>>And good afternoon. Welcome back here on the Cub as to continue our coverage at aws Reinvent 22, win the Venetian here in Las Vegas, day two, it's Wednesday. Thanks. Still rolling. Quite a along. We have another segment for you as part of the Global Startup program, which is under the AWS Startup Showcase. I'm joined now by Vink at Viera, who is the CEO and co-founder of R Set. And good to see you, >>Sir. Thanks for having me here. Yeah, >>No, a real pleasure. Looking forward to it. So first off, for some of, for yours who might not be familiar with Roxette, I know you've been on the cube a little bit, so you're, you're an alum, but, but why don't you set the stage a little bit for Rock set and you know, where you're engaged with in terms of, with aws? >>Definitely. Rock Set is a realtime analytics database that is built for the cloud. You know, we make realtime applications possible in the cloud. You know, realtime applications need high concurrency, low latency query processing data needs to be fresh, your analytic needs to be fast. And, you know, we built on aws and that's why we are here. We are very, very proud partners of aws. We are in the AWS Accelerate program, and also we are in the startup program of aws. We are strategic ISV partner. And so yeah, we make real time analytics possible without all the cost and complexity barriers that are usually associated with it. And very, very happy to be part of this movement from batch to real time that is happening in the world. >>Right. Which is certainly an exciting trend. Right. I know great news for you, you made news yesterday, had an announcement involved with the intel with aws, who wants to share some of that >>With us too? Definitely. So, you know, one, one question that I always ask people is like, you know, if you go perspective that I share is like, if you go ask a hundred people, do you want fast analytics on fresh data or slow analytics on stale data? You know, a hundred out of a hundred would say fast and fresh, right? Sure. So then the question is, why hasn't this happened already? Why is this still a new trend that is emerging as opposed to something that everybody's taking for granted? It really comes down to compute efficiency, right? I think, you know, at the end of the day, real time analytics was always in using, you know, technologies that are, let's say 10 years ago using let's say processors that were available 10 years ago to, you know, three cloud, you know, days. There was a lot of complexity barriers associated with realtime analytics and also a lot of cost and, and performance barriers associated with it. >>And so Rox said from the, you know, from the very beginning, has been obsessing about building the most compute efficient realtime database in the world. And, you know, AWS on one hand, you know, allows us to make a consumption based pricing model. So you only pay for what you use. Sure. And that shatters all the cost barriers. But in terms of computer efficiency, what we announced yesterday is the Intel's third generation Zon scalable processors, it's code named Intel Ice Lake. When we port it over Rock said to that architecture, taking advantage of some of the instructions sets that Intel has, we got an 84% performance boost, 84, 84, 84. >>It's, it's incredible, right? >>It's, it's an incredible charts, it's an incredible milestone. It reduces the barrier even more in terms of cost and, you know, and, and pushes the efficiency and sets a, a really new record for how efficient realtime, you know, data processing can be in the cloud. And, and it's very, very exciting news. And so we used to benchmark ourselves against some of our other, you know, realtime, you know, did up providers and we were already faster and now we've set a, a much, much higher bar for other people to follow. >>Yep. And, and so what is, or what was it about real time that, that, you know, was such a barrier because, and now you've got the speed of, of course, obviously, and maybe that's what it was, but I think cost is probably part of that too, right? That's all part of that equation. I mean, real time, so elusive. >>Yeah. So real time has this inherent pattern that your data never stops coming. And when your data never stops coming, and you can now actually do analytics on that. Now, initially people start with saying, oh, I just want a real time dashboard. And then very quickly they realize, well, the dashboard is actually in real time. I'm not gonna be staring at the 24 7. Can you tap on my shoulder when something is off, something needs to be looked at. So in which case you're constantly also asking the question, is everything okay? Is everything all right? Do I need to, is is that something that I need to be, you know, double clicking on and, and following up on? So essentially very quickly in real time analytics, what happens is your queries never stop. The questions that you're asking on your data never stops. And it's often a program asking the question to detect anomalies and things like that. >>And your data never stops coming. And so compute is running 24 7. If you look at traditional data warehouses and data lakes, they're not really optimized for these kinds of workloads. They're optimized to store massive volumes of data and in a storage efficient format. And when an analyst comes and asks a question to generate a report, you can spin up a whole bunch of compute, generate the report and tear it all down when you're done. Well, that is not compute running 24 7 to continuously, you know, you know, keep ingesting the data or continuously keep answering questions. So the compute efficiency that is needed is, is much, much, much higher. Right? And that is why, you know, Rox was born. So from the very beginning, we're only built, you know, for these use cases, we have a, an extremely powerful SQL engine that can give you full feature SQL analytics in a very, very compute efficient way in the cloud. >>Right. So, so let's talk about the leap that you've made, say in the last two years and, and, and what's been the spur of that? What has been allowed you to, to create this, you know, obviously a, a different kind of an array for your customers from which to choose, but, but what's been the spark you think >>We touched upon this a little earlier, right? This spark is really, you know, the world going from batch to real time. So if you look at mainstream adoption of technologies like Apache, Kafka and Confluent doing a really good job at that. In, in, in growing that community and, and use cases, now businesses are now acquiring business data, really important business data in real time. Now they want to operationalize it, right? So, you know, extract based static reports and bi you know, business intelligence is getting replaced in all modern enterprises with what we call operational intelligence, right? Don't tell me what happened last quarter and how to plan this quarter better. Tell me what's happening today, what's happening right now. And it's, it's your business operations using data to make day to day decisions better that either grows your top line, compresses your bottom line, eliminates risk that are inherently creeping up in your business. >>Sure. You know, eliminate potential churn from a customer or fraud, you know, deduction and, and getting on top of, you know, that, you know, a minute into this, into, into an outage as opposed to an hour into the outage. Right? And so essentially I think businesses are now realizing that operational intelligence and operational analytics really, you know, allows them to leverage data and especially real time data to make their, you know, to grow their businesses faster and more efficiently. And especially in this kind of macro environment that is, you know, more important to have better unit economics in your business than ever before. Sure. And so that is really, I think that is the real market movement happening. And, and we are here to just serve that market. We are making it much, much easier for companies that have already adopted, you know, streaming technologies like Kafka and, and, and knows Canis MSK and all these technologies. Now businesses are acquiring these data in real time now. They can also get realtime analytics on the other end of it. Sure. >>You know, you just touched on this and, and I'd like to hear your thoughts about this, about, about the economic environment because it does drive decisions, right? And it does motivate people to look for efficiencies and maybe costs, you know, right. Cutting costs. What are you seeing right now in terms of that, that kind of looming influence, right? That the economy can have in terms of driving decisions about where investments are being made and what expectations are in terms of delivering value, more value for the buck? >>Exactly. I think we see across the board, all of our customers come back and tell us, we don't want to manage data infrastructure and we don't want to do kind of DIY open source clusters. We don't wanna manage and scale and build giant data ops and DevOps teams to manage that, because that is not really, you know, in their business. You know, we have car rental companies want to be better at car rentals, we want airlines to be a better airline, and they don't, don't want their, you know, a massive investment in DevOps and data ops, which is not really their core business. And they really want to leverage, you know, you know, fully managed and, you know, cloud offerings like Rock said, you know, built on aws, massively scalable in the cloud with zero operational overhead, very, very easy to get started and scale. >>And so that completely removes all the operational overhead. And so they can invest the resources they have, the manpower, they have, the calories that they have on actually growing their businesses because that is what really gonna allow them to have better unit economics, right? So everybody that is on my payroll is helping me grow my top line or shrink my bottom line, eliminate risk in my business and, and, and, and churn and, and fraud and other, and eliminate all those risks that are inherent in my business. So, so that is where I think a lot of the investments going. So gone are the days where, you know, you're gonna have these in like five to 10% team managing a very hard to operate, you know, open source data management clusters on EC two nodes in, in AWS and, and kind of DIYing it their way because those 10 people, you know, if all they do is just operational maintenance of infrastructure, which is a means to an end, you're way better off, you know, using a cloud, you know, a bond in the cloud built for the cloud solution like rock and eliminate all that cost and, and replace that with an operationally much, much simpler, you know, system to op, you know, to to work with such as, such as rock. >>So that is really the big trend that we are seeing why, you know, not only real time is going more and more mainstream cloud native solutions or the real future even when it comes to real time because the complexity barrier needs to be shattered and only cloud native solutions can actually, >>You get the two Cs cost and complexity, right. That you, you need to address. Exactly. Yeah, for sure. You know, what is it about building trust with your, with your clients, with your partners? Because you, you're talking about this cloud environment that, that everyone is talking about, right? Not everyone's made that commitment. There are still some foot draggers out there. How are you going about establishing confidence and establishing trust and, and, and providing them with really concrete examples of the values and the benefits that you can provide, you know, with, with these opportunities? >>So, you know, I grew up, so there's a few ways to to, to answer this question. I'll, I'll, I'll come, I'll cover all the angles. So in, in order to establish trust, you have to create value. They, you know, your customer has to see that with you. They were able to solve the problem faster, better, cheaper, and they're able to, you know, have a, the business impact they were looking for, which is why they started the project in the first place. And so establishing that and proving that, I think there's no equivalence to that. And, you know, I grew up at, at, you know, at Facebook back in the day, you know, I was managing online data infrastructure, okay. For Facebook from 2007 and 2015. And internally we always had this kind of culture of all the product teams building on top of the infrastructure that my team was responsible for. >>And so they were not ever, there was never a, a customer vendor relationship internally within Facebook that we're all like, we're all part of the same team. We're partnering here to have you, you know, to help you have a successful product launch. There's a very similar DNA that, that exists in Rock said, when our customers work with us and they come to us and we are there to make them successful, our consumption based pricing model also forces us to say they're not gonna really use Rock said and consume more. I mean, we don't make money until they consume, right? And so their success is very much integral part of our, our success. And so that I think is one really important angle on, you know, give us a shot, come and do an evaluation, and we will work with you to build the most efficient way to solve your problem. >>And then when you succeed, we succeed. So that I think is a very important aspect. The second one is AWS partnership. You know, we are an ISV partner, you know, AWS a lot of the time. That really helps us establish trust. And a lot of the time, one of the, the, the people that they look up to, when a customer comes in saying, Hey, what is, who is Rock? Said? You know, who are your friends? Yeah. Who are your friends? And then, you know, and then the AWS will go like, oh, you know, we'll tell you, you know, all these other successful case studies that R has, you know, you know, built up on, you know, the world's largest insurance provider, Europe's largest insurance provider. We have customers like, you know, JetBlue Airlines to Klarna, which is a big bator company. And so, so all these case studies help and, and, and, and platform and partners like AWS helps us, helps you amplify that, that, you know, and, and, and, and, and give more credibility. And last but not least, compliance matters. You know, being Soto type two compliant is, is a really important part of establishing trust. We are hip hop compliant now so that, you know, we can, you know, pi I phi data handling that. And so I think that will continue to be a part, a big part of our focus in improving the security, you know, functionality and, and capabilities that R set has in the cloud, and also compliance and, and the set of com, you know, you know, standards that we are gonna be compliant against. >>Well, I'm glad you hit on the AWS too, cause I did wanna bring that up. I, I appreciate that and I know they appreciate the relationship as well. Thanks for the time here. It's been a pleasure. Awesome. Learning about Rockette and what you're up to. Thank you. >>You bet. >>It's a pleasure. Thank you. Vi ka. All right. You are watching the cube coverage here at AWS Reinvent 22. And on the cube, of course, the leader, the leader in high tech coverage.

