>>Good morning friends. Live from Las Vegas. It's the Cube Day four of our coverage of AWS. Reinvent continues. Lisa Martin here with Dave Valante. You >>Can tell it's day four. Yeah. >>You can tell, you >>Get punchy. >>Did you? Yes. Did you know that the Vegas rodeo is coming into town? I'm kind of bummed down, leaving tonight. >>Really? You rodeo >>Fan this weekend? No, but to see a bunch of cowboys in Vegas, >>I'd like to see the Raiders. I'd like to see the Raiders get tickets. >>Yeah. And the hockey team. Yeah. We have had an amazing event, Dave. The cubes. 10th year covering reinvent 11th. Reinvent >>Our 10th year here. Yeah. Yes. Yeah. I mean we covered remotely in during Covid, but >>Yes, yes, yes. Awesome content. Anything jump out at you that we really, we, we love talking to aws, the ecosystem. We got a customer next. Anything jump out at you that's really a kind of a key takeaway? >>Big story. The majority of aws, you know, I mean people ask me what's different under a Adam than under Andy. And I'm like, really? It's the maturity of AWS is what's different, you know, ecosystem, connecting the dots, moving towards solutions, you know, that's, that's the big thing. And it's, you know, in a way it's kind of boring relative to other reinvents, which are like, oh wow, oh my god, they announced outposts. So you don't see anything like that. It's more taking the platform to the next level, which is a good >>Thing. The next level it is a good thing. Speaking of next level, we have a couple of next level guests from T-Mobile joining us. We're gonna be talking through their customers story, their business transformation with aws. Glenn Curing joins us, the director product and technology. And Nick Chris, senior manager, product and technology guys. Welcome. Great to have you on brand. You're on T-Mobile brand. I love it. >>Yeah, >>I mean we are always T-Mobile. >>I love it. So, so everyone knows T-Mobile Blend, you guys are in the digital commerce domain. Talk to us about what that is, what functions that delivers for T-Mobile. Yeah, >>So the digital commerce domain operates and runs a platform called the Digital commerce platform. What this essentially does, it's a set of APIs that are headless that power the shopping experiences. When you talk about shopping experiences at T-Mobile, a customer comes to either a T-Mobile website or goes to a store. And what they do is they start with the discovery process of a phone. They take it through the process, they decide to purchase the phone day at, at the phone to cart, and then eventually they decide to, you know, basically pull the trigger and, and buy the phone at, at which point they submit the order. So that whole experience, essentially from start to finish is powered by the digital commerce platform. Just this year we have processed well over three and a half million orders amounting to a billion and a half dollars worth of business for T-Mobile. >>Wow. Big outcomes. Nick, talk about the before stage, obviously the, the customer experience is absolutely critical because if, if it goes awry, people churn. We know that and nobody wants, you know, brand reputation is is at stake. Yep. Talk about some of the challenges before that you guys faced and how did you work with AWS and part its partner ecosystem to address those challenges? >>Sure. Yeah. So actually before I started working with Glen on the commerce domain, I was part of T-Mobile's cloud team. So we were the team that kind of brought in AWS and commerce platform was really the first tier one system to go a hundred percent cloud native. And so for us it was very much a learning experience and a journey to learn how to operate on the cloud and which was fundamentally different from how we were doing things in the old on-prem days. When >>You talk about headless APIs, you talk, I dunno if you saw Warren a Vogel's keynote this morning, but you're talking about loosely coupled, a loosely coupled system that you can evolve without ripping out the whole system or without bringing the whole system down. Can you explain that in a little bit more >>Detail? Absolutely. So the concept of headless API exactly opens up that possibility. What it allows us to do is to build and operator platform that runs sort of loosely coupled from the user experiences. So when you think about this from a simplistic standpoint, you have a set of APIs that are headless and you've got the website that connects to it, the retail store applications that connect to it, as well as the customer care applications that connect to it. And essentially what that does is it allows us to basically operate all these platforms without being sort of tightly coupled to >>Each other. Yeah, he was talking about this morning when, when AWS announced s3, you know, there was just a handful of services maybe at just two or three. I think now there's 200 and you know, it's never gone down, it's never been, you know, replaced essentially. And so, you know, the whole thing was it's an asynchronous system that's loosely coupled and then you create that illusion of synchronicity for the customer. >>Exactly. >>Which was, I thought, you know, really well described, but maybe you guys could talk about what the genesis was for this system. Take us kind of to the, from the before or after, you know, the classic as as was and the, and as is. Did you talk about that? >>Yeah, I can start and then hand it off to Nick for some more details. So we started this journey back in 2016 and at that point T-Mobile had seven or eight different commerce platforms. Obviously you can think about the complexity involved in running and operating platforms. We've all talked about T-Mobile being the uncarrier. It's a brand that we have basically popularized in the telco industry. We would come out with these massive uncarrier moves and every time that announcement was made, teams have to scramble because you've got seven systems, seven teams, every single system needs to be updated, right? So that's where we started when we kicked off this transformational journey over time, essentially we have brought it down to one platform that supports all these experiences and what that allows us to do is not only time to market gets reduced immensely, but it also allows us to basically reduce our operational cost. Cuz we don't have to have teams running seven, eight systems. It's just one system with one team that can focus on making it a world class, you know, platform. >>Yeah, I think one of the strategies that definitely paid off for us, cuz going all the way back to the beginning, our little platform was powering just a tiny little corner of the, of the webspace, right? But even in those days we approached it from we're gonna build functions in a way that is sort of agnostic to what the experience is gonna be. So over time as we would build a capability that one particular channel needed primary, we were still thinking about all the other channels that needed it. So now over a few years that investment pays off and you have basically the same capabilities working in the same way across all the channels. >>When did the journey start? >>2016. >>2016, yeah. It's been, it's been six years. >>What are some of the game changers in, in this business transformation that you would say these are some of the things that really ignited our transformation? >>Yeah, there's particularly one thing that we feel pretty proud about, which is the fact that we now operate what we call active active stacks. And what that means is you've got a single stack of the eCommerce platform start to finish that can run in an independent manner, but we can also start adding additional stacks that are basically loosely coupled from each other but can, but can run to support the business. What that basically enables is it allows us to run in active active mode, which itself is a big deal from a system uptime perspective. It really changes the game. It allows us to push releases without worrying about any kind of downtime. We've done canary releases, we are in the middle of retail season and we can introduce changes without worrying about it. And more importantly, I think what it has also allowed us to do is essentially practice disaster recovery while doing a release. Cuz that's exactly what we do is every time we do a release we are switching between these separate stacks and essentially are practicing our DR strategy. >>So you do this, it's, it's you separate across regions I presume? Yes. Is that right? Yes. This was really interesting conversation because as you well know in the on-prem world, you never tested that disaster recovery was too risky because you're afraid you're gonna take your whole business down and you're essentially saying that the testing is fundamental to the implementation. >>Absolutely. >>It, it is the thing that you do for every release. So you know, at least every week or so you are doing this and you know, in the old world, the active passive world on paper you had a bunch of capabilities and in in incidents that are even less than say a full disaster recovery scenario, you would end up making the choice not to use that capability because there was too much complexity or risk or problem. When we put this in place. Now if I, I tell people everything we do got easier after that. >>Is it a challenge for you or how do you deal with the challenge? Correct me if it's not a, a challenge that sometimes Amazon services are not available in both regions. I think for instance, the observability thing that they just announced this week is it's not cross region or maybe I'm getting that wrong, but there are services where, you know, you might not be able to do data sharing across region. How do you manage that? Or maybe there's different, you know, levels of certifications. How do you manage that discontinuity or is that not an issue for you? >>Yeah, I mean it, it is certainly a concern and so the stacks, like Glen said, they are largely decoupled and that what that means is practically every component and there's a lot of lot of components in there. I have redundancy from an availability zone point of view. But then where the real magic happens is when you come in as a user to the stack, we're gonna initially kind of lock you on one stack. And then the key thing that we do is we, we understand the difference between what, what we would call the critical data. So think of like your shopping carts and then contextual data that we can relatively easily reload if we need to. And so that critical data is constantly in an async fashion. So it's not interrupting your performance, being broadcast out to a place where we can recover it if we need to, if we need to send you to another stack and then we call that dehydration. And if you end up getting bumped to a new stack, we rehydrate you on that stack and reload that, that contextual data. So to make that whole thing happen, we rely on something we call the global cart store and that's basically powered by Dynamo. So Dynamo is highly, highly reliable and multi >>Reason. So, and, and presume you're doing some form of server list for the stateless stuff and, and maybe taking control of the run time for the stateful things you, are you leaning into to servers and lambda or Not yet cuz you want control over the, the, the EC two and the memory configs. What, what's, I mean, I know we're going inside the plumbing a little bit, but it's kind of fun. >>That's always fun. You >>Went Yeah, and, and it has been a journey. Back in 2016 when we started, we were all on EC twos and across, you know, over the last three or four years we have kind of gone through that journey where we went from easy two to, to containers and we are at some point we'll get to where we will be serverless, we've got a few functions running. But you know, in that journey, I think when you look at the full end of the spectrum, we are somewhere towards the, the process of sort of going from, you know, containers to, to serverless. >>Yeah. So today your team is setting up the containers, they're fencing 'em off, fencing off the app and doing all that sort of sort of semi heavy lifting. Yeah. How do you deal with the, you know, this is one of the things Lisa, you and I were talking about is the skill sets. We always talk about this. What's that? What's your team look like and what are the skill sets that you've got that you're deploying? >>Yeah, I mean, as you can imagine, it's a challenge and it's a, a highly specialized skill set that you need. And you talk about cloud, you know, I, I tell developers when we bring new folks in, in the old days, you could just be like really good at Java and study that for and be good at that for decades. But in the cloud world, you have to be wide in, in your breadth. And so you have to understand those 200 services, right? And so one of the things that really has helped us is we've had a partner. So UST Global is a digital services company and they've really kind of been on the journey up the same timeline that we were. And I had worked with them on the cloud team, you know, before I came to commerce. And when I came to, to the commerce team, we were really struggling, especially from that operational perspective. >>The, the team was just not adapting to that new cloud reality. They were used to the on-prem world, but we brought these folks in because not only were they really able to understand the stuff, but they had built a lot of the platforms that we were gonna be leveraging for commerce with us on the cloud team. So for example, we have built, T-Mobile operates our own customized Kubernetes platform. We've done some stuff for serverless development, C I C D, cloud security. And so not only did these folks have the right skill sets, but they knew how we were approaching it from a T-mobile cloud perspective. And so it's kind of kind of fun to see, you know, when they came on board with this journey with us, we were both, both companies were relatively new and, and learning. Now I look and, you know, I I think that they're like a, a platinum sponsor these days here of aws and so it's kind of cool to see how we've all grown together, >>A lot of evolution, a lot of maturation. Glen, I wanna know from you when we're almost out of time here, but tell me the what the digital commerce domain, you kind of talked about this in the beginning, but I wanna know what's the value in it for me as a customer? All of this under the hood plumbing? Yeah, the maturation, the transformation. How does it benefit mean? >>Great question. So as a customer, all they care about is coming into, going to the website, walking into a store, and without spending too much time completed that transaction and walkout, they don't care about what's under the hood, right? So this transformational journey from, you know, like I talked about, we started with easy twos back in the day. It was what we call the wild west in the, on a cloud native platform to where we have reached today. You know, the journey we have collectively traversed with the USD has allowed us to basically build a system that allows a customer to walk into a store and not spend a whole hour dealing with a sales rep that's trying to sell them things. They can walk in and out quickly, they go to the website, literally within a couple minutes they can complete the transaction and leave. That's what customers want. It is. And that has really sort of helped us when you think about T-Mobile and the fact that we are now poised to be a leader in the US in telco at this whole concept of systems that really empower the customers to quickly complete their transaction has been one of the key components of allowing us to kind of make that growth. Right. So >>Right. And a big driver of revenue. >>Exactly. >>I have one final question for each of you. We're making a Instagram reel, so think about if you had 30 seconds to describe T-Mobile as a technology company that sells phones or a technology company that delights people, what, what would you say if you had a billboard, what would it say about that? Glen, what do you think? >>So T-Mobile, from a technology company perspective, the, the whole purpose of setting up T-mobile's, you know, shopping experience is about bringing customers in, surprising and delighting them with the frictionless shopping experiences that basically allow them to come in and complete the transaction and move on with their lives. It's not about keeping them in the store for too long when they don't want to do it. And essentially the idea is to just basically surprise and delight our customers. >>Perfect. Nick, what would you say, what's your billboard about T-Mobile as a technology company that's delivering great services to its customers? >>Yeah, I think, you know, Glen really covered it well. What I would just add to that is I think the way that we are approaching it these days, really starting from that 2016 period is we like to say we don't think of ourselves as a telco company anymore. We think of ourselves as a technology company that happens to do telco among other things, right? And so we've approached this from a point of view of we're here to provide the best possible experience we can to our customers and we take it personally when, when we don't reach that high bar. And so what we've done in the last few years as a transformation is really given us the toolbox that we need to be able to meet that promise. >>Awesome. Guys, it's been a pleasure having you on the program, talking about the transformation of T-Mobile. Great to hear what you're doing with aws, the maturation, and we look forward to having you back on to see what's next. Thank you. >>Awesome. Thank you so much. >>All right, for our guests and Dave Ante, I'm Lisa Martin, you watching The Cube, the leader in live enterprise and emerging tech coverage.

>>Good afternoon and welcome back to the Classiest Show in Technology. This is the Cube we are at AWS Reinvent 2022 in Fabulous Sin City. That's why I've got my sequence on. We love a little Vegas, don't we? I'm joined by John Farer, another, another Vegas >>Fan. I don't have my sequence, I left it in my room. We're >>Gonna have to figure out how to get us 20 as soon as possible. What's been your biggest shock for you at the show so far? >>Well, I think the data story and security is so awesome. I love how that's front and center. If you look at the minutes of the keynote of Adamski, the CEO on day one, it's all bulked into data and security. All worked hand in hand. That's on top of already the innovation of their infrastructure. So I think you're gonna see a lot of interplay going on in this next segment. It's gonna tell a lot of that innovation story that's coming next. It's pretty awesome. >>It is pretty awesome, and I'm super excited. It's not only what we do here on the Cube, it's also in my show notes. We are gonna be geeking out for the next segment. Please welcome Paul and Puja. Wonderful to have you both here. Paul from Amazon, s3, glacier, and Pujan, CEO of kuo. I wanna turn to you Pujan, to start us off, just in case the audience isn't familiar, give us the Kuo pitch. >>Yeah, so basically Kuo is a, a backup as a service offering, right? Built in AWS four aws, right? And effectively going after, you know, any service that a customer uses on top of aws, right? And so a lot of the data sitting on s3, right? So that's been like our, our big use case going and basically building backup and air gap protection for, for s3. But we basically go to every other service, e c two, ebs, dynamo, you know, you name it, right? So basically do the whole thing >>And the relationship with aws. Can you guys share, I mean, you got you here together. You guys are a great partnership. Born in the cloud, operation in the cloud. Absolutely. I think talk about the partnership with aws. >>Absolutely. I think the last five years of building on AWS has been phenomenal, right? And I love the platform. It's, it's a very pure platform for us. You know, the APIs and, and the access you get and access you get to the service teams like Paul sitting here and the other teams you have gotten access to, I think has been phenomenal. But we also have, I would say, pushed the envelope in terms of how innovative we have been and how aggressive we have been in utilizing all the innovation that AWS has built in over the last few years. But it would not have happened without the fantastic partnership with the service teams. >>Paul, talk about the, AM the S3 part of this. What's the story there? >>Well, it's been great working with the CUO team over the course of the last few years. We were just upstairs diving deep into the, to the features that they're taking advantage of. They really push us hard on behalf of customers, and it's been a, it's just been a great relationship over the last years. >>That's awesome. And the ecosystem at such a, we're gonna hear tomorrow, the keynote on the, from Aruba who's gonna tend over the ecosystem. You guys are working together. There's a lot of strategic partnerships, so much collaboration between you guys that makes it very, this is the next gen cloud of cloud environment we're seeing. And you heard the, the economies around the corner. It's still gonna be challenging, but still there's more growth in the cloud. This is not stopping. This is impacts the customers. What are the customers saying to you guys when you work backwards from their needs? They want it faster, easier, cheaper. They want it more integrated. What are some of the things, all those you guys hearing from customers? >>So for us, you know, if you think about it, like, you know, as people are moving to the cloud, especially like take a use case like s3, right? So much of critical data sitting on top of S3 today. And so what folks have realized that as they're, you know, putting all of those, you know, what, over two 50 trillion objects, you know, sitting on s3, a lot of them need backup and data protection because there could be accidental deletions, there could be software bugs, there could be a ransomware type event due to which you need a second copy of the data that is outside of your security domain, right? But again, that needs to get be done at the, at the right price point, right? And that's where like a technology like Columbia comes in because since we've been built on the cloud, we've optimized it correctly. So especially for folks who are very cost conscious, given the macroeconomic conditions, we are heading into a technology that's built correctly so that, you know, you get the right architecture and the right solution at the right price point and the scale, right? Talking about trillions of objects, billions of objects within a single customer, within a single bucket sometimes. And that's where Columbia comes in. Cause we basically do that at scale without, again, impacting the, the customer's wallet more than it needs to. >>The porridge has to be the right temperature and the right size bowl. With the right spoon. You've got a lot of complexity when it comes to solving those customer challenges. You have a couple customer story examples you're allowed to share with us. Correct? Paul, do you want to kick one off? Go ahead. Oh, puja. All right. >>No, absolutely. I think there's a ton of them. I, I'll talk about, you know, want to begin with like Cox Automotive, right? A phenomenal customer that we, all of us have worked together with them. And again, looking for a solution to backup S3 to essentially go air gap protection outside of their account, right? They looked at doing it themselves, right? They thought they'll go and basically do it themselves. And then they fortunately bumped into Columbia, they looked at our architecture, looked at what it would really go and take to build it. And guess what, sitting in 2022, getting 23 right now, nobody wants to go and build this themselves. They actually want a turnkey solution that just does it, right? And so, again, we are a phenomenal joint customer of ours doing this at a pretty massive scale, right? And there are many more like that. There's Warner Brothers that are essentially going into the cloud from on premises, right? And they're going really fast accelerating the usage on aws again, looking at, you know, backup and data protection and using clum because of our extreme simplicity that we provide. >>Yeah, I think it's, you've got a, a lot of different people solving different problems that you're working with all the time. Millions of customers. Well, how do you prioritize? >>Well, for us, it really all comes down to fundamentals, right? So Amazon, s3 s unique distributed architecture delivers industry leading durability, availability, performance and security at virtually unlimited scale, right? And it's really been delivering on the fundamentals that has earned the trust of so many customers of all sizes and industries over the course of over 16 years. Now, in terms of how we prioritize on behalf of those customers, we always say that 90% of our roadmap comes directly from what customers are telling us is important. And a large number of our customers now are using S3 through lumino, which is why the relationship is so important. We're here talking about customer use cases here at the show, and we do that regularly throughout the year as well. And that's, that's how we land on a road. >>And what are the, what are the top stories from customers? What, what are they telling you? What's the number one top three things you're hearing? >>I tell you, like, again, it just comes down to the fundamentals, right? Of security, availability, durability and performance at virtually unlimited scale. Like that is the first customer first discussions that we have with customers talking about durable storage, for >>Sure. What I find interesting in, you mentioned scale, right? That comes up a lot scale with data. Yeah. That we heard data. The big theme here, security, what's in my S3 bucket? Can you find out what's in there? Is it backed up properly? How do I get it back? Where's the ransomware? Why not just target the ransomware? So how do you navigate the, the security challenges, the, the need to store all that scale data? What's the secret sauce? >>Yeah, so I think the, the big thing is we'll start with the, you know, how we have architected the product, right? If you think about it, this, you're dealing with a lot of scale, right? You get to a hundred million, a billion and billions very fast on S3 few, especially on a cloud native application. So it starts with the visibility, right? It's basically about, like we have things where you do, where you create a subset of your buckets called protection groups that you can essentially, you know, do it based on prefixes. So now you can essentially figure out what prefix you want to back up and what you don't want to back up. Maybe there's log data that you don't care about, so you don't back that up, right? And it all starts with that visibility that you give. And the prefix level data protection then comes the scale, which is where I was telling you, right? We have basically built an orchestration engine, right? It's like we call the ES for Lambdas, right? So we have a internal orchestration engine and essentially what what we have done is we have our own language internally that spawns off these lambdas, right? And they go after these S3 partitions do the right things and then you basically reel them back. So things like that that we do that are not possible if you're not built on the >>Clock. Well also, I mean, just mind blowing and go back 10 years. Yeah. I mean you got Lambda. What you're talking about here is the gift of the cloud innovation. Yeah. So the benefit of S3 is now accelerated. This is the story this year. Yeah. I mean they're highlighting it at scale, not just in the data, but like what we knew when Lambda came out and what S3 could do. But now mainstream solutions are coming in. Does that change your backup plans? Because we're gonna see a lot more end to end, lot more solutions. We heard that on the keynote. Some are saying it's more complexity. Of course it might, but you can abstract another way with the cloud that's the best part of the cloud. So these abstraction leads. So what's your view on that? But I wanna get your thoughts because you guys are perfectly positioned for this scale, but there's more coming. Yes. Yes. Exactly. What, how are you looking at that? >>So again, I think the, you know, obviously the, the S3 teams and every team in AWS is basically pushing the envelope in terms of innovation. But the key for a partner like us is to go and take that innovation. A lot of complex architectures behind the scene. But what you deliver to the customer is simple. I'll give you one more example. One of the things we launched that, you know, Paul and others are very excited about, is this ability to do instant access on the backup, right? So you could have billions of objects that you backed up. Maybe you need just 10,000 of them for a DR test. And we can basically create like an instant virtual bucket on top of that backup that you can instantly restore >>Spinning up a sandbox of temporary data to go check it >>Out. Exactly. Offer an inte application. >>Think we're geeking out right now. >>Yeah, I know. Brought that part of the segment, John. Don't worry, we're safely there. But, >>But that's the thing, right? That all that is possible because of all the, the scale and innovation and all the APIs and everything that, you know, Paul and the team gives us that we go and build on top of >>Paul, geek out on with us on this. We >>Are super excited for instant restore >>For store. I mean, automation programmability. >>It is, I mean it's the logical next step for backup in the cloud. Exactly. Yeah. But it's a super hard engineering problem to go solve for customers. I mean, the RTO benefits alone are super compelling, but then there's a cost element as well of not having to bring back all that stuff for a test restore, for example. And so it's, it's been really great to, to work with the team on that. We have some ideas on how we may help solve it from our side, and we're looking forward to collaborating on it. >>This is a great illustration of what I was writing about this week around the classic cloud, which is great. And as Adam said, and used like to use the word and, and you got this new functionality we're seeing emerge from the growth. Yes. From the companies that are built on Amazon web services that are growing. You're a partner, they have a lot of other partners and people are taking over restaurant here off action. I mean, there's real growth and new functionality on top of aws. You guys are no different. What's, are you prepared for that? Are you ready to go? >>Yeah, no, absolutely. And I think if you think about, if you think about it, right, I think it's also about doing this without impacting the primary application. Like if the customer is running a primary application at scale on s3, a backup application like ours can't come in and really mess with that. So I think being able to do things where, and this is where you solve really hard computer science problems, right? Where you're bottling yourself. If you are essentially seeing any kind of, you know, interfering with the primary, you're going to cut yourself down. You're gonna go after a different partition. So there are a lot of things you need to do behind the scenes, which is again, all the complexity, all of that, but deliver the, to the customer a very, very simple thing. >>You know, Paul, I wanna get your thoughts and I want you to chime in. Yeah. In 2014, I interviewed Steven Schmidt, my first interview with the, he was the CISO then, and now he's a CSO and, and former ciso, he's back at that time, the word was the cloud's not secure. Now we're talking about security. Just in the complexity of how you're partitioning and managing your sub portions, how you explained it, it's harder for the attackers. The cloud in its in its architecture has become a more secure environment. Yeah. Well, and getting more secure as you have laying out this, this is a new dynamic. This is good. Can you explain the, >>I mean, I, I can just tell you that at AWS security is job zero and that it will always be our number one priority, right? We have a, an infrastructure with under AWS that is vetted and approved to run even top secret workloads, which benefits all customers in all regions. >>And your, your security posture is embedded on top of that. And you got your own stuff. >>Yeah. And if you think of it as a shared responsibility model, so security of the cloud is the responsibility of the cloud provider, but then security of the data on top of it. Like you, you go and delete stuff, your software goes and does something that resiliency, the integrity of the data is your responsibility as a customer. And that's where, you know, we come in. Who >>Shared responsibility has been such a hot topic all week. Yeah. >>I gotta ask him one more question. Cause this is fascinating. And we are talking about on the cube all day today after we saw the announcement and Adam's comment on the cube, Adams LE's comment on the keynote. I mean, he said, if you're gonna tighten your belt, meaning economic cost recovery, re right sizing. If you want to tighten your belt, come to the cloud. So I have to ask you guys, Puja, if you can comment, that'd be great. There's a lot of other competitors out there that aren't born on aws. What is the customer gonna do when they tighten the build? What does that mean? They're gonna go to, to the individual contracts. They're gonna work in the marketplace. I mean this, there's a new dynamic in town. It's called AWS 2022. They weren't really around much in the recession of 2008. They were just starting to grow. Now they're an economic force. People like yourselves have embedded in there. There's a lot of competition. What's gonna happen? >>I think people are gonna just go to a place like, you know, AWS marketplace. You're going to essentially look for solutions and essentially like, and, and the right solutions built in are going to be self-service like aws. It's a very self-service thing. A hundred percent. So you go and do self-service, you figure out what's working, what's not working. Also, the model has to be consumption oriented. No longer can you expect the customer to go and pay a bunch of money for shelfware, right? It's like, like how we charge how AWS charges, which is you pay for what you consume. That and all has to be front and center, >>Right? I think that's a really, I think that's a really important >>Point. It's time >>And I think it's time. So we have a new challenge on the cube. We give you 30 seconds roughly to give us your extraordinarily hot take your shining thought leadership moment and, and highlight what you think is the most important takeaway from the show. The biggest soundbite, the juiciest announcement. Paul, I'll >>Start with an Instagram. Real basically. Yeah. Okay. >>Yeah. Hi. Go. I would just say from an S3 perspective, over the course of the last several years, we've really seen workloads shift from just backup and recovery and static images on websites to data lake analytics applications. And you continue to see that here. And I can tell you that some of these scaled applications are running at enormous mind blowing scale, right? And so, so every year we come here, we talk to customers, and it's just every year it sort of blows me away. And I've been in the storage industry for a long time and it's just is, it blows me away. Just the scale at customers are running in >>And >>Blowing scale. And when it comes to backup, let me just say that it's easy to back up and recover a single object, but doing an easy thing, a billion or 10 billion times over, that's actually quite hard. >>And just to, just to bold that a little bit, just pull out my highlighter. S3 now has over 280 trillion objects. That's a lot. >>That's a lot of objects. >>Yeah. You are not, you are not kidding. When you talk about scale, I mean, this is the most scalable. >>That's not solution's not there. Yeah. That, that's right. And we wake up every, we have a culture of durability and we wake up every single day to raise the bar on the fundamentals and make sure that every single one of those objects is protected and safe. >>Okay. You, I, >>I can't imagine worrying about two, two 80 trillion different things. >>Let's go. You're Instagram real >>For me again, you know, between S3 and us, we are two players out there that are really, you know, processing the data at the end of the day, right? And so I'm very excited about, you know, what we are going to do more and more with the instant restore capability where we can integrate third party services on top of it that can do more things with the data that is not, not passively sitting, but now becomes active data that you can analyze and do things with. So that's something where we take this to the next level is something that I'm super excited about. >>There's a lot to be excited about and, and we're excited to have you. We're excited to hear what happens next. Excited to see more collaboration like this. Paul Pon, thank you so much for joining us here on the show. Thank all of you from for tuning into our continuous wall to wall super thrilling live coverage of AWS reinvent here in fabulous Las Vegas, Nevada, with John Furrier. I'm Savannah Peterson. We're the cube, the leading source for high tech coverage.

