>> Announcer: It's theCUBE covering the Virtual Vertica Big Data Conference 2020, brought to you by Vertica. >> Hi, everybody. Welcome to this digital coverage of the Vertica Big Data Conference. You're watching theCUBE and my name is Dave Volante. It's my pleasure to invite in Ben White, who's the Senior Database Engineer at Domo. Ben, 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 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 Domo as the company. Share with us what Domo does and what your role is there. >> Well, if I can go straight to the official what Domo does is we provide, we process data at BI scale, we-we-we provide BI leverage at cloud scale in record time. And so what that means is, 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 differentiates us quite a bit. >> So a lot of your work, if I understand it, and just in terms of understanding what Domo does, 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-hoc. I wonder if you could sort of talk about that, confirm that, maybe add a little color to it. >> Yeah, absolutely, absolutely. That's probably the biggest challenge it is to being, to 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 with out very... With very few limitations. So from an engineering standpoint, that challenge in that of course is that you don't have this predictable dashboard to plan for, when it comes to performance planning. So it definitely presents some challenges for us that we've done some pretty unique things, I think, to address those. >> So it sounds like your background fits well with that. I understand your people have called you a database whisperer and an envelope pusher. What does that mean to a DBA in this day and age? >> 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 where analytics comes in, right? That's where pushing the envelope comes in. And in a lot of ways that's where Vertica 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 Vertica, with such an open architecture, kind of lets you push those limits quite a bit. >> I mean I've always felt like, you know, Vertica, when I first saw the stone breaker architecture and talked to 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 regard. But talk a little bit more about how you use Vertica, why, you know, why MPP, why Vertica? You know, why-why can't you do this with RDBMS? Educate us, a little bit, on, sort of, the basics. >> For us it was, part of what I mentioned when we started, when we talked about the very nature of the Domo platform, where there's an incredible amount of resiliency required. And so Vertica, the MPP platform, of course, allows us to build individual database clusters that can perform best for the workload that might be assigned to them. So the open, the expandable, the... The-the ability to grow Vertica, right, as your base grows, those are all important factors, when you're choosing early on, right? Without a real idea of how growth would be or what it will look like. If you were kind of, throwing up something to the dark, you look at the Vertica platform and you can see, well, as I grow, I can, kind of, build with this, right? I can do some unique things with the platform in terms of this open architecture that will allow me to not have to make all my decisions today, right? (mutters) >> So, you're using Vertica, I know, at least in part, you're working with AWS as well, can you describe sort of your environment? Do you give anything on-prem, is everything in cloud? What's your set up look like? >> 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 would say, in AWS. So, they're definitely the partner of our when it comes to providing the databases and the server power that we need to operate on. >> From a standpoint of engineering and architecting a database, what were 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 were some things we faced in terms of, one, it made it easy that Vertica and AWS have their own... They play well together, we'll say that. And so, Vertica was designed to work on AWS. So that part of it took care of it's self. Now our own private cloud and being able to connect that to our public cloud has been a part of our own engineering abilities. And again, I don't want to make little, make light of it, it certainly not impossible. And so we... Some of the challenges that pertain to the database really were in the early days, that you mentioned, when we talked a little bit earlier about Vertica's most recent eon mode. And I'm sure you'll get to that. But when I think of early challenges, some of the early challenges were the architecture of enterprise mode. When I talk about all of these, this idea that we can have unique databases or database clusters of different sizes, or this elasticity, because really, if you know the enterprise architecture, that's not necessarily the enterprise architecture. So we had to do some unique things, I think, to overcome that, right, early. To get around the rigidness of enterprise. >> Yeah, I mean, I hear you. Right? Enterprise is complex and you like when things are hardened and fossilized but, in your ad hoc environment, that's not what you needed. So talk more about eon mode. What is eon mode for you and how do you apply it? What are some of the challenges and opportunities there, that you've found? >> So, the opportunities were certainly in this elastic architecture and the ability to separate in 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. More importantly, now you can reduce. Because previously, in the past, right, when I mentioned the enterprise architecture, the idea of growing a database in itself has it's pain. As far as the time it takes to (mumbles) the data, and that. Then think about taking that database back down and (telephone interference). All of a sudden, with eon, right, we had this elasticity, where you could, kind of, start to think about auto scaling, where you can go up and down and maybe you could save some money or maybe you could improve performance or maybe you could meet demand, At a time where customers need it most, in a real way, right? So it's definitely a game changer in that regard. >> I always love to talk to the customers because I get to, you know, I hear from the vendor, what they say, and then I like to, sort of, validate it. So, you know, Vertica talks a lot about separating compute and storage, and they're not the only one, from an architectural standpoint who do that. But Vertica stresses it. They're the only one that does that with a hybrid architecture. They can do it on-prem, they can do it in the cloud. From your experience, well first of all, is that true? You may or may not know, but is that advantageous to you, and if so, why? >> Well, first of all, it's certainly true. Earlier in some of the original beta testing for the on-prem eon modes that we... I was able to participate in it and be aware of it. So it certainly a realty, they, it's actually supported on Pure storage with FlashBlade and it's quite impressive. You know, for who, who will that be for, tough one. It's probably Vertica's 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 they themselves, want to make use of. We certainly would probably fit into one of their, you know, their market segments. That they would say that we might be the ones to look at on-prem eon mode. Again, the beauty of it is, the elasticity, right? The idea that you could have this... So a lot of times... So I want to go back real quick to separating compute. >> Sure. Great. >> You know, we start by separating