(upbeat music) >> Hey everyone. Welcome to theCUBE's presentation of the AWS Startup Showcase. This is Data as Code, The Future of Enterprise Data and Analytics. This is also season two, episode two of our ongoing series with exciting partners from the AWS ecosystem who are here to talk with us about data and analytics. I'm your host, Lisa Martin. Two guests join me, one, a cube alumni. Venkat Venkataramani is here CEO & Co-Founder of Rockset. Good to see you again. And Doug Moore, VP of cloud platforms at Command Alkon. You're here to talk to me about how Command Alkon implemented real time analytics in just days with Rockset. Guys, welcome to the program. >> Thanks for having us. >> Yeah, great to be here. >> Doug, give us a little bit of a overview of Command Alkon, what type of business you are? what your mission is? That good stuff. >> Yeah, great. I'll pref it by saying I've been in this industry for only three years. The 30 years prior I was in financial services. So this was really exciting and eye opening. It actually plays into the story of how we met Rockset. So that's why I wanted to preface that. But Command Alkon is in the business, is in the what's called The Heavy Building Materials Industry. And I had never heard of it until I got here. But if you think about large projects like building buildings, cities, roads anything that requires concrete asphalt or just really big trucks, full of bulky materials that's the heavy building materials industry. So for over 40 years Command Alkon has been the north American leader in providing software to quarries and production facilities to help mine and load these materials and to produce them and then get them to the job site. So that's what our supply chain is, is from the quarry through the development of these materials, then out to the to a heavy building material job site. >> Got it, and now how historically in the past has the movement of construction materials been coordinated? What was that like before you guys came on the scene? >> You'll love this answer. So 'cause, again, it's like a step back in time. When I got here the people told me that we're trying to come up with the platform that there are 27 industries studied globally. And our industry is second to last in terms of automation which meant that literally everything is still being done with paper and a lot of paper. So when one of those, let's say material is developed, concrete asphalt is produced and then needs to get to the job site. They start by creating a five part printed ticket or delivery description that then goes to multiple parties. It ends up getting touched physically over 50 times for every delivery. And to give you some idea what kind of scale it is there are over 330 million of these type deliveries in north America every year. So it's really a lot of favor and a lot of manual work. So that was the state of really where we were. And obviously there are compelling reasons certainly today but even 3, 4, 5 years ago to automate that and digitize it. >> Wow, tremendous potential to go nowhere but up with the amount of paper, the lack of, of automation. So, you guys Command Alkon built a platform, a cloud software construction software platform. Talk to me of about that. Why you built it, what was the compelling event? I mean, I think you've kind of already explained the compelling event of all the paper but give us a little bit more context. >> Yeah. That was the original. And then we'll get into what happened two years ago which has made it even more compelling but essentially with everything on premises there's really in a huge amount of inefficiency. So, people have heard the enormous numbers that it takes to build up a highway or a really large construction project. And a lot of that is tied up in these inefficiencies. So we felt like with our significant presence in this market, that if we could figure out how to automate getting this data into the cloud so that at least the partners in the supply chain could begin sharing information. That's not on paper a little bit closer to real time that we could make has an impact on everything from the timing it takes to do a project to even the amount of carbon dioxide that's admitted, for example from trucks running around and being delayed and not being coordinated well. >> So you built the connect platform you started on Amazon DynamoDB and ran into some performance challenges. Talk to us about the, some of those performance bottlenecks and how you found Venkat and Rockset. >> So from the beginning, we were fortunate, if you start building a cloud three years ago you're you have a lot of opportunity to use some of the what we call more fully managed or serverless offerings from Amazon and all the cloud vendors have them but Amazon is the one we're most familiar with throughout the past 10 years. So we went head first into saying, we're going to do everything we can to not manage infrastructure ourselves. So we can really focus on solving this problem efficiently. And it paid off great. And so we chose dynamo as our primary database and it still was a great decision. We have obviously hundreds of millions of billions of these data points in dynamo. And it's great from a transactional perspective, but at some point you need to get the data back out. And what plays into the story of the beginning when I came here with no background basically in this industry, is that, and as did most of the other people on my team, we weren't really sure what questions were going to be asked of the data. And that's super, super important with a NoSQL database like dynamo. You sort of have to know in advance what those usage patterns are going to be and what people are going to want to get back out of it. And that's what really began to strain us on both performance and just availability of information. >> Got it. Venkat, let's bring you into the conversation. Talk to me about some of the challenges that Doug articulated the, is industry with such little automation so much paper. Are you finding that still out there for in quite a few industries that really have nowhere to go but up? >> I think that's a very good point. We talk about digital transformation 2.0 as like this abstract thing. And then you meet like disruptors and innovators like Doug, and you realize how much impact, it has on the real world. But now it's not just about disrupting, and digitizing all of these records but doing it at a faster pace than ever before, right. I think this is really what digital transformation in the cloud really enable tools you do that, a small team in a, with a very very big mission and responsibility like what Doug team have been, shepherding here. They're able to move very, very, very fast, to be able to kind of accelerate this. And, they're not only on the forefront of digitizing and transforming a very big, paper-heavy kind of process, but real-time analytics and real time reporting is a requirement, right? Nobody's wondering where is my supply chain three days ago? Are my, one of the most important thing in heavy construction is to keep running on a schedule. If you fall behind, there's no way to catch up because there's so many things that falls apart. Now, how do you make sure you don't fall behind, realtime analytics and realtime reporting on how many trucks are supposed to be delivered today? Halfway through the day, are they on track? Are they getting behind? And all of those things is not just able to manage the data but also be able to get reporting and analytics on that is a extremely important aspect of this. So this is like a combination of digital transformation happening in the cloud in realtime and realtime analytics being in the forefront of it. And so we are very, very happy to partner with digital disruptors like Doug and his team to be part of this movement. >> Doug, as Venkat mentioned, access to real time data is a requirement that is just simple truth these days. I'm just curious, compelling event wise was COVID and accelerator? 'Cause we all know of the supply chain challenges that we're all facing in one way or the other, was that part of the compelling event that had you guys go and say, we want to do DynamoDB plus Rockset? >> Yeah, that is a fantastic question. In fact, more so than you can imagine. So anytime you come into an industry and you're going to try to completely change or revolutionize the way it operates it takes a long time to get the message out. Sometimes years, I remember in insurance it took almost 10 years really to get that message out and get great adoption and then COVID came along. And when COVID came along, we all of a sudden had a situation where drivers and the foreman on the job site didn't want to exchange the paperwork. I heard one story of a driver taping the ticket for signature to the foreman on a broomstick and putting it out his windows so that he didn't get too close. It really was that dramatic. And again, this is the early days and no one really has any idea what's happening and we're all working from home. So we launched, we saw that as an opportunity to really help people solve that problem and understand more what this transformation would mean in the long term. So we launched internally what we called Project Lemonade obviously from, make lemonade out of lemons, that's the situation that we were in and we immediately made some enhancements to a mobile app and then launched that to the field. So that basically there's now a digital acceptance capability where the driver can just stay in the vehicle and the foreman can be anywhere, look at the material say it's acceptable for delivery and go from there. So yeah, it made a, it actually immediately caused many of our customers hundreds to begin, to want to push their data to the cloud for that reason just to take advantage of that one capability >> Project lemonade, sounds like it's made a lot of lemonade out of a lot of lemons. Can you comment Doug on kind of the larger trend of real time analytics and logistics? >> Yeah, obviously, and this is something I didn't think about much either not knowing anything about concrete other than it was in my driveway before I got here. And that it's a perishable product and you've got that basically no more than about an hour and a half from the time you mix it, put it in the drum and get it to the job site and pour it. And then the next one has to come behind it. And I remember I, the trend is that we can't really do that on paper anymore and stay on top of what has to be done we'll get into the field. So a foreman, I recall saying that when you're in the field waiting on delivery, that you have people standing around and preparing the site ready to make a pour that two minutes is an eternity. And so, working a real time is all always a controversial word because it means something different to anyone, but that gave it real, a real clarity to mean, what it really meant to have real time analytics and how we are doing and where are my vehicles and how is this job performing today? And I think that a lot of people are still trying to figure out how to do that. And fortunately, we found a great tool set that's allowing us to do that at scale. Thankfully, for Rockset primarily. >> Venkat talk about it from your perspective the larger trend of real time analytics not just in logistics, but in other key industries. >> Yeah. I think we're seeing this across the board. I think, whether, even we see a huge trend even within an enterprise different teams from the marketing team to the support teams to more and more business operations team to the security team, really moving more and more of their use cases from real time. So we see this, the industries that are the innovators and the pioneers here are the ones for whom real times that requirement like Doug and his team here or where, if it is all news, it's no news, it's useless, right? But I think even within, across all industries, whether it is, gaming whether it is, FinTech, Bino related companies, e-learning platforms, so across, ed tech and so many different platforms, there is always this need for business operations. Some, certain aspects certain teams within large organizations to, have to tell me how to win the game and not like, play Monday morning quarterback after the game is over. >> Right, Doug, let's go back at you, I'm curious with connects, have you been able to scale the platform since you integrated with Rockset? Talk to us about some of the outcomes that you've achieved so far? >> Yeah, we have, and of course we knew and we made our database selection with dynamo that it really doesn't have a top end in terms of how much information that we can throw at it. But that's very, very challenging when it comes to using that information from reporting. But we've found the same thing as we've scaled the analytics side with Rockset indexing and searching of that database. So the scale in terms of the number of customers and the amount of data we've been able to take on has been, not been a problem. And honestly, for the first time in my career, I can say that we've always had to add people every time we add a certain number of customers. And that has absolutely not been the case with this platform. >> Well, and I imagine the team that you do have is far more, sorry Venkat, far more strategic and able to focus on bigger projects. >> It, is, and, you've amazed at, I mean Venkat hit on a couple of points that it's in terms of the adoption of analytics. What we found is that we are as big a customer of this analytic engine as our customers are because our marketing team and our sales team are always coming to us. Well how many customers are doing this? How many partners are connected in this way? Which feature flags are turned on the platform? And the way this works is all data that we push into the platform is automatically just indexed and ready for reporting analytics. So we really it's no additional ad of work, to answer these questions, which is really been phenomenal. >> I think the thing I want to add here is the speed at which they were able to build a scalable solution and also how little, operational and administrative overhead that it has cost of their teams, right. I think, this is again, realtime analytics. If you go and ask hundred people, do you want fast analytics on realtime data or slow analytics on scale data, people, no one would say give me slow and scale. So, I think it goes back to again our fundamental pieces that you have to remove all the cost and complexity barriers for realtime analytics to be the new default, right? Today companies try to get away with batch and the pioneers and the innovators are forced to solve, I know, kind of like address some of these realtime analytics challenges. I think with the platforms like the realtime analytics platform, like Rockset, we want to completely flip it on its head. You can do everything in real time. And there may be some extreme situations where you're dealing with like, hundreds of petabytes of data and you just need an analyst to generate like, quarterly reports out of that, go ahead and use some really, really good batch base system but you should be able to get anything, and everything you want without additional cost or complexity, in real time. That is really the vision. That is what we are really enabling here. >> Venkat, I want to also get your perspective and Doug I'd like your perspective on this as well but that is the role of cloud native and serverless technologies in digital disruption. And what do you see there? >> Yeah, I think it's huge. I think, again and again, every customer, and we meet, Command Alkon and Doug and his team is a great example of this where they really want to spend as much time and energies and calories that they have to, help their business, right? Like what, are we accomplishing trying to accomplish as a business? How do we enable, how do we build better products? How do we grow revenue? How do we eliminate risk that is inherent in the business? And that is really where they want to spend all of their energy not trying to like, install