>>It's the queue covering the virtual vertical Big Data Conference 2020. Brought to you by vertical. >>Hello, everybody. Welcome to the new Normal. You're watching the Cube, and it's remote coverage of the vertical big data event on digital or gone Virtual. My name is Dave Volante, and I'm here with Colin Mahoney, who's a senior vice president at Micro Focus and the GM of Vertical Colin. Well, strange times, but the show goes on. Great to see you again. >>Good to see you too, Dave. Yeah, strange times indeed. Obviously, Safety first of everyone that we made >>a >>decision to go Virtual. I think it was absolutely the right all 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, Joe and we're super excited to be here. As you know, the Cube has been at every single BDC since its inception. It's a great event. You just you just presented the key note to your to your audience, You know, it was remote. You didn't have that that live vibe. And you have a lot of fans in the vertical community But could you feel the love? >>Yeah, you know, it's >>it's hard to >>feel the love virtually, but I'll tell you what. The silver lining in all this is the reach that we have for this event now is much broader than it would have been a Z you know, you know, we brought this event back. It's been a few years since we've done it. We're super excited to do it, obviously, you know, 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 there's a lot of love out there we're getting, too. I have a lot of participants who otherwise would not have been able to participate in this. Both live as well. It's a lot of these assets that we're gonna have available. So, um, you know, it's out there. We've got an amazing customers and of practitioners with vertical. We've got so many have been with us for a long time. We've of course, have a lot of new customers as well that we're welcoming, so it's exciting. >>Well, it's been a while. Since you've had the BDC event, a lot of transpired. You're now part of micro focus, but I know you and I know the vertical team you guys have have not stopped. You've kept the innovation going. We've been following the announcements, but but bridge the gap between the last time. You know, 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 it's the software industry, right? So nothing stays the same. We constantly have Teoh keep going. Probably the only thing that stays the same is the name Vertical. Um and, uh, you know, you're not spending 10 which is just a phenomenal released for us. So, you know, overall, the the organization continues to grow. The dedication and commitment to this great form of vertical 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 are our main road map and direction that we're going towards. And I think one of the things have been great about it is that we've stayed true that from day one we haven't tried to deviate too much and get into things that are barred to outside your box. But we've really done, I think, a great job of extending vertical into places where people need a lot of help. And with vertical 10 we know we're going to talk more about that. But we've done a lot of that. 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 has been saying this for years. It was one of the best conferences we've been to just so really it's. It's developers giving tech talks, its customers giving talks. And we have more customers that wanted to give talks than we had slots to fill this year at the event, which is another benefit, a little bit of going virtually accommodate a little bit more about obviously still a tight schedule. But it really was an opportunity for our community to come together and talk about not just America, but how to deal with data, you know, we know the volumes are slowing down. We know the complexity isn't slowing down. The things that people want to do with AI and machine learning are moving forward in a rapid pace as well. There's a lot talk about and share, and that's really huge part of what we try to do with it. >>Well, let's get into some of that. Um, your customers are making bets. Micro focus is actually making a bet on one vertical. I wanna get your perspective on one of the waves that you're riding and where are you placing your bets? >>Yeah, No, it's great. So, you know, I think that one of the waves that we've been writing for a long time, obviously Vertical started out as a sequel platform for analytics as a sequel, database engine, relational engine. But we always knew that was just sort of takes 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's lots of analytics to take advantage of that data in the lots of tools and capabilities to shape that data to get into the right format. The operational reporting but also in this day and age for machine learning and from some pretty advanced regressions and other techniques of things. So a huge part of vertical 10 is just doubling down on that commitment to what we call in database machine learning and ai. Um, 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 made some announcements about incorporating PM ML models into the product. We continue to deepen our python integration. Building off of a new open source project we started with uber has been a great customer and partner on This is 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 big shape the data get the data in the right format, apply the model, fit the model test a model operationalized model and is a great platform to do that. So that's a huge bet that were, um, continuing to ride on, taking advantage of and then some of the other things that we've just been seeing. You continue. I'll take object. Storage is an example on, I think Hadoop and what would you point 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 S three early we created America Yang mode, which separates computing story. And so for us that separation is not just about being able to take care of your 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 databases in the database could actually start self analysing without impacting many operational workloads, and so that continues with our partnership with pure storage. On premise, we just announced that we're supporting beyond Google Cloud now. In addition to Amazon, we supported on we've got a CFS now being supported by are you on mode. So we continue to ride on that mega trend as well. 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 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 our customers that they want that flexibility to be able to run anywhere. They want the ease of use and simplicity of native cloud experiences, which we're giving them as well. >>I want to stay in that architectural component for a minute. Talk about separating compute from storage is not just about economics. I mean apart Is that you, you know, green, really scale compute separate from storage as opposed to in chunks. It's more efficient, but you're saying there's other advantages to operational and workload. Specificity. Um, what is unique about vertical In this regard, however, many others separate compute from storage? What's different about vertical? >>Yeah, I think you know, there's a lot of differences about how we do it. It's one thing if you're a cloud native company, you do it and you have a shared catalog. That's key value store that all of your customers are using and are 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, they're fully protected. They're not sharing it with any other customers. And that's something that we hear a lot of insights from our customers. They want to be able to separate compute and storage. But they want to be able to do this in their own environment so that they know that in their data catalog there's no one else is. You share in that catalog, there's no single point of failure. So, um, that's one huge advantage that we have. And frankly, I think it just comes from being a company that's operating on premise and, uh, up in the cloud. I think another huge advantages for us is we don't know what object storage platform is gonna win, nor do we necessarily have. We designed the young vote so that it's an sdk. We started with us three, but it could be anything. It's DFS. That's three. Who knows what what object storage formats were 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 and 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 you know, fully automated, putting this information in different places. They don't have to completely reload everything to take advantage of the Arctic analytics. We can go where the data is connected into it, and we offer them a lot of different ways to take advantage of those analytics. So there are a couple of unique differences with verdict, and again, I think are really advance. You know, in many ways, by not being a cloud native platform is that we're very good at operating in different environments with different formats that changing formats over time. And I don't think a lot of the other companies out there that I think many, particularly many of the SAS companies were scrambling. They even have challenges moving from saying Amazon environment to a Microsoft azure environment with their office 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. >>Good. I'm gonna summarize. I'm hearing from you your Ferrari of databases that we've always known. Your your object store agnostic? Um, it's any. It's the cloud experience that you can bring on Prem to virtually any cloud. All the popular clouds hybrid. You know, aws, azure, now Google or on Prem and in a variety of different data formats. And that is, I think, you know, you need the combination of those I think is unique in the marketplace. Um, before we get into the news, I want to ask you about data silos and data silos. You mentioned H DFs where you and I met back in the early days of big data. You know, in some respects, you know, Hadoop help break down the silos with distributing the date and leave it in place, and in other respects, they created Data Lakes, which became silos. And so we have. Yet all these other sales people are trying to get to, Ah, digital transformation meeting, putting data at their core virtually obviously, and leave it in place. What's your thoughts on that in terms of data being a silo buster Buster, How does verdict of way there? >>Yeah, so And you're absolutely right, I think if even if you look at his due for all the new data that gets into the do. 