it. And I like to think of it, maybe more of, like, the up link. Because in a true way, it's not necessarily separated because ultimately, you're bringing the compute and the storage back together. But to be able to decouple it quickly, replace nodes, bring in nodes, that certainly fits, I think, what we were trying to do in building this kind of ecosystem that could respond to unknown of a customer query or of a customer demand. >> I see, thank you for that clarification because you're right, it's really not separating, it's decoupling. And that's important because you can scale them independently, but you still need compute and you still need storage to run your work load. But from a cost standpoint, you don't have to buy it in chunks. You can buy in granular segments for whatever your workload requires. Is that, is that the correct understanding? >> Yeah, and to, the ability to able to reuse compute. So in the scenario of AWS or even in the scenario of your on-prem solution, you've got this data that's safe and secure in (mumbles) computer storage, but 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-more fire power, more memory, more what have you that you have. And so you can kind of move between, and that's important, right? That's maybe more important than can I grow them separately. Can I, can I borrow it. Can I borrow that compute you're using for my (cuts out) and give it back? And you can do that, when you're so easily able to decouple the compute and put it where you want, 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 it, you're not going to get it back. 'Cause it-it opened the door to a lot of those things that allowed performance and process department to meet up. >> I wonder if I can ask you a question, you mentioned Pure a couple of times, are you using Pure FlashBlade on-prem, is that correct? >> That is the solution that is supported, that is supported by Vertica for the on-prem. (cuts out) So at this point, we have been discussing with them about some our own POCs for that. Before, again, we're back to the idea of how do we see ourselves using it? And so we certainly discuss the feasibility of bringing it in and giving it the (mumbles). But that's not something we're... Heavily on right now. >> And what is Domo for Domo? Tell us about that. >> Well it really started as this idea, even in the company, where we say, we should be using Domo in our everyday business. From the sales folk to the marketing folk, right. Everybody is going to use Domo, it's a business platform. For us in 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 at itself, right? Vertica's running Domo in the background to some degree and then we turn around and say, "Hey Domo, how can we better at running you?" So it became this kind of cool thing we'd play with. We're now able to put some, some methods together where we can actually do that, right. Where we can monitor using our platform, that's really good at processing large amounts of data and spitting out useful analytics, right. We take those analytics down, make recommendation changes at the-- For now, you've got Domo for Domo happening and it allows us to sit at home and work. Now, even when we have to, even before we had to. >> Well, you know, look. Look at us here. Right? We couldn't meet in Boston physically, we're now meeting remote. You're on a hot spot because you've got some weather in your satellite internet in Atlanta and we're having a great conversation. So-so, we're here with Ben White, who's a 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 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 you're doing? >> You know, I... Autonomous and the idea idea of autonomy is something that I don't even know if that I have already, ready to define. And so, even in my discussion, I often mention it as a road to it. Because exactly where it is, it's hard to pin down, because there's always this idea of how much trust do you give, right, to the system or how much, how much is truly autonomous? How much already is being intervened by us, the engineers. So I do hedge on using that. But on this road towards autonomy, when we look at, what we're, how we're using Domo. And even what that really means for Vertica, because in a lot of my examples and a lot of the things that we've engineered at Domo, were designed to maybe overcome something that I thought was a limitation thing. And so many times as we've done that, Vertica has kind of met us. Like right after we've kind of engineered our architecture stuff, that we thought that could help on our side, Vertica has a release that kind of addresses it. So, the autonomy idea and the idea that we could analyze metadata, make recommendations, and then execute those recommendations without innervation, is that road to autonomy. Once the database is properly able to do that, you could see in our ad hoc environment how that would be pretty useful, where with literally millions of queries every hour, trying to figure out what's the best, you know, profile. >> You know for- >> (overlapping) probably do a better job in that, than we could. >> For years I felt like IT folks sometimes were really, did not want that automation, they wanted the knobs to turn. But I wonder if you can comment. I feel as though the level of complexity now, with cloud, with on-prem, with, you know, hybrid, multicloud, the scale, the speed, the real time, it just gets, the pace is just too much for humans. And so, it's almost like the industry is going to have to capitulate to the machine. And then, really trust the machine. But I'm still sensing, from you, a little bit of hesitation there, but light at the end of the tunnel. I wonder if you can comment? >> Sure. I think the light at the end of the tunnel is even in the recent months and recent... We've really begin to incorporate more machine learning and artificial intelligence into the model, right. And back to what we're saying. So I do feel that we're getting closer to 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 we should be looking for, these are the things that we think are a problem." To mature to the point where the database is recognizing anomalies and taking on pattern (mutters). 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 the path we're on now. And it's probably more exciting even than, kind of, nailing down all the things you think you know. We figure out what we don't know yet. >> So I want to close with, I know you're a prominent member of the, a respected member of the Vertica Customer Advisory Board, and you know, without divulging anything confidential, what are the kinds of things that you want Vertica to do going forward? >> Oh, I think, some of the in dated base for autonomy. The ability to take some of the recommendations that we know can derive from the metadata that already exists in the platform and start to execute some of the recommendations. And another thing we've talked about, and I've been pretty open about talking to it, talking about it, is the, a new version of the database designer, I think, is something that I'm sure they're working on. Lightweight, something that can give us that database design without the overhead. Those are two things, I think, as they nail or basically the database designer, as they respect that, they'll really have all the components in play to do in based autonomy. And I think that's, to some degree, where they're heading. >> Nice. Well Ben, listen, I really appreciate you coming on. You're a thought leader, you're very open, open minded, Vertica is, you know, a 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 theCUBE to-to share with our community. So thank you so much and hopefully we can meet face-to-face shortly. >> Absolutely. Well you stay safe in Boston, one of my favorite towns and so no doubt, when the doors get back open, I'll be coming down. Or coming up as it were. >> Take care. All right, and thank you for watching everybody. Dave Volante with theCUBE, we're here covering the Virtual Vertica Big Data Conference. (electronic music)

