(upbeat music) >> Narrator: It's theCUBE, covering the Virtual Vertica Big Data Conference 2020. Brought to you by Vertica. >> Dave Vellante: Hello everyone, and welcome to theCUBE's exclusive coverage of the Vertica Virtual Big Data Conference. You're watching theCUBE, the leader in digital event tech coverage. And we're broadcasting remotely from our studios in Palo Alto and Boston. And, we're pleased to be covering wall-to-wall this digital event. Now, as you know, originally BDC was scheduled this week at the new Encore Hotel and Casino in Boston. Their theme was "Win big with big data". Oh sorry, "Win big with data". That's right, got it. And, I know the community was really looking forward to that, you know, meet up. But look, we're making the best of it, given these uncertain times. We wish you and your families good health and safety. And this is the way that we're going to broadcast for the next several months. Now, we want to unpack Colin Mahony's keynote, but, before we do that, I want to give a little context on the market. First, theCUBE has covered every BDC since its inception, since the BDC's inception that is. It's a very intimate event, with a heavy emphasis on user content. Now, historically, the data engineers and DBAs in the Vertica community, they comprised the majority of the content at this event. And, that's going to be the same for this virtual, or digital, production. Now, theCUBE is going to be broadcasting for two days. What we're doing, is we're going to be concurrent with the Virtual BDC. We got practitioners that are coming on the show, DBAs, data engineers, database gurus, we got a security experts coming on, and really a great line up. And, of course, we'll also be hearing from Vertica Execs, Colin Mahony himself right of the keynote, folks from product marketing, partners, and a number of experts, including some from Micro Focus, which is the, of course, owner of Vertica. But I want to take a moment to share a little bit about the history of Vertica. The company, as you know, was founded by Michael Stonebraker. And, Verica started, really they started out as a SQL platform for analytics. It was the first, or at least one of the first, to really nail the MPP column store trend. Not only did Vertica have an early mover advantage in MPP, but the efficiency and scale of its software, relative to traditional DBMS, and also other MPP players, is underscored by the fact that Vertica, and the Vertica brand, really thrives to this day. But, I have to tell you, it wasn't without some pain. And, I'll talk a little bit about that, and really talk about how we got here today. So first, you know, you think about traditional transaction databases, like Oracle or IMBDB tour, or even enterprise data warehouse platforms like Teradata. They were simply not purpose-built for big data. Vertica was. Along with a whole bunch of other players, like Netezza, which was bought by IBM, Aster Data, which is now Teradata, Actian, ParAccel, which was the basis for Redshift, Amazon's Redshift, Greenplum was bought, in the early days, by EMC. And, these companies were really designed to run as massively parallel systems that smoked traditional RDBMS and EDW for particular analytic applications. You know, back in the big data days, I often joked that, like an NFL draft, there was run on MPP players, like when you see a run on polling guards. You know, once one goes, they all start to fall. And that's what you saw with the MPP columnar stores, IBM, EMC, and then HP getting into the game. So, it was like 2011, and Leo Apotheker, he was the new CEO of HP. Frankly, he has no clue, in my opinion, with what to do with Vertica, and totally missed one the biggest trends of the last decade, the data trend, the big data trend. HP picked up Vertica for a song, it wasn't disclosed, but my guess is that it was around 200 million. So, rather than build a bunch of smart tokens around Vertica, which I always call the diamond in the rough, Apotheker basically permanently altered HP for years. He kind of ruined HP, in my view, with a 12 billion dollar purchase of Autonomy, which turned out to be one of the biggest disasters in recent M&A history. HP was forced to spin merge, and ended up selling most of its software to Microsoft, Micro Focus. (laughs) Luckily, during its time at HP, CEO Meg Whitman, largely was distracted with what to do with the mess that she inherited form Apotheker. So, Vertica was left alone. Now, the upshot is Colin Mahony, who was then the GM of Vertica, and still is. By the way, he's really the CEO, and he just doesn't have the title, I actually think they should give that to him. But anyway, he's been at the helm the whole time. And Colin, as you'll see in our interview, is a rockstar, he's got technical and business jobs, people love him in the community. Vertica's culture is really engineering driven and they're all about data. Despite the fact that Vertica is a 15-year-old company, they've really kept pace, and not been polluted by legacy baggage. Vertica, early on, embraced Hadoop and the whole open-source movement. And that helped give it tailwinds. It leaned heavily into cloud, as we're going to talk about further this week. And they got a good story around machine intelligence and AI. So, whereas many traditional database players are really getting hurt, and some are getting killed, by cloud database providers, Vertica's actually doing a pretty good job of servicing its install base, and is in a reasonable position to compete for new workloads. On its last earnings call, the Micro Focus CFO, Stephen Murdoch, he said they're investing 70 to 80 million dollars in two key growth areas, security and Vertica. Now, Micro Focus is running its Suse play on these two parts of its business. What I mean by that, is they're investing and allowing them to be semi-autonomous, spending on R&D and go to market. And, they have no hardware agenda, unlike when Vertica was part of HP, or HPE, I guess HP, before the spin out. Now, let me come back to the big trend in the market today. And there's something going on around analytic databases in the cloud. You've got companies like Snowflake and AWS with Redshift, as we've reported numerous times, and they're doing quite well, they're gaining share, especially of new workloads that are merging, particularly in the cloud native space. They combine scalable compute, storage, and machine learning, and, importantly, they're allowing customers to scale, compute, and storage independent of each other. Why is that important? Because you don't have to buy storage every time you buy compute, or vice versa, in chunks. So, if you can scale them independently, you've got granularity. Vertica is keeping pace. In talking to customers, Vertica is leaning heavily into the cloud, supporting all the major cloud platforms, as we heard from Colin earlier today, adding Google. And, why my research shows that Vertica has some work to do in cloud and cloud native, to simplify the experience, it's more robust in motor stack, which supports many different environments, you know deep SQL, acid properties, and DNA that allows Vertica to compete with these cloud-native database suppliers. Now, Vertica might lose out in some of those native workloads. But, I have to say, my experience in talking with customers, if you're looking for a great MMP column store that scales and runs in the cloud, or on-prem, Vertica is in a very strong position. Vertica claims to be the only MPP columnar store to allow customers to scale, compute, and storage independently, both in the cloud and in hybrid environments on-prem, et cetera, cross clouds, as well. So, while Vertica may be at a disadvantage in a pure cloud native bake-off, it's more robust in motor stack, combined with its multi-cloud strategy, gives Vertica a compelling set of advantages. So, we heard a lot of this from Colin Mahony, who announced Vertica 10.0 in his keynote. He really emphasized Vertica's multi-cloud affinity, it's Eon Mode, which really allows that separation, or scaling of compute, independent of storage, both in the