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 some of the things that verdict it doesn't part of the announcement just attend his migration tools to make it really easy. If you do want to move it from one platform to another inter vertical, but you don't have to move it, you can actually take advantage of a lot of the data where it resides with vertical, especially in the Hadoop brown with our external table storage with our building or compartment natively. So we're very pragmatic about how our customers go about this. Very few customers, Many of them tried it with Hadoop and realize that didn't work. But very few customers want a wholesale. Just say we're going to throw everything out. We're gonna get rid of our data warehouse. We're gonna hit the pause button and we're going to go from there. Just it's not possible to do that. So we've spent a lot of time investing in the product, really work with them to go where the data is and then seamlessly migrate. And when it makes sense to migrate, you mentioned the performance of America. Um, and you talked about it is the variety. It definitely is. And one other thing that we're really proud of this is that it actually is not a gas guzzler. Easy either One of the things that we're seeing, a lot of the other cloud databases pound for pound you get on the 10th the hardware vertical running up there. You get over 10 x performance. We're seeing that a lot, so it's Ah, 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 there's just only so much horsepower out there. And it's easier for companies to play tricks and lots of servers environment when they start up for so many organizations and cloud and frankly, looking at the bills they're getting from these cloud workloads that are running. They really conscious of that. >>Yeah. The big, big energy companies love the gas guzzlers. A lot of a lot of cloud. Cute. But let's get into the news. Uh, 10 dot io you shared with your the audience in your keynote. One of the one of the highlights of data. What do we need to know? >>Yeah, so, you know, 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 PM ml models, bring them into vertical, run them massively parallel and help shape you know your data and prepare it. Do all the work that we know is required true machine learning. And for all the hype that there is around it, this is really you know, people want to 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 and, you know, as I mentioned, we're not going to come up with the best algorithms. Our job is really to ensure that those algorithms that people coming up with could be incorporated, that we can run them against massive data sets super efficiently. So that's that's number one number two on object storage. We continue to support Mawr object storage platforms for ya mode in the cloud we're expanding to Google G CPI, Google's cloud beyond just Amazon on premise or in the cloud. Now we're also supporting HD fs with beyond. Of course, we continue to have a great relationship with our partners, your storage on premise. Well, what we continue to invest in the 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 AP I calls and how to save our customers money and tricks on performance and things on the third areas. We definitely continue to build on that flexibility of deployment, which is related to young vote with. Some are 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 road map on these abuse on security, on performance and scale. I mean, for us. Those are the things that we're working on 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, you know, Version 10 is. It is a huge release for any product, especially an analytic database platform. And so there's We're just constantly revisiting you know, 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 really one of the things we've noticed is that you know the new innovation cocktail. It's not being driven by Moore's law anymore. It's really a combination of you. You've collected all this data over the last 10 years through Hadoop and other data stores, object stores, etcetera. And 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 etcetera. The reason why I think this is important 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, you know, a lot of compute. Yeah, but they also had 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 driving, you know, turning data into insights and from what I'm hearing is you played directly in that and your differentiation is a lot of the things that we talk about including the ability to do that on from and in the cloud and across clouds. >>Yeah, I mean, I think that's a great point. We were a great cloud database. We run very well upon three major clouds, and you could argue some of the other plants as well in other parts of the world. Um, if you talk to our customers and we have hundreds of customers who are running vertical in the cloud, the experience is very good. I think it would always be better. We've invested a lot in taking advantage of the native cloud ecosystem, so that provisioning and managing vertical is seamless when you're in that environment will continue to do that. But vertical excuse me as a cloud platform is phenomenal. And, um, you know, there's a There's a lot of confusion out there, you know? I think there's a lot of marketing dollars spent that won't name many of the companies here. You know who they are, You know, the cloud Native Data Warehouse and it's true, you know their their software as a service. But if you talk to a lot of our customers, they're getting very good and very similar. experiences with Bernie comic. We stopped short of saying where software is a service because ultimately our customers have that control of flexibility there. They're putting verdict on whichever cloud they want to run it on, managing it. Stay tuned on that. I think you'll you'll hear from or more from us about, you know, that going going even further. But, um, you know, we do really well in the cloud, and I think he on so much of yang. And, you know, this has really been a sort of 2.5 years and never for us. But so much of eon is was designed around. The cloud was designed around Cloud Data Lakes s three, separation of compute and storage on. And if you look at the work that we're doing around container ization and a lot of these other elements, it just takes that to the next level. And, um, there's a lot of great work, so I think we're gonna get 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 open the door I got to ask you. So it's e. I hear you from a performance and architectural perspective, but you're also alluding two. 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. That am I getting that right and you share >>your difficult in that right? And actually, I'm glad you wrote something. I think a huge part of Cloud is also has nothing to do with the technology. I think it's how you and seeing 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. You know, I am opening the door Ah, a little bit. But look, we have customers that ask us that we're in offer them or, you know, we can offer them platforms, brawl in. We've had customers come to us and say please take over systems, um, and offer something as a distribution as I said, though I think one thing that we've been really good at is focusing on on what is our core and where we really offer offer value. But I can tell you that, um, we introduced something called the Verdict Advisor Tool this year. One of the things that the 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 have said to us, You know, it's funny. We've tried managed service, tried SAS off, and you guys blow them away in terms of your ability to help us, like automatically managed the verdict, environment and the system. Why don't you guys just take this product and converted into a SAS 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 that ease of use, the going above and beyond. Its really excited to have an analytic platform because we can do so much automation off ourselves. And just like we're doing with Perfect Advisor Tool, we're leveraging our own Kool Aid or Champagne Dawn. However you want to say Teoh, in fact, tune up and solve, um, some optimization for our customers automatically, and I think you're going to see that continue. And I think that could work really well in a bunch of different wallets. >>Welcome. 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, uh, this will blow over. I loved last summer when we got together. We had the verdict throwback. We had Stone Breaker, Palmer, Lynch and Mahoney. We did a great series, and that was a lot of fun. So it's really it's a pleasure. And thanks so much. Stay safe out there and, uh, we'll talk to you soon. >>Yeah, you too did stay safe. I really appreciate it up. Unity and, you know, this is what it's all about. It's Ah, 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 looking forward to that, but I really appreciate it. >>Alright. And thank you, everybody for watching. This is the Cube coverage of the verdict. Big data conference gone, virtual going digital. I'm Dave Volante. We'll be right back right after this short break. >>Yeah.

>> David: It's the cube covering the virtual Vertica Big Data conference 2020 brought to you by Vertica. Hello, buddy, welcome to this special digital presentation of the cube. We're tracking the Vertica virtual Big Data conferences, the cubes. I think fifth year doing the BDC. We've been to every big data conference that they've held and really excited to be helping with the digital component here in these interesting times. Ron Cormier is here, Principal database engineer at the Trade Desk. Ron, great to see you. Thanks for coming on. >> Hi, David, my pleasure, good to see you as well. >> So we're talking a little bit about your background you got, you're basically a Vertica and database guru, but tell us about your role at Trade Desk and then I want to get into a little bit about what Trade Desk does. >> Sure, so I'm a principal database engineer at the Trade Desk. The Trade Desk was one of my customers when I was working with Hp, at HP, as a member of the Vertica team, and I joined the Trade Desk in early 2016. And since then, I've been working on building out their Vertica capabilities and expanding the data warehouse footprint and as ever growing database technology, data volume environment. >> And the Trade Desk is an ad tech firm and you are specializing in real time ad serving and pricing. And I guess real time you know, people talk about real time a lot we define real time as before you lose the customer. Maybe you can talk a little bit about you know, the Trade Desk in the business and maybe how you define real time. >> Totally, so to give everybody kind of a frame of reference. Anytime you pull up your phone or your laptop and you go to a website or you use some app and you see an ad what's happening behind the scenes is an auction is taking place. And people are bidding on the privilege to show you an ad. And across the open Internet, this happens seven to 13 million times per second. And so the ads, the whole auction dynamic and the display of the ad needs to happen really fast. So that's about as real time as it gets outside of high frequency trading, as far as I'm aware. So we put the Trade Desk participates in those auctions, we bid on behalf of our customers, which are ad agencies, and the agencies represent brands so the agencies are the madman companies of the world and they have brands that under their guidance, and so they give us budget to spend, to place the ads and to display them and once the ads get displayed, so we bid on the hundreds of thousands of auctions per second. Once we make those bids, anytime we