 It's theCUBE covering the virtual Vertica big data conference 2020 brought to you by Vertica. Hello everybody, welcome to the new normal. You're watching theCUBE and it's remote coverage of the Vertica big data event, gone digital or gone virtual. My name is Dave Vellante and I'm here with Colin Mahoney who's a senior vice president at Micro Focus and the GM of Vertica. Colin, well, strange times, but the show goes on. Great to see you again. Great to see you too, Dave. Yeah, strange times indeed. Obviously safety first of everyone and we made the decision to go virtual. I think it was absolutely the right call. We made it in advance of how things have transpired but we're making the best of it and appreciate your time here going virtual with us. Well, Ditto, and we're super excited to be here. As you know, theCUBE has been at every single BDC since its inception. It's a great event. You just presented the keynote to your audience. You know, it was remote. You didn't have that live vibe and you have a lot of fans in the Vertica community but could you feel the love? Yeah, you know, it's hard to feel the love virtually but I'll tell you what, the silver lining in all of this is the reach that we have for this event now is much broader than it would have been. As you know, we brought this event back. It's been a few years since we've done it. We were super excited to do it, obviously, in Boston where it was supposed to be on location but there wouldn't have been as many people that could participate. So the silver lining in all of this is that I think there's a lot of love out there. We're getting to have a lot of participants who otherwise would not have been able to participate in this both live as well as a lot of these assets that we're gonna have available on that. So, you know, it's out there. We've got an amazing community of customers and of practitioners with Vertica. We've got so many that have been with us for a long time and we've of course have a lot of new customers as well that we're welcoming in the community. So it's exciting. Well, it's been a while since you've had the BDC event. A lot has transpired. You're now part of Micro Focus but I know you and I know the Vertica team, you guys have not stopped. You've kept the innovation going. We've been following the announcements but bridged the gap between the last time we had coverage of this event and where we are today. A lot has changed. Oh yeah, a lot, a lot has changed. I mean, you know, it's the software industry, right? So nothing stays the same. We constantly have to keep going. Probably the only thing that stays the same is the name Vertica. And as you know, we announced Vertica 10 which is just a phenomenal release for us. So, you know, overall the organization continues to grow the dedication and commitment to this great platform of Vertica continues. Every single release we do, as you know, and this hasn't changed, it's always about performance and scale and adding a whole bunch of new capabilities on that front. But it's also about our main road map and direction that we're going towards. And I think one of the things that's been great about Vertica is that we've stayed true to that from day one. We haven't tried to deviate too much and get into things that are far too outside our box. But we've really done, I think, a great job at extending Vertica into places where people need a lot of help. And with Vertica 10, I know we're going to talk more about that, but we've done a lot of that. And it's super exciting for our customers. And all of this, of course, is driven by our customers. But back to the big data conference, you know, everybody's been saying to us for years. You know, that was one of the best conferences we've been to. It was just so real. It's developers giving tech talks. It's customers giving talks. And we had more customers that wanted to give talks than we had slots to fill this year at the event, which is another, you know, benefit, a little bit of going virtual. We can accommodate a little bit more, but obviously still a tight schedule. But it really was an opportunity for our community to come together and talk about not just Vertica, but how to deal with data. You know, we know the volumes aren't slowing down. We know the complexity isn't slowing down. Things that people want to do with AI and machine learning are moving forward in a rapid pace as well. So there's a lot to talk about and share. And that's really a huge part of what we try to do with Vertica Big Data Conference. Well, let's get into some of that. Your customers are making bets. Microfocus is actually making a bet on Vertica. I want to get your perspective on what are the waves that you're riding and where are you placing your bets? Yeah, no, it's great. So, you know, I think one of the waves that we've been riding for a long time, and obviously Vertica started out as a SQL platform for analytics as a SQL database engine, for relational engine. But we always knew that was just sort of the base that we wanted to do. People were going to trust us to put enormous amounts of data in our platform. And what we owe everyone else is lots of analytics to take advantage of that data in the platform. Lots of tools and capabilities to shape that data, to get into the right format for operational reporting, but also in this day and age for machine learning and for some pretty advanced regressions and other types of things. So a huge part of Vertica 10 is just doubling down on that commitment to what we call in-database machine learning and AI. And to do that, you know, we know that we're not going to come up with the world's best algorithms, nor is that our focus to do. Our advantage is we have this massively parallel platform to ingest, store, manage, and analyze the data. So we've made some announcements about incorporating PMML models into the product. We continue to deepen our Python integration, building off of an open source project. We started with Uber, who's been a great customer and partner on this, as one of our great talks here at the event. So, you know, we're