some backend software, administer build IDL pipelines and so on and so forth. And so, doing serverless on the compute side of that things like AWS lambda does and what have you. And, it's a very important innovation but that isn't, complete the story or your data stack also have to become serverless. And, that is really the vision with Rockset that your entire realtime analytics stack can be operating and managing. It could be as simple as managing a serverless stack for your compute environments like your APS servers and what have you. And so I think that is going to be a that is for here to stay. This is a path towards simplicity and simplicity scales really, really well, right? Complexity will always be the killer that'll limit, how far you can use this solution and how many problems can you solve with that solution? So, simplicity is a very, very important aspect here. And serverless helps you, deliver that. >> And Doug your thoughts on cloud native and serverless in terms of digital disruption >> Great point, and there are two parts to the scalability part. The second one is the one that's more subtle unless you're in charge of the budget. And that is, with enough effort and enough money that you can make almost any technology scale whether it's multiple copies of it, it may take a long time to get there but you can get there with most technologies but what is least scalable, at least that I as I see that this industry is the people, everybody knows we have a talent shortage and these other ways of getting the real time analytics and scaling infrastructure for compute and database storage, it really takes a highly skilled set of resources. And the more your company grows, the more of those you need. And that is what we really can't find. And that's actually what drove our team in our last industry to even go this way we reached a point where our growth was limited by the people we could find. And so we really wanted to break out of that. So now we had the best of both scalable people because we don't have to scale them and scalable technology. >> Excellent. The best of both worlds. Isn't it great when those two things come together? Gentlemen, thank you so much for joining me on "theCUBE" today. Talking about what Rockset and Command Alkon are doing together better together what you're enabling from a supply chain digitization perspective. We appreciate your insights. >> Great. Thank you. >> Thanks, Lisa. Thanks for having us. >> My pleasure. For Doug Moore and Venkat Venkatramani, I'm Lisa Martin. Keep it right here for more coverage of "theCUBE", your leader in high tech event coverage. (upbeat music)

(upbeat music) >> Hello, welcome to this CUBE Conversation featuring Rockset CEO and co-founder Venkat Venkataramani who selected season two of the AWS Startup Showcase featured company. Before co-founding Rockset Venkat was the engineering director at Facebook, infrastructure team responsible for all the data infrastructure, storing all there at Facebook and he's here to talk real-time analytics. Venkat welcome back to theCUBE for this CUBE Conversation. >> Thanks John. Thanks for having me again. It's a pleasure to be here. >> I'd love to read back and I know you don't like to take a look back but Facebook was huge hyperscale data at scale, really a leading indicator of where everyone is kind of in now so this is about real-time analytics moving from batch to theme here. You guys are at the center, we've talked about it before here on theCUBE, and so let's get in. We've a couple different good talk tracks to dig into but first I want to get your reaction to this soundbite I read on your blog post. Fast analytics on fresh data is better than slow analytics on stale data, fresh beats stale every time, fast beats slow in every space. Where does that come from obviously it makes a lot of sense nobody wants slow data, no one wants to bail data.(giggles) >> Look, we live in the information era. Businesses do want to track, ask much information as possible about their business and want to use data driven decisions. This is now like motherhood and apple pie, no business would say that is not useful because there's more information than what can fit in one person's head that the businesses want to know. You can either do Monday morning quarterback or in the middle of the third quarter before the game is over, you're maybe six points down, you look at what plays are working today, you look at who's injured in your team and who's injured in your opponent and you try to come up with plays that can change the outcome of the game. You still need Monday morning quarterbacking that's not going anywhere, that's batch analytics, that's BI, classic BI, and what the world is demanding more and more is operational intelligence like help me run my business better, don't just gimme a great report at the end of the quarter. >> Yeah, this is the whole trend. Looking back is key to post more like all that good stuff but being present to make future decisions is a lot more mainstream now than ever was you guys are the center of it, and I want to get your take on this data driven culture because the showcase this year for this next episode of the showcase for Startup says, cloud stuff says, data as code something I'm psyched for because I've been saying in theCUBE for many years, data as code is almost as important as infrastructure as code. Because when you think about the application of data in real-time, it's not easy, it's a hard problem and two, you want to make it easy so this is the whole point of this data driven culture that you're on right now. Can you talk about how you see that because this is really one of the most important stories we've seen since the last inflection point. >> Exactly right. What is data driven culture which basically means you stop guessing. You look at the data, you look at what the data says and you try to come up with hypothesis it's still guardrail, it's a guiding light it's not going to tell you what to do, but you need to be able to interrogate your data. If every time you ask a question and it takes 20 minutes for you to get an answer from your favorite Alexa CD or what have you you are probably not going to ever use that device you will not try to be data driven and you can't really build that culture, so it's not just about visibility it's not just about looking back and getting analytics on how the business is doing, you need to be able to interrogate your data in real-time in an interactive fashion, and that I think is what real-time analytics gives you. This is what we say when we say fast analytics on real-time data that's what we mean, which is, as you make changes to your business on the course of your day-to-day work, week-to-week work, what changes are working? How much impact is it having? If something isn't working you have more questions to figure out why and being able to answer all of that is how you really build the data driven culture and it isn't really going to come from just looking at static reports at the end of the week and at the end of the quarter. >> To talk about the latency aspect of the term and how it relates to where it could be a false flag in the sense of you could say, well, we have low latency but you're not getting all the data. You got to get the data, you got to ingest it, make it addressable, query it, represent it, these are huge things when you factor in every single data where you're not guessing latency is a factor. Can you unpack what this new definition is all about and how do people understand whether they got it right or not. >> A great question. A lot of people say, is five minutes real-time? Because I used to run my thing every six hours. Now for us, if it's more than two seconds behind in terms of your data latency, data freshness, it's too old. When does the present become the past and the future hasn't arrived yet and we think it's about one to two seconds. And so everything we do at Rockset we only call it real-time if it can be within one to two seconds 'cause that's the present, that's what's happening now, if it's five minutes ago, it's already five minutes ago it's already past tense. So if you kind of break it down, you're absolutely right that you have to be able to bring data into a system in real-time without sacrificing freshness, and you store it in a way where you can get fast analytics out of that so Rockset is the only real-time data platform real-time analytics platform with built-in connectors so this is why we have built-in connectors where without writing a single line of code, you can bring in data in real-time from wherever you happen to be managing it today. And when data comes into Rockset now the latency is about query processing. What is the point of bringing in data in real-time if every question you're going to ask is going to still take 20 minutes to come back. Well, then you might as well batch data in order to load it, so there I think we have a conversion indexing, we have a real-time indexing technology that allows data as it comes in real-time to be organized in a way and how a distributor SQL engine on top of that so as long as you can frame your question using a SQL query you can ask any question on your real-time data and expect subsequent response time. So that I think is the the combination of the latency having two parts to it, one is how fresh is your data and how fast is your analytics, and you need both, with the simplicity of the cloud for you to really unlock and make real-time analytics to default, as opposed to let me try to do it and batch and see if I can get away with it, but if you really need real-time you have to be able to do both cut down and control your data latency on how fresh your data is, and also make it fast. >> You talk about culture, can you talk about the people you're working with and how that translates into your next topic which is business observability, the next play on words obviously observability if you can measure everything, there shouldn't be any questions that you can't ask. But it's important this culture is shifting from hardcore data engineering to business value kind of coming together at scale. This is kind of where you see the hardcore data folks really bringing that into the business can you talk about this? The people you're working with, and how that's translating to this business observability. >> Absolutely. We work with the world's probably largest Buy Now Pay Later company maybe they're in the top three, they have hundreds of millions of users 300,000+ merchants, working in so many different countries so many different payment methods and there's a very simple problem they have. Some part of their product, some part of their payment system is always down at any given point in time or it has a very high chance of not working. It's not the whole thing is down but, for this one merchant in Switzerland, Apple Pay could be not working and so all of those kinds of transactions might not be processing, and so they had a very classic cloud data warehouse based solution, accumulate all these payments, every six hours they would kind of process and look for anomalies and say, hey, these things needs to be investigated and a response team needs to be tackling these. The business was growing so fast. Those analytical jobs that would run every six hours in batch mode was taking longer than six hours to run and so that was a dead end. They came to Rockset, simply using SQL they're able to define all the metrics they care about across all of their dimensions and they're all accurate up to the second, and now they're able to run their models every minute. And in sort of six hours, every minute they're able find anomalies and run their statistical models, so that now they can protect their business better and more than that, the real side effect of that is they can offer much better quality of a product, much better quality of service to their customer so that the customers are very sticky because now they're getting into the state where they know something is wrong with one of their more merchants, even before the merchants realize that, and that allows them to build a much better product to their end users. So business observability is all about that. It's about do you know really what's happening in your business and can you keep tabs on it, in real-time, as you go about your business and this is what we call operational intelligence, businesses are really demanding operational intelligence a lot more than just traditional BI. >> And we're seeing it in every aspect of a company the digital transformation affects every single department. Sales use data to get big sales better, make the product better people use data to make product usage whether it's A/B testing whatnot, risk management, OPS, you name it data is there to drill down so this is a huge part of real-time. Are you finding that the business observability is maturing faster now or where do you put the progress of companies with respect to getting on board with the idea that this wave is here. >> I think it's a very good question. I would say it has gone mainstream primarily because if you look at technologies like Apache Kafka, and you see Confluent doing really really well, those technologies have really enabled now customers and business units, business functions across the spectrum, to be able to now acquire really really important business data in real-time. If you didn't have those mechanisms to acquire the data in real-time, well, you can't really do analytics and get operational intelligence on that. And so the majority is getting there and things are growing very fast as those kinds of technologies get better and better. SaaSification also is a very big component to it which is like more and more business apps are basically becoming SaaS apps. Now that allows everything to be in the cloud and being interconnected and now when all of those data systems are all interconnected, you can now have APIs that make data flow from one system to another all in happening in real-time, and that also unlocks a lot more potential for again, getting better operational intelligence for your enterprise, and there's a subcategory to this which is like B2B SaaS companies also having to build real-time interactive analytics embedded as part of their offering otherwise people wouldn't even want to buy it and so that it's all interconnected. I think the market is emerging, market is growing but it is gone mainstream I would say predominantly because, Kafka, Confluent, and these kinds of real-time data collection and aggregation kind of systems have gone mainstream and now you actually get to dream about operational intelligence which you couldn't even think about maybe five or 10 years ago. >> They're getting all their data together. So to close it out, take us through the bottom line real-time business observability, great for companies collecting their data, but now you got B2B, you got B2C, people are integrating partnerships where APIs are connecting, it could be third party business relationships, so the data collection is not just inside the company it's also outside. This is more value. This is the more of what's going on. >> Exactly. So more and more, instead of going to your data team and demanding real-time analytics what a lot of business units are doing is, they're going to the product analytics platform, the SaaS app they're using for covering various parts of their business, they go to them and demand, either this is my recruiting software, sales software, customer support, gimme more real-time insights otherwise it's not really that useful. And so there is really a huge uptake on all these SaaS companies now building real-time infrastructure powered by Rockset in many cases that actually ends up giving a lot of value to their end customers and that I think is kind of the proof of value for a SaaS product, all the workflows are all very, very important absolutely but almost every amazing SaaS product has an analytics tab and it needs to be fast, interactive and it needs to be real-time. It needs you talking about fresh insights that are happening and that is often in a B2B SaaS, application developers always comes and tell us that's the proof of value that we can show how much value that that particular SaaS application is creating for their customer. So I think it's all two sides of the same coin, large enterprises want to build it themselves because now they get more control about how exactly the problem needs to be solved and then there are also other solutions where you rely on a SaaS application, where you demand that particular application gives you. But at the end of the day, I think the world is going real-time and we are very, very happy to be part of this moment, operational intelligence. For every classic BI use case I think there are 10 times more operational intelligence use cases. As Rockset we are on a mission to eliminate all cost and complexity barriers and really really provide fast analytics on real-time data with the simplicity of the cloud and really be part of this moment. >> You guys having some fun right now these days through in the middle of all the action. >> Absolutely. I think we're growing very fast, we're hiring, we are onboarding as many customers as possible and really looking forward to being part of this moment and really accelerate this moment from business intelligence to operational intelligence. >> Well, Venkat great to see you. Thanks for coming on theCUBE as part of this CUBE Conversation, you're in the class of AWS Startup Showcase season two, episode two. Thanks for coming on. Keep it right there everyone watch more action from theCUBE. Your leader in tech coverage, I'm John Furrier your host. Thanks for watching. (upbeat music)