do make a bid some data flows into our data platform, which is powered by Vertica. And, so we're getting hundreds of thousands of events per second. We have other events that flow into Vertica as well. And we clean them up, we aggregate them, and then we run reports on the data. And we run about 40,000 reports per day on behalf of our customers. The reports aren't as real time as I was talking about earlier, they're more batch oriented. Our customers like to see big chunks of time, like a whole day or a whole week or a whole month on a single report. So we wait for that time period to complete and then we run the reports on the results. >> So you you have one of the largest commercial infrastructures, in the Big Data sphere. Paint a picture for us. I understand you got a couple of like 320 node clusters we're talking about petabytes of data. But describe what your environment looks like. >> Sure, so like I said, we've been very good customers for a while. And we started out with with a bunch of enterprise clusters. So the Enterprise Mode is the traditional Vertica deployment where the compute and the storage is tightly coupled all raid arrays on the servers. And we had four of those and we're doing okay, but our volumes are ever increasing, we wanted to store more data. And we wanted to run more reports in a shorter period of time, was to keep pushing. And so we had these four clusters and then we started talking with Vertica about Eon mode, and that's Vertica separation of compute and storage where you get the compute and the storage can be scaled independently, we can add storage without adding compute or vice versa or we can add both, like. So that was something that we were very interested in for a couple reasons. One, our enterprise clusters, we're running out of disk, like when adding disk is expensive. In Enterprise Mode, it's kind of a pain, you got to add, compute at the same time, so you kind of end up in an unbalanced place. So beyond mode that problem gets a lot better. We can add disk, infinite disk because it's backed by S3. And we can add compute really easy to scale, the number of things that we run in parallel concurrency, just add a sub cluster. So they are two US East and US west of Amazon, so reasonably diverse. And and the real benefit is that they can, we can stop nodes when we don't need them. Our workload is fairly lumpy, I call it. Like we, after the day completes, we do the ingest, we do the aggregation for ingesting and aggregating all day, but the final hour, so it needs to be completed. And then once that's done, then the number of reports that we need to run spikes up, it goes really high. And we run those reports, we spin up a bunch of extra compute on the fly, run those reports and then spin them down. And we don't have to pay for that, for the rest of the day. So Eon has been a nice Boone for us for both those reasons. >> I'd love to explore you on little bit more. I mean, it's relatively new, I think 2018 Vertica announced Eon mode, so it's only been out there a couple years. So I'm curious for the folks that haven't moved the Eon mode, can you which presumably they want to for the same reasons that you mentioned why by the stories and chunks when you're on Storage if you don't have to, what were some of the challenges that you had to, that you faced in going to Eon mode? What kind of things did you have to prepare for? Were there any out of scope expectations? Can you share that experience with us? >> Sure, so we were an early adopter. We participated in the beta program. I mean, we, I think it's fair to say we actually drove the requirements and a lot of ways because we approached Vertica early on. So the challenges were what you'd expect any early adopter to be going through. The sort of getting things working as expected. I mean, there's a number of cases, which I could touch upon, like, we found an efficiency in the way that it accesses the data on S3 and it was accessing the data too frequently, which ended up was just expensive. So our S3 bill went up pretty significantly for a couple of months. So that was a challenge, but we worked through that another was that we recently made huge strides in with Vertica was the ability to stop and start nodes and not have to start them very quickly. And when they start to not interfere with any running queries, so when we create, when we want to spin up a bunch to compute, there was a point in time when it would break certain queries that were already running. So that that was a challenge. But again, the very good team has been quite responsive to solving these issues and now that's behind us. In terms of those who need to get started, there's or looking to get started. there's a number of things to think about. Off the top of my head there's sort of new configuration items that you'll want to think about, like how instance type. So certainly the Amazon has a variety of instances and its important to consider one of Vertica's architectural advantages in these areas Vertica has this caching layer on the instances themselves. And what that does is if we can keep the data in cache, what we've found is that the performance is basically the same performance of Enterprise Mode. So having a good size cast when needed, can be a little worrying. So we went with the I three instance types, which have a lot of local NVME storage that we can, so we can cache data and get good performance. That's one thing to think about. The number of nodes, the instance type, certainly the number of shards is a sort of technical item that needs to be considered. It's how the data gets, its distributed. It's sort of a layer on top of the segmentation that some Vertica engineers will be familiar with. And probably I mean, the, one of the big things that one needs to consider is how to get data in the database. So if you have an existing database, there's no sort of nice tool yet to suck all the data into an Eon database. And so I think they're working on that. But we're at the point we got there. We had to, we exported all our data out of enterprise cluster as cache dumped it out to S3 and then we had the Eon cluster to suck that data. >> So awesome advice. Thank you for sharing that with the community. So but at the end of the day, so it sounds like you had some learning to do some tweaking to do and obviously how to get the data in. At the end of the day, was it worth it? What was the business impact? >> Yeah, it definitely was worth it for us. I mean, so right now, we have four times the data in our Eon cluster that we have in our enterprise clusters. We still run some enterprise clusters. We started with four at the peak. Now we're down to two. So we have the two young clusters. So it's been, I think our business would say it's been a huge win, like we're doing things that we really never could have done before, like for accessing the data on enterprise would have been really difficult. It would have required non trivial engineering to do things like daisy chaining clusters together, and then how to aggregate data across clusters, which would, again, non trivial. So we have all the data we want, we can continue to grow data, where running reports on seasonality. So our customers can compare their campaigns last year versus this year, which is something we just haven't been able to do in the past. We've expanded that. So we grew the data vertically, we've expanded the data horizontally as well. So we were adding columns to our aggregates. We are, in reaching the data much more than we have in the past. So while we still have enterprise kicking around, I'd say our clusters are doing the majority of the heavy lifting. >> And the cloud was part of the enablement, here, particularly with scale, is that right? And are you running certain... >> Definitely. >> And you are running on prem as well, or are you in a hybrid mode? Or is it all AWS? >> Great question, so yeah. When I've been speaking about enterprise, I've been referring to on prem. So we have a physical machines in data centers. So yeah, we are running a hybrid now and I mean, and so it's really hard to get like an apples to apples direct comparison of enterprise on prem versus Eon in the cloud. One thing that I touched upon in my presentation is it would require, if I try to get apples to apples, And I think about how I would run the entire workload on enterprise or on Eon, I had to run the entire thing, we want both, I tried to think about how many cores, we would need CPU cores to do that. And basically, it would be about the same number of cores, I think, for enterprise on prime versus Eon in the cloud. However, Eon nodes only need to be running half the course only need to be running about six hours out of the day. So the other the other 18 hours I can shut them down and not be paying for them, mostly. >> Interesting, okay, and so, I got to ask you, I mean, notwithstanding the fact that you've got a lot invested in Vertica, and get a lot of experience there. A lot of you know, emerging cloud databases. Did you look, I mean, you know, a lot about database, not just Vertica, your database guru in many areas, you know, traditional RDBMS, as well as MPP new cloud databases. What is it about Vertica that works for you in this specific sweet spot that you've chosen? What's really the difference there? >> Yeah, so I think the key differences is the maturity. There are a number, I am familiar with another, a number of other database platforms in the cloud and otherwise, column stores specifically, that don't have the maturity that we're used to and we need at our scale. So being able to specify alternate projections, so different sort orders on my data is huge. And, there's other platforms where we don't have that capability. And so the, Vertica is, of course, the original column store and they've had time to build up a lead in terms of their maturity and features and I think that other other column stores cloud, otherwise are playing a little bit of catch up in that regard. Of course, Vertica is playing catch up on the cloud side. But if I had to pick whether I wanted to write a column store, first graph from scratch, or use a defined file system, like a cloud file system from scratch, I'd probably think it would be easier to write the cloud file system. The column store is where the real smarts are. >> Interesting, let's talk a little bit about some of the challenges you have in reporting. You have a very dynamic nature of reporting, like I said, your clients want to they want to a time series, they just don't want to snap snapshot of a slice. But at the same time, your reporting is probably pretty lumpy, a very dynamic, you know, demand curve. So first of all, is that accurate? Can you describe that sort of dynamic, dynamism and how are you handling that? >> Yep, that's exactly right. It is lumpy. And that's the exact word that I use. So like, at the end of the UTC day, when UTC midnight rolls around, that's we do the final ingest the final aggregate and then the queue for the number of reports that need to run spikes. So the majority of those 