continuing to do that. And it turns out that when it comes to anything analytics, machine learning, certainly so much of what you have to do is actually prepare the data, shape the data, get the data into the right format, apply the model, fit the model, test the model, operationalize the model, and Vertica's a great platform to do that. So that's a huge bet that we're continuing to ride and taking advantage of. And then some of the other things that we've just been seeing continue. I'll take object storage as an example. And I think Hadoop and what Hadoop went through ultimately was a huge part of this, but there's just a massive disruption going on in the world around object storage. You know, we've made several bets on S3 early. We created Vertica Eon mode, which separates compute and storage. And so for us, that separation is not just about being able to take care of, you know, take advantage of cloud economics as we do or the economics of object storage. It's also about being able to truly isolate workloads and start to set the sort of platform to be able to do very autonomous things in the database, so that the database could actually start self-analyzing without impacting any operational workloads. And so that continues with our partnership with Pure Storage on premise. We just announced that we're supporting Eon and Google Cloud now, in addition to Amazon, which we've supported. And we've got HDFS now being supported by our Eon mode. So we continue to ride on that mega trend as well. And then just the clouds in general, whether it's a public cloud, it's a private cloud on premise, giving our customers the flexibility and choice to run Vertica wherever it makes sense for them is something that we are very committed to from a flexibility standpoint. There's a lot of lock-in products out there. There's a lot of cloud-only products. Now more than ever, we're hearing from our customers that they want that flexibility to be able to run anywhere, but they want the ease of use and simplicity of native cloud experiences, which we're giving them as well. I want to stay on that architectural component for a minute and talk about separating compute from storage. It's not just about economics. I mean, in a part it is because you can, you know, gradually scale compute separate from storage as opposed to in chunks, it's more efficient. But you're saying there are other advantages to operational and workload specificity. What is unique about Vertica in this regard, however? I mean, others separate compute from storage. What's different about Vertica in that regard? Yeah, I think there's a lot of differences about how we do it. It's one thing if you're a cloud native company and you do it and you have a shared catalog that's a key value store that all of your customers are using and they're on the same one, frankly, it's probably more of a security concern than anything. But it's another thing when you give that capability to each customer on their own and they're fully protected, they're not sharing it with any other customers. And that's something that we hear a lot on the security side from our customers. They want to be able to separate compute and storage, but they wanna be able to do this in their own environment so that they know that in their data catalog, there's no one else's data being shared in that catalog. There's no single point of failure. So that's one huge advantage that we have. And frankly, I think it just comes from being a company that's operated on premise and up in the cloud. I think another huge advantage is that for us is, we don't know what object storage platform is gonna win nor do we necessarily have to. We designed EonMode so that it's an SDK. We started with S3, but it could be anything, HDFS, S3. I mean, who knows what object storage formats are going to be there. And then finally, beyond just the object storage, we're really one of the only database companies that actually allows our customers to natively operate on data in very different formats, like Parquet or if you're familiar with those in the Hadoop community. So we not only embrace this kind of object storage disruption, but we really embrace the different data formats. And what that means is our customers that have data pipelines that are fully automated and they're putting this information in different places, they don't have to completely reload everything to take advantage of the Vertica analytic platform. We can go where the data is, connect into it, and we offer them a lot of different ways to take advantage of those analytics. So those are a couple of unique differences with the Vertica. And again, I think our real advantage, in many ways by not being a cloud native platform is that we're very good at operating in different environments with different formats, changing formats over time. And I don't think a lot of the other companies out there are good at that. I think many, particularly many of the SaaS companies are scrambling, they even have challenges moving from say an Amazon environment to a Microsoft Azure environment with their offerings, because they've got so much unique Band-Aid, excuse me, in the background, just holding the system up that is native to any of those one particular cloud. All right, good. I'm going to summarize. I'm hearing from you, you're the Ferrari of databases, that we've always known. You're object store agnostic. It's the cloud experience that you can bring on-prem to virtually any cloud, all the popular clouds, hybrid, AWS, Azure, now Google, or on-prem and a variety of different data formats. And that is, I think, unique, the combination of those, I think is unique in the marketplace. Before we get into the news, I want to ask you about data silos, data silos. You mentioned HDFS, you and I met back in the early days of big data. In some respects, Hadoop helped break down the silos with distributing the data and leaving it in place. And in other respects, it created data lakes, which became silos. And so we have yet all these other silos. People are trying to get to a digital