>>Okay, welcome back everyone to the cubes coverage here in Las Vegas for AWS re Mars, Remar stands for machine learning, automation, robotics, and space. Part of thehow is reinforces security. And the big show reinvent at the end of the year is the marquee event. Of course, the queues at all three and more coverage here. We've got a great guest here. Ryan re practice lead data analytics, machine learning at mission cloud. Ryan. Thanks for joining me. Absolutely >>Glad. >>So we were talking before he came on camera about mission cloud. It's not a mission as in a space mission. That's just the name of the company to help people with their mission to move to the cloud. And we're a space show to make that it's almost like plausible. I can see a mission cloud coming someday. >>Yeah, absolutely. >>You got >>The name. We got it. We're ready. >>You guys help customers get to the cloud. So you're working with all the technologies on AWS stack and people who are either lifting and shifting or cloud native born in the cloud, right? Absolutely. >>Yeah. I mean, we often see some companies talk about lift and shift, but you know, we try to get them past that because often a lift and shift means like, say you're on Oracle, you're bringing your Oracle licensing, but a lot of companies want to, you know, innovate and migrate more than they want to lift and shift. So that's really what we're seeing in market. >>You see more migration. Yeah. Less lift and shift. >>Yeah, exactly. Because they, they're trying to get out of an Oracle license. Right. They're seeing if that's super expensive and you know, you can get a much cheaper product on AWS. >>Yeah. What's the cutting up areas right now that you're seeing with cloud Amazon. Cause you know, Amazon, you know, is at their, their birthday, you know, dynamo you to sell with their 10th birthday. Where are they in your mind relative to the enterprise in terms of the services and where this goes next in terms of the on-prem you got the hybrid model. Everyone sees that, but like you got outpost. Mm. Not doing so as good as say EKS or other cool serverless stuff. >>Yeah. I mean, that's a great question. One of the things that's you see from AWS is really innovation, right? They're out there, they have over 400 microservices. So they're looking at all the different areas you have on the cloud and that people are trying to use. And they're creating these microservices that you string together, you architect them all up so that you can create what you're looking for. One of the big things we're seeing, right, is with SageMaker. A lot of people are coming in, looking for ML projects, trying to use all the hype that you see around that doing prediction, NLP and computer vision are super hot right now we've helped a lot of companies, you know, start to build out these NLP models where they're doing, you know, all kinds of stuff you use. 'em in gene research, you know, they're trying to do improvements in drugs and therapeutics. It's really awesome. And then we do some eCommerce stuff where people are just looking at, you know, how do I figure out what are similar things on similar websites, right. For, for search companies. So >>Awesome. Take me through the profile of your customer. You have the mix of business. Can you break down the, the target of the small, medium size enterprise, large all the above. >>Yeah. So mission started working with a lot of startups and SMBs and then as we've grown and become, you know, a much larger company that has all the different focus areas, we started to get into enterprise as well and help a lot of pretty well known enterprises out there that are, you know, not able to find the staff that they need and really want to get into >>The cloud. I wanted to dig into the staffing issues and also to the digital transformation journey. Okay. It okay. We all kind of know what's turning into the more dashboards, more automation, DevOps, cloud, native applications. All good. Yeah. And I can see that journey path. Now the reality is how do you get people who are gonna be capable of doing the ML, doing the DevOps dev sec ops. But what about cyber security? I mean is a ton of range of issues that you gotta be competent on to kind of survive in this multi-disciplined world, just to the old days of I'm the top of rack switch guy is over. >>Absolutely. Yeah. You know, it's a really good question. It's really hard. And that's why, you know, AWS has built out that partner ecosystem because they know companies can't hire enough people to do that. You know, if you look at just a migration into a data lake, you know, on-prem often you had one guy doing it, but if you want to go to the cloud, it's like you said, right, you need a security guy. You need to have a data architect. You need to have a cloud architect. You need to have a data engineer. So, you know, in the old days maybe you needed one guy. Now you have to have five. And so that's really why partners are valuable to customers is we're able to come in, bring those resources, get everything done quickly, and then, you know, turn >>It over. Yeah. We were talking again before we came on camera here live, you, you guys have a service led business, but the rise of MSPs managed service providers is huge. We're seeing it everywhere mainly because the cloud actually enables that you're seeing it for things like Kubernetes, serverless, certain microservices have certain domain expertise and people are making a living, providing great managed services. You guys have managed services. What's that phenomenon. Do you agree with it? And how do you, why did that come about and what, how does it keep going? Is it a trend or is it a one trick pony? >>I think it's a trend. I mean, what you have, it's the same skills gap, right? Is companies no longer want that single point of failure? You know, we have a pool model with our managed services where your team's working with a group of people. And so, you know, we have that knowledge and it's spread out. And so if you're coming in and you need help with Kubernetes, we got a Kubernetes guy in that pool to help you, right. If you need, you know, data, we got a data guy. And so it just makes it a lot easier where, Hey, I can pay the same as one guy and get a whole team of like 12 people that can be interchangeable onto my project. So, you know, I think you're gonna see managed services continue to rise and companies, you know, just working in that space. >>Do you see a new skill set coming? That's kind of got visibility right now, but not full visibility. That's going to be needed. I asked this because the environment's changing for the better obviously, but you're seeing companies that are highly valued, like data bricks, snowflake, they're getting killed on valuation. So they gotta have a hard time retaining talent. In my opinion, my opinion probably be true, but you know, you can't, you know, if you're data breach, you can't raise that 45 billion valuation try to hire senior people. They're gonna be underwater from day one. So there's gonna be a real slow down in these unicorns, these mega unicorns, deck, unicorns, whatever they're called because they gotta refactor the company, stock equity package. They attract people. So they gotta put them on a flat foot. And the next question is, do they actually have the juice, the goods to go to the new market? That's another question. So what I mean, what's your take on you're in the trenches. You're in the front lines. >>Yeah, that's a great question. I mean, and it's hard for me to think about whether they have the juice. I think snowflake and data bricks have been great for the market. They've come in. They've innovated, you know, snowflake was cloud native first. So they were built for the cloud. And what that's done is push all the hyperscalers to improve their products, right. AWS has gone through and you know, drastically over the last three years, improved Redshift. Like, I mean it's night and day from three years ago. Did, >>And you think snowflake put that pressure on them? >>Snowflake. Absolutely. Put that pressure on them. You know, I don't know whether they would've gotten to that same level if snowflake wasn't out there stealing market share. But now when you look at it, Redshift is much cheaper than snowflake. So how long are people gonna pay that tax to have snowflake versus switching over snowflakes? >>Got a nice data. Clean room, had some nice lock in features. Only on snowflake. The question is, will that last clean room? I see you smiling. Go ahead. >>Clean. Room's a concept that was actually made by Google. I know Snowflake's trying to capture it as their own, but, but Google's the one that actually launched the clean room concept because of marketing and, and all of that. >>Google also launches semantic layer, which Snowflake's trying to copy that. Does that, what does that mean to you when you hear the word semantic layer? What does that mean? >>And semantic layer just is really all about meta tags, right? How am I going through to figure out what data do I actually have in my data lake so that I can pull it for whatever I'm trying to do, whether it's dashboarding or whether it's machine learning. You're just trying to organize your data better. >>Ryan, you should be a cue post. You're like a masterclass here in, in it and cloud native. I gotta ask you since you're here, since we're having the masterclass being put in a clinic here, lot of clients are confused between how to handle the control plane and the data plane cause machine learning right now is at an all time high. You're seeing deep racer. You're seeing robotic space, all driving by machine learning. SW. He said it today, the, the companion coder, right? The, the code whisperer, that's only gonna get stronger. So machine learning needs data. It feeds on data. So everyone right now is trying to put data in silos. Okay? Cause they think, oh, compliance, you gotta create a data plane and a control plane that makes it highly available. So that can be shared >>Right >>Now. A lot of people are trying to own the data plane and some are trying to own the control plane or both. Right? What's your view on that? Because I see customers say, look, I want to own my own data cause I can control it. Control plane. I can maybe do other things. And some are saying, I don't know what to do. And they're getting forced to take both to control plane and a data plane from a vendor, right? What's your, what's your reaction to that? >>So it's pretty interesting. I actually was presenting at a tech target conference this week on exactly this concept, right, where we're seeing more and more words out there, right? It was data warehouse and it was data lake and it's lake house. And it's a data mesh and it's a data fabric. And some of the concepts you're talking about really come into that data, match data fabric space. And you know, what you're seeing is data's gonna become a product right, where you're gonna be buying a product and the silos yes. Silos exist. But what, what companies have to start doing is, and this is the whole data mesh concept is, Hey yes, you finance department. You can own your silo, but now you have to have an output product. That's a data product that every other part of your company can subscribe to that data product and use it in their algorithms or their dashboard so that they can get that 360 degree view of the customer. So it's really, you know, key that, you know, you work within your business. Some business are gonna have that silo where the data mesh works. Great. Others are gonna go. >>And what do you think about that? Because I mean, my thesis would be, Hey, more data, better machine learning. Right. Is that the concept? >>So, or that's a misconception or, >>Okay. So what's the, what's the rationale to share the data like that and data mission. >>So having more of the right data here, it is improves. Just having more data in general, doesn't improve, right? And often the problem is in the silos you're getting to is you don't have all the data you want. Right. I was doing a big project about shipping and there's PII data. When you talk about shipping, right? Person's addresses, that's owned by one department and you can't get there. Right. But how am I supposed to estimate the cost of shipping if I can't get, you know, data from where a person lives. Right. It's just >>Not. So none of the wrinkle in the equation is latency. Okay. The right data at the right time is another factor is that factored into data mesh versus these other approaches. Because I mean, you can, people are streaming data. I get that. We're seeing a lot of that. But talking about getting data fast enough before the decisions are made, is that an issue or is this just BS? >>I'm going with BS. Okay. So people talk about real time real. Time's great if you need it, but it's really expensive to do. Most people don't need real time. Right. They're really looking for, I need an hourly dashboard or I need a daily dashboard. And so pushing into real time, just gonna be an added expense that you don't >>Really need. Like cyber maybe is that not maybe need real time. >>Well, cyber security add. I mean, there's definitely certain applications that you need real time, >>But don't over invest in fantasy if you don't need an an hour's fine. Right, >>Right. Yeah. If you're, if you're a business and you're looking at your financials, do you need your financials every second? Is that gonna do anything for you? Got >>It. Yeah. Yeah. And so this comes back down to data architecture. So the next question I asked, cause I had a great country with the Fiddler AI CEO, CEO earlier, and he was at Facebook and then Pinterest, he was a data, you know, an architect and built everything. He said themselves. We were talking about all the stuff that's available now are all the platforms and tools available to essentially build the next Facebook if someone wanted to from scratch. I mean, hypothetically thought exercise. So the ability to actually ramp up and code a complete throwaway and rebuild from the ground up is possible. >>Absolutely. >>And so the question is, okay, how do you do it? How long would it take? I mean, in an ideal scenario, not, you know, make some assumptions here, you got the budget, you got the people, how long to completely roll out a brand new platform. >>Now it's funny, you asked that because about a year ago I was asked that exact same question by a customer that was in the religious space that basically wanted to build a combination of Facebook, Netflix, and Amazon altogether for the religious space, for religious goods and you know, church sermons, we estimated for him about a year and about $9 million to do it. >>I mean, that's a, that's a, a round these days. Yeah. Series a. So it's possible. Absolutely. So enterprises, what's holding them back, just dogma process, old school legacy, or are people taking the bold move to take more aggressive, swiping out old stuff and just completely rebuilding? Or is it a talent issue? What's the, what's the enterprise current mode of reset, >>You know, I think it really depends on the enterprise and their aversion to risk. Right. You know, some enterprises and companies are really out there wanting to innovate, you know, I mean there's companies, you know, an air conditioning company that we worked with, that's totally, you know, nest was eaten all their business. So they came in and created a whole T division, you know, to, to chase that business, that nest stole from them. So I think it, I think often a company's not necessarily gonna innovate until somebody comes in and starts stealing their >>Lunch. You know, Ryan, Andy, Jess, we talked about this two reinvents ago. And then Adam Eski said the same thing this year on a different vector, but kind of building on what Andy Jessey said. And it's like, you could actually take new territory down faster. You don't have to kill the old, no I'm paraphrasing. You don't have to kill the old to bring in the new, you can actually move on new ideas with a clean sheet of paper if you have that builder mindset. And I think that to me is where I'm seeing. And I'd love to get your reaction because if you see an opportunity to take advantage and take territory and you have the right budget time and people, you can get it. Oh absolutely. It's gettable. So a lot of people have this fear of, oh, we're, that's not our core competency. And, and they they're the frog and boiling water. >>You know, my answer to that is I think part of it's VCs, right? Yeah. VCs have come in and they see the value of a company often by how many people you hire, right. Hire more people. And the value is gonna go up. But often as a startup, you can't hire good people. So I'm like, well, why are you gonna go hire a bunch of random people? You should go to a firm like ours that knows AWS and can build it quickly for you, cuz then you're gonna get to the market faster versus just trying to hire a bunch of people in >>Someone. Right. I really appreciate you coming on. I'd love to have you back on the cube again, sometime your expertise and your insights are awesome. Give a commercial for the company, what you guys are doing, who you're looking for, what you want to do, hiring or whatever your goals are. Take a minute to explain what you guys are doing and give a quick plug. >>Awesome. Yeah. So mission cloud, you know, we're a premier AWS consulting firm. You know, if you're looking to go to AWS or you're in AWS and you need help and support, we have a full team, we do everything. Resell, MSP professional services. We can get you into the cloud optimize. You make everything run as fast as possible. I also have a full machine learning team. Since we're here at re Mars, we can build you models. We can get 'em into production, can make sure everything's smooth. The company's hiring. We're looking to double in size this year. So, you know, look me up on LinkedIn, wherever happy to, to take, >>You mentioned the cube, you get a 20% discount. He's like, no, I don't approve that. Thanks for coming on the key. Really appreciate it. Again. Machine learning swaping said on stage this, you can be a full time job just tracking just the open source projects. Never mind all the different tools and like platform. So I think you're gonna have a good, good tailwind for your business. Thanks for coming on the queue. Appreciate it. Ryan Reese here on the queue. I'm John furry more live coverage here at re Mars 2022. After this short break, stay with us.

>>Hi, everyone. Welcome to the cubes presentation of the AWS startup showcase the theme. This episode is data as code, and this is season two, episode two of the ongoing series covering exciting startups from the ecosystem in cloud and the future of data analytics. I'm your host, John furry. You're getting great featured panel here with AWS heroes, Lynn blankets, the CEO of Lindbergh Lega consulting, Peter Hanson's, founder of cloud Cedar and Alex debris, principal of debris advisory. Great to see all of you here and, uh, remotely and look forward to see you in person at the next re-invent or other event. >>Thanks for having us. >>So Lynn, you're doing a lot of work in healthcare, Peter you're in the middle of all the action as data as code Alex. You're in deep on the databases. We've got a good round up of, of topics here ranging from healthcare to getting under the hood on databases. So as we'll start with you, what are you working on right now? What trends do you see in the database space? >>Yeah, sure. So I do, uh, I do a lot of consulting work working with different people and, you know, often with, with dynamo DB or, or just general serverless technology type stuff. Um, if you want to talk about trends that I'm seeing right now, I would say trends you're seeing as a lot, just more serverless native databases or cloud native databases where you're seeing these cool databases come out that really take advantage of, uh, this new cloud environment, right? Where you have scalability, you have plasticity of the clouds. So you're not having, you know, instant space environments anymore. You're paying for capacity, you're paying for throughput. You're able to scale up and down. You're not managing individual instances. So a lot of cool stuff that we're seeing, you know, um, with this new generation of, of infrastructure and in particular database is taking advantage of this, this new cloud world >>And really lot deep into the database side in terms of like cloud native impact, diversity of database types, when to use certain databases that also a big deal. >>Yeah, absolutely. I like, I totally agree. I love seeing the different types of databases and, you know, AWS has this whole, uh, purpose-built database strategy. And I think that, that makes a lot of sense. Um, you know, I want to go too far with it. I would, I would more think about purpose-built categories and things like that, you know, specialize in an OLTB database within your, within your organization, whether that's dynamo DB or document DB or relational database Aurora or something like that. But then also choose some sort of analytics database, you know, if it's drew it or Redshift or Athena, and then, you know, if you have some specialized needs, you want to show some real time stuff to your users, check out rock site. If you want to, uh, you know, do some graph analytics, fraud detection, checkout tiger graph, a lot of cool stuff that we're seeing from the startup showcase here. >>Looking forward to unpacking that Lynn you've been in love now, a healthcare action with cloud ops, the pandemic pushes hard core on everybody. What are you working on? >>Yeah, it's all COVID data all the time. Uh, before the pandemic, I was supporting research groups for cancer genomics, which I still do, but, um, what's, uh, impactful is the explosive data volumes. You know, when you there's big data and there's genomic data, you know, I've worked with clients that have broken data centers, broken public cloud provider data centers because of the daily volume they're putting in. So there's this volume aspect. And then there's a collaboration, particularly around COVID research because of pandemic. And so you have this explosive volume, you have this, um, need for, uh, computational complexity. And that means cloud the challenge is it, you know, put the pedal to the metal. So you've got all these bioinformatics researchers that are used to single machine. Suddenly they have to deal with distributed compute. So it's a wild time to be in this space. >>What was the big change that you've seen with the, uh, the pandemic and in genomic cloud genomic specifically what's the big change has happened. >>The amount of data that is being put into the public cloud, um, previously people would have their data on their local, uh, capacity, and then they would publish their paper and the data may or may not become available for, uh, reproducing the research, uh, to accelerate for drug discovery and even variant identification. The data sets are being pushed to public cloud repositories, which is a whole new set of concerns. You have not only dealing with the volume and cost, but security, you know, there's federated security is non-trivial and not well understood by this domain. So there's so much work available here. >>Awesome. Peter, you're doing a lot with the data as a platform kind of view and platform engineering data as code is, is something that's being kicked around. What are you working on and how does platform engineering change as data becomes so much more prevalent in its value proposition? >>Yeah. So I'm the founder of cloud Cedar and, um, we sort of built this company out, this consultancy all around the challenges that a lot of companies have got with getting their data sorted, getting it organized, getting it ready for other use cases, such as analytics and machine learning, um, AI workloads and the like. So typically a platform engineering team will look after the organization of a company infrastructure, making sure that it's coherent across the company and a data platform, engineering teams doing something similar in that sense where they're, they're looking at making sure that, uh, data teams have a solid foundation to build upon, uh, that everything's quite predictable and what that enables is a faster velocity and the ability to use data as code as a way of specifying and onboarding data, building that, translating it, transforming it out into its specific domains and then on to data products. >>I have to ask you while you're here. Um, there's a big trend around data meshes right now. You're hearing, we've had a lot of stuff on the cube. Um, what are practical that people are using data mesh, first of all, is it relevant and how are people looking at this data mesh conversation? >>I think it becomes more and more relevant, uh, the bigger the organization that you're dealing with. So, you know, often times in the enterprise, you've got, uh, projects with timelines of five to 10 years often outlasting technology life cycles. The technology that you're building on is probably irrelevant by the time that you complete it. And what we're seeing is that data engineering teams and data teams more broadly, this organizational bottleneck and data mesh is all about, uh, breaking down that, um, bottleneck and decentralizing the work, shifting that work back onto, uh, development teams who oftentimes have got more of the context and a centralized data engineering team. And we're seeing a lot of, uh, Philocity increases as a result of that. >>It's interesting. There's so many different aspects of how data is changing the world. Lynn talks about the volume with the cloud and genomics. We're hearing data engineering at a platform level. You're talking about slicing and dicing and real-time information. You mentioned rock set, Alex. So I'd like to ask each of you to answer this next question, which is how has the team dynamics changed with data engineering because every single company's impacted. So if you're researchers, Lynn, you're pumping more data into the cloud, that's got a little bit of data engineering to it. Do they even understand that is that impacting them? So how has data changed the responsibilities or roles in this new emerging area of data engineering or whatever you want to call it? Lynn, we'll start with you. What do you, what do you see this impact? >>Well, you know, I mean, dev ops becomes data ops and ML ops and, uh, you know, this is a whole emergent area of work and it starts with an understanding of container technologies, which, you know, in different verticals like FinTech, that's a given, right, but in bioinformatics building an appropriately optimized Docker container is something I'm still working with customers now on because they have the concept of a Docker container is just a virtual machine, which obviously it isn't, or shouldn't be. So, um, you have, again, as I mentioned previously, this humongous skill gap, um, concepts like D, which are prevalent in ad tech FinTech, that's not available yet for most of my customers. So those are the things that I'm building. So the whole ops space is, um, this a wide open area. And really it's a question of practicality. Um, you know, I have, uh, a lot of experience with data lakes and, you know, containerizing and using the data lake platform. But a lot of my customers are going to move to like an interim pass based solutions. If they're using spark, for example, they might use to use a managed spark solution as an interim, um, step up to the cloud before they build their own containers. Because the amount of knowledge to do that effectively is non-trivial >>Peter, you mentioned data, you mentioned data lakes, onboarding data into lake house architectures, for instance, something that you're familiar with. Um, this is not obvious to some verticals obvious to others. What do you see this data engineering impact from a personnel standpoint? And then ultimately how things get built, >>You know, are you directing that to me, >>Peter? >>Yeah. So I think, um, first and foremost, you know, the workload that data engineering teams are dealing with is ever increasing. Usually there's a 10 X ratio of, um, software engineers to data engineers within a business and usually double the amount of analysts to data engineers again. And so they're, they're fighting it ever increasing backload. And, uh, so they're fighting an ever increasing backlog of, of, uh, tasks to do and tickets to, to, to churn through. And so what we're seeing is that data engineering teams are becoming data platform engineering teams where they're building capability instead of constantly hamster wheels spinning if you will. And so with that in mind, with onboarding data into, uh, a Lakehouse architecture or a data lake where data engineering teams, uh, uh, getting wins is developing a very good baseline of structure where they're getting the categorization, the data tagging, whether this data is of a particular domain, does it contain some, um, PII data, for instance, uh, and, and, and, and then the security aspects, and also, you know, the mechanisms on which to do the data transformations, >>Alex, on the database side, those are known personas in an enterprise, a them, the database team, but now the scale is so big. Um, and there's so much going on in databases. How does the data engineering impact organizations from your standpoint? >>Yeah, absolutely. I think definitely, you know, gone are the days where you have a single relational database that is serving operational queries for your users, and you can also serve analytics queries, you know, for your internal teams. It's, it's now split up into those purpose-built databases, like we've said. Uh, but now you've got two different teams managing it and they're, they're designing their data model for different things. You know? So L LLTP might have a more de-normalized model, something that works for very fast operations and it's optimized for that, but now you need to suck that data out and get it elsewhere so that your, your PM or your business analyst, or whoever can crunch through some of that. And, you know, now it needs to be in a more normalized format. How do you sort of bridge that gap? That's a tough one. I think you need to, you know, build empathy on each side of, of what each side is doing and, and build the tools to say, Hey, this is going to help you, uh, you know, LLTP team, if we know what, what users are actually doing, and, and if you can get us into the right format there, so that then I can, you know, we can analyze it, um, on the backend. >>So I think, I think building empathy across those teams is helpful. >>When I left to come back to, you mentioned a health and informatics is coming back. Um, but it's interesting, you know, I look at a database world and you look at the solutions that are out there. A lot of companies that build data solutions don't have a data problem. They've never, they're not swimming in a lot of data, but then you look at like the field that you're working in right now with the genomics and health and, and quantum, they're always, they're dealing with data all the time. So you have people who deal with a lot of data all the time are breaking through New Zealand. People who are don't have that experience are now becoming data full, right? So people are now either it's a first time problem, or they've always been swimming in a ton of data. So it's more of what's the new playbook. And then, wow, I've never had to deal with a lot of data before. What's your take? >>It's interesting. Cause they know, uh, bioinformatics hires, um, uh, grad students. So grad students, you know, use their, our scripts with their file on their laptop. And so, um, to get those folks to understand distributed container-based computing is like I said, a not non-trivial problem. What's been really interesting with the money pouring in to COVID research is when I first started, some of the workflows would take, you know, literally 500 hours and that was just okay. And coming out of FinTech, I was, uh, I could, I was blown away like FinTech is like, could that please take a millisecond rather than a second? Right. And so what has now happened, which makes it, you know, like I said, even more fun to work in this domain is, uh, the research dollars have really gone up because of the pandemic. And so there are, there are, there's this blending of people like me with more of a big data background coming into bioinformatics and working side by side. >>So it's this interesting sort of translation because you have the whole taxonomy of bioinformatics with genomics and sequencers and all the weird file types that you get. And then you have the whole taxonomy of dev ops data ops, you know, containers and Kubernetes and all that. And trying to get that into pipelines that can actually, you know, be efficient, given the constraints. Of course, we, on the tech side, we always want to make it super optimized. I had a customer that we got it down from 500 hours to minutes, but they wanted to stay with the past solution because it was easier for them to go from 500 hours to five hours was good enough, but you know, the techies want to get it down to five minutes. >>This is, this is, we've seen this movie before dev ops, um, edge and op operations, you know, IOT, world scenes, the convergence of cultures. Now you have data and then old, old school operations kind of coming up. So this kind of supports the thesis. That data as code is the next infrastructure as code. What do you guys, what's the reaction there for you guys? What do you think about that? What does data's code mean? If infrastructure's code was cloud and dev ops, what is data as code? What does that mean? >>I could take it if you like. I think, um, data teams, organizations, um, have been long been this bottleneck within the organization and there's like this dark matter of untapped energy and potential waiting to be unleashed a data with the advent of open source projects like DBT, um, have been slowly sort of embracing software development, lifecycle practices. And this is really sort of seeing a, a big steep increase in, um, in their velocity. And, and this is only going to increase and improve as we're seeing data teams, um, embrace starter as code. I think it's, uh, the future is bright for data. So I'm very excited. >>Lynn Peter reaction. I mean, agility data is code is developer concept CICB pipeline. You mentioned it new operational workflows coming into traditional operations reaction. >>Yeah. I mean, I think Peter's right on there. I'd say, you know, some of those tools we're seeing come in from, from software, like, like DBT, basically giving you that infrastructure as code, but applied to that data realm. Also there have been a few, like get for data type things, pack a derm, I believe is one and a few other ones where you bring that in and you also see a lot of immutability concepts flowing into the data realm. So I think just seeing some of those software engineering concepts come over to the data world has, has been pretty interesting >>What we'll literally just versioning datasets and the identification of what's in a data set. What's not in a data set. Some of this is around ethical AI as well, um, is a whole, uh, area that has come out of research groups. Um, mostly AI research groups, but is being applied to medical data and needs to be obviously, um, so this, this, this, um, metadata and versioning around data sets is really, I think, a very of the moment area. >>Yeah, I think we, we, you guys are bringing up a really good kind of direction that's happening in data. And that is something that you're seeing on the software side, open source and now dev ops. And now going to data is that the supply chain challenges of we've been talking about it here on the cube and this, this, um, this episode is, you know, we've seen Ukraine war, but some open source, you know, malware hitting datasets is data secure. What is that going to look like? So you starting to get into this what's the supply chain, is it verified data sets if data sets have to be managed a whole nother level of data supply chain comes up, what do you guys think about that? >>I'll jump in. Oh, sorry. I'll jump in again. I think that, you know, there's, there's, um, some, some of the compliance requirements, um, around financial data are going to be applied to other types of data, probably health data. So immutability reproducibility, um, that is, uh, legally required. Um, also some of the privacy requirements that originated in Europe with GDPR are going to be replicated as more and more, um, types of data. And again, I'm always going to speak for health, but there's other types as well coming out of personal devices and that kind of stuff. So I think, you know, this idea of data as code is it's, it goes down to versioning and controlling and, um, that's, uh, that's sort of a real succinct way to say it that we didn't used to think about that. We just put it in our, you know, relational database and we were good to go, but, um, versioning and controlling in the global ecosystem is kind of, uh, where I'm focusing my efforts. >>It brings up a good question. If databases, if data is going to be part of the development process has to be addressable, which means horizontally scalable. That means it has to be accessible and open. How do you make that work and not foreclose it with a lot of restrictions? >>I think the use of data catalogs and appropriate tagging and categorization, you know, I think, you know, everyone's heard of the term data swamp, and I think that just came about because that everyone saw like, oh, wow, S3, you know, infinite storage. We just, you know, throw whatever in there for as long as we want. And I think at times, you know, the proliferation of S3 buckets, um, and the like, you know, we've just seen, uh, perhaps security, not maintained as well as it could have been. And I think that's kind of where data platform engineering teams have really sort of, uh, come into the, for, you know, creating a governance set of buckets like formation on top. But I think that's kind of where we need to see a lot more work with appropriate tags and also the automatic publishing of metadata into data catalogs so that, um, folks can easily search and address particular data sets and also control the access. You know, for instance, you've got some PII data, perhaps really only your marketing folks should be looking at email addresses and the like not perhaps your finance folks. So I think, you know, there's, there's a lot to be leveraged there in formation and other solutions, >>Alex, let's back up and talk about what's in it for the customer, right. Let's zoom back and saying reality is I just got to get my data to make sure it's secure always on and not going to be hackable. And I just got to get my data available on river performance. So then, then I got to start thinking about, okay, how do I intersect it? So what should teams be thinking about right now as I look up all their data options or databases across their enterprise? >>Yeah, it's, it's a, it's a good question. I just, you know, I think Peter made some good points there and you can think of history as sort of ebbing and flowing between centralization and decentralization a lot of times. And you know, when storage was expensive, data was going to be sort of centralized and Maine maintained, sort of a, you know, by the, uh, the people that are in charge of it. But then when, when S3 comes along, it really decreases storage. Now we can do a lot more experiments on it. We can store a lot more of our data, keep it around and do different things on it. You know, now we've got regulations again, we were, we gotta, we gotta be more realistic about, about keeping that data secure and make sure we're, we're doing the right things with it. So it's, we're gonna probably go through a period of, of centralization as we work out some of this tooling around, you know, tagging and, and ethical AI that, that both Peter. And when we're talking about here and maybe get us into that, that next wearable world of de-centralization again. But I, I think that ebb and flow is going to be natural in response to, you know, the problems of the, the other extreme, >>Where are we in the market right now from progress standpoint, because data lakes don't want to be data swamps. You seeing lake formation as a data architecture, as an example, where are we with customers? What are they doing right now? Where would you put them in the progress bar of, of evolution towards the Nirvana of having this data sovereignty? And this data is code environment. Are they just now in the data lake store, everything real-time and historical? >>Well, I can jump in there. Um, SQL on files is the, is the driver. And so we know when Amazon got Athena, um, that really drove a lot of the customers to really realistically look at data lake technologies, but data warehouses are not going away. And the integration between the two is not seamless. No, we, we are partners with AWS, but we don't work for them. So we can tell you the truth here. Um, there's, there's work to it, but it really, for my customers, it really upped the ante around data lake, uh, because Athena and technologies like that, the serverless, um, SQL queries or the familiar quarry, um, uh, libraries really drove a movement away from either OLTB or OLAP, more expensive, more cumbersome structures, >>But they still need that. Oh, LTP, like if they have high latency issues, they want to be low latency. Can they have the best of both worlds? That's the question. >>I mean, I w I would say we're getting, you know, we're getting closer. We're always going to be, uh, you know, that technology is going to be moving forward, and then we'll just move the goalpost again, in terms of, of what we're asking from it. But I think, you know, the technology that's getting out there, you can get, get really well. And then, you know, just what I work in the dynamo DB world. So you can get really great low latency. So, you know, single digit millisecond LLTP response times on that. I think some of the analytics stuff has been a problem with that. And there, there are different solutions out there to where you can export dynamo to S3, and then you can be doing SQL on your FA your files with Athena Lakeland's talking about, or now you see, you know, rock set of partner here that that'll just ingest your dynamo, DB data, you know, make all those changes. So if you're doing a lot of, uh, changes to your data and dynamo is going to reflect in Roxanna, and then you can do analytics queries, you can do complex filters, different things like that. So, you know, I, I think we continue to push the envelope and then we moved the goalpost again. But, um, you know, I think we're in a, a lot better place than we were a few years ago, for sure. >>Where do you guys see this going relative to the next level? If data as code becomes that next agile, um, software defined environment with open source? Well, all of these new tools with serverless things happening with data lakes are built in with nice architectures with data warehouses, where does it go next? What happens next? If this becomes an agile environment, what's the impact? >>Well, I don't want to be so dominant, but I have, I feel strongly, so I'm going to jump in here. So, so I, um, I feel like, you know, now for my, my, my most computationally intensive workloads, I'm using GPS, I'm bursting to GPU for TensorFlow neural networks. So I've been doing quite a bit of exploration around Amazon bracket for QPS and it's early. Um, and it's specialty. It's not, you know, for everybody. And the learning curve again is pretty daunting, but, um, there are some use cases out there. I mean, I got ahold of a paper where some people did some, um, it was a Q CNN, um, quantum convolutional neural network for lung cancer images, um, from COVID patients and the, the, uh, the QP Hugh, um, algorithm pipeline performed more accurately and faster. So I think, um, bursting to quantum is something to pay attention to. >>Awesome. Peter, what's your take on what's next? >>Well, I think there's still, um, that, that was absolutely fascinating from Lynn, but I think also there's, there's, uh, you know, some more sort of low-level, uh, low-hanging fruit available in, in the data stack. I think there's a lot of, there's still a lot of challenges around the transformation there, getting our data from sort of raw landed data into business domains, and that sort of talks to a lot of what data mesh is all about. I think if we can somehow make that a little more frictionless, because that that's really where the like labor intensive work is. That's, that's kinda dominating, uh, data engineering teams and where we're sort of trying to push that, that workload back onto, um, you know, software engineering teams. >>Alice will give you the final word. What's the impact. What's the next step? What's it look like in the future? >>Yeah, for sure. I mean, I've never had the, uh, breaking a data center problem that wind's had, or the bursting the quantum problem, for sure. But, you know, if you're in that, you know, the pool I swim and of terabytes of data and below and things like that, I think it's a good time. It just like we saw, you know, like we were talking about dev ops and, and pushing, uh, you know, allowing software engineers to handle more of, of the operation stuff. I think the same thing with data can happen where, you know, software engineering teams can handle not just their code, not just, you know, deploying and operating it, but also thinking about their data around the code. And that doesn't mean you won't have people assist you within your organization. You won't have some specialists in there, but I think pushing more stuff, even onto the individual development teams where they have ownership of that. And they're thinking about it through all this different life cycle. I mean, I'm pretty bullish on that. And I think that's an exciting development >>Was that shift, what left with left is security. What does that mean to >>Shipped so much stuff left, but now, you know, the things that were at the end are back at the end again, but, uh, you know, at least we think we can think about that stuff early in the process, which is good, >>Great conversation, very provocative, very realistic and great impact on the future data as code is real, the developers I do believe will have a great operational role and the data stack concept and impacting things like quantum, it's all kind of lining up nicely. Um, and it's a great opportunity to be in this field from a science and policy standpoint. Um, data engineering is legit. It's going to continue to grow and thanks for unpacking that here on the queue. Appreciate it. Okay. Great panel D AWS heroes. They work with AWS and the ecosystem independently out there. They're in the trenches doing the front lines, cracking the code here with data as code season two, episode two of the ongoing series of the 80, but startups I'm John for your host. Thanks for watching.