40,000 reports that we run per day are run in the four to six hours after that spikes up. And so that's when we need to have all the compute come online. And that's what helps us answer all those queries as fast as possible. And that's a big reason why Eon is advantage for us because the rest of the day we kind of don't necessarily need all that compute and we can shut it down and not pay for it. >> So Ron, I wonder if you could share with us just sort of the wrap here, where you want to take this you're obviously very close to Vertica. Are you driving them in a heart and Eon mode, you mentioned before you'd like, you'd have the ability to load data into Eon mode would have been nice for you, I guess that you're kind of over that hump. But what are the kinds of things, If Column Mahoney is here in the room, what are you telling him that you want the team, the engineering team at Vertica to work on that would make your life better? >> I think the things that need the most attention sort of near term is just the smoothing out some of the edges in terms of making it a little bit more seamless in terms of the cloud aspects to it. So our goal is to be able to start instances and have them join the cluster in less than five minutes. We're not quite there yet. If you look at some of the other cloud database platforms, they're beating that handle it so I know the team is working on that. Some of the other things are the control. Like I mentioned, while we like control in the column store, we also want control on the cloud side of things in terms of being able to dedicate cluster, some clusters specific. We can pin workloads against a specific sub cluster and take advantage of the cast that's over there. We can say, okay, this resource pool. I mean, the sub cluster is a new concept, relatively new concept for Vertica. So being able to have control of many things at sub cluster level, resource pools, configuration parameters, and so on. >> Yeah, so I mean, I personally have always been impressed with Vertica. And their ability to sort of ride the wave adopt new trends. I mean, they do have a robust stack. It's been, you know, been 10 plus years around. They certainly embraced to do, the embracing machine learning, we've been talking about the cloud. So I actually have a lot of confidence to them, especially when you compare it to other sort of mid last decade MPP column stores that came out, you know, Vertica is one of the few remaining certainly as an independent brand. So I think that speaks the team there and the engineering culture. But give your final word. Just final thoughts on your role the company Vertica wherever you want to take it. >> Yeah, no, I mean, we're really appreciative and we value the partners that we have and so I think it's been a win win, like our volumes are, like I know that we have some data that got pulled into their test suite. So I think it's been a win win for both sides and it'll be a win for other Vertica customers and prospects, knowing that they're working with some of the highest volume, velocity variety data that (mumbles) >> Well, Ron, thanks for coming on. I wish we could have met face to face at the the Encore in Boston. I think next year we'll be able to do that. But I appreciate that technology allows us to have these remote conversations. Stay safe, all the best to you and your family. And thanks again. >> My pleasure, David, good speaking with you. >> And thank you for watching everybody, we're covering this is the Cubes coverage of the Vertica virtual Big Data conference. I'm Dave volante. We'll be right back right after this short break. (soft music)

>> Announcer: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. >> Hi, everybody, welcome back to theCUBE's coverage of the Virtual Vertica BDC, Big Data Conference. It was, of course, going to be in Boston, but now we're covering it online. It's really our pleasure to invite back Joy King, she's the vice president of product and go-to-market strategy at Vertica. She also manages marketing and education programs. Joy, great to see you. >> It's great to be back, as always, Dave, thank you. >> Let's talk about BDC, Virtual BDC. We took a break. theCUBE has been at every Big Data Conference. I love that show, great customers, awesome buzz, great outside speakers. I actually had the pleasure of being up on stage with some database experts, of which I'm not, but I'm a (laughs) inch deep and a mile wide. >> I remember that! (laughs) >> And it was a lot of fun going head to head with some of the folks, and just really a great vibe over that conference. But, so, now, you had to make the decision, because of the coronavirus, to go digital. You didn't delay, and I love the fact that you guys leaned right in, you've got all this content. So talk about what we can expect at BDC. >> Well, you know, Dave, the BDC is really special, and I have to give Colin Mahoney, our GM, the credit for the idea. Sometimes his ideas are really good, and the execution can be, well, challenging. But when we started the BDC, he had an idea. He said, "You know, we have such a passionate "community, we need to get them together. "We need, like, a user group." Well, that user group, for the first BDC, was the first and only event I have ever been responsible for where, yes, it's true, we exceeded the fire code of the venue, and we had more people that registered than we were allowed to accept. That's never happened before. It's because the passion was so real. We made a commitment. We said the only people that could speak at the BDC were engineers who architected and write the code, and customers who've used the code. We were determined to keep the technical credibility, the value of best practices, the sharing among the community. Marketing was responsible for appropriate amounts of coffee and alcohol at the appropriate times, (Dave laughs) but today, that is still why the BDC is so special. Now, I have to tell you, we have been somewhat limited in our ability to confirm coffee, alcohol, et cetera in the Virtual BDC, but we are still true to our mission. The people that will be speaking during the sessions that we have, and for all of the recordings that we will do in addition after we complete the live BDC, are engineers and architects who design and write the code, hands on the keyboard, and customers who use Vertica to power their businesses every day. That's the rule. Some people don't like it, but that's how we play. >> Well, and to your point, and we've interviewed a number of your customers, and I can second that. The database engineers are proud to put Vertica in their title. >> Yes. >> They embrace it, they love to train people and get adoption going, so that's awesome. Let's talk about some of the logistics of the BDC, the Virtual BDC. Tuesday, March 31st, and then the next day, April 1st, you've got keynotes, you've got breakouts, and of course, we've got theCUBE. After the keynotes, we'll be doing CUBE coverage for two days, wall-to-wall coverage of Virtual BDC. And to your point, and I think this is a nuance that I think people are going to learn with digital, is there's a post-event that really is going to continue that engagement with your community. >> That's right. As much as everybody knows there's nothing that replaces face-to-face interaction, there are advantages to the virtual world. First of all, people are getting pretty creative, I've got to say, and second, it gives global reach to people who would have loved to come to the BDC but couldn't. They couldn't travel, there were restrictions, they were busy with other things. So, yes, all day Tuesday and all day Wednesday. After the keynote on Tuesday will be two parallel tracks, and this is East Coast time, from U.S. East Coast time, on Tuesday afternoon, and then two parallel tracks all day Wednesday. And then on Thursday, in addition to all of those webinars, all of those sessions being available on demand, we are also, right now, recording additional sessions because we just didn't have enough slots, but we had more speakers, both customers and engineers, that wanted to, and all of that will be available on the BDC website on Thursday and beyond. And we're going to continue with two webinar series that we're very proud of. One is called "Under the Hood," which is technical webinars, and the other is called "Data Disruptors," and those are the customers that love to tell their stories. And that, in parallel with ongoing CUBE interviews, will keep the energy all the way up until late March of 2021, when we have already confirmed the next live BDC. >> Awesome, so go to vertica.com/bdc2020, register, you got to register, to see the keynotes. It's lightweight registration, it's not a hundred fields, we want you to come in. And then, of course, theCUBE.net is going to be covering, theCUBE interviews, and SiliconANGLE.com will have editorial. Joy, looking forward to it. Thanks so much for giving us the update, and we'll see you online. >> It will be a pleasure, see ya, bye. >> And we'll see you. Thank you, everybody, and go, like I said, go register, again, it's vertica.com/bdc2020. This is Dave Vellante from theCUBE, and we'll see you at the Virtual Vertica Big Data Conference. (upbeat music)

>>from the Cube Studios in Palo Alto and Boston connecting with thought leaders all around the world. >>This is a cube conversation. >>Hi, everybody. Dave Vellante here with the Cube. And we're getting ready for the verdict. A big data conference. 2020. The conference has gone virtual, and this is our digital presentation of the conference. I'm here with Colin Mahoney. Who's the general manager of Vertical? How you doing, Colin? >>Great day. Great to see you. >>Hey, let's set it up. What should we expect? That BBC 2020 get people excited? >>Yeah. So look, I mean, it's it's part of the times. We made the decision to go Virtual way made that decision a little bit earlier, and now we know it was absolutely the right thing to do. And as much as we love getting everybody together and the community around vertical being together first and look at the bright side, we've got the opportunity to hear bring the critical big data conference virtual to a lot of people in the comfort of whatever they are right now. That's exciting, But we're still gonna have great presentations. Speakers true to form, way don't really allow any marketing into the critical big data conference. It's all presentations given by either our engineering team for our customers on how you can actually take advantage and use the father. Then, I think, on years past it's been a few years since we've done it, but we got great agenda. The team is doing an incredible job, as we were to virtual as you could imagine. It's never easy to pull off one of these events, and it's certainly not easy to do change course a few weeks before they get virtual. But everybody's doing a great job of customers, have been so supportive and you're going to help. And like I said, the good news is our reach is going through the roof in terms of the numbers and the number of people that actually participate. So it's gonna be fun. It's It's all about data. It's not just about the data itself. We all know that may be boring. If you're just talking about is really about what you can do with data, how you can take advantage of some of the incredible things that our customers are hearing with data to change the world for the better and no type of it. Now, I think we all understand how critically important that it's >>That's awesome. Colin and I understand from talking books the vertical team that registrations are are going to the roof. So Goto find vertical BDC 2020. Just Google it. You'll find it. Sign up, um, And then give us the last word. >>Yeah. Come, come, come see it. And you know what? It's going to be on demand as well, Which is one of the benefits of, uh, you know, vertical going virtual for the big data conference. But come and learn. Come learn about data. Come to see the community we hear from our customers directly and enjoy. Have fun. We can forward to seeing you there. Thanks, Dave. >>Yeah, awesome. And then, you know that's the thing to the Cube Will be. There will be streaming ah of interviews all throughout the next several weeks and months, so check it out. Thanks for watching everybody. We'll see you at the verdict of Big Data Conference. 2020. Yeah, Yeah, yeah, yeah, yeah