hello everybody and thank you for joining us today at the virtual Vertica BBC 2020 today's breakout session is entitled the road to autonomous database management how Domo is delivering SLA for less my name is su LeClair I'm the director of marketing at Vertica and I'll be your host for this webinar joining me is Ben white senior database engineer at Domo but before we begin I want to 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 below the slides and click Submit there will be a Q&A session at the end of the presentation we'll answer as many questions as we're able to during that time any questions that we aren't able to address or drew our best to answer them offline alternatively you can visit vertical forums to post your questions there after the session our engineering team is planning to join the forum to keep the conversation going also as a reminder you can maximize your screen by clicking the double arrow button in the lower right corner of the slide and yes this virtual session is being recorded and will be available to view on demand this week we'll send you notification as soon as it's ready now let's get started then over to you greetings everyone and welcome to our virtual Vertica Big Data conference 2020 had we been in Boston the song you would have heard playing in the intro would have been Boogie Nights by heatwaves if you've never heard of it it's a great song to fully appreciate that song the way I do you have to believe that I am a genuine database whisperer then you have to picture me at 3 a.m. on my laptop tailing a vertical log getting myself all psyched up now as cool as they may sound 3 a.m. boogie nights are not sustainable they don't scale in fact today's discussion is really all about how Domo engineers the end of 3 a.m. boogie nights again well I am Ben white senior database engineer at Domo and as we heard the topic today the road to autonomous database management how Domo is delivering SLA for less the title is a mouthful in retrospect I probably could have come up with something snazzy er but it is I think honest for me the most honest word in that title is Road when I hear that word it evokes for me thoughts of the journey and how important it is to just enjoy it when you truly embrace the journey often you look up and wonder how did we get here where are we and of course what's next right now I don't intend to come across this too deep so I'll submit there's nothing particularly prescient and simply noticing the elephant in the room when it comes to database economy my opinion is then merely and perhaps more accurately my observation the office context imagine a place where thousands and thousands of users submit millions of ad-hoc queries every hour now imagine someone promised all these users that we could deliver bi leverage at cloud scale in record time I know what many of you should be thinking who in the world would do such a thing of course that news was well received and after the cheers from executives and business analysts everywhere and chance of Keep Calm and query on finally started to subside someone that turns an ass that's possible we can do that right except this is no imaginary place this is a very real challenge we face the demo through imaginative engineering demo continues to redefine what's possible the beautiful minds at Domo truly embrace the database engineering paradigm that one size does not fit all that little philosophical nugget is one I would pick up while reading the white papers and books of some guy named stone breaker so to understand how I and by extension Domo came to truly value analytic database administration look no further than that philosophy and what embracing it would mean it meant really that while others were engineering skyscrapers we would endeavor to build Datta neighborhoods with a diverse kapala G of database configuration this is where our journey at Domo really gets under way without any purposeful intent to define our destination not necessarily thinking about database as a service or anything like that we had planned this ecosystem of clusters capable of efficiently performing varied workloads we achieve this with custom configurations for node count resource pool configuration parameters etc but it also meant concerning ourselves with the unattended consequences of our ambition the impact of increased DDL activities on the catalog system overhead in general what would be the management requirements of an ever-evolving infrastructure we would be introducing multiple points of failure what are the advantages the disadvantages those types of discussions and considerations really help to define what would be the basic characteristics of our system the database itself needed to be trivial redundant potentially ephemeral customizable and above all scalable and we'll get more into that later with this knowledge of what we were getting into automation would have to be an integral part of development one might even say automation will become the first point of interest on our journey now using popular DevOps tools like saltstack terraform ServiceNow everything would be automated I mean it discluded everything from larger multi-step tasks like database designs database cluster creation and reboots to smaller routine tasks like license updates move-out and projection refreshes all of this cool automation certainly made it easier for us to respond to problems within the ecosystem these methods alone still if our database administration reactionary and reacting to an unpredictable stream of slow query complaints is not a good way to manage a database in fact that's exactly how three a.m. Boogie Nights happen and again I understand there was a certain appeal to them but ultimately managing that level of instability is not sustainable earlier I mentioned an elephant in the room which brings us to the second point of interest on our road to autonomy analytics more specifically analytic database administration why our analytics so important not just in this case but generally speaking I mean we have a whole conference set up to discuss it domo itself is self-service analytics the answer is curiosity analytics is the method in which we feed the insatiable human curiosity and that really is the impetus for analytic database administration analytics is also the part of the road I like to think of as a bridge the bridge if you will from automation to autonomy and with that in mind I say to you my fellow engineers developers administrators that as conductors of the symphony of data we call analytics we have proven to be capable producers of analytic capacity you take pride in that and rightfully so the challenge now is to become more conscientious consumers in some way shape or form many of you already employ some level of analytics to inform your decisions far too often we are using data that would be categorized as nagging perhaps you're monitoring slow queries in the management console better still maybe you consult the workflows