(upbeat music) >> Welcome to this special cloud virtual event, theCUBE on cloud. This is our continuing editorial series of the most important stories in cloud. We're going to explore the cutting edge most relevant technologies and companies that will impact business and society. We have special guests from Jeff Barr, Michael Liebow, Jerry Chen, Ben Haynes, Michael skulk, Mike Feinstein from AWS all today are presenting the top startups in the AWS ecosystem. This is the AWS showcase of startups. I'm showing with Dave Vellante. Dave great to see you. >> Hey John. Great to be here. Thanks for having me. >> So awesome day today. We're going to feature a 10 grade companies amplitude, auto grid, big ID, cordial Dremio Kong, multicloud, Reltio stardog wire wheel, companies that we've talked to. We've researched. And they're going to present today from 10 for the rest of the day. What's your thoughts? >> Well, John, a lot of these companies were just sort of last decade, they really, were keyer kicker mode, experimentation mode. Now they're well on their way to hitting escape velocity which is very exciting. And they're hitting tens of millions dollars of ARR, many are planning IPO's and it's just it's really great to see what the cloud has enabled and we're going to dig into that very deeply today. So I'm super excited. >> Before we jump into the keynote (mumbles) our non Huff from AWS up on stage Jeremy is the brains behind this program that we're doing. We're going to do this quarterly. Jeremy great to see you, you're in the global startups program at AWS. Your job is to keep the crops growing, keep the startups going and keep the flow of innovation. Thanks for joining us. >> Yeah. Made it to startup showcase day. I'm super excited. And as you mentioned my team the global startup program team, we kind of provide white glove service for VC backed startups and help them with go to market activities. Co-selling with AWS and we've been looking for ways to highlight all the great work they're doing and partnering with you guys has been tremendous. You guys really know how to bring their stories to life. So super excited about all the partner sessions today. >> Well, I really appreciate the vision and working with Amazon this is like truly a bar raiser from theCUBE virtual perspective, using the virtual we can get more content, more flow and great to have you on and bring that the top hot startups around data, data ops. Certainly the most important story in tech is cloud scale with data. You you can't look around and seeing more innovation happening. So I really appreciate the work. Thanks for coming on. >> Yeah, and don't forget, we're making this a quarterly series. So the next one we've already been working on it. The next one is Wednesday, June 16th. So mark your calendars, but super excited to continue doing these showcases with you guys in the future. >> Thanks for coming on Jeremy. I really appreciate it,. Dave so I want to just quick quickly before we get Jeff up here, Jeff Barr who's a luminary guests for us this week who has been in the industry has been there from the beginning of AWS the role of data, and what's happened in cloud. And we've been watching the evolution of Amazon web services from the beginning, from the startup market to dominate in the enterprise. If you look at the top 10 enterprise companies Amazon wasn't on that list in 2010 they weren't even bringing the top 10 Andy Jassy's keynote at reinvent this past year. Highlighted that fact, I think they were number five or four as vendor in just AWS. So interesting to see that you've been reporting and doing a lot of analysis on the role of data. What's your analysis for these startups and as businesses need to embrace the new technologies and be on the right side of history not part of that old guard, incumbent failed model. >> Well, I think again, if you look back on the early days of cloud, it was really about storage and networking and compute infrastructure. And then we collected all this data and now you're seeing the next generation of innovation and value. We're going to talk to Michael Liebow about this is really if you look at all the value points in the leavers, it's all around data and data is going through a massive change in the way that we think about it, that we talk about it. And you hear that a lot. Obviously you talk about the volumes, the giant volumes but there's something else going on as AWS brings the cloud to the edge. And of course it looks at the data centers, just another edge device, data is getting highly decentralized. And what we're seeing is data getting into the hands of business owners and data product builders. I think we're going to see a new parlance emerge and that's where you're seeing the competitive advantage. And if you look at all the real winners these days in the marketplace especially in the digital with COVID, it all comes back to the data. And we're going to talk about that a lot today. >> One of the things that's coming up in all of our cube interviews, certainly we've seen, I mean we've had a great observation space across all the ecosystems, but the clear thing that's coming out of COVID is speed, agility, scale, and data. If you don't have that data you are going to be a non-player. And I think I heard some industry people talking about the future of how the stock market's going to work and that if you're not truly in market with an AI or machine learning data value play you probably will be shorted on the stock market or delisted. I think people are looking at that as a table stakes competitive advantage item, where if you don't have some sort of data competitive strategy you're going to be either delisted or sold short. And that's, I don't think delisted but the point is this table-stakes Dave. >> Well, I think too, I think the whole language the lingua franca of data is changing. We talk about data as an asset all the time, but you think about it now, what do we do with assets? We protect it, we hide it. And we kind of we don't share it. But then on the other hand, everybody talks about sharing the data and that is a huge trend in the marketplace. And so I think that everybody is really starting to rethink the whole concept of data, what it is, its value and how we think about it, talk about it, share it make it accessible, and at the same time, protect it and make it governed. And I think you're seeing, computational governance and automation really hidden. Couldn't do this without the cloud. I mean, that's the bottom line. >> Well, I'm super excited to have Jeff Barr here from AWS as our special keynote guests. I've been following Jeff's career for a long, long time. He's a luminaries, he's a technical, he's in the industry. He's part of the community, he's been there from the beginning AWS just celebrate its 15th birthday as he was blogging hard. He's been a hardcore blogger. I think Jeff, you had one of the original ping service. If I remember correctly, you were part of the web services foundational kind of present at creation. No better guests to have you Jeff thanks for coming up on our stage. >> John and Dave really happy to be here. >> So I got to ask you, you've been blogging hard for the past decade or so, going hard and your job has evolved from blogging about what's new with Amazon. A couple of building blocks a few services to last reinvent them. You must have put out I don't know how many blog posts did you put out last year at every event? I mean, it must have been a zillion. >> Not quite a zillion. I think I personally wrote somewhere between 20 and 25 including quite a few that I did in the month or so run up to reinvent and it's always intense, but it's always really, really fun. >> So I've got to ask you in the past couple of years, I mean I quoted Andy Jassy's keynote where we highlight in 2010 Amazon wasn't even on the top 10 enterprise players. Now in the top five, you've seen the evolution. What is the big takeaway from your standpoint as you look at the enterprise going from Amazon really dominating the start of a year startups today, you're in the cloud, you're born in the cloud. There's advantage to that. Now enterprises are kind of being reborn in the cloud at the same time, they're building these new use cases rejuvenating themselves and having innovation strategy. What's your takeaway? >> So I love to work with our customers and one of the things that I hear over and over again and especially the last year or two is really the value that they're placing on building a workforce that has really strong cloud skills. They're investing in education. They're focusing on this neat phrase that I learned in Australia called upskilling and saying let's take our set of employees and improve their skill base. I hear companies really saying we're going to go cloud first. We're going to be cloud native. We're going to really embrace it, adopt the full set of cloud services and APIs. And I also see that they're really looking at cloud as part of often a bigger picture. They often use the phrase digital transformation, in Amazon terms we'd say they're thinking big. They're really looking beyond where they are and who they are to what they could be and what they could grow into. Really putting a lot of energy and creativity into thinking forward in that way. >> I wonder Jeff, if you could talk about sort of how people are thinking about the future of cloud if you look at where the spending action is obviously you see it in cloud computing. We've seen that as the move to digital, serverless Lambda is huge. If you look at the data it's off the charts, machine learning and AI also up there containers and of course, automation, AWS leads in all of those. And they portend a different sort of programming model a different way of thinking about how to deploy workloads and applications maybe different than the early days of cloud. What's driving that generally and I'm interested in serverless specifically. And how do you see the next several years folding out? >> Well, they always say that the future is the hardest thing to predict but when I talked to our enterprise customers the two really big things that I see is there's this focus that says we need to really, we're not simply like hosting the website or running the MRP. I'm working with one customer in particular where they say, well, we're going to start on the factory floor all the way up to the boardroom effectively from IOT and sensors on the factory floor to feed all the data into machine learning. So they understand that the factory is running really well to actually doing planning and inventory maintenance to putting it on the website to drive the analytics, to then saying, okay, well how do we know that we're building the right product mix? How do we know that we're getting it out through the right channels? How are our customers doing? So they're really saying there's so many different services available to us in the cloud and they're relatively easy and straightforward to deploy. They really don't think in the old days as we talked about earlier that the old days where these multi-year planning and deployment cycles, now it's much more straightforward. It's like let's see what we can do today. And this week and this month, and from idea to some initial results is a much, much shorter turnaround. So they can iterate a lot more quickly which is just always known to produce better results. >> Well, Jeff and the spirit of the 15th birthday of AWS a lot of services have been built from the original three. I believe it was the core building blocks and there's been a lot of history and it's kind of like there was a key decoupling of compute from storage, those innovations what's the most important architectural change if any has happened or built upon those building blocks with AWS that you could share with companies out there as many people are coming into the cloud not just lifting and shifting and having that innovation but really building cloud native and now hybrid full cloud operations, day two operations. However you want to look at it. That's a big thing. What architecturally has changed that's been innovative from those original building blocks? >> Well, I think that the basic architecture has proven to be very, very resilient. When I wrote about the 15 year birthday of Amazon S3 a couple of weeks ago one thing that I thought was really incredible was the fact that the same APIs that you could have used 15 years ago they all still work. The put, the get, the list, the delete, the permissions management, every last one of those were chosen with extreme care. And so they all still work. So one of the things you think about when you put APIs out there is in Amazon terms we always talk about going through a one-way door and a one way door says, once you do it you're committed for the indefinite future. And so you we're very happy to do that but we take those steps with extreme care. And so those basic building blocks so the original S3 APIs, the original EC2 APIs and the model, all those things really worked. But now they're running at this just insane scale. One thing that blows me away I routinely hear my colleagues talking about petabytes and exabytes, and we throw around trillions and quadrillions like they're pennies. It's kind of amazing. Sometimes when you hear the scale of requests per day or request per month, and the orders of magnitude are you can't map them back to reality anymore. They're simply like literally astronomical. >> If I can just jump in real quick Dave before you ask Jeff, I was watching the Jeff Bezos interview in 1999 that's been going around on LinkedIn in a 60 minutes interview. The interviewer says you are reporting that you can store a gigabyte of customer data from all their purchases. What are you going to do with that? He basically nailed the answer. This is in 99. We're going to use that data to create, that was only a gig. >> Well one of the things that is interesting to me guys, is if you look at again, the early days of cloud, of course I always talked about that in small companies like ours John could have now access to information technology that only big companies could get access to. And now you've seen we just going to talk about it today. All these startups rise up and reach viability. But at the same time, Jeff you've seen big companies get the aha moment on cloud and competition drives urgency and that drives innovation. And so now you see everybody is doing cloud, it's a mandate. And so the expectation is a lot more innovation, experimentation and speed from all ends. It's really exciting to see. >> I know this sounds hackneyed and overused but it really, really still feels just like day one. We're 15 plus years into this. I still wake up every morning, like, wow what is the coolest thing that I'm going to get to learn about and write about today? We have the most amazing customers, one of the things that is great when you're so well connected to your customers, they keep telling you about their dreams, their aspirations, their use cases. And we can just take that and say we can actually build awesome things to help you address those use cases from the ground on up, from building custom hardware things like the nitro system, the graviton to the machine learning inferencing and training chips where we have such insight into customer use cases because we have these awesome customers that we can make these incredible pieces of hardware and software to really address those use cases. >> I'm glad you brought that up. This is another big change, right? You're getting the early days of cloud like, oh, Amazon they're just using off the shelf components. They're not buying these big refrigerator sized disc drives. And now you're developing all this custom Silicon and vertical integration in certain aspects of your business. And that's because workload is demanding. You've got to get more specialized in a lot of cases. >> Indeed they do. And if you watch Peter DeSantis' keynote at re-invent he talked about the fact that we're researching ways to make better cement that actually produces less carbon dioxide. So we're now literally at the from the ground on up level of construction. >> Jeff, I want to get a question from the crowd here. We got, (mumbles) who's a good friend of theCUBE cloud Arate from the beginning. He asked you, he wants to know if you'd like to share Amazon's edge aspirations. He says, he goes, I mean, roadmaps. I go, first of all, he's not going to talk about the roadmaps, but what can you share? I mean, obviously the edge is key. Outpost has been all in the news. You obviously at CloudOps is not a boundary. It's a distributed network. What's your response to-- >> Well, the funny thing is we don't generally have technology roadmaps inside the company. The roadmap is always listen really well to customers not just where they are, but the customers are just so great at saying, this is where we'd like to go. And when we hear edge, the customers don't generally come to us and say edge, they say we need as low latency as possible between where the action happens within our factory floors and our own offices and where we might be able to compute, analyze, store make decisions. And so that's resulted in things like outposts where we can put outposts in their own data center or their own field office, wavelength, where we're working with 5G telecom providers to put computing storage in the carrier hubs of the various 5G providers. Again, with reducing latency, we've been doing things like local zones, where we put zones in an increasing number of cities across the country with the goal of just reducing the average latency between the vast majority of customers and AWS resources. So instead of thinking edge, we really think in terms of how do we make sure that our customers can realize their dreams. >> Staying on the flywheel that AWS has built on ship stuff faster, make things faster, smaller, cheaper, great mission. I want to ask you about the working backwards document. I know it's been getting a lot of public awareness. I've been, that's all I've learned in interviewing Amazon folks. They always work backwards. I always mentioned the customer and all the interviews. So you've got a couple of customer references in there check the box there for you. But working backwards has become kind of a guiding principles, almost like a Harvard Business School case study approach to management. As you guys look at this working backwards and ex Amazonians have written books about it now so people can go look at, it's a really good methodology. Take us back to how you guys work back from the customers because here we're featuring 10 startups. So companies that are out there and Andy has been preaching this to customers. You should think about working backwards because it's so fast. These companies are going into this enterprise market your ecosystem of startups to provide value. What things are you seeing that customers need to think about to work backwards from their customer? How do you see that? 