cloud and on-prem. Vertica 10, according to Mahony, is making big bets on in-database machine learning, he talked about that, AI, and along with some advanced regression techniques. He talked about PMML models, Python integration, which was actually something that they talked about doing with Uber and some other customers. Now, Mahony also stressed the trend toward object stores. And, Vertica now supports, let's see S3, with Eon, S3 Eon in Google Cloud, in addition to AWS, and then Pure and HDFS, as well, they all support Eon Mode. Mahony also stressed, as I mentioned earlier, a big commitment to on-prem and the whole cloud optionality thing. So 10.0, according to Colin Mahony, is all about really doubling down on these industry waves. As they say, enabling native PMML models, running them in Vertica, and really doing all the work that's required around ML and AI, they also announced support for TensorFlow. So, object store optionality is important, is what he talked about in Eon Mode, with the news of support for Google Cloud and, as well as HTFS. And finally, a big focus on deployment flexibility. Migration tools, which are a critical focus really on improving ease of use, and you hear this from a lot of customers. So, these are the critical aspects of Vertica 10.0, and an announcement that we're going to be unpacking all week, with some of the experts that I talked about. So, I'm going to close with this. My long-time co-host, John Furrier, and I have talked some time about this new cocktail of innovation. No longer is Moore's law the, really, mainspring of innovation. It's now about taking all these data troves, bringing machine learning and AI into that data to extract insights, and then operationalizing those insights at scale, leveraging cloud. And, one of the things I always look for from cloud is, if you've got a cloud play, you can attract innovation in the form of startups. It's part of the success equation, certainly for AWS, and I think it's one of the challenges for a lot of the legacy on-prem players. Vertica, I think, has done a pretty good job in this regard. And, you know, we're going to look this week for evidence of that innovation. One of the interviews that I'm personally excited about this week, is a new-ish company, I would consider them a startup, called Zebrium. What they're doing, is they're applying AI to do autonomous log monitoring for IT ops. And, I'm interviewing Larry Lancaster, who's their CEO, this week, and I'm going to press him on why he chose to run on Vertica and not a cloud database. This guy is a hardcore tech guru and I want to hear his opinion. Okay, so keep it right there, stay with us. We're all over the Vertica Virtual Big Data Conference, covering in-depth interviews and following all the news. So, theCUBE is going to be interviewing these folks, two days, wall-to-wall coverage, so keep it right there. We're going to be right back with our next guest, right after this short break. This is Dave Vellante and you're watching theCUBE. (upbeat music)

>> Voiceover: 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, our continuing coverage of our event, Big Data SV. We're on day two of this event. I'm Lisa Martin, with George Gilbert. We've had a great day yesterday learning a lot and really peeling back the layers of big data, looking at it from different perspectives, from challenges to opportunities. Joining us next is one of our Cube alumni, Chris Selland, the VP of Strategic Alliances from Unifi Software. Chris, great to meet you, welcome back! >> Thank you Lisa, it's great to be here. I have to say, as a alumni and a many time speaker, this venue is spectacular. Congratulations on the growth of The Cube, and this is an awesome venue. I've been on The Cube a bunch of times and this is as nice as I've ever seen it, >> Yeah, this is pretty cool, >> Onward and upward. This place is great. Isn't it cool? >> It really is. This is our 10th Big Data event, we've been having five now in San Jose, do our fifth one in New York City in the fall, and it's always interesting because we get the chance, George and I, and the other hosts, to really look at what is going on from different perspectives in the industry of big data. So before we kind of dig into that, tell us a little bit about Unifi Software, what do you guys do, what is unique and differentiating about Unifi. >> Sure, yeah, so I joined Unifi a little over a year ago. You know, I was attracted to the company because it really, I think, is aligned with where the market is going, and Peter talked this morning, Peter Burris was talking this morning about networks of data. Unifi is fundamentally a data catalog and data preparation platform, kind of combined or unified together. So, you know, so people say, "What do you do?" We're a data catalog with integrated data preparation. And the idea behind that, to go to Peter's, you know, mention of networks of data, is that data is becoming more and more distributed in terms of where it is, where it lives, where it sits. This idea of we're going to put everything in the data warehouse, and then we're going to put everything in the data lake, well, in reality, some of the data's in the warehouse, some of the data's in the lake, some of the data's in SAS applications, some of the data's in blob storage. And where is all of that data, what is it, and what can I do with it, that's really the fundamental problem that we solve. And, by the way, solve it for business people, because it's not just data scientists anymore, it's really going out into the entire business community now, you know, marketing people, operations people, finance people, they need data to do their jobs. Their jobs are becoming more data driven, but they're not necessarily data people. They don't know what schemas are, or joins are, but they know, "I need better data "to be able to do my job more effectively." So that's really what we're helping with. So, Chris, this is, it's kind of interesting, if you distill, you know, the capability down to the catalog and the prep-- >> Chris: Yep. So that it's ready for a catalog, but that sort of thing is, it's like investment in infrastructure, in terms of like building the highway system, but there're going to be, you know, for those early highways, there's got to be roots that you, a reason to build them out. What are some of those early use cases that justifies the investment in data infrastructure? >> There absolutely are, I mean, and by the way, those roots don't go away, those roots, you know, just like cities, right? New roots get built on top of them. So we're very much, you know, about, there's still data sitting in mainframes and legacy systems and you know, that data is absolutely critical for many large organizations. We do a lot of working in banking and financial services, and healthcare. They're still-- >> George: Are there common use cases that they start with? >> A lot of times-- >> Like, either by industry or just cross-sectional? >> Well, it's interesting, because, you know, analysts like yourselves have tended to put data catalog, which is a relatively new term, although some other big analyst firm that's having another conference this week, they were telling us recently that, starts with a "G," right? They were telling us that data catalog is now the number one search term they're getting. But it's been, by many annals, also kind of lumped in, lumped in's the wrong word, but incorporated with data governance. So traditionally, governance, another word that starts with "G," it's been the term. So, we often, we're not a traditional data governance platform, per se, but cataloging data has to have a foundation of security in governance. You know, think about what's going on in the world right now, both in the court of law and the court of public opinion, things like GDPR, right? So GDPR sort of says any customer data you have needs to be managed a certain way, with a certain level of sensitivity, and then there's other capabilities you need to open up to customers, like the right to be forgotten, so that means I need to have really