(mid tempo digital music) >> Welcome to today's session of theCUBE' presentation of the AWS startup showcase. This is New Breakthroughs and DevOps, Data Analytics, and Cloud Management Tools. The segment is featuring Rockset and we're going to be talking about data analytics. I'm your host, Lisa Martin, and today I'm joined by one of our alumni, Venkat Venkataramani, the co-founder and CEO of Rockset, and Carl Sjogreen, the co-founder and CPO of Seesaw Learning. We're going to be talking about the fast path to real-time analytics at Seesaw. Guys, Thanks so much for joining me today. >> Thanks for having us >> Thank you for having us. >> Carl, let's go ahead and start with you. Give us an overview of Seesaw. >> Yeah, so Seesaw is a platform that brings educators, students, and families together to create engaging and learning experiences. We're really focused on elementary aged students, and have a suite of creative tools and engaging learning activities that helps get their learning and ideas out into the world and share that with family members. >> And this is used by over 10 million teachers and students and family members across 75% of the schools in the US and 150 countries. So you've got a great big global presence. >> Yeah, it's really an honor to serve so many teachers and students and families. >> I can imagine even more so now with the remote learning being such a huge focus for millions and millions across the country. Carl, let's go ahead and get the backstory. Let's talk about data. You've a ton of data on how your product is being used across millions of data points. Talk to me about the data goals that you set prior to using Rockset. >> Yeah, so, as you can imagine with that many users interacting with Seesaw, we have all sorts of information about how the product is being used, which schools, which districts, what those usage patterns look like. And before we started working with Rockset, a lot of data infrastructure was really custom built and cobbled together a bit over the years. We had a bunch of batch jobs processing data, we were using some tools, like Athena, to make that data visible to our internal customers. But we had a very sort disorganized data infrastructure that really as we've grown, we realized was getting in the way of helping our sales and marketing and support and customer success teams, really service our customers in the way that we wanted to past. >> So operationalizing that data to better serve internal users like sales and marketing, as well as your customers. Give me a picture, Carl, of those key technology challenges that you knew you needed to solve. >> Yeah, well, at the simplest level, just understanding, how an individual school or district is using Seesaw, where they're seeing success, where they need help, is a critical question for our customer support teams and frankly for our school and district partners. a lot of what they're asking us for is data about how Seesaw is being used in their school, so that they can help target interventions, They can understand where there is an opportunity to double down on where they are seeing success. >> Now, before you found Rockset, you did consider a more traditional data warehouse approach, but decided against it. Talk to me about the decision why was a traditional data warehouse not the right approach? >> Well, one of the key drivers is that, we are heavy users of DynamoDB. That's our main data store and has been tremendous aid in our scaling. Last year we scaled with the transition to remote learning, most of our metrics by, 10X and Dynamo didn't skip a beat, it was fantastic in that environment. But when we started really thinking about how to build a data infrastructure on top of it, using a sort of traditional data warehouse, a traditional ETL pipeline, it wasn't going to require a fair amount of work for us to really build that out on our own on top of Dynamo. And one of the key advantages of Rockset was that it was basically plug and play for our Dynamo instance. We turned Rockset on, connected it to our DynamoDB and were able within hours to start querying that data in ways that we hadn't before. >> Venkat let's bring you into the conversation. Let's talk about the problems that you're solving for Seesaw and also the complimentary relationship that you have with DynamoDB. >> Definitely, I think, Seesaw, big fan of the product. We have two kids in elementary school that are active users, so it's a pleasure to partner with Seesaw here. If you really think about what they're asking for, what Carl's vision was for their data stack. The way we look at is business observability. They have many customers and they want to make sure that they're doing the right thing and servicing them better. And all of their data is in a very scalable, large scale, no SEQUEL store like DynamoDB. So it makes it very easy for you to build applications, but it's very, very hard to do analytics on it. Rockset had comes with all batteries included, including real-time data connectors, with Amazon DynamoDB. And so literally you can just point Rockset at any of your Dynamo tables, even though it's a no SEQUEL store, Rockset will in real time replicate the data and automatically convert them into fast SEQUEL tables for you to do analytics on. And so within one to two seconds of data getting modified or new data arriving in DynamoDB from your application, within one to two seconds, it's available for query processing in Rockset with full feature SEQUEL. And not just that, I think another very important aspect that was very important for Seesaw is not just that they wanted me to do batch analytics. They wanted their analytics to be interactive because a lot of the time we just say something is wrong. It's good to know that, but oftentimes you have a lot more followup questions. Why is it wrong? When did it go wrong? Is it a particular release that we did? Is it something specific to the school district? Are they trying to use some part of the product more than other parts of the product and struggling with it? Or anything like that. It's really, I think it comes down to Seesaw's and Carl's vision of what that data stack should serve and how we can use that to better serve the customers. And Rockset's indexing technology, and whatnot allows you to not only get real-time in terms of data freshness, but also the interactivity that comes in ad-hoc drilling down and slicing and dicing kind of analytics that is just our bread and butter . And so that is really how I see not only us partnering with Seesaw and allowing them to get the business observerbility they care about, but also compliment Dynamo transactional databases that are massively scalable, born in the cloud, like DynamoDB. >> Carl talked to me about that complimentary relationship that Venkat just walked us through and how that is really critical to what you're trying to deliver at Seesaw. >> Yeah, well, just to reiterate what Venkat said, I think we have so much data that any question you ask about it, immediately leads to five other questions about it. We have a very seasonal business as one example. Obviously in the summertime when kids aren't in school, we have very different usage patterns, then during this time right now is our critical back to school season versus a steady state, maybe in the middle of the school year. And so really understanding how data is trending over time, how it compares year over year, what might be driving those things, is something that frankly we just haven't had the tools to really dig into. There's a lot about that, that we are still beginning to understand and dig into more. And so this iterative exploration of data is incredibly powerful to expose to our product team, our sales and marketing teams to really understand where Seesaw's working and where we still have work do with our customers. And that's so critical to us doing a good job for schools in districts. >> And how long have you been using Rockset, Carl? >> It's about six months now, maybe a little bit longer. >> Okay, so during the pandemic. So talk to me a little bit about in the last 18 months, where we saw the massive overnight transition to remote learning and there's still a lot of places that are in that or a hybrid environment. How critical was it to have Rockset to fuel real-time analytics interactivity, particularly in a very challenging last 18 month time period? >> The last 18 months have been hard for everyone, but I think have hit teachers and schools maybe harder than anyone, they have been struggling with. And then, overnight transition to remote learning challenges of returning to the classroom hybrid learning, teachers and schools are being asked to stretch in ways they have never been stretched before. And so, our real focus last year was in doing whatever we could to help them manage those transitions. And data around student attendance in a remote learning situation, data around which kids were completing lessons and which kids weren't, was really critical data to provide to our customers. And a lot of our data infrastructure had to be built out to support answering those questions in this really crazy time for schools. >> I want to talk about the data set, but I'd like to go back to Venkat 'cause what's interesting about this story is Seesaw is a customer of Rockset, Venkat, is a customer of Seesaw. Talk to me Venkat about how this has been helpful in the remote learning that your kids have been going through the last year and a half. >> Absolutely. I have two sons, nine and ten year olds, and they are in fourth and fifth grade now. And I still remember when I told them that Seesaw is considering using Rockset for the analytics, they were thrilled, they were overjoyed because finally they understood what I do for a living. (chuckling) And so that was really amazing. I think, it was a fantastic dual because for the first time I actually understood what kids do at school. I think every week at the end of the week, we would use Seesaw to just go look at, "Hey, well, let's see what you did last week." And we would see not only what the prompts and what the children were doing in the classroom, but also the comments from the educators, and then they comment back. And then we were like, "Hey, this is not how you speak to an educators." So it was really amazing to actually go through that, and so we are very, very big fans of the product, we really look forward to using it, whether it is remote learning or not, we try to use it as a family, me, my wife and the kids, as much as possible. And it's a very constant topic of conversation, every week when we are working with the kids and seeing how we can help them. >> So from an observability perspective, it sounds like it's giving parents and teachers that visibility that really without it, you don't get. >> That's absolutely correct . I think the product itself is about making connections, giving people more visibility into things that are constantly happening, but you're not in the know. Like, before Seesaw, I used to ask the kids, "How was school today? "what happened in the class?" And they'll say, "It was okay." It would be a very short answer, it wouldn't really have the depth that we are able to get from Seesaw. So, absolutely. And so it's only right that, that level of observability and that level of... Is also available for their business teams, the support teams so that they can also service all the organizations that Seesaw's working with, not only the parents and the educators and the students that are actually using the product. >> Carl, let's talk about that data stack And then I'm going to open the can on some of those impacts that it's making to your internal folks. We talked about DynamoDB, but give me an visual audio, visual picture of the data stack. >> Yeah. So, we use DynamoDB as our database of record. We're now in the process of centralizing all of our analytics into Rockset. So that rather than having different BaaS jobs in different systems, querying that data in different ways, trying to really set Rockset up as the source of truth for analytics on top of Dynamo. And then on top of Rockset, exposing that data, both to internal customers for that interactive iterative SEQUEL style queries, but also bridging that data into the other systems our business users use. So Salesforce, for example, is a big internal tool and have that data now piped into Salesforce so that a sales rep can run a report on a prospect to reach out to, or a customer that needs help getting started with Seesaw. And it's all plumbed through the Rockset infrastructure. >> From an outcome standpoint, So I mentioned sales and marketing getting that visibility, being able to act on real time data, how has it impacted sales in the last year and a half? six months rather since , it's now since months using it. >> Well, I don't know if I can draw a direct line between those things, but it's been a very busy year for Seesaw, as schools have transitioned to remote learning. And our business is really largely driven by teachers discovering our free product, finding it valuable in their classroom, and then asking their school or district leadership to purchase a school wide subscription. It's a very bottoms up sales motion. And so data on where teachers are starting to use Seesaw is the key input into our sales and marketing discussions with schools and districts. And so understanding that data quickly in real time is a key part of our sales strategy and a key part of how we grow at Seesaw over time. >> And it sounds like Rockset is empowering those users, the sales and marketing folks to really fine tune their interactions with existing customers, prospective customers. And I imagine you on the product side in terms of tuning the product. What are some of the things Carl that you've learned in the last six months that have helped you make better decisions on what you want Seesaw to deliver in the future? >> Well, one of the things that I think has been really interesting is how usage patterns have changed between the classroom and remote learning. We saw per student usage of Seesaw increased dramatically over the past year, and really understanding what that means for how the product needs to evolve to better meet teacher needs, to help organize that information, since it's now a lot more of it, really helped motivate our product roadmap over the last year. We