'Cause you've been on the community side, you see the tech side customers have to move fast and work backwards. What are the things that they need to focus on? What's your observation? >> So there's actually a brand new book called "Working Backwards," which I actually learned a lot about our own company from simply reading the book. And I think to me, a principal part of learning backward it's really about humility and being able to be a great listener. So you don't walk into a customer meeting ready to just broadcast the latest and greatest that we've been working on. You walk in and say, I'm here from AWS and I simply want to learn more about who you are, what you're doing. And most importantly, what do you want to do that we're not able to help you with right now? And then once we hear those kinds of things we don't simply write down kind of a bullet item of AWS needs to improve. It's this very active listening process. Tell me a little bit more about this challenge and if we solve it in this way or this way which one's a better fit for your needs. And then a typical AWS launch, we might talk to between 50 and 100 customers in depth to make sure that we have that detailed understanding of what they would like to do. We can't always meet all the needs of these customers but the idea is let's see what is the common base that we can address first. And then once we get that first iteration out there, let's keep listening, let's keep making it better and better and better as quickly. >> A lot of people might poopoo that John but I got to tell you, John, you will remember this the first time we ever met Andy Jassy face-to-face. I was in the room, you were on the speaker phone. We were building an app on AWS at the time. And he was asking you John, for feedback. And he was probing and he pulled out his notebook. He was writing down and he wasn't just superficial questions. He was like, well, why'd you do it that way? And he really wanted to dig. So this is cultural. >> Yeah. I mean, that's the classic Amazon. And that's the best thing about it is that you can go from zero startups zero stage startup to traction. And that was the premise of the cloud. Jeff, I want to get your thoughts and commentary on this love to get your opinion. You've seen this grow from the beginning. And I remember 'cause I've been playing with AWS since the beginning as well. And it says as an entrepreneur I remember my first EC2 instance that didn't even have custom domain support. It was the long URL. You seen the startups and now that we've been 15 years in, you see Dropbox was it just a startup back in the day. I remember these startups that when they were coming they were all born on Amazon, right? These big now unicorns, you were there when these guys were just developers and these gals. So what's it like, I mean, you see just the growth like here's a couple of people with them ideas rubbing nickels together, making magic happen who knows what's going to turn into, you've been there. What's it been like? >> It's been a really unique journey. And to me like the privilege of a lifetime, honestly I've like, you always want to be part of something amazing and you aspire to it and you study hard and you work hard and you always think, okay, somewhere in this universe something really cool is about to happen. And if you're really, really lucky and just a million great pieces of luck like lineup in series, sometimes it actually all works out and you get to be part of something like this when it does you don't always fully appreciate just how awesome it is from the inside, because you're just there just like feeding the machine and you are just doing your job just as fast as you possibly can. And in my case, it was listening to teams and writing blog posts about their launches and sharing them on social media, going out and speaking, you do it, you do it as quickly as possible. You're kind of running your whole life as you're doing that as well. And suddenly you just take a little step back and say, wow we did this kind of amazing thing, but we don't tend to like relax and say, okay, we've done it at Amazon. We get to a certain point. We recognize it. And five minutes later, we're like, okay, let's do the next amazingly good thing. But it's been this just unique privilege and something that I never thought I'd be fortunate enough to be a part of. >> Well, then the last few minutes we have Jeff I really appreciate you taking the time to spend with us for this inaugural launch of theCUBE on cloud startup showcase. We are showcasing 10 startups here from your ecosystem. And a lot of people who know AWS for the folks that don't you guys pride yourself on community and ecosystem the global startups program that Jeremy and his team are running. You guys nurture these startups. You want them to be successful. They're vectoring out into the marketplace with growth strategy, helping customers. What's your take on this ecosystem? As customers are out there listening to this what's your advice to them? How should they engage? Why is these sets of start-ups so important? >> Well, I totally love startups and I've spent time in several startups. I've spent other time consulting with them. And I think we're in this incredible time now wheres, it's so easy and straightforward to get those basic resources, to get your compute, to get your storage, to get your databases, to get your machine learning and to take that and to really focus on your customers and to build what you want. And we see this actual exponential growth. And we see these startups that find something to do. They listen to one of their customers, they build that solution. And they're just that feedback cycle gets started. It's really incredible. And I love to see the energy of these startups. I love to hear from them. And at any point if we've got an AWS powered startup and they build something awesome and want to share it with me, I'm all ears. I love to hear about them. Emails, Twitter mentions, whatever I'll just love to hear about all this energy all those great success with our startups. >> Jeff Barr, thank you for coming on. And congratulations, please pass on to Andy Jassy who's going to take over for Jeff Bezos and I saw the big news that he's picking a successor an Amazonian coming back into the fold, Adam. So congratulations on that. >> I will definitely pass on your congratulations to Andy and I worked with Adam in the past when AWS was just getting started and really looking forward to seeing him again, welcoming back and working with him. >> All right, Jeff Barr with AWS guys check out his Twitter and all the social coordinates. He is pumping out all the resources you need to know about if you're a developer or you're an enterprise looking to go to the next level, next generation, modern infrastructure. Thanks Jeff for coming on. Really appreciate it. Our next guests want to bring up stage Michael Liebow from McKinsey cube alumni, who is a great guest who is very timely in his McKinsey role with a paper he and his colleagues put out called cloud's trillion dollar prize up for grabs. Michael, thank you for coming up on stage with Dave and I. >> Hey, great to be here, John. Thank you. >> One of the things I loved about this and why I wanted you to come on was not only is the report awesome. And Dave has got a zillion questions, he want us to drill into. But in 2015, we wrote a story called Andy Jassy trillion dollar baby on Forbes, and then on medium and silken angle where we were the first ones to profile Andy Jassy and talk about this trillion dollar term. And Dave came up with the calculation and people thought we were crazy. What are you talking about trillion dollar opportunity. That was in 2015. You guys have put this together with a serious research report with methodology and you left a lot on the table. I noticed in the report you didn't even have a whole section quantified. So I think just scratching the surface trillion. I'd be a little light, Dave, so let's dig into it, Michael thanks for coming on. >> Well, and I got to say, Michael that John's a trillion dollar baby was revenue. Yours is EBITDA. So we're talking about seven to X, seven to eight X. What we were talking back then, but great job on the report. Fantastic work. >> Thank you. >> So tell us about the report gives a quick lowdown. I got some questions. You guys are unlocking the value drivers but give us a quick overview of this report that people can get for free. So everyone who's registered will get a copy but give us a quick rundown. >> Great. Well the question I think that has bothered all of us for a long time is what's the business value of cloud and how do you quantify it? How do you specify it? Because a lot of people talk around the infrastructure or technical value of cloud but that actually is a big problem because it just scratches the surface of the potential of what cloud can mean. And we focus around the fortune 500. So we had to box us in somewhat. And so focusing on the fortune 500 and fast forwarding to 2030, we put out this number that there's over a trillion dollars worth of value. And we did a lot of analysis using research from a variety of partners, using third-party research, primary research in order to come up with this view. So the business value is two X the technical value of cloud. And as you just pointed out, there is a whole unlock of additional value where organizations can pioneer on some of the newest technologies. And so AWS and others are creating platforms in order to do not just machine learning and analytics and IOT, but also for quantum or mixed reality for blockchain. And so organizations specific around the fortune 500 that aren't leveraging these capabilities today are going to get left behind. And that's the message we were trying to deliver that if you're not doing this and doing this with purpose and with great execution, that others, whether it's others in your industry or upstarts who were motioning into your industry, because as you say cloud democratizes compute, it provides these capabilities and small companies with talent. And that's what the skills can leverage these capabilities ahead of slow moving incumbents. And I think that was the critical component. So that gives you the framework. We can deep dive based on your questions. >> Well before we get into the deep dive, I want to ask you we have startups being showcased here as part of the, it will showcase, they're coming out of the ecosystem. They have a lot of certification from Amazon and they're secure, which is a big issue. Enterprises that you guys talk to McKinsey speaks directly to I call the boardroom CXOs, the top executives. Are they realizing that the scale and timing of this agility window? I mean, you want to go through these key areas that you would break out but as startups become more relevant the boardrooms that are making these big decisions realize that their businesses are up for grabs. Do they realize that all this wealth is shifting? And do they see the role of startups helping them? How did you guys come out of them and report on that piece? >> Well in terms of the whole notion, we came up with this framework which looked at the opportunity. We talked about it in terms of three dimensions, rejuvenate, innovate and pioneer. And so from the standpoint of a board they're more than focused on not just efficiency and cost reduction basically tied to nation, but innovation tied to analytics tied to machine learning, tied to IOT, tied to two key attributes of cloud speed and scale. And one of the things that we did in the paper was leverage case examples from across industry, across-region there's 17 different case examples. My three favorite is one is Moderna. So software for life couldn't have delivered the vaccine as fast as they did without cloud. My second example was Goldman Sachs got into consumer banking is the platform behind the Apple card couldn't have done it without leveraging cloud. And the third example, particularly in early days of the pandemic was Zoom that added five to 6,000 servers a night in order to scale to meet the demand. And so all three of those examples, plus the other 14 just indicate in business terms what the potential is and to convince boards and the C-suite that if you're not doing this, and we have some recommendations in terms of what CEOs should do in order to leverage this but to really take advantage of those capabilities. >> Michael, I think it's important to point out the approach at sometimes it gets a little wonky on the methodology but having done a lot of these types of studies and observed there's a lot of superficial studies out there, a lot of times people will do, they'll go I'll talk to a customer. What kind of ROI did you get? And boom, that's the value study. You took a different approach. You have benchmark data, you talked to a lot of companies. You obviously have a lot of financial data. You use some third-party data, you built models, you bounded it. And ultimately when you do these things you have to ascribe a value contribution to the cloud component because fortunate 500 companies are going to grow even if there were no cloud. And the way you did that is again, you talk to people you model things, and it's a very detailed study. And I think it's worth pointing out that this was not just hey what'd you get from going to cloud before and after. This was a very detailed deep dive with really a lot of good background work going into it. >> Yeah, we're very fortunate to have the McKinsey Global Institute which has done extensive studies in these areas. So there was a base of knowledge that we could leverage. In fact, we looked at over 700 use cases across 19 industries in order to unpack the value that cloud contributed to those use cases. And so getting down to that level of specificity really, I think helps build it from the bottom up and then using cloud measures or KPIs that indicate the value like how much faster you can deploy, how much faster you can develop. So these are things that help to kind of inform the overall model. >> Yeah. Again, having done hundreds, if not thousands of these types of things, when you start talking to people the patterns emerge, I want to ask you there's an exhibit tool in here, which is right on those use cases, retail, healthcare, high-tech oil and gas banking, and a lot of examples. And I went through them all and virtually every single one of them from a value contribution standpoint the unlocking value came down to data large data sets, document analysis, converting sentiment analysis, analytics. I mean, it really does come down to the data. And I wonder if you could comment on that and why is it that cloud is enabled that? >> Well, it goes back to scale. And I think the word that I would use would be data gravity because we're talking about massive amounts of data. So as you go through those kind of three dimensions in terms of rejuvenation one of the things you can do as you optimize and clarify and build better resiliency the thing that comes into play I think is to have clean data and data that's available in multiple places that you can create an underlying platform in order to leverage the services, the capabilities around, building out that structure. >> And then if I may, so you had this again I want to stress as EBITDA. It's not a revenue and it's the EBITDA potential as a result of leveraging cloud. And you listed a number of industries. And I wonder if you could comment on the patterns that you saw. I mean, it doesn't seem to be as simple as Negroponte bits versus Adam's in terms of your ability to unlock value. What are the patterns that you saw there and why are the ones that have so much potential why are they at the top of the list? >> Well, I mean, they're ranked based on impact. So the five greatest industries and again, aligned by the fortune 500. So it's interesting when you start to unpack it that way high-tech oil, gas, retail, healthcare, insurance and banking, right? Top. And so we did look at the different solutions that were in that, tried to decipher what was fully unlocked by cloud, what was accelerated by cloud and what was perhaps in this timeframe remaining on premise. And so we kind of step by step, expert by expert, use case by use case deciphered of the 700, how that applied. >> So how should practitioners within organizations business but how should they use this data? What would you recommend, in terms of how they think about it, how they apply it to their business, how they communicate? >> Well, I think clearly what came out was a set of best practices for what organizations that were leveraging cloud and getting the kind of business return, three things stood out, execution, experience and excellence. And so for under execution it's not just the transaction, you're not just buying cloud you're changing their operating model. And so if the organization isn't kind of retooling the model, the processes, the workflows in order to support creating the roles then they aren't going to be able, they aren't going to be successful. In terms of experience, that's all about hands-on. And so you have to dive in, you have to start you have to apply yourself, you have to gain that applied knowledge. And so if you're not gaining that experience, you're not going to move forward. And then in terms of excellence, and it was mentioned earlier by Jeff re-skilling, up-skilling, if you're not committed to your workforce and pushing certification, pushing training in order to really evolve your workforce or your ways of working you're not going to leverage cloud. So those three best practices really came up on top in terms of what a mature cloud adopter looks like. >> That's awesome. Michael, thank you for coming on. Really appreciate it. Last question I have for you as we wrap up this trillion dollar segment upon intended is the cloud mindset. You mentioned partnering and scaling up. The role of the enterprise and business is to partner with the technologists, not just the technologies but the companies talk about this cloud native mindset because it's not just lift and shift and run apps. And I have an IT optimization issue. It's about innovating next gen solutions and you're seeing it in public sector. You're seeing it in the commercial sector, all areas where the relationship with partners and companies and startups in particular, this is the startup showcase. These are startups are more relevant than ever as the tide is shifting to a new generation of companies. >> Yeah, so a lot of think about an engine. A lot of things have to work in order to produce the kind of results that we're talking about. Brad, you're more than fair share or unfair share of trillion dollars. And so CEOs need to lead this in bold fashion. Number one, they need to craft the moonshot or the Marshot. They have to set that goal, that aspiration. And it has to be a stretch goal for the organization because cloud is the only way to enable that achievement of that aspiration that's number one, number two, they really need a hardheaded economic case. It has to be defined in terms of what the expectation is going to be. So it's not loose. It's very, very well and defined. And in some respects time box what can we do here? I would say the cloud data, your organization has to move in an agile fashion training DevOps, and the fourth thing, and this is where the startups come in is the cloud platform. There has to be an underlying platform that supports those aspirations. It's an art, it's not just an architecture. It's a living, breathing live service with integrations, with standardization, with self service that enables this whole program. >> Awesome, Michael, thank you for coming on and sharing the McKinsey perspective. The report, the clouds trillion dollar prize