. So I think what the cloud has enabled us to do is put some very innovative features onto a very easy to use platform while still maintaining that core feature that customers are wanting, which is I don't really want to have to know how to install it. I don't want to know how to run it. I just want to push a button, have it instantiated, and then I want to use it. And that's where we're focused on is giving that usability experience and then the compatibility with all the other things I was talking about. So the MPP crowd created a better data warehouse for, certainly for many workloads that could take advantage of the architecture. And then Hadoop comes along and it's like, okay, it's a cheaper data store. Sure. And we now realize, okay, it's not an either or. It's sort of all of the above. AI now comes in as this automation layer. So what do you see as sort of the next wave in data stores and value stores? Sure, so another great kind of question there. So one of the things that we've focused on, again, is kind of that integration. So we recently, in our last version of Green Plum, we introduced Kafka streaming into Green Plum. So you literally take a topic inside of Kafka, you assign it to a table inside of Green Plum, and we just load it for you as data rolls. That actually allows you to have this continuous stream of analytics that can happen through this MPP, this massively parallel Postgres system. Now, if you look at kind of the future, what we see is more and more desire to push as much of those analytics out to what I call the edge as possible. And so the edge can be anything from an IoT device out there, or perhaps it's really just the cloud with on-prem being kind of the base. So our Postgres compatibility and the fact that we're open source and the fact that you can run it anywhere allows us to basically have a core analytics engine as close to you as possible. We have the ability to take those same analytics and run them in the cloud, any cloud. And then we have the ability to actually shrink those and run those same analytics on like a single instance of say Postgres, again kind of out on the edge. And so that gives you much faster innovation time, much easier integration time because you're talking about the same base platform across all of that ecosystem. So talking about the marketplace sales, one of the things that's a proxy for us when we look at some of the signals in the marketplace because you're right, users want to spin up basically analytics infrastructure. And cloud is infrastructure service. But the software for analytics is a little bit different in software, right? So okay, so I can see spinning up a cloud instance. Basically, multi-tenant, isolated infrastructure, no problem, check. Get workloads going, test and dev, whatever you want to do with it easily. So sales on marketplace should be an indicator of activity. Do you guys track that? And can you share any kind of anecdotal or data on how marketplace sales are going on Amazon and Azure? So as a public company, we don't really share that level of detail. What I can tell you though, is since we launched in our first cloud about a year and a half ago, which was Amazon. And then based on market demand, we've been launching in the other ones. So Azure was kind of next. Pretty much with the exception of what I'm going to call seasonal, so like IE, you know, the holidays, pretty much month over month, our usage has grown literally month over month in the last year and a half. And it's interesting to me, if you look at kind of our user base, we've been around for 10 years. And so we've got a very core group of customers that have literally been with us for three, four, five, six years. They like our products, they continue to expand. As you know, the cloud is part of the architecture for every organization now. And our ability to take what they're doing now and roll into the cloud exists. So we see that adoption. On the flip side, what we see is a lot of very niche customers who actually make a business out of analyzing data, loving the cloud for what the cloud is, right? I don't have to have something running forever. I don't have to, you know, install a bunch of hardware. I can literally instantiate it and go. And we've got some customers now that have, you know, in that idea of, I've got my core analytics and then I have the edge. We've got some customers that are actually spinning up Green Plum ephemeral clusters in order to serve as their specific needs as a side analysis from their core product. So for example, if my analysis engine is around healthcare analytics, you know, for a specific provider, I may want to provide them the capability to do further analysis. So the ability to spin up a Green Plum cluster and leverage that completely independent of your core engine exists today. Talk about the impact of use cases because, you know, okay, this low hanging fruit, sometimes that can be a distraction or misdata point. The real value we're seeing now is new opportunities. Okay, you mentioned these people doing, you know, spot analytics essentially, you're saying. But then you've got practices operationalizing analytics within an organization. A little bit more complex, you've got legacy. So what are some of those new opportunities? What's the low hanging fruit? I can imagine the low hanging fruit is just get data set up and then do some analytics on some core systems. But what are the new opportunities that you guys are seeing at Pivotal that give us an indicator of where the market might be going? So one, on the low hanging fruit, it's everything from, you know, the traditional security analytics to even things like ERP consolidation. So, you know, you think about large organizations that have had multiple ERPs for a variety of reasons. One would be like, you know, acquisition, right? So being able to actually query and analyze that data is somewhat laborious. So being able to plug it into an open source engine like Green Plum, very nice predictive maintenance. You know, we were actually just asked by a retailer, you know, we're shifting a lot of our legacy Teradata over to the cloud. And they're looking at things like, you know, what Google has and what Amazon has. And they'd like to understand, you know, where does Green Plum fit in all that? Because the experiments that they've