>> from Cambridge, Massachusetts. It's three Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back. We're here to wrap up the M I T. Chief data officer officer, information quality. It's hashtag m i t CDO conference. You're watching the Cube. I'm David Dante, and Paul Gill is my co host. This is two days of coverage. We're wrapping up eyes. Our analysis of what's going on here, Paul, Let me let me kick it off. When we first started here, we talked about that are open. It was way saw the chief data officer role emerged from the back office, the information quality role. When in 2013 the CEO's that we talked to when we asked them what was their scope. We heard things like, Oh, it's very wide. Involves analytics, data science. Some CEOs even said Oh, yes, security is actually part of our purview because all the cyber data so very, very wide scope. Even in some cases, some of the digital initiatives were sort of being claimed. The studios were staking their claim. The reality was the CDO also emerged out of highly regulated industries financialservices healthcare government. And it really was this kind of wonky back office role. And so that's what my compliance, that's what it's become again. We're seeing that CEOs largely you're not involved in a lot of the emerging. Aye, aye initiatives. That's what we heard, sort of anecdotally talking to various folks At the same time. I feel as though the CDO role has been more fossilized than it was before. We used to ask, Is this role going to be around anymore? We had C I. Ose tell us that the CEO Rose was going to disappear, so you had both ends of the spectrum. But I feel as though that whatever it's called CDO Data's our chief analytics off officer, head of data, you know, analytics and governance. That role is here to stay, at least for for a fair amount of time and increasingly, issues of privacy and governance. And at least the periphery of security are gonna be supported by that CD a role. So that's kind of takeaway Number one. Let me get your thoughts. >> I think there's a maturity process going on here. What we saw really in 2016 through 2018 was, ah, sort of a celebration of the arrival of the CDO. And we're here, you know, we've got we've got power now we've got an agenda. And that was I mean, that was a natural outcome of all this growth and 90% of organizations putting sea Dios in place. I think what you're seeing now is a realization that Oh, my God, this is a mess. You know what I heard? This year was a lot less of this sort of crowing about the ascendance of sea Dios and Maura about We've got a big integration problem of big data cleansing problem, and we've got to get our hands down to the nitty gritty. And when you talk about, as you said, we had in here so much this year about strategic initiatives, about about artificial intelligence, about getting involved in digital business or customer experience transformation. What we heard this year was about cleaning up data, finding the data that you've got organizing it, applying meditator, too. It is getting in shape to do something with it. There's nothing wrong with that. I just think it's part of the natural maturation process. Organizations now have to go through Tiu to the dirty process of cleaning up this data before they can get to the next stage, which was a couple of three years out for most of >> the second. Big theme, of course. We heard this from the former head of analytics. That G s K on the opening keynote is the traditional methods have failed the the Enterprise Data Warehouse, and we've actually studied this a lot. You know, my analogy is often you snake swallowing a basketball, having to build cubes. E D W practitioners would always used to call it chasing the chips until we come up with a new chip. Oh, we need that because we gotta run faster because it's taking us hours and hours, weeks days to run these analytics. So that really was not an agile. It was a rear view mirror looking thing. And Sarbanes Oxley saved the E. D. W. Business because reporting became part of compliance thing perspective. The master data management piece we've heard. Do you consistently? We heard Mike Stone Breaker, who's obviously a technology visionary, was right on. It doesn't scale through this notion of duping. Everything just doesn't work and manually creating rules. It's just it's just not the right approach. This we also heard the top down data data enterprise data model doesn't works too complicated, can operationalize it. So what they do, they kick the can to governance. The Duke was kind of a sidecar, their big data that failed to live up to its promises. And so it's It's a big question as to whether or not a I will bring that level of automation we heard from KPMG. Certainly, Mike Stone breaker again said way heard this, uh, a cz well, from Andy Palmer. They're using technology toe automate and scale that big number one data science problem, which is? They spend all their time wrangling data. We'll see if that if that actually lives up >> to his probable is something we did here today from several of our guests. Was about the promise of machine learning to automate this day to clean up process and as ah Mark Ramsay kick off the conference saying that all of these efforts to standardize data have failed in the past. This does look, He then showed how how G s K had used some of the tools that were represented here using machine learning to actually clean up the data at G S. K. So there is. And I heard today a lot of optimism from the people we talked to about the capability of Chris, for example, talking about the capability of machine learning to bring some order to solve this scale scale problem Because really organizing data creating enterprise data models is a scale problem, and the only way you can solve that it's with with automation, Mike Stone breaker is right on top of that. So there was optimism at this event. There was kind of an ooh, kind of, ah, a dismay at seeing all the data problems they have to clean up, but also promised that tools are on the way that could do that. >> Yeah, The reason I'm an optimist about this role is because data such a hard problem. And while there is a feeling of wow, this is really a challenge. There's a lot of smart people here who are up for the challenge and have the d n a for it. So the role, that whole 360 thing. We talked about the traditional methods, you know, kind of failing, and in the third piece that touched on, which is really bringing machine intelligence to the table. We haven't heard that as much at this event. It's now front and center. It's just another example of a I injecting itself into virtually every aspect every corner of the industry. And again, I often jokes. Same wine, new bottle. Our industry has a habit of doing that, but it's cyclical, but it is. But we seem to be making consistent progress. >> And the machine learning, I thought was interesting. Several very guest spoke to machine learning being applied to the plumbing projects right now to cleaning up data. Those are really self contained projects. You can manage those you can. You can determine out test outcomes. You can vet the quality of the of the algorithms. It's not like you're putting machine learning out there in front of the customer where it could potentially do some real damage. There. They're vetting their burning in machine, learning in a environment that they control. >> Right, So So, Amy, Two solid days here. I think that this this conference has really grown when we first started here is about 130 people, I think. And now it was 500 registrants. This'd year. I think 600 is the sort of the goal for next year. Moving venues. The Cube has been covering this all but one year since 2013. Hope to continue to do that. Paul was great working with you. Um, always great work. I hope we can, uh we could do more together. We heard the verdict is bringing back its conference. You put that together. So we had column. Mahoney, um, had the vertical rock stars on which was fun. Com Mahoney, Mike Stone breaker uh, Andy Palmer and Chris Lynch all kind of weighed in, which was great to get their perspectives kind of the days of MPP and how that's evolved improving on traditional relational database. And and now you're Stone breaker. Applying all these m i. Same thing with that scale with Chris Lynch. So it's fun to tow. Watch those guys all Boston based East Coast folks some news. We just saw the news hit President Trump holding up jet icon contractors is we've talked about. We've been following that story very closely and I've got some concerns over that. It's I think it's largely because he doesn't like Bezos in The Washington Post Post. Exactly. You know, here's this you know, America first. The Pentagon says they need this to be competitive with China >> and a I. >> There's maybe some you know, where there's smoke. There's fire there, so >> it's more important to stick in >> the eye. That's what it seems like. So we're watching that story very closely. I think it's I think it's a bad move for the executive branch to be involved in those type of decisions. But you know what I know? Well, anyway, Paul awesome working with you guys. Thanks. And to appreciate you flying out, Sal. Good job, Alex Mike. Great. Already wrapping up. So thank you for watching. Go to silicon angle dot com for all the news. Youtube dot com slash silicon angles where we house our playlist. But the cube dot net is the main site where we have all the events. It will show you what's coming up next. We've got a bunch of stuff going on straight through the summer. And then, of course, VM World is the big kickoff for the fall season. Goto wicked bond dot com for all the research. We're out. Thanks for watching Dave. A lot day for Paul Gillon will see you next time.