transformation, meaning putting data at their core, virtually, obviously, and leave it in place. What are your thoughts on that in terms of data being a silo buster? How does Vertica play there? Yeah, and you're absolutely right. I think even if you look at Hadoop, for all the new data that gets into Hadoop, in many ways it's created yet another large island of data that many organizations are struggling with because it's separate from their core traditional data warehouse. It's separate from some of the operational systems that they have. And so there might be a lot of data in there, but they're still struggling with how do I break it out of that large silo and or combine it? Again, I think some of the things that Vertica does and part of the announcement, which is part of Vertica 10, is migration tools to make it really easy if you do want to move it from one platform to another into Vertica, but you don't have to move it. You can actually take advantage of a lot of the data, where it resides with Vertica, especially in the Hadoop realm, with our external table storage, with our ability to read or can partake natively. So we're very pragmatic about how our customers go about this. Very few customers, many of them tried it with Hadoop and realized it didn't work, but very few customers want a wholesale, just say, all right, we're going to throw everything out, we're going to get rid of our data warehouse, we're going to hit the pause button and we're going to go from there. It's just, it's not possible to do that. So we've spent a lot of time investing in the product that can really work with them to go where the data is and then seamlessly migrate in when it makes sense to migrate it in. You mentioned the performance of Vertica and you talked about it as the Ferrari, it definitely is. And one of the things that we're really proud of with this is that it actually is not a gas gunsler easy either. I mean, one of the things that we're seeing against a lot of the other cloud databases is pound for pound, you get on a 10th the hardware of Vertica running up there, you get over 10th performance. We're seeing that a lot. So it's not just about the performance but it's about the efficiency as well. And I think that efficiency is really important when it comes to silos because there's just only so much horsepower out there and it's easy for cloud companies to play tricks and throw lots of servers at the environment when they start up. But for so many organizations that deal with on-prem and cloud and frankly looking at the bills they're getting from these cloud workloads that are running, they really have to be conscious of that. Yeah, the big energy companies love the gas guzzlers. There's a lot of cloud puke. But all right, let's get into the news. 10.0, you shared with the audience and your keynote. What are the highlights of 10.0? What do we need to know? Yeah, so again, doubling down on these mega trends, I'll start with machine learning and AI. We've done a lot of work to integrate so that you can take native PMML models, bring them into Vertica, run them massively parallel in Vertica, help shape your data, prepare it, do all the work that we know is required to do true machine learning and AI. And for all the hype that there is around it, this is real. People wanna do a lot of unsupervised machine learning whether it's for healthcare, fraud detection, financial services. So we've doubled down on that. We now also support things like TensorFlow. As I mentioned, we're gonna come up with the best algorithms. Our job is really to ensure that those algorithms that people are coming up with can be incorporated and we can run them against massive data sets super efficiently. So that's number one. Number two, on object storage, we continue to support more object storage platforms for Eon mode in the cloud. We're expanding to Google, GCP, Google's cloud beyond just Amazon, on-premise or in the cloud now. We're also supporting HDFS with Eon mode. And of course, we continue to have a great relationship with our partner Pure Storage for on-premise as well. But we continue to invest in Eon mode especially. I'm not gonna go through all the different things here, but it's not just sort of, hey, you support this and then you move on. There's so many different things that we learn about API calls and how to save our customers money and tricks on performance and things. And then the third area is we definitely continue to build on that flexibility of deployment, which is related to Eon mode with some of what I've already described. But it's also about simplicity. It's also about some of the migration tools that we've announced to make it easy to go from one platform to another. We have a great roadmap on ease of use, on security, on performance and scale. I mean, for us, those are the things that we're working on in every single release. We probably don't talk about them as much as we need to, but obviously they're critically important. And so we constantly look at every component in this product. Version 10 is a huge release for any product, especially an analytic database platform. And so we're just constantly revisiting some of the code base and figuring out how we can do it in new and better ways. And that's a big part of 10 as well. I'm glad you brought up the machine intelligence, the machine learning and AI piece because we would agree that it is real. And one of the things we've noted is that, the new innovation cocktail, it's not being driven by Moore's law anymore. It's really a combination of you've collected all this data over the last 10 years through Hadoop and other data stores, object stores, et cetera. Now you're applying machine intelligence to that. And then you've got the cloud for scale. And of course we talked about you bringing the cloud experience, whether it's on-prem or hybrid, et cetera. The reason why I think this is important, and I wanted to get your take on this is because you do see a lot of emerging analytic databases, cloud native, yes, they do suck up a lot of compute, but they also add a lot of value. And I