good control, first of all, knowledge of, control over, and governance over my customer data. I talked about all those business people before. Certainly marketers are a great example. Marketers want all the customer data they can get, right? But there's social security numbers, PII, who should be able to see and use what? Because, if this data is used inappropriately, then it can cause a lot of problems. So, IT kind of sits in a-- they want to enable the business, but at the same time, there's a lot of risk there. So, anyway, going back to your question, you know, the catalog market is kind of evolved out of the governance market with more of a focus on kind of, you know, enabling the business, but making sure that it's done in a secure and well-governed way. >> George: Guard rails. >> Yes, guard rails, exactly, good way to say it. So, yep, that's good, I said about 500 words, and you distilled it to about two, right? Perfect, yep. >> So, in terms of your role in strategic alliances, tell us a little about some of the partnerships that Unifi is forging, to help customers understand where all this data is, to your point earlier, the different lines of business that need it to drive, identify where's their value, and drive the business forward, can actually get it. >> Absolutely, well, certainly to your point, our customers are our partners, and we can talk about some of them. But also, strategic alliances, we work very closely with a number of, you know, larger technology companies, Microsoft is a good example. We were actually part of the Microsoft Accelerator Program, which I think they've now rebranded Microsoft for Startups, but we've really been given tremendous support by that team, and we're doing a lot of work to, kind of, we're to some degree cloud agnostic, we support AWS, we support Azure, we support Google Cloud, but we're doing a lot of our development also on the Azure cloud platform. But you know, customers use all of the above, so we need to support all of the above. So Microsoft's a very close partner of ours. Another, I'll be in two weeks, and we've got some interesting news pending, which unfortunately I can't get into today, but maybe in a couple weeks, with Adobe. We're working very closely with them on their marketing cloud, their experience cloud, which is what they call their enterprise marketing cloud, which obviously, big, big focus on customer data, and then we've been working with a number of organizations and the sort of professional services system integration. We've had a lot of success with a firm called Access Group. We announced the partnership with them about two weeks ago. They've been a great partner for us, as well. So, you know, it's all about an ecosystem. Making customers successful is about getting an ecosystem together, so it's a really exciting place to be. >> So, Chris, it's actually interesting, it sounds like there's sort of a two classic routes to market. One is essentially people building your solution into theirs, whether it's an application or, you know, >> Chris: An enabling layer. >> Yes. >> Chris: Yes. >> Even higher layer. But with corporate developers, you know, it's almost like we spent years experimenting with these data lakes. But they were a little too opaque. >> Chris: Yes. >> And you know, it's not just that you provide the guard rails, but you also provide, sort of some transparency-- >> Chris: Yes. >> Into that. Have you seen a greater success rate within organizations who curate their data lakes, as opposed to those who, you know, who don't? >> Yes, absolutely. I think Peter said it very well in his presentation this morning, as well. That, you know, generally when you see data lake, we associate it with Hadoop. There are use cases that Hadoop is very good for, but there are others where it might not be the best fit. Which, to the early point about networks of data and distributed data, so companies that have, or organizations that have approached Hadoop with a "let's use it what it's good for," as opposed to "let's just dump "everything in there and figure it out later," and there have been a lot of the latter, but the former have done, generally speaking, a lot better, and that's what you're seeing. And we actually use Hadoop as a part of our platform, at least for the data preparation and transformation side of what we do. We use it in its enabling technology, as well. >> You know, it's funny, actually, when you talk about, as Peter talked about, networks of data versus centralized repositories. Scott Gnau, CTO of Hortonworks, was on yesterday, and he was talking about how he had originally come from Teradata, and that they had tried to do work, that he had tried to push them in the direction of recognizing that not all the analytic data was going to be in Teradata, you know, but they had to look more broadly with Hadapt, and I forgot what the rest of, you know-- >> Chris: Right, Aster, and-- >> Aster, yeah. >> Chris: Yes, exactly, yep. >> But what was interesting is that Hortonworks was moving towards the "we believe "everything is going to be in the data lake," but now, with their data plane service, they're talking about, you know, "We have to give you visibility and access." You mediate access to data everywhere. >> Chris: Right. >> So maybe help, so for folks who aren't, like, all bought into Hortonworks, for example, how much, you know, explain how you work relative to data plane service. >> Well, you know, maybe I could step back and give you a more general answer, because I agree with that philosophically, right? That, as I think we've been talking about here, with the networks of data, that goes back to my prior statement that there's, you know, there's different types of data platforms that have different use cases, and different types of solutions should be built on top of them, so things are getting more distributed. I think that, you know, Hortonworks, like every company, has to make the investments that are, as we are, making their customers successful. So, using Hadoop, and Hortonworks is one of our supported Hadoop platforms, we do work with them on engagements, but you know, it's all about making customers successful, ultimately. It's not about a particular product, it's about, you know, which data belongs in which location, and for what use case and what purpose, and then at the same time, when we're taking all of these different data sets and data sources, and cataloging them and preparing them and creating our output, where should we put that and catalog that, so we can create kind of a continuous improvement cycle, as well, and for those types-- >> A flywheel. >> A flywheel, exactly, continuous improvement flywheel, and for those types of purposes, you know, that's actually great use case for, you know, Hortonworks, Hadoop. That's a lot of what we typically use it for. We can actually put the data any place our customers define, but that's very often what we do with it, and then, but doing it in a very structured and organized way. As opposed to, you know, a lot of the early Hadoop, and not specific to any particular distro that went bad, were, it was just like, "Let's just dump it all "into Hadoop because it's cheaper." You know, "Let's, 'cause it's cheaper than the warehouse, "so let's just put it all in there, "and we'll figure what to do with it later." That's bad, but if you're using it in a structured way, it can be extremely useful. At the same point, and at the same time, not everything's going to go there belongs there, if you're being thoughtful about it. So you're seeing a lot more thoughtfulness these days, which is good. Which is good for customers, and it's good for us in the vendor side. Us, Hortonworks, everybody, so. >> So is there, maybe you can tell us of the different approaches to, like, the advantage of integrating the data prep with the catalogized service, because as soon as you're done with data prep it's visible within the catalog. >> Chris: Absolutely, that's one, yep. >> When, let's say when people do derive additional views into the data, how are they doing that in a way that