>> Announcer: It's theCUBE! Covering the Virtual Vertica Big Data Conference 2020 brought to you by Vertica. >> Hi, everybody. Welcome back. You're watching theCUBE's coverage of the Vertica Virtual Big Data Conference. It was, of course, going to be in Boston at the Encore Hotel. Win big with big data with the new casino but obviously Coronavirus has changed all that. Our hearts go out and we are empathy to those people who are struggling. We are going to continue our wall-to-wall coverage of this conference and we're here with Larry Lancaster who's the founder and CTO of Zebrium. Larry, welcome to theCUBE. Thanks for coming on. >> Hi, thanks for having me. >> You're welcome. So first question, why did you start Zebrium? >> You know, I've been dealing with machine data a long time. So for those of you who don't know what that is, if you can imagine servers or whatever goes on in a data center or in a SAS shop. There's data coming out of those servers, out of those applications and basically, you can build a lot of cool stuff on that. So there's a lot of metrics that come out and there's a lot of log files that come. And so, I've built this... Basically spent my career building that sort of thing. So tools on top of that or products on top of that. The problem is that since at least log files are completely unstructured, it's always doing the same thing over and over again, which is going in and understanding the data and extracting the data and all that stuff. It's very time consuming. If you've done it like five times you don't want to do it again. So really, my idea was at this point with machine learning where it's at there's got to be a better way. So Zebrium was founded on the notion that we can just do all that automatically. We can take a pile of machine data, we can turn it into a database, and we can build stuff on top of that. And so the company is really all about bringing that value to the market. >> That's cool. I want to get in to that, just better understand who you're disrupting and understand that opportunity better. But before I do, tell us a little bit about your background. You got kind of an interesting background. Lot of tech jobs. Give us some color there. >> Yeah, so I started in the Valley I guess 20 years ago and when my son was born I left grad school. I was in grad school over at Berkeley, Biophysics. And I realized I needed to go get a job so I ended up starting in software and I've been there ever since. I mean, I spent a lot of time at, I guess I cut my teeth at Nedap, which was a storage company. And then I co-founded a business called Glassbeam, which was kind of an ETL database company. And then after that I ended up at Nimble Storage. Another company, EMC, ended up buying the Glassbeam so I went over there and then after Nimble though, which where I build the InfoSight platform. That's where I kind of, after that I was able to step back and take a year and a half and just go into my basement, actually, this is my kind of workspace here, and come up with the technology and actually build it so that I could go raise money and get a team together to build Zebrium. So that's really my career in a nutshell. >> And you've got Hello Kitty over your right shoulder, which is kind of cool >> That's right. >> And then up to the left you got your monitor, right? >> Well, I had it. It's over here, yeah. >> But it was great! Pull it out, pull it out, let me see it. So, okay, so you got that. So what do you do? You just sit there and code all night or what? >> Yeah, that's right. So Hello Kitty's over here. I have a daughter and she setup my workspace here on this side with Hello Kitty and so on. And over on this side, I've got my recliner where I basically lay it all the way back and then I pivot this thing down over my face and put my keyboard on my lap and I can just sit there for like 20 hours. It's great. Completely comfortable. >> That's cool. All right, better put that monitor back or our guys will yell at me. But so, obviously, we're talking to somebody with serious coding chops and I'll also add that the Nimble InfoSight, I think it was one of the best pick ups that HP, HPE, has had in a while. And the thing that interested me about that, Larry, is the ability that the company was able to take that InfoSight and poured it very quickly across its product lines. So that says to me it was a modern, architecture, I'm sure API, microservices, and all those cool buzz words, but the proof is in their ability to bring that IP to other parts of the portfolio. So, well done. >> Yeah, well thanks. Appreciate that. I mean, they've got a fantastic team there. And the other thing that helps is when you have the notion that you don't just build on top of the data, you extract the data, you structure it, you put that in a database, we used Vertica there for that, and then you build on top of that. Taking the time to build that layer is what lets you build a scalable platform. >> Yeah, so, why Vertica? I mean, Vertica's been around for awhile. You remember you had the you had the old RDBMS, Oracles, Db2s, SQL Server, and then the database was kind of a boring market. And then, all of a sudden, you had all of these MPP companies came out, a spade of them. They all got acquired, including Vertica. And they've all sort of disappeared and morphed into different brands and Micro Focus has preserved the Vertica brand. But it seems like Vertica has been able to survive the transitions. Why Vertica? What was it about that platform that was unique and interested you? >> Well, I mean, so they're the first fund to build, what I would call a real column store that's kind of market capable, right? So there was the C-Store project at Berkeley, which Stonebreaker was involved in. And then that became sort of the seed from which Vertica was spawned. So you had this idea of, let's lay things out in a columnar way. And when I say columnar, I don't just mean that the data for every column is in a different set of files. What I mean by that is it takes full advantage of things like run length and coding, and L file and coding, and block--impression, and so you end up with these massive orders of magnitude savings in terms of the data that's being pulled off of storage as well as as it's moving through the pipeline internally in Vertica's query processing. So why am I saying all this? Because it's fundamentally, it was a fundamentally disruptive technology. I think column stores are ubiquitous now in analytics. And I think you could name maybe a couple of projects which are mostly open source who do something like Vertica does but name me another one that's actually capable of serving an enterprise as a relational database. I still think Vertica is unique in being that one. >> Well, it's interesting because you're a startup. And so a lot of startups would say, okay, we're going with a born-in-the-cloud database. Now Vertica touts that, well look, we've embraced cloud. You know, we have, we run in the cloud, we run on PRAM, all different optionality. And you hear a lot of vendors say that, but a lot of times they're just taking their stack and stuffing it into the cloud. But, so why didn't you go with a cloud-native database and is Vertica able to, I mean, obviously, that's why you chose it, but I'm interested from a technologist standpoint as to why you, again, made that choice given all these other choices around there. >> Right, I mean, again, I'm not, so... As I explained a column store, which I think is the appropriate definition, I'm not aware of another cloud-native-- >> Hm, okay. >> I'm aware of other cloud-native transactional databases, I'm not aware of one that has the analytics form it and I've tried some of them. So it was not like I didn't look. What I was actually impressed with and I think what let me move forward using Vertica in our stack is the fact that Eon really is built from the ground up to be cloud-native. And so we've been using Eon almost ever since we started the work that we're doing. So I've been really happy with the performance and with reliability of Eon. >> It's interesting. I've been saying for years that Vertica's a diamond in the rough and it's previous owner didn't know what to do with it because it got distracted and now Micro Focus seems to really see the value and is obviously putting some investments in there. >> Yeah >> Tell me more about your business. Who are you disrupting? Are you kind of disrupting the do-it-yourself? Or is there sort of a big whale out there that you're going to go after? Add some color to that. >> Yeah, so our broader market is monitoring software, that's kind of the high-level category. So you have a lot of people in that market right now. Some of them are entrenched in large players, like Datadog would be a great example. Some of them are smaller upstarts. It's a pretty, it's a pretty saturated market. But what's happened over the last, I'd say two years, is that there's been sort of a push towards what's called observability in terms of at least how some of the products are architected, like Honeycomb, and how some of them are messaged. Most of them are messaged these days. And what that really means is there's been sort of an understanding that's developed that that MTTR is really what people need to focus on to keep their customers happy. If you're a SAS company, MTTR is going to be your bread and butter. And it's still measured in hours and days. And the biggest reason for that is because of what's called unknown unknowns. Because of complexity. Now a days, things are, applications are ten times as complex as they used to be. And what you end up with is a situation where if something is new, if it's a known issue with a known symptom and a known root cause, then you can setup a automation for it. But the ones that really cost a lot of time in terms of service disruption are unknown unknowns. And now you got to go dig into this massive mass of data. So observability is about making tools to help you do that, but it's still going to take you hours. And so our contention is, you need to automate the eyeball. The bottleneck is now the eyeball. And so you have to get away from this notion of a person's going to be able to do it infinitely more efficient and recognize that you need automated help. When you get an alert agent, it shouldn't be that, "Hey, something weird's happening. Now go dig in." It should be, "Here's a root cause and a symptom." And that should be proposed to you by a system that actually does the observing. That actually does the watching. And that's what Zebrium does. >> Yeah, that's awesome. I mean, you're right. The last thing you want is just another alert and it say, "Go figure something out because there's a problem." So how does it work, Larry? In terms of what you built there. Can you take us inside the covers? >> Yeah, sure. So there's really, right now there's two kinds of data that we're ingesting. There's metrics and there's log files. Metrics, there's actually sort of a framework that's really popular in DevOp circles especially but it's becoming popular everywhere, which is called Prometheus. And it's a way of exporting metrics so that scrapers can collect them. And so if you go look at a typical stack, you'll find that most of the open source components and many of the closed source components are going to have exporters that export all their stacks to Prometheus. So by supporting that stack we can bring in all of those metrics. And then there's also the log files. And so you've got host log files in a containerized environment, you've got container logs, and you've got application-specific logs, perhaps living on a host mount. And you want to pull all those back and you want to be able to associate this log that I've collected here is associated with the same container on the same host that this metric is associated with. But now what? So once you've got that, you've got a pile of unstructured logs. So what we do is we take a look at those logs and we say, let's structure those into tables, right? So where I used to have a log message, if I look in my log file and I see it says something like, X happened five times, right? Well, that event types going to occur again and it'll say, X happened six times or X happened three times. So if I see that as a human being, I can say, "Oh clearly, that's the same thing." And what's interesting here is the times that X, that X happened, and that this number read... I may want to know when the numbers happened as a time series, the values of that column. And so you can imagine it as a table. So now I have table for that event type and every time it happens, I get a row. And then I have a column with that number in it. And so now I can do any kind of analytics I want almost instantly across my... If I have all my event types structured that way, every thing changes. You can do real anomaly detection and incident detection on top of that data. So that's really how we go about doing it. How we go about being able to do autonomous monitoring in a way that's effective. >> How do you handle doing that for, like the Spoke app? Do you have to, does somebody have to build a connector to those apps? How do you handle that? >> Yeah, that's a really good question. So you're right. So if I go and install a typical log manager, there'll be connectors for different apps and usually what that means is pulling in the stuff on the left, if you were to be looking at that log line, and it will be things like a time stamp, or a severity, or a function name, or various other things. And so the connector will know how to pull those apart and then the stuff to the right will be considered the message and that'll get indexed for search. And so our approach is we actually go in with machine learning and we structure that whole thing. So there's a table. And it's going to have a column called severity, and timestamp, and function name. And then it's going to have columns that correspond to the parameters that are in that event. And it'll have a name associated with the constant parts of that event. And so you end up with a situation where you've structured all of it automatically so we don't need collectors. It'll work just as well on your home-grown app that has no collectors or no parsers to find or anything. It'll work immediately just as well as it would work on anything else. And that's important, because you can't be asking people for connectors to their own applications. It just, it becomes now they've go to stop what they're doing and go write code for you, for your platform and they have to maintain it. It's just untenable. So you can be up and running with our service in three minutes. It'll just be monitoring those for you. >> That's awesome! I mean, that is really a breakthrough innovation. So, nice. Love to see that hittin' the market. Who do you sell to? Both types of companies and what role within the company? >> Well, definitely there's two main sort of pushes that we've seen, or I should say pulls. One is from DevOps folks, SRE folks. So these are people who are tasked with monitoring an environment, basically. And then you've got people who are in engineering and they have a staging environment. And what they actually find valuable is... Because when we find an incident in a staging environment, yeah, half the time it's because they're tearing everything up and it's not release ready, whatever's in stage. That's fine, they know that. But the other half the time it's new bugs, it's issues and they're finding issues. So it's kind of diverged. You have engineering users and they don't have titles like QA, they're Dev engineers or Dev managers that are really