(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)

>>Hello and welcome to the AWS partners showcase season one, episode two. I'm your host of the cube John ferry. We're here with two great guests who John Kumar, CEO of and Sabina Joseph, the general manager of AWS. Welcome to the show. Welcome to welcome to the cube, >>John. Good to see you >>Again. Great to see both of you both cube. Alumna's great to see how the businesses is going, going very well. Cloud scale, continuing to dominate Columbia is doing extremely well. Tell us more about what's going on in Columbia. What's your mission? What kinds of use cases are you seeing? Napa John, that's helping you guys keep your growth trajectory and solve your customer problems. >>Yeah. Firstly, thank you, John. Thank you, Sabina. Great to be here is a backup as a service platform. That's built natively on AWS for AWS, and we do support other use cases beyond AWS. But our primary mission is to basically deliver, you know, a ransomware data protection solution, you know, on AWS for AWS customers. Right? So if we think about it, you know, one of the things that's, you know, typically holding back any company to put mission critical workloads on a fantastic platform, a public cloud platform like AWS is to make sure that the data is protected in the event of any attack. And it's also done with extreme amount of simplicity, right? So that nobody is doing the heavy lift of doing backup themselves, right? So that's what really drew me or provides. It's a service. It's a turnkey service that provides, you know, data protection on AWS, whatever. >>Well, you're a frequent cube alumni. We're always talking about the importance of that, but I want to ask you this year more than ever, you're seeing it at the center of the conversation built in from day one, you're seeing a lot more threats, certainly mentioned ransomware and more there's more and more online attacks that's impacting this particular area more than ever before. Can you comment on what your focus has been this year around that? >>Yeah, I see it. If you think about tumor's evolution, our primary mission has been to go and protect every data source, but guess what? Right with more and more move to the public cloud and you look just AWS is journey and that pioneer in public cloud going from, you know, whatever 3 billion in revenues, 10 years ago to north of 70 billion run rate today, there's so much of data that is in the public cloud and the, and the most important thing that customers need is they want to free themselves from going and protecting this data themselves. Right? And, and there's a lot of scale in these environments, right? If you look at customers running hundreds of thousands of AWS accounts across every region on AWS, and if you give them that kind of flexibility and that kind of scale, what they want is give me a turnkey solution that just allows me to go and protect all of these workloads running across all of these regions in a service that takes the data out of my accounts separately in an air gap fashion, right. And that's really what we basically provide. And that's what we focused on over the last 12 months. Right? So if you look at what we have done is we've gone after every important service on AWS TC to EBS RDS, S3, dynamo, sequel databases, and other databases running on top of BC too. So now that becomes the comprehensive set of things that somebody needs to use to really deliver an application on top of the public cloud. And that's where we want for, >>And the growth has been there and the results on Amazon because of the refactoring has been huge. Can you share any examples of some successes that you've had with, with the AWS refactoring and all that good stuff going on? >>Yeah. I mean, I think that what we have seen is, you know, customers that basically told us that before you guys existed, we had to go and build these things ourselves, right. Again, you know, they had all the, the, the blocks to go and do it themselves, but it was so much of a heavy lift to go and do it themselves. And again, they didn't want to be in a, you know, in that business. So, so what we have done essentially for, and we have, you know, we have some joint customers at a pretty massive scale that basically have said that, okay, let me just use your solution to protect my critical assets. Like, you know, things, you know, sitting in S3 and really, you know, we'll use gloomy as a, as a >>Yeah, I think that's a great example of the refactoring Sabina. Gotta, I gotta ask you, you obviously you're at the center of this. You have your hand on the wheel of the partnerships and all the innovators out there. The growth of AWS just has been spectacular because there's value being created. Again, companies are refactoring their business on the cloud and you're at the center of it. So talk about the partnership with Clooney. Can you tell us how it all started and where it's going? >>Yeah, thanks for having me here, John, and good to see you again, Fujian, if I'm not mistaken for John, we met each other at the San Francisco summit, the AWS San Francisco summit, actually I believe it was in 2016 or 2017. You can correct me if I'm wrong here, but yes, I think so. It was, it was in the 8% a month of April. I still remember it. And that's when, you know, you kind of mentioned to me about and this modern backup as a service solution that you were creating, you're still in stealth mode. So you couldn't talk a lot about it. And B started to engage deeply on the partnership, right from 2017. And initially we were kind of focused around helping Colombia build a solution using our well-architected review. And then as soon as we all came out of stealth mode, we started to engage more deeply around deeper integrations and also on go to market activities. >>As you know, AWS has a very prescriptive approach to our partnerships. So we started to work with around the five pillars of security, reliability, cost optimization, performance, and operational excellence to really help them tune the solution on AWS. And we also started to engage with our service teams and I have to thank Paul John and his team here. They really embraced those deeper and broader integrations, many services that Pooja mentioned, but also specifically want to mention S3 EBS. And our Columbia was also a launch partner for AWS outpost when AWS in fact, launched outpost. So I want to kind of commend CLU, CLU MEO, and the entire team kind of embracing this technology and innovation and this modern backup as a service approach. And also also embracing how we want to focus on the five key pillars that I mentioned. >>And that's a great example of success when you ride the wave, which I talk about the ACLU, Colombia trends in the data protection, because one of the things that you pointed out earlier is the ransomware. Okay. That's a big one, right? That's a big, hot area. How, how is the cloud, first of all, how is that going? And then how has the cloud equation changed the ransomware defense and protection piece of it? >>Yeah. Now I just, I wonder I had a little bit on what Sabina mentioned before I answered the question, John, if you don't mind. Sure. I think that collaboration is where is the reason why we are here today, right? Like if you think about it, like we were the first design partners to go and build, you know, the EBS direct API, right. And we work closely with the EBS teams, not just for the API, but the cost structure of it. How would somebody like us use it? So we are at the bleeding edge of some of these services that we are using and that has enabled us, you know, to be where we are today. So again, thank you very much to be enough for this fantastic partnership. And again, there's so much to go and do to really go and nail this in a, in a, in a, in a great way on, on the public cloud. >>So now coming back to your question, John, you know, fundamentally, if you see right, you know, what happened is when, when, when customers move to the public cloud, you know, right there, you know, the ease of use with which, you know, AWS provides these services, right? And the consumption of these services actually drives some amazing behavior, right? Where people actually want to go and build, build, build, and build. But then it comes a time where somebody comes in and says, okay, you know, are you compliant? Right. You know, do you have the right compliance in place? You have all these accounts that you have, but what is running in each of these accounts, you have visibility in those accounts. And are these accounts that the data in these accounts is this gap, right? This is getting air gap in the same region, or does it need to be across regions? >>Right. You know, I'm in the east, do I need to, you know, have an air gap in the west and so on and so forth. Right? So all of these, you know, confluence of all these things come in and by the, all these problems existed in on-premise world, they get translated in, in the public cloud, where do I need to replicate my data, doing it to back it up? Do I need air gapped in a, like an on-prem world? You had a data domain of plans, which was separate from your primary storage for a reason, same similar something similar now needs to happen here for compliance reasons and for ransomware reason. So a lot of parallels here is just that here we are, it almost feels like, you know, as they say, right, the more things changed. The more they remain the same. That's what it is in the public cloud again. >>Well, that's a good point. I mean, let's take that example of on premises versus the cloud. Also, the clouds got more scale too, by the way. So now you've got regions, this is a common problem that customers are having, you can build your own and, or use solutions, but if you don't get ahead of it, the compliance question can bite you in the, you know what, because you then got to go back and retrofit everything. So, so that's kind of what I hear a lot on my end is like, okay, I want to be compliant from day one. I want to have an answer when asked, I don't want to have to go to old techniques that don't fit the cloud. That comes up a lot. What's your answer to that? >>Yeah, no, no. We were pretty much right. I think it's like, you know, when it, when it comes to compliance and all of these things, you know, people at the end of the day are looking for that same foundation of, of things. The same questions are asked for an encryption. You know, you know, I is my data where it needs to be when it needs to be right. What is my recovery point? Objective? What is my recovery time objective? All of these things basically come together. And now, as you said, it's just the scale that you're dealing is, is extremely different in the cloud and the, and the services, right? The easier it is that, you know, it is to use these services. And especially what AWS does, it makes it so easy. So compelling that same ease of use needs to get translated with a SAS service, like what we are doing with data protection, right? That that ease of use is very important. You have to preserve that sanctity >>Sabina. Let's get back to you. You mentioned earlier about the design partner, that benefits for Colombia. Now let's take it to the next level. As customers really realize they have a problem, they need solutions and you're on the AWS side. So you gotta have the answers for the customers. You've got to put people together, make things work. There's a variety of things that you guys offer. What are some of the different facets of the ISV or the partner programs that you offer to partners like Clooney, you know, that they can benefit from? >>Absolutely John, we believe in a win-win approach to the partnerships because that's what makes partnerships durable over time. We're always striving to do better here. And we continue to broaden our investments. As you know, John, the AWS management team, right from Adam Phillipsky, our CEO down firmly believe that partners are critical to our success, our longterm success, and as partners like CLU MEO work to lean in with us with more investment resources, our technology innovation. We also ensure that we are doing our part by providing value back to Cleo about a few years ago, as you might recall, right. We really did a lot of investment in our sales team on the AWS side. Well, one of the tanks me and also our partners observed is while we were making investments in the AWS sales team, I don't think we were doing a great job at helping our partners with reaching out to those customers. >>What we call as co-sale and partners gave us feedback on this. We are very partner and customer feedback driven, and we introduced in fact, a new role called the ISP success manager, ISS, who are basically embedded in our field. And they work with partners to help them close opportunities. And also net new opportunities are we've also in 2020. I believe that re-invent, we launched the ISB accelerate program whereby we offer incentives to the AWS field team to work with our partners to close existing opportunities and also bring in net new opportunities. So all of this has led to closer collaboration in the field between both our field teams, Muir's field team and our field team, but also accelerated mutual customer wins. I'm not saying that we are doing everything great. We still have a long ways to go. And we are constantly getting feedback from cluneal and also some of our other key partners, and we'll continue to get better at it. But I think the role of the ISV success manager and also the ISP accelerate program has been key to bringing in cold cell success. >>Well, John, what's your take on, is this a good partnership for you? I mean, see, the wave of Vegas has got the growth numbers. You mentioned that, but from a partnership standpoint, you're closing business, they got scale. Is it working? How do you organize your company to take advantage of these benefits? Can you share your thoughts? >>Absolutely not. We have embraced the ecosystem wholeheartedly 100%, but if you think about it, what we have done is look at our offering on AWS marketplace. There's an example, right? We are the only company I would say in our domain, obviously that routes our entire business through AWS marketplace. Whether obviously we get a lot of organic benefit from AWS marketplace, people go and search for a solution and from your shows up, and obviously they go and onboard self onboard themselves, and guess what? We let them self onboard themselves. And we rely on AWS's billing automatically. So you don't need to talk to us. You can just get billed automatically in your AWS bill and you get your data protection solution. Or if you directly reached out to us, guess what we do. We actually route you through AWS marketplace. All the onboarding is just to one place and it's a fantastic experience. >>So we have gone like all in, on that experience and completely like, you know, internalized that that's the right way to do things. And of course, thanks to, you know, Sabina's team and the marketplace team to create that platform so that we could actually plug it into it. But that's the kind of benefits that we have that we have, you know, taken advantage of a DWI. That's one example, another example that Sabina mentioned, right, which is the whole ACE program. We put a ton of registrations on AIS and with all the wins that we get on AWS, they could broadcast it to the sellers. So that creates its own vicious cycle in terms of more coming into the pipeline and more closing in. So, so these are just two small examples, but there's other examples that we look at our recent press release, where AWS, you know, when we, when we launched yesterday data protection and backup, the GM of AWSs three supported us in the press release. So there's things like that, that it's a, it's a fantastic collaboration. That's working really well for our joint customers. Sorry. >>And tell us something about the partnership between 80 of us, including, you know, that people might not be aware of some of the things that Poojan said that they're different out there that, that are, co-selling go marketing, that you guys offer people you guys work together on. >>Yeah. The, the ISV accelerate program that was created, it was really created with partners like Klunier in mind, our SAS partners. I think that that is something very, very unique between our partnership and, you know, I, I want to double click on what Poojan said, which is riding their opportunities through marketplace, right? All of their opportunities. That is something pretty unique. They understand the richness of the platform and also how customers are procuring software today in this world. And they've embraced that. And we really appreciate that. And I want to say, you know, another thing about Qumulo is they're all in on AWS, which is another unique thing. There are not a lot of, I would say all in partnerships in my world and I manage infrastructure, business apps, applications, and industry partnerships from the Americas globally. And all of those things are very, very unique in our partnership, which has led to success. Right. We started very, very early stage when Columbia was in stealth mode in 2017 and look where we've come today. And it's really kudos to Paul, John and his entire team for believing in the partnership for leaning in with us and for placing that trust with us. >>Awesome. Pooja, any final words you'd like to share for folks out there about the conversation and what's going on in Columbia? >>Yeah, no, absolutely. You know, as I said, I think we have been fortunate to be very early adopters of all these technologies and go and really build what a true cloud native solution has to be. Right. And, and again, right, you know, this is what customers are really looking for. And people are looking for, you know, at least on the data protection side, you know, ransomware air gap solution, people are looking for a solution natively built on the cloud because that's the only way a solution can deliver something at the scale and the cost structure that is needed to have, you know, a data protection solution in the public cloud. So, so this has been just a fantastic thing end to end, you know, for us overall. And we really look forward to, you know, going, you know, doing much more with AWS as we essentially go and scale, >>I have to ask, but before we, before we go, cause you're the CEO of the company and founder having all that backend infrastructure from Amazon, just on the resources, great. It creates a market for your product, but also the sales piece, you know, they got the marketplace, you mentioned, that's a big expense that you don't have to carry, you know, and you get revenue and top line. I mean, that's an impact for startups out there and growing companies. That's a pretty big deal. What's your, what's your advice to folks out there who are trying to think about the buy versus use the leverage of the, of the marketplace, which is, which is at large scale, because as a CEO, you're, you've got to make these decisions. What's your opinion on that? >>It's not, it's not as, as easy as I make it sound to do your own part. You know, AWS is, is, is, is huge, right? It's huge. And so we have to do our part to educate everybody within the, you know, even the AWS seller base to make sure that they internalize the fact that this is the right solution for the customers, for our joint customers, right? So we have to do that all day long. So there's no running away the no shortcut to everything, but obviously AWS does its part to make it very, as easy as possible, but there's a lot of heavy lifting we still have to do. And I think that'll only become easier and easier over the next few years >>And Sabina your takeout at AVS. You've got a great job. You were with all the hot growth companies. This is the big wave we're on right now with the cloud next generation clouds here, a lot of opportunities. >>Absolutely. And it's, and it's thanks to Pooja and, and partners like Lumeo that really understand what it takes to build a cloud native solution because it's part of it is building. And part of it is the co-selling go-to-market engine and embracing both of that is critical to success. >>Well, thank you both for coming on this journey here on the cube, as part of the showcase, push on. Great to see you to being a great to see you as well. And thanks for sharing that insight. Appreciate it. >>Thank you very much. >>Okay. AWS partners showcase speeding innovation with AWS. I'm John Ford, your host of the cube. Thanks for watching.

>>And welcome to this special cube conversation. I'm John for a, your host of the cube. We're here in Palo Alto, California, and I'm here with a very special guest coming down from Seattle remotely into the cube studios is the leader at AWS Amazon web services, the vice president of database analytics and machine learning Swami. Great to see you cube alumni recently taking over the database business at AWS as a leader. Congratulations. And thanks for coming on the cube. >>Hey, my pleasure to be here, John, very excited to talk to you. >>Yeah. We've had many conversations on the cube and also in person and also online around all the major mega trends. You've had your hand in all the action, going back to your days when you were in school learning and, and writing papers. And 10 years ago, Amazon web services launched AWS dynamo, DB, fast, flexible, no SQL database that everyone loves today, which has inspired a generation of what I would call database distributing cloud scale, single digit millisecond performance at scale. And again, the key scale. And again, this is 10 years ago, so it seems like yesterday, but you guys are celebrating and your name was on the original paper with CTO Verner. Vogel's your celebrity. Congratulations. >>Thank you. Not sure about the celebrating part, but I'm very excited. At least I played a hand in building such an amazing technology that has enabled so many amazing customers along the way as well. So >>Trivia on the, on the paper as you were an intern at AWS, so you're getting your PhD. And then since, since rising through the ranks and involved in a lot of products over the years, and then leading the machine learning and AI, which is now changing the game at the industry level, but I got to ask you getting back to the story here. A lot of customers have built amazing things on top of dynamo DB, not to mention lots of other AWS and Amazon tech riding on it. Can you share some of the highlights that came out of the original paper? And so with some examples, because I think this is a point in time, 10 years ago, where you start to, so the KickUp of cloud scale, not just, just for developers and building startups, you're really starting to see the scale rise. >>Yeah, I actually, I mean, as you probably know, based on what he read to explain the Genesis of dynamo DB itself had to explain the Genesis of how Amazon got into building the original dynamo, right? And this was during the time when miner, I joined Ron esteem as an intern and, and Amazon was one of the pioneers in pushing the boundary of scale. And a year over year, our Q4 holiday season tends to be really, really bad for all the right reasons. We all want our holiday shopping done during that time. And you want to be able to scale your website, arters fulfillment centers, all of them at that time. And those are the times around 2005. And the answer is when people think our database, they think of a single database server that actually runs on a box and has a certain characteristics and does a scale and availability and whatnot. >>And it's usually relational. And then when we had a major disruption during Q4 that's when yeah, ask ourselves the question, why are we actually using a relational database for some of these things when they really didn't need the data model complexity of relational database. And normally I would say most companies where to actually ask an intern or a few engineers who are early in the career saying like, what the hell are you suggesting? Just go away. But Amazon being enabling Buddhists to build what they want. And they actually let us start reimagining what a database or our scale could look like. And that led to dynamo. And since she unstained mine, then we migrated from an traditional relational database stair this one for some of the amazon.com services. And then I moved on to actually start building some butts off our storage service and then our managed relational database service, I explicitly remember. >>And one of our customer advisory board, we're just the set off some of our leading customers who actually give us feedback on roadmap. Another son, Don, who's the CEO and chief geek of spunk bargain faker. And him actually looking at the Trinity me, I was starting in the corner and saying like you all, both tomorrow and why do I need to keep shotting my, my sequel database and reshooting assigned scaling. And this is the time when the state of the art in most databases were around. Like, you start sharding your relational database and constantly reshaping. And this is when most websites are starting to experience the kind of scale which we consider a normal month. During those times it was mostly, most companies used to have a single relational database backend and start scaling that way. And that conversation led entirely under duress, unaided read, lot of AWS leaders and myself saying like, Hey, what is a cloud database reimagined without the hampering SQL look like? And that led us to start building dynamo DB, but just a key value database at that time. Now we support document might've too, but that single digit millisecond latency at any scale imagine. So >>I think about that time at that time, 10 years ago, when you were having this conversation and I know the smug mug and I, he said, he's in totally geek and he's, he's good to point that out. You also have Netflix as customers too. I'd like to hear how that's evolved, but, but I think back at the time, if you look back then I got to ask you most people we've talked about this before. No one database rules, a world that's now standard people now don't see one database back then it was a one database kind of mindset back then. Yeah. And then you had that big data movement happening with Hadoop. You had the object store developing. So you're in you're you're circling around that area. What was it like then? I mean, take, take us through that because there was obvious visibility that, Hey, let's just store this. Now you see data lakes and that's all happening. But back then object store was kind of new. Yeah. >>Ah, it's a great question. Now, one of the things I realized early on, especially when I was working with binary, when you're saying amazon.com itself as an example, that the access patterns for various applications and Amazon, but let alone AWS customers tend to be very, very, very, some of them really just needed an object store. Some of them needed a relational database. Some of them really wanted a key value store within a fast latency. Some of them really needed a durable cash. And, but it so happens when you have a giant hammer. You use that for everything looks like a map, which is essentially the story at that time. And so everyone kept using the same database, irrespective of what the problem was because nobody else, I mean, thought about like, what else can we build that is better? So this let us do, literally I remember writing a paper with Bernard internally that is widely used in Amazon explaining what are all the menu of booklets that access. >>And then how do we go about actually solving for each of these things so that they can actually grow and innovate faster. And, and this was led to actually the Genesis of not only building IDs and so forth, but also dynamo and various other non-relational data. There's a still let alone not so storage access patterns and what not. So, and this was one of the big revelations he had just that there is not a single database that is going to meet the customer, needs us. The diversity of workloads in the internet is growing. And this was a key pivotal moment because with cloud now applications can scale very more instantly than before now. Building an application for Superbowl is very easier than before. That means that on, I mean, everybody is pushing the boundaries of what scale means, and they are expecting more from their obligations. That's when you need technologies like dynamo, DB, and that's exactly what dynamo already be set out to do. And since then, we are continuing to innovate on behalf of our customers and the purpose of the database story as well. And this concept has resonated well across the board. If you see that the database industry has also embraced this method, >>It's natural that you obviously evolved into the machine learning side of it because that's data is big part of that. And you see back then you, you bringing up kind of like flashes for me where it's like those, the data conversations back then and the data movement was just beginning. So the idea that you can have diversity in access methods of the kind of databases was a use case driven by the application, not so much database saying, this is how you have to work, that the script was flipped. It it's changed from infrastructure dictating to the applications, what to do. Now, the applications are going to the infrastructure and saying, give me what I want. I want to access something here in an office store, something here in no SQL that became the Genesis of infrastructure as code at a, at a global level. And so your paper kind of set the, the, the wave, the influence for this, no SQL did big data movement. It's created tons of value, maybe a third Mongo might've been influenced by this other people have been influenced. Can you share some stories of how people adopted the concept of dynamo DB and how that's changed in the industry and how has that helped the industry evolve? >>I mean, plus file data. Most share our experience of building and dynamo style data store. Very, it is a non-relational API and showing what are some of the experiences that the Venter in building such an paper and these set out early on itself, that it is should not be just a design paper, but it should be something that we shared our experiences. So even now, when I talked to my friends and colleagues and various other companies, one thing they always tell me is they appreciated the openness with which we were sharing. Some of the examples and learnings that we learned to not optimizing for percentile latencies, and what are some of the scalability challenges, how we solved and some of the techniques around things like sloppy Cora or various other stuff. We invented a lot of towns along the way too, but people really appreciated several of some of our findings and as talking about it. >>And since then I met so many other innovations are happening in the industry and the AWS, but also across the entire academia and industry in this space, the databases I've been going through what I call as a period of Renaissance, where one of the things, if you see our own arc, when Roger and I started on the database, front Disney started over the promo saying like, if you were to build a database where cloud is the new normal, this is again in 2008, we asked ourselves that question and what the belt that led us to start building things like dynamo, DB, RDS star. I know that alone, we reimagined data viruses with Redshift and several, and then several other databases like time stream for time series workloads started running Neptune for graph and whatnot. But at the moment we started actually asking that question and working backwards from customers. Then you will start being able to innovate accordingly. And this has worked really well. Then more than a hundred thousand AWS customers have chosen dynamo DB for mobile gaming tech IOT. Many of these are fast growing businesses, such as ledge, Darryl BNB, red fan, as soon as enterprises like Samsung Toyota, capital one and so far. So these are like really some meaningful clouds, let alone amazon.com. I run this. >>We have an internal customer is always good to have that entire inside customer. You know, I really find this a really profound use case because you're just talking, you know, in Amazonian terms, I'll just translate for the audience working backwards from the customer, which is the customer obsession you guys have. So here's, what's going on off the way I see it. You got dynamo, DB, paper, you and Verner, and the team Paul was a great as a great video on your blog posts that goes into the, to the talk he gave at around that time, which is fun to watch if you look back, but you have a radical enabler here, that's disrupting and changing S3 RDS, Aurora. These are game-changing concepts inside the, the landscape of AWS at the same time, you're working backwards from the customer. So the question I have for you as a leader and as a builder, how did you balance the working backwards from the customer while bringing something brand new and radical at that time to the market? >>Yeah, this is one of the S I mean hardest things to be, as leaders need to balance on. If you see many times, then we actually worked backwards from customers. The literal later translated this, literally do what customers are asking for, which is true nine out of 10 times, but there is one or a 10 times, you got to read between the lines on what they are asking. Because many times customers when are articulate that they need to go fast. If in the right way, they might say, Hey, I wish my heart storage goes faster, but they're not going to tell you they need a car, but you need to know and be able to translate and read between the lines we call it under the bucket of innovate on behalf of customers. And that is exactly the kind of a mantra we had when we were thinking about concepts like dynamo DB, because essentially at that time, almost everybody would, if I asked, they would just say, I wish a relational database could actually be able to scale from not just like a hundred gigabyte to one terabyte are, it can take up to like 2 million transactions, a second and so forth and still be cheap and made in reality as relational databases, the way they were engineered at that time, those are not going to meet the scale needs. >>So this is fair. We hunted read between the lines on what are some of the key Mustang needs from customers and then work backwards and then innovate on behalf of these workloads, be enabled by the sun oh four, which are some of the reasons that led to us launching some of the initial sets on dynamo on a single digit millisecond latency and seamless scale. At that time, databases didn't have the elasticity to go from like 10 requests, a second to like a hundred thousand or 1 million requests a second, and then scaled right back in an hour. So that was not possible. And we kind of enabled that. And that was an, a pretty big game changer that showed the elasticity of the cloud to a database. Well, >>Yeah, I think also just to, not to nerd out on this, but it enables a lot of other kind of cool scaled concepts, like queuing storage. It's all kind of together. This database piece of that you guys are solving. And again, props to you guys on the team. Congratulations. I have to ask, you know, more generally, how has your thinking changed since the paper? I'll see, you've got more experience under your belt. You don't yet have the gray hairs yet, but we'll see those soon come in, but you know, you're, you got a lot more experience. You're running teams, you're launching a lot of products. How has your thinking changed in the industry since the paper what's happening now? What's the big evolution. What are those new things now that are in the innovate on behalf of the customer? What's between the lines now, how do you see this happening? >>I mean, now since wanting dynamo via a victim, I had the opportunity to work on various problems in the big data space. There we've worked on some are fire things that you might be aware of in the analytics all the way from Redshift to quick side, too. Then I moved on to start some of our efforts, having built systems that enabled customer to store process and credit, and then analyze them. One of the realizations, I had this, the in around 2015 or 2016, I kinda had that machine learning was hitting a critical point where now it is ready for being scaled at option. Their cloud has basically enabled limitless compute and limitless storage, which are the factors that are holding back machine learning technology. Then I realized that now we have a unique opportunity to bring machine learning BI to everybody, not just folks with PhD in machine learning. >>And that's when I moved on from database and analytics areas, they started machine learning. We're just a descent area because machine learning is powered by data and then started building capabilities like SageMaker, which is our end to end ML platform to build, train and deploy them on models. And this, what does the leading enterprise platform by several gaggled users and then also a bunch of our AI services since then, I view the reason I'm giving all this historical context is one of the biggest realization I had early on itself. And 2016 as first machine learning is one of the most disruptive technologies. She will then country in our generation. This is right after cloud. I think these still are the most amazing combination that is going to revolutionize how we build applications and how we actually reason about that. Now, the second thing is that at the end of the day, when you look at the ANC and journey, it is not just about one database or one data Varroa. >>So one data lake product, or even 1:00 AM out platform. It is about the end to end journey where a customer is storing their order database. And then they are actually building a data lake that test customer history and order history. And they want to be able to personalize. And for their viewer experience are actually forecast what products to staff in their fulfillment center, but then all these things need to work and to handle. And that view is one of the big things that struck me for the past five years. And I've been on this journey in addition to building this Emma building blocks to connect the dots so that customers can go on this modern end to end data strategy as I call it, right. It goes beyond a single database technology or data technology, but putting now all of these end to end together so that customers don't end up spending six months connecting the dots, which has been the state of the down for the last couple of years. And we are bringing it down to matter of the Sundays. Now >>He's incredible Swami. Thank you so much for spending the time with us here in the, >>Yeah, my pleasure. Thanks again, Sean. Thanks for having me.