launched a new progress dashboard that helps teachers get an added glance view of what's happening in their classroom. That was really in direct response to the changing usage patterns, that we were able to understand with better insights into data. >> And those insights allow you to pivot and iterate on the product. Venkat I want to just go back to the AWS relationship for a second. You both talked about the complimentary nature of Rockset and DynamoDB. Here we are at the AWS Startup Showcase. Venkat just give the audience a little overview of the partnership that you guys have with AWS. >> Rockset fully runs on AWS, so we are customer of AWS. We are also a partner. There are lots of amazing cloud data products that AWS has, including DynamoDB or AWS Kinesis. And so one with which we have built in integrations. So if you're managing data in AWS, we compliment and we can provide, very, very fast interactive real-time analytics on all of your datasets. So the partnership has been wonderful, we're very excited to be in the Startup Showcase. And so I hope this continuous for years to come. >> Let's talk about the synergies between a Rockset and Seesaw for a second. I know we talked about the huge value of real time analytics, especially in today's world, where we've learned many things in the last year and a half, including that real-time analytics is no longer a nice to have for a lot of industries, 'cause I think Carl as you said, if you can't get access to the data, then there's questions we can't ask. Or we can't iterate on operations, if we wait seconds for every query to load, then there's questions we can't ask. Talk to me Venkat, about how Rockset is benefiting from what you're learning from Seesaw's usage of the technology? >> Absolutely. I mean, if you go to the first part of the question on why do businesses really go after real time. What is the drive here? You might have heard the phrase, the world is going from batch to real-time. What does it really mean? What's the driving factor there? Our take on it is, I think it's about accelerating growth. Seesaw's product being amazing and it'll continue to grow, it'll continue to be a very, very important product in the world. With or without Rockset, that will be true. The way we look at once they have real-time business observability, is that inherent growth that they have, they can reach more people, they can put their product in the hands of more and more people, they can iterate faster. And at the end of the day, it is really about having this very interesting platform, very interesting architecture to really make a lot more data driven decisions and iterate much more quickly. And so in batch analytics, if you were able to make, let's say five decisions a quarter, in real time analytics you can make five decisions a day. So that's how we look at it. So that is really, I think, what is the underpinnings of why the world is going from batch to real time. And what have we learned from having a Seesaw as a customer? I think Seesaw has probably one of the largest DynamoDB installations that we have looked at. I think, we're talking about billions and billions of records, even though they have tens of millions of active users. And so I think it has been an incredible partnership working with them closely, and they have had a tremendous amount of input on our product roadmap and some of that like role-based access control and other things have already being a part of the product, thanks to the continuous feedback we get from their team. So we're delighted about this partnership and I am sure there's more input that they have, that we cannot wait to incorporate in our roadmap. >> I imagine Venkat as well, you as the parent user and your kids, you probably have some input that goes to the Seesaw side. So this seems like a very synergistic relationship. Carl, a couple more questions for you. I'd love to know how in this... Here we are kind of back to school timeframe, We've got a lot of students coming back, they're still remote learning. What are some of the things that you're excited about for this next school year that do you think Rockset is really going to fuel or power for Seesaw? >> Yeah, well, I think schools are navigating yet another transition now, from a world of remote learning to a world of back to the classroom. But back to the classroom feels very different than it does at any other back to school timeframe. Many of our users are in first or second grade. We serve early elementary age ranges and some of those students have never been in a classroom before. They are entering second grade and never having been at school. And that's hard. That's a hard transition for teachers in schools to make. And so as a partner to those schools, we want to do everything we can to help them manage that transition, in general and with Seesaw in particular. And the more we can understand how they're using Seesaw, where they're struggling with Seesaw, as part of that transition, the more we can be a good partner to them and help them really get the most value out of Seesaw, in this new world that we're living in, which is sort of like normal, and in many ways not. We are still not back to normal as far as schools are concerned. >> I'm sure though, the partnership that you provide to the teachers and the students can be a game changer in these, and still navigating some very uncertain times. Carl, last question for you. I want you to point folks to where they can go to learn more about Seesaw, and how for all those parents watching, they might be able to use this with their families. >> Yeah, well, seesaw.me is our website, and you can go to seesaw.me and learn more about Seesaw, and if any of this sounds interesting, ask your teacher, if they're not using Seesaw, to give it a look. >> Seesaw.me, excellent. Venkat, same question for you. Where do you want folks to go to learn more about Rockset and its capabilities? >> Rockset.com is our website. There is a free trial for... $300 worth of free trial credits. It's a self service platform, you don't need to talk to anybody, all the pricing and everything is out there. So, if real-time analytics and modernizing your data stack is on your roadmap, go give it a spin. >> Excellent guys. Thanks so much for joining me today, talking about real-time analytics, how it's really empowering both the data companies and the users to be able to navigate in challenging waters. Venkat, thank you, Carl, thank you for joining us. >> Thanks everyone. >> Thanks Lisa. >> For my guests, this has been our coverage of the AWS Startup Showcase, New Breakthroughs in DevOps, Data Analytics and Cloud Management Tools. I am Lisa Martin. Thanks for watching. (mid tempo music)

(bright intro music) >> Welcome to this "Cube Conversation". I'm your host, Lisa Martin. This is part of our third AWS Start-up Showcase. And I'm pleased to welcome two gentlemen from Rockset, Venkat Venkataramani is here, the CEO and co-founder and Dhruba Borthakur, CTO and co-founder. Gentlemen, welcome to the program. >> Thanks for having us. >> Thank you. >> Excited to learn more about Rockset, Venkat, talk to me about Rockset and how it's putting real-time analytics within the reach of every company. >> If you see the confluent IPO, if you see where the world is going in terms of analytics, I know, we look at this, real-time analytics is like the lost frontier. Everybody wants fast queries on fresh data. Nobody wants to say, "I don't need that. You know, give me slow queries on stale data," right? I think if you see what data warehouses and data lakes have done, especially in the cloud, they've really, really made batch analytics extremely accessible, but real-time analytics still seems too clumsy, too complex, and too expensive for most people. And we are on a mission to make, you know, real-time analytics, make it very, very easy and affordable for everybody to be able to take advantage of that. So that's our, that's what we do. >> But you're right, nobody wants a stale data or slower queries. And it seems like one of the things that we learned, Venkat, sticking with you in the last 18 months of a very strange world that we're living in, is that real-time is no longer a nice to have. It's really a differentiator and table stakes for businesses in every industry. How do you make it more affordable and accessible to businesses in so many different industries? >> I think that's a great question. I think there are, at a very high level, there are two categories of use cases we see. I think there is one full category of use cases where business teams and business units are demanding almost like business observability. You know, if you think about one domain that actually understood real-time and made everything work in real-time is the DevOps world, you know, metrics and monitoring coming out of like, you know, all these machines and because they really want to know as soon as something goes wrong, immediately, I want to, you know, be able to dive in and click and see what happens. But now businesses are demanding the same thing, right? Like a CEO wants to know, "Are we on track to hit our quarterly estimates or not? And tell me now what's happening," because you know, the larger the company, the more complex that have any operations dashboards are. And, you know, if you don't give them real-time visibility, the window of opportunity to do something about it disappears. And so they are really, businesses is really demanding that. And so that is one big use case we have. And the other strange thing we're also seeing is that customers are demanding real-time even from the products they are using. So you could be using a SaaS product for sales automation, support automation, marketing automation. Now I don't want to use a product if it doesn't have real-time analytics baked into the product itself. And so all these software companies, you know, providing a SaaS service to their cloud customers and clients, they are also looking to actually, you know, their proof of value really comes from the analytics that they can show within the product. And if that is not interactive and real-time, then they are also going to be left behind. So it's really a huge differentiator whether you're building a software product or your running a business, the real-time observability gives you a window of opportunity to actually do something about, you know, when something goes wrong, you can actually act on it very, very quickly. >> Right, which is absolutely critical. Dhruba, I want to get your take on this. As the CTO and co-founder as I introduced you, what were some of the gaps in the market back in 2016 that you saw that really necessitated the development of this technology? >> Yeah, for real-time analytics, the difference compared to what it was earlier is that all your things used to be a lot of batch processes. Again, the reason being because there was something called MapReduce, and that was a scanning system that was kind of a invention from Google, which talked about processing big data sets. And it was about scanning, scanning large data sets to give answers. Whereas for real-time analytics, the new trend is that how can you index these big datasets so that you can answer queries really fast? So this is what Rockset does as well, is that we have capabilities to index humongous amounts of data cheaply, efficiently, and economically feasible for our customers. And that's why query is the leverage the index to give fast (indistinct). This is one of the big changes. The other change obviously is that it has moved to the cloud, right? A lot of analytics have moved to the cloud. So Rockset is built natively for the cloud, which is why we can scale up, scale down resources when queries come and we can provide a great (indistinct) for people as data latency, and as far as query latencies comes on, both of these things. So these two trends, I think, are kind of the power behind moving, making people use more real-time analytics. >> Right, and as Venkat was talking about how it's an absolute differentiator for businesses, you know, last year we saw this really, this quick, all these quick pivots to survive and ultimately thrive. And we're seeing the businesses now coming out of this, that we're able to do that, and we're able to pivot to digital, to be successful and to out-compete those who maybe were not as fast. I saw that recently, Venkat, you guys had a new product release a few weeks ago, major product release, that is making real-time analytics on streaming data sources like Apache Kafka, Amazon Kinesis, Amazon DynamoDB, and data lakes a lot more accessible and affordable. Breakdown that launch for me, and how is it doing the accessibility and affordability that you talked about before? >> Extremely good question. So we're really excited about what we call SQL-based roll-ups, is what we call that release. So what does that do? So if you think about real-time analytics and even teeing off the previous question you asked on what is the gap in the market? The gap in the market is really, all that houses and lakes are built for batch. You know, they're really good at letting people accumulate huge volumes of data, and once a week, analyst asking a question, generating a report, and everybody's looking at it. And with real-time, the data never stops coming. The queries never stop coming. So how do you, if I want real-time metrics on all this huge volumes of data coming in, now if I drain it into a huge data lake and then I'm doing analytics on that, it gets very expensive and very complex very quickly. And so the new release that we had is called SQL-based roll-ups, where simply using SQL, you can define any real-time metric that you want to track across any dimensions you care about. It could be geo demographic and other dimensions you care about that and Rockset will automatically maintain all those real-time metrics for you in real-time in a highly accurate fashion. So you never have to doubt whether the metrics are valid and it will be accurate up to the second. And the best part is you don't have to learn a new language. You can actually use SQL to define those metrics and Rockset will automatically maintain that and scale that for you in the cloud. And that, I think, reduces the barrier. So like if somebody wants to build a real-time, you know, track something for their business in real-time, you know, you have to duct tape together multiple, disparate components and systems that were never meant to work with each other. Now you have a real-time database built for the cloud that is fully, you know, supports full feature SQL. So you can do this in a matter of minutes, which would probably take you days or weeks with alternate technologies. >> That's a dramatic X reduction in time there. I want to mention the Snowflake IPO since you guys mentioned the Confluent IPO. You say that Rockset does for real-time, what Snowflake did for batch. Dhruba, I want to get your perspective on that. Tell me about that. What do you mean by that? >> Yeah, so like we see this trend in the market where lot of analytics, which are very batch, they get a lot of value if they've moved more real-time, right? Like Venkat mentioned, when analytics powers, actual products, which need to use analytics into their, to make the product better. So Rockset very much plays in this area. So Rockset is the only solution. I shouldn't say solution. It's a database, it's a real-time database, which powers these kind of analytic systems. If you don't use Rockset, then you might be using maybe a warehouse or something, but you cannot get real-time because there is always a latency of putting data into the warehouse. It could be minutes, it could be hours. And then also you don't get too many people making concurrent queries on the warehouse. So this is another difference for real-time analytics because it powers applications, the query volume could be large. So that's why you need a real-time database and not a real-time warehouse or any other technologies for this. And this trend has really caught up because most