interested. And then you've got DevOps and SRE people there (mumbles). >> And how do I consume your product? Is the SAS... I sign up and you say within three minutes I'm up and running. I'm paying by the drink. >> Well, (laughs) right. So there's a couple ways. So, right. So the easiest way is if you use Kubernetes. So Kubernetes is what's called a container orchestrator. So these days, you know Docker and containers and all that, so now there's container orchestrators have become, I wouldn't say ubiquitous but they're very popular now. So it's kind of on that inflection curve. I'm not exactly sure the penetration but I'm going to say 30-40% probably of shops that were interested are using container orchestrators. So if you're using Kubernetes, basically you can install our Kubernetes chart, which basically means copying and pasting a URL and so on into your little admin panel there. And then it'll just start collecting all the logs and metrics and then you just login on the website. And the way you do that is just go to our website and it'll show you how to sign up for the service and you'll get your little API key and link to the chart and you're off and running. You don't have to do anything else. You can add rules, you can add stuff, but you don't have to. You shouldn't have to, right? You should never have to do any more work. >> That's great. So it's a SAS capability and I just pay for... How do you price it? >> Oh, right. So it's priced on volume, data volume. I don't want to go too much into it because I'm not the pricing guy. But what I'll say is that it's, as far as I know it's as cheap or cheaper than any other log manager or metrics product. It's in that same neighborhood as the very low priced ones. Because right now, we're not trying to optimize for take. We're trying to make a healthy margin and get the value of autonomous monitoring out there. Right now, that's our priority. >> And it's running in the cloud, is that right? AWB West-- >> Yeah, that right. Oh, I should've also pointed out that you can have a free account if it's less than some number of gigabytes a day we're not going to charge. Yeah, so we run in AWS. We have a multi-tenant instance in AWS. And we have a Vertica Eon cluster behind that. And it's been working out really well. >> And on your freemium, you have used the Vertica Community Edition? Because they don't charge you for that, right? So is that how you do it or... >> No, no. We're, no, no. So, I don't want to go into that because I'm not the bizdev guy. But what I'll say is that if you're doing something that winds up being OEM-ish, you can work out the particulars with Vertica. It's not like you're going to just go pay retail and they won't let you distinguish between tests, and prod, and paid, and all that. They'll work with you. Just call 'em up. >> Yeah, and that's why I brought it up because Vertica, they have a community edition, which is not neutered. It runs Eon, it's just there's limits on clusters and storage >> There's limits. >> But it's still fully functional though. >> So to your point, we want it multi-tenant. So it's big just because it's multi-tenant. We have hundred of users on that (audio cuts out). >> And then, what's your partnership with Vertica like? Can we close on that and just describe that a little bit? >> What's it like. I mean, it's pleasant. >> Yeah, I mean (mumbles). >> You know what, so the important thing... Here's what's important. What's important is that I don't have to worry about that layer of our stack. When it comes to being able to get the performance I need, being able to get the economy of scale that I need, being able to get the absolute scale that I need, I've not been disappointed ever with Vertica. And frankly, being able to have acid guarantees and everything else, like a normal mature database that can join lots of tables and still be fast, that's also necessary at scale. And so I feel like it was definitely the right choice to start with. >> Yeah, it's interesting. I remember in the early days of big data a lot of people said, "Who's going to need these acid properties and all this complexity of databases." And of course, acid properties and SQL became the killer features and functions of these databases. >> Who didn't see that one coming, right? >> Yeah, right. And then, so you guys have done a big seed round. You've raised a little over $6 million dollars and you got the product market fit down. You're ready to rock, right? >> Yeah, that's right. So we're doing a launch probably, well, when this airs it'll probably be the day before this airs. Basically, yeah. We've got people... Like literally in the last, I'd say, six to eight weeks, It's just been this sort of pique of interest. All of a sudden, everyone kind of gets what we're doing, realizes they need it, and we've got a solution that seems to meet expectations. So it's like... It's been an amazing... Let me just say this, it's been an amazing start to the year. I mean, at the same time, it's been really difficult for us but more difficult for some other people that haven't been able to go to work over the last couple of weeks and so on. But it's been a good start to the year, at least for our business. So... >> Well, Larry, congratulations on getting the company off the ground and thank you so much for coming on theCUBE and being part of the Virtual Vertica Big Data Conference. >> Thank you very much. >> All right, and thank you everybody for watching. This is Dave Vellante for theCUBE. Keep it right there. We're covering wall-to-wall Virtual Vertica BDC. You're watching theCUBE. (upbeat music)

>> Paige: Hello, everybody, and thank you for joining us today for the virtual Vertica BDC 2020. Today's breakout session is entitled, Vertica next generation architecture. I'm Paige Roberts, open social relationship Manager at Vertica, I'll be your host for this session. And joining me is Vertica Chief Architect, Chuck Bear, before we begin, I encourage you to submit questions or comments during the virtual session. You don't have to wait, just type your question or comment, in the question box that's below the slides and click submit. So as you think about it, go ahead and type it in, there'll be a Q&A session at the end of the presentation, where we'll answer as many questions, as we're able to during the time. Any questions that we don't get a chance to address, we'll do our best to answer offline. Or alternatively, you can visit the Vertica forums to post your questions there, after the session. Our engineering team is planning to join the forum and keep the conversation going, so you can, it's just sort of like the developers lounge would be in delight conference. It gives you a chance to talk to our engineering team. Also, as a reminder, you can maximize your screen by clicking the double arrow button in the lower right corner of the slide. And before you ask, yes, this virtual session is being recorded, and it will be available to view on demand this week, we'll send you a notification, as soon as it's ready. Okay, now, let's get started, over to you, Chuck. >> Chuck: Thanks for the introduction, Paige, Vertica vision is to help customers, get value from structured data. This vision is simple, it doesn't matter what vertical the customer is in. They're all analytics companies, it doesn't matter what the customers environment is, as data is generated everywhere. We also can't do this alone, we know that you need other tools and people to build a complete solution. You know our database is key to delivering on the vision because we need a database that scales. When you start a new database company, you aren't going to win against 30 year old products on features. But from day one, we had something else, an architecture built for analytics performance. This architecture was inspired by the C-store project, combining the best design ideas from academics and industry veterans like Dr. Mike Stonebreaker. Our storage is optimized for performance, we use many computers in parallel. After over 10 years of refinements against various customer workloads, much of the design held up and serendipitously, the fact that we don't store in place updates set Vertica up for success in the cloud as well. These days, there are other tools that embody some of these design ideas. But we have other strengths that are more important than the storage format, where the only good analytics database that runs both on premise and in the cloud, giving customers the option to migrate their workloads, in most convenient and economical environment, or a full data management solution, not just the query tool. Unlike some other choices, ours comes with integration with a sequel ecosystem and full professional support. We organize our product roadmap into four key pillars, plus the cross cutting concerns of open integration and performance and scale. We have big plans to strengthen Vertica, while staying true to our core. This presentation is primarily about the separation pillar, and performance and scale, I'll cover our plans for Eon, our data management architecture, Mart analytic clusters, or fifth generation query executer, and our data storage layer. Let's start with how Vertica manages data, one of the central design points for Vertica was shared nothing, a design that didn't utilize a dedicated hardware shared disk technology. This quote here is how Mike put it politely, but around the Vertica office, shared disk with an LMTB over Mike's dead body. And we did get some early field experience with shared disk, customers, well, in fact will learn on anything if you let them. There were misconfigurations that required certified experts, obscure bugs extent. Another thing about the shared nothing designed for commodity hardware though, and this was in the papers, is that all the data management features like fault tolerance, backup and elasticity have to be done in software. And no matter how much you do, procuring, configuring and maintaining the machines with disks is harder. The software configuration process to add more service may be simple, but capacity planning, racking and stacking is not. The original allure of shared storage returned, this time though, the complexity and economics are different. It's cheaper, even provision storage with a few clicks and only pay for what you need. It expands, contracts and brings the maintenance of the storage close to a team is good at it. But there's a key difference, it's an object store, an object stores don't support the API's and access patterns used by most database software. So another Vertica visionary Ben, set out to exploit Vertica storage organization, which turns out to be a natural fit for modern cloud shared storage. Because Vertica data files are written once and not updated, they match the object storage model perfectly. And so today we have Eon, Eon uses shared storage to hold Vertica data with local disk depot's that act as caches, ensuring that we can get the performance that our customers have come to expect. Essentially Eon in enterprise behave similarly, but we have the benefit of flexible storage. Today Eon has the features our customers expect, it's been developed in tune for years, we have successful customers such as Redpharma, and if you'd like to know more about Eon has helped them succeed in Amazon cloud, I highly suggest reading their case study, which you can find on vertica.com. Eon provides high availability and flexible scaling, sometimes on premise customers with local disks get a little jealous of how recovery and sub-clusters work in Eon. Though we operate on premise, particularly on pure storage, but enterprise also had strengths, the most obvious being that you don't need and short shared storage to run it. So naturally, our vision is to converge the two modes, back into a single Vertica. A Vertica that runs any combination of local disks and shared storage, with full flexibility and portability. This is easy to say, but over the next releases, here's what we'll do. First, we realize that the query executer, optimizer and client drivers and so on, are already the same. Just the transaction handling and data management is different. But there's already more going on, we have peer-to-peer depot operations and other internode transfers. And enterprise also has a network, we could just get files from remote nodes over that network, essentially mimicking the behavior and benefits of shared storage with the layer of software. The only difference at the end of it, will be which storage hold the master copy. In enterprise, the nodes can't drop the files because they're the master copy. Whereas in Eon they can be evicted because it's just the cache, the masters, then shared storage. And in keeping with versus current support for multiple storage locations, we can intermix these approaches at the table level. Getting there as a journey, and we've already taken the first steps. One of the interesting design ideas of the C-store paper is the idea that redundant copies, don't have to have the same physical organization. Different copies can be optimized for different queries, sorted in different ways. Of course, Mike also said to keep the recovery system simple, because it's hard to debug, whenever the recovery system is being used, it's always in a high pressure situation. This turns out to be a contradiction, and the latter idea was better. No down performing stuff, if you don't keep the storage the same. Recovery hardware if you have, to reorganize data in the process. Even query optimization is more complicated. So over the past couple releases, we got rid of non identical buddies. But the storage files can still diverge at the fifth level, because tuple mover operations are synchronized. The same record can end up in different files than different nodes. The next step in our journey, is to make sure both copies are identical. This will help with backup and restore as well, because the second copy doesn't need backed up, or if it is backed up, it appears identical to the deduplication that is going to look present in both backup systems. Simultaneously, we're improving the Vertica networking service to support this new access pattern. In conjunction with identical storage files, we will converge to a recovery system that instantaneous nodes can process queries immediately, by retrieving data they need over the network from the redundant copies as they do in Eon day with even higher performance. The final step then is to unify the catalog and transaction model. Related concepts such as segment and shard, local catalog and shard catalog will be coalesced, as they're really represented the same concepts all along, just in different modes. In the catalog, we'll make slight changes to the definition of a projection, which represents the physical storage organization. The new definition simplifies segmentation and introduces valuable granularities of sharding to support evolution over time, and offers a straightforward migration path for both Eon and enterprise. There's a lot more to our Eon story than just the architectural roadmap. If you missed yesterday's Vertica, in Eon mode presentation about supported cloud, on premise storage option, replays are available. Be sure to catch the upcoming presentation on sizing and configuring vertica and in beyond doors. As we've seen with Eon, Vertica can separate data storage from the compute nodes, allowing machines to quickly fill in for each other, to rebuild fault tolerance. But separating compute and storage is used for much, much more. We now offer powerful, flexible ways for Vertica to add servers and increase access to the data. Vertica nine, this