is up for grabs. Everyone who's registered for this event will get a copy. We will appreciate it's also on the website. We'll make sure everyone gets a copy. Thanks for coming, I appreciate it. Thank you. >> Thanks, Michael. >> Okay, Dave, big discussion there. Trillion dollar baby. That's the cloud. That's Jassy. Now he's going to be the CEO of AWS. They have a new CEO they announced. So that's going to be good for Amazon's kind of got clarity on the succession to Jassy, trusted soldier. The ecosystem is big for Amazon. Unlike Microsoft, they have the different view, right? They have some apps, but they're cultivating as many startups and enterprises as possible in the cloud. And no better reason to change gears here and get a venture capitalist in here. And a friend of theCUBE, Jerry Chen let's bring them up on stage. Jerry Chen, great to see you partner at Greylock making all the big investments. Good to see you >> John hey, Dave it's great to be here with you guys. Happy marks.Can you see that? >> Hey Jerry, good to see you man >> So Jerry, our first inaugural AWS startup showcase we'll be doing these quarterly and we're going to be featuring the best of the best, you're investing in all the hot startups. We've been tracking your careers from the beginning. You're a good friend of theCUBE. Always got great commentary. Why are startups more important than ever before? Because in the old days we've talked about theCUBE before startups had to go through certain certifications and you've got tire kicking, you got to go through IT. It's like going through security at the airport, take your shoes off, put your belt on thing. I mean, all kinds of things now different. The world has changed. What's your take? >> I think startups have always been a great way for experimentation, right? It's either new technologies, new business models, new markets they can move faster, the experiment, and a lot of startups don't work, unfortunately, but a lot of them turned to be multi-billion dollar companies. I thing startup is more important because as we come out COVID and economy is recovery is a great way for individuals, engineers, for companies for different markets to try different things out. And I think startups are running multiple experiments at the same time across the globe trying to figure how to do things better, faster, cheaper. >> And McKinsey points out this use case of rejuvenate, which is essentially retool pivot essentially get your costs down or and the next innovation here where there's Tam there's trillion dollars on unlock value and where the bulk of it is is the innovation, the new use cases and existing new use cases. This is where the enterprises really have an opportunity. Could you share your thoughts as you invest in the startups to attack these new waves these new areas where it may not look the same as before, what's your assessment of this kind of innovation, these new use cases? >> I think we talked last time about kind of changing the COVID the past year and there's been acceleration of things like how we work, education, medicine all these things are going online. So I think that's very clear. The first wave of innovation is like, hey things we didn't think we could be possible, like working remotely, e-commerce everywhere, telemedicine, tele-education, that's happening. I think the second order of fact now is okay as enterprises realize that this is the new reality everything is digital, everything is in the cloud and everything's going to be more kind of electronic relation with the customers. I think that we're rethinking what does it mean to be a business? What does it mean to be a bank? What does it mean to be a car company or an energy company? What does it mean to be a retailer? Right? So I think the rethinking that brands are now global, brands are all online. And they now have relationships with the customers directly. So I think if you are a business now, you have to re experiment or rethink about your business model. If you thought you were a Nike selling shoes to the retailers, like half of Nike's revenue is now digital right all online. So instead of selling sneakers through stores they're now a direct to consumer brand. And so I think every business is going to rethink about what the AR. Airbnb is like are they in the travel business or the experience business, right? Airlines, what business are they in? >> Yeah, theCUBE we're direct to consumer virtual totally opened up our business model. Dave, the cloud premise is interesting now. I mean, let's reset this where we are, right? Andy Jassy always talks about the old guard, new guard. Okay we've been there done that, even though they still have a lot of Oracle inside AWS which we were joking the other day, but this new modern era coming out of COVID Jerry brings this up. These startups are going to be relevant take territory down in the enterprises as new things develop. What's your premise of the cloud and AWS prospect? >> Well, so Jerry, I want to to ask you. >> Jerry: Yeah. >> The other night, last Thursday, I think we were in Clubhouse. Ben Horowitz was on and Martine Casado was laying out this sort of premise about cloud startups saying basically at some point they're going to have to repatriate because of the Amazon VIG. I mean, I'm paraphrasing and I guess the premise was that there's this variable cost that grows as you scale but I kind of shook my head and I went back. You saw, I put it out on Twitter a clip that we had the a couple of years ago and I don't think, I certainly didn't see it that way. Maybe I'm getting it wrong but what's your take on that? I just don't see a snowflake ever saying, okay we're going to go build our own data center or we're going to repatriate 'cause they're going to end up like service now and have this high cost infrastructure. What do you think? >> Yeah, look, I think Martin is an old friend from VMware and he's brilliant. He has placed a lot of insights. There is some insights around, at some point a scale, use of startup can probably run things more cost-effectively in your own data center, right? But I think that's fewer companies more the vast majority, right? At some point, but number two, to your point, Dave going on premise versus your own data center are two different things. So on premise in a customer's environment versus your own data center are two different worlds. So at some point some scale, a lot of the large SaaS companies run their own data centers that makes sense, Facebook and Google they're at scale, they run their own data centers, going on premise or customer's environment like a fortune 100 bank or something like that. That's a different story. There are reasons to do that around compliance or data gravity, Dave, but Amazon's costs, I don't think is a legitimate reason. Like if price is an issue that could be solved much faster than architectural decisions or tech stacks, right? Once you're on the cloud I think the thesis, the conversation we had like a year ago was the way you build apps are very different in the cloud and the way built apps on premise, right? You have assume storage, networking and compute elasticity that's independent each other. You don't really get that in a customer's data center or their own environment even with all the new technologies. So you can't really go from cloud back to on-premise because the way you build your apps look very, very different. So I would say for sure at some scale run your own data center that's why the hyperscale guys do that. On-premise for customers, data gravity, compliance governance, great reasons to go on premise but for vast majority of startups and vast majority of customers, the network effects you get for being in the cloud, the network effects you get from having everything in this alas cloud service I think outweighs any of the costs. >> I couldn't agree more and that's where the data is, at the way I look at it is your technology spend is going to be some percentage of revenue and it's going to be generally flat over time and you're going to have to manage it whether it's in the cloud or it's on prem John. >> Yeah, we had a quote on theCUBE on the conscious that had Jerry I want to get your reaction to this. The executive said, if you don't have an AI strategy built into your value proposition you will be shorted as a stock on wall street. And I even went further. So you'll probably be delisted cause you won't be performing with a tongue in cheek comment. But the reality is that that's indicating that everyone has to have AI in their thing. Mainly as a reality, what's your take on that? I know you've got a lot of investments in this area as AI becomes beyond fashion and becomes table stakes. Where are we on that spectrum? And how does that impact business and society as that becomes a key part of the stack and application stack? >> Yeah, I think John you've seen AI machine learning turn out to be some kind of novelty thing that a bunch of CS professors working on years ago to a funnel piece of every application. So I would say the statement of the sentiment's directionally correct that 20 years ago if you didn't have a web strategy or a website as a company, your company be sure it, right? If you didn't have kind of a internet website, you weren't real company. Likewise, if you don't use AI now to power your applications or machine learning in some form or fashion for sure you'd be at a competitive disadvantage to everyone else. And just like if you're not using software intelligently or the cloud intelligently your stock as a company is going to underperform the rest of the market. And the cloud guys on the startups that we're backing are making AI so accessible and so easy for developers today that it's really easy to use some level of machine learning, any applications, if you're not doing that it's like not having a website in 1999. >> Yeah. So let's get into that whole operation side. So what would you be your advice to the enterprises that are watching and people who are making decisions on architecture and how they roll out their business model or value proposition? How should they look at AI and operations? I mean big theme is day two operations. You've got IT service management, all these things are being disrupted. What's the operational impact to this? What's your view on that? >> So I think two things, one thing that you and Dave both talked about operation is the key, I mean, operations is not just the guts of the business but the actual people running the business, right? And so we forget that one of the values are going to cloud, one of the values of giving these services is you not only have a different technology stack, all the bits, you have a different human stack meaning the people running your cloud, running your data center are now effectively outsource to Amazon, Google or Azure, right? Which I think a big part of the Amazon VIG as Dave said, is so eloquently on Twitter per se, right? You're really paying for those folks like carry pagers. Now take that to the next level. Operations is human beings, people intelligently trying to figure out how my business can run better, right? And that's either accelerate revenue or decrease costs, improve my margin. So if you want to use machine learning, I would say there's two areas to think about. One is how I think about customers, right? So we both talked about the amount of data being generated around enterprise individuals. So intelligently use machine learning how to serve my customers better, then number two AI and machine learning internally how to run my business better, right? Can I take cost out? Can I optimize supply chain? Can I use my warehouses more efficiently my logistics more efficiently? So one is how do I use AI learning to be a more familiar more customer oriented and number two, how can I take cost out be more efficient as a company, by writing AI internally from finance ops, et cetera. >> So, Jerry, I wonder if I could ask you a little different subject but a question on tactical valuations how coupled or decoupled are private company valuations from the public markets. You're seeing the public markets everybody's freaking out 'cause interest rates are going to go up. So the future value of cash flows are lower. Does that trickle in quickly into the private markets? Or is it a whole different dynamic? >> If I could weigh in poly for some private markets Dave I would have a different job than I do today. I think the reality is in the long run it doesn't matter as much as long as you're investing early. Now that's an easy answer say, boats have to fall away. Yes, interest rates will probably go up because they're hard to go lower, right? They're effectively almost zero to negative right now in most of the developed world, but at the end of the day, I'm not going to trade my Twilio shares or Salesforce shares for like a 1% yield bond, right? I'm going to hold the high growth tech stocks because regardless of what interest rates you're giving me 1%, 2%, 3%, I'm still going to beat that with a top tech performers, Snowflake, Twilio Hashi Corp, bunch of the private companies out there I think are elastic. They're going to have a great 10, 15 year run. And in the Greylock portfolio like the things we're investing in, I'm super bullish on from Roxanne to Kronos fear, to true era in the AI space. I think in the long run, next 10 years these things will outperform the market that said, right valuation prices have gone up and down and they will in our careers, they have. In the careers we've been covering tech. So I do believe that they're high now they'll come down for sure. Will they go back up again? Definitely, right? But as long as you're betting these macro waves I think we're all be good. >> Great answer as usual. Would you trade them for NFTs Jerry? >> That $69 million people piece of artwork look, I mean, I'm a longterm believer in kind of IP and property rights in the blockchain, right? And I'm waiting for theCUBE to mint this video as the NFT, when we do this guys, we'll mint this video's NFT and see how much people pay for the original Dave, John, Jerry (mumbles). >> Hey, you know what? We can probably get some good bang for that. Hey it's all about this next Jerry. Jerry, great to have you on, final question as we got this one minute left what's your advice to the people out there that either engaging with these innovative startups, we're going to feature startups every quarter from the in the Amazon ecosystem, they are going to be adding value. What's the advice to the enterprises that are engaging startups, the approach, posture, what's your advice. >> Yeah, when I talk to CIOs and large enterprises, they often are wary like, hey, when do I engage a startup? How, what businesses, and is it risky or low risk? Now I say, just like any career managing, just like any investment you're making in a big, small company you should have a budget or set of projects. And then I want to say to a CIO, Hey, every priority on your wish list, go use the startup, right? I mean, that would be 10 for 10 projects, 10 startups. Probably too much risk for a lot of tech companies. But we would say to most CIOs and executives, look, there are strategic initiatives in your business that you want to accelerate. And I would take the time to invest in one or two startups each quarter selectively, right? Use the time, focus on fewer startups, go deep with them because we can actually be game changers in terms of inflecting your business. And what I mean by that is don't pick too many startups because you can't devote the time, but don't pick zero startups because you're going to be left behind, right? It'd be shorted as a stock by the John, Dave and Jerry hedge fund apparently but pick a handful of startups in your strategic areas, in your top tier three things. These really, these could be accelerators for your career. >> I have to ask you real quick while you're here. We've got a couple minutes left on startups that are building apps. I've seen DevOps and the infrastructure as code movement has gone full mainstream. That's really what we're living right now. That kind of first-generation commercialization of DevOps. Now DevSecOps, what are the trends that you've seen that's different from say a couple of years ago now that we're in COVID around how apps are being built? Is it security? Is it the data integration? What can you share as a key app stack impact (mumbles)? >> Yeah, I think there're two things one is security is always been a top priority. I think that was the only going forward period, right? Security for sure. That's why you said that DevOps, DevSecOps like security is often overlooked but I think increasingly could be more important. The second thing is I think we talked about Dave mentioned earlier just the data around customers, the data on premise or the cloud, and there's a ton of data out there. We keep saying this over and over again like data's new oil, et cetera. It's evolving and not changing because the way we're using data finding data is changing in terms of sources of data we're using and discovering and also speed of data, right? In terms of going from Basser real-time is changing. The speed of business has changed to go faster. So I think these are all things that we're thinking about. So both security and how you use your data faster and better. >> Yeah you were in theCUBE a number of years ago and I remember either John or I asked you about you think Amazon is going to go up the stack and start developing applications and your answer was you know what I think no, I think they're going to enable a new set of disruptors to come in and disrupt the SaaS world. And I think that's largely playing out. And one of the interesting things about Adam Selipsky appointment to the CEO, he comes from Tableau. He really helped Tableau go from that sort of old guard model to an ARR model obviously executed a great exit to Salesforce. And now I see companies like Salesforce and service now and Workday is potential for your scenario to really play out. They've got in my view anyway, outdated pricing models. You look at what's how Snowflake's pricing and the consumption basis, same with Datadog same with Stripe and new startups seem to really be a leading into the consumption-based pricing model. So how do you, what are your thoughts on that? And maybe thoughts on Adam and thoughts on SaaS disruption? >> I think my thesis still holds that. I don't think Selipsky Adam is going to go into the app space aggressively. I think Amazon wants to enable next generation apps and seeing some of the new service that they're doing is they're kind of deconstructing apps, right? They're deconstructing the parts of CRM or e-commerce and they're offering them as services. So I think you're going to see Amazon continue to say, hey we're the core parts of an app like payments or custom prediction or some machine learning things around applications you want to buy bacon, they're going to turn those things to the API and sell those services, right? So you look at things like Stripe, Twilio which are two of the biggest companies out there. They're not apps themselves, they're the components of the app, right? Either e-commerce or messaging communications. So I can see Amazon going down that path. I think Adam is a great choice, right? He was a longterm early AWS exact from the early days latent to your point Dave really helped take Tableau into kind of a cloud business acquired by Salesforce work there for a few years under Benioff the guy who created quote unquote cloud and now him coming home again and back to Amazon. So I think it'll be exciting to see how Adam runs the business. >> And John I think he's the perfect choice because he's got operations chops and he knows how to... He can help the startups disrupt. >> Yeah, and he's been a trusted soldier of Jassy from the beginning, he knows the DNA. He's got some CEO outside experience. I think that was the key he knows. And he's not going to give up Amazon speed, but this is baby, right? So he's got him in charge and he's a trusted lieutenant. >> You think. Yeah, you think he's going to hold the mic? >> Yeah. We got to go. Jerry Chen thank you very much for coming on. Really appreciate it. Great to see you. Thanks for coming on our inaugural cube on cloud AWS startup event. Now for the 10 startups, enjoy the sessions at 12:30 Pacific, we're going to have the closing keynote. I'm John Ferry for Dave Vellante and our special guests, thanks for watching and enjoy the rest of the day and the 10 startups. (upbeat music)