analyzing how about a logging and alerting system like sumo logic if you're lucky you do have demo where you monitor and alert on query metrics like this all examples of analytics that help inform our decisions being a Domo the incorporation of analytics into database administration is very organic in other words pretty much company mandated as a company that provides BI leverage a cloud scale it makes sense that we would want to use our own product could be better at the business of doma adoption of stretches across the entire company and everyone uses demo to deliver insights into the hands of the people that need it when they need it most so it should come as no surprise that we have from the very beginning use our own product to make informed decisions as it relates to the application back engine in engineering we call it our internal system demo for Domo Domo for Domo in its current iteration uses a rules-based engine with elements through machine learning to identify and eliminate conditions that cause slow query performance pulling data from a number of sources including our own we could identify all sorts of issues like global query performance actual query count success rate for instance as a function of query count and of course environment timeout errors this was a foundation right this recognition that we should be using analytics to be better conductors of curiosity these types of real-time alerts were a legitimate step in the right direction for the engineering team though we saw ourselves in an interesting position as far as demo for demo we started exploring the dynamics of using the platform to not only monitor an alert of course but to also triage and remediate just how much economy could we give the application what were the pros and cons of that Trust is a big part of that equation trust in the decision-making process trust that we can mitigate any negative impacts and Trust in the very data itself still much of the data comes from systems that interacted directly and in some cases in directly with the database by its very nature much of the data was past tense and limited you know things that had already happened without any reference or correlation to the condition the mayor to those events fortunately the vertical platform holds a tremendous amount of information about the transaction it had performed its configurations the characteristics of its objects like tables projections containers resource pools etc this treasure trove of metadata is collected in the vertical system tables and the appropriately named data collector tables as a version 9 3 there are over 190 tables that define the system tables while the data collector is the collection of 215 components a rich collection can be found in the vertical system tables these tables provide a robust stable set of views that let you monitor information about your system resources background processes workload and performance allowing you to more efficiently profile diagnose and correlate historical data such as low streams query profiles to pool mover operations and more here you see a simple query to retrieve the names and descriptions of the system tables and an example of some of the tables you'll find the system tables are divided into two schemas the catalog schema contains information about persistent objects and the monitor schema tracks transient system States most of the tables you find there can be grouped into the following areas system information system resources background processes and workload and performance the Vertica data collector extends system table functionality by gathering and retaining aggregating information about your database collecting the data collector mixes information available in system table a moment ago I show you how you get a list of the system tables in their description but here we see how to get that information for the data collector tables with data from the data collecting tables in the system tables we now have enough data to analyze that we would describe as conditional or leading data that will allow us to be proactive in our system management this is a big deal for Domo and particularly Domo for demo because from here we took the critical next step where we analyze this data for conditions we know or suspect lead to poor performance and then we can suggest the recommended remediation really for the first time we were using conditional data to be proactive in a database management in record time we track many of the same conditions the Vertica support analyzes via scrutinize like tables with too many production or non partition fact tables which can negatively affect query performance and life in vertical in viral suggests if the table has a data a time step column you recommend the partitioning by the month we also can track catalog sizes percentage of total memory and alert thresholds and trigger remediations requests per hour is a very important metric in determining when a trigger are scaling solution tracking memory usage over time allows us to adjust resource pool parameters to achieve the optimal performance for the workload of course the workload analyzer is a great example of analytic database administration I mean from here one can easily see the logical next step where we were able to execute these recommendations manually or automatically be of some configuration parameter now when I started preparing for this discussion this slide made a lot of sense as far as the logical next iteration for the workload analyzing now I left it in because together with the next slide it really illustrates how firmly Vertica has its finger on the pulse of the database engineering community in 10 that OS management console tada we have the updated work lies will load analyzer we've added a column to show tuning commands the management console allows the user to select to run certain recommendations currently tuning commands that are louder and alive statistics but you can see where this is going for us using Domo with our vertical connector we were able to then pull the metadata from all of our clusters we constantly analyze that data for any number of known conditions we build these recommendations into script that we can then execute immediately the actions or we can save it to a later time for manual execution and as you would expect those actions are triggered by thresholds that we can set from the moment nyan mode was released to beta our team began working on a serviceable auto-scaling solution the elastic nature of AI mode separated store that compute clearly lent itself to our ecosystems requirement for scalability in building our system we worked hard to overcome many of the obstacles they came with the more rigid architecture of enterprise mode but with the introduction is CRM mode we now have a practical way of giving our ecosystem at Domo the architectural elasticity our model requires using analytics we can now scale our environment to match demand what we've built is a system that scales without adding management overhead or our necessary cost all the while maintaining optimal performance well we're really this is just our journey up to now and which begs the question what's next for us we expand the use of Domo for Domo within our own application stack maybe more importantly we continue to build logic into the tools we have by bringing machine learning and artificial intelligence to our analysis and decision making really do to further illustrate those priorities we announced the support for Amazon sage maker autopilot at our demo collusive conference just a couple of weeks ago for vertical the future must include in database economy the enhanced capabilities in the new management console to me are clear nod to that future in fact with a streamline and lightweight database design process all the pieces should be in place versions deliver economists database management itself we'll see well I would like to thank you for listening and now of course we will have a Q&A session hopefully very robust thank you [Applause]