feature is called sub-clusters. It allows computing capacity to be added quickly and incrementally, and isolates workloads from each other. If your exploratory analytics team needs direct access to the source data, they need a lot of machines and not the same number all the time, and you don't 100% trust the kind of queries and user defined functions, they might be using sub-clusters as the solution. While there's much more expensive information available in our other presentation. I'd like to point out the highlights of our latest sub-cluster best practices. We suggest having a primary sub-cluster, this is the one that runs all the time, if you're loading data around the clock. It should be sized for the ETL workloads and also determines the natural shard count. Additional read oriented secondary sub-clusters can be added for real time dashboards, reports and analytics. That way, subclusters can be added or deep provisioned, without disruption to other users. The sub-cluster features of Vertica 9.3 are working well for customers. Yesterday, the Trade Desk presented their use case for Vertica over 300,000 in 5 sub clusters running in the cloud. If you missed a presentation, check out the replay. But we have plans beyond sub-clusters, we're extending sub-clusters to real clusters. For the Vertica savvy, this means the clusters bump, share the same spread ring network. This will provide further isolation, allowing clusters to control their own independent data sets. While replicating all are part of the data from other clusters using a publish subscribe mechanism. Synchronizing data between clusters is a feature customers want to understand the real business for themselves. This vision effects are designed for ancillary aspects, how we should assign resource pools, security policies and balance client connection. We will be simplifying our data segmentation strategy, so that when data that originate in the different clusters meet, they'll still get fully optimized joins, even if those clusters weren't positioned with the same number of nodes per shard. Having a broad vision for data management is a key component to political success. But we also take pride in our execution strategy, when you start a new database from scratch as we did 15 years ago, you won't compete on features. Our key competitive points where speed and scale of analytics, we set a target of 100 x better query performance in traditional databases with path loads. Our storage architecture provides a solid foundation on which to build toward these goals. Every query starts with data retrieval, keeping data sorted, organized by column and compressed by using adaptive caching, to keep the data retrieval time in IO to the bare minimum theoretically required. We also keep the data close to where it will be processed, and you clusters the machines to increase throughput. We have partition pruning a robust optimizer evaluate active use segmentation as part of the physical database designed to keep records close to the other relevant records. So the solid foundation, but we also need optimal execution strategies and tactics. One execution strategy which we built for a long time, but it's still a source of pride, it's how we process expressions. Databases and other systems with general purpose expression evaluators, write a compound expression into a tree. Here I'm using A plus one times B as an example, during execution, if your CPU traverses the tree and compute sub-parts from the whole. Tree traversal often takes more compute cycles than the actual work to be done. Especially in evaluation is a very common operation, so something worth optimizing. One instinct that engineers have is to use what we call, just-in-time or JIT compilation, which means generating code form the CPU into the specific activity expression, and add them. This replaces the tree of boxes that are custom made box for the query. This approach has complexity bugs, but it can be made to work. It has other drawbacks though, it adds a lot to query setup time, especially for short queries. And it pretty much eliminate the ability of mere models, mere mortals to develop user defined functions. If you go back to the problem we're trying to solve, the source of the overhead is the tree traversal. If you increase the batch of records processed in each traversal step, this overhead is amortized until it becomes negligible. It's a perfect match for a columnar storage engine. This also sets the CPU up for efficiency. The CPUs look particularly good, at following the same small sequence of instructions in a tight loop. In some cases, the CPU may even be able to vectorize, and apply the same processing to multiple records to the same instruction. This approach is easy to implement and debug, user defined functions are possible, then generally aligned with the other complexities of implementing and improving a large system. More importantly, the performance, both in terms of query setup and record throughput is dramatically improved. You'll hear me say that we look at research and industry for inspiration. In this case, our findings in line with academic binding. If you'd like to read papers, I recommend everything you always wanted to know about compiled and vectorized queries, don't afraid to ask, so we did have this idea before we read that paper. However, not every decision we made in the Vertica executer that the test of time as well as the expression evaluator. For example, sorting and grouping aren't susceptible to vectorization because sort decisions interrupt the flow. We have used JIT compiling on that for years, and Vertica 401, and it provides modest setups, but we know we can do even better. But who we've embarked on a new design for execution engine, which I call EE five, because it's our best. It's really designed especially for the cloud, now I know what you're thinking, you're thinking, I just put up a slide with an old engine, a new engine, and a sleek play headed up into the clouds. But this isn't just marketing hype, here's what I mean, when I say we've learned lessons over the years, and then we're redesigning the executer for the cloud. And of course, you'll see that the new design works well on premises as well. These changes are just more important for the cloud. Starting with the network layer in the cloud, we can't count on all nodes being connected to the same switch. Multicast doesn't work like it does in a custom data center, so as I mentioned earlier, we're redesigning the network transfer layer for the cloud. Storage in the cloud is different, and I'm not referring here to the storage of persistent data, but to the storage of temporary data used only once during the course of query execution. Our new pattern is designed to take into account the strengths and weaknesses of cloud object storage, where we can't easily do a path. Moving on to memory, many of our access patterns are reasonably effective on bare metal machines, that aren't the best choice on cloud hyperbug that have overheads, page faults or big gap. Here again, we found we can improve performance, a bit on dedicated hardware, and even more in the cloud. Finally, and this is true in all environments, core counts have gone up. And not all of our algorithms take full advantage, there's a lot of ground to cover here. But I think sorting in the perfect example to illustrate these points, I mentioned that we use JIT in sorting. We're getting rid of JIT in favor of a data format that can be treated efficiently, independent of what the data types are. We've drawn on the best, most modern technology from academia and industry. We've got our own analysis and testing, you know what we chose, we chose parallel merge sort, anyone wants to take a guess when merge sort was invented. It was invented in 1948, or at least documented that way, like computing context. If you've heard me talk before, you know that I'm fascinated by how all the things I worked with as an engineer, were invented before I was born. And in Vertica , we don't use the newest technologies, we use the best ones. And what is noble about Vertica is the way we've combined the best ideas together into a cohesive package. So all kidding about the 1940s aside, or he redesigned is actually state of the art. How do we know the sort routine is state of the art? It turns out, there's a pretty credible benchmark or at the appropriately named historic sortbenchmark.org. Anyone with resources looking for fame for their product or academic paper can try to set the record. Record is last set in 2016 with Tencent Sort, 100 terabytes in 99 seconds. Setting the records it's hard, you have to come up with hundreds of machines on a dedicated high speed switching fabric. There's a lot to a distributed sort, there all have core sorting algorithms. The authors of the paper conveniently broke out of the time spent in their sort, 67 out of 99 seconds want to know local sorting. If we break this out, divided by two CPUs and each of 512 nodes, we find that each CPU so there's almost a gig and a half per second. This is for what's called an indy sort, like an Indy race car, is in general purpose. It only handles fixed hundred five records with 10 byte key. There is a record length can vary, then it's called daytona sort, a 10 set daytona sort, is a little slower. One point is 10 gigabytes per second per CPU, now for Verrtica, We have a wide variety ability in record sizes, and more interesting data types, but still no harm in setting us like phone numbers, comfortable to the world record. On my 2017 era AMD desktop CPU, the Vertica EE5 sort to store about two and a half gigabytes per second. Obviously, this test isn't apply to apples because they use their own open power chip. But the number of DRM channels is the same, so it's pretty close the number that says we've hit on the right approach. And it performs this way on premise, in the cloud, and we can adapt it to cloud temp space. So what's our roadmap for integrating EE5 into the product and compare replacing the query executed the database to replacing the crankshaft and other parts of the engine of a car while it's been driven. We've actually done it before, between Vertica three and a half and five, and then we never really stopped changing it, now we'll do it again. The first part in replacing with algorithm called storage merge, which combines sorted data from disk. The first time has was two that are in vertical in incoming 10.0 patch that will be EE5 or resegmented storage merge, and then convert sorting and grouping into do out. There the performance results so far, in cases where the Vertica execute is doing well today, simple environments with simple data patterns, such as this simple capitalistic query, there's a lot of speed up, when we ship the segmentation code, which didn't quite make the freeze as much like to bump longer term, what we do is grouping into the storage of large operations, we'll get to where we think we ought to be, given a theoretical minimum work the CPUs need to do. Now if we look at a case where the current execution isn't doing as well, we see there's a much stronger benefit to the code shipping in Vertica 10. In fact, it turns a chart bar sideways to try to help you see the difference better. This case also benefit from the improvements in 10 product point releases and beyond. They will not happening to the vertical query executer, That was just the taste. But now I'd like to switch to the roadmap first for our adapters layer. I'll start with a story about, how our storage access layer evolved. If you go back to the academic ideas, if you start paper that persuaded investors to fund Vertica, read optimized store was the part that had substantiation in the form of performance data. Much of the paper was speculative, but we tried to follow it anyway. That paper talked about the WS with RS, The rights are in the read store, and how they work together for transaction processing and how there was a supernova. In all honesty, Vertica engineers couldn't figure out from the paper what to do next, incase you want to try, and we asked them they would like, We never got enough clarification to build it that way. But here's what we built, instead. We built the ROS, read optimized store, introduction on steep major revision. It's sorted, ordered columnar and compressed that follows a table partitioning that worked even better than the we are as described in the paper. We also built the last byte optimized store, we built four versions of this over the years actually. But this was the best one, it's not a set of interrelated V tree. It's just an append only, insertion order remember your way here, am sorry, no compression, no base, no partitioning. There is, however, a tuple over which does what we call move out. Move the data from WOS to ROS, sorting and compressing. Let's take a moment to compare how they behave, when you load data directly to the ROS, there's a data parsing operation. Then we finished the sorting, and then compressing right out the columnar data files to stay storage. The next query through executes against the ROS and it runs as it should because the ROS is read optimized. Let's repeat the exercise for WOS, the load operation response before the sorting and compressing, and before the data is written to persistent storage. Now it's possible for a query to come along, and the query could be responsible for sorting the lost data in addition to its other processes. Effect on query isn't predictable until the TM comes along and writes the data to the ROS. Over the years, we've done a lot of comparisons between ROS and WOS. ROS has always been better for sustained load throughput, it achieves much higher records per second without pushing back against the client and hasn't Vertica for when we developed the first usable merge out algorithm. ROS has always been better for predictable query performance, the ROS has never had the same management complexity and limitations as WOS. You don't have to pick a memory size and figure out which transactions get to use the pool. A non persistent nature of ROS always cause headaches when there are unexpected cluster shutdowns. We also looked at field usage data, we found that few customers were using a lot, especially among those that studied the issue carefully. So how we set out on a mission to improve the ROS to the point where it was always better than both the WOS and the profit of the past. And now it's true, ROS is better than the WOS and the loss of a couple of years ago. We implemented storage bundling, better catalog object storage and better tuple mover merge outs. And now, after extensive Q&A and customer testing, we've now succeeded, and in Vertica 10, we've removed the whys. Let's talk for a moment about simplicity, one of the best things Mike Stonebreaker said is no knobs. Anyone want to guess how many knobs we got rid of, and we took the WOS out of the product. 