run have shown them that while it's easy to get started in a lot of these niche products, it's actually really hard to take the traditional enterprise things that have been working for, you know, 20, 30 years and move them over there that they don't support at all. And so our ability to do business intelligence to actually be, you know, a full rounded data warehouse plus a data platform that actually analyzes unstructured and structured data, you know, we actually toss around a what we deem, what we call a magic query. And so the question really is, and it's a relatively simple question, but if you were to ask of your data, you know, I'm looking for any transactions that either Pavin or Peter, and their names sound like Pavin or Peter, have where they've withdrawn an amount from an ATM of around $200 within a radius of let's say four blocks of this particular data point. You know, like how would I answer that? And that has everything from, you know, kind of advanced analytics to geospatial to even some unstructured, you know, with the sound. And all of that can be answered literally in like eight lines of code within Green Plum. And by code, I mean SQL, you know, the same SQL that these folks have been running for 35 years, all of that can be answered leveraging our platform that is integrated solar, it's integrated, you know, Apache Madlib, it's integrated Kafka, et cetera, et cetera. All of that can be answered through a very simple, you know, thing. And then from there, you can actually push a query like that out and you can start to, you know, in your mind think about like, oh, you know, if I'm at an ATM with a very limited amount of CPU and even network capabilities, what can I push down to that particular ATM to prevent, you know, anything from fraud to theft, et cetera. That's a huge use case. Well, Jack, thanks for coming on theCUBE. I really appreciate it. I know you guys have been very successful in this open concept. I know you guys have your own event, you're engaging in a community, you guys are building a community, Pivotal had a community, but now with Cloud Foundry, you have Cloud, you got DevOps, you got data, how are you guys bringing that together? Because, you know, talk about your event that you do, talk about how you guys are building community and sustaining and growing that community. So earlier this year, we actually teamed up with the PostgresCon folks and alongside of that created Green Plum Summit. Hugely successful, we had customers from around the globe come over to, not far away from here. We're looking to do that event, you know, on a regular basis now. It helps foster the community and as we've said, I mean, these are the folks that are actually solving hard problems. With regards to how we're bringing it all together, it's funny, but if you look at the three parts of Pivotal, one is, you know, Cloud Foundry for a platform for deploying applications. One is, you know, kind of our data products for a platform for deploying specifically analytics on your data. And then one is around, you know, agile development. We leverage that agile development on both the data side and the application side such that customers see progress faster than they ever have before. What we see is some customers are ready to go on the application side and we go lickety-split on that and when they turn around, they kind of think, okay, we've solved some of the low-hanging fruits. Now, how do we make it smarter? And in comes the data side where we can plug in microservice, you know, architecture such that they can very easily plug it into something they've already built. Other folks are actually coming to us and saying, hey, we've got analytical problems and they're too big for my existing system. You know, in the case of like netizens going away, you know, help me like, help me make sure that the stuff I have right now continues to work but also prepare for the future. And so we can help them immediately on this side and by the way, you know, most of those organizations are actually looking to transform how they build software as well. There's a nice little tie-in there. So I think the original premise of Pivotal, the three legs, you know, Agile, you know, a cloud platform and data platform continue to exist. It's up to the individual organization where we plug in but we've literally on any of those three pillars, we plug in pretty nice. And just to get the last point in for the folks watching, where are you guys going to be in the next couple months for events? If they want to hook up with you guys. So probably the biggest event, again, will be Green Plum Summit at PostgresConf early next year. We're also hosting spring one here in two weeks in DC where it will primarily be on our developer community in the Cloud Foundry side, but both Dell and the Green Plum folks will be there for the analytics side. We've got a bunch of events actually scheduled with Dell as well around artificial intelligence and some of our new hardware because believe it or not, people are still buying stuff to not be in the cloud and we're there with a very nice solution. That kind of November timeframe, right? November, yes. That there's an analyst meeting there and it's sort of a yearly thing now. Yes. And we know there's an AI theme. We don't know much about it beyond that but there's chatter going on in the industry. We found out something last night. Not surprising, you guys have found it. Should we tell everyone we found out last night? Yeah. Let's just say AI, purpose-built hardware for AI, like you guys shown with Green Plum, I think is a no-brainer, you see. You guys NVIDIA, probably Dell EMC building, AI-specific chips to software. Correct. And then of course, right? Since we're all on a journey to the cloud, your ability to take that and leverage it in both places exists on any cloud. And the cloud is not, cloud and on-premises are operating edges, if you will, how you're going to look at their operating environments. Consistency is critical, 100% agree. Jacques, thanks so much, Colin, thank you, appreciate it. Jacques, with pivotal data, head of data at Pivotal, sharing his insights here in theCUBE, we're bringing you all the data here in New York City for the next two days. We're in day one of CUBE NYC, stay with us for more coverage here in day one after this short break.