people have either, are pretty much into this journey. You asked me this previous question about what has changed since 2016 as well. And this is a journey that most enterprises we see are already embarking upon. >> One thing too, that we're seeing is that more and more applications are becoming data intensive applications, right? We think of whether it's Instagram or DoorDash or whatnot, or even our banking app, we expect to have the information updated immediately. How do you help, Dhruba, sticking with you, how do you help businesses build and power those data intensive applications that the consumers are demanding? >> That's a great question. And we have booked, me and Venkat, we have seen these data applications at large scale when we were at Facebook earlier. We were both parts of the Facebook team. So we saw how real-time was really important for building that kind of a business, that was social media. But now we are taking the same kind of back ends, which can scale to like huge volumes of data to the enterprises as well. Venkat, do you have anything to add? >> Yeah, I think when you're trying to go from batch to real-time, you're 100% spot on that, a static report, a static dashboard actually becomes an application, becomes a data application, and it has to be interactive. So you're not just showing a newspaper where you just get to read. You want to click and deep dive, do slice and dice the data to not only understand what happened, but why it happened and come up with hypotheses to figure out what I want to do with it. So the interactivity is important and the real-timeliness now it becomes important. So the way we think about it is like, once you go into real-time analytics, you know, the data never stops coming. That's obvious. Data freshness is important. But the queries never stop coming also because one, when your dashboards and metrics are getting up to date real-time, you really want alerts and anomaly detection to be automatically built in. And so you don't even have to look at the graphs once a week. When something is off, the system will come and tap on your shoulder and say, "Hey, something is going on." And so that really is a real-time application at that point, because it's constantly looking at the data and querying on your behalf and only alerting you when something, actually, is interesting happening that you might need to look at. So yeah, the whole movement towards data applications and data intensive apps is a huge use case for us. I think most of our customers, I would say, are building a data application in one shape or form or another. >> And if I think of use cases like cutthroat customer 360, you know, as customers and consumers of whatever product or solution we're talking about, we expect that these brands know who we are, know what we've done with them, what we've bought, what to show me next is what I expect whether again, it's my bank or it's Instagram or something else. So that personalization approach is absolutely critical, and I imagine another big game changer, differentiator for the customers that use Rockset. What do you guys think about that? >> Absolutely, personalized recommendation is a huge use case. We see this all where we have, you know, Ritual is one of the customers. We have a case study on that, I think. They want to personalize. They generate offline recommendations for anything that the user is buying, but they want to use behavioral data from the product to personalize that experience and combine the two before they serve anything on the checkout lane, right? We also see in B2B companies, real-time analytics and data applications becoming a very important thing. And we have another customer, Command Alkon, who, you know, they have a supply chain platform for heavy construction and 80% of concrete in North America flows through their platform, for example. And what they want to know in real-time is reporting on how many concrete trucks are arriving at a big construction site, which ones are late and whatnot. And the real-time, you know, analytics needs to be accurate and needs to be, you know, up to the second, you know, don't tell me what trucks were, you know, coming like an hour ago. No, I need this right now. And so even in a B2B platform, we see that very similar trend trend where real-time reporting, real-time search, real-time indexing is actually a very, very important piece to the puzzle. And not just for B to C examples that you said, and the Instagram comment is also very appropriate because a hedge fund customer came to us and said, "I have kind of a dashboards built on top of like Snowflake. They're taking two to five seconds and I have certain parts of my dashboards, but I am actually having 50/60 visualizations. You do the math, it takes many minutes to load. And so they said, "Hey, you have some indexing deck. Can you make this faster?" Three weeks later, the queries that would take two to five seconds on a traditional warehouse or a cloud data warehouse came back in 18 milliseconds with Rockset. And so it is so fast that they said, you know, "If my internal dashboards are not as fast as Instagram, no one in my company uses it." These are their words. And so they are really, you know, the speed is really, really important. The scale is really, really important. Data freshness is important. If you combine all of these things and also make it simple for people to access with SQL-based, that's really the real unique value prop that we have a Rockset, which is what our customers love. >> You brought up something interesting, Venkat, that kind of made me think of the employee experience. You know, we always think of the customer 360. The customer experience with the employee experience, in my opinion, is inextricably linked. The employees have to have access to what they need to deliver and help these great customer relationships. And as you were saying, you know, the employees are expecting databases to be as fast as they see on Instagram, when they're, you know, surfing on their free time. Then adoption, I imagine, gets better, obviously, than the benefit from the end user and customers' perspective is that speed. Talk to me a little bit about how Rockset, and I would like to get both of your opinions here, is a facilitator of that employee productivity for your customers. >> This is a great question. In fact, the same hedge fund, you know, customer, I pushed them to go and measure how many times do people even look at all the data that you produce? (laughs) How many analysts and investors actually use your dashboards and ask them to go investigate at that. And one of the things that they eventually showed me was there was a huge uptake and their dashboards went from two to three second kind of like, you know, lags to 18 milliseconds. They almost got the daily active user for their own internal dashboards to be almost going from five people to the entire company, you know, so I think you're absolutely spot on. So it really goes back to, you know, really leveraging the data and actually doing something about it. Like, you know, if I ask a question and it's going to, you know, system is going to take 20 minutes to answer that, you know, I will probably not ask as many questions as I want to. When it becomes interactive and very, very fast, and all of a sudden, I not only start with a question and, you know, I can ask a follow-up question and then another follow-up question and make it really drive that to, you know, a conclusion and I can actually act upon it. And this really accelerates. So even if you kind of like, look at the macro, you hear these phrases, the world is going from batch to real-time, and in my opinion, when I look at this, people want to, you know, accelerate their growth. People want to make faster decisions. People want to get to, what can I do about this and get actionable insights. And that is not really going to come from systems that take 20 minutes to give a response. It's going to really come from systems that are interactive and real-time, and that's really the need for acceleration is what's really driving this movement from batch to real-time. And we're very happy to facilitate that and accelerate that moment. >> And it really drives the opportunity for your customers to monetize more and more data so that they can actually act on it, as you said, in real-time and do something about it, whether it's a positive experience or it is, you know, remediating a challenge. Last question guys, since we're almost out of time here, but I want to understand, talk to me about the Rockset-AWS partnership and what the value is for your customers. >> Okay, yeah. I'll get to that in a second, but I wanted to add something to your previous question. I think my opinion for all the customers that we see is that real-time analytics is addictive. Once they get used to it, they can go back to the old stuff. So this is what we have found with all our customers. So, yeah, for the AWS question, I think maybe Venkat can answer that better than me. >> Yeah, I mean, we love partnering with AWS. I think, they are the world's leader when it comes to public clouds. We have a lot of joint happy customers that are all AWS customers. Rockset is entirely built on top of AWS, and we love that. And there is a lot of integrations that Rockset natively comes with. So if you're already managing your data in AWS, you know, there are no data transfer costs or anything like that involved for you to also, you know, index that data in Rockset and actually build real-time applications and stream the data to Rockset. So I think the partnership goes in very, very deep in terms of like, we are an AWS customer, we are a partner and we, you know, our go-to market teams work with them. And so, yeah, we're very, very happy, you know, like, AWS fanboys here, yeah. >> Excellent, it sounds like a very great synergistic collaborative relationship, and I love, Dhruba, what you said. This is like, this is a great quote. "Real-time analytics is addictive." That sounds to me like a good addiction (all subtly laugh) for businesses and every industry to take out. Guys, it's been a pleasure talking to you. Thank you for joining me, talking to the audience about Rockset, what differentiates you, and how you're helping customers really improve their customer productivity, their employee productivity, and beyond. We appreciate your time. >> Thanks, Lisa. >> Thank you, thanks a lot. >> For my guests, I'm Lisa Martin. You're watching this "Cube Conversation". (bright ending music)

[Music] we're on welcome to the special cube conversation we're here with some breaking news we got some startup investment news here in the Q studios palo alto I'm John for your host here at Jerry Chen partnered Greylock and the CEO of rock said Venkat Venkat Rahmani welcome to the cube you guys announcing hot news today series a and seed and Series A funding 21 million dollars for your company congratulations thank you Roxette is a data company jerry great this is one of your nest you kept this secret forever it was John was really hard you know over the past two years every time I sat in this seat I'd say and one more thing you know I knew that part of the advantage was rocks I was a special company and we were waiting to announce it and that's right time so it's been about two and half years in the making I gotta give you credit Jerry I just want to say to everyone I try to get the secrets out of you so hard you are so strong and keeping a secret I said you got this hot startup this was two years ago yeah I think the probe from every different angle you can keep it secrets all the entrepreneurs out there Jerry Chen's your guide alright so congratulations let's talk about the startup so you guys got 21 million dollars how much was the seed round this is the series a the seed was three million dollars both Greylock and Sequoia participating and the series a was eighteen point five all right so other investors Jerry who else was in on this I just the two firms former beginning so we teamed up with their French from Sequoia and the seed round and then we over the course of a year and half like this is great we're super excited about the team bank had Andrew bhai belt we love the opportunity and so Mike for an office coin I said let's do this around together and we leaned in and we did it around alright so let's just get into the other side I'm gonna read your your about section of the press release roxette's visions to Korea to build the data-driven future provide a service search and analytics engine make it easy to go from data to applications essentially building a sequel layer on top of the cloud for massive data ingestion I want to jump into it but this is a hot area not a lot of people are doing this at the level you guys are now and what your vision is did this come from what's your background how did you get here did you wake up one Wednesday I'm gonna build this awesome contraction layer and build an operating system around data make this thing scalable how did it all start I think it all started from like just a realization that you know turning useful data to useful apps just requires lots of like hurdles right you have to first figure out what format the data is in you got to prepare the data you gotta find the right specialized you know data database or data management system to load it in and it often requires like weeks to months before useful data becomes useful apps right and finally you know after I you know my tenure at Facebook when I left the first thing I did was I was just talking you know talking to a lot of people with real-world companies and reload problems and I started walking away from moremore of them thinking that this is way too complex I think the the format in which a lot of the data is coming in is not the format in which traditional sequel based databases are optimized for and they were built for like transaction processing and analytical processing not for like real-time streams of data but there's JSON or you know you know parque or or any of these other formats that are very very popular and more and more data is getting produced by one set of applications and getting consumed by other applications but what we saw it was what is this how can we make it simpler why do we need all this complexity right what is a simple what is the most simple and most powerful system we can build and pulled in the hands of as many people as possible and so we very sort of naturally relate to developers and data scientists people who use code on data that's just like you know kind of like our past lives and when we thought about it well why don't we just index the data you know traditional databases were built when every byte mattered every byte of memory every byte on disk now in the cloud the economics are completely different right so when you rethink those things with fresh perspective what we said was like what if we just get all of this data index it in a format where we can directly run very very fast sequel on it how simple would the world be how much faster can people go from ideas to do experiments and experiments to production applications and how do we make it all faster also in the cloud right so that's really the genesis of it well the real inspiration came from actually talking to a lot of people with real-world problems and then figuring out what is the simplest most powerful thing we can build well I want to get to the whole complexity conversation cuz we were talking before we came on camera here about how complexity can kill and why and more complexity on top of more complexity I think there's a simplicity angle here that's interesting but I want to get back to your background of Facebook and I want to tell a story you've been there eight years but you were there during a very interesting time during that time in history Facebook was I think the first generation we've taught us on the cube all the time about how they had to build their own infrastructure at scale while they're scaling so they were literally blitzscaling as reid hoffman and would say and