22 were five knobs to control whether it didn't went to ROS as well. Six controlling the ROS itself, Six more to set policies for the typical remove out and so on. In my honest opinion is still wasn't enough control over to achieve excess in a multi tenant environment, the big reason to get rid of the WOS for simplicity. Make the lives of DBAs and users better, we have a long way to go, but we're doing it. On my desk, I keep a jar with the knob in it for each knob in Vertica. When developers add a knob to the product, they have to add a knob to the jar. When they remove a knob, they get to choose one to take out, We have a lot of work to do, but I'm thrilled to report that in 15 years 10 is the first release with a number of knobs ticked downward. Get back to the WOS, I've said the most important thing get rid of it for last. We're getting rid of it so we can deliver our vision of the future to our customer. Remember how he said an Eon and sub-clusters we got all these benefits from shared storage? Guess what can't live in shared storage, the WOS. Remember how it's been a big part of the future was keeping the copies that identical to the primary copy? Independent actions of the WOS took a little at the root of the divergence between copies of the data. You have to admit it when you're wrong. That was in the original design and held up to the a selling point of time, without onto the idea of a separate ROS and WOS for too long. In Vertica, 10, we can finally bid, good reagents. I've covered a lot of ground, so let's put all the pieces together. I've talked a lot about our vision and how we're achieving it. But we also still pay attention to tactical detail. We've been fine tuning our memory management model to enhance performance. That involves revisiting tens of thousands of satellite of code, much like painting the inside of a large building with small paintbrushes. We're getting results as shown in the chart in Vertica nine, concurrent monitoring queries use memory from the global catalog tool, and Vertica 10, they don't. This is only one example of an important detail we're improving. We've also reworked the monitoring tables without network messages into two parts. The increased data we're collecting and analyzing and our quality assurance processes, we're improving on everything. As the story goes, I still have my grandfather's axe, of course, my father had to replace the handle, and I had to replace the head. Along the same lines, we still have Mike Stonebreaker Vertica. We didn't replace the query optimizer twice the debate database designer and storage layer four times each. The query executed is and it's a free design, like charted out how our code has changed over the years. I found that we don't have much from a long time ago, I did some digging, and you know what we have left in 2007. We have the original curly braces, and a little bit of percent code for handling dates and times. To deliver on our mission to help customers get value from their structured data, with high performance of scale, and in diverse deployment environments. We have the sound architecture roadmap, reviews the best execution strategy and solid tactics. On the architectural front, we're converging in an enterprise, we're extending smart analytic clusters. In query processing, we're redesigning the execution engine for the cloud, as I've told you. There's a lot more than just the fast engine. that you want to learn about our new data support for complex data types, improvements to the query optimizer statistics, or extension to live aggregate projections and flatten tables. You should check out some of the other engineering talk that the big data conference. We continue to stay on top of the details from low level CPU and memory too, to the monitoring management, developing tighter feedback cycles between development, Q&A and customers. And don't forget to check out the rest of the pillars of our roadmap. We have new easier ways to get started with Vertica in the cloud. Engineers have been hard at work on machine learning and security. It's easier than ever to use Vertica with third Party product, as a variety of tools integrations continues to increase. Finally, the most important thing we can do, is to help people get value from structured data to help people learn more about Vertica. So hopefully I left plenty of time for Q&A at the end of this presentation. I hope to hear your questions soon.

>> Joy: Welcome to the Virtual Big Data Conference. Vertica is so excited to host this event. I'm Joy King, and I'll be your host for today's Big Data Conference Keynote Session. It's my honor and my genuine pleasure to lead Vertica's product and go-to-market strategy. And I'm so lucky to have a passionate and committed team who turned our Vertica BDC event, into a virtual event in a very short amount of time. I want to thank the thousands of people, and yes, that's our true number who have registered to attend this virtual event. We were determined to balance your health, safety and your peace of mind with the excitement of the Vertica BDC. This is a very unique event. Because as I hope you all know, we focus on engineering and architecture, best practice sharing and customer stories that will educate and inspire everyone. I also want to thank our top sponsors for the virtual BDC, Arrow, and Pure Storage. Our partnerships are so important to us and to everyone in the audience. Because together, we get things done faster and better. Now for today's keynote, you'll hear from three very important and energizing speakers. First, Colin Mahony, our SVP and General Manager for Vertica, will talk about the market trends that Vertica is betting on to win for our customers. And he'll share the exciting news about our Vertica 10 announcement and how this will benefit our customers. Then you'll hear from Amy Fowler, VP of strategy and solutions for FlashBlade at Pure Storage. Our partnership with Pure Storage is truly unique in the industry, because together modern infrastructure from Pure powers modern analytics from Vertica. And then you'll hear from John Yovanovich, Director of IT at AT&T, who will tell you about the Pure Vertica Symphony that plays live every day at AT&T. Here we go, Colin, over to you. >> Colin: Well, thanks a lot joy. And, I want to echo Joy's thanks to our sponsors, and so many of you who have helped make this happen. This is not an easy time for anyone. We were certainly looking forward to getting together in person in Boston during the Vertica Big Data Conference and Winning with Data. But I think all of you and our team have done a great job, scrambling and putting together a terrific virtual event. So really appreciate your time. I also want to remind people that we will make both the slides and the full recording available after this. So for any of those who weren't able to join live, that is still going to be available. Well, things have been pretty exciting here. And in the analytic space in general, certainly for Vertica, there's a lot happening. There are a lot of problems to solve, a lot of opportunities to make things better, and a lot of data that can really make every business stronger, more efficient, and frankly, more differentiated. For Vertica, though, we know that focusing on the challenges that we can directly address with our platform, and our people, and where we can actually make the biggest difference is where we ought to be putting our energy and our resources. I think one of the things that has made Vertica so strong over the years is our ability to focus on those areas where we can make a great difference. So for us as we look at the market, and we look at where we play, there are really three recent and some not so recent, but certainly picking up a lot of the market trends that have become critical for every industry that wants to Win Big With Data. We've heard this loud and clear from our customers and from the analysts that cover the market. If I were to summarize these three areas, this really is the core focus for us right now. We know that there's massive data growth. And if we can unify the data silos so that people can really take advantage of that data, we can make a huge difference. We know that public clouds offer tremendous advantages, but we also know that balance and flexibility is critical. And we all need the benefit that machine learning for all the types up to the end data science. We all need the benefits that they can bring to every single use case, but only if it can really be operationalized at scale, accurate and in real time. And the power of Vertica is, of course, how we're able to bring so many of these things together. Let me talk a little bit more about some of these trends. So one of the first industry trends that we've all been following probably now for over the last decade, is Hadoop and specifically HDFS. So many companies have invested, time, money, more importantly, people in leveraging the opportunity that HDFS brought to the market. HDFS is really part of a much broader storage disruption that we'll talk a little bit more about, more broadly than HDFS. But HDFS itself was really designed for petabytes of data, leveraging low cost commodity hardware and the ability to capture a wide variety of data formats, from a wide variety of data sources and applications. And I think what people really wanted, was to store that data before having to define exactly what structures they should go into. So over the last decade or so, the focus for most organizations is figuring out how to capture, store and frankly manage that data. And as a platform to do that, I think, Hadoop was pretty good. It certainly changed the way that a lot of enterprises think about their data and where it's locked up. In parallel with Hadoop, particularly over the last five years, Cloud Object Storage has also given every organization another option for collecting, storing and managing even more data. That has led to a huge growth in data storage, obviously, up on public clouds like Amazon and their S3, Google Cloud Storage and Azure Blob Storage just to name a few. And then when you consider regional and local object storage offered by cloud vendors all over the world, the explosion of that data, in leveraging this type of object storage is very real. And I think, as I mentioned, it's just part of this broader storage disruption that's been going on. But with all this growth in the data, in all these new places to put this data, every organization we talk to is facing even more challenges now around the data silo. Sure the data silos certainly getting bigger. And hopefully they're getting cheaper per bit. But as I said, the focus has really been on collecting, storing and managing the data. But between the new data lakes and many different cloud object storage combined with all sorts of data types from the complexity of managing all this, getting that business value has been very limited. This actually takes me to big bet number one for Team Vertica, which is to unify the data. Our goal, and some of the announcements we have made today plus roadmap announcements I'll share with you throughout this presentation. Our goal is to ensure that all the time, money and effort that has gone into storing that data, all the data turns into business value. So how are we going to do that? With a unified analytics platform that analyzes the data wherever it is HDFS, Cloud Object Storage, External tables in an any format ORC, Parquet, JSON, and of course, our own Native Roth Vertica format. Analyze the data in the right place in the right format, using a single unified tool. This is something that Vertica has always been committed to, and you'll see in some of our announcements today, we're just doubling down on that commitment. Let's talk a little bit more about the public cloud. This is certainly the second trend. It's the second wave maybe of data disruption with object storage. And there's a lot of advantages when it comes to public cloud. There's no question that the public clouds give rapid access to compute storage with the added benefit of eliminating data center maintenance that so many companies, want to get out of themselves. But maybe the biggest advantage that I see is the architectural innovation. The public clouds have introduced so many methodologies around how to provision quickly, separating compute and storage and really dialing-in the exact needs on demand, as you change workloads. When public clouds began, it made a lot of sense for the cloud providers and their customers to charge and pay for compute and storage in the ratio that each use case demanded. And I think you're seeing that trend, proliferate all over the place, not just up in public cloud. That architecture itself is really becoming the next generation architecture for on-premise data centers, as well. But there are a lot of concerns. I think we're all aware of them. They're out there many times for different workloads, there are higher costs. Especially if some of the workloads that are being run through analytics, which tend to run all the time. Just like some of the silo challenges that companies are facing with HDFS, data lakes and cloud storage, the public clouds have similar types of siloed challenges as well. Initially, there was a belief that they were cheaper than data centers, and when you added in all the costs, it looked that way. And again, for certain elastic workloads, that is the case. I don't think that's true across the board overall. Even to the point where a lot of the cloud vendors aren't just charging lower costs anymore. We hear from a lot of customers that they don't really want to tether themselves to any one cloud because of some of those uncertainties. Of course, security and privacy are a concern. We hear a lot of concerns with regards to cloud and even some SaaS vendors around shared data catalogs, across all the customers and not enough separation. But security concerns are out there, you can read about them. I'm not going to jump into that bandwagon. But we hear about them. And then, of course, I think one of the things we hear the most from our customers, is that each cloud stack is starting to feel even a lot more locked in than the traditional data warehouse appliance. And as everybody knows, the industry has been running away from appliances as fast as it can. And so they're not eager to get locked into another, quote, unquote, virtual appliance, if you will, up in the cloud. They really want to make sure they have flexibility in which clouds, they're going to today, tomorrow and in the future. And frankly, we hear from a lot of our customers that they're very interested in eventually mixing and matching, compute from one cloud with, say storage from another cloud, which I think is something that we'll hear a lot more about. And so for us, that's why we've got our big bet number two. we love the cloud. We love the public cloud. We love the private clouds on-premise, and other hosting providers. But our passion and commitment is for Vertica to be able to run in any of the clouds that our customers choose, and make it portable across those clouds. We have supported on-premises and all public clouds for years. And today, we have announced even more support for Vertica in Eon Mode, the deployment option that leverages the separation of compute from storage, with even more deployment choices, which I'm going to also touch more on as we go. So super excited about our big bet number two. And finally as I mentioned, for all the hype that there is around machine learning, I actually think that most importantly, this third trend that team Vertica is determined to address is the need to bring business critical, analytics, machine learning, data science projects into production. For so many years, there just wasn't enough data available to justify the investment in machine learning. Also, processing power was expensive, and storage was prohibitively expensive. But to train and score and evaluate all the different models to unlock the full power of predictive analytics was tough. Today you have those massive data volumes. You have the relatively cheap processing power and storage to make that dream a reality. And if you think about this, I mean with all the data that's available to every company, the real need is to operationalize the speed and the scale of machine learning so that these organizations