>>Hey everyone. Welcome back to the cubes. Continuous coverage of AWS reinvent 2021 live. Yes. Live in Las Vegas, Lisa Martin, with Dave Nicholson. David's great to co-host with you. How you doing >>Fantastic. Great to be here with >>You, Lisa, as always, we're going to have a great conversation. Next to Cuba actually is two lifestyles, two remote studios. We've got over a hundred guests on the program talking about the next decade and cloud innovation and Dave and I are pleased to welcome Scott Carter, the CTO of TSS to the program. Scott. Welcome. >>Thank you. It's really, really great to be here. Really >>This a little bit. Great to have you on the program. Talk to us a little bit about, about TCIs and let's talk about your kind of journey to the cloud and your relationship with AWS. >>Absolutely. Um, you know, TCIs, we've been around as a company for about 40 years. We specialize in, uh, payment products specifically on the issuing side. So card issuing, we've worked with some of the largest financial brands in the world and retailers as well. Uh, and, and a lot of, you know, what I always tell people is if you have a card in your wallet today, uh, you could probably pull it out. And at least one of those cards is something that we manage and service for our customers. And, and we, uh, do everything full lifecycle of those payment products for our customers around the globe >>On behalf of being a cardholder. Thank you. Talk to me a little bit about the AWS partnership here we are at re-invent. >>Yeah, well, we started a very special, uh, partnership with AWS about 18 months ago. We're about 18 months into the journey, uh, and really our goal and our vision is to build out a financial services cloud for all of our clients and our retailers and fintechs. Uh, we're really focused right now on migrating some of our key products to the AWS cloud environment. We built we've used us a variety of AWS technology by some on-premise and in the cloud environment to migrate our processing platforms and all of our customer servicing systems. So we're in the middle of that journey. Uh, we've had a lot of successes so far. AWS is helping us out. Our engineering team is working side by side with the AWS engineering team to produce what we believe is going to be the next generation of payments, especially on the card issuing side, >>Next gen that's, that's important as a consumers, consumer life business life. We have that expectation that we're going to be able to transact whatever we want anytime day or night, >>Absolutely choice is key, uh, virtual physical, no matter where you are, we want to be able to facilitate your payment and make sure you have everything you need to support you through the full card life cycles, the life cycle of your account. >>So you talk about those cards being in our wallets and handbags. I know there's one that's actually smoking. It's so hot from use in my co-hosts handbag, but, >>Uh, we appreciate that >>Talk, talk, talk about this journey from the perspective of someone who, um, I assume like me is not just out of college, right? You've working, you've been working in this business for a while. And so you're going through the transition from the world of what some will refer to as legacy it into the world of cloud. Uh, talk about the challenges there. How do you go after the low hanging fruit versus the high hanging fruit? How do you evaluate something from an ROI perspective? Talk about that. >>Yeah, and I, you know, uh, I get that quite a similar question a lot. I get, you know, people are, are interested in the journey and especially CTOs and CEOs who were starting journeys at their own. I get a chance to talk with a lot of banks and retailers about their individual like modernization and transformation journeys. Um, and you know, the, the basics are true about the journey. And I had somebody tell me years ago that it's, it's, it's psychology, it's not technology. Uh, you've really got to address the people's side of the equation. First, you've got to focus on training and upskilling, make sure that the team comes along on the journey. And then you've gotta be a really good recruiter. You've got to go out and get the talent, the skills you need to build a good foundation. You gotta have the right partners. >>You know, we have partners like PWC and, and, uh, AWS and others that are really helping us with the journey. So that part of it's really, really important. The key is, and I think for us, uh, we really started building our talent pool, uh, probably more than five years ago. And so we were able to bring in some skill sets in dev ops and some skill sets. And, you know, nowadays AI we'd do a lot with ML and AI skill sets. Uh, but we were able to build in a lot of cloud skills and start to build out our development environments first, very, very early on. That's what we did. And we used those development environments for our engineers to cut their teeth and really get comfortable in the cloud. Um, I remember probably about three years ago, we installed our first Kubernetes cluster. Um, and we did it with a small team. >>And then over time we really incented the team by allowing them to get more and more certifications and grow their skills. And we really built up a really large team around just our on-premise cloud first. And then later that helped us with the migration, the journey into the actual public cloud for those same services. Um, and we use that, that same team as there today, we really invest in our people. We think it's important to have a staff that's there. We insource our staff. We really believe in that. Um, that's super important, even though we have partners that we really value, we make sure that we've got a core group of people that are really passionate about the journey and about cloud. And so that >>You mentioned that, that kind of cultural aspect. Yeah. And you mentioned bringing in a team starting years ago with a specific focus. What about the transition of folks who have been it practitioners for maybe decades making that transition? How has, how has that worked out culturally? Have you adopted a policy where you're basically saying, look, if you have experience with this stuff, great, stay with it. Yeah. But we're hiring net new people for the new stuff. Is that the strategy or is it >>Look like I've seen some do that? I personally don't feel that that works because you need some subject matter experts. You need people who really know your products and your company and your solutions and your customers. You really need those people to come along the journey. So what we've done internally is we created, for example, a digital boot camps where our team members could sign up that could come in. We actually construct the boot boot camps on about a six week schedule. Uh, we do two week sprints. So we do three sprints. We, we get them sort of inculcated and agile from the very beginning, we have demos at the end of each sprint. So they're working in an agile way as they're going through their training course. And then of course we, that gives us a chance to identify people who are really high potential to move into some of our cloud teams and our dev ops teams. >>And so that's been really, really beneficial for us. And I would tell you that today we've got people that have a broad range of skills just because of that digital bootcamp. So they may have started their career doing assembler or COBOL or something like that. But now they've tacked on some dev ops and some cloud skills. Uh, we have some that know dynamo DB, and they also know DB too. And we like that. So they have a broad range and those people bring a lot of deep expertise that you're not going to necessarily get with somebody that you're bringing, you know, new, you know, sometimes straight out of college into your company. You've got to grow those people too, but you need the experience, people there to help develop them. >>No, we often talk about people, process and technology, and it's kind of a phrase that's thrown around right. At every event with every vendor. But I really admire the focus on the people, part that you're talking about there and how it's really essential to enable, to enable the people, how you started very strategically starting with the people in the focus and the training on-prem then making the decision that they've, they've got the foundation. Now we need to migrate to the cloud. I'm curious the why AWS, you have a lot of choice course here we are at reinvent. But talk to me about why AWS is that strategic partner. >>We've, we've looked at a number of different cloud platforms for our business. And in fact, uh, global payments is a large company. So TCIs is sort of the issuing part of that. And so we have really great relationships with GCP and other cloud platforms, even some Azure in certain pockets of the company for the issuing side of the business, we went through a thorough evaluation and we felt like the tools, the technology, the platforms, really the, the maturity of that platform. And then the scale, you know, scale matters in our business. And a lot of businesses, it matters, uh, you know, the locations of all of the, uh, uh, availability zones and the regions that was really important to us. We were able to align all of the different AWS regions to where our customer locations are. And that's becoming more and more important as we, you know, we try to be more flexible now about where we, uh, you know, deploy our products around the globe. We want to make sure that whoever we partner with has a point of presence in those markets and that we can do that very, very quickly. We can stand up a new environment when we need to. And so that's what that's been really beneficial that we made that choice with AWS. Um, you know, there's a lot of cloud platforms out out there there's a lot of choice, but we just felt like AWS was the best for us. >>AWS is also very, very, very customer focused, but they probably would say customer obsessed, really that customer flywheel that generates everything that we'd even heard this morning in the keynote culturally, is TCIs similar to AWS in that respect. And can you share a little bit about that? >>Very much. So our reputation as a business is based on the relationships that we built with our customers, and we're known for that in financial services, the TCIs brand and the way that we think about our customers and the way that we partner with them. Um, you know, we, when we taught with the AWS team, we, we try to explain, you know, our history is, you know, w we're kind of the cloud for our customers. So they have a number of products and services. We support those, we manage those products. We, we build on top of, of those products for them. And so we really understand that it's important, not only that you're building a platform, but that platform has got to be able to support all the different things that our customers do every day. And we want that to be broad. We don't want it to be narrow. It's not just focused in one area. If our customers come to us and they say, well, you know, I need to build a data and an analytics platform, or I need some really specific fraud capabilities. We want to be able to support that on demand with our customers. And that's really the journey that we've taken with AWS. AWS is enabling that for us. >>And on-demand is key. I think we've one of the things that's been in short supply during the last 22 months is patients, right? That's >>Right. Absolutely. >>So describe the role of a CTO in that process. What does that look like? Because this isn't, you're not making unilateral decisions here, obviously you're working with the team, but talk about the CTO's perspective as you make decisions about whether AWS is the right fit for a part of your environment or GCP or something else. >>Yeah. I think, you know, um, we, we have, uh, a long history of supporting our own solutions and supporting our systems. And we run some of the world's largest like authorizations platforms, which those are the platforms where when you go into the store and you swipe your card, you, you have to get a response back from us. Like we have to give you that and we have to give it, we have a really specific amount of time. We have to give that back to you. And so we really understand operations and support and how to scale, uh, applications and systems and, and, and how to build really, really reliable solutions. We really understand that part of the business. So whoever we partner with, and, and you asked about my decision to CTO, it was really a group decision. You know, I have to partner with our business team, I have to get their buy-in. Um, they have to support the decision, whatever we do, it's a big investment, we're making the move to the cloud. And so, um, but we have to make sure that we, we cover off the basis. They've gotta be able to at least whatever, whoever our partner is, they've got to be able to at least provide the operational support and the reliability that we're able to give our customers today. So it's just a spreadsheet that's right. Technical qualifier, >>And whoever has the most boxes checked wins. That's right. You're taking into consideration all of those cultural aspects and the goals of the business. That's right. So as a chief technology officer, it's not just about the technology, it's about the business >>That's right, right. So I have a very, very close relationship with the president of our business, Galen, Jowers, um, and, and we built a team and we have on, on the, uh, the actual modernization or transformation team, we have members that represent that from a business perspective there I report into, uh, directly into the business teams. And then we have, uh, people from my, from my side of the, of the company. And we work every single day together and we're driving this forward. So the important part of that is at some point, we, we go to our customers and we show them, Hey, for this particular product or service that we're offering, we're going to be moving that to cloud on this kind of a schedule. And we're there together as a unified front and a unified communication with our customer to explain that journey. And we think that's really important that we do it that way and not do it. You know, like I've seen some companies they'll segment it and sort of technology, or it goes off and they kind of do their own sort of cloud initiative to us that wouldn't work for our business. It's gotta be together and enjoy it with the business. >>You sound like a very much a transformational CTO to me versus a traditional CTO and working at a legacy company that's been around for 40 years. That's impressive that the company is that forward in thinking, first of all, about its people, but also about that business, it partnership. But that has to be in lock step. We talk about that all the time, but it's hard to facilitate that, but you really sound like you guys have done a phenomenal job with some key strategic foresight is not the word. Um, I liked, like Dave was saying, it's not a spreadsheet. It's a checklist of technology requirements that people element is absolutely. >>Absolutely. And you have to, you have to, you have to be all in together on it because you know that as you go on the journey, you're going to have some failure. You're going to experience some challenges. Your customers might not be happy with every decision you make. So you have to be in it together. You're going to have to make that commitment as a company. And that's what we decided early earlier on is that we were going to do that and it's worked out well for us. >>What are some of the things that are going to be happening next for TCIs as we hopefully round out the year 2021 and go into a much better 20, 22, >>We've got a, we've got some really big things on the horizon. One of the things that we're working on right now is, um, we've, since we've been at this for 18 months, we're starting to get to a point where we have certain solutions that are ready to go. We're ready. We're going to be able in 2022 to make some key announcements around some parts of our platform, they're going to be available in AWS as a, as an offering. So we're excited about that. A lot of our customer servicing and some of the things that we do outside of our core processing platform are already cloud native. We run them in a cloud environment on our premise and some of those services, we're going to be able to go ahead and launch into the AWS in 2022. So we're really excited about that. We're right now in the throws of building an onboarding team, that's going to be working with both our customers and with our internal teams to make that shift and start migrating those applications out to the environment. >>So big, big things underway there. We've got a couple of, uh, really key strategic relationships that we've built over the last 12 months or so, um, that are all in, on our cloud journey. And so we're going to be able to announce some of those, uh, pretty soon as some of our customers and prospects, uh, that really want to be on the journey with us. So we're pretty excited about that. And I don't want to spoil any surprises there, so we'll wait and let that come out with the, with the schedule. But yeah, we've got a lot of great things ahead and we're very, very excited for where we're going. >>Awesome, Scott, great stuff. I love how transformational you are, the focus that you guys have on the people, as well as the technologies and the processes. Exciting. Congratulations on your, on your 18 month journey. And we'll have to have you back on so we can hear some of those, those, uh, you know, little, uh, Easter eggs that you just dropped. >>I'd love to, I'd love to be back on. This has been great. All right. >>And how did you know I have a credit card in my wallet running a whole. >>I've been feeling bad about saying that the whole time. He's not going to go well when we're done here, >>Wherever in Vegas, we hope you've enjoyed this. Like for Dave Nicholson, I'm Lisa Martin. You're watching the cube, the global leader in a live chat coverage.

(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)

(upbeat music) >> Welcome to theCUBE and our Q3 AWS Startup Showcase. I'm Lisa Martin. I've got two guests here with me, Erez Berkner is back, the Co-Founder and CEO of Lumigo. Hey, Erez, good to see you. >> Hey, Lisa, great to be here again. >> And Kevin O'Neill, the CTO at Flex is here as well. Kevin, welcome. >> Hi, Lisa, nice to meet you. >> Likewise, we're going to give the audience an overview of Lumigo and Flex. Let's go ahead, Erez, and start with you. Talk to us about Lumigo, and I think you have a slide to pull up to walk us through? >> Yeah, I have a couple, so, great to be here again. And just as an overview, Lumigo is a serverless monitoring and debugging platform. Basically allowing the user, the developer to get an end-to-end view of every transaction in his cloud. It's basically distributed tracing that allows you from one hand to monitor, to see a visual representation of your transaction, but also allows you to drill down and debug the failure to get to the root cause. So essentially, once you have the visualization and if we'll move to the next slide, you can actually click and drill down and see all the relevant debug information like environment variables, duct rays, inputs, outputs, and so on and so forth. And by that, understanding the root cause. And sometimes those root causes of the problems are not just errors, they are latencies, they are hiccups. And for that, we can see on the next slide, where Lumigo allows you to see where do you spend your time? Where are the hiccups in your system? What's running in Paula to what in the same transaction, where you can optimize. And that's the essence of what Lumigo provides in a distributed environment and focusing on serverless. >> Got it, focusing on serverless, we'll dig into that in a second. Kevin, give us an overview of Flex. You're a customer of Lumigo? >> We are indeed. So Flex is a build smoothing platform. We help people pay their rent and other bills, in these times of uncertainty and cashflow, the first of the month for your rent, it's a big bill. Being able to split that up into multiple payments is a lot easier. And when we entered the market, you were looking at a place where people were using things like payday loans, which are just ridiculous, really hurting, hurt people in the longterm. So we want to come in with something that is a little more equitable, little fairer and help people who can well afford their rent. They just can't afford it on the first, right? And so we started with rent, and now we cover all the bills like utilities and things like that. >> What a great use case, and I can't even imagine, Kevin, in the last year and a half, how helpful that's been as the world has been so dynamic. So talk to me a little bit about what you were doing before Lumigo and we'll get into then why you went the serverless route. >> Right, so I came to Flex to help them out with some problems that we're having as our servers were scaling up. Obviously, when the business hit, it was really, it went from zero to 100 miles an hour so quickly. And so I came in to help sort out some of the growing issues. And so when I started looking at that, we were three developers and didn't want to spend time on ops, didn't want to spend time on all of the things that you have to do just to be in business, right? And it's really expensive in the technical space. If you get into something about Kubernetes or things like that, you spend a lot of time building that infrastructure, making sure, and that's really minimal value to your business. It's there for reliability, but it doesn't really focus in on the thing that is important to you. So we wanted to build something that minimized that, we talk about DevOps, we want it ops zero, right? So that's like DevOps is a really nice practice, but having people in that role, it seems like you're still doing ops, right? You still got people who are doing those things, and we want it to kind of eliminate that. So I had some experience with serverless before joining Flex. I thought we'll run up a few things and spike up a few things. When you come out of environments like Kubernetes or your more traditional AC to type infrastructure, you'd lose some things. And one of the big things you'd lose is platforms of visibility. So things like OpenTrace and Datadog, and things like that, that do these jobs of telling you what's going on in your infrastructure, you've got fairly complex infrastructure going on, lots of things happening. And so, we initially started with what was available on the platforms, right? So we started with your CloudWatch logs and New Relics, right? Which got us somewhere. But as soon as we started to get into more complex scenarios where we're talking across multiple hops, so through SQS and then through EventBridge and Dynamo, it was very difficult to be able to retrace a piece of information. And that's when we started looking around for solutions, we looked at big traditional pliers, the Datadogs, the New Relics and people like that. And then the serverless specific players, and we ended up landing on Lumigo, and I couldn't have been happier with the results, from day one, I was getting results. >> That's great, I want to talk about that too, especially as you say, we wanted to be able to focus on our core competencies and not spend time in resources that we didn't have in areas where we could actually outsource. So I want to go back to Erez, talk to me about some of the challenges that Kevin articulated, are those common across the board, across industries that Lumigo sees? >> Yeah, I think the main thing when we met Kevin main were about visibility and about ability to zoom out, see the bigger picture and when something actually fails or about to fail in production, being able to drill down to understand what happened, what is the root cause, and go ahead and fix it instead of going through different CloudWatch logs, and log groups and connecting the dots manually. And that's one of the most common challenges when enterprise, where software engineers are heading toward serverless, toward managed services. So, definitely we'll hear that it was many of our customers. >> So Kevin, talk about the infrastructure that you've set up with serverless and go through some of the main benefits that Flex is getting. >> Right, so look, the day one thing of course, is the number of people we need doing operations as we've grown is next to nothing, right? We are able to create in that, we all want this independence of execution, right? So as you scale, I think there's two ways really to scale a system, right? You can build a monolith and shot it, that works really, really well, right? You can just build something that just holds a ton of data and everything seems connected when you release it all in one place, or you build something that's a little more distributed and relies on asynchronous interactions effectively, like in everywhere but the edges, both of those things scale. The middle ground doesn't scale, right? That middle ground of synchronous systems talking to synchronous systems, at some point, your lightency is your sum of all the things you're talking to, right? So doing anything in a quick way is not possible. So when we started to look at things like, I'm sorry, so the other challenge is things like logging and understanding what's happening in your system. Logging is one of those things that you always don't have the thing logged that you're interested in, right? You put in whatever logging you like, but the thing you need will always be missing, which is why we've always taken a tracing approach, right? Why you want to use something like Lumigo or an OpenTrace, you don't sit there and say, "Hey, log this specifically," you log the information that's moving through the system. At that point, you can then look at what's happening specifically. So again, the biggest challenge for us is that we run 1500 landlords, right? We run 600 queues. There's a lot of information. We use an EventBridge, we use Dynamo, we use RDS, we've got information spread out. We moved stuff, but to third party vendors, we're talking out to say, two guys like Stripe and Co, and we're making calls out of those. And we want to understand when we've made those calls, what's the latency on those calls. And for a given interaction, it might touch 20 or 30 of those components. And so for us, the ability to say, "Hey, I want to know why this file to write down here." We need to actually look through everywhere, explain, and understand how it's complex, right? Where this piece of data that was wrong come from? And so, yeah, which is difficult in a distributed environment where your infrastructure is so much a part of somebody else's systems, you don't have direct access to assistance. You'd only got the side effects of the system. >> Right, so talk to me in that distributed environment, Kevin, how does Lumigo help to improve that? Especially as we're talking about payments and billing and sensitive financial information. >> Right, so in a couple of ways, the nice part about Lumigo is I really don't have to do much in order for it to just do its thing, right? This comes back to that philosophy of zero ops, right? Zero effort. I don't want to be concentrating on how I build my tracing infrastructure, right? I just want it to work. I want it to work out of the box when something happens, I want it to have happened. So Lumigo, when I looked at it, when I was looking at the platforms, the integration's so straightforward, the cost integration being straightforward is kind of useless, if it doesn't actually give you the information you want. And we had a challenge initially, which was, we use a lot of EventBridge, and of course, nothing tries to EventBridge until we got, I mentioned this to Erez and Co, and said, "Hey guys, we really need to try to EventBridge, and a little while later, we were tracing through EventBridge, which was fantastic. And because I would say 70% of our transactions evolve something that goes through EventBridge, the other thing there. We're also from an architectural standpoint, we're also what's known as an event source system. So we derive the state of the information from the things that have occurred rather than a current snapshot of what something looks like, right? So rather than you being Lisa with a particular phone number and particular email address stored in a database as a record, you are, Lisa changed the phone number, Lisa changed her email address. And then we take that sequence of things and create a current view of Lisa. So that also helps us with ordering, right? And at those lower levels, we can do a lot of our security. We can do a lot of our encryption, we can say that this particular piece of information, for example, a social security number is encrypted and never is available as plain text. And you need the keys to be able to unlock that particular piece of information. So we can do a lot of that, a lower level infrastructure, but that does generate a lot of movement of information. >> Right. >> And if you can't trace that movement of information, you're in a hurting place. >> So Erez, we just got a great testimonial from Kevin on how Lumigo's really fundamental to their environment and what they're able to deliver to customers, and also Kevin talked about, it sounds like some of the collaboration that went on to help get that EventBridge. Talk to me, Erez, about the collaborative partnership that you have with Flex. >> Yeah, so I think that it's more of a, I would say a philosophy of customers, the users come first. So this is what we're really trying to about. We always try to make sure there's an open communication with all of our customers and for us customer is a key and user's a key, not even a customer. And this is why we try to accommodate the different requests, specifically on this event, this was actually a while after AWS released the service and due to the partnership that we have with AWS, we were able to get this supported relatively fast and first to market supporting EventBridge, and connecting the dots around it. So that's one of the things that we really, really focused on. >> Kevin, back to you, how do you quantify the ROI of what Lumigo is delivering to Flex? >> That's a really good question. And Erez, and I've talked about this a few times, because the simple fact is if I add up the numbers, it costs me more to trace than it does to execute. But if I look at the slightly bigger picture, I also don't have op stuff, right? And I also have an ability to look at things very quickly. The service cost is nothing compared to what I would need if I was running my own tracing through OpenTrace with my own database, monitor the staff to support those things. But the management of those things, the configuration of those things, the multiple touchpoints I'd need for those things, they're not the simple thing. So, if you look at a raw cost, you go, oh man, that part is actually more than my execution costs at least certainly in the early days, but when I look at the entire cost of what it takes to watch manage and trace a system, it's a really easy song, right? And a lot of these things don't pay off until something goes wrong. Now we're heavy users of EventBridge. EventBridge has had two incidents in USA in the last six months, right? And we were able to say through our traffic, that was going through EventBridge, that the slowdown was occurring in EventBridge. In fact, we were saying that before was alerted in the IDR VUS dashboards, to say, "Hey, EventBridge is having problems," like we watch all their alerts, but we were saying an hour before leading into Titus saying, "Hey, there's something going wrong here." Right? Because we were seeing delays in the system. So things like that give you an opportunity to adjust, right? You can't do it. You're not going to be able to get everything off of EventBridge for that period. But at least I can talk to the business and say, "Hey, we're having an impact here, and this is what's going on. We don't think it's our systems, we think it's actually something external. We can see the tries, we see it going in, we see it coming out, it's a 20 minute delay." >> There's a huge amount of value in that, sorry, Kevin, in that visibility alone, as you said, and even maybe even some cost avoidance is there, if you're seeing something going wrong, you maybe can pivot and adjust as needed. But without that visibility, you don't have that. There's a lot of potential loss. >> Yeah, and it's one of those things that doesn't pay for itself until it pays for itself, right? It's like insurance, you don't need insurance until you need insurance. These sort of things, people look at these things and go, "Ah, what am I getting it from day to day?" And day to day, I'll use Lumigo, right? When I'm developing now, Lumigo is part of my development process, in that, I use it to make sure the information is flowing in the way I expect it to, right? Which wasn't what I expected to be able to do with it, right? It wasn't even a plan or anything I intended to use it for, but day to day now, when I buy something off, one of the checks I go through when I'm debugging or when I'm looking at a problem, especially distributed problem is what went through Lumigo. What happened here, here and here, and why did that happen in response to this? So, these things are, again, it's that insurance thing, you don't need it until you need it, and when you need it, you're so glad you've got it. >> Right, exactly. >> Actually it's already said, I have a question because, yeah, I think that it's clear on that part. And how did this, if it change the developer work in Flex, do you feel different on that part? >> I think it's down to individual developers, how they use the different tools, just like individual developers use different tools. I tend to, and a couple of people that I work closely with tend to use these tools in this way, probably where the more advanced users of serverless in general inside the organization. So we were more aware of these weird little things that occur and justly double-checks you want to do. But I feel like when I don't have something like Lumigo in place, it's very hard for me to understand, did everything happen? I can write my acceptance tests, but I want to make sure that, testing is a really fun art, right? And it's picking my cabinets nice and easy, and you can run all these formulas to do things, it's just not right, and there's just too many, especially in distributed space, too many cases where things look odd, things look strange, you've got weird edge cases. We get new timeouts in Dynamo. We hit the 100,000 limit in fresh hall on Dynamo, right? In production, that was really interesting because it meant we needed to do some additional things. >> Lisa: Kevin, oh, go ahead. >> Go ahead, no, go ahead, Lisa. >> I was just going to ask you, I'd love to get your perspective. It sounds like, you look at other technologies, there's been some clear benefits and differentiators that you saw, which is why you chose Lumigo, but it also sounds like there were some things that surprised you. So in your opinion, what are some of the key differentiators of Lumigo versus its competitors? >> So I guess I've been a partner with Lumigo for like eight months now, right? Which is a long time in the history of Flex, right? 'Cause we're just out of two and a half years old. So, when I did the initial evaluation, I was looking for the things. I'm lazy, so I wanted something that I could just drop in and it would just work, right? And get the information I wanted to ask. I wanted something that was giving me information consistently. So I try to figure these things out and hit them with some load. I wanted it to have coverage of the assistance that we use. We use Dynamo a lot. We use Lambros a lot, and I want it not just cursory coverage, how it's just another one of the 20,000 things that they do, I wanted something that was dedicated to it. That gave me information that was useful for me. And really the specialist serverless providers were the obvious choice there. When you looked at the more general providers, the Datadogs and New Relics, I think if you're in an environment that has a lot of other different types of systems running on, then maybe the specificity that you'd lose is worthwhile, right? There's trade off you can make, but we're in a highly serverless environment, so one of the specificity. When I looked at the vendors, Lumigo was the one that worked best straight out of the box for me, it gave me the information I wanted. It gave me the experience I wanted, and to be frank, they've reached out really quickly and had a chat about what were my specific problems, what I was thinking. And all of those things add up, a proactive vendor, just doing the things you wanted to do, and what became and has become a lasting partnership, and I don't say partnership lightly 'cause we've worked with a number of other vendors, right? For different things. But Lumigo, I have turned to these guys, 'cause these guys know serverless, right? So I've turned to these guys when I've gone, "Look, I am not sure what the best approach here is." You have trusted me about it, this is vendor, right? >> Right, but it sounds like it's very synergistic, collaborative trusted relationship. And to your point, not using the term partner lightly, I think arises, probably couldn't have been a better testimonial for Lumigo, its capabilities, and what you guys are able to do. So I'll give you, Erez the last word, just give the audience a little bit of an overview of the AWS partnership. >> Sure, so AWS has been a very strategic partner for Lumigo, and that means that, I would say the most critical part is a product, is a technology. And we are design partners with the serverless team. And that means that we work with AWS to make sure that before new services are released, they get our feedback on whether we can integrate easily or not, and making sure that on the launch date, we are able to be a launch partner for a lot of their services. And this strong partnership with R&D team is what's allowing Lumigo to support new services out of the box like Kevin mentioned. >> Excellent, gentlemen, thank you so much for joining me today, talking about, not just about Lumigo, but getting this great perspective of it through the CTO lens with Kevin, we appreciate your insights, your time, and what a great testimonial. >> Thank you very much, thank you, Kevin. >> Thanks, Lisa, thanks Erez. >> You're most welcome. For Erez Berkner and Kevin O'Neill, I'm Lisa Martin, you're watching the AWS Startup Showcase for Q3. (gentle music)