then gets also registered back in the catalog, for further discovery? >> Yeah, well, having the integrated data preparation which is a huge differentiator from us, there are a lot of data catalog products out there, but our huge differentiator, one of them, is the fact that we have integrated data preparation. We don't have to hand off to another product, so that, as you said, gives us the ability to then catalog our output and build that flywheel, that continuous improvement flywheel, and it also just basically simplifies things for customers, hence our name. So, you know, it really kind of starts there. I think I, the second part of your question I didn't really, rewind back on that for me, it was-- >> Go ahead. >> Well, I'm not sure I remember it, right now, either. >> We all need more coffee. >> Exactly, we all need more coffee. >> So I'll ask you this last question, then. >> Yes, please. >> What are, so here we are in March 2018, what are you looking forward to, in terms of momentum and evolution of Unifi this year? >> Well, a lot of it, and tying into my role, I mentioned we will be at Adobe Summit in two weeks, so if you're going to be at Adobe Summit, come see us there, some of the work that we're doing with our partner, some of the events we're doing with people like Microsoft and Access, but really it's also just customer success, I mean, we're seeing tremendous momentum on the customer side, working with our customers, working with our partners, and again, as I mentioned, we're seeing so much more thoughtfulness in the market, these days, and less talk about, you know, the speeds and feeds, and more around business solutions. That's really also where our professional services, system integration partners, many of whom I've been with this week, really help, because they're building out solutions. You know, GDPR is coming in May, right? And you're starting to really see a groundswell of, okay, you know, and that's not about, you know, speeds and feeds. That's ultimately about making sure that I'm compliant with, you know, this huge regulatory environment. And at the same time, the court of public opinion is just as important. You know, we want to make sure that we're doing the right thing with data. Spread it throughout organization, make ourselves successful and make our customers successful. So, it's a lot of fun. >> That's, fun is good. >> Exactly, fun is good. >> Well, we thank you so much, Chris, for stopping back by The Cube and sharing your insights, what you're hearing in the big data industry, and some of the momentum that you're looking forward to carrying throughout the year. >> It's always a pleasure, and you, too. So, love the venue. >> Lisa: All right. >> Thank you, Lisa, thank you, George. >> Absolutely. We want to thank you for watching The Cube. You're watching our coverage of our event, Big Data SV, hashtag BigDataSV, for George, I almost said George Martin. For George Gilbert. >> George: I wish. >> George R.R., yeah. You would not be here if you were George R.R. Martin. >> George: No, I wouldn't. >> That was a really long way to say thank you for watching. I'm Lisa Martin, for this George. Stick around, we'll be right back with our next guest. (techno music)

>> Narrator: Live from Las Vegas it's the Cube. Covering Veritas Vision 2017. Brought to you by Veritas. >> Welcome back to Las Vegas everybody. This is the wrap for Veritas 2017. This is the Cube, the leader in live tech coverage, I'm Dave Vellante with Stu Mindeman. And Stu, two days where we're witnessing the evolution transformation of Veritas. Veritas used to be the gold standard for what wasn't known at the time as software design but just software function to deliver storage capabilities, no hardware agenda and now you're seeing investment under the leadership of new management. Some innovation, a cycle that's quite rapid. It's hard to tell how much of that is really taking shape in the customer base. Seems like the channel, partners are picking up on it. Customers are still sort of trying to figure out how to move beyond so their existing legacy situation, it's like Heath Townsend says. The vendor community tends to move at the speed of CIO. It's a great quote. But overall, I think very good show. Some surprises here in terms of specifically the breadth of the Veritas portfolio not just a backup company. Really focused on data management, focused on information management which obviously is relevant in the digital economy. What were your takeaways? >> So Dave the big strategy is the 360 data management. And I think one of the things we teased out in here is first of all, nobody thinks the cloud is simple. Multicloud, where customers are and when you dig into it and what Veritas has learned in the last year is that there's a lot of work to be done. Where are their deeper integrations that they need to have. There's different requirements from the different partners here. See Microsoft, the top level sponsor. Russinovich up on stage, giving kind of his usual hybrid cloud with a lot of open source pitch there but seems a good fit from the customers and partners that we talked to here to say Microsoft aligns well with what Veritas is doing. Amazon big player here. Lot of integration is happening behind the scenes to make sure that Veritas can work there. And then you follow Google of course, big focus around data, good to see where Veritas is going. We had a nice conversation with Google. Google seems very open on a lot of these not as much focus on some of the functionality that Veritas has so it's a good natural fit and then IBM and Oracle kind of rounding out the big players here. The thing I've come in, I think every show I've gone to this year Dave, is where do companies that have been around for more than a couple of years fit in this multicloud world and absolutely that's where the puck's going as Bill Coleman said that's where they're betting the company and putting it forward and we wondered coming in would it be like ah, yeah. This is a net backup and Veritas foundation suite with a new coat of paint on it? And no, I mean they really brought in a lot of new management team sure there's engineers here with a lot of expertise and experience to build on to know how to do this but I was pretty impressed with what I saw this week Dave. >> So no hardware agenda is evolving to no cloud agenda. That's one of the things we learned here and we had a good discussion. Got a little bit awkward at times but good discussion about why Veritas relative to the other players here. And what the answer we got back which we had to tease it out a little bit was essentially the upstart guys, the Rubrics, the Cohesity's to a certain extent Zerto I think they tried to put Veeam in that category we'll come back to Veeam it's kind of interesting Maybe not big enough to deliver on that multicloud vision. And they're really not even trying. Cohesity and Rubric I don't know. >> They've added a lot of cloud recently, actually Rubric's been doing it for a while, Cohesity definitely seen there. They understand that cloud but I think what maybe I'd say Dave, they tend to start from an on premises piece as opposed to you say this Veritas strategy is it doesn't matter and what many of the player, right, where is there natural gravity? Is it on premises or is in the public cloud and Nutanix, they partner with Google, they're doing the cloud. But absolutely, most of their >> Dave: They make more money. >> Stu: Most of their revenue is, you know, is found there. >> So the upstarts I kind of buy the Veritas argument that there maybe doesn't have the Gravitas and the heft to attack that multicloud other than pick at it and grow and they'll do hundreds of millions of dollars in revenue and maybe get to a billion and have a great exit. I think that'll happen. And then the other guys, the big guys, HPE, Dell EMC, IBM, they certainly have the capabilities to do that. But is it going to be the main focus of those companies? HPE maybe. We'll see. HPE and Veeam are an interesting partnership. My information suggests that Veeam is driving many