(upbeat music) >> Welcome to today's session of the AWS Startup Showcase, featuring Dremio. I'm your host, Lisa Martin. And today we're joined by Robert Maybin, Principal Architect at Dremio. Robert is going to talk to us about democratizing your data by eliminating data copies. Robert, welcome. It's great to have you in today's session. >> Great. Thank you, Lisa. It's great to be here. >> So talk to me a little bit about why data copies, as Dremio says, are the key obstacle to data democratization? >> Oh, sure. Sure. Well, I think when you think about data democratization and really what that means, what people mean when they talk about data democratization, what they're really speaking to is kind of the desire for people in the organization to be able to, you know, work with the enterprises data, discover data, really, in a more self-service way. And you know, when you think about democratization, you might say, "Well, what's wrong with copies? What could be more democratic than giving everybody their own copy of the data?" But I think when you really think about that and how that ties into, you know, traditional architectures and environments, there are a lot of problems that come with copies, and those are real impediments. And so, you know, traditionally, in the data warehousing world, what often happens is that there are numerous sources of data that are coming in in all different formats, all different structures. These things, typically, for people that query them, have got to be, you know, loaded into some sort of a data warehousing tool. You know, maybe they land in cloud storage, but before they can be queried, you know, somebody has to go in and basically reformat those data sets, transform them in ways that make them more useful and make them more performant. And so this is very, very common. Like I think many, many organizations do this, and it makes a lot of sense to do it, because, you know, traditionally, the formats of the data is sourced in is pretty hard to work with and it's very slow to query. So copies is kind of a natural thing to do, but it comes at a real cost, right? There's a tremendous complexity that can come about, and having to do all these transformations. There's a real dollar cost, and there's a lot of time involved too. So, you know, if you could kind of take all of these middle steps out, where you're copying and transforming, and then transforming again, and then, potentially, persisting very high-performance structures for fast BI queries, you can reduce a lot of those impediments. >> So talk to me about... Oh, I'm sorry. Go ahead. >> Go ahead. >> I was just going to say, you know, of the things that is even in more demand now is the need for real time data access. I think real-time is no longer a nice-to-have. And I think what we've been through the last year has really shown that. So given the legacy architectures and some of the challenges with copies being an obstacle to that true democratization, how can data teams actually get in there and solve this challenge? >> Yeah, so, you know, I think going back a little bit to the prior question, and I can fill out a little bit more of the detail, and that'll lead us to your point, that one of the things that is also really born as a cost, when you have to go through and make multiple copies, is that, you know, typically you need experts in the organization, who are the ones who are going to, you know, write the ETL scripts, or, you know, kind of do the data architecture and design the structures that have to be performant for real-time BI queries, right? So typically these take the form of things like, you know, OLAP cubes, or, you know, big flattened data structures with all of the attributes joined in, or there's a lot of different ways that you can get query performance. Typically that's not available directly against the source data. So, you know, one of the things that data teams can do, and, you know, there's really two ways to go about this, right? One is you can really go all in on the data copy approach, and kind of home grow or build yourself a lot of the automation and tooling, and, you know, parts that it would take to basically transform the data. You can build UIs for people to go in, and kind of request data, and you can automate this whole process. And we found that a number of large organizations have actually gone this route. And they've kind of been at these projects for, in some cases, years, and they're still not completely there. And so I wouldn't really recommend that approach. I think that the real approach, and this is really available today with kind of the the rise of cloud technologies, is that we can shift our thinking a bit, right? And so we can think about how do we take some of these, you know, features and capabilities that one would expect in a data warehousing environment, and how can we bring that directly to the data? So, you know, with the shift in thinking, it requires kind of new technology to do this, right? So if you could imagine a lot of these traditional data warehousing features, like interactive speed, and, you know, the ability to kind of build structures, or, you know, views or things on top of your data, but do that directly on the data itself without having to transform and copy, transform and copy. So that's really something that we kind of call the next generation data lake architecture, is bringing those capabilities directly to the data that's on the lake. >> So leaving the data where it is, next generation is a term like future-ready, that's used a lot. Let's unpack that and dig into why what you're talking about is the next generation data lake architecture. >> Sure, sure. And I think to talk about that, the first thing that we really have to discuss is, really, a fundamental shift in technologies that's come about really in the last few years. So, you know, as really cloud services, like AWS, who've have risen to prominence, there are some capabilities that are available to us now that just weren't, you know, three, four or five years ago. And so what we can do now is that we have the ability to truly separate compute and storage, connected together with really fast networking. And we can, you know, provision storage, and we can provision compute. And from the perspective of the user, those two things can basically be scaled infinitely, right? And if you contrast that with what used to have to happen, or what we used to have to do in platforms like Hadoop or in scale-out MPP data warehouses, is that we didn't have, not only the the flexibility to scale compute and storage independently, but we didn't have the kind of networking that we have today. And so it was a requirement to take, you know, basically the compute, and push it as close to the data as we could, which is what you would get in a large Hadoop cluster. You've got, you know, nodes, which have compute right next to the storage, and you try to push as much work as you can onto each node before you start to transfer the data to other nodes for further processing. And now what we've got with some of the new cloud technology is the ability to, basically, do away with that requirement, right? So now we can have very, very large provision pools of data that can grow and grow and grow, really, without the limitations of nodes of hardware. And we can spin up and down compute process that. And the thing that we need, though, is a way of processing it, a query processing engine that's built for those dynamics, right? That's built, so that it performs really, really well when compute and storage are decoupled. So I think that that's really the trick, is that once we really, you know, come into the fact that we've got this new paradigm with separate compute, separate storage, very fast networking, if we start to look for technologies that can scale out and back, and do really performance query in that environment, then that's really what we're talking about. Now, I think the very last piece, and what I would call kind of next gen data lake architecture, is very common even today for organizations to have a data lake, right? That contains a tremendous amount of data, but in order to do actual BI queries at that interactive speed that people expect, they still have to take portions of the data from the lake and go load it into a warehouse, right? And then probably from there build, you know, OLAP cubes, or, you know, extracts into a BI tool. So the last piece, really, in the next gen data lake architecture puzzle, is once you've got that fast query engine foundation, how do you then move those interactive workloads into that platform, so they don't have to be in a data warehouse, right? How do you take some of those data warehousing expectations and put those into a platform that can query data directly? So that that's really what the next generation means to us. >> So let's talk about Dremio now. I see that just in January of 2021, Series D funding of $135 million. And then I saw that Datanami actually coined Dremio as a unicorn, as it's reached a $1 billion valuation. Talk to us about what Dremio is, and how you're part of this modern data architecture. >> Absolutely. Yeah. So, you know, you can think about Dremio as a... You know, in the technology context, really, is solving that problem that I just laid out, which is we're in the business of, you know, building technology that allows users to query very large data sets in a scale-out, very performant way, you know, directly on the data where it lives. So there's no real need for data movement. And in fact, we can also not only query one source of data, but we can query multiple sources of data, and, you know, join those things together in the context of the same query. So, you know, you may have most of your data in a data lake, but then you may have some relational sources. So there's a potent story there, in that you don't have to consolidate all of your data into one place. You don't have to load all of your data into, you know, a data warehouse or a cloud data warehouse. You can query it where it is. That's the first piece. I think the next piece that the Dremio provides is kind of, as we mentioned before, we're giving almost a data warehouse-like user experience in terms of very, very fast response times for things like BI dashboards, right? So really interactive queries. And the ability to do things, like you would normally expect to do inside a warehouse. So you can, you know, create schemas, for instance, you can create layers of views and accelerations, and effectively allow users to build out virtually in the form of views, what they would have done before with all of their various ETL pipelines, to, you know, scrub and prepare and transform the data to get it in shape to query. And at the very end, what we can do is selectively, kind of in an internally managed way, we can accelerate certain query patterns by creating something that we call reflections, which is an internally managed, you know, persistence of data that accelerates certain queries, but it's entirely internally managed by Dremio. The user doesn't have to worry with anything to do with setup, or configuration, or clean up, or maintenance, or any of that stuff. >> So does reflections really provide a differentiator for Dremio, if you look in the market and you see competitors, like Snowflake, SingleStore, for example, is this really kind of that competitive differentiator? >> I think it's one of them. I think the ability to create reflections is it's certainly a differentiator, because what it allows is it allows you to basically accelerate different kinds of query patterns against the same underlying source data, right? So rather than have to go build a transformation for a user, that, you know, potentially aggregates data a certain way, and persist that somewhere, and have to build all the machinery to do that and maintain it, in Dremio, literally, it's a button click. You can, you know, go in and look at the dataset, identify those dimensions that you need to, say, aggregate by, the measures that you want to compute, and Dremio will just manage that for you, and any query that comes in, that may be going after this massive detail table with a trillion rows, that has a GROUP BY in it, for instance, will just match that reflection and use it. And that query can respond in less than a second, where typically the work that would have to happen on the backend engine might take a minute to process that query. So really that's the edge piece that gives us that BI acceleration without having to use additional tools or in any additional complexity for the user. >> And I assume you're talking about like millisecond response times, right? You said under a second, but I'm sure milliseconds? >> Hundreds of milliseconds, typically. So we're not really in the one to two millisecond range. That's pretty, pretty rare (chuckles), but certainly sub-second response times is very, very common with very, very large backend data sets when you use reflections, mm-hmm. >> Got it, and that speed and performance is absolutely table stakes today for organizations to succeed and thrive. So is what Dremio delivers a no-copy data strategy? Is that what you consider it? >> It's that, and it's actually much more than that, right? So I think, you know, when you talk to, really, users of the platform, there are a number of layers of Dremio, and, you know, we often get asked, I get asked, you know, who are our direct competitors, right? And I think that when you think about that question, it's really interesting, because we're not just the backend high-performance query engine. We aren't just the acceleration layer, right? We also have a very rich, fully-featured UI environment, that allows users to actually log in, find data, curate data, you know, reflect data, build their own views, et cetera. So there's really a whole suite of services that are built in to the Dremio platform, that make it very, very easy to install Dremio on, you know... You know, install it on AWS, get started right away, and be querying data, kind of building these virtual views, adding accelerations. All this can happen within minutes. And so it's really interesting that there's kind of a wide spectrum of services that allow us to really power a data lake in its entirety, really, without too many other technologies that have to be involved there. >> What are some of key use cases that you've seen, especially in the last year, as we've seen this rapid acceleration of digital transformation, this adoption of SaaS applications, more and more and more data, some of those key use cases that Dremio is helping customers solve? >> Sure. Yeah. I think there's a number of verticals, and there's some that I'm very familiar with, because I've worked very closely with customers, and in financial services is a large one, you know, and that would include, you know, banking, insurance, investment, you know, a lot of the large fortune 500 companies that maybe in manufacturing, or, you know, transportation, shipping, et cetera. You know, I think lately I'm most familiar with some of the transformation that's going on in the financial services space, and what's happening there, you know, companies have typically started with very, very large data warehouses, and often for the last four or five years, maybe a little longer, they've been in this transition to building kind of an in-house data lake, typically on a Hadoop platform of some flavor, with a lot of additional services that they've created to try to enable this data democratization. But these are huge efforts. And, you know, typically these are on-prem, and, you know, lots of engineers working on these things, really, full-time, to build out this full spectrum of capabilities. The way that Dremio really impacts that is, you know, we can come in and actually take the place of a lot of parts of that puzzle. And we give a really rich experience to the user, you know, allow customers to kind of retire some of these acceleration layers that they've put in to try to make BI queries fast, get rid of a lot of the transformations, like the ETL jobs or ELT processes that have to run. So, you know, there's a really wide swath of that puzzle that we can solve. And then when you look at the cloud, because all of these organizations, they've got a toe in the water, or they're halfway down the path, of really exploring how do we take all of this on-prem data and processing and everything else, and get it into AWS, you know, put it in the cloud? What does that architecture look like? And we're ideally positioned for that story. You know, we've got an offering that runs, you know, natively on AWS, and takes full advantage of kind of the decoupling of compute and storage. So we give organizations a really good path to solve some of their on-prem problems today, and then give them a clear path as they migrate into cloud. >> Can you walk me through a customer example that you think really underscores what you just described as what Dremio delivers, and helping customers with this migration, and to be able to take advantage and find value in volumes and volumes of data? >> Yeah, absolutely. Unfortunately, I can't mention their name, but I have worked very, very closely with a large customer, as I mentioned in financial services. And one of the things that they're very keenly interested in is, you know, they've had a pretty large deployment that traditionally has been both Hadoop-based, and they've got a large, several large on-prem relational data warehouses as well. And Dremio has been able to come in and actually provide that BI performance piece, basically, you know, the very, very fast, you know, second, two second, three-second performance that people would expect from the data warehouse, but we're able to do that directly on, you know, the files and tables that are in their Hadoop cluster. And that project's been going on for quite some time, and we've had success there. I think that where it really starts to get exciting though, and this is just beginning, is this customer also is, you know, investigating and actually prototyping and building out a lot of these functions in the AWS cloud. And so, you know, the nice thing that we're able to offer is, really, a consistent technology