can actually take advantage of it where they need to. I mean, we've seen this for years with Vertica, going back to some of the most advanced gaming companies in the early days, they were incorporating this with live data directly into their gaming experiences. Well, every organization wants to do that now. And the accuracy for clickability and real time actions are all key to separating the leaders from the rest of the pack in every industry when it comes to machine learning. But if you look at a lot of these projects, the reality is that there's a ton of buzz, there's a ton of hype spanning every acronym that you can imagine. But most companies are struggling, do the separate teams, different tools, silos and the limitation that many platforms are facing, driving, down sampling to get a small subset of the data, to try to create a model that then doesn't apply, or compromising accuracy and making it virtually impossible to replicate models, and understand decisions. And if there's one thing that we've learned when it comes to data, prescriptive data at the atomic level, being able to show end of one as we refer to it, meaning individually tailored data. No matter what it is healthcare, entertainment experiences, like gaming or other, being able to get at the granular data and make these decisions, make that scoring applies to machine learning just as much as it applies to giving somebody a next-best-offer. But the opportunity has never been greater. The need to integrate this end-to-end workflow and support the right tools without compromising on that accuracy. Think about it as no downsampling, using all the data, it really is key to machine learning success. Which should be no surprise then why the third big bet from Vertica is one that we've actually been working on for years. And we're so proud to be where we are today, helping the data disruptors across the world operationalize machine learning. This big bet has the potential to truly unlock, really the potential of machine learning. And today, we're announcing some very important new capabilities specifically focused on unifying the work being done by the data science community, with their preferred tools and platforms, and the volume of data and performance at scale, available in Vertica. Our strategy has been very consistent over the last several years. As I said in the beginning, we haven't deviated from our strategy. Of course, there's always things that we add. Most of the time, it's customer driven, it's based on what our customers are asking us to do. But I think we've also done a great job, not trying to be all things to all people. Especially as these hype cycles flare up around us, we absolutely love participating in these different areas without getting completely distracted. I mean, there's a variety of query tools and data warehouses and analytics platforms in the market. We all know that. There are tools and platforms that are offered by the public cloud vendors, by other vendors that support one or two specific clouds. There are appliance vendors, who I was referring to earlier who can deliver package data warehouse offerings for private data centers. And there's a ton of popular machine learning tools, languages and other kits. But Vertica is the only advanced analytic platform that can do all this, that can bring it together. We can analyze the data wherever it is, in HDFS, S3 Object Storage, or Vertica itself. Natively we support multiple clouds on-premise deployments, And maybe most importantly, we offer that choice of deployment modes to allow our customers to choose the architecture that works for them right now. It still also gives them the option to change move, evolve over time. And Vertica is the only analytics database with end-to-end machine learning that can truly operationalize ML at scale. And I know it's a mouthful. But it is not easy to do all these things. It is one of the things that highly differentiates Vertica from the rest of the pack. It is also why our customers, all of you continue to bet on us and see the value that we are delivering and we will continue to deliver. Here's a couple of examples of some of our customers who are powered by Vertica. It's the scale of data. It's the millisecond response times. Performance and scale have always been a huge part of what we have been about, not the only thing. I think the functionality all the capabilities that we add to the platform, the ease of use, the flexibility, obviously with the deployment. But if you look at some of the numbers they are under these customers on this slide. And I've shared a lot of different stories about these customers. Which, by the way, it still amaze me every time I talk to one and I get the updates, you can see the power and the difference that Vertica is making. Equally important, if you look at a lot of these customers, they are the epitome of being able to deploy Vertica in a lot of different environments. Many of the customers on this slide are not using Vertica just on-premise or just in the cloud. They're using it in a hybrid way. They're using it in multiple different clouds. And again, we've been with them on that journey throughout, which is what has made this product and frankly, our roadmap and our vision exactly what it is. It's been quite a journey. And that journey continues now with the Vertica 10 release. The Vertica 10 release is obviously a massive release for us. But if you look back, you can see that building on that native columnar architecture that started a long time ago, obviously, with the C-Store paper. We built it to leverage that commodity hardware, because it was an architecture that was never tightly integrated with any specific underlying infrastructure. I still remember hearing the initial pitch from Mike Stonebreaker, about the vision of Vertica as a software only solution and the importance of separating the company from hardware innovation. And at the time, Mike basically said to me, "there's so much R&D in innovation that's going to happen in hardware, we shouldn't bake hardware into our solution. We should do it in software, and we'll be able to take advantage of that hardware." And that is exactly what has happened. But one of the most recent innovations that we embraced with hardware is certainly that separation of compute and storage. As I said previously, the public cloud providers offered this next generation architecture, really to ensure that they can provide the customers exactly what they needed, more compute or more storage and charge for each, respectively. The separation of compute and storage, compute from storage is a major milestone in data center architectures. If you think about it, it's really not only a public cloud innovation, though. It fundamentally redefines the next generation data architecture for on-premise and for pretty much every way people are thinking about computing today. And that goes for software too. Object storage is an example of the cost effective means for storing data. And even more importantly, separating compute from storage for analytic workloads has a lot of advantages. Including the opportunity to manage much more dynamic, flexible workloads. And more importantly, truly isolate those workloads from others. And by the way, once you start having something that can truly isolate workloads, then you can have the conversations around autonomic computing, around setting up some nodes, some compute resources on the data that won't affect any of the other data to do some things on their own, maybe some self analytics, by the system, etc. A lot of things that many of you know we've already been exploring in terms of our own system data in the product. But it was May 2018, believe it or not, it seems like a long time ago where we first announced Eon Mode and I want to make something very clear, actually about Eon mode. It's a mode, it's a deployment option for Vertica customers. And I think this is another huge benefit that we don't talk about enough. But unlike a lot of vendors in the market who will dig you and charge you for every single add-on like hit-buy, you name it. You get this with the Vertica product. If you continue to pay support and maintenance, this comes with the upgrade. This comes as part of the new release. So any customer who owns or buys Vertica has the ability to set up either an Enterprise Mode or Eon Mode, which is a question I know that comes up sometimes. Our first announcement of Eon was obviously AWS customers, including the trade desk, AT&T. Most of whom will be speaking here later at the Virtual Big Data Conference. They saw a huge opportunity. Eon Mode, not only allowed Vertica to scale elastically with that specific compute and storage that was needed, but it really dramatically simplified database operations including things like workload balancing, node recovery, compute provisioning, etc. So one of the most popular functions is that ability to isolate the workloads and really allocate those resources without negatively affecting others. And even though traditional data warehouses, including Vertica Enterprise Mode have been able to do lots of different workload isolation, it's never been as strong as Eon Mode. Well, it certainly didn't take long for our customers to see that value across the board with Eon Mode. Not just up in the cloud, in partnership with one of our most valued partners and a platinum sponsor here. Joy mentioned at the beginning. We announced Vertica Eon Mode for Pure Storage FlashBlade in September 2019. And again, just to be clear, this is not a new product, it's one Vertica with yet more deployment options. With Pure Storage, Vertica in Eon mode is not limited in any way by variable cloud, network latency. The performance is actually amazing when you take the benefits of separate and compute from storage and you run it with a Pure environment on-premise. Vertica in Eon Mode has a super smart cache layer that we call the depot. It's a big part of our secret sauce around Eon mode. And combined with the power and performance of Pure's FlashBlade, Vertica became the industry's first advanced analytics platform that actually separates compute and storage for on-premises data centers. Something that a lot of our customers are already benefiting from, and we're super excited about it. But as I said, this is a journey. We don't stop, we're not going to stop. Our customers need the flexibility of multiple public clouds. So today with Vertica 10, we're super proud and excited to announce support for Vertica in Eon Mode on Google Cloud. This gives our customers the ability to use their Vertica licenses on Amazon AWS, on-premise with Pure Storage and on Google Cloud. Now, we were talking about HDFS and a lot of our customers who have invested quite a bit in HDFS as a place, especially to store data have been pushing us to support Eon Mode with HDFS. So as part of Vertica 10, we are also announcing support for Vertica in Eon Mode using HDFS as the communal storage. Vertica's own Roth format data can be stored in HDFS, and actually the full functionality of Vertica is complete analytics, geospatial pattern matching, time series, machine learning, everything that we have in there can be applied to this data. And on the same HDFS nodes, Vertica can actually also analyze data in ORC or Parquet format, using External tables. We can also execute joins between the Roth data the External table holds, which powers a much more comprehensive view. So again, it's that flexibility to be able to support our customers, wherever they need us to support them on whatever platform, they have. Vertica 10 gives us a lot more ways that we can deploy Eon Mode in various environments for our customers. It allows them to take advantage of Vertica in Eon Mode and the power that it brings with that separation, with that workload isolation, to whichever platform they are most comfortable with. Now, there's a lot that has come in Vertica 10. I'm definitely not going to be able to cover everything. But we also introduced complex types as an example. And complex data types fit very well into Eon as well in this separation. They significantly reduce the data pipeline, the cost of moving data between those, a much better support for unstructured data, which a lot of our customers have mixed with structured data, of course, and they leverage a lot of columnar execution that Vertica provides. So you get complex data types in Vertica now, a lot more data, stronger performance. It goes great with the announcement that we made with the broader Eon Mode. Let's talk a little bit more about machine learning. We've been actually doing work in and around machine learning with various extra regressions and a whole bunch of other algorithms for several years. We saw the huge advantage that MPP offered, not just as a sequel engine as a database, but for ML as well. Didn't take as long to realize that there's a lot more to operationalizing machine learning than just those algorithms. It's data preparation, it's that model trade training. It's the scoring, the shaping, the evaluation. That is so much of what machine learning and frankly, data science is about. You do know, everybody always wants to jump to the sexy algorithm and we handle those tasks very, very well. It makes Vertica a terrific platform to do that. A lot of work in data science and machine learning is done in other tools. I had mentioned that there's just so many tools out there. We want people to be able to take advantage of all that. We never believed we were going to be the best algorithm company or come up with the best models for people to use. So with Vertica 10, we support PMML. We can import now and export PMML models. It's a huge step for us around that operationalizing machine learning projects for our customers. Allowing the models to get built outside of Vertica yet be imported in and then applying to that full scale of data with all the performance that you would expect from Vertica. We also are more tightly integrating with Python. As many of you know, we've been doing a lot of open source projects with the community driven by many of our customers, like Uber. And so now with Python we've integrated with TensorFlow, allowing data scientists to build models in their preferred language, to take advantage of TensorFlow. But again, to store and deploy those models at scale with Vertica. I think both these announcements are proof of our big bet number three, and really our commitment to supporting innovation throughout the community by operationalizing ML with that accuracy, performance and scale of Vertica for our customers. Again, there's a lot of steps when it comes to the workflow of machine learning. These are some of them that you can see on the slide, and it's definitely not linear either. We see this as a circle. And companies that do it, well just continue to learn, they continue to rescore, they continue to redeploy and they want to operationalize all that within a single platform that can take advantage of all those capabilities. And that is the platform, with a very robust ecosystem that Vertica has always been committed to as an organization and will continue to be. This graphic, many of you have seen it evolve over the years. Frankly, if we put everything and everyone on here wouldn't fit on a slide. But it will absolutely continue to evolve and grow as we support our customers, where they need the support most. So, again, being able to deploy everywhere, being able to take advantage of Vertica, not just as a business analyst or a business user, but as a data scientists or as an operational or BI person. We want Vertica to be leveraged