 And good afternoon, welcome back here on theCUBE as we continue our coverage at AWS re-invent 22 when the Venetian here in Las Vegas, day two, it's Wednesday, they can still be rolling quite a long. 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 Venkat Venkateramani, who is the CEO and co-founder of Rockset, 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 our viewers who might not be familiar with Rockset, I know you've been on theCUBE a little bit, so you're an alum, but why don't you set the stage a little bit for Rockset and where you're engaged in terms of with AWS? Definitely. Rockset is a real-time analytics database that is built for the cloud. We make real-time applications possible in the cloud. Real-time applications need high concurrency, low latency, query processing. Data needs to be fresh, your analytics needs to be fast, and we built on AWS, and that's why we're here. We're very, very proud partners of AWS. We are in the AWS Accelerate Program, and also we are in the startup program of AWS. We're a 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 Intel, with AWS. Why don't you share some of that with us, too? Definitely, so one question that I always ask people is like, if you go, perspective that I shared is like, if you go ask 100 people, do you want fast analytics on fresh data, or slow analytics on stale data? 100 out of 100 would say fast and fresh, right? 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 at the end of the day, real-time analytics was always in using technologies that are, let's say, 10 years ago, using, let's say, processors that were available 10 years ago to pre-cloud days. There was a lot of complexity barriers associated with real-time analytics, and also a lot of cost and performance barriers associated with it. And so, rock said, from the very beginning, has been obsessing about building the most compute-efficient real-time database in the world. And AWS, on one hand, allows us to make a consumption-based pricing model, so you only pay for what you use, and that shatters all the cost barriers. But in terms of compute efficiency, what we announced yesterday is the Intel's third-generation Xeon scalable processors. It's codenamed Intel Ice Lake. When we ported over, rock said, to that architecture, taking advantage of some of the instruction sets that Intel has, we got an 84% performance boost. 8484. It's incredible, right? It's an incredible milestone. It reduces the barrier even more in terms of cost and pushes the efficiency and sets a really new record for how efficient real-time data processing can be in the cloud. And it's very, very exciting news. And so we used to benchmark ourselves against some of our other real-time data providers, and we were already faster, and now we've set a much, much higher bar for other people to follow. Yeah, and so what was it about real-time that was such a barrier? Because, and now you've got the speed, 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's 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 going to be staring at it 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? Is that something that I need to be double-clicking on 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 keep ingesting the data or continuously keep answering questions. So the compute efficiency that is needed is much, much, much higher, right? And that is why, you know, Roxette was born. So from the very beginning, we're only built, you know, for these use cases. We have an extremely powerful SQL engine that can give you a full-feature SQL analytics in a very, very compute efficient way in the cloud. So let's talk about the leap that you've made, say, in the last two years. And what's been the spur of that? What has been allowed you to create this, you know, obviously a different kind of an array for your customers from which to choose, but what's been the spark you think? We touched upon this a little earlier, right? The 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 of that in growing that community 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 your business operations using data to make day-to-day decisions better that either grows your top line, compresses your bottom line, eliminates risks that are inherently creeping up in your business, you know, eliminate potential churn from a customer or fraud, you know, deduction and getting on top of, you know, that, you know, a minute into an outage as opposed to an hour into the outage. 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. And so that is really, I think, that is the real market movement happening. 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 AWS Kinesis, MSK and all these technologies. Now, businesses are acquiring these data in real-time. Now they can also get real-time analytics on the other end of it. Sure. You know, you just touched on this and I'd like to hear your thoughts about this, 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 want to manage and scale and build giant data ops and DevOps teams to manage that because that is not really 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 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, fully managed and, you know, cloud offerings like Rockset, 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 going to 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 churn and fraud and eliminate all those risks that are inherent in my business. So that is where I think a lot of the investment's going. So gone are the days where, you know, you're going to have this five to 10% team managing a very hard to operate, you know, open source data management clusters on EC2 nodes and on AWS 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 Rockset and eliminate all that cost and replace that with an operationally much, much simpler, you know, system to work with such as Rockset. So that is really the big trend that we're seeing why, you know, not only real time is going more and more mainstream, cloud native solutions are 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 get the two C's cost and complexity, right? That you have, you need to address. Exactly. Yeah, for sure. You know, what is it about building trust with your clients, with your partners? Because you're talking about this cloud environment 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 providing them with really concrete examples of the values and the benefits that you can provide, you know, with these opportunities? So, you know, I grew up, so there's a few ways to answer this question. I'll cover all the angles. So in order to establish trust, you have to create value. 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 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 Facebook, back in the day, you know, I was managing online data infrastructure 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 customer-vendor relationship internally within Facebook. They were 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 exists in Rockset 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 going to really use Rockset and consume more. I mean, we don't make money until they consume. And so their success is very much integral part of 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 people that they look up to when a customer comes in saying, hey, what is, who is Rockset? 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 Rockset has, you know, built upon, 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 vinyl pay later company. And so all these case studies help and partners like AWS helps us, helps you amplify that, you know, and give more credibility. And last but not least, compliance matters. You know, being SOC type two compliant is a really important part of establishing trust. We are HIPAA compliant now, so that, you know, we can, you know, PII, PHI data, handling that. And so I think that will continue to be a big part of our focus in improving the security, you know, functionality and capabilities that Rockset has in the cloud and also compliance and the set of, you know, you know, standards that we're going to be compliant against. Well, I'm glad you hit on the AWS too, because I did want to bring that up. I appreciate that. And I know they appreciate the relationship as well. Thanks for the time here. Thank you. It's been a pleasure learning about Rockset and what you're up to. Thank you. It's a pleasure. Thank you, Venkat. 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