 Everyone, welcome to the special CUBE conversation. We're here with some breaking news. We've got some startup investment news here in the CUBE, students of Palo Alto, I'm John Furrier, your host. We're here with Jerry Chen, partner at Greylock and the CEO of Rockset, Venkat, Venkat Ramani. Welcome to the CUBE. You guys announcing hot news today, Series A in SEED and Series A funding, $21 million for your company. Congratulations. Thank you. Rockset is a data company. Jerry, great, this is one of your investors. You kept this secret for how many years? It was, John, it was really hard. You know, the past two years, every time I sat in the seat, I'd just want to say, and one more thing. But, you know, I knew that part of the advantage was Rockset was a special company and we were waiting to announce it and now it's the right time. So it's been about two and a half years of making. I got to 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 at keeping a secret. You said you got this hot startup this was two years ago. Yeah. From every different angle, you can keep your secrets. All the entrepreneurs out there, Jerry, change your guide. All right, so congratulations. Let's talk about the startup. So you guys got $21 million. How much was the seed round? And how much was the Series A? The seed was $3 million, both Greylock and Sequoia participating and the Series A was $18.5. All right, so other investors, Jerry, who else was in on this? Just the two firms from the beginning. So we teamed up with their friends from Sequoia in the seed round and then we, over the course of a year and a half, like, this is great. We're super excited about the team, VanCAD and Druba have built. We love the opportunity. And so, Mike Vernaufer, Sequoia and I said, let's do this A round together. And we leaned in and we did the A round. All right, so let's just get into the, sorry, I'm going to read your about section of the press release. Rockset's vision is to build a data-driven future, provide a serverless search and analytics engine, making it easy to go from data to applications, essentially building a SQL 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. Where did this come from? What's your background? How did you get here? Did you wake up one day and want to build this awesome abstraction layer and build an operating system around data, make this thing scalable? How did it all start? I think it all started from just the realization that turning useful data to useful apps just requires lots of hurdles. You have to first figure out what format the data is in. You got to prepare the data. You got to find the right specialized database or data management system to load it in. And it often requires weeks to months before useful data becomes useful apps, right? And when I, after I, my tenure at Facebook when I left, the first thing I did was I was just talking to a lot of people with real world companies and real world problems. And I started walking away from more and more of them thinking that this is way too complex. I think the format in which a lot of the data is coming in is not the format in which traditional SQL-based databases are optimized for. And they were built for like transaction processing and analytical processing, not for like real-time streams of data, whether it's JSON or, you know, Parquet 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 was, how can we make it simpler? Why do we need all this complexity, right? What is the simple, what is the most simple and most powerful system we can build and put it 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 SQL on it. How simple would the world be? How much faster can people go from ideas to experiments and experiments to production applications? And how do we make it all fast 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 into the whole complexity conversation because we were talking before we came on camera here about how complexity can kill and why it's more complexity on top of more complexity. And I think there's a simplicity angle here that's interesting. But I want to get back to your background on Facebook. And I want to tell the 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 talked to us on theCUBE all the time about how they had to build their own infrastructure at scale while they're scaling. So they were literally blitz scaling as Reid Hoffman would say and you guys do at the Greylock coverage. Unlike other companies at scale, eBay, Microsoft, they had old school 1.0 technology databases. Facebook had to kind of break glass 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 build 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 the systems were serving five plus billion requests per second across 25 plus geographical clusters in half a dozen data centers, I think at that time. And now there's more. And the system continues to check 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 you're at the front lines, but you're kind of the frog in boiling water. And that's because 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 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 proposition has got clear visibility, but the economics behind it, 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 had to rebuild this. Jerry, 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 Rockset that when you first talked to them that 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. Historically when you have these new problems like how to use data, the first thing you try to do is solve with old technology. Old existing data warehouses, existing databases, okay, that doesn't work. And then the next thing you do is like, okay, you know, through my investments in Docker and being in the board observer Cladera, I saw firsthand kind of this rise of stateless apps, but not stateless databases, right? And then through the Cladera and a bunch of companies that I saw as an investor, every