 Welcome to this CUBE Conversation. I'm your host, Lisa Martin. This is part of our third AWS startup showcase and I'm pleased to welcome two gentlemen from Rockset. Venkat Venkataramani is here, the CEO and co-founder, Andruva Borthakar, 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, you 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 there, they've really, really made batch analytics extremely accessible, but real-time analytics still seems too clumsy, too complex, 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 what we do. You're right. Nobody wants to 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 the 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 it'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 the revenue 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 are 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 that product itself. And so all these software companies, you know, providing a SaaS service to their 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 you're 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. Trima, I want to get your take on this. As the CTO and Cal 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 earlier 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 an invention from Google. We 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 data sets 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 queries, they leverage the index to give fast queries. 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 great SLAs for people as far 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 making people use more real-time analytics. Right, and as Bencat 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 were able to do that and were able to pivot to digital to be successful and to out-compete those who maybe were not as fast. I saw that recently, Bencat, you guys had a new product release a few weeks ago, a major product release that it's making real-time analytics on streaming data sources like Apache Kafka, Amazon Kinesis, Amazon DynamoDB, and data lakes a lot more accessible and affordable. Break down 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 taking off the previous question you asked on what is the gap in the market? The gap in the market is really all warehouses and lakes are built for batch. They're really good at letting people accumulate huge volumes of data. And once a week, analysts 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 know 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 wanna track across any dimensions you care about. It could be geo demographic and other dimensions you care about that. And Roxy 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'll 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 Roxy 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 track something for their business in real-time 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 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 wanna mention the Snowflake IPO since you guys mentioned the Confluent IPO. You say that Roxy does for real-time what Snowflake did for batch. Dreeba, I wanna 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 a lot of analytics which are very batch they get a lot of value if they move 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 Roxy very much plays in this area. So Roxy is the only solution. It's a, I shouldn't say solution. It's a database. It's a real-time database which powers these kind of analytics 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 a change 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, Drew, by 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 had both me and Venkat, we had 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, right? That was social media. But now we are taking the same kind of backends 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 back 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 hypothesis to figure out what I want to do with it. So the interactivity is important and the real-time readiness goes important, it becomes important. So the way we think about it is like once you go into real-time analytics, the data never stops coming. That's obvious, data freshness is important but the queries never stop coming also. Because 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 customer 360, 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 Rockside. What do you guys think about that? Absolutely, personalized recommendation is a huge use case. We see this all over. 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 their 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 Alcon, 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 and 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 the very similar 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 B2C examples that you said. And the Instagram comment is also very appropriate because one of H1 customer came to us and said, I have kind of dashboards built on top of like Snowflake. They're taking two to five seconds and I have certain parts of my dashboards where 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 tech. 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 with Rockset, which is what our customers love. You brought up something interesting, Ben, 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. And 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 surfing on their free time, then adoption, I imagine, gets better. And obviously then the benefit from the end user and customer's 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? I, this is a great question. In fact, the same hedge fund, you know, customer, I push them to go and measure how many times do people even look at all the data that you produce? 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 when their dashboards went from two to three seconds, kind of like, you know, lags to 18 milliseconds. They almost got the daily active user, user for their own internal dashboards to be almost going from five people to the entire company. You know, so I think you 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 gonna, you know, a system is gonna take 20 minutes to answer that, you know, I will probably not ask as many questions as I want to. When it comes, 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 a, you know, a conclusion and I can actually act upon it. And this really accelerates this. 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 gonna 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 movement. 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, Drew, but I want to understand from you, 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 you see is that real time analytics is addictive. There, once they get used to it, they can't 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 think I can answer that better than me. Yeah, I mean, we love partnering with AWS. I think they're 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, there are no data transfer costs or anything like that involved for you to also 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 are go-to-market teams work with them. And so, yeah, we're very, very happy, like AWS fanboys here, yeah. Excellent, it sounds like a very great synergistic collaborative relationship. And I love, Drew, but what you said, this is a great quote, real time analytics is addictive. That sounds to me like a good addiction for businesses and every industry to take up. 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.