tens of millions of dollars through Hewlett Packard Enterprise now that the microfocus deal has been done and they got rid of data protector. IBM they're kind of re-invigorating the storage business, data production is part of that. Dell EMC is I think challenged to invest They can't invest in as much as they used to certainly not in acquisitions. The acquisition pipeline is basically dried up. >> Stu: Dave, Dave, look at the DataMain was a great acquisition by EMC at the time now under Dell EMC. I mean, you're probably closer to it than me. I don't hear a strong cloud message coming out of that group when we talk about backup and the like. Dell corporate, of course they've got Microsoft partnerships Veeam has Amazon partnerships but it very much is tied to appliances or arrays or servers at the main piece, it's not a software message which is where Veritas is. >> Dave: If you look at Dell EMC's acquisitions recently, Isilon a couple billion, two and a half billion I think, Data Domain two and a half billion, DSSD a billion, which really hasn't turned into much at this point in time anyway. Extreme IO, not sure what they paid but you know you're hearing ebbs and flows on that but that my point is that is how under Joe Tucci EMC innovated. They would incrementally add on to their existing platforms. You were there. You saw it. And then they would invest in what Joe Tucci used to call tuck in acquisitions. And all that was well and good and they were able to sort of keep, not sort of, they were able to keep pace with the industry. That's basically stopped. That strategy. We've seen cuts and layoffs but still a financial windfall I think is coming for Dell. And VMware is a secret sauce there so we don't have to dig into that too much but my point is that services is going to be the lynchpin for that company in terms of attacking multicloud services and VMware. So now you >> Stu: And pivotal of course too. >> Dave: And pivotal as well, that's right. Great point. Now you come back to Veritas. Focused on that strategy of information management. Investing apparently in RND. Seemingly patient capital with Carlyle so you know me, I like to unpack the numbers. From what I can tell, my sources and got to do some more digging on this but when Veritas was acquired by Carlyle it was about 2.3 billion dollar company, wouldn't surprise me if on an income statement basis it's actually shrunk. It wouldn't surprise me at all. In fact, Bill Coleman kind of hinted to that. And especially if you start looking at rateable revenue models, maybe bookings could be up and I've heard numbers as high as 2.6, 2.7 billion but who knows. I've also heard now, the evaluation at the time of the acquisition was 7 billion and change. I've heard numbers as high as 14, 15 billion now, maybe a little inflated but I think easily over ten. And I think this company has an opportunity to get to three billion, get the evaluation up to 15, maybe even 20 billion. Big win for the private equity investors and the key to that, I think, is going to be a continuous investment. Go to market that aligns to those new areas that they're talking about and very importantly the ecosystem. I want to see this thing start exploding. The big highlights here were the cloud guys. What else would you highlight? You know, you walk around the shows a lot of smaller partners here Really would like to see that ecosystem grow. That's something that we're going to watch. And the audience grow. I think this show is up from last year next year I believe it's in Las Vegas again moving to the Cosmopolitan little bit better venue, bigger venue we'll see if they can get up to where the big boys go over time but overall I'd say pretty good second year for Veritas Vision. >> Yeah, you know Dave, when you look at the different areas Veritas has a full suite of software to find storage. The analogy I've used all the time storage industry is a knife fight in a dark alley. So you've got some big players out there that all have their software defined storage messaging out there of course Veritas would say they all have the hardware agenda. There's some truth to that but Veritas also has to partner with a bunch of these players to get there so where did they get the reach, how does the channel help them punch above their white, the differences there a two and a half, 2.6 billion dollar run rate company, revenue company that is private. So you know, they're trusted because they have history. They're not a small startup can this innovation and all the new team members come in and definitely the cloud piece is pretty interesting, Dave we see, we'll be back at Reinvent with the Cube and Veritas will have a presence there. Amazon, huge ecosystem, where do they play where do they show up, data, we've said so many times on here it becomes repetitive data is the new oil and customers need to take advantage of them. Can Veritas' message get them at the table and in a conversation where so much, it's about infrastructure and I love the message here at the show. It's not infrastructure technology it's information technology and we want to put a highlight on that so like the message, like where it's going, here are the customers but can they get at the table when there's so many different there's the startups, there's the big players everybody pulling at where the customers are and the GDPR was an interesting angle 'cause it was the crispest, the most crisp conversation I've heard on GDPR. I know you've been talking about it at least the last six months on some Cube interviews, I've done a number of interviews. But it really crystallized for me this week at the show. >> I'm glad you mentioned that because I've done a couple shows where GDPR has come up and I was like okay, yeah we get it. It's coming. It's nasty. How are you going to help me again? And I think Veritas did a really good job this week of saying look, we are here to help. We're going to start with Discovery and they sort of laid out the journey and I think they made a good case for their portfolio aligning well with solving that problem. So this could be a nice little kicker there. One of the things I wanted to sort of riff on a little bit was the tam, the data protection space. It reminds when ServiceNow went public I know it was a story about Gartner Antlis was very negative on and saying a helpdesk is a dead business and then Frank Sluman did a masterful job of expanding the tam, explaining that tam, guiding the company to a massive opportunity. And I see a similar dynamic here. On the one hand I say wow. Got a lot of companies in this data protection space even though it's exploding lot of VC money coming in, you're seeing new entrants like Datrium now gets in the space even though they're not just backup, that's not their primary but I mean you certainly saw SimpliVity with what's kind of their specialty. But guys like Datos.IO and some of these new guys coming in like we talked about Rubric, etc there's a lot of players here. Is the market big enough to support those? Part of me says ehh, I don't know but then I think back to that ServiceNow example. I think the tam is going to explode because it's not about backup. And it's not even just about data protection. It is about information management and I think Veritas got that right. What I like about their chances is they're big. They've got a big install base and I think their vision is right and they don't have that cloud agenda. They're a pure software company even though they do sell some appliances sometimes. And they got what seemingly is good management. I think I'd like to see them attract even more management as they grow and as they start executing this and as I say, the ecosystem has got to grow. >> Yeah, so Dave, IT has to deal with information governance. That's the defense they need to play. There's going to be money thrown at that. Some of the conversation we had this week IT operations becomes one of those tail winds that should lift companies like Veritas to be able to have further discussion and grow those budgets to be able to be a much more important