>> From Cambridge, Massachusetts, it's theCUBE, covering MIT Chief Data Officer and Information Quality Symposium 2019, brought to you by SiliconANGLE Media. >> Welcome back to Cambridge, Massachusetts everybody, you're watching The Cube, the leader in tech coverage. My name is Dave Vellante here with my cohost Paul Gillin. This is day one of our two day coverage of the MIT CDOIQ conferences. CDO, Chief Data Officer, IQ, information quality. Colin Mahoney is here, he's a good friend and long time CUBE alum. I haven't seen you in awhile, >> I know >> But thank you so much for taking some time, you're like a special guest here >> Thank you, yeah it's great to be here, thank you. >> Yeah, so, this is not, you know, something that you would normally attend. I caught up with you, invited you in. This conference has started as, like back office governance, information quality, kind of wonky stuff, hidden. And then when the big data meme took off, kind of around the time we met. The Chief Data Officer role emerged, the whole Hadoop thing exploded, and then this conference kind of got bigger and bigger and bigger. Still intimate, but very high level, very senior. It's kind of come full circle as we've been saying, you know, information quality still matters. You have been in this data business forever, so I wanted to invite you in just to get your perspectives, we'll talk about what's new with what's going on in your company, but let's go back a little bit. When we first met and even before, you saw it coming, you kind of invested your whole career into data. So, take us back 10 years, I mean it was so different, remember it was Batch, it was Hadoop, but it was cool. There was a lot of cool >> It's still cool. (laughs) projects going on, and it's still cool. But, take a look back. >> Yeah, so it's changed a lot, look, I got into it a while ago, I've always loved data, I had no idea, the explosion and the three V's of data that we've seen over the last decade. But, data's really important, and it's just going to get more and more important. But as I look back I think what's really changed, and even if you just go back a decade I mean, there's an insatiable appetite for data. And that is not slowing down, it hasn't slowed down at all, and I think everybody wants that perfect solution that they can ask any question and get an immediate answers to. We went through the Hadoop boom, I'd argue that we're going through the Hadoop bust, but what people actually want is still the same. You know, they want real answers, accurate answers, they want them quickly, and they want it against all their information and all their data. And I think that Hadoop evolved a lot as well, you know, it started as one thing 10 years ago, with MapReduce and I think in the end what it's really been about is disrupting the storage market. But if you really look at what's disrupting storage right now, public clouds, S3, right? That's the new data league. So there's always a lot of hype cycles, everybody talks about you know, now it's Cloud, everything, for maybe the last 10 years it was a lot of Hadoop, but at the end of the day I think what people want to do with data is still very much the same. And a lot of companies are still struggling with it, hence the role for Chief Data Officers to really figure out how do I monetize data on the one hand and how to I protect that asset on the other hand. >> Well so, and the cool this is, so this conference is not a tech conference, really. And we love tech, we love talking about this, this is why I love having you on. We kind of have a little Vertica thread that I've created here, so Colin essentially, is the current CEO of Vertica, I know that's not your title, you're GM and Senior Vice President, but you're running Vertica. So, Michael Stonebreaker's coming on tomorrow, >> Yeah, excellent. >> Chris Lynch is coming on tomorrow, >> Oh, great, yeah. >> we've got Andy Palmer >> Awesome, yeah. >> coming up as well. >> Pretty cool. (laughs) >> So we have this connection, why is that important? It's because, you know, Vertica is a very cool company and is all about data, and it was all about disrupting, sort of the traditional relational database. It's kind of doing more with data, and if you go back to the roots of Vertica, it was like how do you do things faster? How do you really take advantage of data to really drive new business? And that's kind of what it's all about. And the tech behind it is really cool, we did your conference for many, many years. >> It's coming back by the way. >> Is it? >> Yeah, this March, so March 30th. >> Oh, wow, mark that down. >> At Boston, at the new Encore Hotel. >> Well we better have theCUBE there, bro. (laughs) >> Yeah, that's great. And yeah, you've done that conference >> Yep. >> haven't you before? So very cool customers, kind of leading edge, so I want to get to some of that, but let's talk the disruption for a minute. So you guys started with the whole architecture, MPP and so forth. And you talked about Cloud, Cloud really disrupted Hadoop. What are some of the other technology disruptions that you're seeing in the market space? >> I think, I mean, you know, it's hard not to talk about AI machine learning, and what one means versus the other, who knows right? But I think one thing that is definitely happening is people are leveraging the volumes of data and they're trying to use all the processing power and storage power that we have to do things that humans either are too expensive to do or simply can't do at the same speed and scale. And so, I think we're going through a renaissance where a lot more is being automated, certainly on the Vertica roadmap, and our path has always been initially to get the data in and then we want the platform to do a lot more for our customers, lots more analytics, lots more machine-learning in the platform. So that's definitely been a lot of the buzz around, but what's really funny is when you talk to a lot of customers they're still struggling with just some basic stuff. Forget about the predictive thing, first you've got to get to what happened in the past. Let's give accurate reporting on what's actually happening. The other big thing I think as a disruption is, I think IOT, for all the hype that it's getting it's very real. And every device is kicking off lots of information, the feedback loop of AB testing or quality testing for predictive maintenance, it's happening almost instantly. And so you're getting massive amounts of new data coming in, it's all this machine sensor type data, you got to figure out what it means really quick, and then you actually have to do something and act on it within seconds. And that's a whole new area for so many people. It's not their traditional enterprise data network warehouse and you know, back to you comment on Stonebreaker, he got a lot of this right from the beginning, you know, and I think he looked at the architectures, he took a lot of the best in class designs, we didn't necessarily invent everything, but we put a lot of that together. And then I think the other you've got to do is constantly re-invent your platform. We came out with our Eon Mode to run cloud native, we just got rated the best cloud data warehouse from a net promoter score rating perspective, so, but we got to keep going you know, we got to keep re-inventing ourselves, but leverage everything that we've done in the past as well. >> So one of the things that you said, which is kind of relevant for here, Paul, is you're still seeing a real data quality issue that customers are wrestling with, and that's a big theme here, isn't it? >> Absolutely, and the, what goes around comes around, as Dave said earlier, we're still talking about information quality 13 years after this conference began. Have the tools to improve quality improved all that much? >> I think the tools have improved, I think that's another area where machine learning, if you look at Tamr, and I know you're going to have Andy here tomorrow, they're leveraging a lot of the augmented things you can do with the processing to make it better. But I think one thing that makes the problem worse now, is it's gotten really easy to pour data in. It's gotten really easy to store data without having to have the right structure, the right quality, you know, 10 years ago, 20 years ago, everything was perfect before it got into the platform. Right, everything was, there was quality, everything was there. What's been happening over the last decade is you're pumping data into these systems, nobody knows if it's redundant data, nobody knows if the quality's any good, and the amount of data is massive. >> And it's cheap to store >> Very cheap to store. >> So people keep pumping it in. >> But I think that creates a lot of issues when it comes to data quality. So, I do think the technology's gotten better, I think there's a lot of companies that are doing a great job with it, but I think the challenge has definitely upped. >> So, go ahead. >> I'm sorry. You mentioned earlier that we're seeing the death of Hadoop, but I'd like you to elaborate on that becuase (Dave laughs) Hadoop actually came up this morning in the keynote, it's part of what GlaxoSmithKline did. Came up in a conversation I had with the CEO of Experian last week, I mean, it's still out there, why do you think it's in decline? >> I think, I mean first of all if you look at the Hadoop vendors that are out there, they've all been struggling. I mean some of them are shutting down, two of them have merged and they've got killed lately. I think there are some very successful implementations of Hadoop. I think Hadoop as a storage environment is wonderful, I think you can process a lot of data on Hadoop, but the problem with Hadoop is it became the panacea that was going to solve all things data. It was going to be the database, it was going to be the data warehouse, it was going to do everything. >> That's usually the kiss of death, isn't it? >> It's the kiss of death. And it, you know, the killer app on Hadoop, ironically, became SQL. I mean, SQL's the killer app on Hadoop. If you want to SQL engine, you don't need Hadoop. But what we did was, in the beginning Mike sort of made fun of it, Stonebreaker, and joked a lot about he's heard of MapReduce, it's called Group By, (Dave laughs) and that created a lot of tension between the early Vertica and Hadoop. I think, in the end, we embraced it. We sit next to Hadoop, we sit on top of Hadoop, we sit behind it, we sit in front of it, it's there. But I think what the reality check of the industry has been, certainly by the business folks in these companies is it has not fulfilled all the promises, it has not fulfilled a fraction on the promises that they bet on, and so they need to figure those things out. So I don't think it's going to go away completely, but I think its best success has been disrupting the storage market, and I think there's some much larger disruptions of technologies that frankly are better than HTFS to do that. >> And the Cloud was a gamechanger >> And a lot of them are in the cloud. >> Which is ironic, 'cause you know, cloud era, (Colin laughs) they didn't really have a cloud strategy, neither did Hortonworks, neither did MapR and, it just so happened Amazon had one, Google had one, and Microsoft has one, so, it's just convenient to-- >> Well, how is that affecting your business? We've seen this massive migration to the cloud (mumbles) >> It's actually been great for us, so one of the things about Vertica is we run everywhere, and we made a decision a while ago, we had our own data warehouse as a service offering. It might have been ahead of its time, never really took off, what we did instead is we pivoted and we say "you know what? "We're going to invest in that experience "so it's a SaaS-like experience, "but we're going to let our customers "have full control over the cloud. "And if they want to go to Amazon they can, "if they want to go to Google they can, "if they want to go to Azure they can." And we really invested in that and that experience. We're up on the Amazon marketplace, we have lots of customers running up on Amazon Cloud as well as Google and Azure now, and then about two years ago we went down and did this endeavor to completely re-architect our product so that we could separate compute and storage so that our customers could actually take advantage