and used by the broader organization. So I think it's fair to say and I encourage everybody to learn more about Vertica 10, because I'm just highlighting some of the bigger aspects of it. But we talked about those three market trends. The need to unify the silos, the need for hybrid multiple cloud deployment options, the need to operationalize business critical machine learning projects. Vertica 10 has absolutely delivered on those. But again, we are not going to stop. It is our job not to, and this is how Team Vertica thrives. I always joke that the next release is the best release. And, of course, even after Vertica 10, that is also true, although Vertica 10 is pretty awesome. But, you know, from the first line of code, we've always been focused on performance and scale, right. And like any really strong data platform, the execution engine, the optimizer and the execution engine are the two core pieces of that. Beyond Vertica 10, some of the big things that we're already working on, next generation execution engine. We're already actually seeing incredible early performance from this. And this is just one example, of how important it is for an organization like Vertica to constantly go back and re-innovate. Every single release, we do the sit ups and crunches, our performance and scale. How do we improve? And there's so many parts of the core server, there's so many parts of our broader ecosystem. We are constantly looking at coverages of how we can go back to all the code lines that we have, and make them better in the current environment. And it's not an easy thing to do when you're doing that, and you're also expanding in the environment that we are expanding into to take advantage of the different deployments, which is a great segue to this slide. Because if you think about today, we're obviously already available with Eon Mode and Amazon, AWS and Pure and actually MinIO as well. As I talked about in Vertica 10 we're adding Google and HDFS. And coming next, obviously, Microsoft Azure, Alibaba cloud. So being able to expand into more of these environments is really important for the Vertica team and how we go forward. And it's not just running in these clouds, for us, we want it to be a SaaS like experience in all these clouds. We want you to be able to deploy Vertica in 15 minutes or less on these clouds. You can also consume Vertica, in a lot of different ways, on these clouds. As an example, in Amazon Vertica by the Hour. So for us, it's not just about running, it's about taking advantage of the ecosystems that all these cloud providers offer, and really optimizing the Vertica experience as part of them. Optimization, around automation, around self service capabilities, extending our management console, we now have products that like the Vertica Advisor Tool that our Customer Success Team has created to actually use our own smarts in Vertica. To take data from customers that give it to us and help them tune automatically their environment. You can imagine that we're taking that to the next level, in a lot of different endeavors that we're doing around how Vertica as a product can actually be smarter because we all know that simplicity is key. There just aren't enough people in the world who are good at managing data and taking it to the next level. And of course, other things that we all hear about, whether it's Kubernetes and containerization. You can imagine that that probably works very well with the Eon Mode and separating compute and storage. But innovation happens everywhere. We innovate around our community documentation. Many of you have taken advantage of the Vertica Academy. The numbers there are through the roof in terms of the number of people coming in and certifying on it. So there's a lot of things that are within the core products. There's a lot of activity and action beyond the core products that we're taking advantage of. And let's not forget why we're here, right? It's easy to talk about a platform, a data platform, it's easy to jump into all the functionality, the analytics, the flexibility, how we can offer it. But at the end of the day, somebody, a person, she's got to take advantage of this data, she's got to be able to take this data and use this information to make a critical business decision. And that doesn't happen unless we explore lots of different and frankly, new ways to get that predictive analytics UI and interface beyond just the standard BI tools in front of her at the right time. And so there's a lot of activity, I'll tease you with that going on in this organization right now about how we can do that and deliver that for our customers. We're in a great position to be able to see exactly how this data is consumed and used and start with this core platform that we have to go out. Look, I know, the plan wasn't to do this as a virtual BDC. But I really appreciate you tuning in. Really appreciate your support. I think if there's any silver lining to us, maybe not being able to do this in person, it's the fact that the reach has actually gone significantly higher than what we would have been able to do in person in Boston. We're certainly looking forward to doing a Big Data Conference in the future. But if I could leave you with anything, know this, since that first release for Vertica, and our very first customers, we have been very consistent. We respect all the innovation around us, whether it's open source or not. We understand the market trends. We embrace those new ideas and technologies and for us true north, and the most important thing is what does our customer need to do? What problem are they trying to solve? And how do we use the advantages that we have without disrupting our customers? But knowing that you depend on us to deliver that unified analytics strategy, it will deliver that performance of scale, not only today, but tomorrow and for years to come. We've added a lot of great features to Vertica. I think we've said no to a lot of things, frankly, that we just knew we wouldn't be the best company to deliver. When we say we're going to do things we do them. Vertica 10 is a perfect example of so many of those things that we from you, our customers have heard loud and clear, and we have delivered. I am incredibly proud of this team across the board. I think the culture of Vertica, a customer first culture, jumping in to help our customers win no matter what is also something that sets us massively apart. I hear horror stories about support experiences with other organizations. And people always seem to be amazed at Team Vertica's willingness to jump in or their aptitude for certain technical capabilities or understanding the business. And I think sometimes we take that for granted. But that is the team that we have as Team Vertica. We are incredibly excited about Vertica 10. I think you're going to love the Virtual Big Data Conference this year. I encourage you to tune in. Maybe one other benefit is I know some people were worried about not being able to see different sessions because they were going to overlap with each other well now, even if you can't do it live, you'll be able to do those sessions on demand. Please enjoy the Vertica Big Data Conference here in 2020. Please you and your families and your co-workers be safe during these times. I know we will get through it. And analytics is probably going to help with a lot of that and we already know it is helping in many different ways. So believe in the data, believe in data's ability to change the world for the better. And thank you for your time. And with that, I am delighted to now introduce Micro Focus CEO Stephen Murdoch to the Vertica Big Data Virtual Conference. Thank you Stephen. >> Stephen: Hi, everyone, my name is Stephen Murdoch. I have the pleasure and privilege of being the Chief Executive Officer here at Micro Focus. Please let me add my welcome to the Big Data Conference. And also my thanks for your support, as we've had to pivot to this being virtual rather than a physical conference. Its amazing how quickly we all reset to a new normal. I certainly didn't expect to be addressing you from my study. Vertica is an incredibly important part of Micro Focus family. Is key to our goal of trying to enable and help customers become much more data driven across all of their IT operations. Vertica 10 is a huge step forward, we believe. It allows for multi-cloud innovation, genuinely hybrid deployments, begin to leverage machine learning properly in the enterprise, and also allows the opportunity to unify currently siloed lakes of information. We operate in a very noisy, very competitive market, and there are people, who are in that market who can do some of those things. The reason we are so excited about Vertica is we genuinely believe that we are the best at doing all of those things. And that's why we've announced publicly, you're under executing internally, incremental investment into Vertica. That investments targeted at accelerating the roadmaps that already exist. And getting that innovation into your hands faster. This idea is speed is key. It's not a question of if companies have to become data driven organizations, it's a question of when. So that speed now is really important. And that's why we believe that the Big Data Conference gives a great opportunity for you to accelerate your own plans. You will have the opportunity to talk to some of our best architects, some of the best development brains that we have. But more importantly, you'll also get to hear from some of our phenomenal Roth Data customers. You'll hear from Uber, from the Trade Desk, from Philips, and from AT&T, as well as many many others. And just hearing how those customers are using the power of Vertica to accelerate their own, I think is the highlight. And I encourage you to use this opportunity to its full. Let me close by, again saying thank you, we genuinely hope that you get as much from this virtual conference as you could have from a physical conference. And we look forward to your engagement, and we look forward to hearing your feedback. With that, thank you very much. >> Joy: Thank you so much, Stephen, for joining us for the Vertica Big Data Conference. Your support and enthusiasm for Vertica is so clear, and it makes a big difference. Now, I'm delighted to introduce Amy Fowler, the VP of Strategy and Solutions for FlashBlade at Pure Storage, who was one of our BDC Platinum Sponsors, and one of our most valued partners. It was a proud moment for me, when we announced Vertica in Eon mode for Pure Storage FlashBlade and we became the first analytics data warehouse that separates compute from storage for on-premise data centers. Thank you so much, Amy, for joining us. Let's get started. >> Amy: Well, thank you, Joy so much for having us. And thank you all for joining us today, virtually, as we may all be. So, as we just heard from Colin Mahony, there are some really interesting trends that are happening right now in the big data analytics market. From the end of the Hadoop hype cycle, to the new cloud reality, and even the opportunity to help the many data science and machine learning projects move from labs to production. So let's talk about these trends in the context of infrastructure. And in particular, look at why a modern storage platform is relevant as organizations take on the challenges and opportunities associated with these trends. The answer is the Hadoop hype cycles left a lot of data in HDFS data lakes, or reservoirs or swamps depending upon the level of the data hygiene. But without the ability to get the value that was promised from Hadoop as a platform rather than a distributed file store. And when we combine that data with the massive volume of data in Cloud Object Storage, we find ourselves with a lot of data and a lot of silos, but without a way to unify that data and find value in it. Now when you look at the infrastructure data lakes are traditionally built on, it is often direct attached storage or data. The approach that Hadoop took when it entered the market was primarily bound by the limits of networking and storage technologies. One gig ethernet and slower spinning disk. But today, those barriers do not exist. And all FlashStorage has fundamentally transformed how data is accessed, managed and leveraged. The need for local data storage for significant volumes of data has been largely mitigated by the performance increases afforded by all Flash. At the same time, organizations can achieve superior economies of scale with that segregation of compute and storage. With compute and storage, you don't always scale in lockstep. Would you want to add an engine to the train every time you add another boxcar? Probably not. But from a Pure Storage perspective, FlashBlade is uniquely architected to allow customers to achieve better resource utilization for compute and storage, while at the same time, reducing complexity that has arisen from the siloed nature of the original big data solutions. The second and equally important recent trend we see is something I'll call cloud reality. The public clouds made a lot of promises and some of those promises were delivered. But cloud economics, especially usage based and elastic scaling, without the control that many companies need to manage the financial impact is causing a lot of issues. In addition, the risk of vendor lock-in from data egress, charges, to integrated software stacks that can't be moved or deployed on-premise is causing a lot of organizations to back off the all the way non-cloud strategy, and move toward hybrid deployments. Which is kind of funny in a way because it wasn't that long ago that there was a lot of talk about no more data centers. And for example, one large retailer, I won't name them, but I'll admit they are my favorites. They several years ago told us they were completely done with on-prem storage infrastructure, because they were going 100% to the cloud. But they just deployed FlashBlade for their data pipelines, because they need predictable performance at scale. And the all cloud TCO just didn't add up. Now, that being said, well, there are certainly challenges with the public cloud. It has also brought some things to the table that we see most organizations wanting. First of all, in a lot of cases applications have been built to leverage object storage platforms like S3. So they need that object protocol, but they may also need it to be fast. And the said object may be oxymoron only a few years ago, and this is an area of the market where Pure and FlashBlade have really taken a leadership position. Second, regardless of where the data is physically stored, organizations want the best elements of a cloud experience. And for us, that means two main things. Number one is simplicity and ease of use. If you need a bunch of storage experts to run the system, that should be considered a bug. The other big one is the consumption model. The ability to pay for what you need when you need it, and seamlessly grow your environment over time totally nondestructively. This is actually pretty huge and something that a lot of vendors try to solve for with finance programs. But no finance program can address the pain of a forklift upgrade, when you need to move to next gen hardware. To scale nondestructively over long periods of time, five to 10 years plus is a crucial architectural decisions need to be made at the outset. Plus, you need the ability to pay as you use it. And we offer something for FlashBlade called Pure as a Service, which delivers exactly that. The third cloud characteristic that many organizations want is the option for hybrid. Even if that is just a DR site in the cloud. In our