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]

>> Narrator: From theCUBE studios in Palo Alto in Boston, connecting with top leaders all around the world, this is theCUBE Conversation. >> Hi everybody, I'm Dave Vellante, and welcome to the Vertica Big Data Conference virtual. This is our digital presentation, wall to wall coverage actually, of the Vertica Big Data Conference. And with me is Jeff Healy, who directs product marketing at Vertica. Jeff, good to see you. >> Good to see you, Dave. Thanks for the opportunity to chat. >> You're very welcome Now I'm excited about the products that you guys announced and you're hardcore into product marketing, but we're going to talk about the Vertica Big Data Conference. It's been a while since you guys had this. Obviously, new owner, new company, some changes, but that new company Microfocus has announced that it's investing, I think the number was $70 million into two areas. One was security and the other, of course, was Vertica. So we're really excited to be back at the virtual Big Data Conference. And let's hear it from you, what are your thoughts? >> Yeah, Dave, thanks. And we love having theCUBE at all of these events. We're thrilled to have the next Vertica Big Data Conference. Actually it was a physical event, we're moving it online. We know it's going to be a big hit because we've been doing this for some time particularly with two of the webcast series we have every month. One is under the Hood Webcast Series, which is led by our engineers and the other is what we call a Data Disruptors Webcast Series, which is led by all customers. So we're really confident this is going to be a big hit we've seen the registration spike. We just hit 1,000 and we're planning on having about 1,000 at the physical event. It's growing and growing. We're going to see those big numbers and it's not going to be a one time thing. We're going to keep the conversation going, make sure there's plenty of best practices learning throughout the year. >> We've been at all the big BDCs and the first one's were really in the heart of the Big Data Movement, really exciting time and the interesting thing about this event is it was always sort of customers talking to customers. There wasn't a lot of commercials, an intimate event. Of course I loved it because it was in our hometown. But I think you're trying to carry that theme obviously into the digital sphere. Maybe you can talk about that a little bit. >> Yeah, Dave, absolutely right. Of course, nothing replaces face to face, but everything that you just mentioned that makes it special about the Big Data Conference, and you know, you guys have been there throughout and shown great support in talking to so many customers and leaders and what have you. We're doing the same thing all right. So we had about 40 plus sessions planned for the physical event. We're going to run half of those and we're not going to lose anything though, that's the key point. So what makes the Vertica Big Data Conference really special is that the only presenters that are allowed to present are either engineers, Vertica engineers, or best practices engineers and then customers. Customers that actually use the product. There's no sales or marketing pitches or anything like that. And I'll tell you as far as the customer line up that we have, we've got five or six already lined up as part of those 20 sessions, customers like Uber, customers like the Trade Desk, customers like Phillips talking about predictive maintenance, so list goes on and on. You won't want to miss it if you're on the fence or if you're trying to figure out if you want to register for this event. Best part about it, it's all free, and if you can't attend it live, it will be live Q&A chat on every single one of those sessions, we promise we'll answer every question if we don't get it live, as we always do. They'll all be available on demand. So no reason not to register and attend or watch later. >> Thinking about the content over the years, in the early days of the Big Data Conference, of course Vertica started before the whole Big Data Conference meme really took off and then as it took off, plugged right into it, but back then the discussion was a lot of what do I do with big data, Gartner's three Vs and how do I wrangle it all, and what's the best approach and this stuff is, Hadoop is really complicated. Of course Vertica was an alternative to RDBMS that really couldn't scale or give that type of performance for analytical databases so you had your foot in that door. But now the conversation that's interesting your theme, it's win big with data. Of course, the physical event was at the Encore, which is the new Casino in Boston. But my point is, the conversation is no longer about, how to wrangle all this data, you know how to lower the cost of storing this data, how to make it go faster, and actually make it work. It's really about how to turn data into insights and transform your organizations and quote and quote, win with big data. >> That's right. Yeah, that's great point, Dave. And that's why I mean, we chose the title really, because it's about our customers and what they're able to do with our platform. And it's we know, it's not just one platform, all of the ecosystem, all of our incredible partners. Yeah it's funny when I started with the organization about seven years ago, we were closing lots of deals, and I was following up on case studies and it was like, Okay, why did you choose Vertica? Well, the queries went fast. Okay, so what does that mean for your business? We knew we're kind of in the early adopter stage. And we were disrupting the data warehouse market. Now we're talking to our customers that their volumes are growing, growing and growing. And they really have these analytical use cases again, talk to the value at the entire organization is gaining from it. Like that's the difference between now and a few years ago, just like you were saying, when Vertica disrupted the database market, but also the data warehouse market, you can speak to our customers and they can tell you exactly what's happening, how it's moving the needle or really advancing the entire organization, regardless of the analytical use case, whether it's an internet of things around predictive maintenance, or customer behavior analytics, they can speak confidently of it more than just, hey, our queries went faster. >> You know, I've mentioned before the Micro Focus investment, I want to drill into that a bit because the Vertica brand stands alone. It's a Micro Focus company, but Vertica has its own sort of brand awareness. The reason I've mentioned that is because if you go back to the early days of MPP Database, there was a spate of companies, startups that formed. And many if not all of those got acquired, some lived on with the Codebase, going into the cloud, but generally speaking, many of those brands have gone away Vertica stays. And so my point is that we've seen Vertica have staying power throughout, I