(upbeat music) >> Well good to have you with us here as we continue the AWS startup showcase and we're joined now by the CEO of BigID, Dmitri Sirota. And Dmitri good afternoon to you? How are you doing today? >> I'm pretty good, it's Friday, it's sunny, it's warm, I'm doing well. >> Then that's a good start, yeah. Glad to have you with us here. First off, just about BigID and when you look at I would assume these accolades are, they are quite a showcase for you. Well economic forum technology pioneer. Forbes cloud 100, business insider startup the watch. I mean, you are getting a lot of attention, obviously for... >> Yep. >> And well-deserved, but when you see these kinds of recognitions coming your way- >> Yep. >> First of what does that do to inspire, motivate and fuel this great passion that you have? >> Yeah, look I think all of these recognitions help, I think affirm, I think what we aspire to be right? Provide the preeminent solution for helping organizations understand their data and in so doing, be able to address problems in privacy and protection and perspective. And I think that these recognitions are part of that as our customers, as our partners like AWS. So they're all part of that ad mixture. And I think they contribute to a sense that we're doing some pioneering work, right as they work from the world economic forum recognized. So I think it's important. I think it's healthy. It encourages kind of cooperative spirit at the company. And I think it's, you know, it's very encouraging for us to continue and build. >> So let's talk about BigID, a little bit for our viewers who might not be too familiar. You are a fairly new company, raised 200 million so far, five years of operations coming up on five years. >> Yep. >> But talk about your sweet spot in terms of the variety of services they provided in terms of protection and security. >> Yeah, sure. So we were founded with really this kind of precept that organizations need to have a better understanding of their data. I think when we got started about five years ago. Most organizations had some view of their data, maybe a few of their files, maybe their databases. What changed is the emerging privacy regulations like GDPR and CCPA later forced companies to rethink their approach to data understanding data knowledge, because part of the kind of the core consumption of privacy is that you and me and other individuals have a right to their data the data actually belongs to us. Similar to when you deposit a check in a bank. That money you deposited is yours. If you ever want to withdraw it, the bank has to give it back to you. And in a similar way, these privacy regulations require organizations to be able to give back your data or delete it or do other things. And as it happens there was no real technology to help companies do that, to help companies look across their vast data estates and pick out all the pieces of information all the detritus that could belong to Dimitri. So it could be my password, it could be my social security, it could be my click stream, it could be my IP address, my cookie. And so we developed from the ground up a brand new approach to technology that covers the data center and the cloud, and allow organizations to understand their data at a level of detail that never existed before. And still, I would argue doesn't exist today. Separate from BigID. And we describe that as our foundational data discovery in depth, right? We provide this kind of multidimensional view of your data to understand the content and the context of the information. And what that allows organizations to do is better understand the risk better meet certain regulatory requirements like GDPR and CCPA. But ultimately also get better value from their data. And so what was pioneering about us is not only that level of detail that we provided almost like your iPhone provides you four cameras to look at the world. We provide you kind of four lenses to look at your data. But then on top of that we introduced a platform that allowed you to take action on what you found. And that action could be in the realm of privacy so that you could solve for some of the privacy use cases like data rights or consent or consumer privacy preferences or data protection data security, so that you can remediate. You can do deal with data lifecycle management. You could deal with encryption, et cetera. Or ultimately what we call a data governance or data perspective, this idea of being able to get value from your data but doing so in a privacy and security preserving way. So that's kind of the conception we want to help you know, your data. And then we want to help you act on your data so that your data is both secure. It's both compliant , but ultimately you get value from your data. >> Now we get into this, helping me know my data better because you you've talked about data you know and data you don't right? >> Dimitri: Yeah. >> And you're saying there's a lot more that we don't or a company doesn't know. >> Dimitri: Yeah. >> Than it's aware of. And I find that still kind of striking in this day and age. I mean with kind of the sophistication of tools that we have and different capabilities that I think give us better insight. But I'm still kind of surprised when you're saying there's all a lot of data that companies are housing that they're not even aware of right now. >> They're not and candidly they didn't really want to be for a long, long time. I think the more you know sometimes the more you have to fix, right? So there needed to be a catalyzing event like these privacy regulations to essentially kind of unpack, to force a set of actions because the privacy regulation said, no, no, no you need to know whether you want to or not. So I think a lot of organizations for years and years outside of a couple of narrow fields like HIPAA, PCI unless there was a specific regulation, they didn't want to know too much. And as a consequence there, wasn't really technology to keep up with the explosion in data volumes and data platforms. Right? Think about like AWS didn't exist when a lot of these technologies were first built in the early 2000's. And so we had to kind of completely re-think things. And one thing I'll also kind of highlight is the need or necessity is not just driven by some of these emerging privacy regulations, but it's also driven by the shift to the cloud. Because when you have all your data on a server in a data center in New Jersey, you could feel a false sense of security because you have doors to that data center in New Jersey and you have firewalls to that data center in New Jersey. And if anybody asks you where your sensitive data you could say, it's in New Jersey! But now all of a sudden you move it into the cloud and data becomes the perimeter, right? It's kind of naked and exposed it's out there. And so I think there's a much greater need and urgency because now data is kind of in the ethos in the air. And so organizations are really kind of looking for additional ability for them to both understand contextualize and deal with some of the privacy security and data governance aspects of that data. >> So you're talking about data obviously AWS comes to mind, right? >> Dimitri: Yeah. And the relationship that you have with them it's been a couple of years in the making things are going really well for you and ultimately for your customers. What is it about this particular partnership that you have with AWS that you think has allowed you to bring that even more added value at the end of the day to your customer base? >> Look, our customers are going to AWS because its simplicity to kind of provision their applications, their services, the cost is incredibly attractive, the diversity of capabilities that AWS provides our customers. And so we have a lot of larger and midsize and even smaller organizations that are going to AWS. And it's important for us to be where our customers are. And so if our customers are using Red Sheriff, or using S creator, using dynamo or using Kinesis or using security hub. We have to be there, right? So we've kind of followed that pathway because of they're putting data in those places, part of our job is provide that insight and intelligence to our customers around those data assets, wherever they are. And so we build a set of capabilities and expertise around the broader AWS platform. So that we could argue that we can help you, whether you keep your data in S3 whether you keep it Dynamo, whether you keep it in EMR, RDS, Aurora, Athena the list goes on and on. We want to be that expert partner for you to kind of help you know your data and then tend to take action on your data. >> So the question about data security in general, obviously as you know, there are these major stories of tremendous breach that's right. >> Yep. >> Stayed afterwards, in some cases. >> Bad guys. >> Yeah, really bad guys and bad smart guys, unfortunately and persistent to say the least. How do you work with your clients in an environment like that? Where, you know, these threats are never ending, >> Yep. >> They're becoming more and more complex. And the tools that you have are certainly robust but at the end of the day, it's very difficult. If not impossible to say a 100% bulletproof, right? >> Yeah. >> It's if you are absolutely safe with us. But you still try, you give these insurances because of your sophistication that, should give people some peace of mind. Again, it's a tough battle your in. >> Yeah. So I think the first rule of fight club is that, to solve a problem, you need to know the problem, right? You can't fix what you can't find, right? So if you're unaware that there's a potential compromise in your data, potential risk in your data maybe you have passwords in a certain data store and there's no security around that. You need to know that you have passwords in a certain data store and there's no security around that. >> Because unless you know that first, there's no ability for you to solve it. So the first part of what we do that kind of know your data that K-Y-D, is we help organizations understand what data do they have that potentially is at risk, may violate a regulatory requirement like GDPR or CCPA, things of that sort. So that's kind of the first level of value because you can't solve for something you can't, you're unaware of, right? You need to be able to see it and you need to be able to understand it. And so our ability to kind of both understand your data and understand what it is, why it is, whose it is where it came from, the risk around it lets you take action on that. Now we don't stop there. We don't stop at just helping you kind of find the problem. We also help you understand if there's additional levels of exposure. Do you have access control around that data for instance. If that data is open to the world and you just put a bunch of passwords there or API keys or credentials, that's a problem. So we provide this kind of holistic view into your data and to some of the security controls. And then most importantly, through our application platform our own apps, we provide ways for you to take action on that. And that action could take many forms. It could be about remediating where you delegate to a security owner and say, hey, I want you to delete that data. Or I want you to encrypt that data. It could be something more automated where it just encrypts everything. But again, part of the value and virtue of our platform is that we both help you identify the potential risk points. And then we give you in the form of apps that sit on top of our platform, ways to take action on it, to secure it, to reduce it, to minimize the risk. >> Because these threats are ever evolving. Can you give us a little, maybe inside peek under the tent here, a bit about what you're looking at in terms of products or services down the road here. So if somebody is thinking, okay. What enhanced tools might be at my disposal in the near term or even in the longterm to try and mitigate these risks. Can you give us an idea about some things you guys are working on? >> Yeah. So the biggest thing we're working on I've already kind of hinted at this is really the kind of first in industry platform, in our category companies that look at data and by platform i mean, something like where you can introduce apps. So AWS has a platform. People can introduce additional capabilities on top of AWS. In the data discovery classification arena, that had never been the case because the tools were very, very old. So we're introducing these apps and these apps allow you to take a variety of actions. I've mentioned a few of them, there's retention. You can do encryption, you can do access control, you could do remediation, and you could do breach impact analysis. Each of these apps is kind of an atomic unit of functionality. So there's no different than on your iPhone or your Android phone. You may have an Uber app, when you click on it, all of a sudden your phone looks like an Uber application. You may have an app focused on Salesforce, you click on it, all of a sudden your phone looks like a Salesforce application. And so what we've done is we've kind of taken this kind of data discovery, classification and intelligence mechanism that kind of K-Y-D I referenced. And then we built a whole app platform. And what we're going to start announcing over the coming months, is more and more apps in the field of privacy, in the fields of data security or protection, and even the fields of data value what we call perspective and that's and we're actually coming out with an announcement shortly on this app marketplace. And there'll be BigID building apps, but you know what, there's going to be a lot of third parties building apps. So companies that do intrusion detection and integrations and all kinds of other things are also building apps on BigID. And that's an exciting part of what you're going to see coming from us in the coming weeks. >> Great. Well, thanks for the sneak peek and wait I feel like I just barely scratched the surface of it. Governance, compliance, right? Regulation, you have so many balls in the air but obviously you're juggling them quite well and we wish you continued success, job well done. Thanks, Dimitri. >> Dimitri: Thank you very much for having me. (upbeat music)

(upbeat music) >> Well good to have you with us here as we continue the AWS startup showcase and we're joined now by the CEO of BigID, Dmitri Sirota. And Dmitri good afternoon to you? How are you doing today? >> I'm pretty good, it's Friday, it's sunny, it's warm, I'm doing well. >> Then that's a good start, yeah. Glad to have you with us here. First off, just about BigID and when you look at I would assume these accolades are, they are quite a showcase for you. Well economic forum technology pioneer. Forbes cloud 100, business insider startup the watch. I mean, you are getting a lot of attention, obviously for... >> Yep. >> And well-deserved, but when you see these kinds of recognitions coming your way- >> Yep. >> First of what does that do to inspire, motivate and fuel this great passion that you have? >> Yeah, look I think all of these recognitions help, I think affirm, I think what we aspire to be right? Provide the preeminent solution for helping organizations understand their data and in so doing, be able to address problems in privacy and protection and perspective. And I think that these recognitions are part of that as our customers, as our partners like AWS. So they're all part of that ad mixture. And I think they contribute to a sense that we're doing some pioneering work, right as they work from the world economic forum recognized. So I think it's important. I think it's healthy. It encourages kind of cooperative spirit at the company. And I think it's, you know, it's very encouraging for us to continue and build. >> So let's talk about BigID, a little bit for our viewers who might not be too familiar. You are a fairly new company, raised 200 million so far, five years of operations coming up on five years. >> Yep. >> But talk about your sweet spot in terms of the variety of services they provided in terms of protection and security. >> Yeah, sure. So we were founded with really this kind of precept that organizations need to have a better understanding of their data. I think when we got started about five years ago. Most organizations had some view of their data, maybe a few of their files, maybe their databases. What changed is the emerging privacy regulations like GDPR and CCPA later forced companies to rethink their approach to data understanding data knowledge, because part of the kind of the core consumption of privacy is that you and me and other individuals have a right to their data the data actually belongs to us. Similar to when you deposit a check in a bank. That money you deposited is yours. If you ever want to withdraw it, the bank has to give it back to you. And in a similar way, these privacy regulations require organizations to be able to give back your data or delete it or do other things. And as it happens there was no real technology to help companies do that, to help companies look across their vast data estates and pick out all the pieces of information all the detritus that could belong to Dimitri. So it could be my password, it could be my social security, it could be my click stream, it could be my IP address, my cookie. And so we developed from the ground up a brand new approach to technology that covers the data center and the cloud, and allow organizations to understand their data at a level of detail that never existed before. And still, I would argue doesn't exist today. Separate from BigID. And we describe that as our foundational data discovery in depth, right? We provide this kind of multidimensional view of your data to understand the content and the context of the information. And what that allows organizations to do is better understand the risk better meet certain regulatory requirements like GDPR and CCPA. But ultimately also get better value from their data. And so what was pioneering about us is not only that level of detail that we provided almost like your iPhone provides you four cameras to look at the world. We provide you kind of four lenses to look at your data. But then on top of that we introduced a platform that allowed you to take action on what you found. And that action could be in the realm of privacy so that you could solve for some of the privacy use cases like data rights or consent or consumer privacy preferences or data protection data security, so that you can remediate. You can do deal with data lifecycle management. You could deal with encryption, et cetera. Or ultimately what we call a data governance or data perspective, this idea of being able to get value from your data but doing so in a privacy and security preserving way. So that's kind of the conception we want to help you know, your data. And then we want to help you act on your data so that your data is both secure. It's both compliant , but ultimately you get value from your data. >> Now we get into this, helping me know my data better because you you've talked about data you know and data you don't right? >> Dimitri: Yeah. >> And you're saying there's a lot more that we don't or a company doesn't know. >> Dimitri: Yeah. >> Than it's aware of. And I find that still kind of striking in this day and age. I mean with kind of the sophistication of tools that we have and different capabilities that I think give us better insight. But I'm still kind of surprised when you're saying there's all a lot of data that companies are housing that they're not even aware of right now. >> They're not and candidly they didn't really want to be for a long, long time. I think the more you know sometimes the more you have to fix, right? So there needed to be a catalyzing event like these privacy regulations to essentially kind of unpack, to force a set of actions because the privacy regulation said, no, no, no you need to know whether you want to or not. So I think a lot of organizations for years and years outside of a couple of narrow fields like HIPAA, PCI unless there was a specific regulation, they didn't want to know too much. And as a consequence there, wasn't really technology to keep up with the explosion in data volumes and data platforms. Right? Think about like AWS didn't exist when a lot of these technologies were first built in the early 2000's. And so we had to kind of completely re-think things. And one thing I'll also kind of highlight is the need or necessity is not just driven by some of these emerging privacy regulations, but it's also driven by the shift to the cloud. Because when you have all your data on a server in a data center in New Jersey, you could feel a false sense of security because you have doors to that data center in New Jersey and you have firewalls to that data center in New Jersey. And if anybody asks you where your sensitive data you could say, it's in New Jersey! But now all of a sudden you move it into the cloud and data becomes the perimeter, right? It's kind of naked and exposed it's out there. And so I think there's a much greater need and urgency because now data is kind of in the ethos in the air. And so organizations are really kind of looking for additional ability for them to both understand contextualize and deal with some of the privacy security and data governance aspects of that data. >> So you're talking about data obviously AWS comes to mind, right? >> Dimitri: Yeah. And the relationship that you have with them it's been a couple of years in the making things are going really well for you and ultimately for your customers. What is it about this particular partnership that you have with AWS that you think has allowed you to bring that even more added value at the end of the day to your customer base? >> Look, our customers are going to AWS because its simplicity to kind of provision their applications, their services, the cost is incredibly attractive, the diversity of capabilities that AWS provides our customers. And so we have a lot of larger and midsize and even smaller organizations that are going to AWS. And it's important for us to be where our customers are. And so if our customers are using Red Sheriff, or using S creator, using dynamo or using Kinesis or using security hub. We have to be there, right? So we've kind of followed that pathway because of they're putting data in those places, part of our job is provide that insight and intelligence to our customers around those data assets, wherever they are. And so we build a set of capabilities and expertise around the broader AWS platform. So that we could argue that we can help you, whether you keep your data in S3 whether you keep it Dynamo, whether you keep it in EMR, RDS, Aurora, Athena the list goes on and on. We want to be that expert partner for you to kind of help you know your data and then tend to take action on your data. >> So the question about data security in general, obviously as you know, there are these major stories of tremendous breach that's right. >> Yep. >> Stayed afterwards, in some cases. >> Bad guys. >> Yeah, really bad guys and bad smart guys, unfortunately and persistent to say the least. How do you work with your clients in an environment like that? Where, you know, these threats are never ending, >> Yep. >> They're becoming more and more complex. And the tools that you have are certainly robust but at the end of the day, it's very difficult. If not impossible to say a 100% bulletproof, right? >> Yeah. >> It's if you are absolutely safe with us. But you still try, you give these insurances because of your sophistication that, should give people some peace of mind. Again, it's a tough battle your in. >> Yeah. So I think the first rule of fight club is that, to solve a problem, you need to know the problem, right? You can't fix what you can't find, right? So if you're unaware that there's a potential compromise in your data, potential risk in your data maybe you have passwords in a certain data store and there's no security around that. You need to know that you have passwords in a certain data store and there's no security around that. >> Because unless you know that first, there's no ability for you to solve it. So the first part of what we do that kind of know your data that K-Y-D, is we help organizations understand what data do they have that potentially is at risk, may violate a regulatory requirement like GDPR or CCPA, things of that sort. So that's kind of the first level of value because you can't solve for something you can't, you're unaware of, right? You need to be able to see it and you need to be able to understand it. And so our ability to kind of both understand your data and understand what it is, why it is, whose it is where it came from, the risk around it lets you take action on that. Now we don't stop there. We don't stop at just helping you kind of find the problem. We also help you understand if there's additional levels of exposure. Do you have access control around that data for instance. If that data is open to the world and you just put a bunch of passwords there or API keys or credentials, that's a problem. So we provide this kind of holistic view into your data and to some of the security controls. And then most importantly, through our application platform our own apps, we provide ways for you to take action on that. And that action could take many forms. It could be about remediating where you delegate to a security owner and say, hey, I want you to delete that data. Or I want you to encrypt that data. It could be something more automated where it just encrypts everything. But again, part of the value and virtue of our platform is that we both help you identify the potential risk points. And then we give you in the form of apps that sit on top of our platform, ways to take action on it, to secure it, to reduce it, to minimize the risk. >> Because these threats are ever evolving. Can you give us a little, maybe inside peek under the tent here, a bit about what you're looking at in terms of products or services down the road here. So if somebody is thinking, okay. What enhanced tools might be at my disposal in the near term or even in the longterm to try and mitigate these risks. Can you give us an idea about some things you guys are working on? >> Yeah. So the biggest thing we're working on I've already kind of hinted at this is really the kind of first in industry platform, in our category companies that look at data and by platform i mean, something like where you can introduce apps. So AWS has a platform. People can introduce additional capabilities on top of AWS. In the data discovery classification arena, that had never been the case because the tools were very, very old. So we're introducing these apps and these apps allow you to take a variety of actions. I've mentioned a few of them, there's retention. You can do encryption, you can do access control, you could do remediation, and you could do breach impact analysis. Each of these apps is kind of an atomic unit of functionality. So there's no different than on your iPhone or your Android phone. You may have an Uber app, when you click on it, all of a sudden your phone looks like an Uber application. You may have an app focused on Salesforce, you click on it, all of a sudden your phone looks like a Salesforce application. And so what we've done is we've kind of taken this kind of data discovery, classification and intelligence mechanism that kind of K-Y-D I referenced. And then we built a whole app platform. And what we're going to start announcing over the coming months, is more and more apps in the field of privacy, in the fields of data security or protection, and even the fields of data value what we call perspective and that's and we're actually coming out with an announcement shortly on this app marketplace. And there'll be BigID building apps, but you know what, there's going to be a lot of third parties building apps. So companies that do intrusion detection and integrations and all kinds of other things are also building apps on BigID. And that's an exciting part of what you're going to see coming from us in the coming weeks. >> Great. Well, thanks for the sneak peek and wait I feel like I just barely scratched the surface of it. Governance, compliance, right? Regulation, you have so many balls in the air but obviously you're juggling them quite well and we wish you continued success, job well done. Thanks, Dimitri. >> Dimitri: Thank you very much for having me. (upbeat music)