pitch I saw for two or three years, trying to solve this data and state problem in the cloud, you would just add more boxes, right? Here's a box database or S3. Let me solve it with like, oh, another database, Elastic or Kafka or Mongo or Apache Arrow. And it just got like a mess because if I'm an enterprise IT shop, there's no way can I have the skill of the developers to manage this like as like to call it Rube Goldberg, the imagination of data pipelines. And I first met Venkat three years ago and one of the conversations was complexity, you can't solve complexity with more complexity. You can only solve complexity with simplicity. And Rockset 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 box to solve a problem, but how to remove boxes to solve this problem. And he and I got along with that vision and I was excited from the beginning to leave the seed. All right, so let's go about what you guys now, I got the funding, so you did a couple stealth years too with three million, which is gonna be a small team and that goes a long way. It's certainly 21 total, 18 fresh money. It's going to help you guys build out the team and whatnot, get that later. But what did you guys do in those two years? Where are you now? SQL, obviously it's Lingua Franklin, people love SQL. But all this data doesn't need to be scheme it up and built out. So where are you guys at now? 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 VMs 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 one CPU for 100 minutes is cost you exactly the same. So then if you really ask why is any of my query slow, right? I think because your software sucks, right? So basically what I'm trying to say is if you can actually paralyze it 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 understands the economics are different now. And a system software at the end of the day is how do I get the best performance and efficiency for the price being paid, right? And the really building, 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 SQL engine that is cloud native, built by ground up in the cloud and C++ and really high performance technologies. And we can actually run distributed SQL on this raw data very, very fast in the cloud. And this is why I brought up your background on Facebook. I think there's a parallel there from this ground up kind of philosophy. If you think of SQL as like a Google search results, Google search keyword, it's the keyword for machines. In most database worlds, that is the standard. So you can just use that as your interface, right? And then you're using the cloud goodness to optimize for more of the results. Crofty index, is that right? Correct, yes. If you can ask your question, if you're app, if you know how to use SQL, you know how to use Rockset. If you can frame the question that you're asking in order to answer an API request, it could be a microservice that you're building, it could be a recommendation engine that you're building or you could have recommendations to kind of personalize it on top of real time data. Any of those kinds of applications, where it's a service that you're building and application you're building, if you can represent, ask a question in SQL, 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 are the winners here, end of the day. So when we were covering the Hadoop ecosystem, you know from the Cloudera days, and now we're working with the Cloudera merger, that kind of consolidates that kind of open source pool. The big complaint that we used to hear from practitioners was, it's time consuming talent, but we used to kind of get down and dirty with 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. And the other answer was, we're using Hadoop for blank. There was not a lot of those responses. In other words, it 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 going to run it? Management of it, new things came in, so it became complex overnight, kind of had to get hair on it basically as we would say. So how do you guys see your solution being used? So how do you solve that? Well, we're running Rockset. Oh, okay, that's great. For what? What did developers use Rockset for? So there are two big personas that we currently have as users, right? They are developers and data scientists, people who program on data, right? To, you know, on one hand, developers want to build applications that are making either an existing application better. It could be a microservice that, you know, I want to personalize the recommendations. It's generated online, I mean offline, but it's served online. But whether it is somebody, you know, asking, shopping for cars on San Francisco, what's the shopping, you know, what's 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 you need to do is, you need to combine real-time streams coming in semi-structured format with structured data. And you have no SQL type of systems that are very good at semi-structured data, but they don't give you joins, they don't give you full SQL. And then traditional SQL systems are a little bit cumbersome if you think about it. It's like elastic search, but you can do joins in much more complex things. Correct, exactly. Build for the cloud and with full feature SQL and joins, that's how, that's the best way to think about it. And that's how developers use it. On the other side, because it's SQL, now all of a sudden, data scientists also love it. They want to run a lot of experiments, they are sitting on a lot of data, they want to play with it, one experiments test hypotheses before they say, all right, I got something here, I found a pattern that I don't know I had before, which is why when you go and try to standard a traditional database infrastructure, they don't know what indexes to build, how do I optimize it, so that I can ask, you know, interrogate the data. So you're taking all that complexity away from those people. Right. From basically provisioning a sandbox, if you