piece. >> Alright good, Stu. Thank you. Good working with you again. It's been a long few weeks here but we're at it again next week. The Cube is at Big Data NYC which is done in conjunction with Strata in New York City. We've got a big party on Wednesday night. Actually we've got a presentation, Peter Burrows, Neil Raden, Jim Cubillas and we got a panel. Talking about software eating the edge. That's on Wednesday at 37 Pillars. Tweet me at @dvellante if you don't have an invitation I'll get you one although I heard there was a waitlist last week but we'll get you in, don't worry. And then we're also at Splunk next week, I'm going to be at Dotconf in DC. We've done Dotconf since I think 2011 was the first year we did Dotconf. >> And I'll be keeping a big eye on Microsoft Ignite next week while we don't have the Cube there. Obviously pretty important things like Aster Stack expected to roll out and got so many shows Dave. >> So the Cube, we love digital content creating content, sharing with you our community. Follow @thecube that handle for the Cube gems, you'll see a bunch of videos. Go to thecube.net, that's where we host all the videos from all of our shows. And then siliconangle.com is where we write up our news and analysis of these events and news of the day and of course wikibon.com is our research site. A lot of really good deep work going on there. So thanks for watching everybody. This is Dave Vellante with Stu Mindeman. We're out from Veritas Vision 2017. We'll see you next time. (music)

>> Announcer: Live, from San Jose, in the heart of Silicon Valley, it's theCUBE, covering DataWorks Summit 2017. Brought to you by Hortonworks. (electronic music fading) >> Hi everybody, this is George Gilbert. We're watching theCUBE. We're at DataWorks 2017 with my good friend Sri Raghavan from Teradata, and Sri, let's kick this off. Tell us, bring us up to date with what Teradata's been doing in the era of big data and advanced analytics. >> First of all, George, it's always great to be back with you. I've done this before with you, and it's a pleasure coming back, and I always have fun doing this. So thanks for having me and Teradata on theCUBE. So, a lot of things have been going on at Teradata. As you know, we are the pioneer in the enterprise data warehouse space. We've been so for the past 25 plus years, and, you know, we've got an incredible amount of goodwill in the marketplace with a lot of our key customers and all that. And as you also know, in the last, you know, five or seven years or so, between five and seven years, we've actually expanded our portfolio significantly to go well beyond the enterprise data warehouse into advanced analytics. We've got solutions for the quote-unquote the big data, advanced analytics space. We've acquired organizations which have significant amount of core competence with enormous numbers of years of experience of people who can deliver us solutions and services. So it's fair to say, as an understatement, that we have, we've come a long way in terms of being a very formidable competitor in the marketplace with the kinds of, not only our core enterprise data warehouse solutions, but also advanced analytics solutions, both as products and solutions and services that we have developed over time. >> So I was at the Influencer Summit, not this year but the year before, and the thing, what struck me was you guys articulated very consistently and clearly the solutions that people build with the technology as opposed to just the technology. Let's pick one, like Customer Journey that I remember that was used last year. >> Sri: Right. >> And tell us, sort of, what are the components in it, and, sort of, what are the outcomes you get using it? >> Sure. First of all, thanks for picking on that point because it's a very important point that you mentioned, right? It's not- in today's world, it can't just be about the technology. We just can't go on and articulate things around our technology and the core competence, but we also have to make a very legitimate case for delivering solutions to the business. So, our, in fact, our motto is: Business solutions that are technology-enabled. We have a strong technology underpinning to be able to deliver solutions like Customer Journey. Let me give you a view into what Customer Journey is all about, right? So the idea of the Customer Journey, it's actually pretty straightforward. It's about being able to determine the kind of experience a customer is having as she or he engages with you across the various channels that they do business with you at. So it could be directly they come into the store, it could be online, it could be through snail mail, email, what have you. The point is not to look at Customer Journey as a set of disparate channels through which they interact with you, but to look at it holistically. Across the various areas of encounters they have with you and engagements they have with you, how do you determine what their overall experience is, and, more importantly, once you determine what their overall experience is, how can you have certain kinds of treatments that are very specific to the different parts of the experience and make their experience and engagement even better? >> Okay, so let me jump in for a second there. >> We've seen a lot of marketing automation companies come by and say, you know, or come and go having said over many generations, "We can help you track that." And they all seem to, like, target either ads or email. >> Correct. >> There's like, the touchpoints are constrained. How do you capture a broader, you know, a broader journey? >> Yeah, to me it's not just the touchpoints being constrained, although all the touchpoints are constrained. To me, it's almost as if those touchpoints are looked at very independently, and it's very orthogonal too, right? I look at only my online experience versus a store experience versus something else, right? And the assumption in most cases is that they're all not related. You know, sometimes, I may not come directly to the store, right, but the reason why I'm not coming to the store is because, to buy things, because, you know, I have seen an advertisement somewhere which says, "Look, go online and purchase a product." So whatever the case might be, the point is each part of the journey is very interrelated, and you need to understand this is as well. Now, the question that you asked is, "How do you, for instance, collect all this information? "Where do you store it?" >> George: And how do you relate it ... >> And, exactly, and how do you connect the various points of interaction, right? So for one thing, and let me just, sort of, go a little bit tangential and go into some architecture, the marchitecture, if you will that allows us to be able to, first of all, access all of this data. As you can imagine, the types and the sources of data are quite a bit, are pretty disparate, particularly as the number of channels by which you can engage with me as an organization has expanded, so do the number of sources. So, you know, we have to go to place A, where there's a lot of CRM information for instance, or place B, where it's a lot of online information, weblogs and web servers and what have you, right? So, we have to go to, for instance, some of these guys would have put all this information in a big data lake. Or they could have stored it in an EDW, in an enterprise data warehouse. So we've put in place a technology, an architecture, which allows us to be able to connect to all these various sources, be it Teradata products, or non-Terada- third-party sources, we don't care. We have the capability to connect all to, to these different data sources to be able to access information. So that's number one. Number two is how do you normalize all of this information? So as you can well imagine, right, webs logs servers are very different in their data makeup as apposed to CRM solutions, highly structured information. So we need a way to be able to bring them together, to connect a singular user ID across the different sources, so we have filtering, you know, data