you guys do it the Greylock coverage unlike other companies at scale eBay Microsoft they had old-school one dotto Technology databases Facebook had to kind of you know break glass you know and build the DevOps out from generation one from scratch correct it was a fantastic experience I think when I started in 2007 Facebook had about 40 million monthly actives and I had the privilege of working with some of the best people and a lot of the problems we were very quickly around 2008 when I went and said hey I want to do some infrastructure stuff the mandate that was given to me and my team was we've been very good at taking open source software and customizing it to our needs what would infrastructure built by Facebook for Facebook look like and we then went into this journey that ended up being building the online data infrastructure at Facebook by the time I left the collectively these systems were surveying 5 plus billion requests per second across 25 plus geographical clusters and half a dozen data centers I think at that time and now there's more and the system continues to chug along so it was just a fantastic experience I think all the traditional ways of problem solving just would not work at that scale and when the user base was doubling early in the early days every four months every five months yeah and what's interesting you know you're young and here at the front lines but you're kind of the frog in boiling water and that's because you are you were at that time building the power DevOps equation automating scale growth everything's happening at once you guys were right there building it now fast forward today everyone who's got an enterprise it's it wants to get there they don't they're not Facebook they don't have this engineering staff they want to get scale they see the cloud clearly the value property has got clear visibility but the economics behind who they hire so they have all this data and they get more increasing amount of data they want to be like Facebook but can't be like Facebook so they have to build their own solutions and I think this is where a lot of the other vendors have to rebuild this cherry I want to ask you because you've been looking at a lot of investments you've seen that old guard kind of like recycled database solutions coming to the market you've seen some stuff in open source but nothing unique what was it about Roxette that when you first talk to them that but you saw that this is going to be vectoring into a trend that was going to be a perfect storm yeah I think you nailed it John historic when we have this new problems like how to use data the first thing trying to do you saw with the old technology Oh existing data warehouses akin databases okay that doesn't work and then the next thing you do is like okay you know through my investments in docker and B and the boards or a cloud aerosol firsthand you need kind of this rise of stateless apps but not stateless databases right and then I through the cloud area and a bunch of companies that I saw has an investor every pitch I saw for two or three years trying to solve this data and state problem the cloud dudes add more boxes right here's here's a box database or s3 let me solve it with like Oh another database elastic or Kafka or Mongo or you know Apache arrow and it just got like a mess because if almond Enterprise IT shop there's no way can I have the skill the developers to manage this like as Beckett like to call it Rube Goldberg machination of data pipelines and you know I first met Venkat three years ago and one of the conversations was you know complexity you can't solve complex with more complexity you can only solve complexity with simplicity and Roxette and the vision they had was the first company said you know what let's remove boxes and their design principle was not adding another boxes all a problem but how to remove boxes to solve this problem and you know he and I got along with that vision and excited from the beginning stood to leave the scene ah sure let's go back with you guys now I got the funding so use a couple stealth years to with three million which is good a small team and that goes a long way it certainly 2021 total 18 fresh money it's gonna help you guys build out the team and crank whatnot get that later but what did you guys do in the in those two years where are you now sequel obviously is lingua franca cool of sequel but all this data is doesn't need to be scheming up and built out so were you guys that now so since raising the seed I think we've done a lot of R&D I think we fundamentally believe traditional data management systems that have been ported over to run on cloud Williams does not make them cloud databases I think the cloud economics is fundamentally different I think we're bringing this just scratching the surface of what is possible the cloud economics is you know it's like a simple realization that whether you rent 100 CPUs for one minute or or one CPU 400 minutes it's cost you exactly the same so then if you really ask why is any of my query is slow right I think because your software sucks right so basically what I'm trying to say is if you can actually paralyze that and if you can really exploit the fluidity of the hardware it's not easy it's very very difficult very very challenging but it's possible I think it's not impossible and if you can actually build software ground-up natively in the cloud that simplifies a lot of this stuff and and understands the economics are different now and it's system software at the end of the day is how do I get the best you know performance and efficiency for the price being paid right and the you know really building you know that is really what I think took a lot of time for us we have built not only a ground-up indexing technique that can take raw data without knowing the shape of the data we can turn that and index it in ways and store them maybe in more than one way since for certain types of data and then also have built a distributed sequel engine that is cloud native built by ground up in the cloud and C++ and like really high performance you know technologies and we can actually run distributor sequel on this raw data very very fast my god and this is why I brought up your background on Facebook I think there's a parallel there from the ground this ground up kind of philosophy if you think of sequel as like a Google search results search you know keyword it's the keyword for machines in most database worlds that is the standard so you can just use that as your interface Christ and then you using the cloud goodness to optimize for more of the results crafty index is that right correct yes you can ask your question if your app if you know how to see you sequel you know how to use Roxette if you can frame your the question that you're asking in order to answer an API request it could be a micro service that you're building it could be a recommendation engine that you're that you're building or you could you could have recommendations you know trying to personalize it on top of real time data any of those kinds of applications where it's a it's a service that you're building an application you're building if you can represent ask a question in sequel we will make sure it's fast all right let's get into the how you guys see the application development market because the developers will other winners here end of the day so when we were covering the Hadoop ecosystem you know from the cloud era days and now the important work at the Claire merger that kind of consolidates that kind of open source pool the big complaint that we used to hear from practitioners was its time consuming Talent but we used to kind of get down and dirty the questions and ask people how they're using Hadoop and we had two answers we stood up Hadoop we were running Hadoop in our company and then that was one answer the other answer was we're using Hadoop for blank there was not a lot of those responses in other words there has to be a reason why you're using it not just standing it up and then the Hadoop had the problem of the world grew really fast who's gonna run it yeah management of it Nukem noose new things came in so became complex overnight it kind of had took on cat hair on it basically as we would say so how do you guys see your solution being used so how do you solve that what we're running Roxette oh okay that's great for what what did developers use Roxette for so there are two big personas that that we currently have as users right there are developers and data scientists people who program on data right - you know on one hand developers want to build applications that are making either an existing application better it could be a micro service that you know I want to personalize the recommendations they generated online I mean offline but it's served online but whether it is somebody you know asking shopping for cars on San Francisco was the shopping you know was the shopping for cars in Colorado we can't show the same recommendations based on how do we basically personalize it so personalization IOT these kinds of applications developers love that because often what what you need to do is you need to combine real-time streams coming in semi structured format with structured data and you have no no sequel type of systems that are very good at semi structured data but they don't give you joins they don't give you a full sequel and then traditional sequel systems are a little bit cumbersome if you think about it I new elasticsearch but you can do joins and much more complex correct exactly built for the cloud and with full feature sequel and joins that's how that's the best way to think about it and that's how developers you said on the other side because its sequel now all of a sudden did you know data scientist also loved it they had they want to run a lot of experiments they are the sitting on a lot of data they want to play with it run experiments test hypotheses before they say all right I got something here I found a pattern that I don't know I know I had before which is why when you go and try to stand up traditional database infrastructure they don't know how what indexes to build how do i optimize it so that I can ask you know interrogatory and all that complexity away from those people right from basically provisioning a sandbox if you will almost like a perpetual sandbox of data correct except it's server less so like you don't you never think about you know how many SSDs do I need how many RAM do I need how many hosts do I need what configure your programmable data yes exactly so you start so DevOps for data is finally the interview I've been waiting for I've been saying it for years when's is gonna be a data DevOps so this is kind of what you're thinking right exactly so you know you give us literally you you log in to rocks at you give us read permissions to battle your data sitting in any cloud and more and more data sources we're adding support every day and we will automatically cloudburst will automatically interested we will schematize the data and we will give you very very fast sequel over rest so if you know how to use REST API and if you know how to use sequel you'd literally need don't need to think about anything about Hardware anything about standing up any servers shards you know reindex and restarting none of that you just go from here is a bunch of data here are my questions here is the app I want to build you know like you should be bottleneck by your career and imagination not by what can my data employers give me through a use case real quick island anyway the Jarius more the structural and architectural questions around the marketplace take me through a use case I'm a developer what's the low-hanging fruit use case how would I engage with you guys yeah do I just you just ingest I just point data at you how do you see your market developing from the customer standpoint cool I'll take one concrete example from a from a developer right from somebody we're working with right now so they have right now offline recommendations right or every night they generate like if you're looking for this car or or this particular item in e-commerce these are the other things are related well they show the same thing if you're looking at let's say a car this is the five cars that are closely related this car and they show that no matter who's browsing well you might have clicked on blue cars the 17 out of 18 clicks you should be showing blue cars to them right you may be logging in from San Francisco I may be logging in from like Colorado we may be looking for different kinds of cars with different you know four-wheel drives and other options and whatnot there's so much information that's available that you can you're actually by personalizing it you're adding creating more value to your customer we make it very easy you know live stream all the click stream beta to rock set and you can join that with all the assets that you have whether it's product data user data past transaction history and now if you can represent the joins or whatever personalization that you want to find in real time as a sequel statement you can build that personalization engine on top of Roxanne this is one one category you're putting sequel code into the kind of the workflow of the code saying okay when someone gets down to these kinds of interactions this is the sequel query because it's a blue car kind of go down right so like tell me all the recent cars that this person liked what color is this and I want to like okay here's a set of candidate recommendations I have how do I start it what are the four five what are the top five I want to show and then on the data science use case there's a you know somebody building a market intelligence application they get a lot of third-party data sets it's periodic dumps of huge blocks of JSON they want to combine that with you know data that they have internally within the enterprise to see you know which customers are engaging with them who are the persons churning out what are they doing and they in the in the market and trying to bring they bring it all together how do you do that when you how do you join a sequel table with a with a JSON third party dumb and especially for coming and like in the real-time or periodic in a week or week month or one month literally you can you know what took this particular firm that we're working with this is an investment firm trying to do market intelligence it used age to run ad hoc scripts to turn all of this data into a useful Excel report and that used to take them three to four weeks and you know two people working on one person working part time they did the same thing in two days and Rock said I want to get to back to microservices in a minute and hold that thought I won't go to Jerry if you want to get to the business model question that landscape because micro services were all the world's going to Inc so competition business model I'll see you gets are funded so they said love the thing about monetization to my stay on the core value proposition in light of the red hat being bought by by IBM had a tweet out there kind of critical of the transactions just in terms of you know people talk about IBM's betting the company on RedHat Mike my tweet was don't get your reaction will and tie it to the visible here is that it seems like they're going to macro services not micro services and that the world is the stack is changing so when IBM sell out their stack you have old-school stack thinkers and then you have new-school stack thinkers where cloud completely changes the nature of the stack in this case this venture kind of is an indication that if you think differently the stack is not just a full stack this way it's this way in this way yeah as we've been saying on the queue for a couple of years so you get the old guard trying to get a position and open source all these things but the stacks changing these guys have the cloud out there as a tailwind which is a good thing how do you see the business model evolving do you guys talk about that in terms of you can