will. Correct. A perpetual sandbox of data. Correct. Except it's serverless. So you never think about, how many SSDs do I need? How many RAM do I need? How many hosts do I need? What configuration is that? So your programmable data. Yes, exactly. So you start. So DevOps for data, finally the interview I've been waiting for, I've been saying it for years. When's this going to be a data DevOps? So this is kind of what you're thinking, right? Exactly. So you know, you give us literally, you log into Rockset, you give us read permissions to whatever your data is sitting in any cloud and more and more data sources we are adding support every day. And we will automatically, cloud-based, we'll automatically ingest it. We will schematize the data and we will give you very, very fast SQL over rest. So if you know how to use REST APIs and if you know how to use SQL, you literally don't need to think about anything about hardware, anything about standing up any servers, shards, you know, re-indexing, resharding, none of that. You just go from, here is a bunch of data, here are my questions, here is the app I wanna build. You know, like you should be bottlenecked by your creativity and imagination, not by what can my data infrastructure do. Give me through a use case real quick. I don't know any of the gerry on some of the structural, 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? Do I just, you just ingest, I just point data at you. How do you see your market developing from a customer standpoint? Cool. I'll take one concrete example from a developer, like from somebody we're working with right now. So they have right now offline recommendations, right? Over every night they generate, like if you're looking for this car or this particular item in e-commerce, these are the other things that 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 to 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 four-wheel drives and other options and whatnot. There's so much information that's available that 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 clickstream data to Rockset 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 of whatever personalization that you want to find in real time, as a SQL statement, you can build that personalization engine on top of Rockset. This is one category of cases. So essentially, you're putting SQL code into the kind of the workflow of the code, saying, okay, when someone gets down to these kinds of interactions, this is the SQL 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 sort it? What are the first five? What are the top five I want to show? And then on the data science use case, there's, 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 turning out? What are they doing in the market? And trying to bring it all together. And how do you do that? When you, how do you join a SQL table with a JSON third-party dump? And especially if it coming in like, either real time or periodic, you know, week over week, month over 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 these 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 in Rockset. I want to get back to microservices in a minute, but hold that thought. I want to go to Jerry, because I want to get to the business model question in the landscape. Because microservices is where all the world's going to. So competition, business model, obviously you guys are funded. So these are not the thing about monetization too much. Stay on the core value proposition. You know, in light of the red hat being bought by IBM, I had a tweet out there, kind of critical of the transaction, just in terms of, you know, people talk about IBM's betting the company on red hat. My tweet was, don't get your reaction when I tie it to the business model 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 talks about 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, and this way, as we've been saying on theCUBE for a couple of years. So you get the old guard trying to get a position in open source, all these things, but the stack's 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 group swing, get customers, don't worry about the monetization, how are you charging? So how do you guys talk about the business model? Is it specific? Have you guys have clear visibility on that? What's the story on that? I mean, I think, yeah, I always tell bank how there's kind of three hurdles to know if you have something worthwhile. Well, someone listened to your pitch, right? People are busy, like, hey, John, you get pitched 100 times a day by startups, right? Will you take 30 seconds to listen to it? That's hurdle one. Her role too is will you spend time, hands on keyboards, playing around with the code? And step three is will they write you a check? And as an enterprise software investor and a former operator, we don't overly focus on the revenue model now. I think writing a check in the business model just means you're creating value. And I think people write you a check and you're creating value. But, you know, the feedback I always give Venkat and the founders to work with, don't overthink pricing for 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 of the business model, you know, will figure out down the line. I mean, it's a cloud service, it's, you know, serverless, technically too many servers in that sentence, but it's, to your point, it's born on the cloud. Born on the cloud. Academics are good, so if it works, it's going to be profitable. Yeah, it's born on the cloud, multi-cloud, right, across whatever cloud I want to be in, it's the way application architecture is going, right? 