stack, consistent interfaces you know, consistent look and feel of the UI, both on-prem and in the cloud. And so we can really, once they start that move, now they've got kind of the familiar place to connect to for their data and to run their queries. And that's a nice seamless transition as they migrate. >> What about other verticals? Like, I can imagine healthcare and government services, are you seeing traction in those segments as well? >> Yeah, absolutely. We are. There are a number of companies in the healthcare space. I think that one of the larger ones in the government space, which I have some exposure to, is CMS, which is one that we had done some work through a partner to implement Dremio there. And, you know, this was a project, I think, that was undertaken about a year ago. They implemented our technology as part of a larger data lake architecture, and had a good bit of success there. So what's been interesting, when you talk about the funding and the valuation, and the kind of the buzz that's going on around Dremio is that we really have customers in so many different verticals, right? So we've got certainly financials and healthcare, and, you know, insurance, and, you know, big commercials, like in manufacturing, et cetera. So we're seeing a lot of interest across a number of different verticals, and customers are are buying and implementing the product in all those verticals, yeah. >> All right, so take us out with where customers can go, and prospects that are interested, and even investors, in finding out more about this next generation data engine that is Dremio. >> Absolutely. So I think the first thing that people can do is they can go to our website, which is dremio.com, and they can go to dremio.com/labs. And from there they can launch a self-guided product tour. I think that's probably a very quick way to get an overview of the product, and who we are, what we do, what we offer. And then there's also a free trial that's actually on the AWS marketplace. So if you want to actually try Dremio out, and, you know, spin up an instance, you can get us on the marketplace. >> Do most of your customers do that, like doing a trial with a proof of concept, for example, to see really how, from an architecture perspective, how these technologies are synergistic? >> Absolutely. Yeah. I think that probably every large enterprise, you know, there's a number of ways that customers find us. And so, you know, often customers may just try the trial on the marketplace. But, you know, customers may also, you know, reach out to our sales team, et cetera, but it's very, very common for us to do a proof of concept, that's not just architecture, but it would cover, you know, performance requirements and things like that. So I think pretty much all of our very largest enterprise customers would go through some sort of a proof of concept, and that would be done with the support of our field teams. >> Excellent, well, Robert, thanks for joining me today, and sharing all about Dremio with our audience. We appreciate your time. >> Great. Thank you, Lisa. It was a pleasure. >> Likewise, for Robert Maybin, I'm Lisa Martin. Thanks for watching. (upbeat music)

>>Good day. And thanks for joining us as we continue our series here on the Coupa, the AWS startup showcase featuring today, big ID and what this is, will Murphy was the vice president of business development and alliances at big idea. Well, good day to you. How are you going today? Thanks John. I'm doing well. I'm glad to be here. That's great. And acute belong to, I might add, so it's nice to have you back. Um, let's first off, let's share the big ID story. Uh, you've been around for just a handful of years accolades coming from every which direction. So obviously, uh, what you're doing, you're doing very well, but for our viewers who might not be too familiar with big ID, just give us a 30,000 foot level of your core competence. Yeah, absolutely. So actually we just had our five-year anniversary for big ID, uh, which we're quite excited about. >>Um, and that five-year comes with some pretty big red marks. We've raised over $200 million for a unicorn now. Um, but where that comes to and how that came about was that, um, we're dealing with, um, longstanding problems with modern data landscape security governance, privacy initiatives, um, and starting in 2016 with the, uh, authorship of GDPR, the European privacy law organizations, how to treat data differently than they did before they couldn't afford to just sit on all this data that was collected for a couple of reasons, right? Uh, one of them being that it's expensive. So you're constantly storing data, whether that's on-prem or in the cloud is we're going to talk about there's expense that you have to pay to secure the data and keep it from being leaked. You have to pay for access control. It's paid for a lot of different things and you're not getting any value out of that. >>And then there's the idea of like the customer trust piece, which is like, if anything happens to that data, um, your reputational, uh, your reputation as a company and the trust you have between your customers and your organization is broken. So big ID. What we did is we decided that there was a foundation that needed to be built. The foundation was data discovery. If you even an organization knows where its data is, whose data it is, where it is, um, and what it is, and also who has access to it, they can start to make actionable decisions based on the data and based on this new data intelligence. So we're trying to help organizations keep up with modern data initiatives and we're empowering organizations to handle their data sensitive, personal regulated. And what's actually quite interesting is we allow organizations to define what's sensitive to them because like people, organizations are all different. >>And so what's sensitive to one organization might not be to another, it goes beyond the wall. And so we're giving organizations that new power and flexibility, and this is what I still find striking is that obviously with this exponential growth of data and machine learning, just bringing billions of inputs, it seems like right. All of a sudden you have this fast reservoir data, is that the companies in large part, um, don't know a lot about the data that they're harvest state and where it is. And so it's not actionable, it's kind of dark data, right. Just out there reciting. >>Um, and so as I understand it, this, this is your focus basically is tell people, Hey, here's your landscape. Uh, here's how you can better put it to action, why it's valuable and we're going to help them protect it. Um, and they're not aware of these things, which I still find a little striking in this day and age, >>And it goes even further. So, you know, when you start to, when you start to reveal the truth and what's going on with data, there's a couple things that some organizations do. Uh, and I think human instincts, some organizations want to bury their head in the sand. I'm like, everything's fine. Uh, which is, as we know, and we've seen the news frequently, not a sustainable approach. Uh, there's the, there's the, like, let's be a, we're overwhelmed. We don't, we don't even know. We don't even know where to start. Then there's the natural reaction, which is okay. We have to centralize and control everything which defeats the purpose of having, um, shared drives and collaboration and, um, geographically disparate workforces, which we've seen particularly over the last year, how important that resiliency within organizations is to be able to work in different areas. And so, um, it really restricts the value that, um, organizations can get from their data, which is important. And it's important in a ton of ways. Um, and for customers that have allowed their, their data to be, to be stored and harvested by these organizations, they're not getting value out of it either. It's just risk. And we've got to move data from the liability side of the balance sheet, um, to the assets out of the balance sheet. And that comes first and foremost with knowledge. >>So everybody's vote cloud, right? Everybody was on prem and also we build a bigger house and build a bigger house, better security, right in front of us, got it, got to grow. And that's where I assume AWS has come in with you. And, and this was a two year partnership that you've been engaged with in AWS. So maybe shine a little light on that, about the partnership that you've created with AWS, and then how you then in turn transition that, to leverage that for the betterment of your >>Customer base. Yeah. So AWS has been a great partner. Um, they are very forward-looking for an organization, as large as they are very forward looking that they can't do everything that their customers need. And it's better for the ecosystem as a whole to enable small companies like us. And we were very small when we started our relationship with them, uh, to, to join their partner organization. So we're an advanced partner. Now we're part of ISV accelerate. So it's a slightly more lead partner organization. Um, and we're there because our customers are there and AWS like us, but we both have a customer obsessed culture. Um, but organizations are embracing the cloud and there's fear of the cloud. There's there really shouldn't be in the, in the way that we thought of it, maybe five or 10 years ago. And that, um, companies like AWS are spending a lot more money on security than most organizations can. >>So like they have huge security teams, they're building massive infrastructure. And then on top of that, companies themselves can do, can use, uh, products like big ID and other products to make themselves more secure, um, from outside threats and from, from inside threats as well. So, um, we are trying to with them approach modern data challenge as well. So even within AWS, if you put all the information in, like, let's say S3 buckets, that doesn't really tell you anything. It's like, you know, I, I make this analogy. Sometimes I live in Manhattan. If I were to collect all the keys of everybody that lived in a 10 block radius around me and put it into a dumpster, uh, and keep doing that, I would theoretically know where all the keys were there in the dumpster. Now, if somebody asked me, I'd like my keys back, uh, I'd have a really hard time giving them that because I've got to sort through, you know, 10,000 people's keys. >>And I don't really know a lot about it, but those key sale a lot, you know, it says, are you in an old building, are you in a new building? You have a bike, do you have a car? Do you have a gym locker? There's all sorts of information. And I think this analogy holds up for data because of the way you store your data is important, but, um, you can gain a lot of theoretically innocuous, but valuable information from the data that's there while not compromising the sensitive data. And as an AWS has been a fabulous partner in this, they've helped us build a AWS security, have integration out of the box. Um, we now work with over 12 different AWS native, uh, applications from anything like S3 Redshift and Sienna, uh, Kinesis, as well as, um, apps built on AWS like snowflake and Databricks that we, that we connect to. >>And AWS, the technical team of department teams have been an enormous part of our success there. We're very proud of joining the marketplace to be where our customers want to buy enterprise software more and more. Um, and that's another area that we're collaborating, uh, in, in, in joint accounts now to bring more value in simplicity to our joint customers. What's your process in terms of your customer and, uh, evaluating their needs because you just talked about earlier, you had different approaches to security. Some people put their head in the sand, right? Some people admit that there's a problem. Some people fully engaged. So I assume there's also different levels of sophistication in terms of whatever you have in place and then what their needs are. So if you would shine a little light on that, you know, where they are in terms of their data landscape and AWS and its tools, but you just touched them on multiple tools you have in your service. >>Now, all that comes together to develop what would be, I guess, a unique program for a company's specific needs. It is. We started talking to the largest enterprise accounts when we were founded and we still have a real proclivity and expertise in that area. So the issues with the large enterprise accounts and the uniqueness there is scale. They have a tremendous amount of data, HR data, financial data, customer data, you name it, right? Like, we'll go. We can, we can go dry mouth talking about how many you're saying data. So many times with, with these large customers, um, freight Ws scale, wasn't an issue. They can store it, they can analyze it. They can do tons. It where we were helping is that we could make that safer. So if you want to perform data analytics, you want to ensure that sensitive data is not being, or that you want to make sure you're not violating local, not national or industry specific regulations. >>Financial services is a great example. There's dozens of regulations at the federal level in the United States and each state has their own regulations. This becomes increasingly complex. So AWS handles this by, by allowing an amazing amount of customization for their customers. They have data centers in the right places. They have experts on, on, uh, vertical, specific issues. Big ID handles this similarly in some ways, but we handle it through ostensive ability. So one of our big things is we have to be able to connect to every everywhere where our customers have data. So we want to build a foundation of like, let's say first let's understand the goals is the goal compliance with the law, which it should be for everybody that should just be like, we need to, we need to comply with the law. Like that's, that's easy. Yeah. Then as the next piece, like, are we dealing with something legacy? >>Was there a breach? Do we need to understand what happened? Are we trying to be forward-looking and understanding? We want to make sure we can lock down our most sensitive data, tier our storage tier, our security tier are our analytics efforts, which also is cost-effective. So you don't have to do, uh, everything everywhere, um, or is the goal a little bit like we needed to get a return on investment faster, and we can't do that without de-risking some of that. So we've taken those lessons from the enterprise where it's exceedingly difficult, uh, to work because of the strict requirements, because the customers expect more. And I think like AWS, we're bringing a down market. Uh, we have some, a new product coming out. Uh, it's exclusive for, uh, AWS now called small ID, which is a cloud native, a smaller version, lighter weight version of our product for customers in the more commercial space in the SMB space where they can start to build a foundation of understanding their data or, um, protection for security for, for, for privacy. >>And, and before I let you go here, what I'd like to hear about is practical application. You know, somebody that, that you've, you know, that you were able to help and assist you evaluated. Cause you've talked about the format here. You've talked about your process and talk about some future, I guess, challenges, opportunities, but, but just to give our viewers an idea of maybe the kind of success you've already had to, uh, give them a perspective on that, this share a couple stories. If you wouldn't mind with some work that you guys did and rolled up your sleeves and, and, uh, created that additional value >>For your customers. Yeah, absolutely. So I'll give a couple examples. I'm going to, I'm going to keep everyone anonymized, uh, as a privacy based company, in many ways, what we, we try to respect colors. Um, but let's talk about different types of sensitive data. So we have customers that, um, intellectual property is their biggest concern. So they, they do care about compliance. They want to comply with all local and national laws where they, where they, their company resides all their offices are, but they were very concerned about sensitive data sprawl around intellectual property. They have a lot of patents. They have a lot of sensitive data that way. So one of the things we did is we were able to provide custom tags and classifications for their sensitive data based on intellectual property. And they could see across their cloud environment, across their on-premise environment across shared drives, et cetera. >>We're sensitive data had sprawl where it had moved, who's having access to it. And they were able to start realigning their storage strategy and their content management strategy, data governance strategy, based on that, and start to, uh, move sensitive data back to certain locations, lock that down on a higher level could create more access control there, um, but also proliferate and, uh, share data that more teams needed access to. Um, and so that's an example of a use case that I don't think we imagined necessarily in 2016 when we were focused on privacy, but we've seen that the value can come from it. Um, so yeah, no, I mean, the other piece is, so we've worked with some of the largest AWS customers in the world. Their concern is how do we even start to scan the Tedder, terabytes and petabytes of data in any reasonable fashion? >>Uh, without it being out of date, if we create this data map, if we prayed this data inventory, uh, it's going to be out of date day one, as soon as we say, it's complete, we've already added more. That's where our scalability fit Sam. We were able to do a full scan of their entire AWS environment and, uh, months, and then keep up with the new data that was going into their AWS environment. This is a, this is huge. This was groundbreaking for them. So our hyper scan capability, uh, that we've wrote, brought out that we rolled out to AWS first, um, was a game changer for them to understand what data they had and where it is who's it is et cetera at a way that they never thought they could keep up with. You know, I I'm, I brought back to the beginning of code when the British government was keeping track of all the COVID cases on spreadsheets and spreadsheet broke. >>Um, it was also out of date, as soon as they entered something else. It was already out of date. They couldn't keep up with them. Like there's better ways to do that. Uh, luckily they think they've moved on from, from that, uh, manual system, but automation using the correct human inputs when necessary, then let, let machine learning, let, uh, big data take care of things that it can, uh, don't waste human hours that are precious and expensive unnecessarily and make better decisions based on that data. You know, you raised a great point too, which I hadn't thought of about the fact is you do your snapshot today and you start evaluating all their needs for today. And by the time you're going to get that done, their needs have now exponentially grown. It's like painting the golden gate bridge, right. You get that year and now you've got to pay it again. I said it got bigger, but anyway, they will. Thanks for the time. We certainly appreciate it. Thanks for joining us here on the sort of showcase and just remind me that if you ever asked for my keys, keep them out of that dumpster to be here.