think it's a function of the architecture that Stonebraker originally envisioned, you guys were early on the market had a lot of good customer traction, and you've been very responsive to a lot of the trends. Colin Mahony will talk about how you adopted and really embrace cloud, for example, and different data formats. And so you've really been able to participate in a lot of the new emerging waves that have come out to the market. And I would imagine some of that's cultural. I wonder if you could just address that in the context of BDC. >> Oh, yeah, absolutely. You hit on all the key points here, Dave. So a lot of changes in the industry. We're in the hottest industry, the tech industry right now. There's lots of competition. But one of the things we'll say in terms of, Hey, who do you compete with? You compete with these players in the cloud, open source alternatives, traditional enterprise data warehouses. That's true, right. And one of the things we've stayed true within calling is really kind of led the charge for the organization is that we know who we are right. So we're an analytical database platform. And we're constantly just working on that one sole Source Code base, to make sure that we don't provide a bunch of different technologies and databases, and different types of technologies need to stitch together. This platform just has unbelievable universal capabilities from everything from running analytics at scale, to in Database Machine Learning with the different approach to all different types of deployment models that are supported, right. We don't go to our companies and we say, yeah, we take care of all your problems but you have to stitch together all these different types of technologies. It's all based on that core Vertica engine, and we've expanded it to meet all these market needs. So Colin knows and what he believes and what he tells the team what we lead with, is that it lead with that one core platform that can address all these analytical initiatives. So we know who we are, we continue to improve on it, regardless of the pivots and the drastic measures that some of the other competitors have taken. >> You know, I got to ask you, so we're in the middle of this global pandemic with Coronavirus and COVID-19, and things change daily by the hour sometimes by the minute. I mean, every day you get up to something new. So you see a lot of forecasts, you see a lot of probability models, best case worst case likely case even though nobody really knows what that likely case looks like, So there's a lot of analytics going on and a lot of data that people are crunching new data sources come in every day. Are you guys participating directly in that, specifically your customers? Are they using your technology? You can't use a traditional data warehouse for this. It's just you know, too slow to asynchronous, the process is cumbersome. What are you seeing in the customer base as it relates to this crisis? >> Sure, well, I mean naturally, we have a lot of customers that are healthcare technology companies, companies, like Cerner companies like Philips, right, that are kind of leading the charge here. And of course, our whole motto has always been, don't throw away any the data, there's value in that data, you don't have to with Vertica right. So you got petabyte scale types of analytics across many of our customers. Again, just a few years ago, we called the customers a petabyte club. Now a majority of our large enterprise software companies are approaching those petabyte volumes. So it's important to be able to run those analytics at that scale and that volume. The other thing we've been seeing from some of our partners is really putting that analytics to use with visualizations. So one of the customers that's going to be presenting as part of the Vertica Big Data conferences is Domo. Domo has a really nice stout demo around be able to track the Coronavirus the outbreak and how we're getting care and things like that in a visual manner you're seeing more of those. Well, Domo embeds Vertica, right. So that's another customer of ours. So think of Vertica is that embedded analytical engine to support those visualizations so that just anyone in the world can track this. And hopefully as we see over time, cases go down we overcome this. >> Talk a little bit more about that. Because again, the BDC has always been engineers presenting to audiences, you guys have a lot of you just mentioned the demo by Domo, you have a lot of brand names that we've interviewed on theCUBE before, but maybe you could talk a little bit more about some of the customers that are going to be speaking at the virtual event, and what people can expect. >> Sure, yeah, absolutely. So we've got Uber that's presenting just a quick fact around Uber. Really, the analytical data warehouse is all Vertica, right. And it works very closely with Open Source or what have you. Just to quick stat on on Uber, 14 million rides per day, what Uber is able to do is connect the riders with the drivers so that they can determine the appropriate pricing. So Uber is going to be a great session that everyone will want to tune in on that. Others like the Trade Desk, right massive Ad Tech company 10 billion ad auctions daily, it may even be per second or per minute, the amount of scale and analytical volume that they have, that they are running the queries across, it can really only be accomplished with a few platforms in the world and that's Vertica that's another a hot one is with the Trade Desk. Philips is going to be presenting IoT analytical workloads we're seeing more and more of those across not only telematics, which you would expect within automotive, but predictive maintenance that cuts across all the original manufacturers and Philips has got a long history of being able to handle sensor data to be able to apply to those business cases where you can improve customer satisfaction and lower costs related to services. So around their MRI machines and predictive maintenance initiative, again, Vertica is kind of that heartbeat, that analytical platform that's driving those initiatives So list goes on and on. Again, the conversation is going to continue with the Data Disruptors in the Under Hood webcast series. Any customers that weren't able to present and we had a few that just weren't able to do it, they've already signed up for future months. So we're already booked out six months out more and more customer stories you're going to hear from Vertica.com. >> Awesome, and we're going to be sharing some of those on theCUBE as well, the BDC it's always been intimate event, one of my favorites, a lot of substance and I'm sure the online version, the virtual digital version is going to be the same. Jeff Healey, thanks so much for coming on theCUBE and give us a little preview of what we can expect at the Vertica BDC 2020. >> You bet. >> Thank you. >> Yeah, Dave, thanks to you and the whole CUBE team. Appreciate it >> Alright, and thank you for watching everybody. Keep it right here for all the coverage of the virtual Big Data conference 2020. You're watching theCUBE. I'm Dave Vellante, we'll see you soon