of the cloud economics as well. That's been huge for us, >> So you scale independent-- >> Scale independently, cloud native, add compute, take away compute, and for our existing customers, they're loving the hybrid aspect, they love that they can still run on Premise, they love that they can run up on a public cloud, they love that they can run in both places. So we will continue to invest a lot in that. And it is really, really important, and frankly, I think cloud has helped Vertica a lot, because being able to provision hardware quickly, being able to tie in to these public clouds, into our customers' accounts, give them control, has been great and we're going to continue on that path. >> Because Vertica's an ISV, I mean you're a software company. >> We're a software company. >> I know you were a part of HP for a while, and HP wanted to mash that in and run it on it's hardware, but software runs great in the cloud. And then to you it's another hardware platform. >> It's another hardware platform, exactly. >> So give us the update on Micro Focus, Micro Focus acquired Vertica as part of the HPE software business, how many years ago now? Two years ago? >> Less than two years ago. >> Okay, so how's that going, >> It's going great. >> Give us the update there. >> Yeah, so first of all it is great, HPE and HP were wonderful to Vertica, but it's great being part of a software company. Micro Focus is a software company. And more than just a software company it's a company that has a lot of experience bridging the old and the new. Leveraging all of the investments that you've made but also thinking about cloud and all these other things that are coming down the pike. I think for Vertica it's been really great because, as you've seen Vertica has gotten its identity back again. And that's something that Micro Focus is very good at. You can look at what Micro Focus did with SUSE, the Linux company, which actually you know, now just recently spun out of Micro Focus but, letting organizations like Vertica that have this culture, have this product, have this passion, really focus on our market and our customers and doing the right thing by them has been just really great for us and operating as a software company. The other nice thing is that we do integrate with a lot of other products, some of which came from the HPE side, some of which came from Micro Focus, security products is an example. The other really nice thing is we've been doing this insource thing at Micro Focus where we open up our source code to some of the other teams in Micro Focus and they've been contributing now in amazing ways to the product. In ways that we would just never be able to scale, but with 4,000 engineers strong in Micro Focus, we've got a much larger development organization that can actually contribute to the things that Vertica needs to do. And as we go into the cloud and as we do a lot more operational aspects, the experience that these teams have has been incredible, and security's another great example there. So overall it's been great, we've had four different owners of Vertica, our job is to continue what we do on the innovation side in the culture, but so far Micro Focus has been terrific. >> Well, I'd like to say, you're kind of getting that mojo back, because you guys as an independent company were doing your own thing, and then you did for a while inside of HP, >> We did. >> And that obviously changed, 'cause they wanted more integration, but, and Micro Focus, they know what they're doing, they know how to do acquisitions, they've been very successful. >> It's a very well run company, operationally. >> The SUSE piece was really interesting, spinning that out, because now RHEL is part of IBM, so now you've got SUSE as the lone independent. >> Yeah. >> Yeah. >> But I want to ask you, go back to a technology question, is NoSQL the next Hadoop? Are these databases, it seems to be that the hot fad now is NoSQL, it can do anything. Is the promise overblown? >> I think, I mean NoSQL has been out almost as long as Hadoop, and I, we always say not only SQL, right? Mike's said this from day one, best tool for the job. Nothing is going to do every job well, so I think that there are, whether it's key value stores or other types of NoSQL engines, document DB's, now you have some of these DB's that are running on different chips, >> Graph, yeah. >> there's always, yeah, graph DBs, there's always going to be specialty things. I think one of the things about our analytic platform is we can do, time series is a great example. Vertica's a great time series database. We can compete with specialized time series databases. But we also offer a lot of, the other things that you can do with Vertica that you wouldn't be able to do on a database like that. So, I always think there's going to be specialty products, I also think some of these can do a lot more workloads than you might think, but I don't see as much around the NoSQL movement as say I did a few years ago. >> But so, and you mentioned the cloud before as kind of, your position on it I think is a tailwind, not to put words in your mouth, >> Yeah, yeah, it's a great tailwind. >> You're in the Amazon marketplace, I mean they have products that are competitive, right? >> They do, they do. >> But, so how are you differentiating there? >> I think the way we differentiate, whether it's Redshift from Amazon, or BigQuery from Google, or even what Azure DB does is, first of all, Vertica, I think from, feature functionality and performance standpoint is ahead. Number one, I think the second thing, and we hear this from a lot of customers, especially at the C-level is they don't want to be locked into these full stacks of the clouds. Having the ability to take a product and run it across multiple clouds is a big thing, because the stack lock-in now, the full stack lock-in of these clouds is scary. It's really easy to develop in their ecosystems but you get very locked into them, and I think a lot of people are concerned about that. So that works really well for Vertica, but I think at the end of the day it's just, it's the robustness of the product, we continue to innovate, when you look at separating compute and storage, believe it or not, a lot of these cloud-native databases don't do that. And so we can actually leverage a lot of the cloud hardware better than the native cloud databases do themselves. So, like I said, we have to keep going, those guys aren't going to stop, and we actually have great relationships with those companies, we work really well with the clouds, they seem to care just as much about their cloud ecosystem as their own database products, and so I think that's going to continue as well. >> Well, Colin, congratulations on all the success >> Yeah, thank you, yeah. >> It's awesome to see you again and really appreciate you coming to >> Oh thank you, it's great, I appreciate the invite, >> MIT. >> it's great to be here. >> All right, keep it right there everybody, Paul and I will be back with our next guest from MIT, you're watching theCUBE. (electronic jingle)

live from Boston Massachusetts extracting the signal from the noise it's the kue covering HP big data conference 2015 brought to you by HP software now your host John furrier and Dave vellante okay welcome back everyone we are here live in boston massachusetts for HP's big data conference this is a special presentation of the cube our flagship program where we go out to the events and extract the season for the noise I'm John furrier with Dave allante here Wikibon down on research our next guest Stephanie McReynolds VP margon elation hot new startup that's been kind of coming out of stealth that's out there big data a lot of great stuff Stephanie welcome to the cube great see you great to be here tell us what the start at first of all because good buzz going on it's kind of stealth buzz but it's really with the fought leaders and really the you know the people in the industry who know what they're talking about like what you guys are doing so so introduce the company tells me you guys are doing and relationship with Vertica and exciting stuff absolutely a lesion is a exciting company we just started to come out of south in March of this year and we came out of self with some great production customers so eBay is a customer they have hundreds of analysts using our systems we also have square as a customer smaller analytics team but the value that you Neelix teams are getting out of this product is really being able to access their data in human context so we do some machine learning to look at how individuals are using data in an organization and take that machine learning and also gather some of the human insights about how that data is being used by experts surface that all in line with in work so what kind of data cuz Stonebreaker was kind of talking yesterday about the 3 v's which we all know but the one that's really coming mainstream in terms of a problem space is variety variety you have the different variety of schema sources and then you have a lot of unstructured exhaust or data flying around can you be specific on what you guys do yeah I mean it's interesting because there's several definitions of data and big data going around right and so I'm you know we connect to a lot of database systems and we also connect to a lot of Hadoop implementations so we deal with both structured data as well as what i consider unstructured data and i think the third part of what we do is bring in context from human created data or cumin information with which robert yesterday was talking about a little bit which is you know what happens in a lot of analytic organizations is that and there's a very manual process of documenting some of the data that's being used in these projects and that's done on wiki pages or spreadsheets that are floating around the organization and that's actually a really black base camp all these collaboration all these collaboration platforms and what you realize when you start to really get into the work of using that information to try to write your queries is that trying to reference a wiki page and then write your sequel and flip back and forth between maybe ten different documents is not very productive for the analyst so what our customers are seeing is that by consolidating all of that data and information in one place where the tables are actually reference side by side with the annotations their analysts can get from twenty to fifty percent savings and productivity and new analysts maybe more importantly new analyst can get up to speed quite a bit quicker and that square the day I was talking to one of the the data scientists and he was was talking about you know his process for finding data in the organization which prior to using elation it would take about 30 minutes going two maybe three or four people to find the data he needed for his analysis and with elation in five seconds he can run a query search for the date he wants gets it back gets all kind of all that expert annotation already around that base data said he's ready to roll he can start I'm testing some of us akashi go platform right they've heard it was it a platform and it and you said you work with a lot of database the databases right so it's tightly integrated with the database in this use case so it's interesting and you know we see databases as a source of information so we don't create copies of the data on our platform we go out and point to the data where it lies and surface that you know that data to to the end user now in the case of verdict on our relationship with Vertica and we've also integrated verdict in our stack to support we call data forensics which is the building for not an analyst who's using the system day to day but for NIT individual to understand where the behaviors around this data and the types of analysis that are being done and so verdicts a great high performance platform for dashboarding and business intelligence a back end of that providing you know quick access to aggregates so one of they will work on a vertica you guys just the engine what specifically again yeah so so we use the the vertica the vertical engine underneath our forensics product and then the that's you know one portion of our platform the rest of our platform is built out on other other technologies so verdict is part of your solution it's