>>Well, good day. And thank you for joining us here on the cube, John Walls here, uh, bringing you to this conversation as part of the AWS startup showcase. And we're joined by Justin bough, who is the SVP of product for amplitude and Justin. Good to see you today. How are you? >>I'm doing great. Thank you for having me, John. >>No pleasure. Looking forward to it. Um, you know, personalization that everybody's talking about these days and then how do we better personalize our, our digital presence, our digital products, um, you know, how do we get much more acutely aware of the end user at the end of the day and grow? I know that's what Amplitude's all about. So maybe if you just give us a 30,000 foot, um, perspective on that, about your thoughts about personalization today and how amplitude tries to affect >>For sure. Yeah. So I think first off personalization matters because it actually works. I think we live in a world where, as you know, we're drowning in content and distraction, uh, and it's been proven that customers respond better to digital experiences that are more personalized, that are more relevant for them. And frankly just save them time. Um, and the nice thing about this is not only the customers benefit, but companies do too. Uh, we actually see that a big impact on a company's bottom line, if they're able to, uh, deliver a more relevant customer experience to them because that leads to better engagement, better return, higher loyalty and lifetime value, uh, for those customers. >>So, um, well, let's, let's just go right to an example then, uh, I know you worked with a lot of different people, um, but there's anybody in particular that stands out, um, maybe give us an idea of a case study here about what practices you put into place, the kind of evaluations that you do, and ultimately the service that you're providing that allows them to increase sales and, and get a little more stickiness with them. >>Yeah, that's great. That's great. So I think one, uh, company customer of ours we're working with right now on this is actually Chick-fil-A. Uh, so people probably familiar with Chick-fil-A. Their mission is to be the most customer caring company in the world, uh, which I love in personalization is critical to that strategy because it helps them create a more relevant and seamless experience for their customers. Um, and the experience itself, and the app is actually pretty simple, which is the magic of personalization. So you open the Chick-fil-A app, uh, you see a list of menu items and those items are relevant to you based on your previous behavior. Um, after you order your entree, you're then offered a list of personalized sides. And then after that Alyssa personalized drinks, um, and the great thing is that as new items, uh, get introduced to the menu by Chick-fil-A you see the ones that are most relevant to you based on predicted affinity and all of the machine learning that we're doing in the background. And so really now Chick-fil-A is actually they're able to deliver a customized menu for everyone that automatically updates based on your behavior and your preferences. Um, and I think the real beauty of this is that they're able to configure all of this by a marketer through a simple UI. This did not require an army of data scientists or engineers. Uh, they're able to use the amplitude platform, uh, to build out this entire experience for their customers. >>Right. Cause I mean, it seems like there'd be an enormous amount of analytics that you have to apply here, right. Um, because you got all this structured and unstructured data, uh, you know, it's, it's all over the place, right. And a lot of times people don't even know what they have on hand. Um, and so you gotta, you gotta help them sift through all this. Right. So let's talk about that process a little bit for somebody who's watching and thinking about, well, that's all sounds well and good, but, but how do you kind of automate this? How do you make it so that we don't have to invest a lot in a team dedicated solely to, you know, sipping through our data and making it valuable for us? >>Yeah. I mean, I think that's the beauty of, uh, of amplitude actually offering this in that that's actually our original first product product analytics. That's what we've done. Um, so we've actually made an out of the box system that can read from all your different data sources. Um, so whether those be your product sources, marketing channels, data that sits in your data warehouse, um, but it's not just piping that data. Uh, we then combine that into a unique identity, uh, profile for that customer, um, across all those different touch points, um, and also have out of the box data governance, um, so that you can make sure you maintain, uh, the quality of that data profile, uh, over time. And then that gets fed into, um, our, what we call our behavioral graph. It's our database, uh, that's actually built to both understand and predict future behavior. And so all of this happens effectively out of the box for our customer. They don't need to do any of this, uh, themselves. Uh, we're managing all this for them. And then what they experience is, uh, an analytics application. So they can analyze that user behavior understand kind of what the drivers of different things like engage in retention are, and then use that to actually personalize the product experience. >>And, and you mentioned machine learning, um, talk about that aspect of this. I mean, how much more capability you have now because of what I know can deliver and, and, um, in some ways it adds some complexity, um, but also obviously it delivers exponentially, I would think in benefit at the end of the day. >>Yeah, for sure. I mean, it's just not possible to do one to one personalization without machine learning. I think that's actually, when we talk about the benefits and the advantages of personalization, it's probably even worth taking a step back. Like there's a lot of different types of personalization. Um, I think when you want to do behavioral personalization where you truly getting to one-to-one experiences, you have to use machine learning. Now you compare that to maybe like demographic personalization, which is actually, I think when most companies talk about when they're doing personalization, they're actually doing demographic personalization. That's like, are you a male or female? Um, what's your, you live in a city or a suburb. Um, uh, but the reality is like that light segmentation, it's not really that effective. Like do all women who live in a city behave the same, obviously not. Uh, and so, uh, we want instead to use behavior because your past behavior is the best predictor of your future behavior. >>Um, and, uh, and you need machine learning to be able to actually come up with, for an individual. What is their likelihood propensity to actually engage on any piece of content of which think about for you think about Chick-fil-A, how many different items they have in a menu. Um, you can think about like, we work with, um, a content company that has millions of different articles and they want to figure out what's the right article to put in front of you. Like, that's just not possible to actually analyze that by hand, uh, nor actually work working straight that, uh, uh, in real time without actually leveraging machine learning. Um, and so that's the exciting thing that's happened with, uh, new advances in, uh, supervisor and supervised learning models that we can actually do those in generalizable ways, uh, for our customers, >>Wait, we've talked a lot about behavioral, so that's obviously metrics you've been tracked. Right. I saw something and I clicked on something and I acted on something or watch something. These are all very measurable activities. On the other hand, though, as you know, in the consumer space, a lot of it's emotion too, you know, I make decisions based on, on my feelings or my thoughts or whatever. Can you, can you do any kind of unpeeling of my motivation in this almost like empathetic, uh, investigation so that you have an idea of what social cues on emanating or sending off? So, Hey, yeah, we can, we can get John this way too. >>Yeah. So I think a lot of it is, I mean, we're talking a lot about the science of, uh, product development, uh, for sure. And how do you bring personalization leveraging data? There is then the art of actually understanding, like what are the emotional States that users are in and like this isn't to say that the ability to personalize the product means that you're not actually bringing the heart as well. Like you act, it actually is a, both about the art and the science coming together. Um, and so you still need to, like, you're still gonna talk to your customers. You're still going to understand, uh, them and kind of what their, uh, different need States are, but this is then taking what you have, which you've built as a great product, then how do you optimize that? So we call it an optimization system, um, and actually deliver, uh, the best experience, uh, based on that customer's behavior. >>So just to kind of flip this a little bit, then what are you doing? Amplitude? What are you doing that, um, that hasn't been done before? I couldn't, I didn't understand that a lot of people think personalization just hasn't has a great horizon, has a lot of great promise. Well, but we're not there yet. I mean, what haven't we delivered on yet that you think amplitude is improving on and refining this capability? >>Yeah. So I think there are a couple of things there as to why we haven't fully seen the promise of personalization deliver no way. And I would say we're really starting to see that chasm emerge, where there are some companies that, you know, you think of, um, you know, Netflix, like obviously Amazon and others, who've done, who've been really successful here, but they've done it through armies of people. Um, what hasn't happened is a self-serve way of doing this so that it does not require massive investments, uh, in technical resources. Um, and so what we've solved for three things, um, one we've already talked about it, but it's just so true. Like this actually in and of itself is not an ML problem. First, it's actually a trustworthy data problem. Do you actually have the behavioral data that you can trust? Can you actually capture that across the entire customer journey because you can't personalize a journey if you don't even know what your users are doing to begin with. >>So you have to start there at that foundational level. Um, and that is a big part of our secret sauce is that we've built a database specifically catered to helping you understand that journey of that customer across all the different platforms and channels that they do. That's not easy to actually unify behavior in that fashion and allow you to analyze that in real time. Um, so that's the first thing that we did, um, is build that, uh, that database. So that's number one. And that's just the foundation. You have to have that, like, I, I think so many companies fail because they think we can go hire ML engineers, but if you don't have the foundation, it's not going to work. Um, the second thing isn't necessarily technological. It's more cultural, but it is really critical. And I think our analytics applications helped, uh, helped a lot here, which is you gotta break down the silos between marketing product engineering and data science. >>You actually have, you have to have all of them working together, um, to really be able to fulfill the promise of personalization because you have to be aligned and what's the outcome we're trying to drive, but that's actually how I literally can walk you through like the, how the, how the actual product works. But the first starting point is what are we trying to accomplish? Like in the Chick-fil-A example, it is, we want people to buy more than one item. Okay. So that's your goal. Like you have to get alignment that that is the goal. Cause if everyone's arguing about different goals, it doesn't matter what ammo model, like the model needs to know what we're trying to actually focus in on. Uh, and so how do you bring people together? And you do that through shared understanding of data. You do that through, we call it a North star, like we're aligned in what is the North star that we're focused on. >>And can you measure that? And that's analytics is focused in on that. And then when you have both of those, you've got behavioral data, you understand the journey of a customer you're aligned in the goals and outcomes you care about. Then you can leverage machine learning to actually deliver that personalized experience. And the advances that we're making there are actually doing that in a generalizable fashion. And so that does not have to be custom built for every single use case. Um, and our models are now able that we can run a model basically, uh, every hour to update for a customer. Um, and that scales horizontally, >>Well, I know of Chick-fil-A certainly has a track record that, um, is an arguable, right? And, and, and you've had a lot to do with satisfying that appetite for success. So, uh, Justin, uh, congratulations to amplitude. It's been a real pleasure speaking with you and thanks for the time today. >>Of course. >>Excellent speaking with Justin Bauer, the senior vice president of product at amplitude, and you've been watching the AWS startup showcase here on the cube.