case, that means supporting appplication of S3, at the AWS. And the final trend, which to me represents the biggest opportunity for all of us, is the need to help the many data science and machine learning projects move from labs to production. This means bringing all the machine learning functions and model training to the data, rather than moving samples or segments of data to separate platforms. As we all know, machine learning needs a ton of data for accuracy. And there is just too much data to retrieve from the cloud for every training job. At the same time, predictive analytics without accuracy is not going to deliver the business advantage that everyone is seeking. You can kind of visualize data analytics as it is traditionally deployed as being on a continuum. With that thing, we've been doing the longest, data warehousing on one end, and AI on the other end. But the way this manifests in most environments is a series of silos that get built up. So data is duplicated across all kinds of bespoke analytics and AI, environments and infrastructure. This creates an expensive and complex environment. So historically, there was no other way to do it because some level of performance is always table stakes. And each of these parts of the data pipeline has a different workload profile. A single platform to deliver on the multi dimensional performances, diverse set of applications required, that didn't exist three years ago. And that's why the application vendors pointed you towards bespoke things like DAS environments that we talked about earlier. And the fact that better options exists today is why we're seeing them move towards supporting this disaggregation of compute and storage. And when it comes to a platform that is a better option, one with a modern architecture that can address the diverse performance requirements of this continuum, and allow organizations to bring a model to the data instead of creating separate silos. That's exactly what FlashBlade is built for. Small files, large files, high throughput, low latency and scale to petabytes in a single namespace. And this is importantly a single rapid space is what we're focused on delivering for our customers. At Pure, we talk about it in the context of modern data experience because at the end of the day, that's what it's really all about. The experience for your teams in your organization. And together Pure Storage and Vertica have delivered that experience to a wide range of customers. From a SaaS analytics company, which uses Vertica on FlashBlade to authenticate the quality of digital media in real time, to a multinational car company, which uses Vertica on FlashBlade to make thousands of decisions per second for autonomous cars, or a healthcare organization, which uses Vertica on FlashBlade to enable healthcare providers to make real time decisions that impact lives. And I'm sure you're all looking forward to hearing from John Yavanovich from AT&T. To hear how he's been doing this with Vertica and FlashBlade as well. He's coming up soon. We have been really excited to build this partnership with Vertica. And we're proud to provide the only on-premise storage platform validated with Vertica Eon Mode. And deliver this modern data experience to our customers together. Thank you all so much for joining us today. >> Joy: Amy, thank you so much for your time and your insights. Modern infrastructure is key to modern analytics, especially as organizations leverage next generation data center architectures, and object storage for their on-premise data centers. Now, I'm delighted to introduce our last speaker in our Vertica Big Data Conference Keynote, John Yovanovich, Director of IT for AT&T. Vertica is so proud to serve AT&T, and especially proud of the harmonious impact we are having in partnership with Pure Storage. John, welcome to the Virtual Vertica BDC. >> John: Thank you joy. It's a pleasure to be here. And I'm excited to go through this presentation today. And in a unique fashion today 'cause as I was thinking through how I wanted to present the partnership that we have formed together between Pure Storage, Vertica and AT&T, I want to emphasize how well we all work together and how these three components have really driven home, my desire for a harmonious to use your word relationship. So, I'm going to move forward here and with. So here, what I'm going to do the theme of today's presentation is the Pure Vertica Symphony live at AT&T. And if anybody is a Westworld fan, you can appreciate the sheet music on the right hand side. What we're going to what I'm going to highlight here is in a musical fashion, is how we at AT&T leverage these technologies to save money to deliver a more efficient platform, and to actually just to make our customers happier overall. So as we look back, and back as early as just maybe a few years ago here at AT&T, I realized that we had many musicians to help the company. Or maybe you might want to call them data scientists, or data analysts. For the theme we'll stay with musicians. None of them were singing or playing from the same hymn book or sheet music. And so what we had was many organizations chasing a similar dream, but not exactly the same dream. And, best way to describe that is and I think with a lot of people this might resonate in your organizations. How many organizations are chasing a customer 360 view in your company? Well, I can tell you that I have at least four in my company. And I'm sure there are many that I don't know of. That is our problem because what we see is a repetitive sourcing of data. We see a repetitive copying of data. And there's just so much money to be spent. This is where I asked Pure Storage and Vertica to help me solve that problem with their technologies. What I also noticed was that there was no coordination between these departments. In fact, if you look here, nobody really wants to play with finance. Sales, marketing and care, sure that you all copied each other's data. But they actually didn't communicate with each other as they were copying the data. So the data became replicated and out of sync. This is a challenge throughout, not just my company, but all companies across the world. And that is, the more we replicate the data, the more problems we have at chasing or conquering the goal of single version of truth. In fact, I kid that I think that AT&T, we actually have adopted the multiple versions of truth, techno theory, which is not where we want to be, but this is where we are. But we are conquering that with the synergies between Pure Storage and Vertica. This is what it leaves us with. And this is where we are challenged and that if each one of our siloed business units had their own stories, their own dedicated stories, and some of them had more money than others so they bought more storage. Some of them anticipating storing more data, and then they really did. Others are running out of space, but can't put anymore because their bodies aren't been replenished. So if you look at it from this side view here, we have a limited amount of compute or fixed compute dedicated to each one of these silos. And that's because of the, wanting to own your own. And the other part is that you are limited or wasting space, depending on where you are in the organization. So there were the synergies aren't just about the data, but actually the compute and the storage. And I wanted to tackle that challenge as well. So I was tackling the data. I was tackling the storage, and I was tackling the compute all at the same time. So my ask across the company was can we just please play together okay. And to do that, I knew that I wasn't going to tackle this by getting everybody in the same room and getting them to agree that we needed one account table, because they will argue about whose account table is the best account table. But I knew that if I brought the account tables together, they would soon see that they had so much redundancy that I can now start retiring data sources. I also knew that if I brought all the compute together, that they would all be happy. But I didn't want them to tackle across tackle each other. And in fact that was one of the things that all business units really enjoy. Is they enjoy the silo of having their own compute, and more or less being able to control their own destiny. Well, Vertica's subclustering allows just that. And this is exactly what I was hoping for, and I'm glad they've brought through. And finally, how did I solve the problem of the single account table? Well when you don't have dedicated storage, and you can separate compute and storage as Vertica in Eon Mode does. And we store the data on FlashBlades, which you see on the left and right hand side, of our container, which I can describe in a moment. Okay, so what we have here, is we have a container full of compute with all the Vertica nodes sitting in the middle. Two loader, we'll call them loader subclusters, sitting on the sides, which are dedicated to just putting data onto the FlashBlades, which is sitting on both ends of the container. Now today, I have two dedicated storage or common dedicated might not be the right word, but two storage racks one on the left one on the right. And I treat them as separate storage racks. They could be one, but i created them separately for disaster recovery purposes, lashing work in case that rack were to go down. But that being said, there's no reason why I'm probably going to add a couple of them here in the future. So I can just have a, say five to 10, petabyte storage, setup, and I'll have my DR in another 'cause the DR shouldn't be in the same container. Okay, but I'll DR outside of this container. So I got them all together, I leveraged subclustering, I leveraged separate and compute. I was able to convince many of my clients that they didn't need their own account table, that they were better off having one. I eliminated, I reduced latency, I reduced our ticketing I reduce our data quality issues AKA ticketing okay. I was able to expand. What is this? As work. I was able to leverage elasticity within this cluster. As you can see, there are racks and racks of compute. We set up what we'll call the fixed capacity that each of the business units needed. And then I'm able to ramp up and release the compute that's necessary for each one of my clients based on their workloads throughout the day. And so while they compute to the right before you see that the instruments have already like, more or less, dedicated themselves towards all those are free for anybody to use. So in essence, what I have, is I have a concert hall with a lot of seats available. So if I want to run a 10 chair Symphony or 80, chairs, Symphony, I'm able to do that. And all the while, I can also do the same with my loader nodes. I can expand my loader nodes, to actually have their own Symphony or write all to themselves and not compete with any other workloads of the other clusters. What does that change for our organization? Well, it really changes the way our database administrators actually do their jobs. This has been a big transformation for them. They have actually become data conductors. Maybe you might even call them composers, which is interesting, because what I've asked them to do is morph into less technology and more workload analysis. And in doing so we're able to write auto-detect scripts, that watch the queues, watch the workloads so that we can help ramp up and trim down the cluster and subclusters as necessary. There has been an exciting transformation for our DBAs, who I need to now classify as something maybe like DCAs. I don't know, I have to work with HR on that. But I think it's an exciting future for their careers. And if we bring it all together, If we bring it all together, and then our clusters, start looking like this. Where everything is moving in harmonious, we have lots of seats open for extra musicians. And we are able to emulate a cloud experience on-prem. And so, I want you to sit back and enjoy the Pure Vertica Symphony live at AT&T. (soft music) >> Joy: Thank you so much, John, for an informative and very creative look at the benefits that AT&T is getting from its Pure Vertica symphony. I do really like the idea of engaging HR to change the title to Data Conductor. That's fantastic. I've always believed that music brings people together. And now it's clear that analytics at AT&T is part of that musical advantage. So, now it's time for a short break. And we'll be back for our breakout sessions, beginning at 12 pm Eastern Daylight Time. We have some really exciting sessions planned later today. And then again, as you can see on Wednesday. Now because all of you are already logged in and listening to this keynote, you already know the steps to continue to participate in the sessions that are listed here and on the previous slide. In addition, everyone received an email yesterday, today, and you'll get another one tomorrow, outlining the simple steps to register, login and choose your session. If you have any questions, check out the emails or go to www.vertica.com/bdc2020 for the logistics information. There are a lot of choices and that's always a good thing. Don't worry if you want to attend one or more or can't listen to these live sessions due to your timezone. All the sessions, including the Q&A sections will be available on demand and everyone will have access to the recordings as well as even more pre-recorded sessions that we'll post to the BDC website. Now I do want to leave you with two other important sites. First, our Vertica Academy. Vertica Academy is available to everyone. And there's a variety of very technical, self-paced, on-demand training, virtual instructor-led workshops, and Vertica Essentials Certification. And it's all free. Because we believe that Vertica expertise, helps everyone accelerate their Vertica projects and the advantage that those projects deliver. Now, if you have questions or want to engage with our Vertica engineering team now, we're waiting for you on the Vertica forum. We'll answer any questions or discuss any ideas that you might have. Thank you again for joining the Vertica Big Data Conference Keynote Session. Enjoy the rest of the BDC because there's a lot more to come