filters in place that extracts information from weblogs, let's say it's a XML file. So we extract all that information, and we connect it. We, ultimately, all of that information comes to you in a structured manner. >> And can it, can it be realtime reactive? In other words when- >> Sri: Absolutely. >> someone comes to- >> Sri: Absolutely. >> you know, a channel where you need to anticipate and influence. >> Very good question. In fact, I think we will be doing a big disservice to our customers if we did not have realtime decisioning in place. I mean, the whole idea is for us to be able to provide certain treatments based on what we anticipate your reactions are going to be to certain, let's say if it's a retail store, let's say to certain product coupons we've placed, which says, you know, come online, and basically behavior we think there's a 90% chance that tomorrow morning you're going to come back, you know, through our online portal and buy the products. And because of the fact that our analytics allows us to be able to predict your behavior tomorrow morning, as soon as you land on the online portal, we will be able to provide certain treatment to you that takes advantage of that. Absolutely. >> Techy question: because you're anticipating, does that mean you've done the prediction runs, batch, >> Sri: Absolutely. >> And so you're just serving up the answer. >> Yeah, the business level answer is absolutely. In fact, we have, as part of our advanced analytics solution, we have pre-built algorithms that take all this information that I've talked to you about, where it's connected all that information across the different sources, and we apply algorithms on top of that to be able to deliver predictive models. Now, these models, once they are actually applied as and when the data comes in, you know, you can operationalize them. So the thing to be very clear here, a key part of the Teradata story, is that not only are we in a position to be able to provide the infrastructure which allows you to be able to collect all the information, but we provide the analytic capabilities to be able to connect all of the data across the various sources and at scale, to do the analytics on top of all that disparate data, to deliver the model, and, as an important point, to operationalize that model, and then to connect it back in the feedback loop. We do the whole thing. >> That's, there's a lot to unpack in there, and I called our last guest dense. What I was actually trying to say, we had to unpack a dense answer, so it didn't come out quite that, quite right. So I won't make that mistake. >> Sri: That's a very backhanded compliment there. (George laughing) >> So, explain to me though, the, I know from all the folks who are trying to embed predictive analytics in their solutions, the operationalizing of the model is very difficult, you know, to integrate it with the system of record. >> Yeah, yeah, yeah. >> How do, you know, how do you guys do that? >> So a good point. There are two ways by which we do it. One is we have something called the AppCenter. It's called Teradata AppCenter. The AppCenter is a core capability of some of the work we've done so far, in fact we've had it for the last, I don't know, four years or so. We've actually expanded it across, uh, to include a lot of the apps. So the idea behind the AppCenter is that it's a framework for us to be able to develop very specific apps for us to be able to deliver the model so that next time, as and when realtime data comes in, when you connect to a database for instance. So the way the app works is that you set up the app. There's a code that we've created, it's all prebuilt code that he put behind that app, and it runs, the app runs. Every time the data is refreshed, you can run the app, and it automatically comes up with visualizations which allow you to be able to see what's happening with your customers in realtime. So that's one way to operationalize. In fact, you know, if you come by to our booth, we can show you a demo as to how the AppCenter works. The other say by which we've done it is to develop a software development kit where we actually have created an operationalization. So, as an, I'll give you an example, right? We developed an app, a realtime operationalization app where the folks in the call center are assessing whether you should be given a loan to buy a certain kind of car, a used car, brand new car, what have you the case might be. So what happens is the call center person takes information from you, gets information about, you know, what your income level is, you know, how long you've been working in your existing job, what have you. And those are parameters that are passed into the screen- >> By the way, I should just say, on the income level, it's way too low for my taste. >> Those are, um, those are comments I'll take, uh, later. >> Off slide. >> But, I mean, you got a brand new Armani suit, so you're not doing badly. But, uh, so what happens is, you know, as and when the data goes into the parameters, right, the call center person just clicks on the button, and the model which sits behind the app picks up all the parameters, runs it, and spews out a likelihood score saying that this person is 88% likely- >> So an AppCenter is not just a full end to end app, it also can be a model. >> AppCenter can include the model which can be used to operationalize as and when the data comes in. >> George: Okay. >> It's a very core part of our offering. In fact, AppCenter is, I can't stress how important, I can't stress enough how important it is to our ability to operationalize our various analytic models. >> Okay, one more techy question in terms of how that's supported. Is the AppCenter running on Aster or the models, are they running on Aster, uh, the old Aster database or Teradata? >> Well, just to be clear, right, so the Aster solution is called Aster Analytics of which one foreign factor contains a database, but you have Aster which is in Hadoop, you have Aster in the Cloud, you have Aster software only, so there's a lot of difference between these two, right? So AppCenter sits on Aster, but right now, it's not just the Aster AppCenter. It's called the Teradata AppCenter which sits on, with the idea is that it will sit on Teradata products as well. >> George: Okay. >> So again, it's a really core part of our evolution that we've come up with. We're very proud of it. >> On that note, we have to wrap it up for today, but to be continued. >> Sri: Time flies when you're having fun. >> Yes. So this is George Gilbert. I am with Sri Raghavan from Teradata. We are at DataWorks 2017 in San Jose, and we will be back tomorrow with a whole lineup of exciting new guests. Tune in tomorrow morning. Thanks. (electronic music)

>>Live from Las Vegas, Nevada. It's a queue at HP. Discover 2014 brought to you by HP. >>Welcome back, everyone live here in Las Vegas for HP. Discover 2014. This is the cube we're out. We go where the action is. We're on the ground here at HP. Discover getting all the signals, sharing them with you, extracting the signal from the noise. I'm John furrier, founder of SiliconANGLE. I joined Steve Woolwich VP of product marketing at map art technologies. Great to see you welcome to the cube. Thank you. I know you got a plane to catch up, but I really wanted to squeeze you in because you guys are a leader in the big data space. You guys are in the top three, the three big whales map are Hortonworks, Cloudera. Um, you know, part of the original big data industry, which, you know, when we did the cube, when we first started the industry, you had like 30, 34 employees, total combined with three, one company Cloudera, and then Matt are announced and then Hortonworks, you guys have been part of that. Holy Trinity of, of early pioneers. Give us the update you guys are doing very, very well. Uh, we talked to you guys at the dupe summit last week. So Jack Norris for the party, give us the update what's going on with the momentum and the traction. And then I want to talk about some of the things with the product. >>Yeah. So we've seen