part of our solution it's it's one application that we part of one application we deliver so we've been talking all week about this week Colin Mahoney in his talk yesterday and I saw a pretty little history on erp how initially was highly customized and became packaged apps and he sort of pointed to a similar track with analytics although he said it's not going to be the same it's going to be more composable sort of applications I wonder and historically the analytics in the database have been closely aligned I'll say maybe not integrated you see that model continuing do you see it more packaged apps or will thus what Collins calling composable apps what's the relationship between your platforming and the application yeah so our platform is is really more tooling for those individuals that are building or creating those applications so we're helping data scientists and analysts find what algorithms they want to use as a foundation for those applications so a little bit more on the discovery side where folks are doing a lot of experiment and experimentation they may be having to prepare data in different ways in order to figure out what might work for those applications and that's where we fit in as a vendor and what's your license model and so you know we're on a subscription model we have customers that have data teams in the in the hundreds at a place like eBay you know the smaller implementations could be maybe just teams of five analyst 10a analyst fairly small spatial subscription and it's a seat base subscription but we can run in the cloud we can run on premise and we do some interesting things around securing the data where you can and see your columns bommana at the data sets for financial services organizations and our customers that have security concerns and most of those are on premise top implementation 70 talk about the inspiration of the company in about the company he's been three years since then came out of stealth what's the founders like what's the DNA the company what do you guys do differently and what was the inspiration behind this yeah what's really what's really interesting I think about the founding of the company is that and the technical founders come from both Google and Apple so you have an interesting observation that both individuals had made independently hardcore algorithmic guy and then like relevant clean yeah and both those kind of made interesting observations about how Google and Apple two of the most data-driven companies you know on the planet we're struggling and their analytics teams were struggling with being able to share queries and share data sets and there was a lot of replication of work that was happening and so much for the night you know but both of these folks from different angles kind of came together at adulation said look there's there's a lot of machine learning algorithms that could help with this process and there's also a lot of good ways with natural language processing to let people interact with their data in more natural ways the founder from from Apple Aaron key he was on the Siri team so we had a lot of experience designing products for navigability and ease of use and natural language learning and so those two perspectives coming together have created some technology fundamentals in our product and it's an experience to some scar tissue from large-scale implementations of data yeah very large-scale implementations of data and also a really deep awareness of what the human equation brings to the table so machine learning algorithms aren't enough in and of themselves and I think ken rudin had some interesting comments this morning where you know he kind of pushed it one step further and said it's not just about finding insight data science about is about having impact and you can't have impact unless you create human contacts and you have communication and collaboration around the data so we give analyst a query tool by which we surface the machine learning context that we have about the data that's being used in the organization and what queries have been running that data but we surface in a way where the human can get recommendations about how to improve their their sequel and drive towards impact and then share that understanding with other analysts in the organization so you get an innovation community that's started so who you guys targets let's step back on the page go to market now you guys are launched got some funding can you share the amount or is it private confidential or was how much did you raise who are you targeting what's your go-to market what's the value proposition give us the give us this data yeah so its initial value proposition is just really about analyst productivity that's where we're targeted how can you take your teams of analysts and everyone knows it's hard to hire these days so you're not going to be able to grow those teams out overnight how do you make the analyst the data scientist the phd's you have on staff much more productive how do you take that eighty to ninety percent of the time that they make them using stuff sharing data because I stuff you in the sharing data try to get them out of the TD of trying to just find eight in the organization and prepare it and let them really innovate and and use that to drive value back to the to the organization so we're often selling to individual analysts to analytics teams the go to market starts there and the value proposition really extends much further in the organization so you know you find teams and organizations that have been trying to document their data through traditional data governance means or ETL tools for a very long time and a lot of those projects have stalled out and the way that we crawl systems and use machine learning automation and to automate some of that documentation really gives those projects and new life in our enterprise data has always been elusive I mean do you go back decades structured day to all these pre pre built databases it's been hard right so it's you can crack that nut that's going to be a very lucrative in this opportunity I got the Duke clusters now storing everything I mean some clients we talked to here on the key customers of a CHP or IBM big companies they're storing everything just because they don't know they do it again yeah I mean if the past has been hard in part because we in some cases over manage the modeling of the data and I think what's exciting now about storing all your data in Hadoop and storing first and then asking questions later is you're able to take a more discovery oriented hypothesis testing iterative approach and if you think about how true innovation works you know you build insights on top of one another to get to the big breakthrough concepts and so I think we're at an interesting point in the market for a solution like this that can help with that increasing complexity of data environment so you just raise your series a raised nine million you maybe did some seed round before that so pretty early days for you guys you mentioned natural language processing before one of your founders are you using NLP and in your solution in any way or so we have a we have a search interface that allows you to look for that technical data to look for metadata and for data objects and by entering a simple simple natural language search terms so we are using that as part of our interface in solution right and so kind of early customer successes can you talk about any examples or yeah you know there's some great examples and jointly with Vertica square is as a customer and their analytics team is using us on a day-to-day basis not only to find data sets and the organization but to document those those data sets and eBay has hundreds of analysts that are using elation today in a day to day manner and they've seen quite a bit of productivity out of their new analysts that are coming on the system's it used to take analysts about 18 months to really get their feet around them in the ebay environment because of the complexity of all of the different systems at ebay and understanding where to go for that customer table you know that they needed to use and now analysts are up and running about six months and their data governance team has found that elation has really automated and prioritized the process around documentation for them and so it's a great light a great foundation for them there and data curators and data stewards to go in and rich the data and collaborate more with the analysts and the actual data users to get to a point of catalogued catalog data disease so what's the next you guys going to be on the road in New York Post Radek hadoop world big data NYC is coming up a big event in New York I'm Cuba visa we're getting the word out about elation and then what we're doing we have customers that are you know starting to speak about their use cases and the value that they're seeing and will be in New York market share I believe will be speaking on our behalf there to share their stories and then we're also going to a couple other conferences after that you know the fall is an exciting time which one's your big ones there so i will be at strada in New York and a September early October and then mid-october we're going to be at both teradata partners and tableaus conference as well so we connect not only to databases of all set different sorts but also to go with users are the tools yeah awesome well anything else you'd like to add share at the company is awesome we're some great things about you guys been checking around I'll see you found out about you guys and a lot of people like the company I mean a lot of insiders like moving little see you didn't raise too much cash that's raised lettin that's not the million zillion dollar round I think what led you guys take nine million yeah raised a million and I you know I think we're building this company in a traditional value oriented way great word hey stay long bringing in revenue and trying to balance that out with the venture capital investment it's not that we won't take money but we want to build this company in a very durable so the vision is to build a durable company absolutely absolutely and that may be different than some of our competitors out there these days but that's that we've and I have not taken any financing and SiliconANGLE at all so you know we're getting we believe in that and you might pass up some things but you know what have control and you guys have some good partners so congratulations um final word what's this conference like you go to a lot of events what's your take on this on this event yeah I do i do end up going to a lot of events that's part of the marketing role you know i think what's interesting about this conference is that there are a lot of great conversations that are happening and happening not just from a technology perspective but also between business people and deep thinking about how to innovate and verticals customers i think are some of the most loyal customers i've seen in the in the market so it's great in their advanced to they're talking about some pretty big problems but they're solving it's not like little point solutions it's more we architecting some devops i get a dev I'm good I got trashed on Twitter private messages all last night about me calling this a DevOps show it's not really a DevOps cloud show but there's a DevOps vibe here the people who are working on the solutions I think they're just a real of real vibe people are solving real problems and they're talking about them and they're sharing their opinions and I I think that's you know that's similar to what you see in DevOps the guys with dev ops are in the front line the real engineers their engineering so they have to engineer because of that no pretenders here that's for sure are you talking about it's not a big sales conference right it's a lot of customer content their engineering solutions talking to Peter wants a bullshit they want reaiah I mean I got a lot on the table i'm gonna i'm doing some serious work and i want serious conversations and that's refreshing for us but we love love of hits like it's all right Stephanie thinks for so much come on cubes sharing your insight congratulations good luck with the new startup hot startups here in Boston hear the verdict HP software show will be right back more on the cube after this short break you you