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 as a developer had to make compromises. If I wanted joins and SQL queries, I had to use like Postgres. If I wanted like document database, I had to use like Mongo. If I wanted index, I had to use like Elastic. And so either one, I had to pick one, or two, I had to use all three, you know, and none of the world was great. And then all three of those products have different business models. With Rockset, you actually don't need to make choices, right? Yeah, so this is classic Greylock investment, you got Sequoia the same way. Go out, get a position in the market, don't overthink the revenue model, you're funded for, grow the company, let's scale a little bit, and figure out that blitz scale moment, 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, we're 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. Elastic search is a good proxy. I mean, that's, they did well that way too. And Rockset's free to get started, right? So right now they go to rockset.com, get started for free, and just start playing around with it. 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, but I think the trend is, it looks a lot like that DevOps ethos of automation, scale, get to value quickly, not over thinking it, the value proposition, and let it organically become part of the operation. Yeah, I think 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're obsessed about. I think we say like, this is what excellence looks like, and we pursue that the logos and the revenue would be a second order effect. Yeah, and it's good. You build that core kernel. This is a classic, classic build out. So I want to ask you about the multicloud, you mentioned that earlier. I want to get your thoughts on Kubernetes. Obviously there's a lot of great projects going on and CNCF around Istio and this new state problem that you're solving. You know, stateless has been an easy solution, APIs. 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 at a party, hey, Bencat, why is, what's all this Kubernetes? What party's it going to? I mean, all we do is talk about Kubernetes and operating systems. It's in handout candy last night. No, we are huge fans of Kubernetes and Docker. In fact, in the entire rock set, backend is built on top of that. So we run in AWS, but with the insight that we run our entire infrastructure in one Kubernetes cluster. And that is something that I think is here to stay. I think this is 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 for good. Why is it important in your mind? Because of the orchestration and what's the, why is it so important? It's a lot of people are jazzed about it. I have an opinion on it. What's the key thing? I think it makes your entire infrastructure programmable. I think it turns every aspect of, for example, I'll take a concrete example. We wanted to build this infrastructure so that when somebody points at like, it's a 10 terabytes of data, we want to very quickly autoscale that out and be able to grow 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 at two levels. It's one microservice 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 nodes that 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 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 cloud bursting and whatnot, when I say cloud, it's something we do take advantage of that. With respect to stateful services, I think it's still early days. So our position on that, it's a lot harder. So our position on that is continue to use Kubernetes and continue to make things as stateless as possible and send your real-time streams to a service like Rockset, not necessarily that, pick something like that where you separate state and keep it in the backend that is very much suited to your microservice and the business logic that needs to live there. It 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, Rockset is one that we are proud of building and have your stateless application logic and continue to have that, maybe use Kubernetes, scale it and lambdas for all we care. But you can take something that is very hard to manage and scale today, break it into the stateful part and the stateless part and the serverless backend like Rockset will sort of hopefully give you a huge boost in being able to go from an experiment to okay, I'm gonna roll it out to a small set of audience to like I want to do a worldwide launch. You can do all of that without having to worry about what happens. And think about the alternative if you did it the old way. I mean, Dan, that's like talent you'd need. It would be a wire that's spaghetti everywhere. So Jerry, this is like Kubernetes is really kind of a benefit off your investment in Docker. You must be proud and that the industry's gone to a home of the level because containers really enable all this. Right, yeah. So this is an example where I think Cloud is going to go to a home of the level that no one's seen before and these kinds of opportunities that you're investing in. So I got to ask you directly as you're looking at as a knowledgeable Cloud guy as well as an investor, Cloud changes things. How does that change? How is Cloud native in these kinds of new opportunities that are built from the ground up change a company's network, network security, application performance? Because certainly this is a game changer. So those are the three areas I see a lot of impact. Compute, check, storage, check, networking. It's early days. You know, it's funny. It kind of seems so long ago yet so briefly when you know, I first talked five years ago when I first met Mayor Vessner and Docker and almost from the beginning people were like, okay, I got stateless applications but stateful containers, stateless apps. And then for the next three or four years we saw a bunch of companies like how do we handle state in a Docker based application and lots of stars have tried and it was the wrong approach. The right approach is what these guys have cracked is separate the state from the application, build your app stateless containers, store your state on an indexing layer like Rockset. That's hopefully one of the better ways to solve the problem. But as you kind of unearth one problem and solve it with something like Rockset to your