(gentle music) >> From the Cube Studios in Palo Alto and Boston, connecting with thought leaders all around the world. This is theCube Conversation. >> Hi everybody, this is Dave Vellante, we're back at Cube on Cloud, and with me is Muddu Sudhakar. He's a long time alum of theCube, a technologist and executive, a serial entrepreneur and an investor. Welcome my friend, good to see you. >> Good to see you, Dave. Pleasure to be with you. Happy elections, I guess. >> Yeah, yeah. So I wanted to start, this work from home, pivot's been amazing, and you've seen the enterprise collaboration explode. I wrote a piece a couple months ago, looking at valuations of various companies, right around the snowflake IPO, I want to ask you about that, but I was looking at the valuations of various companies, at Spotify, and Shopify, and of course Zoom was there. And I was looking at just simple revenue multiples, and I said, geez, Zoom actually looks, might look undervalued, which is crazy, right? And of course the stock went up after that, and you see teams, Microsoft Teams, and Microsoft doing a great job across the board, we've written about that, you're seeing Webex is exploding, I mean, what do you make of this whole enterprise collaboration play? >> No, I think the look there is a trend here, right? So I think this probably trend started before COVID, but COVID is going to probably accelerate this whole digital transformation, right? People are going to work remotely a lot more, not everybody's going to come back to the offices even after COVID, so I think this whole collaboration through Slack, and Zoom, and Microsoft Teams and Webex, it's going to be the new game now, right? Both the video, audio and chat solutions, that's really going to help people like eyeballs. You're not going to spend time on all four of them, right? It's like everyday from a consumer side, you're going to spend time on your Gmail, Facebook, maybe Twitter, maybe Instagram, so like in the consumer side, on your personal life, you have something on the enterprise. The eyeballs are going to be in these platforms. >> Yeah. Well. >> But we're not going to take everything. >> Well, So you are right, there's a permanence to this, and I got a lot of ground to cover with you. And I always like our conversations mood because you tell it like it is, I'm going to stay on that work from home pivot. You know a lot about security, but you've seen three big trends, like mega trends in security, Endpoint, Identity Access Management, and Cloud Security, you're seeing this in the stock prices of companies like CrowdStrike, Zscaler, Okta- >> Right >> Sailpoint- >> Right, I mean, they exploded, as a result of the pandemic, and I think I'm inferring from your comment that you see that as permanent, but that's a real challenge from a security standpoint. What's the impact of Cloud there? >> No, it isn't impact but look, first is all the services required to be Cloud, right? See, the whole ideas for it to collaborate and do these things. So you cannot be running an application, like you can't be running conference and SharePoint oN-Prem, and try to on a Zoom and MS teams. So that's why, if you look at Microsoft is very clever, they went with Office 365, SharePoint 365, now they have MS Teams, so I think that Cloud is going to drive all these workloads that you have been talking about a lot, right? You and John have been saying this for years now. The eruption of Cloud and SAS services are the vehicle to drive this next-generation collaboration. >> You know what's so cool? So Cloud obviously is the topic, I wonder how you look at the last 10 years of Cloud, and maybe we could project forward, I mean the big three Cloud vendors, they're running it like $20 billion a quarter, and they're growing collectively, 35, 40% clips, so we're really approaching a hundred billion dollars for these three. And you hear stats like only 20% of the workloads are in the public Cloud, so it feels like we're just getting started. How do you look at the impact of Cloud on the market, as you say, the last 10 years, and what do you expect going forward? >> No, I think it's very fascinating, right? So I remember when theCube, you guys are talking about 10 years back, now it's been what? More than 10 years, 15 years, since AWS came out with their first S3 service back in 2006. >> Right. >> Right? so I think look, Cloud is going to accelerate even more further. The areas is going to accelerate is for different reasons. I think now you're seeing the initial days, it's all about startups, initial workloads, Dev test and QA test, now you're talking about real production workloads are moving towards Cloud, right? Initially it was backup, we really didn't care for backup they really put there. Now you're going to have Cloud health primary services, your primary storage will be there, it's not going to be an EMC, It's not going to be a NetApp storage, right? So workloads are going to shift from the business applications, and these business applications, will be running on the Cloud, and I'll make another prediction, make customer service and support. Customer service and support, again, we should be running on the Cloud. You're not want to run the thing on a Dell server, or an IBM server, or an HP server, with your own hosted environment. That model is not because there's no economies of scale. So to your point, what will drive Cloud for the next 10 years, will be economies of scale. Where can you take the cost? How can I save money? If you don't move to the Cloud, you won't save money. So all those workloads are going to go to the Cloud are people who really want to save, like global gradual custom, right? If you stay on the ASP model, a hosted, you're not going to save your costs, your costs will constantly go up from a SaaS perspective. >> So that doesn't bode well for all the On-prem guys, and you hear a lot of the vendors that don't own a Cloud that talk about repatriation, but the numbers don't support that. So what do those guys do? I mean, they're talking multi-Cloud, of course they're talking hybrid, that's IBM's big play, how do you see it? >> I think, look, see there, to me, multi-Cloud makes sense, right? You don't want one vendor that you never want to get, so having Amazon, Microsoft, Google, it gives them a multi-Cloud. Even hybrid Cloud does make sense, right? There'll be some workloads. It's like, we are still running On-prem environment, we still have mainframe, so it's never going to be a hundred percent, but I would say the majority, your question is, can we get to 60, 70, 80% workers in the next 10 years? I think you will. I think by 2025, more than 78% of the Cloud Migration by the next five years, 70% of workload for enterprise will be on the Cloud. The remaining 25, maybe Hybrid, maybe On-prem, but I get panics, really doesn't matter. You have saved and part of your business is running on the Cloud. That's your cost saving, that's where you'll see the economies of scale, and that's where all the growth will happen. >> So square the circle for me, because again, you hear the stat on the IDC stat, IBM Ginni Rometty puts it out there a lot that only 20% of the workloads are in the public Cloud, everything else is On-prem, but it's not a zero sum game, right? I mean the Cloud native stuff is growing like crazy, the On-prem stuff is flat to down, so what's going to happen? When you talk about 70% of the workloads will be in the Cloud, do you see those mission critical apps and moving into the car, I mean the insurance companies going to put their claims apps in the Cloud, or the financial services companies going to put their mission critical workloads in the Cloud, or they just going to develop new stuff that's Cloud native that is sort of interacts with the On-prem. How do you see that playing out? >> Yeah, no, I think absolutely, I think a very good question. So two things will happen. I think if you take an enterprise, right? Most businesses what they'll do is the workloads that they should not be running On-prem, they'll move it up. So obviously things like take, as I said, I use the word SharePoint, right? SharePoint and conference, all the knowledge stuff is still running on people's data centers. There's no reason. I understand, I've seen statistics that 70, 80% of the On-prem for SharePoint will move to SharePoint on the Cloud. So Microsoft is going to make tons of money on that, right? Same thing, databases, right? Whether it's CQL server, whether there is Oracle database, things that you are running as a database, as a Cloud, we move to the Cloud. Whether that is posted in Oracle Cloud, or you're running Oracle or Mongo DB, or Dynamo DB on AWS or SQL server Microsoft, that's going to happen. Then what you're talking about is really the App concept, the applications themselves, the App server. Is the App server is going to run On-prem, how much it's going to laureate outside? There may be a hybrid Cloud, like for example, Kafka. I may use a Purse running on a Kafka as a service, or I may be using Elasticsearch for my indexing on AWS or Google Cloud, but I may be running my App locally. So there'll be some hybrid place, but what I would say is for every application, 75% of your Comprende will be on the Cloud. So think of it like the Dev. So even for the On-prem app, you're not going to be a 100 percent On-prem. The competent, the billing materials will move to the Cloud, your Purse, your storage, because if you put it On-prem, you need to add all this, you need to have all the whole things to buy it and hire the people, so that's what is going to happen. So from a competent perspective, 70% of your bill of materials will move to the Cloud, even for an On-prem application. >> So, Of course, the susification of the industry in the last decade and in my three favorite companies last decade, you've worked for two of them, Tableau, ServiceNow, and Splunk. I want to ask you about those, but I'm interested in the potential disruption there. I mean, you've got these SAS companies, Salesforce of course is another one, but they can't get started in 1999. What do you see happening with those? I mean, we're basically building these sort of large SAS, platforms, now. Do you think that the Cloud native world that developers can come at this from an angle where they can disrupt those companies, or are they too entrenched? I mean, look at service now, I mean, I don't know, $80 billion market capital where they are, they bigger than Workday, I mean, just amazing how much they've grown and you feel like, okay, nothing can stop them, but there's always disruption in this industry, what are your thoughts on that. >> Not very good with, I think there'll be disrupted. So to me actually to your point, ServiceNow is now close to a 100 billion now, 95 billion market coverage, crazy. So from evaluation perspective, so I think the reason they'll be disrupted is that the SAS vendors that you talked about, ServiceNow, and all this plan, most of these services, they're truly not a multi-tenant or what do you call the Cloud Native. And that is the Accenture. So because of that, they will not be able to pass the savings back to the enterprises. So the cost economics, the economics that the Cloud provides because of the multi tenancy ability will not. The second reason there'll be disrupted is AI. So far, we talked about Cloud, but AI is the core. So it's not really Cloud Native, Dave, I look at the AI in a two-piece. AI is going to change, see all the SAS vendors were created 20 years back, if you remember, was an operator typing it, I don't respond administered we'll type a Splunk query. I don't need a human to type a query anymore, system will actually find it, that's what the whole security game has changed, right? So what's going to happen is if you believe in that, that AI, your score will disrupt all the SAS vendors, so one angle SAS is going to have is a Cloud. That's where you make the Cloud will take up because a SAS application will be Cloudified. Being SAS is not Cloud, right? Second thing is SAS will be also, I call it, will be AI-fied. So AI and machine learning will be trying to drive at the core so that I don't need that many licenses. I don't need that many humans. I don't need that many administrators to manage, I call them the tuners. Once you get a driverless car, you don't need a thousand tuners to tune your Tesla, or Google Waymo car. So the same philosophy will happen is your Dev Apps, your administrators, your service management, people that you need for service now, and these products, Zendesk with AI, will tremendously will disrupt. >> So you're saying, okay, so yeah, I was going to ask you, won't the SAS vendors, won't they be able to just put, inject AI into their platforms, and I guess I'm inferring saying, yeah, but a lot of the problems that they're solving, are going to go away because of AI, is that right? And automation and RPA and things of that nature, is that right? >> Yes and no. So I'll tell you what, sorry, you have asked a very good question, let's answer, let me rephrase that question. What you're saying is, "Why can't the existing SAS vendors do the AI?" >> Yes, right. >> Right, >> And there's a reason they can't do it is their pricing model is by number of seats. So I'm not going to come to Dave, and say, come on, come pay me less money. It's the same reason why a board and general lover build an electric car. They're selling 10 million gasoline cars. There's no incentive for me, I'm not going to do any AI, I'm going to put, I'm not going to come to you and say, hey, buy me a hundred less license next year from it. So that is one reason why AI, even though these guys do any AI, it's going to be just so I call it, they're going to, what do you call it, a whitewash, kind of like you put some paint brush on it, trying to show you some AI you did from a marketing dynamics. But at the core, if you really implement the AI with you take the driver out, how are you going to change the pricing model? And being a public company, you got to take a hit on the pricing model and the price, and it's going to have a stocking part. So that, to your earlier question, will somebody disrupt them? The person who is going to disrupt them, will disrupt them on the pricing model. >> Right. So I want to ask you about that, because we saw a Snowflake, and it's IPO, we were able to pour through its S-1, and they have a different pricing model. It's a true Cloud consumption model, Whereas of course, most SAS companies, they're going to lock you in for at least one year term, maybe more, and then, you buy the license, you got to pay X. If you, don't use it, you still got to pay for it. Snowflake's different, actually they have a different problem, that people are using it too much and the sea is driving the CFO crazy because the bill is going up and up and up, but to me, that's the right model, It's just like the Amazon model, if you can justify it, so how do you see the pricing, that consumption model is actually, you're seeing some of the On-prem guys at HPE, Dell, they're doing as a service. They're kind of taking a page out of the last decade SAS model, so I think pricing is a real tricky one, isn't it? >> No, you nailed it, you nailed it. So I think the way in which the Snowflake there, how the disruptors are data warehouse, that disrupted the open source vendors too. Snowflake distributed, imagine the playbook, you disrupted something as the $ 0, right? It's an open source with Cloudera, Hortonworks, Mapper, that whole big data that you want me to, or that market is this, that disrupting data warehouses like Netezza, Teradata, and the charging more money, they're making more money and disrupting at $0, because the pricing models by consumption that you talked about. CMT is going to happen in the service now, Zen Desk, well, 'cause their pricing one is by number of seats. People are going to say, "How are my users are going to ask?" right? If you're an employee help desk, you're back to your original health collaborative. I may be on Slack, I could be on zoom, I'll maybe on MS Teams, I'm going to ask by using usage model on Slack, tools by employees to service now is the pricing model that people want to pay for. The more my employees use it, the more value I get. But I don't want to pay by number of seats, so the vendor, who's going to figure that out, and that's where I look, if you know me, I'm right over as I started, that's what I've tried to push that model look, I love that because that's the core of how you want to change the new game. >> I agree. I say, kill me with that problem, I mean, some people are trying to make it a criticism, but you hit on the point. If you pay more, it's only because you're getting more value out of it. So I wanted to flip the switch here a little bit and take a customer angle. Something that you've been on all sides. And I want to talk a little bit about strategies, you've been a strategist, I guess, once a strategist, always a strategist. How should organizations be thinking about their approach to Cloud, it's cost different for different industries, but, back when the cube started, financial services Cloud was a four-letter word. But of course the age of company is going to matter, but what's the framework for figuring out your Cloud strategy to get to your 70% and really take advantage of the economics? Should I be Mono Cloud, Multi-Cloud, Multi-vendor, what would you advise? >> Yeah, no, I mean, I mean, I actually call it the tech stack. Actually you and John taught me that what was the tech stack, like the lamp stack, I think there is a new Cloud stack needs to come, and that I think the bottomline there should be... First of all, anything with storage should be in the Cloud. I mean, if you want to start, whether you are, financial, doesn't matter, there's no way. I come from cybersecurity side, I've seen it. Your attackers will be more with insiders than being on the Cloud, so storage has to be in the Cloud then come compute, Kubernetes. If you really want to use containers and Kubernetes, it has to be in the public Cloud, leverage that have the computer on their databases. That's where it can be like if your data is so strong, maybe run it On-prem, maybe have it on a hosted model for when it comes to database, but there you have a choice between hybrid Cloud and public Cloud choice. Then on top when it comes to App, the app itself, you can run locally or anywhere, the App and database. Now the areas that you really want to go after to migrate is look at anything that's an enterprise workload that you don't need people to manage it. You want your own team to move up in the career. You don't want thousand people looking at... you don't want to have a, for example, IT administrators to call central people to the people to manage your compute storage. That workload should be more, right? You already saw Sierra moved out to Salesforce. We saw collaboration already moved out. Zoom is not running locally. You already saw SharePoint with knowledge management mode up, right? With a box, drawbacks, you name anything. The next global mode is a SAS workloads, right? I think Workday service running there, but work data will go into the Cloud. I bet at some point Zendesk, ServiceNow, then either they put it on the public Cloud, or they have to create a product and public Cloud. To your point, these public Cloud vendors are at $2 trillion market cap. They're they're bigger than the... I call them nation States. >> Yeah, >> So I'm servicing though. I mean, there's a 2 trillion market gap between Amazon and Azure, I'm not going to compete with them. So I want to take this workload to run it there. So all these vendors, if you see that's where Shandra from Adobe is pushing this right, Adobe, Workday, Anaplan, all the SAS vendors we'll move them into the public Cloud within these vendors. So those workloads need to move out, right? So that all those things will start, then you'll start migrating, but I call your procurement. That's where the RPA comes in. The other thing that we didn't talk about, back to your first question, what is the next 10 years of Cloud will be RPA? That third piece to Cloud is RPA because if you have your systems On-prem, I can't automate them. I have to do a VPN into your house there and then try to automate your systems, or your procurement, et cetera. So all these RPA vendors are still running On-prem, most of them, whether it's UI path automation anywhere. So the Cloud should be where the brain should be. That's what I call them like the octopus analogy, the brain is in the Cloud, the tentacles are everywhere, they should manage it. But if my tentacles have to do a VPN with your house to manage it, I'm always will have failures. So if you look at the why RPA did not have the growth, like the Snowflake, like the Cloud, because they are running it On-prem, most of them still. 80% of the RP revenue is On-prem, running On-prem, that needs to be called clarified. So AI, RPA and the SAS, are the three reasons Cloud will take off. >> Awesome. Thank you for that. Now I want to flip the switch again. You're an investor or a multi-tool player here, but so if you're, let's say you're an ecosystem player, and you're kind of looking at the landscape as you're in an investor, of course you've invested in the Cloud, because the Cloud is where it's at, but you got to be careful as an ecosystem player to pick a spot that both provides growth, but allows you to have a moat as, I mean, that's why I'm really curious to see how Snowflake's going to compete because they're competing with AWS, Microsoft, and Google, unlike, Frank, when he was at service now, he was competing with BMC and with on-prem and he crushed it, but the competitors are much more capable here, but it seems like they've got, maybe they've got a moat with MultiCloud, and that whole data sharing thing, we'll see. But, what about that? Where are the opportunities? Where's that white space? And I know there's a lot of white space, but what's the framework to look at, from an investor standpoint, or even a CEO standpoint, where you want to put place your bets. >> No, very good question, so look, I did something. We talk as an investor in the board with many companies, right? So one thing that says as an investor, if you come back and say, I want to create a next generation Docker or a computer, there's no way nobody's going to invest. So that we can motor off, even if you want to do object storage or a block storage, I mean, I've been an investor board member of so many storage companies, there's no way as an industry, I'll write a check for a compute or storage, right? If you want to create a next generation network, like either NetSuite, or restart Juniper, Cisco, there is no way. But if you come back and say, I want to create a next generation Viper for remote working environments, where AI is at the core, I'm interested in that, right? So if you look at how the packets are dropped, there's no intelligence in either not switching today. The packets come, I do it. The intelligence is not built into the network with AI level. So if somebody comes with an AI, what good is all this NVD, our GPS, et cetera, if you cannot do wire speed, packet inspection, looking at the content and then route the traffic. If I see if it's a video package, but in UN Boston, there's high interview day of they should be loading our package faster, because you are a premium ISP. That intelligence has not gone there. So you will see, and that will be a bad people will happen in the network, switching, et cetera, right? So that is still an angle. But if you work and it comes to platform services, remember when I was at Pivotal and VMware, all models was my boss, that would, yes, as a platform, service is a game already won by the Cloud guys. >> Right. (indistinct) >> Silicon Valley Investors, I don't think you want to invest in past services, right? I mean, you might come with some lecture edition database to do some updates, there could be some game, let's say we want to do a time series database, or some metrics database, there's always some small angle, but the opportunity to go create a national database there it's very few. So I'm kind of eliminating all the black spaces, right? >> Yeah. >> We have the white spaces that comes in is the SAS level. Now to your point, if I'm Amazon, I'm going to compete with Snowflake, I have Redshift. So this is where at some point, these Cloud platforms, I call them aircraft carriers. They're not going to stay on the aircraft carriers, they're going to own the land as well. So they're going to move up to the SAS space. The question is you want to create a SAS service like CRM. They are not going to create a CRM like service, they may not create a sales force and service now, but if you're going to add a data warehouse, I can very well see Azure, Google, and AWS, going to create something to compute a Snowflake. Why would I not? It's so close to my database and data warehouse, I already have Redshift. So that's going to be nightlights, same reason, If you look at Netflix, you have a Netflix and you have Amazon prime. Netflix runs on Amazon, but you have Amazon prime. So you have the same model, you have Snowflake, and you'll have Redshift. The both will help each other, there'll be a... What do you call it? Coexistence will happen. But if you really want to invest, you want to invest in SAS companies. You do not want to be investing in a compliment players. You don't want to a feature. >> Yeah, that's great, I appreciate that perspective. And I wonder, so obviously Microsoft play in SAS, Google's got G suite. And I wonder if people often ask the Andy Jassy, you're going to move up the stack, you got to be an application, a SAS vendor, and you never say never with Atavist, But I wonder, and we were talking to Jerry Chen about this, years ago on theCube, and his angle was that Amazon will play, but they'll play through developers. They'll enable developers, and they'll participate, they'll take their, lick off the cone. So it's going to be interesting to see how directly Amazon plays, but at some point you got Tam expansion, you got to play in that space. >> Yeah, I'll give you an example of knowing, I got acquired by a couple of times by EMC. So I learned a lot from Joe Tucci and Paul Merage over the years. see Paul and Joe, what they did is to look at how 20 years, and they are very close to Boston in your area, Joe, what games did is they used to sell storage, but you know what he did, he went and bought the Apps to drive them. He bought like Legato, he bought Documentum, he bought Captiva, if you remember how he acquired all these companies as a services, he bought VMware to drive that. So I think the good angle that Microsoft has is, I'm a SAS player, I have dynamics, I have CRM, I have SharePoint, I have Collaboration, I have Office 365, MS Teams for users, and then I have the platform as Azure. So I think if I'm Amazon, (indistinct). I got to own the apps so that I can drive this workforce on my platform. >> Interesting. >> Just going to developers, like I know Jerry Chan, he was my peer a BMF. I don't think just literally to developers and that model works in open source, but the open source game is pretty much gone, and not too many companies made money. >> Well, >> Most companies pretty much gone. >> Yeah, he's right. Red hats not bad idea. But it's very interesting what you're saying there. And so, hey, its why Oracle wants to have Tiktok, running on their platform, right? I mean, it's going to. (laughing) It's going to drive that further integration. I wanted to ask you something, you were talking about, you wouldn't invest in storage or compute, but I wonder, and you mentioned some commentary about GPU's. Of course the videos has been going crazy, but they're now saying, okay, how do we expand our Team, they make the acquisition of arm, et cetera. What about this DPU thing, if you follow that, that data processing unit where they're like hyper dis-aggregation and then they reaggregate, and as an offload and really to drive data centric workloads. Have you looked at that at all? >> I did, I think, and that's a good angle. So I think, look, it's like, it goes through it. I don't know if you remember in your career, we have seen it. I used to get Silicon graphics. I saw the first graphic GPU, right? That time GPU was more graphic processor unit, >> Right, yeah, work stations. >> So then become NPUs at work processing units, right? There was a TCP/IP office offloading, if you remember right, there was like vector processing unit. So I think every once in a while the industry, recreated this separate unit, as a co-processor to the main CPU, because main CPU's inefficient, and it makes sense. And then Google created TPU's and then we have the new world of the media GPU's, now we have DPS all these are good, but what's happening is, all these are driving for machine learning, AI for the training period there. Training period Sometimes it's so long with the workloads, if you can cut down, it makes sense. >> Yeah. >> Because, but the question is, these aren't so specialized in nature. I can't use it for everything. >> Yup. >> I want Ideally, algorithms to be paralyzed, I want the training to be paralyzed, I want so having deep use and GPS are important, I think where I want to see them as more, the algorithm, there should be more investment from the NVIDIA's and these guys, taking the algorithm to be highly paralyzed them. (indistinct) And I think that still has not happened in industry yet. >> All right, so we're pretty much out of time, but what are you doing these days? Where are you spending your time, are you still in Stealth, give us a little glimpse. >> Yeah, no, I'm out of the Stealth, I'm actually the CEO of Aisera now, Aisera, obviously I invested with them, but I'm the CEO of Aisero. It's funded by Menlo ventures, Norwest, True, along with Khosla ventures and Ram Shriram is a big investor. Robin's on the board of Google, so these guys, look, we are going out to the collaboration game. How do you automate customer service and support for employees and then users, right? In this whole game, we talked about the Zoom, Slack and MS Teams, that's what I'm spending time, I want to create next generation service now. >> Fantastic. Muddu, I always love having you on you, pull punches, you tell it like it is, that you're a great visionary technologist. Thanks so much for coming on theCube, and participating in our program. >> Dave, it's always a pleasure speaking to you sir. Thank you. >> Okay. Keep it right there, there's more coming from Cuba and Cloud right after this break. (slow music)