really wanted to understand how you guys play in that new trend, that sort of cloud database, high performance, bringing in machine learning and AI and ML tools, and then turning data into insights. But what I'm hearing is you play directly in that and your differentiation is a lot of the things that we talk about, including the ability to do that both on-prem and in the cloud and across cloud. Yeah, I mean, I think that's a great point. We are a great cloud database. We run very well up on the three major clouds and you could already use some of the other clouds as well in other parts of the world. If you talk to our customers we have hundreds of customers who are running Vertica in the cloud, the experience is very good. I think it could always be better. We've invested a lot in taking advantage of the native cloud ecosystem so that provisioning and managing Vertica is seamless when you're in that environment and we'll continue to do that. But Vertica, excuse me, has a cloud platform is phenomenal and I think there's a lot of confusion out there. I think there's a lot of marketing dollars spent that won't name any of the companies here. I think we know who they are. You know, they're the cloud native data warehouse and it's true, you know, they're software as a service but if you talk to a lot of our customers they're getting very good and very similar experiences with Vertica on the cloud. We stop short of saying we're software as a service because ultimately our customers have that control and flexibility. They're putting Vertica on whichever cloud they wanna run it on, they're managing it. Stay tuned on that. I think you'll hear more from us about that front going even further. But we do really well in the cloud and I think Eon, so much of Eon and this has really been a sort of two and a half year endeavor for us but so much of Eon was designed around the cloud. It was designed around cloud, data lakes, S3, the separation of compute and storage. And if you look at the work that we're doing around containerization and a lot of these other elements it just takes that to the next level and there's a lot of great work. So I think we're going to continue to get better at cloud but I would argue that we're already and have been for some time very good at being a cloud analytic data platform. Well, since you opened the door I got to ask you, so I hear you from performance and architectural perspective but you're also alluding to, I think something else I don't know what you can share with us. You said stay tuned on that but I think you're talking about optionality maybe different consumption models. Am I getting that right? And can you share anything with us there? You're definitely getting that right and actually I'm glad you brought up the consumption model. I think a huge part of cloud is also has nothing to do with the technology. I think it's how you consume the product. Some companies want to rent the product and they want to rent it for a certain period of time. And so we allow our customers to do that. We have incredibly flexible models of how you provision and purchase our product. And I think that helps a lot. I am opening the door a little bit but look and we have customers that ask us if we can offer them more. If we can offer them platforms brought in we've had customers come to us and say please take over our HUPI systems and offer something as a distribution. As I said though, I think one thing that we've been really good at is focusing on what is our core and where we really offer value. But I can tell you that we introduced something called the Vertica Advisor Tool this year. And one of the things that the Vertica Advisor Tool does is it collects information from our customer environments on-premise or the cloud and we run through our own machine learning. We analyze the customer's environment and we make some recommendations automatically. And a lot of our customers are said to us, it's funny, we've tried managed service, tried SaaS offering. And you guys blow them away in terms of your ability to help us like automatically manage the Vertica environment and the system. Why don't you guys just take this product and convert it into a SaaS offering? So I won't go much further than that but you can imagine that there's a lot of innovation and a lot of thoughts going into how we can do that. But there's no reason that we have to wait and do that today and being able to offer our customers on-premise customers, that same sort of experience from a managed capability is something that we spend a lot of time thinking about as well. So again, just back to the automation, the ease of use, the going above and beyond, it's really exciting to have an analytic platform because we can do so much automation in the platform ourselves. And just like we're doing with the Vertica Advisor tool, we're leveraging our own Kool-Aid or Champagne dog food, however you want to say it, to in fact tune up and solve some optimizations for our customers automatically. And I think you're going to see that continue. And I think that could work really well on a bunch of different models. Well, Colin, just on a personal note, I've always enjoyed our conversations. I've learned a lot from you over the years. I'm bummed that we can't hang out in Boston, but hopefully soon this will blow over. I loved last summer when we got together, we had the Vertica throwback, we had Stormbreaker, Palmer, Lynch and Mahoney. We did a great series and that was a lot of fun. So it's a pleasure and thanks so much. Stay safe out there and we'll talk to you soon. Yeah, you too, Dave. Stay safe. I really appreciate the opportunity and this is what it's all about. It's a lot of fun. I know we're going to see each other in person soon and it's the people in the community that really make this happen. So we're looking forward to that, but I really appreciate it. All right, and thank you everybody for watching. This is theCUBE coverage of the Vertica big data conference gone virtual, gone digital. I'm Dave Vellante. We'll be right back right after this short break.