point all of a sudden like networking's issue because all of a sudden like I think service mesh and like Istio and console or kind of the technologies people talk about because as these microservices come up and down they're pretty dynamic. And partially as a developer I don't want to care about that, right? That's the value like a Rockset is serverless but still as the operator of the cloud or the IT person on the other side of the proverbial curtain I probably care. Security I matters because all of a sudden data is flowing from multiple locations and multiple destinations using all these APIs and then you have kind of compliance like GDPR 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 Rockset? Or do I have to build that in my app natively leveraging the Rockset abstraction? We're talking about security features? Yes, just say I'm a region GDPR. Hey, you know what? I got a website and social network out in London and Europe and I got the GDPR nightmare. I don't, we don't have a great answer for GDPR. We are, we're not a controller of the data, right? We are just a processor. So I think for GDPR I think there is still, the controller still has to do a lot of work to be compliant with GDPR. 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, you can't store data in Rockset without encrypting it. Like it's just on by default. The only way, all transit is all over HTTPS and in SSL. And so we never forget that we're building for enterprises. And so we're baked in for enterprise customers. 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 KMS key support, private VPC links. Like we have a plethora of security features so that the control of the data is still with the data controller which is our customer. But we will be the processor and a lot of the time we can process it using that encryption keys. Awesome. But if I'm going to build a GDPR solution, a security solution, I would probably build it on Rockset. And some of the early developers kick around Rockset are security companies that are trying to track where all the data is coming and going. So they're the processor. And then one of the companies we hope to enable with Rockset is another generation of security and privacy companies that in the past had a hard time tracking all this data. So I can build on top of Rockset. Correct. So you can build security and GDPR solution on top of Rockset because Rockset gives you the power to process all the data, index all the data. And then so one of the early developers still in stealth is looking at the data flows coming and going using them and they'll apply the context, right? You'll say, oh, this is your credit card. This is your social security, this is your birthday, et cetera, your favorite colors and they'll apply that. But I think to your point, it's game changing like not just Rockset 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 of problems like privacy in 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, that's why I come up with the Red Hat IBM thing is that's not the answer at the root level. I feel like Red Hat IBM, I think it's fascinating for a bunch of reasons but it's almost like you're almost doubling down to your comment on the old stack, right? It's almost a double down on 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 wish them the best in this innovation. No, they're going to do well with it. I think IBM's going to do great with open stack but again, they're a product company that has people that happen to contribute to open source. I think it was a great move for both companies but it doesn't mean that they can't do well without a new stack doing well. And I think you're going to see this world where we have, to your point, these old stacks but then a category of new stack companies that are being born in the cloud, they're just fun to watch. All big investments that would be blitz scaling criteria all start out organically on a wave in a market that has problems. And that's growing. So I think cloud native ground up kind of clean sheet of paper is the new. You know what I say, you've got to pick the right wave and you've got to pick a big wave bigger. The cloud wave is not a bad wave to be on right now. And the data wave is part of the cloud. Correct. And it's been growing bigger. 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 we're going to pick the next wave that's kind of cloud native born in the cloud infrastructure that is still early days and companies that are riding that wave are going to do well. And so I'm pretty excited. There's a lot of opportunities certainly in this whole idea that this change has come and societal change is going on. Mission based companies from whether it's an NGO to full scale start up. All the applications that the cloud is going to enable from data, privacy, your wearables or cars or health thing, we're seeing it every single day. I'm pretty excited. So you took Amazon's revenue and added to the revenue. Cloud revenue, the whole revenue. You look at their AWS cloud revenue. So it's like 20 billion a run right now. Microsoft bundles in a lot of their office stuff as well. If you took Amazon's customers that are in the marketplace and took their revenue, there clearly would be number one by my 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 going to be a few big ones, I think coming on. They're going to do well. So I think this is a good opportunity. Congratulations. Thank you. Ben Kat, $21 million. Final question before we go. What are you going to spend it on? We're going to spend it on our go to market strategy and hiring amazing people as many as we can get. Good answer. Didn't say launch party. I just don't say launch party. I'm sitting right here. Okay. We're here at Rackset C and trust Jerry Chen. Cube Royalty, number two all-time on our Cube alumni list, partner at Greylock. Guys, thanks for coming in. I'm Joe Furrier. Thanks for watching this special Cube conversation.