>> From The Cube Studios in Palo Alto and Boston connecting with thought-leaders all around the world, this is a Cube Conversation. >> Hello, everyone, and welcome to this Cube Conversation, I'm Dave Vellante. Now, you know I love data, and today we're going to introduce you to a new data and analytical platform, and we're going to take it to the world of cloud database and data warehouses. And with me is Sagar Kadakia who's the head of Enterprise IT (indistinct) 7Park Data. Sagar, welcome back to the Cube. Good to see you. >> Thank you so much, David. I appreciate you having me back on. >> Hey, so new gig for you, how's it going? Tell us about 7Park Data. >> Yeah. Look, things are going well. It started at about two months ago, just a, you know, busy. I had a chance last, you know a few months to kind of really dig into the dataset. We have a tremendous amount of research coming out in Q4 Q1 around kind of the public cloud database market public cloud analytics market. So, you know, really looking forward to that. >> Okay, good. Well, let's bring up the first slide. Let's talk about where this data comes from. Tell us a little bit more about the platform. Where's the insight. >> Yeah, absolutely. So I'll talk a little about 7Park and then we'd kind of jump into the data a little bit. So 7Park was founded in 2012 in terms of differentiator, you know with other alternative data firms, you know we use NLP machine learning, you know AI to really kind of, you know, structure like noisy and unstructured data sets really kind of generate insight from that. And so, because a lot of that know how we ended up being acquired by Vista back in 2018. And really like for us, you know the mandate there is to really, you know look across all their different portfolio companies and try to generate insight from all the data assets you know, that these portfolio companies have. So, you know, today we're going to be talking about you know, one of the data sets from those companies it's that cloud infrastructure data set. We get it from one of the portfolio companies that you know, helps organizations kind of manage and optimize their cloud spend. It's real time data. We essentially get this aggregated daily. So this certainly different than, you know your traditional providers maybe giving you quarterly or kind of by annual data. This is incredibly granular, real time all the way down to the invoice level. So within this cloud infrastructure dataset we're tracking several billion dollars worth of spend across AWS, Azure and GCP. Something like 350 services across like 20 plus markets. So, you know, security machine learning analytics database which we're going to talk about today. And again like the granularity of the KPIs I think is kind of really what kind of you know, differentiates this dataset you know, with just within database itself, you know we're tracking over 20 services. So, you know, lots to kind of look forward to kind of into Q4 and Q1. >> So, okay. So the main spring of your data is if I'm a customer and I there's a service out there there are many services like this that can help me optimize my spend and the way they do that is I basically connect their APIs. So they have visibility on what the transactions that I'm making my usage statistics et cetera. And then you take that and then extrapolate that and report on that. Is that right? >> Exactly. Yeah. We're seeing just on this one data set that we're going to talk about today, it's something like six 700 million rows worth of data. And so kind of what we do is, you know we kind of have the insight layer on top of that or the analytics layer on top of all that unstructured data, so that we can get a feel for, you know a whole host of different kind of KPIs spend, adoption rates, market share, you know product size, retention rates, spend, you know, net price all that type of stuff. So, yeah, that's exactly what we're doing. >> Love it, there's more transparency the better. Okay. So, so right, because this whole world of market sizing has been very opaque you know, over the years, and it's like you know, backroom conversations, whether it's IDC, Gartner who's got what don't take, you know and the estimations and it's very, very, you know it's not very transparent so I'm excited to see what you guys have. Okay. So, so you have some data on the public cloud and specifically the database market that you want to share with our audience. Let's bring up the next graphic here. What are we looking at here Sagar? What are these blue lines and red lines what's this all about? >> Yeah. So and look, we can kind of start at the kind of the 10,000 foot view kind of level here. And so what we're looking at here is our estimates for the entire kind of cloud database market, including data warehousing. If you look all the way over to the right I'll kind of explain some of these bars in a minute but just high level, you know we're forecasting for this year, $11.8 billion. Now something to kind of remember about that is that's just AWS, Azure and GCP, right? So that's not the entire cloud database market. It's just specific to those three providers. What you're looking at here is the breakout and blue and purple is SQL databases and then no SQL databases. And so, you know, to no one's surprise here and you can see, you know SQL database is obviously much larger from a revenue standpoint. And so you can see just from this time last year, you know the database market has grown 40% among these three cloud providers. And, you know, though, we're not showing it here, you know from like a PI perspective, you know database is playing a larger and larger role for all three of these providers. And so obviously this is a really hot market, which is why, you know we're kind of discussing a lot of the dynamics. You don't need to Q and Q Q4 and Q1 >> So, okay. Let's get into some of the specific firm-level data. You have numbers that you want to share on Amazon Redshift and Google BigQuery, and some comments on Snowflake let's bring up the next graphic. So tell us, it says public cloud data, warehousing growth tempered by Snowflake, what's the data showing. And let's talk about some of the implications there. >> Yeah, no problem. So yeah, this is kind of one of the markets, you know that we kind of did a deep dive in tomorrow and we'll kind of get this, you know, get to this in a few minutes, we're kind of doing a big CIO panel kind of covering data, warehousing, RDBMS documents store key value, graph all these different database markets but I thought it'd be great, you know just cause obviously what's occurring here and with snowflake to kind of talk about, you know the data warehousing market, you know, look if you look here, these are some of the KPIs that we have you know, and I'll kind of start from the left. Here are some of the orange bars, the darker orange bars. Those are our estimates for AWS Redshift. And so you can see here, you know we're projecting about 667 million in revenue for Redshift. But if you look at the lighter arm bars, you can see that the service went from representing about 2% of you know, AWS revenue to about 1.5%. And we think some of that is because of Snowflake. And if we kind of, take a look at some of these KPIs you know, below those bar charts here, you know one of the things that we've been looking at is, you know how are longer-term customer spending and how are let's just say like newer customers spending, so to speak. So kind of just like organic growth or kind of net expansion analysis. And if you look at on the bottom there, you'll see, you know customers in our dataset that we looked at, you know that were there 3Q20 as well as 3Q19 their spend on AWS Redshift is 23%. Right? And then look at the bifurcation, right? When we include essentially all the new customers that onboard it, right after 3Q19, look at how much they're bringing down the spend increase. And it's because, you know a lot of spend that was perhaps meant for Redshift is now going to Snowflake. And look, you would expect longer-term customers to spend more than newer customers. But really what we're doing is here is really highlighting the stark contrast because you have kind of back to back KPIs here, you know between organic spend versus total spend and obviously the deceleration in market share kind of coming down. So, you know, something that's interesting here and we'll kind of continue tracking that. >> Okay. So let's maybe come back to this mass Colombo questions here. So the start with the orange side. So we're talking about Snowflake being 667 million. These are your estimates extrapolated based on what we talked about earlier, 1.5% of the AWS portfolio of course you see things like, they continue to grow. Amazon made a bunch of storage announcements last week at the first week of re-invent (indistinct) I mean just name all kinds of databases. And so it's competing with a lot of other services in the portfolio and then, but it's interesting to see Google BigQuery a much larger percentage of the portfolio, which again to me, makes sense people like BigQuery. They like the data science components that are built in the machine learning components that are built in. But then if you look at Snowflake's last quarter and just on a run rate basis, it's over there over $600 million. Now, if you just multiply their last quarter by four from a revenue standpoint. So they got Redshift in their sites, you know if this is, you know to the extent this is the correct number and I know it's an estimate but I haven't seen any better numbers out there. Interesting Sagar, I mean Snowflake surpassed the value of snowflakes or past service now last Friday, it's probably just in trading today you know, on Monday it's maybe Snowflake is about a billion dollars less than the in value than IBM. So you're saying snowflake in a lot of attention, post IPO the thing is even exploded more. I mean, it's crazy. And I presume that's rippled into the customer interest areas. Now the ironic thing here of course, is that that snowflake most of its revenue comes from AWS running on AWS at the same time, AWS and or Redshift and snowflake compete. So you have this interesting dynamic going on. >> Yeah. You know, we've spoken to so many CIOs about kind of the dynamics here with Redshift and BigQuery and Snowflake, you know as it kind of pertains to, you know, Redshift and Snowflake. I think, you know, what I've heard the most is, look if you're using Redshift, you're going to keep using it. But if you're new to data warehousing kind of, so to speak you're going to move to Snowflake, or you're going to start with Snowflake, you know, that and I think, you know when it comes to data warehousing, you're seeing a lot of decisions kind of coming from, you know, bottom up now. So a lot of developers and so obviously their preference is going to be Snowflake. And then when you kind of look at BigQuery here over to the right again, like look you're seeing revenue growth, but again, as a as a percentage of total, you know, GCP revenue you're seeing it come down and look, we don't show it here. But another dynamic that we're seeing amongst BigQuery is that we are seeing adoption rates fall versus this time last year. So we think, again, that could be because of Snowflake. Now, one thing to kind of highlight here with BigQuery look it's kind of the low cost alternative, you know, so to speak, you know once Redshift gets too expensive, so to speak, you know you kind of move over to, to BigQuery and we kind of put some price KPIs down here all the way at the bottom of the chart, you know kind of for both of them, you know when you kind of think about the net price per kind of TB scan, you know, Redshift does it pro rate right? It's five bucks or whatever you, you know whatever you scan in, whereas, you know GCP and get the first terabyte for free. And then everything is prorated after that. And so you can see the net price, right? So that's the price that people actually pay. You can see it's significantly lower that than Redshift. And again, you know it's a lower cost alternative. And so when you think about, you know organizations or CIO's that want to save some money certainly BigQuery, you know, is an option. But certainly I think just overall, you know, Snowflake is is certainly having, you know, an impact here and you can see it from, you know the percentage of total revenue for both these coming down. You know, if we look at other AWS database services or you mentioned a few other services, you know we're not seeing that trend, we're seeing, you know percentage of total revenue hang in or accelerate. And so that's kind of why we want to point this out as this is something unique, you know for AWS and GCP where even though you're seeing growth, it's decelerating. And then of course you can kind of see the percentage of revenue represents coming down. >> I think it's interesting to look at these two companies and then of course Snowflake. So if you think about Snowflake and BigQuery both of those started in the cloud they were true born in the cloud databases. Whereas Redshift was a deal that Amazon did, you know with parxl back in the day, one time license fee and then they re-engineered it to be kind of cloud based. And so there is some of that historical o6n-prem baggage in there. I know that AWS did a tremendous job in rearchitecting that but nonetheless, so I'll give you a couple of examples. If you go back to last year's reinvent 2019 of course Snowflake was really the first to popularize this idea of separating compute from storage and even compute from compute, which is kind of nuance. So I won't go into that, but the idea being you can dial up or dial down compute as you need it you can even turn off compute in the world of Snowflake and just, you know, you're paying an S3 for storage charges. What Amazon did last reinvent was they announced the separation of compute and storage, but what the way they did it was they did it with a tiering architecture. So you can't ever actually fully turn off the compute, but it's great. I mean, it's customers I've talked to say, yes I'm saving a lot of money, you know, with this approach. But again, there's these little nuances. So what Snowflake announced this year was their data cloud and what the data cloud is as a whole new architecture. It's based on this global mesh. It lives across both AWS and Azure and GCP. And what Snowflake has done is they've taken they've abstracted the complexity of the clouds. So you don't even necessarily have to know what you're running on. You have to worry about it any Snowflake user inside of that data cloud if given access can share data with any other user. So it's a very powerful concept that they're doing. AWS at reinvent this year announced something called AWS glue elastic views which basically allows you to take data across their entire database portfolio. And I'm going to put, share in quotes. And I put it in quotes because it's essentially doing copying from a source pushing to a target AWS database and then doing a change data management capture and pushes that over time. So it, it feels like kind of an attempt to do their own data cloud. The advantages of AWS is that they've got way more data stores than just Snowflake cause it's one data store. So was AWS says Aurora dynamo DB Redshift on and on and on streaming databases, et cetera where Snowflake is just Snowflake. And so it's going to be interesting to see, you know these two juxtaposing philosophies but I want it to sort of lay that out because this is just it's setting up as a really interesting dynamic. Then you can bring in Azure as well with Microsoft and what they're doing. And I think this is going to be really fascinating to see how this plays out over the next decade. >> Yeah. I think some of the points you brought up maybe a little bit earlier were just around like the functional limits of a Redshift. Right. And I think that's where, you know Snowflake obviously does it does very, very well you know, you kind of have these, you know kind of to come, you know, you kind of have these, you know if you kind of think about like the market drivers right? Like, let's think about even like the prior slide that we showed, where we saw overall you know, database growth, like what's driving all of that what's driving Redshift, right. Obviously proximity application, interdependencies, right. Costs. You get all the credits or people are already working with the big three providers. And so there's so many reasons to continue spending with them, obviously, you know, COVID-19 right. Obviously all these apps being developed right in the cloud versus data centers and things of that nature. So you have all of these market drivers, you know for the cloud database services for Redshift. And so from that perspective, you know you kind of think, well why are people even to go to a third party vendor? And I think, you know, at that point it has to be the functional superiority. And so again, like a lot of times it depends on, you know, where decisions are coming from you know, top down or bottom up obviously at the engineering at the developer level they're going to want better functionality. Maybe, you know, top-down sometimes, you know it's like, look, we have a lot of credits, you know we're trying to save money, you know from a security perspective it could just be easier to spin something up you know, in AWS, so to speak. So, yeah, I think these are all the dynamics that, you know organizations have to figure out every day, but at least within the data warehousing space, you are seeing spend go towards Snowflake and it's going away to an extent as we kind of see, you know growth decelerate for both of these vendors, right. It's not that revenue's not going out there is growth which is that growth is, it's just not the same as it used to be, you know, so to speak. So yeah, this is a interesting area to kind of watch and I think across all the other markets as well, you know when you think about document store, right you have AWS document DB, right. What are the impacts there with with Mongo and some of these other kind of third party data warehousing vendors, right. Having to compete with all the, you know all the different services offered by AWS Azure like the cosmos and all that stuff. So, yeah, it's definitely kind of turning into a battle Royal, you know as we kind of head into, into 2021. And so I think having all these KPIs is really helping us kind of break down and figure out, you know which areas like data warehousing are slowing down. But then what other areas in database where they're seeing a tremendous amount of acceleration, like as we said, database revenue is driving. Like it's becoming a bigger part of their overall revenue. And so they are doing well. It just, you know, there's obviously snowflake they have to compete with here. >> Well, and I think maybe to your point I infer from your point, it's not necessarily a zero sum game. And as I was discussing before, I think Snowflake's really trying to create a new market. It's not just trying to steal share from the Terra datas and the Redshifts and the PCPs of the world, big queries and and Azure SQL server and Oracle and so forth. They're trying to create a whole new concept called the data cloud, which to me is really important because my prediction is what Snowflake is doing. And they don't even really talk a ton about this but they sort of do, if you squint through the lines I think what they're doing is first of all, simplicity is there, what they're doing. And then they're putting data in the hands of business people, business line people who have domain context, that's a whole new way of thinking about a data architecture versus the prevalent way to do a data pipeline is you got data engineers and data scientists, and you ingest data. It's goes to the beginning of the pipeline and that's kind of a traditional way to do it. And kind of how I think most of the AWS customers do it. I think over time, because of the simplicity of Snowflake you're going to see people begin to look at new ways to architect data. Anyway, we're almost out of time here but I want to bring up the next slide which is a graphic, which talks about a database discussion that you guys are having on 12/8 at 2:00 PM Eastern time with Bain and Verizon who what's this all about. >> Yeah. So, you know, one of the things we wanted to do is we kind of kick off a lot of the, you know Q4 Q1 research or putting on the database spark. It is just like kind of, we did, you know we did today, which obviously, you know we're really going to expand on tomorrow at a at 2:00 PM is discuss all the different KPIs. You know, we track something like 20 plus database services. So we're going to be going through a lot more than just kind of Redshift and BigQuery. Look at all the dynamics there, look at, you know how they're very against some of the third party vendors like the Snowflake, like a Mongo DB, as an example we got some really great, you know, thought leaders you know, Michael Delzer and Praveen from verizon they're going to kind of help, or they're going to opine on all the dynamics that we're seeing. And so it's going to be a very kind of, you know structured wise, it's going to be very quantitative but then you're going to have this beautiful qualitative discussion to kind of help support a lot of the data points that we're capturing. And so, yeah, we're really excited about the panel you know, from, you know, why you should join standpoint. Look, it's just, it's great, competitive Intel. If you're a third party, you know, database, data warehousing vendor, this is the type of information that you're going to want to know, you know, adoption rates market sizing, retention rates, you know net price reservers, on demand dynamics. You know, we're going through a lot that tomorrow. So I'm really excited about that. I'm just in general, really excited about a lot of the research that we're kind of putting out. So >> That's interesting. I mean, and we were talking earlier about AWS glue elastic views. I'd love to see your view of all the database services from Amazon. Cause that's where it's really designed to do is leverage those across those. And you know, you listen to Andrew, Jesse talk they've got a completely different philosophy than say Oracle, which says, Hey we've got one database to do all things Amazon saying we need that fine granularity. So it's going to be again. And to the extent that you're providing market context they're very excited to see that data Sagar and see how that evolves over time. Really appreciate you coming back in the cube and look forward to working with you. >> Appreciate Dave. Thank you so much. >> All right. Welcome. Thank you everybody for watching. This is Dave Vellante for the cube. We'll see you next time. (upbeat music)

>> Announcer: From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is theCUBE conversation. >> Hi everyone, this is Dave Vellante and welcome to this CUBE conversation where we're going to dig in to this, the area of cloud databases. And Gartner just published a series of research in this space. And it's really a growing market, rapidly growing, a lot of new players, obviously the big three cloud players. And with me are three experts in the field, two long time industry analysts. Marc Staimer is the founder, president, and key principal at Dragon Slayer Consulting. And he's joined by David Floyer, the CTO of Wikibon. Gentlemen great to see you. Thanks for coming on theCUBE. >> Good to be here. >> Great to see you too Dave. >> Marc, coming from the great Northwest, I think first time on theCUBE, and so it's really great to have you. So let me set this up, as I said, you know, Gartner published these, you know, three giant tomes. These are, you know, publicly available documents on the web. I know you guys have been through them, you know, several hours of reading. And so, night... (Dave chuckles) Good night time reading. The three documents where they identify critical capabilities for cloud database management systems. And the first one we're going to talk about is, operational use cases. So we're talking about, you know, transaction oriented workloads, ERP financials. The second one was analytical use cases, sort of an emerging space to really try to, you know, the data warehouse space and the like. And, of course, the third is the famous Gartner Magic Quadrant, which we're going to talk about. So, Marc, let me start with you, you've dug into this research just at a high level, you know, what did you take away from it? >> Generally, if you look at all the players in the space they all have some basic good capabilities. What I mean by that is ultimately when you have, a transactional or an analytical database in the cloud, the goal is not to have to manage the database. Now they have different levels of where that goes to as how much you have to manage or what you have to manage. But ultimately, they all manage the basic administrative, or the pedantic tasks that DBAs have to do, the patching, the tuning, the upgrading, all of that is done by the service provider. So that's the number one thing they all aim at, from that point on every database has different capabilities and some will automate a whole bunch more than others, and will have different primary focuses. So it comes down to what you're looking for or what you need. And ultimately what I've learned from end users is what they think they need upfront, is not what they end up needing as they implement. >> David, anything you'd add to that, based on your reading of the Gartner work. >> Yes. It's a thorough piece of work. It's taking on a huge number of different types of uses and size of companies. And I think those are two parameters which really change how companies would look at it. If you're a Fortune 500 or Fortune 2000 type company, you're going to need a broader range of features, and you will need to deal with size and complexity in a much greater sense, and a lot of probably higher levels of availability, and reliability, and recoverability. Again, on the workload side, there are different types of workload and there're... There is as well as having the two transactional and analytic workloads, I think there's an emerging type of workload which is going to be very important for future applications where you want to combine transactional with analytic in real time, in order to automate business processes at a higher level, to make the business processes synchronous as opposed to asynchronous. And that degree of granularity, I think is missed, in a broader view of these companies and what they offer. It's in my view trying in some ways to not compare like with like from a customer point of view. So the very nuance, what you talked about, let's get into it, maybe that'll become clear to the audience. So like I said, these are very detailed research notes. There were several, I'll say analysts cooks in the kitchen, including Henry Cook, whom I don't know, but four other contributing analysts, two of whom are CUBE alum, Don Feinberg, and Merv Adrian, both really, you know, awesome researchers. And Rick Greenwald, along with Adam Ronthal. And these are public documents, you can go on the web and search for these. So I wonder if we could just look at some of the data and bring up... Guys, bring up the slide one here. And so we'll first look at the operational side and they broke it into four use cases. The traditional transaction use cases, the augmented transaction processing, stream/event processing and operational intelligence. And so we're going to show you there's a lot of data here. So what Gartner did is they essentially evaluated critical capabilities, or think of features and functions, and gave them a weighting, or a weighting, and then a rating. It was a weighting and rating methodology. On a s... The rating was on a scale of one to five, and then they weighted the importance of the features based on their assessment, and talking to the many customers they talk to. So you can see here on the first chart, we're showing both the traditional transactions and the augmented transactions and, you know, the thing... The first thing that jumps out at you guys is that, you know, Oracle with Autonomous is off the charts, far ahead of anybody else on this. And actually guys, if you just bring up slide number two, we'll take a look at the stream/event processing and operational intelligence use cases. And you can see, again, you know, Oracle has a big lead. And I don't want to necessarily go through every vendor here, but guys, if you don't mind going back to the first slide 'cause I think this is really, you know, the core of transaction processing. So let's look at this, you've got Oracle, you've got SAP HANA. You know, right there interestingly Amazon Web Services with the Aurora, you know, IBM Db2, which, you know, it goes back to the good old days, you know, down the list. But so, let me again start with Marc. So why is that? I mean, I guess this is no surprise, Oracle still owns the Mission-Critical for the database space. They earned that years ago. One that, you know, over the likes of Db2 and, you know, Informix and Sybase, and, you know, they emerged as number one there. But what do you make of this data Marc? >> If you look at this data in a vacuum, you're looking at specific functionality, I think you need to look at all the slides in total. And the reason I bring that up is because I agree with what David said earlier, in that the use case that's becoming more prevalent is the integration of transaction and analytics. And more importantly, it's not just your traditional data warehouse, but it's AI analytics. It's big data analytics. It's users are finding that they need more than just simple reporting. They need more in-depth analytics so that they can get more actionable insights into their data where they can react in real time. And so if you look at it just as a transaction, that's great. If you're going to just as a data warehouse, that's great, or analytics, that's fine. If you have a very narrow use case, yes. But I think today what we're looking at is... It's not so narrow. It's sort of like, if you bought a streaming device and it only streams Netflix and then you need to get another streaming device 'cause you want to watch Amazon Prime. You're not going to do that, you want one, that does all of it, and that's kind of what's missing from this data. So I agree that the data is good, but I don't think it's looking at it in a total encompassing manner. >> Well, so before we get off the horses on the track 'cause I love to do that. (Dave chuckles) I just kind of let's talk about that. So Marc, you're putting forth the... You guys seem to agree on that premise that the database that can do more than just one thing is of appeal to customers. I suppose that makes, certainly makes sense from a cost standpoint. But, you know, guys feel free to flip back and forth between slides one and two. But you can see SAP HANA, and I'm not sure what cloud that's running on, it's probably running on a combination of clouds, but, you know, scoring very strongly. I thought, you know, Aurora, you know, given AWS says it's one of the fastest growing services in history and they've got it ahead of Db2 just on functionality, which is pretty impressive. I love Google Spanner, you know, love the... What they're trying to accomplish there. You know, you go down to Microsoft is, they're kind of the... They're always good enough a database and that's how they succeed and et cetera, et cetera. But David, it sounds like you agree with Marc. I would say, I would think though, Amazon kind of doesn't agree 'cause they're like a horses for courses. >> I agree. >> Yeah, yeah. >> So I wonder if you could comment on that. >> Well, I want to comment on two vectors. The first vector is that the size of customer and, you know, a mid-sized customer versus a global $2,000 or global 500 customer. For the smaller customer that's the heart of AWS, and they are taking their applications and putting pretty well everything into their cloud, the one cloud, and Aurora is a good choice. But when you start to get to a requirements, as you do in larger companies have very high levels of availability, the functionality is not there. You're not comparing apples and... Apples with apples, it's two very different things. So from a tier one functionality point of view, IBM Db2 and Oracle have far greater capability for recovery and all the features that they've built in over there. >> Because of their... You mean 'cause of the maturity, right? maturity and... >> Because of their... Because of their focus on transaction and recovery, et cetera. >> So SAP though HANA, I mean, that's, you know... (David talks indistinctly) And then... >> Yeah, yeah. >> And then I wanted your comments on that, either of you or both of you. I mean, SAP, I think has a stated goal of basically getting its customers off Oracle that's, you know, there's always this urinary limping >> Yes, yes. >> between the two companies by 2024. Larry has said that ain't going to happen. You know, Amazon, we know still runs on Oracle. It's very hard to migrate Mission-Critical, David, you and I know this well, Marc you as well. So, you know, people often say, well, everybody wants to get off Oracle, it's too expensive, blah, blah, blah. But we talked to a lot of Oracle customers there, they're very happy with the reliability, availability, recoverability feature set. I mean, the core of Oracle seems pretty stable. >> Yes. >> But I wonder if you guys could comment on that, maybe Marc you go first. >> Sure. I've recently done some in-depth comparisons of Oracle and Aurora, and all their other RDS services and Snowflake and Google and a variety of them. And ultimately what surprised me is you made a statement it costs too much. It actually comes in half of Aurora for in most cases. And it comes in less than half of Snowflake in most cases, which surprised me. But no matter how you configure it, ultimately based on a couple of things, each vendor is focused on different aspects of what they do. Let's say Snowflake, for example, they're on the analytical side, they don't do any transaction processing. But... >> Yeah, so if I can... Sorry to interrupt. Guys if you could bring up the next slide that would be great. So that would be slide three, because now we get into the analytical piece Marc that you're talking about that's what Snowflake specialty is. So please carry on. >> Yeah, and what they're focused on is sharing data among customers. So if, for example, you're an automobile manufacturer and you've got a huge supply chain, you can supply... You can share the data without copying the data with any of your suppliers that are on Snowflake. Now, can you do that with the other data warehouses? Yes, you can. But the focal point is for Snowflake, that's where they're aiming it. And whereas let's say the focal point for Oracle is going to be performance. So their performance affects cost 'cause the higher the performance, the less you're paying for the performing part of the payment scale. Because you're paying per second for the CPUs that you're using. Same thing on Snowflake, but the performance is higher, therefore you use less. I mean, there's a whole bunch of things to come into this but at the end of the day what I've found is Oracle tends to be a lot less expensive than the prevailing wisdom. So let's talk value for a second because you said something, that yeah the other databases can do that, what Snowflake is doing there. But my understanding of what Snowflake is doing is they built this global data mesh across multiple clouds. So not only are they compatible with Google or AWS or Azure, but essentially you sign up for Snowflake and then you can share data with anybody else in the Snowflake cloud, that I think is unique. And I know, >> Marc: Yes. >> Redshift, for instance just announced, you know, Redshift data sharing, and I believe it's just within, you know, clusters within a customer, as opposed to across an ecosystem. And I think that's where the network effect is pretty compelling for Snowflake. So independent of costs, you and I can debate about costs and, you know, the tra... The lack of transparency of, because AWS you don't know what the bill is going to be at the end of the month. And that's the same thing with Snowflake, but I find that... And by the way guys, you can flip through slides three and four, because we've got... Let me just take a quick break and you have data warehouse, logical data warehouse. And then the next slide four you got data science, deep learning and operational intelligent use cases. And you can see, you know, Teradata, you know, law... Teradata came up in the mid 1980s and dominated in that space. Oracle does very well there. You can see Snowflake pop-up, SAP with the Data Warehouse, Amazon with Redshift. You know, Google with BigQuery gets a lot of high marks from people. You know, Cloud Data is in there, you know, so you see some of those names. But so Marc and David, to me, that's a different strategy. They're not trying to be just a better data warehouse, easier data warehouse. They're trying to create, Snowflake that is, an incremental opportunity as opposed to necessarily going after, for example, Oracle. David, your thoughts. >> Yeah, I absolutely agree. I mean, ease of use is a primary benefit for Snowflake. It enables you to do stuff very easily. It enables you to take data without ETL, without any of the complexity. It enables you to share a number of resources across many different users and know... And be able to bring in what that particular user wants or part of the company wants. So in terms of where they're focusing, they've got a tremendous ease of use, tremendous focus on what the customer wants. And you pointed out yourself the restrictions there are of doing that both within Oracle and AWS. So yes, they have really focused very, very hard on that. Again, for the future, they are bringing in a lot of additional functions. They're bringing in Python into it, not Python, JSON into the database. They can extend the database itself, whether they go the whole hog and put in transaction as well, that's probably something they may be thinking about but not at the moment. >> Well, but they, you know, they obviously have to have TAM expansion designs because Marc, I mean, you know, if they just get a 100% of the data warehouse market, they're probably at a third of their stock market valuation. So they had better have, you know, a roadmap and plans to extend there. But I want to come back Marc to this notion of, you know, the right tool for the right job, or, you know, best of breed for a specific, the right specific, you know horse for course, versus this kind of notion of all in one, I mean, they're two different ends of the spectrum. You're seeing, you know, Oracle obviously very successful based on these ratings and based on, you know their track record. And Amazon, I think I lost count of the number of data stores (Dave chuckles) with Redshift and Aurora and Dynamo, and, you know, on and on and on. (Marc talks indistinctly) So they clearly want to have that, you know, primitive, you know, different APIs for each access, completely different philosophies it's like Democrats or Republicans. Marc your thoughts as to who ultimately wins in the marketplace. >> Well, it's hard to say who is ultimately going to win, but if I look at Amazon, Amazon is an all-cart type of system. If you need time series, you go with their time series database. If you need a data warehouse, you go with Redshift. If you need transaction, you go with one of the RDS databases. If you need JSON, you go with a different database. Everything is a different, unique database. Moving data between these databases is far from simple. If you need to do a analytics on one database from another, you're going to use other services that cost money. So yeah, each one will do what they say it's going to do but it's going to end up costing you a lot of money when you do any kind of integration. And you're going to add complexity and you're going to have errors. There's all sorts of issues there. So if you need more than one, probably not your best route to go, but if you need just one, it's fine. And if, and on Snowflake, you raise the issue that they're going to have to add transactions, they're going to have to rewrite their database. They have no indexes whatsoever in Snowflake. I mean, part of the simplicity that David talked about is because they had to cut corners, which makes sense. If you're focused on the data warehouse you cut out the indexes, great. You don't need them. But if you're going to do transactions, you kind of need them. So you're going to have to do some more work there. So... >> Well... So, you know, I don't know. I have a different take on that guys. I think that, I'm not sure if Snowflake will add transactions. I think maybe, you know, their hope is that the market that they're creating is big enough. I mean, I have a different view of this in that, I think the data architecture is going to change over the next 10 years. As opposed to having a monolithic system where everything goes through that big data platform, the data warehouse and the data lake. I actually see what Snowflake is trying to do and, you know, I'm sure others will join them, is to put data in the hands of product builders, data product builders or data service builders. I think they're betting that that market is incremental and maybe they don't try to take on... I think it would maybe be a mistake to try to take on Oracle. Oracle is just too strong. I wonder David, if you could comment. So it's interesting to see how strong Gartner rated Oracle in cloud database, 'cause you don't... I mean, okay, Oracle has got OCI, but you know, you think a cloud, you think Google, or Amazon, Microsoft and Google. But if I have a transaction database running on Oracle, very risky to move that, right? And so we've seen that, it's interesting. Amazon's a big customer of Oracle, Salesforce is a big customer of Oracle. You know, Larry is very outspoken about those companies. SAP customers are many, most are using Oracle. I don't, you know, it's not likely that they're going anywhere. My question to you, David, is first of all, why do they want to go to the cloud? And if they do go to the cloud, is it logical that the least risky approach is to stay with Oracle, if you're an Oracle customer, or Db2, if you're an IBM customer, and then move those other workloads that can move whether it's more data warehouse oriented or incremental transaction work that could be done in a Aurora? >> I think the first point, why should Oracle go to the cloud? Why has it gone to the cloud? And if there is a... >> Moreso... Moreso why would customers of Oracle... >> Why would customers want to... >> That's really the question. >> Well, Oracle have got Oracle Cloud@Customer and that is a very powerful way of doing it. Where exactly the same Oracle system is running on premise or in the cloud. You can have it where you want, you can have them joined together. That's unique. That's unique in the marketplace. So that gives them a very special place in large customers that have data in many different places. The second point is that moving data is very expensive. Marc was making that point earlier on. Moving data from one place to another place between two different databases is a very expensive architecture. Having the data in one place where you don't have to move it where you can go directly to it, gives you enormous capabilities for a single database, single database type. And I'm sure that from a transact... From an analytic point of view, that's where Snowflake is going, to a large single database. But where Oracle is going to is where, you combine both the transactional and the other one. And as you say, the cost of migration of databases is incredibly high, especially transaction databases, especially large complex transaction databases. >> So... >> And it takes a long time. So at least a two year... And it took five years for Amazon to actually succeed in getting a lot of their stuff over. And five years they could have been doing an awful lot more with the people that they used to bring it over. So it was a marketing decision as opposed to a rational business decision. >> It's the holy grail of the vendors, they all want your data in their database. That's why Amazon puts so much effort into it. Oracle is, you know, in obviously a very strong position. It's got growth and it's new stuff, it's old stuff. It's, you know... The problem with Oracle it has like many of the legacy vendors, it's the size of the install base is so large and it's shrinking. And the new stuff is.... The legacy stuff is shrinking. The new stuff is growing very, very fast but it's not large enough yet to offset that, you see that in all the learnings. So very positive news on, you know, the cloud database, and they just got to work through that transition. Let's bring up slide number five, because Marc, this is to me the most interesting. So we've just shown all these detailed analysis from Gartner. And then you look at the Magic Quadrant for cloud databases. And, you know, despite Amazon being behind, you know, Oracle, or Teradata, or whomever in every one of these ratings, they're up to the right. Now, of course, Gartner will caveat this and say, it doesn't necessarily mean you're the best, but of course, everybody wants to be in the upper, right. We all know that, but it doesn't necessarily mean that you should go by that database, I agree with what Gartner is saying. But look at Amazon, Microsoft and Google are like one, two and three. And then of course, you've got Oracle up there and then, you know, the others. So that I found that very curious, it is like there was a dissonance between the hardcore ratings and then the positions in the Magic Quadrant. Why do you think that is Marc? >> It, you know, it didn't surprise me in the least because of the way that Gartner does its Magic Quadrants. The higher up you go in the vertical is very much tied to the amount of revenue you get in that specific category which they're doing the Magic Quadrant. It doesn't have to do with any of the revenue from anywhere else. Just that specific quadrant is with that specific type of market. So when I look at it, Oracle's revenue still a big chunk of the revenue comes from on-prem, not in the cloud. So you're looking just at the cloud revenue. Now on the right side, moving to the right of the quadrant that's based on functionality, capabilities, the resilience, other things other than revenue. So visionary says, hey how far are you on the visionary side? Now, how they weight that again comes down to Gartner's experts and how they want to weight it and what makes more sense to them. But from my point of view, the right side is as important as the vertical side, 'cause the vertical side doesn't measure the growth rate either. And if we look at these, some of these are growing much faster than the others. For example, Snowflake is growing incredibly fast, and that doesn't reflect in these numbers from my perspective. >> Dave: I agree. >> Oracle is growing incredibly fast in the cloud. As David pointed out earlier, it's not just in their cloud where they're growing, but it's Cloud@Customer, which is basically an extension of their cloud. I don't know if that's included these numbers or not in the revenue side. So there's... There're a number of factors... >> Should it be in your opinion, Marc, would you include that in your definition of cloud? >> Yeah. >> The things that are hybrid and on-prem would that cloud... >> Yes. >> Well especially... Well, again, it depends on the hybrid. For example, if you have your own license, in your own hardware, but it connects to the cloud, no, I wouldn't include that. If you have a subscription license and subscription hardware that you don't own, but it's owned by the cloud provider, but it connects with the cloud as well, that I would. >> Interesting. Well, you know, to your point about growth, you're right. I mean, it's probably looking at, you know, revenues looking, you know, backwards from guys like Snowflake, it will be double, you know, the next one of these. It's also interesting to me on the horizontal axis to see Cloud Data and Databricks further to the right, than Snowflake, because that's kind of the data lake cloud. >> It is. >> And then of course, you've got, you know, the other... I mean, database used to be boring, so... (David laughs) It's such a hot market space here. (Marc talks indistinctly) David, your final thoughts on all this stuff. What does the customer take away here? What should I... What should my cloud database management strategy be? >> Well, I was positive about Oracle, let's take some of the negatives of Oracle. First of all, they don't make it very easy to rum on other platforms. So they have put in terms and conditions which make it very difficult to run on AWS, for example, you get double counts on the licenses, et cetera. So they haven't played well... >> Those are negotiable by the way. Those... You bring it up on the customer. You can negotiate that one. >> Can be, yes, They can be. Yes. If you're big enough they are negotiable. But Aurora certainly hasn't made it easy to work with other plat... Other clouds. What they did very... >> How about Microsoft? >> Well, no, that is exactly what I was going to say. Oracle with adjacent workloads have been working very well with Microsoft and you can then use Microsoft Azure and use a database adjacent in the same data center, working with integrated very nicely indeed. And I think Oracle has got to do that with AWS, it's got to do that with Google as well. It's got to provide a service for people to run where they want to run things not just on the Oracle cloud. If they did that, that would in my term, and my my opinion be a very strong move and would make make the capabilities available in many more places. >> Right. Awesome. Hey Marc, thanks so much for coming to theCUBE. Thank you, David, as well, and thanks to Gartner for doing all this great research and making it public on the web. You can... If you just search critical capabilities for cloud database management systems for operational use cases, that's a mouthful, and then do the same for analytical use cases, and the Magic Quadrant. There's the third doc for cloud database management systems. You'll get about two hours of reading and I learned a lot and I learned a lot here too. I appreciate the context guys. Thanks so much. >> My pleasure. All right, thank you for watching everybody. This is Dave Vellante for theCUBE. We'll see you next time. (upbeat music)