hey just try to find your groove swing get customers don't worry about the monetization how many charging so how's that how do you guys talk about the business model is it specific and you guys have clear visibility on that what's the story on that I mean I think yeah I always tell Bank had this kind of three hurdles you know you have something worthwhile one well someone listen to your pitch right people are busy you like hey John you get pitched a hundred times a day by startups right will you take 30 seconds listen to it that's hurdle one her will to is we spend time hands on keyboards playing around with the code and step threes will they write you a check and I as a as a enter price offered investor in a former operator we don't overly folks in the revenue model now I think writing a check the biz model just means you're creating value and I think people write you checking screening value but you know the feedback I always give Venkat and the founders work but don't overthink pricing if the first 10 customers just create value like solve their problems make them love the product get them using it and then the monetization the actual specifics the business model you know we'll figure out down the line I mean it's a cloud service it's you know service tactically to many servers in that sentence but it's um it's to your point spore on the cloud the one that economists are good so if it works it's gonna be profitable yeah it's born the cloud multi-cloud right across whatever cloud I wanna be in it's it's the way application architects going right you don't you don't care about VMs you don't care about containers you just care about hey here's my data I just want to query it and in the past you us developer he had to make compromises if I wanted joins in sequel queries I had to use like postgrads if I won like document database and he's like Mongo if I wanted index how to use like elastic and so either one I had to pick one or two I had to use all three you know and and neither world was great and then all three of those products have different business models and with rocks head you actually don't need to make choices right yes this is classic Greylock investment you got sequoia same way go out get a position in the market don't overthink the revenue model you'll funded for grow the company let's scale a little bit and figure out that blitzscale moment I believe there's probably the ethos that you guys have here one thing I would add in the business model discussion is that we're not optimized to sell latte machines who are selling coffee by the cup right so like that's really what I mean we want to put it in the hands of as many people as possible and make sure we are useful to them right and I think that is what we're obsessed about where's the search is a good proxy I mean that's they did well that way and rocks it's free to get started right so right now they go to rocks calm get started for free and just start and play around with it yeah yeah I mean I think you guys hit the nail on the head on this whole kind of data addressability I've been talking about it for years making it part of the development process programming data whatever buzzword comes out of it I think the trend is it looks a lot like that depo DevOps ethos of automation scale you get to value quickly not over thinking it the value proposition and let it organically become part of the operation yeah I think we we the internal KPIs we track are like how many users and applications are using us on a daily and weekly basis this is what we obsess about I think we say like this is what excellence looks like and we pursue that the logos in the revenue would would you know would be a second-order effect yeah and it's could you build that core kernels this classic classic build up so I asked about the multi cloud you mention that earlier I want to get your thoughts on kubernetes obviously there's a lot of great projects going on and CN CF around is do and this new state problem that you're solving in rest you know stateless has been an easy solution VP is but API 2.0 is about state right so that's kind of happening now what's your view on kubernetes why is it going to be impactful if someone asked you you know at a party hey thank you why is what's all this kubernetes what party going yeah I mean all we do is talk about kubernetes and no operating systems yeah hand out candy last night know we're huge fans of communities and docker in fact in the entire rock set you know back-end is built on top of that so we run an AWS but with the inside that like we run or you know their entire infrastructure in one kubernetes cluster and you know that is something that I think is here to stay I think this is the the the programmability of it I think the DevOps automation that comes with kubernetes I think all of that is just like this is what people are going to start taking why is it why is it important in your mind the orchestration because of the statement what's the let's see why is it so important it's a lot of people are jazzed about it I've been you know what's what's the key thing I think I think it makes your entire infrastructure program all right I think it turns you know every aspect of you know for example yeah I'll take it I'll take a concrete example we wanted to build this infrastructure so that when somebody points that like it's a 10 terabytes of data we want to very quickly Auto scale that out and be able to grow this this cluster as quickly as possible and it's like this fluidity of the hardware that I'm talking about and it needs to happen or two levels it's one you know micro service that is ingesting all the data that needs to sort of burst out and also at the second level we need to be able to grow more more nodes that we we add to this cluster and so the programmability nature of this like just imagine without an abstraction like kubernetes and docker and containers and pods imagine doing this right you are building a you know a lots and lots of metrics and monitoring and you're trying to build the state machine of like what is my desired state in terms of server utilization and what is the observed state and everything is so ad hoc and very complicated and kubernetes makes this whole thing programmable so I think it's now a lot of the automation that we do in terms of called bursting and whatnot when I say clock you know it's something we do take advantage of that with respect to stateful services I think it's still early days so our our position on my partner it's a lot harder so our position on that is continue to use communities and continue to make things as stateless as possible and send your real-time streams to a service like Roxette not necessarily that pick something like that very separate state and keep it in a backhand that is very much suited to your micro service and the business logic that needs to live there continue should continue to live there but if you can take a very hard to scale stateful service split it into two and have some kind of an indexing system Roxette is one that you know we are proud of building and have your stateless communal application logic and continue to have that you know maybe use kubernetes scale it in lambdas you know for all we care but you can take something that is very hard to you know manage and scale today break it into the stateful part in the stateless part and the serval is back in like like Roxette will will sort of hopefully give you a huge boost in being able to go from you know an experiment to okay I'm gonna roll it out to a smaller you know set of audience to like I want to do a worldwide you know you can do all of that without having to worry about and think about the alternative if you did it the old way yeah yeah and that's like talent you'd need it would be a wired that's spaghetti everywhere so Jerry this is a kubernetes is really kind of a benefit off your your investment in docker you must be proud and that the industry has gone to a whole nother level because containers really enable all this correct yeah so that this is where this is an example where I think clouds gonna go to a whole nother level that no one's seen before these kinds of opportunities that you're investing in so I got to ask you directly as you're looking at them as a as a knowledgeable cloud guy as well as an investor cloud changes things how does that change how is cloud native and these kinds of new opportunities that have built from the ground up change a company's network network security application era formants because certainly this is a game changer so those are the three areas I see a lot of impact compute check storage check networking early days you know it's it's it's funny it gosh seems so long ago yet so briefly when you know I first talked five years ago when I first met mayor of Essen or docker and it was from beginning people like okay yes stateless applications but stateful container stateless apps and then for the next three or four years we saw a bunch of companies like how do I handle state in a docker based application and lots of stars have tried and is the wrong approach the right approach is what these guys have cracked just suffered the state from the application those are app stateless containers store your state on an indexing layer like rock set that's hopefully one of the better ways saw the problem but as you kind of under one problem and solve it with something like rock set to your point awesome like networking issue because all of a sudden like I think service mesh and like it's do and costs or kind of the technologies people talk about because as these micro services come up and down they're pretty dynamic and partially as a developer I don't want to care about that yeah right that's the value like a Roxanna service but still as they operate of the cloud or the IT person other side of the proverbial curtain I probably care security I matters because also India's flowing from multiple locations multiple destinations using all these API and then you have kind of compliance like you know GDP are making security and privacy super important right now so that's an area that we think a lot about as investors so can I program that into Roxette what about to build that in my nap app natively leveraging the Roxette abstraction checking what's the key learning feature it's just a I'd say I'm a prime agent Ariane gdpr hey you know what I got a website and social network out in London and Europe and I got this gdpr nightmare I don't we don't have a great answer for GDP are we are we're not a controller of the data right we're just a processor so I think for GDP are I think there is still the controller still has to do a lot of work to be compliant with GDP are I think the way we look at it is like we never forget that this ultimately is going to be adding value to enterprises so from day one we you can't store data and Roxette without encrypting it like it's just the on you know on by default the only way and all transit is all or HTTPS and SSL and so we never freaked out that we're building for enterprises and so we've baked in for enterprise customers if they can bring in their own custom encryption key and so everything will be encrypted the key never leaves their AWS account if it's a you know kms key support private VP ceilings like we have a plethora of you know security features so that the the control of the data is still with the data controller with this which is our customer but we will be the the processor and a lot of the time we can process it using their encryption keys if I'm gonna build a GDP our sleeves no security solution I would probably build on Roxette and some of the early developers take around rocks at our security companies that are trying to track we're all ideas coming and going so there the processor and then one of the companies we hope to enable with Roxette is another generation security and privacy companies that in the past had a hard time tracking all this data so I can build on top of rocks crack okay so you can built you can build security a gbbr solution on top rock set because rock set gives you the power to process all the data index all the data and then so one of the early developers you know stolen stealth is they looking at the data flows coming and go he's using them and they'll apply the context right they'll say oh this is your credit card the Social Security is your birthday excetera your favorite colors and they'll apply that but I think to your point it's game-changing like not just Roxette but all the stuff in cloud and as an investor we see a whole generation of new companies either a to make things better or B to solve this new category problems like pricing the cloud and I think the future is pretty bright for both great founders and investors because there's just a bunch of great new companies and it's building up from the ground up this is the thing I brought my mother's red hat IBM thing is that's not the answer at the root level I feel like right now I'd be on I I think's fastenings but it's almost like you're almost doubling down to your your comment on the old stack right it's almost a double down the old stack versus an aggressive bet on kind of what a cloud native stack will look like you know I wish both companies are great people I was doing the best and stuff do well with I think I'd like to do great with OpenStack but again their product company as the people that happen to contribute to open source I think was a great move for both companies but it doesn't mean that that's not we can't do well without a new stack doing well and I think you're gonna see this world where we have to your point oh these old stacks but then a category of new stack companies that are being born in the cloud they're just fun to watch it all it's all big all big investments that would be blitzscaling criteria all start out organically on a wave in a market that has problems yeah and that's growing so I think cloud native ground-up kind of clean sheet of paper that's the new you know I say you're just got a pic pick up you got to pick the right way if I'm oh it's gotta pick a big wave big wave is not a bad wave to be on right now and it's at the data way that's part of the cloud cracked and it's it's been growing bigger it's it's arguably bigger than IBM is bigger than Red Hat is bigger than most of the companies out there and I think that's the right way to bet on it so you're gonna pick the next way that's kind of cloud native-born the cloud infrastructure that is still early days and companies are writing that way we're gonna do well and so I'm pretty excited there's a lot of opportunities certainly this whole idea that you know this change is coming societal change you know what's going on mission based companies from whether it's the NGO to full scale or all the applications that the clouds can enable from data privacy your wearables or cars or health thing we're seeing it every single day I'm pretty sad if you took amazon's revenue and then edit edit and it's not revenue the whole ready you look at there a dybbuk loud revenue so there's like 20 billion run which you know Microsoft had bundles in a lot of their office stuff as well if you took amazon's customers to dinner in the marketplace and took their revenue there clearly would be never for sure if item binds by a long shot so they don't count that revenue and that's a big factor if you look at whoever can build these enabling markets right now there's gonna be a few few big ones I think coming on they're gonna do well so I think this is a good opportunity of gradual ations thank you thank you at 21 million dollars final question before we go what are you gonna spend it on we're gonna spend it on our go-to-market strategy and hiding amazing people as many as we can get good good answer didn't say launch party that I'm saying right yeah okay we're here Rex at SIA and Joe's Jerry Chen cube cube royalty number two all-time on our Keeble um nine list partner and Greylock guy states were coming in I'm Jeffrey thanks for watching this special cube conversation [Music]