>> Narrator: Live from Las Vegas, Nevada, it's theCUBE covering AWS re:Invent 2016. Brought to you by AWS and its ecosystem partners. Now here is your host, John Furrier. >> Hey, welcome back everyone. We are here live in Las Vegas for AWS Amazon Web Services re:Invent 2016, their annual industry conference. The center of the universe in the tech world, 32,000 attendees, broke all records. It grew from 16,000 last year, almost double. I'm John Furrier with theCUBE. We are here getting all of the signal from the noise. Three days of wall-to-wall coverage. Our next guest Ramin Sayar, who's the President and CEO of Sumo Logic. Welcome to theCube, welcome back. >> Very well, thank you much. Nice to be here. >> So, when did you move over to Sumo Logic? >> So interestingly enough, it's two years this Friday. >> Okay so give us a quick update and then I want to dive into the relationship with Amazon. You guys clearly doing big data early. In the wave of the Hadoop is big data, but those other methodologies. Quick history of what you guys are doing now and status of the company. >> Sure. So the company is about seven years old. We were founded, born, actually bred on AWS. We don't have a single server in our place and interesting enough, the premise of founding Sumo, seven and a half years ago, actually was to build a multi-tenant SAAS-based machine data analytics platform to start to address a lot of the security, but also the operational issues that customers were facing. Our founders actually came from a security background and realized that rear-view mirror technologies and looking at historical aspects wasn't good enough. So low and behold, they made a big bet at that time, six years, almost seven years ago, to build exclusively on AWS and today, on an average day, we're ingesting about 70 terabytes of data, we're analyzing over 100 petabytes of data on AWS. >> So talk about the specific implementations. Obviously using all of the services, is there any particular ones, obviously storage, Glacier, you must be using some Glacier, but is it mostly S3, is it ElasticBox Storage? >> S3, C2, we use, obviously, some of the other services, but more importantly, we enable all of the services that AWS provides for their customers to be seamlessly supported by Sumo. So when you log into Sumo or you create a brand new account you give us your credentials, everything from Kinesis to Lambda, to EC2, to ElasticBox Storage, all of those are out-of-the-box that are supported. >> And you guys had a great booth last year. This huge booth, right in the front, with sumo wrestlers. I mean that stole the show in the age of Twitter and Instagram. The share of voice on that was pretty significant. >> Yeah I think there's an underpinning tone there, which is we want to wrangle your data, right. And no one knows big data more than a sumo. And we have earned the right now, after seven years in with 100 petabytes of data that we're analyzing every single day, to be a lot more prescriptive for customers in terms of how to approach the way they build, run, and secure these modern apps. >> We've been following you guys in context of the big data space. I don't think we've had a lot of briefings on the analysis side. I think we should get you guys certainly plugged-in with George Gilbert, our analyst, but what's interesting is the predictive marketing and then a lot of certain verticals were really in early on big data and you guys were there. What's evolved since then? Because now you're seeing, with AWS certainly, you've got streaming, you got redshift doing very well, the services that they've added on over the past few years has been pretty significantly and kind of right in your wheelhouse. >> Yeah. >> John: So what new use-cases are popping up now? What are you guys doing for business? What's some of the profile customers? How are they using Sumo and what's the value for them? >> Great question. So a few things we're seeing. One is with the availability of all these services that Amazon is providing, the cycle time for releasing new code and overall applications is becoming much less. And as a result there's not just a need to move to continuous integration or continuous deployment, it's about continuous updates. So the challenge that brings for a lot of our customers they need real-time visibility. We refer to that as continuous intelligence. So our platform is predicated on the fact that we have near real-time analytics streaming engine that as data is coming in, you can get visibility for your developers, you can get visibility for your operations teams, and you can get visibility for your security compliance teams. So let me give you a couple of examples. You asked for customers, Huddle is one of the customers they spoke about today. >> John: Jeff Frick and I love Huddle. >> Football videos, but you know they support Premier League, they support Aussie rule football, I mean there's a lot of sports right? And so they're uploading video and there's a great service not for just college or high school athletes, but professional athletes to understand their game and analyze their games. So underpinning that, actually Huddle's using Sumo to run their service, to manage their service. Not too distinct from Domo or Qualtrics or other customers like SalesForce, Adobe. We have customers like Land-O-Lakes. We do a lot in media and entertainment, gaming, online retailers. So what do they all have in common? They're either migrating to the cloud, one. Two, they're doing digital transformation or some sort of digital application initiative. Three, they need some way to get visibility real-time into their applications and services from a security perspective, but also an operational perspective. >> What's the driver for customers right now? Because one of the things we hear all the time is people are trying to account for their data. So analytics is kind of like this, well data warehouse was this old mentality, but now smart people started putting into mainstream, but now there's more of a data accountability aspect. The metadata, really valuable. How are customers doing that with you guys? 'Cause I can see them getting their toes wet with Sumo and then getting up and saying "Wow I can use some prescriptive analytics, predictive marketing", whatever the use-case could be, but now you gotta start thinking where's the data coming from and where's it accounted for. Is there a data economy? >> So what's interesting about that, you mentioned metadata, and that's what it's about. Our system, we ingest any type of structured or unstructured data. And we actually analyze a lot of the metadata. In fact, like I mentioned earlier, we're analyzing over 100 petabytes every single day on AWS. And so what we're able to actually help our customers do now is be much more prescriptive and provide insights as to the 1300 customers that are on Sumo, the 74% of them that run on AWS, about a quarter of them are using things like Lambda. Another two-thirds are using EC2, but how? And what types of queries are they doing? And what types of services are they building with Docker containers, or Mesosphere, or others of that type of services? So now we've actually entered a position where we're actually the trusted advisor for a lot of these companies in moving to the cloud, building new, modern apps because we've been doing it for seven and a half years. >> Yeah. >> Ramin: And so the metadata starts to become important because we actually put out a recent survey we called "The state of the modern app". And that whole report was premised on the 100 plus petabytes every single day over a six month period, how are customers using AWS, what services are they using and not using, and what should you consider? The number one thing we found in that report was only half of the customers, of which 74% of the 1300 run on AWS, were actually doing anything with CloudTrail with respect