a tremendous uptick in sales at map. Are we tripled revenue? We announced that publicly about a month ago. So we went up 300% in sales, over Q3, I'm sorry, Q1 of 2013. And I think it's really, you know, the maturity of the market. As people move more towards production, they appreciate the enterprise features. We built into the map, our distribution for Hadoop. So, um, you know, the stats I would share is that 80% of our customers triple the size of their cluster within the first 12 months and 50% of them doubled the size of the cluster because there's the, you know, they had that first production success use case and they find other applications and start rolling out more and more. So it's been great for us. >>You know, I always joke with Jack Norris, who's the VP of marketing over there. And John Frodo is the CEO about Matt bars, humbleness. You don't have the fanfare of all the height, depressed love cloud era. Now see they had done some pretty amazing things. They've had a liquidity event, so essentially kind of an IPO, if you will, that huge ex uh, financing from Intel and they're doing great big Salesforce. Hortonworks has got their open source play. You guys got, you got your heads down as well. So talk about that. How many employees you guys have and what's going on with the product? How many, how many new, what, how many products do you guys actually, >>We have, well, we have one product. So we have the map, our distribution for Hadoop, and it's got all the open source packages directly within it, but where we really innovate is in the course. So that's where we, we spent our time early on was really innovating that data platform to give everything within the Hadoop ecosystem, more reliability, better availability, performance, security scale, >>It's open source contributions to the court. And you guys put stuff on top of that, uh, >>And how it works. Yeah. And even some projects we lead the projects like with Apache Mahal and Apache drill, which is coming into beta shortly other projects, we commit and contribute back. But, um, so we take in the distribution, we're distributing all those projects, but where we really innovate is at that data platform level. So >>HP is a big data leader officer. They bought, uh, autonomy. They have HP Vertica. You guys are here. Hey, what are you doing here? Obviously we covered the cube, uh, the announcement with, uh, with, with HP Vertica, you here for that reason, is there other biz dev other activity going on other integration opportunities? >>Yeah, a few things. So, um, obviously the HP Vertica news was big. We went into general availability that solution the first week of may. So, um, what we have is the HP Vertica database integrated directly on top of our data platform. So it's this hybrid solution where you have full SQL database directly within your Hadoop distribution. Um, so it had a couple sessions on that. We had, uh, a nice panel discussion with our friends from Cloudera and Hortonworks. So really good discussion with HP about just the ecosystem and how it's evolving. The other things we're doing with HP now is, you know, we've got reference architectures on their hardware lines. So, um, you know, people can deploy Mapbox on the hardware of HP, but then also we're talking with the, um, the autonomy group about enterprise search and looking at a similar type of integration where you could have the search integrated directly into your Hadoop distro. And we've got some joint accounts we're piloting that she goes, now, >>You guys are integrating with HP pretty significantly that deals is working well. Absolutely. What's the coolest thing that you've seen with an HP that you can share. How so I asked you in the big data landscape, everyone's Bucher, you know, hunkering down, working on their feature, but outside in the real world, big data, it's not on the top of mind of the CIO, 24 7. It's probably an item that they're dressing. What have you seen and what have you been most impressed with at HP here? >>Yeah. Say, you know, this is my first HP event like this. I think the strategy they have is really good. I think in certain areas like the cloud in particular with the helium, I think they made a lot of early investments there and place some bets. And I think that's going to pay off well for them. And that marries pretty nicely with our strategy as well in terms of, you know, we have on-premise deployments, but we're also an OEM if you will, within Amazon web services. So we have a lot of agility in the cloud if you will. And I think as those products and the partnerships with HP, evolvable, we'll be playing a lot more with them in the cloud as well. >>I see that asks you a question. I want you to share with the folks out there in your own words, what is it about map bar that they may or may not understand or might not know about? Um, a little humble brag out there and share some, share some, uh, insight of, into, into map bar for folks that don't know you guys as a company and for the folks that may have a misperception of what you guys do shit share with them, with what, what map map is all about. >>Yeah. I mean, for me, I was in this space with Aster data and kind of the whole Hadoop and MapReduce area since 2008 and pretty familiar with everybody in the space. I really looked at Matt bars, the best technology hands down, you look at the Forrester wave and they rank us as having the best technology today, as well as product roadmap. I think the misperception is people think, oh, it's proprietary and close. It's actually the opposite of that. We have an unbiased open-source approach where we'll ship in support in our distribution, in the entire Apache spark stack. We're not selective over which projects within Apache spark. We support. Um, I feel like SQL on Hadoop. We support Impala as well as hive and other SQL on to do technologies, including the ability to integrate HP Vertica directly in the system. And it's because of the openness of our platform. I'd say it's actually more open because of the standards we've integrated into the data platform to support a lot of third-party tools directly within it. So there is no locked in the storage formats are all the same. The code that runs on top of the distribution from the projects is exactly the same. So you can build a project in hive or some other system, and you can port it between any of the distributions. So there isn't a, lock-in >>The end of the day, what the customers want is they want ease of integration. They want reliability. That's right. And so what are you guys working on next? What's the big, uh, product marketing roadmap that you can share with us? >>Yeah, I think for us, because of the innovations we did in the data platform allows us to support not only more applications, but more types of operational systems. So integrating things like fraud detection and recommendation engines directly with the analytical systems to really speed up that, um, accuracy and, and, uh, in targeting and detecting risk and things like that. So I think now over time, you know, Hadoop has sort of been this batch analytic type of platform, but the ability to converge operations and analytics in one system is really going to be enabled by technology like Matt BARR. >>How many employees do you guys have now? Uh, >>I'm not sure what our CFO would. Let me say that before. You can say we're over 200 at this point >>As well. And over five, the customers which got the data, you guys do summit graduations, we covered your relationship with HP during our big data SV. That was exciting. Good to see John Schroeder, big, very impressive team. I'm impressed with map. I will always have been. You guys have Stephanie kept your knitting saved. Are you going to do, and again, leading the big data space, um, and again, not proprietary is a very key word and that's really cool. So thanks for coming on. Like you really appreciate Steve. We'll be right back. This is the cube live in Las Vegas, extracting the city from the noise with map bar here at the HP discover 2014. We'll be right back here for the short break.