(gentle music) >> From the Cube Studios in Palo Alto and Boston, connecting with thought leaders all around the world. This is theCube Conversation. >> Hi everybody, this is Dave Vellante, we're back at Cube on Cloud, and with me is Muddu Sudhakar. He's a long time alum of theCube, a technologist and executive, a serial entrepreneur and an investor. Welcome my friend, good to see you. >> Good to see you, Dave. Pleasure to be with you. Happy elections, I guess. >> Yeah, yeah. So I wanted to start, this work from home, pivot's been amazing, and you've seen the enterprise collaboration explode. I wrote a piece a couple months ago, looking at valuations of various companies, right around the snowflake IPO, I want to ask you about that, but I was looking at the valuations of various companies, at Spotify, and Shopify, and of course Zoom was there. And I was looking at just simple revenue multiples, and I said, geez, Zoom actually looks, might look undervalued, which is crazy, right? And of course the stock went up after that, and you see teams, Microsoft Teams, and Microsoft doing a great job across the board, we've written about that, you're seeing Webex is exploding, I mean, what do you make of this whole enterprise collaboration play? >> No, I think the look there is a trend here, right? So I think this probably trend started before COVID, but COVID is going to probably accelerate this whole digital transformation, right? People are going to work remotely a lot more, not everybody's going to come back to the offices even after COVID, so I think this whole collaboration through Slack, and Zoom, and Microsoft Teams and Webex, it's going to be the new game now, right? Both the video, audio and chat solutions, that's really going to help people like eyeballs. You're not going to spend time on all four of them, right? It's like everyday from a consumer side, you're going to spend time on your Gmail, Facebook, maybe Twitter, maybe Instagram, so like in the consumer side, on your personal life, you have something on the enterprise. The eyeballs are going to be in these platforms. >> Yeah. Well. >> But we're not going to take everything. >> Well, So you are right, there's a permanence to this, and I got a lot of ground to cover with you. And I always like our conversations mood because you tell it like it is, I'm going to stay on that work from home pivot. You know a lot about security, but you've seen three big trends, like mega trends in security, Endpoint, Identity Access Management, and Cloud Security, you're seeing this in the stock prices of companies like CrowdStrike, Zscaler, Okta- >> Right >> Sailpoint- >> Right, I mean, they exploded, as a result of the pandemic, and I think I'm inferring from your comment that you see that as permanent, but that's a real challenge from a security standpoint. What's the impact of Cloud there? >> No, it isn't impact but look, first is all the services required to be Cloud, right? See, the whole ideas for it to collaborate and do these things. So you cannot be running an application, like you can't be running conference and SharePoint oN-Prem, and try to on a Zoom and MS teams. So that's why, if you look at Microsoft is very clever, they went with Office 365, SharePoint 365, now they have MS Teams, so I think that Cloud is going to drive all these workloads that you have been talking about a lot, right? You and John have been saying this for years now. The eruption of Cloud and SAS services are the vehicle to drive this next-generation collaboration. >> You know what's so cool? So Cloud obviously is the topic, I wonder how you look at the last 10 years of Cloud, and maybe we could project forward, I mean the big three Cloud vendors, they're running it like $20 billion a quarter, and they're growing collectively, 35, 40% clips, so we're really approaching a hundred billion dollars for these three. And you hear stats like only 20% of the workloads are in the public Cloud, so it feels like we're just getting started. How do you look at the impact of Cloud on the market, as you say, the last 10 years, and what do you expect going forward? >> No, I think it's very fascinating, right? So I remember when theCube, you guys are talking about 10 years back, now it's been what? More than 10 years, 15 years, since AWS came out with their first S3 service back in 2006. >> Right. >> Right? so I think look, Cloud is going to accelerate even more further. The areas is going to accelerate is for different reasons. I think now you're seeing the initial days, it's all about startups, initial workloads, Dev test and QA test, now you're talking about real production workloads are moving towards Cloud, right? Initially it was backup, we really didn't care for backup they really put there. Now you're going to have Cloud health primary services, your primary storage will be there, it's not going to be an EMC, It's not going to be a ETAP storage, right? So workloads are going to shift from the business applications, and this business App again, will be running on the Cloud, and I'll make another prediction, make customer service and support. Customer service and support, again, we should be running on the Cloud. You're not want to run the thing on a Dell server, or an IBM server, or an HP server, with your own hosted environment. That model is not because there's no economies of scale. So to your point, what will drive Cloud for the next 10 years, will be economies of scale. Where can you take the cost? How can I save money? If you don't move to the Cloud, you won't save money. So all those workloads are going to go to the Cloud are people who really want to save, like global gradual custom, right? If you stay on the ASP model, a hosted, you're not going to save your costs, your costs will constantly go up from a SAS perspective. >> So that doesn't bode well for all the On-prem guys, and you hear a lot of the vendors that don't own a Cloud that talk about repatriation, but the numbers don't support that. So what do those guys do? I mean, they're talking multi-Cloud, of course they're talking hybrid, that's IBM's big play, how do you see it? >> I think, look, see there, to me, multi-Cloud makes sense, right? You don't want one vendor that you never want to get, so having Amazon, Microsoft, Google, it gives them a multi-Cloud. Even hybrid Cloud does make sense, right? There'll be some workloads. It's like, we are still running On-prem environment, we still have mainframe, so it's never going to be a hundred percent, but I would say the majority, your question is, can we get to 60, 70, 80% workers in the next 10 years? I think you will. I think by 2025, more than 78% of the Cloud Migration by the next five years, 70% of workload for enterprise will be on the Cloud. The remaining 25, maybe Hybrid, maybe On-prem, but I get panics, really doesn't matter. You have saved and part of your business is running on the Cloud. That's your cost saving, that's where you'll see the economies of scale, and that's where all the growth will happen. >> So square the circle for me, because again, you hear the stat on the IDC stat, IBM Ginni Rometty puts it out there a lot that only 20% of the workloads are in the public Cloud, everything else is On-prem, but it's not a zero sum game, right? I mean the Cloud native stuff is growing like crazy, the On-prem stuff is flat to down, so what's going to happen? When you talk about 70% of the workloads will be in the Cloud, do you see those mission critical apps and moving into the car, I mean the insurance companies going to put their claims apps in the Cloud, or the financial services companies going to put their mission critical workloads in the Cloud, or they just going to develop new stuff that's Cloud native that is sort of interacts with the On-prem. How do you see that playing out? >> Yeah, no, I think absolutely, I think a very good question. So two things will happen. I think if you take an enterprise, right? Most businesses what they'll do is the workloads that they should not be running On-prem, they'll move it up. So obviously things like take, as I said, I use the word SharePoint, right? SharePoint and conference, all the knowledge stuff is still running on people's data centers. There's no reason. I understand, I've seen statistics that 70, 80% of the On-prem for SharePoint will move to SharePoint on the Cloud. So Microsoft is going to make tons of money on that, right? Same thing, databases, right? Whether it's CQL server, whether there is Oracle database, things that you are running as a database, as a Cloud, we move to the Cloud. Whether that is posted in Oracle Cloud, or you're running Oracle or Mongo DB, or Dynamo DB on AWS or SQL server Microsoft, that's going to happen. Then what you're talking about is really the App concept, the applications themselves, the App server. Is the App server is going to run On-prem, how much it's going to laureate outside? There may be a hybrid Cloud, like for example, Kafka. I may use a Purse running on a Kafka as a service, or I may be using Elasticsearch for my indexing on AWS or Google Cloud, but I may be running my App locally. So there'll be some hybrid place, but what I would say is for every application, 75% of your Comprende will be on the Cloud. So think of it like the Dev. So even for the On-prem app, you're not going to be a 100 percent On-prem. The competent, the billing materials will move to the Cloud, your Purse, your storage, because if you put it On-prem, you need to add all this, you need to have all the whole things to buy it and hire the people, so that's what is going to happen. So from a competent perspective, 70% of your bill of materials will move to the Cloud, even for an On-prem application. >> So, Of course, the susification of the industry in the last decade and in my three favorite companies last decade, you've worked for two of them, Tableau, ServiceNow, and Splunk. I want to ask you about those, but I'm interested in the potential disruption there. I mean, you've got these SAS companies, Salesforce of course is another one, but they can't get started in 1999. What do you see happening with those? I mean, we're basically building these sort of large SAS, platforms, now. Do you think that the Cloud native world that developers can come at this from an angle where they can disrupt those companies, or are they too entrenched? I mean, look at service now, I mean, I don't know, $80 billion market capital where they are, they bigger than Workday, I mean, just amazing how much they've grown and you feel like, okay, nothing can stop them, but there's always disruption in this industry, what are your thoughts on that. >> Not very good with, I think there'll be disrupted. So to me actually to your point, ServiceNow is now close to a 100 billion now, 95 billion market coverage, crazy. So from evaluation perspective, so I think the reason they'll be disrupted is that the SAS vendors that you talked about, ServiceNow, and all this plan, most of these services, they're truly not a multi-tenant or what do you call the Cloud Native. And that is the Accenture. So because of that, they will not be able to pass the savings back to the enterprises. So the cost economics, the economics that the Cloud provides because of the multi tenancy ability will not. The second reason there'll be disrupted is AI. So far, we talked about Cloud, but AI is the core. So it's not really Cloud Native, Dave, I look at the AI in a two-piece. AI is going to change, see all the SAS vendors were created 20 years back, if you remember, was an operator typing it, I don't respond administered we'll type a Splunk query. I don't need a human to type a query anymore, system will actually find it, that's what the whole security game has changed, right? So what's going to happen is if you believe in that, that AI, your score will disrupt all the SAS vendors, so one angle SAS is going to have is a Cloud. That's where you make the Cloud will take up because a SAS application will be Cloudified. Being SAS is not Cloud, right? Second thing is SAS will be also, I call it, will be AI-fied. So AI and machine learning will be trying to drive at the core so that I don't need that many licenses. I don't need that many humans. I don't need that many administrators to manage, I call them the tuners. Once you get a driverless car, you don't need a thousand tuners to tune your Tesla, or Google Waymo car. So the same philosophy will happen is your Dev Apps, your administrators, your service management, people that you need for service now, and these products, Zendesk with AI, will tremendously will disrupt. >> So you're saying, okay, so yeah, I was going to ask you, won't the SAS vendors, won't they be able to just put, inject AI into their platforms, and I guess I'm inferring saying, yeah, but a lot of the problems that they're solving, are going to go away because of AI, is that right? And automation and RPA and things of that nature, is that right? >> Yes and no. So I'll tell you what, sorry, you have asked a very good question, let's answer, let me rephrase that question. What you're saying is, "Why can't the existing SAS vendors do the AI?" >> Yes, right. >> Right, >> And there's a reason they can't do it is their pricing model is by number of seats. So I'm not going to come to Dave, and say, come on, come pay me less money. It's the same reason why a board and general lover build an electric car. They're selling 10 million gasoline cars. There's no incentive for me, I'm not going to do any AI, I'm going to put, I'm not going to come to you and say, hey, buy me a hundred less license next year from it. So that is one reason why AI, even though these guys do any AI, it's going to be just so I call it, they're going to, what do you call it, a whitewash, kind of like you put some paint brush on it, trying to show you some AI you did from a marketing dynamics. But at the core, if you really implement the AI with you take the driver out, how are you going to change the pricing model? And being a public company, you got to take a hit on the pricing model and the price, and it's going to have a stocking part. So that, to your earlier question, will somebody disrupt them? The person who is going to disrupt them, will disrupt them on the pricing model. >> Right. So I want to ask you about that, because we saw a Snowflake, and it's IPO, we were able to pour through its S-1, and they have a different pricing model. It's a true Cloud consumption model, Whereas of course, most SAS companies, they're going to lock you in for at least one year term, maybe more, and then, you buy the license, you got to pay X. If you, don't use it, you still got to pay for it. Snowflake's different, actually they have a different problem, that people are using it too much and the sea is driving the CFO crazy because the bill is going up and up and up, but to me, that's the right model, It's just like the Amazon model, if you can justify it, so how do you see the pricing, that consumption model is actually, you're seeing some of the On-prem guys at HPE, Dell, they're doing as a service. They're kind of taking a page out of the last decade SAS model, so I think pricing is a real tricky one, isn't it? >> No, you nailed it, you nailed it. So I think the way in which the Snowflake there, how the disruptors are data warehouse, that disrupted the open source vendors too. Snowflake distributed, imagine the playbook, you disrupted something as the $ 0, right? It's an open source with Cloudera, Hortonworks, Mapper, that whole big data that you want me to, or that market is this, that disrupting data warehouses like Netezza, Teradata, and the charging more money, they're making more money and disrupting at $0, because the pricing models by consumption that you talked about. CMT is going to happen in the service now, Zen Desk, well, 'cause their pricing one is by number of seats. People are going to say, "How are my users are going to ask?" right? If you're an employee help desk, you're back to your original health collaborative. I may be on Slack, I could be on zoom, I'll maybe on MS Teams, I'm going to ask by using usage model on Slack, tools by employees to service now is the pricing model that people want to pay for. The more my employees use it, the more value I get. But I don't want to pay by number of seats, so the vendor, who's going to figure that out, and that's where I look, if you know me, I'm right over as I started, that's what I've tried to push that model look, I love that because that's the core of how you want to change the new game. >> I agree. I say, kill me with that problem, I mean, some people are trying to make it a criticism, but you hit on the point. If you pay more, it's only because you're getting more value out of it. So I wanted to flip the switch here a little bit and take a customer angle. Something that you've been on all sides. And I want to talk a little bit about strategies, you've been a strategist, I guess, once a strategist, always a strategist. How should organizations be thinking about their approach to Cloud, it's cost different for different industries, but, back when the cube started, financial services Cloud was a four-letter word. But of course the age of company is going to matter, but what's the framework for figuring out your Cloud strategy to get to your 70% and really take advantage of the economics? Should I be Mono Cloud, Multi-Cloud, Multi-vendor, what would you advise? >> Yeah, no, I mean, I mean, I actually call it the tech stack. Actually you and John taught me that what was the tech stack, like the lamp stack, I think there is a new Cloud stack needs to come, and that I think the bottomline there should be... First of all, anything with storage should be in the Cloud. I mean, if you want to start, whether you are, financial, doesn't matter, there's no way. I come from cybersecurity side, I've seen it. Your attackers will be more with insiders than being on the Cloud, so storage has to be in the Cloud and encompass compute whoever it is. If you really want to use containers and Kubernetes, it has to be in the public Cloud, leverage that have the computer on their databases. That's where it can be like if your data is so strong, maybe run it On-prem, maybe have it on a hosted model for when it comes to database, but there you have a choice between hybrid Cloud and public Cloud choice. Then on top when it comes to App, the app itself, you can run locally or anywhere, the App and database. Now the areas that you really want to go after to migrate is look at anything that's an enterprise workload that you don't need people to manage it. You want your own team to move up in the career. You don't want thousand people looking at... you don't want to have a, for example, IT administrators to call central people to the people to manage your compute storage. That workload should be more, right? You already saw Sierra moved out to Salesforce. We saw collaboration already moved out. Zoom is not running locally. You already saw SharePoint with knowledge management mode up, right? With a box, drawbacks, you name anything. The next global mode is a SAS workloads, right? I think Workday service running there, but work data will go into the Cloud. I bet at some point Zendesk, ServiceNow, then either they put it on the public Cloud, or they have to create a product and public Cloud. To your point, these public Cloud vendors are at $2 trillion market cap. They're they're bigger than the... I call them nation States. >> Yeah, >> So I'm servicing though. I mean, there's a 2 trillion market gap between Amazon and Azure, I'm not going to compete with them. So I want to take this workload to run it there. So all these vendors, if you see that's where Shandra from Adobe is pushing this right, Adobe, Workday, Anaplan, all the SAS vendors we'll move them into the public Cloud within these vendors. So those workloads need to move out, right? So that all those things will start, then you'll start migrating, but I call your procurement. That's where the RPA comes in. The other thing that we didn't talk about, back to your first question, what is the next 10 years of Cloud will be RPA? That third piece to Cloud is RPA because if you have your systems On-prem, I can't automate them. I have to do a VPN into your house there and then try to automate your systems, or your procurement, et cetera. So all these RPA vendors are still running On-prem, most of them, whether it's UI path automation anywhere. So the Cloud should be where the brain should be. That's what I call them like the octopus analogy, the brain is in the Cloud, the tentacles are everywhere, they should manage it. But if my tentacles have to do a VPN with your house to manage it, I'm always will have failures. So if you look at the why RPA did not have the growth, like the Snowflake, like the Cloud, because they are running it On-prem, most of them still. 80% of the RP revenue is On-prem, running On-prem, that needs to be called clarified. So AI, RPA and the SAS, are the three reasons Cloud will take off. >> Awesome. Thank you for that. Now I want to flip the switch again. You're an investor or a multi-tool player here, but so if you're, let's say you're an ecosystem player, and you're kind of looking at the landscape as you're in an investor, of course you've invested in the Cloud, because the Cloud is where it's at, but you got to be careful as an ecosystem player to pick a spot that both provides growth, but allows you to have a moat as, I mean, that's why I'm really curious to see how Snowflake's going to compete because they're competing with AWS, Microsoft, and Google, unlike, Frank, when he was at service now, he was competing with BMC and with on-prem and he crushed it, but the competitors are much more capable here, but it seems like they've got, maybe they've got a moat with MultiCloud, and that whole data sharing thing, we'll see. But, what about that? Where are the opportunities? Where's that white space? And I know there's a lot of white space, but what's the framework to look at, from an investor standpoint, or even a CEO standpoint, where you want to put place your bets. >> No, very good question, so look, I did something. We talk as an investor in the board with many companies, right? So one thing that says as an investor, if you come back and say, I want to create a next generation Docker or a computer, there's no way nobody's going to invest. So that we can motor off, even if you want to do object storage or a block storage, I mean, I've been an investor board member of so many storage companies, there's no way as an industry, I'll write a check for a compute or storage, right? If you want to create a next generation network, like either NetSuite, or restart Juniper, Cisco, there is no way. But if you come back and say, I want to create a next generation Viper for remote working environments, where AI is at the core, I'm interested in that, right? So if you look at how the packets are dropped, there's no intelligence in either not switching today. The packets come, I do it. The intelligence is not built into the network with AI level. So if somebody comes with an AI, what good is all this NVD, our GPS, et cetera, if you cannot do wire speed, packet inspection, looking at the content and then route the traffic. If I see if it's a video package, but in UN Boston, there's high interview day of they should be loading our package faster, because you are a premium ISP. That intelligence has not gone there. So you will see, and that will be a bad people will happen in the network, switching, et cetera, right? So that is still an angle. But if you work and it comes to platform services, remember when I was at Pivotal and VMware, all models was my boss, that would, yes, as a platform, service is a game already won by the Cloud guys. >> Right. (indistinct) >> Silicon Valley Investors, I don't think you want to invest in past services, right? I mean, you might come with some lecture edition database to do some updates, there could be some game, let's say we want to do a time series database, or some metrics database, there's always some small angle, but the opportunity to go create a national database there it's very few. So I'm kind of eliminating all the black spaces, right? >> Yeah. >> We have the white spaces that comes in is the SAS level. Now to your point, if I'm Amazon, I'm going to compete with Snowflake, I have Redshift. So this is where at some point, these Cloud platforms, I call them aircraft carriers. They're not going to stay on the aircraft carriers, they're going to own the land as well. So they're going to move up to the SAS space. The question is you want to create a SAS service like CRM. They are not going to create a CRM like service, they may not create a sales force and service now, but if you're going to add a data warehouse, I can very well see Azure, Google, and AWS, going to create something to compute a Snowflake. Why would I not? It's so close to my database and data warehouse, I already have Redshift. So that's going to be nightlights, same reason, If you look at Netflix, you have a Netflix and you have Amazon prime. Netflix runs on Amazon, but you have Amazon prime. So you have the same model, you have Snowflake, and you'll have Redshift. The both will help each other, there'll be a... What do you call it? Coexistence will happen. But if you really want to invest, you want to invest in SAS companies. You do not want to be investing in a compliment players. You don't want to a feature. >> Yeah, that's great, I appreciate that perspective. And I wonder, so obviously Microsoft play in SAS, Google's got G suite. And I wonder if people often ask the Andy Jassy, you're going to move up the stack, you got to be an application, a SAS vendor, and you never say never with Atavist, But I wonder, and we were talking to Jerry Chen about this, years ago on theCube, and his angle was that Amazon will play, but they'll play through developers. They'll enable developers, and they'll participate, they'll take their, lick off the cone. So it's going to be interesting to see how directly Amazon plays, but at some point you got Tam expansion, you got to play in that space. >> Yeah, I'll give you an example of knowing, I got acquired by a couple of times by EMC. So I learned a lot from Joe Tucci and Paul Merage over the years. see Paul and Joe, what they did is to look at how 20 years, and they are very close to Boston in your area, Joe, what games did is they used to sell storage, but you know what he did, he went and bought the Apps to drive them. He bought like Legato, he bought Documentum, he bought Captiva, if you remember how he acquired all these companies as a services, he bought VMware to drive that. So I think the good angle that Microsoft has is, I'm a SAS player, I have dynamics, I have CRM, I have SharePoint, I have Collaboration, I have Office 365, MS Teams for users, and then I have the platform as Azure. So I think if I'm Amazon, (indistinct). I got to own the apps so that I can drive this workforce on my platform. >> Interesting. >> Just going to developers, like I know Jerry Chan, he was my peer a BMF. I don't think just literally to developers and that model works in open source, but the open source game is pretty much gone, and not too many companies made money. >> Well, >> Most companies pretty much gone. >> Yeah, he's right. Red hats not bad idea. But it's very interesting what you're saying there. And so, hey, its why Oracle wants to have Tiktok, running on their platform, right? I mean, it's going to. (laughing) It's going to drive that further integration. I wanted to ask you something, you were talking about, you wouldn't invest in storage or compute, but I wonder, and you mentioned some commentary about GPU's. Of course the videos has been going crazy, but they're now saying, okay, how do we expand our Team, they make the acquisition of arm, et cetera. What about this DPU thing, if you follow that, that data processing unit where they're like hyper dis-aggregation and then they reaggregate, and as an offload and really to drive data centric workloads. Have you looked at that at all? >> I did, I think, and that's a good angle. So I think, look, it's like, it goes through it. I don't know if you remember in your career, we have seen it. I used to get Silicon graphics. I saw the first graphic GPU, right? That time GPU was more graphic processor unit, >> Right, yeah, work stations. >> So then become NPUs at work processing units, right? There was a TCP/IP office offloading, if you remember right, there was like vector processing unit. So I think every once in a while the industry, recreated this separate unit, as a co-processor to the main CPU, because main CPU's inefficient, and it makes sense. And then Google created TPU's and then we have the new world of the media GPU's, now we have DPS all these are good, but what's happening is, all these are driving for machine learning, AI for the training period there. Training period Sometimes it's so long with the workloads, if you can cut down, it makes sense. >> Yeah. >> Because, but the question is, these aren't so specialized in nature. I can't use it for everything. >> Yup. >> I want Ideally, algorithms to be paralyzed, I want the training to be paralyzed, I want so having deep use and GPS are important, I think where I want to see them as more, the algorithm, there should be more investment from the NVIDIA's and these guys, taking the algorithm to be highly paralyzed them. (indistinct) And I think that still has not happened in industry yet. >> All right, so we're pretty much out of time, but what are you doing these days? Where are you spending your time, are you still in Stealth, give us a little glimpse. >> Yeah, no, I'm out of the Stealth, I'm actually the CEO of Aisera now, Aisera, obviously I invested with them, but I'm the CEO of Aisero. It's funded by Menlo ventures, Norwest, True, along with Khosla ventures and Ram Shriram is a big investor. Robin's on the board of Google, so these guys, look, we are going out to the collaboration game. How do you automate customer service and support for employees and then users, right? In this whole game, we talked about the Zoom, Slack and MS Teams, that's what I'm spending time, I want to create next generation service now. >> Fantastic. Muddu, I always love having you on you, pull punches, you tell it like it is, that you're a great visionary technologist. Thanks so much for coming on theCube, and participating in our program. >> Dave, it's always a pleasure speaking to you sir. Thank you. >> Okay. Keep it right there, there's more coming from Cuba and Cloud right after this break. (slow music)