to security. That means the other half are potentially vulnerable to breach. >> John: Yeah. >> John: What percentage? >> 50%. >> So half were exposed. >> Half are exposed >> John: No audit at all. >> Ramin: No audit at all. So now we're actually proactively notifying them saying, "Hey listen for your type of deployment you're using these types of common services. Others similar to you should use the following." >> That brings up a good point. So let's unpack that because what that brings up is a lot of people get into data and they hear all this stuff in the news. Oh big data driven and you know they can drink the Kool-Aid and go "Okay I buy that vision." But there's some pretty urgent issues on the table that people got to deal with in the enterprise and or if they're cloud native and that is security. You mentioned it. I mean that has become such the low-hanging fruit for data analytics. So Splunk being very successful with that. Cyber, we talked to Teresa Carlson earlier. Their public-sector business is exploding, certainly with the CIA and others. I'm sure you guys got some of those clients. But that highlights that yeah that's all fine and dandy to do some nice stuff over here to figure out recommendation engine for this or that, when you got security holes out there. Are you seeing that on your end too? >> Well interestingly enough, that's how we started. We started with the goal of providing analytics and more importantly we wanted to democratize analytics initially for security in the cloud. And so, we actually before Amazon Web Services really built things like PKI or public key encryption or things around encrypting data transfer, we had built that into our system and service. So what we actually are able to do now is not only show how we can encrypt the data and do all this services, but show them how they should actually start to use CloudTrail and how they should architect these modern apps, and what things they should be concerned about from a vulnerability and risk point of view. One of the newest products that we just announced is in early-access around threat vulnerability and threat intelligence because now we're getting a 360 degree view for a lot of our customers because you saw today the hybrid announcement right? That's going to be there for a while. What Sumo allows a lot of our customers to do is from their on-premise data center to their CDNs to all their SAAS applications like SalesForce, or WorkDay, or DropBox, or Box to all those things running on ASH or Amazon and the like, we provide a whole 360 view. And we can actually now >> John: So you get real-time >> John: as well on that? >> Real-time. >> Ramin: So our system and service is predicated on a real-time data streaming engine. >> Yeah so you guys can coexist in multi-cloud world. >> Absolutely. >> John: That's your premise. >> Ramin: No pun intended right? (laughing) >> All right, let's talk about contextual data and what companies should do and why they should get you guys involved in the use-cases of going forward, planning. A lot of conversation here at re:Invent is AWS 2.0. They go on to the next level, Enterprise, a little bit more complicated than say Cloud Native greefield apps. How should they be thinking about their data? You've been doing this for seven years in AWS and you probably have clients that aren't on AWS some are, some aren't, that's the makeup. But generally what's the architecture? What should be holistic concept for CIO, CXO, or down to the practitioner level, what's the guiding principles? >> It starts with a fundamental principle of form follows function. And you know this is a sports analogy, but if you're not formed right, you're not going to function right. So a lot has to do with a conscious decision customers need to make in terms of how they're going to structure their teams and whether they're going to move to a true dev-ops model where they're pushing hourly, daily, weekly, and whether they need to or not for certain applications versus others. And then it goes into function in terms of how they start to architect their applications. What services they need to use. And we've actually learned that over seven and a half, eight years ourselves, seven which years were running on AWS. And so the advice often times we give to a lot of our customers is understand where the mission critical workloads that you need to migrate, categorize those. Second is, which of the greenfield apps you're building and why. And what type of retention and security policies do you need and these are the common services you should probably consider with AWS. And then third is, the other set of applications you don't really care about, leave them for now. Focus on your expertise here. >> It's really triaging the sequence or order of app rollout, basically. Well thanks for coming on theCube. Really appreciate Ramin. I want you to take a minute to close us out and talk about for the folks watching, what's new with Sumo Logic? Why should they be working with you? What's the pitch? What's new? What's relevant for you guys? >> Great, so obviously we're a big data company, but more specifically our service and our strategy was predicated on democratizing analytics. And so we refer to that as continuous intelligence. And so as this digital transformation is taking place, and we're seeing it here, we're seeing it across every part of the businesses, we are well suited for every company that's got either a migration effort or an active, new project going on AWS. And so we can provide a simple, secure, highly scalable machine data analytics platform as a service and that's what Sumo is all about. >> And your business plan for the next year is what? Knock down more customers? Do more product development? All of the above? Channel? What's the strategy? >> So good question. So on one hand we're introducing a new product. We've kind of hinted to some of that today with some threat intelligence. Second is, we just introduced a new product about a month ago that we're starting to monetize. It's about semi-structured data. And third is, we're gonna start to really expand our routes to market and channels. One of the things that we participated in recently with Amazon is the new Amazon SAAS marketplace program. We're in with a handful of companies that participate in design and development there. And so that allows very seamlessly for customers to come try, buy, and decide whether they go month-to-month, semi-annually, or year. >> Well that will accelerate the operational nature of your product. >> Absolutely, but that's the way we sell today. In fact, our whole business model is predicated on land and expand. You're probably familiar with this whole notion of cohorts. >> Yup. >> And that dollar retention. Well the median, if you look at PACCrest and Morgan Stanely and the other firms, tend to be 103 to 105. Best in class tends to be 110 to 115. We've been well north of 160 for 19 straight quarters. >> Well Jassie said that on his keynote today. The bombastic days of handwaving are over. If you don't see it right there, the value, in front of you, don't buy it. >> Don't buy it. >> It's really the marketplace's vision. >> That's marketplace vision and that's what we're all about at Sumo Logic. >> Ramir Sayar, President and CEO of Sumo Logic. Congratulations on your success. Continued success. This is theCube bringing you all the action live in Las Vegas for re:Invent 2016, I'm John Furrier. Be right back with more after this short break. You're watching theCube.