everybody welcome to this digital coverage of the verdict of big data conference you're watching the cube and my name is Dave Galante it's my pleasure to invite in Ben white who's the senior database engineer at Domo been great to see you man thanks for coming on great to be here and here you know as I said you know earlier when we were off camera I really was hoping I could meet you face to face and in Boston this year but hey I'll take it and you know our community really wants to hear from experts like yourself but let's start with with domo is the company share with us what Domo does and what your role is there well if Parker can go straight to the official what Domo does is we provide we process data at bi to scale with we provide VI leverage a cloud scale in record time and so what that means is that you know we are a business operating system where we provide a number of analytical abilities to companies of all sizes but we do that at cloud scale and so I think that difference is quite a bit so a lot of your work if I understand it and just in terms of understanding with Domo does--is there's a lot of pressure in terms of being real-time it's not like you sometimes don't know what's coming at you so it's AD Hoch I wonder if you could sort of talk about that confirm that and maybe add a little color to it yeah absolutely absolutely that's probably the biggest challenge it is to being the operating Domo is that it is an ad hoc environment and certainly what that means is that you've got analysts and executives that are able to submit their own queries without very with very few limitations so from an engineering standpoint the challenge in that of course is that you don't have this predictable dashboard to plan for when it comes to performance planning and so it definitely presents some challenges for us that we've done some pretty unique things I think to address those right sounds like your background fits well with that I understand here if people have called you a database whisperer and an envelope pusher what does that mean to do a DBA in this in this day and age well the whisperer part is probably a lost art in the sense that it's not really sustainable right the idea that you know whatever it is I'm able to do with the database it has to be repeatable and so that's really what analytics comes in right and that's where pushing the envelope comes in in a little right away that's what vertical comes in with this open architecture and so as a person who has a reputation for saying I understand this is what our limitations should be but I think we can do more having a platform like vertical is such an open architecture kinda lets you push those limits by the bit I mean I've always felt like you know vertical when I first saw the Stonebreaker architecture and doctors some of the early founders I always felt like it was the Ferrari of databases certainly at the time and it sounds like you guys use it in that in that regard but talk a little bit more about how you use Vertica why in a ym ppy Vertica you know why why can't you do this with our DBMS educate us a little bit on some of the basics but for us it was part of what I mentioned when we start and we talked about the very nature of the demo platform where there's a an incredible amount of resiliency required and so Vertica the NPP platform of course allows us to build individual database clusters that can perform best for the workload that may be assigned to them so the the open the expandable the the the ability to grow vertically as your base grow those are all important factors when you're losing early on right without a real idea of how growth would be or what it would look like if you were kind of doing that something to the dark you looked at the vertical platforming you can see well as I grow I can kind of feel with this right I can do some some unique things with the platform in terms of this poking architecture that will allow me to not have to make all my decisions today right about Harlem so you're using Vertica I know at least in part you you working with AWS as well can you describe sort of your environment that you give anything on Prem is everything in the cloud what's your setup sure we have a hybrid cloud environment where we have a significant presence in public files in our own private cloud and so yeah having said that we certainly have a really an extensive presence I will say an AWS and so they're definitely the partner of our when it comes to providing the databases the server power that we need to operator but from the standpoint of engineering and architecting a database what was some of the challenges that you faced when you had to create that hybrid architecture what did you face and how did you overcome that well you know some of the there are some things we need faced in terms of wine and made it easy that Vertica and AWS have their own they play well together we'll say that and so vertical is designed to reprise I'm gonna AWS and so that part of it the care of itself not our own private cloud and being able to connect that because our public clouds has been a part of our own engineering ability and again I don't want to make a little light of it it's certainly not impossible and so we've some of the challenges though this pertains to the database really were in their early days that you mentioned when we talked a little bit earlier about marathas most recent Eon mode and I'm sure you'll get to that but when I think of our early challenges some of the early challenges were the architecture of enterprise mode when I talk about all of these this idea that we could have unique databases or database clusters of different sizes so this elasticity that's really if you know that the enterprise architecture that's not necessarily dandified architecture so we added this Munich things I think to overcome that right early to get around the rigidness though enterprise yeah I mean I hear you right Enterprise is complex and and you like when things are hardened and fossilized but in your ad hoc environment that's not what you needed so talking more about Aeon mode what what is e on mode for you and how do you apply it what are some of the challenges and opportunities there that you found um so the opportunities were certainly in its elastic architecture the ability to separate the storage immediately meant that for some of the unique data paths that we wanted to take right we could do that fairly quickly certainly we could expand databases right quickly but more importantly now you could reduce because previously in the past right when I mention the Enterprise Architect with the idea of growing a database in itself has its pain right as far as the time it takes to speed the data in that but to read to then think about taking that database back down no Innova though all of us under the eon right you had this elasticity where you could kind of start to think about auto scaling where you go up and down and maybe used to save some money or maybe you could improve performance or maybe in needham and at a time when the customers needed most in a real way right so it was definitely a game in that regard I always have to talk to the customers because I get to you know I hear from the vendor what they say and I think they sort of validate it so you know Vertica talks a lot about separating compute and storage they're not the only one from an architectural standpoint to do that but Vertica stresses that they're the only one that does that with a hybrid architecture they can do it off ram they can do it in the cloud from your experience well first of all is it true you may or may not know it is that advantageous to you and if so why well first of all it's certainly true earlier in some of the original beta ethnic for the arm prim GI mode stuff we I was able to participate in it and be aware of it so it's certainly a reality day I'm it's actually supported on pure spirit with flash played and it's time quite impressive you know for who who that who that will be for tough one a Spartacus question that they're probably still answering but I think obviously some enterprise users that probably have some hybrid cloud right they have some architecture they have some hardware that their sales want to make you so we certainly would probably fit into one of their you know their market segments that they would say we might be the wants to look at on pram er mo begin the the beauty of it is the elasticity right that the idea that you could have this and so a lot of times so I want to go back real quick to separating them and you know we start by separating it and I like to think of it maybe more as like decoupling because a new in a true way it's not necessary separated there's ultimately you bring the compute and the doors back together but to be able to typically couple it quickly replace knows bring in those that's certainly fits I think what we were trying to do in building this Emma I'll me let the ecosystem that could respond to a unknown or of a customer demand I see thank you for that clarification because you're right it's really not separating its decoupling in it that's important because you can scale them independently but you still need compute and you still need storage to run you your workloads but from a cost standpoint you're not to buy it in in chunks you can you can't buy granular segments for whatever your workload requires is that is that the correct understanding yeah and to be able to the ability to be able to reuse compute throw it in a scenario of AWS or even in the scenario your on-prem solution you've got this data that's safest here and ask for your in your storage but then the compute that you have you can reuse that right you could have a scenario that you have some query that needs more analytic more firepower more memory more what have you that you haven't so you can kind of move to the next important right that's maybe more important then and I grow them separately can I can I borrow it can I borrow that computer use for my perfect give it back type of thing and you can do that when you're so easily a couple different ooh all right and likewise if you have a down period where customers aren't using it you'd like to be able to not use that if you no longer require if you'd like to give it back go in it open the door to a lot of those things that allow performance and cross the spark to meet up we're going to ask you a question winsome pure a couple times are you using pure flash blade on-prem is that correct that is the solution that is supported that is supported by Vertica for the on print so at this point we were we have been discuss with them about some our own PLC's for that time before again we back to the idea of how do we see ourselves using it and so we've certainly discussed the feasibility of bringing it in and give it a job but that's not something we're Oh happily all right now then what is Domo for Domo tell us about that we really started this this idea even in the company where we say you know we should be using Domo in our everyday business the sales folks the marketing folks right everybody we're gonna use Domo it's a business platform for us in the engineering team it was kind of like well if we use Domo say for instance to be better at the database engineers now we've pointed Domo edits tell fried verdict is running Domo in the background for some degree and then we turn around and say hey Domo how can we better at running you and so it became this kind of cool thing we played with where we're now able to put some dumb methods together where we can actually do their eye we can monitor using our platform it's really good at processing large amounts of data and spitting out useful analytics right we take those analytics out make recommendation changes that the day so now you've got still more for Domo happening it allows us to sit at home and and work now even when we have to even before we had to well you know look look at us here right it couldn't mean in Boston physically we're now meeting remote you're you're on a hot spot because you got some weather and your satellite internet and in Atlanta and we're having a great conversation so so we're here with with Ben white who's the senior database engineer at Domo I want to ask you about some of the envelope-pushing that you've done around autonomous you hear that that word thrown around a lot means a lot of things to a lot of different people how do you look at autonomous and how does it fit with Eon and some of the other things that you're doing you know I'm a tall amidst the idea of economy is something that I don't even know that I'm I have already ready to define and so even in my discussion I often mention it as a road to it exactly where it is it's hard to pin down because there's always this idea how much trust do you give right to the system or how much how much is truly autonomous how much authority is being intervened by us the engineers so I do hate on using it but on this road towards autonomy when we look at what would how we're using Domo and even what that really means to vertical because in a lot of my examples and a lot of the things that we've engineered a demo work designs maybe over something I thought was a limitation day and so many times Oh as we've done that verdict is kind of met us like right after we've kind of engineered our architecture stuff than we thought it felt on our side Vertica has some released it kinda addresses it so the autonomy idea and the idea that we could analyzed metadata make recommendations and then execute those recommendations without intervention is that road to autonomy and once the databases start able to do that you can see in our ad-hoc environment how that would be pretty pretty useful where with literally millions of queries every hour trying to figure out what's the best you know probably for years I felt like I I T folks sometimes we really did not want that automation they wanted the knobs to turn but but I wonder if you comment I feel as though the level of complexity now with cloud with on-prem with you know hybrid multi clouds the scale the speed the real-time it just gets the pace is just too much for for humans and so it's almost like you know the industries is gonna have to capitulate to the Machine and then really trust the machine but I'm sitting I'm still sensing from you a little bit of hesitation there but light at the end of the tunnel I wonder if you could comment sure I think that in the light of the tunnel is even in recent months in recent we've really began incorporating more machine learning in artificial intelligence to the model right and back to where we're saying it so I do feel they were getting close for too finding conditions that we don't know about because right now our system is kind of a rule rules based system where we've said well these are the things that we should be looking for these are the things that we think are a problem to mature to the point where the database is recognized and anomalies and taken on at imagining saying these are problems you didn't know happen and that's kind of the next step right identifying the things you didn't know and that's where that's the path we're on now and that's probably more exciting even then kind of nailing down all the things you think you know and to figure out what we don't know yet so I want to close with I know you're a prominent member of the respected member of the Vertica a customer advisory board you know without divulging anything confidential to me what are the kinds of things that you want Vertica to do going forward I think some of the end a in database autonomy the ability to take some of the recommendations that we know we can derive from the metadata that already exists in the platform and start to execute some of the recommendation another thing we talk about and I'm gonna pretty open about talking to it is talking about it is the new version of the database designer I think it's something that I'm sure they're working on lightweight something that can give us that's database design without the overhead those are two things I think as they nail or particularly the database designer as they respect that they'll really have all the components in place to do in based economy and I think that's just some victory where they're headed yeah nice well Ben listen I really appreciate you coming on your a thought leader be very open open-minded verdict is you know really open community I mean they've always been quite transparent in terms of where they're going it's just awesome to have guys like you on the cube to share with our community so thank you so much and hopefully we can meet face to face currently absolutely will you stay safe in Boston I'm one of my favorite towns and so no doubt when this when the doors get back open I'll be from coming down or coming I'm gonna do work take care all right and thank you for watching everybody Villante with a cube we're here covering the virtual Vertica of big data conference you [Music]

>> 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