(upbeat music) >> For years on the queue, we've talked about the benefits of the cloud going beyond IT cost savings. Sure. You can move your workloads into the cloud and minimize the so-called undifferentiated heavy lifting of IT equipment and deployment and management. And of course increased agility is often the number one benefit customers site from the cloud. But increasingly, the value of the cloud is being seen as applying that agility to change an organization's operating model. This drives business value that can be orders of magnitude greater than savings on tech labor costs. And one of the more interesting examples we found, is using the cloud, data and software technology to find, and flexibly source distributed energy resources so that clean energy, can be delivered efficiently. Hello, and welcome to the startup showcase on the cube brought to you by AWS. We're very excited to have two exacts on from AutoGrid. Wait until you hear about the innovations that they're driving and the problems that they're solving around, some of the world's most pressing problems. Amit Narayan is here. He's the CEO of auto-graded Rajeev Singh is the chief technology officer gentlemen, welcome to the program. >> Thank you. >> Thank you for having us. >> You're very welcome. >> Okay, so heard my summary Amit. Maybe you could add some color about AutoGrid. What's your story? >> Yeah, I mean, undoubtedly climate change is one of the defining challenges of our time, and we're already seeing extreme weather events whether these are wildfires in California, are extreme cold events in Texas, last two weeks. As we tackle the climate change through renewables, this whole volatility challenge that we are seeing is only going to become even more pressing. So we at AutoGrid provide software that creates, a virtualization layer, just like you doing in the cloud world, with hardware around all kinds of energy assets, whether these are your EVs in the homes, our batteries are distributed solar panels. And then we apply intelligence from software, to coordinate and orchestrate all of these assets. So you can think of us as a autopilot for the grid, and our technology is called virtual power plants. Which allows us to harness, the power from all these distributed energy resources. >> Yeah. I was going to say you're essentially creating, a virtual power plant. That's amazing of aggregating these distributed resources. I mean, it sounds very logical but it also sounds non-trivial, its a transformative idea. What exactly is a virtual power plant? I mean, how does that all work? >> Yeah. Well, I mean, if you think about how the grid was designed by Edison and Tesla, they really never envisioned a world where you will have a two way flow of power, not just from generator to the consumers, but potentially from the consumers back to the generators. And certainly they didn't really design the grid to incorporate this amount of renewables, which can be intermittent and volatile. So as we are now transitioning to this new energy world, we have to rethink the entire grid architecture, and reinvent how this control system works. But fortunately for us, unlike Edison and Tesla we have some really powerful tools at our disposal namely the internet and the cloud, and these tools do allow us to rethink how we connect all these different assets and we optimize them. And in a way, we are now rebuilding the grid outside in where if you have a battery in your home, not only can it power your own home when power is out, it can actually provide power back to the grid or to your neighbors. And so with this onslaught of DES, we think that we are living in the most exciting times, since Edison and Tesla in terms of how we are going to transition to a sustainable grid. And we think that our software, can play a foundational role in accelerating that transition. >> Lets stick here the bi-directional flow. It's so simple, but genius. Rajeev, maybe you could talk about the tech behind AutoGrid. I mean the secret sauce, lies I think in that whole flexibility management system but there's data involved, probably a fair amount of computer science. Maybe you could explain it more detail. >> Yeah. just as Amit mentioned now, when we started AutoGrid, we had the luxury of, cloud computing a massive scale, at that massive scale and AutoGrid, what we've been able to do is pull together a cloud native computing. They lost the city, the scale, with cutting edge AI and machine learning, as well as all of the dispatch, and command and control technologies, that are all in one platform. And all of them have to come together, to be able to manage and orchestrate, these a massively distributed energy resources. I mean, these could be small, you know batteries or solar panels, et cetera. So gone are the days of large generators that could be managed with smaller compute now because the sheer number of DER's, you need a new paradigm to be able to manage this. And this is really what is under the hood, that constitutes our virtual power plant. >> Rajeev Can you talk a little bit more about your scale model? I mean, how are you able to do this effectively without imploding, or hitting walls? >> Yeah, so obviously, we've been on AWS for about ten years now. And even prior to that, we had the previous company loaded with AWS. So that kind of gave us a glimpse off the sheer scale of compute, that is available to us on tap, if required. So that was quite comforting, because when we did back one of the calculations on the amount of data, that's coming in through IOT industrial IOT from all the distributed energy resources, the amount of processing required for real-time computing as well as, the sheer variety of the other, we have to tackle in in various geographies around the world. AWS made it happen just because having regions, across the globe, we done in, I believe six or seven different AWS regions. We cover a four continents, twelve plus countries. So just because cloud computing was there, we were able to ramp up the solution, very quickly. Now, one thing we are a big believers in is that you only learn by doing, and the only way to learn, is to run production systems. And when we started, of course we didn't do everything right. But we quickly learned we adapted, we scaled, and we kept on scaling. And this is where we are right now. >> Interesting. That's like Andy Jassy says there's no compression algorithm for experience. We know it well. One more for Rajeev, and I want to come back. With AutoGrid tapping, all these energy sources, you got a pretty major threat surface. How are you dealing with security? >> Yeah, we don't talk a lot about our security posture for obvious reasons. Some of the underlying principles are in reducing the blast radius. It should be quite familiar to people who work in security. The use of wide variety of best of the breed security tools, including, and or the past few years. In fact, past five, six years, AWS itself has rolled out a number of security managed services, which are included. But on top of that views, other solutions as well. And it's all designed in layers, with proper segregation, and we have variety of security certifications. One of the most, the one that we're proud of is we are one of the few if not the only NERC solution SAS solution in this domain on AWS. And it's just a culmination of using security by layers. And reducing the blast radius. >> Yeah. Makes sense. And let's turn to some customer use cases. What are some of the main problems, that your customers come to you to solve? How are you approaching them? Maybe you could address that and add some color. >> Yeah, absolutely. I mean, as Rajeev mentioned. There is a lot of deep tech in the platform, and the optimization complexity, grows exponentially with the number of assets. And as you go from a gigawatt scale power plant and you want to get the same power from Tesla power walls. let's say, for every generator you're replacing it with more than two hundred thousand mini generators. And if the complexity grows exponentially. it's far beyond what the current algorithms can handle. So a lot of customers come to us solve their technical challenges. But even beyond that, the whole complexity of transacting, with small generators is very high, and that our business model issues that we help our customers solve. So the whole energy industry, has been designed to have transactions, between very large generators and utilities, but very few of these transactions. And now when you are talking about DER's, you're having millions of transactions with very small entities and maybe even homeowners, back to the utilities. So neither the utilities, have the capability today, to have these transactions, nor the asset owners, and operators, have the capability to go back and have the transactions of the utilities. So we sort of act as an intermediary, and we provide a one-stop shop, for fleet owners and operators. And we say that if you work with us, we will help you monetize your assets, and get more value from these assets, by interfacing with utilities by interfacing with energy markets which can get very complex. >> I love this. I mean, everybody's winning here. Rajeev. I want to come back to the to the cloud a little bit. You talked about, you've been able to AWS for ten years and then even before that, you've got deep experience. I mean. I can't imagine, how you would do this without the cloud. I mean, maybe it could be a really heavy complicated list lift. I mean, you've seen the AWS cloud evolve over time. It's gone way beyond, of course, compute and storage brought in a lot of machine learning capabilities on and on. And I mean, how are you leveraging that evolution? Those zillion features that AWS puts out every year at reinvent. I mean, maybe you could talk about that a little bit. >> Yeah. So of course, when we started, we used it as an infrastructure provider, you know provided us compute networking, security firewalls, et cetera, just on tap. There's very good. Got us started. Then we started leveraging a lot more managed services, that AWS offered, that allowed us to run. For example, variety of databases right to data stores, in a managed fashion, with a very small startup. You're always, running lean. So that helped us with a small team, of system engineers and engineers, back from engineers to be able to put together and run these systems around the globe, just because enablers was responsible, for managing the services. We always keep an eye on. And one thing we love about AWS is the amount of innovation, that they quickly put into production. So, we're always keeping an eye on, what's coming out. And over the years, it has been quite nice to us in some ways, we directly talk to the solution architects, they tell us what's coming, what should be used, what we should not use in what's in production ready What's not. So that level of kind of deep engagement, really helps us. Kind of keep abreast of the innovations that are constantly being rolled out on AWS. And we keep kind of incorporating those into our platform and making it more and more capable. The one thing I also would like to say, is that to be able to aggregate capacity, from all these DER's, it has to be done in a cost effective fashion. So, this is where AWS helps us with running, last a city at the service level. All the microservices can scale independently. So we don't have to have this massive monolith, and across the globe, we don't need to have, fifty of those to be running. And that's going to add up to a massive cost. So we are able to scale, just the portions of the infrastructure just in time when we need it. And that also helps us greatly, in having a cost effective solution, for our customers. >> That's actually great. That's great. So that granularity is important, for you to have fine grain control of your costs. A lot of people sometimes question that granularity that AWS provides, because it does add a level of complexity, but you guys can deal with complexity. You know, one of the things that we haven't talked about I wonder if we could touch, on it is data. I mean, this is the data flow. I'm imagining the data flow, and the metadata and the decisions that you have to make are are quite complex. Can you address that a little bit? I mean, you guys got to be pretty, sharp data walks. >> Yeah. So the people that we have at the company, including myself have come from a billing lodge, high performance and high large enterprise systems, previously from airlines, Ford motor company or pharmaceuticals. In any system, where we are making a lot of decisions. The first thing you have to do is data integration. And again this is something that you just learn by doing and having done it across the globe with a variety of the DER, systems UVS, you name it. We have to pretty much done one of everything, and of course, and be very quickly abstract and learn, if you do something twice, we abstract it and make it into a library. So that the next time around it's just a simple turn-on switch. So it's no secret sauce there you just learn by doing and you kind of constantly abstract and you expand the solution. >> That's great. let's close. The other thing. We really haven't talked much about your company. Maybe you could, add some. whatever you want to to share, metrics. I mean you must be growing, head count, or whatever you're comfortable sharing. If you could just give us, a little glimpse of of the company. >> Yeah, absolutely. We have been around for close to ten years now. We are based in Silicon Valley. We have multiple locations. Our second primary location is in India. Today We are operating in over twelve countries. We have close to five thousand megawatts of distributed energy acids, that we actively control manage. This includes, everything from a thermostat in the home, to very large scale, wind and solar farms, as well as large scale batteries. EVs as a new emerging category. And, we work with a variety of partners. AWS has been one of our founding partners, on day one, you talked about data. We were the first ones to realize how much data we were going to get from all of these assets. And the current systems will not scale. So we made the decision on day one to be on cloud. And that was foundational year. I just want to say that over the last year or so, we have I think collectively as a society realized how individual actions, impact the overall society. And I think we are really at a great inflection point right now, where if we can harness this newly developed consciousness and awareness to accelerate, our transition to new energy, away from fossil fuel, we can really solve what I think is the biggest challenge that we face as a society going forward. >> Yeah. Micro actions that actually have a huge impact. And so I guess, that's kind of of where you see this heading in the future both the general market, your business. I mean presumably, you've been around a while, maybe you'd welcome competition to really solve this problem. Right? >> I think we are in the same fight. We are all working towards the same goal, of having a clean cheap reliable energy. And we would welcome as much support as we get to build momentum for this absolutely >> Its like the Pharma companies cheering each other on for the, for the vaccine. Again, guys super interesting business solving real problems really thanks gentlemen for coming on the program and we wish you well in the years ahead. >> Thank you for having us. >> It's really been our pleasure. Thank you for watching the AWS startup showcase on the cube. I'm Dave Volante.

>>Well, good day and welcome back to the cube as we continue our segment featuring AWS star showcase we're with now Mike Bilodeau, who's in corporate development and operations at Kong. Mike, uh, thank you for joining us here on the cube and particularly on the startup showcase. Nice to have you and pong represented here today. Thanks for having me, John. Great to be here. You bet. All right, first off, let's just tell us about pong a little bit and, and, uh, con cadet, which I know is your, your feature program, um, or, um, service. Oh, I love the name by the way. Um, but tell us a little bit about home and then what connect is all about to? Sure. So, uh, Kong as a company really came about in the past five years, our two co-founders came over from Italy in, uh, in the late, in the late aughts, early 20 teens and, uh, had a company called Mashape. >>And so what they were looking at and what they were betting on at that time was that API APIs, uh, were going to be the future of how software was built and how developers interacted with software. And so what came from that was a piece of, uh, they were running that shape as a marketplace at the time. So connecting developers sit in for an API so they can consume them and use them to build new software. And what they found was that actually the most valuable piece of technology that they created was the backbone for running that marketplace. And that backbone is what Kong is. And so they created it to be able to handle a massive amount of traffic, a massive amount of API APIs, all simultaneously. This is a problem that a lot of enterprises have, especially now that we've started to get some microservices, uh, started to, to have more distributed technologies. >>And so what Kong is really is it's a way to manage all of those different API APIs, all of the connections between different microservices, uh, through a single platform, which is called connect. And now that we've started to have Coobernetti's, uh, the, sort of the birth and the, the nascent space of service mesh con connect allows all of those connections to be managed and to be secured and made reliable, uh, through a single platform. So what's driving this right. I mean, um, you, you mentioned micro services, um, and Coobernetti's, and that environment, which is kind of facilitating, you know, this, uh, I guess transformation you might say. Um, but what's the big driver in your opinion, in terms of, of what's pushing this microservices phenomenon, if you will, or this revolution. Sure. And when I think it starts out at, at the simple active of technology acceleration in general, um, so when you look at just the, the real shifts that have come in enterprise, uh, especially looking, you know, start with that at the cloud, but you could even go back to VMware and virtualization is it's really about allowing people to build software more rapidly. >>Um, all of these different innovations that have happened, you know, with cloud, with virtualization now with containers, Kubernetes, microservices, they're really focused on making it, uh, so that developers can build software a lot more quickly, uh, develop the, the latest and greatest in a more rapid way. >>A huge driver out of this is just making it easier for developers, for organizations to bring new technologies to market. Uh, and we see that as a kind of a key driver in a lot of these decisions that are being made. I think another piece of it that's really coming about is looking at, uh, security, uh, as a really big component, you know, do you have a huge monolithic app? Uh, it can become very challenging to actually secure that if somebody gets into kind of that initial, uh, into the, the initial ops space, they're really past the point of no return and can get access to some things that you might not want them to similar for compliance and governance reasons that becomes challenging. So I think you're seeing this combination of where people are looking at breaking things into smaller pieces, even though it does come with its own challenges around security, um, that you need to manage, it's making it so that, uh, there's less ability to just get in and cause a lot of damage kind of all at once. Often Melissa malicious attackers. >>Yeah. You bring up security. And so, yeah, to me, it's almost, in some cases it's almost counterintuitive. I think about, I've got the, if I got this model, the gap and I've got a big parameter around it, right. And I know that I can confine this thing. I can contain this. This is good. Now microservices, now I got a lot of, it's almost like a lot of villages, right. They're all around. And, and uh, I don't have the castle anymore. I've got all these villages, so I have to build walls around all these villages. Right. But you're saying that there that's actually easier to do, or at least you're more capable of doing that now as opposed to living >>Three years ago. Well, you can almost think of it, uh, as if you have this little just right, and you might, um, if you have one castle and somebody gets inside, they're going to be able to find whatever treasury may have, you know, to extend the analogy here a bit, but now it's different, uh, 50 different villages that, you know, uh, an attacker needs to look in, it starts to become really time-consuming and really difficult. And now when you're looking at, especially this idea of kind of cybersecurity, um, the ability to secure a monolithic app is typically not all that different from what you can do with a microservice or with a once you get past that initial point, instead of thinking of it, you know, I have my one wall around everything, you know, think of it almost as a series of walls where it gets more and more difficult. Again, this all depends on, uh, that you're, you're managing that security well, which can get really time-consuming more than anything else and challenging from a pure management standpoint, but from an actual security posture, it is a way of where you can strengthen it, uh, because you're, you're creating more, um, more difficult ways of accessing information for attackers, as well as just more layers potentially of security. >>But what do you do to lift that burden then from, from the customer? Because like you said, that that that's a concern they really don't want to have. Right. They want, they want you to do that. They want somebody to do that for them. So what can, what do you do to alleviate those kinds of stress >>On their systems? Yeah, it's a great question. And this is really where the idea of API management and, um, in it's in its infancy came from, was thinking about, uh, how do we extract a way these different tasks that people don't really want to do when they're managing, uh, how API, how people can interact with their API APIs, whether that be a device or another human, um, and part of that is just taking away. So what we do and what API gate management tools have always done is abstract that into a, a new piece of software. So instead of having to kind of individually develop and write code for security, for logging, for, you know, routing logic, all these different pieces of how those different APIs will communicate with each other, we're putting that into a single piece of software and we're allowing that to be done in a really easy way. >>And so what we've done now with con connect and where we've extended that to you, is making it even easier to do that at a microservices level of scale. So if you're thinking about hundreds or thousands of different microservices that you understand and be able to manage, that's what we're really building to allow people to do. And so that comes with, you know, being able to, to make it extremely easy, uh, to, to actually add policies like authentication, you know, rate-limiting, whatever it may be, as well as giving people the choice to use what they want to use. Uh, we have great partners, you know, looking at the Datadog's, the Okta's of the world who provide a pretty, pretty incredible product. We don't necessarily want to reinvent the wheel on some of these things that are already out there, and that are widely loved and accepted by, uh, technology, practitioners and developers. We just want to make it really easy to actually use those, uh, those different technologies. And so that's, that's a lot of what we're doing is providing a, a way to make it easy to add this, you know, these policies and this logic into each one of these different services. >>So w if you're providing these kinds of services, right. And, and, and, and they're, they're, they're new, right. Um, and you're merging them sometimes with kind of legacy, uh, components, um, that transition or that interaction I would assume, could be a little complex. And, and you've, you've got your work cut out for you in some regards to kind of retrofit in some respects to make this seamless, to make this smooth. So maybe shine a little light on that process in terms of not throwing all the, you know, the bath out, you know, with, with the baby, all the water here, but just making sure it all works right. And that it makes it simple and, and, um, takes away that kind of complexity that people might be facing. >>Yeah, that's really the name of the game. Uh, we, we do not believe that there is a one size fits all approach in general, to how people should build software. Uh, there are going to need instances aware of building a monolithic app. It makes the most sense. There are going to be instances where building on Kubernetes makes the most sense. Um, the key thing that we want to solve is making sure that it works and that you're able to, to make the best technical decision for your products and for your organization. And so in looking at, uh, sort of how we help to solve that problem, I think the first is that we have first class support for everything. So we support, you know, everything down to, to kind of the oldest bare metal servers to NAMS, to containers across the board. Uh, and, and we had that mindset with every product that we brought to market. >>So thinking about our service mesh offering, for instance, um, Kula is the open source project that under tens now are even, but looking at Kumo, one of the first things that we did when we brought it out, because we saw this gap in space was to make sure that that adds first-class support for and chance at the time that wasn't something that was commonly done at all. Uh, now, you know, there there's more people are moving in that direction because they do see it as a need, which is great for the space. Um, but that's something where we, we understand that the important thing is making sure your point, you said it kind of the exact way that we like to, which is it needs to be reliable. It needs work. So I have a huge estate of, you know, older applications, older, uh, you know, potentially environments, even. I might have data centers that might've cloud being, trying to do everything all at once. Isn't really a pragmatic approach. Always. It needs to be able to support the journey as you move to, to a more modern way of building. So in terms of going from on-premise to the cloud, running in a hybrid approach, whatever it may be, all of those things shouldn't be an all or nothing proposition. It should be a phase approach and moving to, to really where it makes sense for your business and for the specific problem >>Talking about cloud deployments, obviously AWS comes into play there in a major way for you guys. Um, tell me a little bit about that, about how you're leveraging that relationship and how you're partnering with them, and then bringing the, the value then to your customer base and kind of how long that's been going on and the kinds of work that you guys are doing together, uh, ultimately to provide this kind of, uh, exemplary product or at least options to your customers. >>Yeah, of course. I think the way that we're doing it first and foremost is that, um, we, we know exactly who AWS is and the space and, and, you know, a great number of our customers are running on AWS. So again, I think that first class support in general for AWS environments services, uh, both from the container service, their, their Kubernetes services, everything that they can have and that they offer to their customers, we want to be able to support, uh, one of the first areas of really that comes to mind in terms of first-class integration and support is thinking about Lambda and serverless. Um, so at the time when we first came out, was that, again, it was early for us, uh, or early in our journey as product and as company, uh, but really early for the space. And so how we were able to support that and how we were able to see, uh, that it could support our vision and, and what we wanted to bring as a value proposition to the market has been, you know, really powerful. So I think in looking at, you know, how we work with AWS, certainly on a partnership level of where we share a lot of the same customers, we share a very similar ethos and wanting to help people do things in the most cost-effective rapid manner possible, and to build the best software. Uh, and, you know, I mean, for us, we have a little bit of a backstory with AWS because Jeffrey's us was a, an early investor in, in common. >>Yeah, exactly. I mean, the, the whole memo that he wrote about, uh, you know, build an API or you're fired was, was certainly an inspiration to, to us and it catalyzed, uh, so much change in, in the technology landscape in general, about how everyone viewed API APIs about building a software that could be reused and, and was composable. And so that's something that, you know, we, we look at, uh, kind of carrying forward and we've been building on that momentum ever since. So, >>Well, I mean, it's just kind of take a, again, a high level, look at this in terms of microservices. And now that it's changing in terms of cloud connectivity. Thank you. Actually, I have a graphic to that. Maybe we can pull up and take a look at this and let's talk about this evolution. You know, what's occurring here a little bit, and, and as we take a look at this, um, tell us what you think those, these impacts are at the end of the day for your customers and how they're better able to provide their services and satisfy their customer needs. >>Absolutely. So this is really the heart of the connect platform and of our vision in general. Um, we'd spoken just a minute ago about thinking how we can support the entire journey or, uh, the, the enterprise reality that is managing a, a relatively complex environment of modelists different services, microservices, you know, circle assumptions, whatever it may be, uh, as well as lots of different deployment methods and underlying tech platforms. You know, if you have, uh, virtual machines and Kubernetes, whatever, again, whatever it may be. But what we look at is just the different sort of, uh, design patterns that can occur in thinking about a monolithic application. And, um, okay. Mainly that's an edge concern of thinking about how you're going to handle connectivity coming in from the edge and looking at a Kubernetes environment of where you're going to have, you know, many Kubernetes clusters that need to be able to communicate with each other. >>That's where we start to think about, uh, our ingress products and Kubernetes ingress that allows for that cross applic, uh, across application communication. And then within the application itself, and looking at service mesh, which we talked a little bit about of just how do I make sure that I can instrument and secure every transaction that's happening in a, a truly microservices, uh, deployment within Kubernetes or outside of it? How do I make sure that that's reliable and secure? And so what we look at is this is just a, uh, part of it is evolution. And part of it is going to be figuring out what works best when it, um, certainly if you're, if you're building something from scratch, it doesn't always make sense to build it, your MDP, as, you know, microservices running on Kubernetes. It probably makes sense to go with the shortest path, uh, at the same time, if you're trying to run it at massive scale and big applications and make sure they're as reliable as possible, it very well does make sense to spend the time and the effort to, to make humanize work well for you. >>And I think that's, that's the, the beauty of, of how the space is shifting is that, uh, it's, it's going towards a way of the most practical solution to get towards business value, to, to move software quicker, to give customers the value that they want to delight them to use. Amazon's, uh, you know, phrase ology, if that's, uh, if that's a word, uh, it's, it's something of where, you know, that is becoming more and more standard practice versus just trying to make sure that you're doing the, the latest and greatest for the sake of, of, uh, of doing it. >>So we've been talking about customers in, in rather generic terms in terms of what you're providing them. We talked about new surfaces that are certainly, uh, providing added value and providing them solutions to their problems. Can you give us maybe just a couple of examples of some real life success stories, where, where you've had some success in terms of, of providing services that, um, I assume, um, people needed, or at least maybe they didn't know they needed until, uh, you, you provided that kind of development that, but give us an idea of maybe just, uh, shine, a little light on some success that you've had so that people at home watching this can perhaps relate to that experience and maybe give them a reason to think a little more about calm. >>Yeah, absolutely. Uh, there, there's a number that come to mind, but certainly one of the customers that I spent a lot of time with, uh, you know, become almost friends would be with, uh, with the different, with a couple of the practitioners who work there is company called Cargill. Uh, it's a shared one with us and AWS, you know, it's one we've written about in the past, but this is one of the largest companies in the world. Um, and, uh, the, the way that they describe it is, is that if you've ever eaten a Vic muffin or eaten from McDonald's and had breakfast there, you you've used a Cargill service because they provide so much of the, the food supply chain business and the logistics for it. They had a, uh, it's a, it's an old, you know, it's a century and a half old company. >>It has a really story kind of legacy, and it's grown to be an extremely large company that's so private. Uh, but you know, they have some of the most unique challenges. I think that I've, I've seen in the space in terms of needing to be able to ensure, uh, that they're able to, to kind of move quickly and build a lot of new services and software that touch so many different spaces. So they were, uh, the challenge that was put in front of them was looking at really modernizing, you know, again, a century and a half old company modernizing their entire tech stack. And, you know, we're certainly not all of that in any way, shape or form, but we are something that can help that process quite a bit. And so, as they were migrating to AWS, as they were looking at, you know, creating a CICB process for, for really being able to ship and deploy new software as quickly as possible as they were looking at how they could distribute the, the new API APIs and services that they were building, we were helping them with every piece of that journey, um, by being able to, to make sure that the services that they deployed, uh, performed in the way that they expected them to, we're able to give them a lot of competence and being able to move, uh, more rapidly and move a lot of software over from these tried and true, uh, you know, older or more legacy of doing things to a much more cloud native built as they were looking at using Kubernetes in AWS and, and being able to support that handle scale. >>Again, we are something that was able to, to kind of bridge that gap and make sure that there weren't going to be disruptions. So there, there are a lot of kind of great reasons of why they're their numbers really speak for themselves in terms of how, uh, how much velocity they were able to get. You know, they saying them saying them out loud on the sense fake in some cases, um, because they were able to, you know, I think like something, something around the order of 20 X, the amount of new API APIs and services that they were building over a six month period, really kind of crazy crazy numbers. Um, but it is something where, you know, the, for us, we, we got a lot out of them because they were open source users. So calling is first and foremost, an open source company. >>And so they were helping us before they even became paying customers, uh, just by testing the software and providing feedback, really putting it through its paces and using it at a scale that's really hard to replicate, you know, the scale of a, uh, a couple of hundred thousand person company, right? Yeah. Talking about a win-win yeah. That worked out well. It's certainly the proof is in the pudding and I'm sure that's just one of many examples of success that you've had. Uh, we appreciate the time here and certainly the insights and wish you well on down the road. Thanks for joining us, Mike. Thanks, Sean. Thanks for having me. I've been speaking with Mike Villa from Kong. He is in corporate development and operations there on John Walls, and you're watching on the cube, the AWS startup showcase.

(upbeat music) >> Hello, and welcome to this Cube special presentation of Cube On CloudStartups with AWS Showcase. I'm John Furrier, your host of theCUBE. This session is the accelerate digital transformation and simplify AWS with autonomous cloud operations with Venkat Krishnamachari, who's the CEO and co-founder here with me on remote. Venkat, good to see you. >> Great to see you, John. >> So this is a session on, essentially DAY2 operations. Something that we've been covering on theCUBE as you know, for a long time. But the big trend is as DevOps becomes much more mainstream, intelligent applications or agile applications, have to connect with intelligent infrastructure and your company MontyCloud has the solution that literally turns IT pros into cloud powerhouses as you guys say, it's your tagline. This is a super important area. I want to get your thoughts and showcase what you guys are doing as one of the hot 10 startups. Thanks for coming on. So take a minute to explain real quick. What is MontyCloud all about? >> Great, thank you again for the opportunity. Hey everybody, I'm Venkat Krishnamachari. I represent mandate team at MontyCloud. We are an intelligent cloud management platform company. What we help customers do, is we help them simplify their cloud operations so they can go innovate and develop intelligent applications. Our platform is called DAY2, because everything after the day one of going to Cloud, needs a lot of expertise and we decided that's a fun area to go solve for our customers. We solve everything on starting DAY2 from simplifying provisioning, to management, to operations, to autonomous cloud operations. Our platform does this for our customers so they can innovate faster and they can close the cloud skills gap that is required to empower the developers. >> Venkat, I want to get your thoughts on DAY2 operations. There's been a trend that people talk about for a long time. As people move to the cloud and see the economic advantage of certainly with COVID-19, the market has said, "Hey, if you're on cloud native, you win." Andy Jassy at re:Invent last Keynote really laid out how companies can be proficient in becoming cloud-scale advantages. One of them was have expertise in cloud. So everyone is kind of doing that. You're starting to see enterprises all build the muscle for cloud operations. That's day one, they get started. Then that's kind of the challenges and the opportunities kick in when you have to continue in production. You have things that go on in the software. The underlying scaling infrastructure needs to be scaled out or all these kinds of things happen. This is what DAY2 is all about, keeping track of and maintaining high availability, uptime and keep the cost structure in line. This is what people discover. If they don't think properly about the architecture, they have huge problems. You guys solve this problem. Could you explain why this is important. >> Sure thing, John. So cloud operations, as you described, it's a continuous operations and continuous improvement in cloud environments. What efficient cloud operations does for customers is it accelerates innovation, reduces the risk, and more importantly, all the period of time that they are using their applications in the cloud, which is future, reduces the total cost of cloud operations. This is important because there is a huge gap in cloud skills. The surface area of cloud that customers need to manage is growing by the day. And most importantly, developers are increasingly and rightfully so, getting a seat at the table in defining and accelerating company's cloud journey. Which means, now they're proposing, microservices based application, container based application. Traditional applications are still in the mix. Now the surface area becomes a challenge for the IT operators to manage. That's why it's very important to start right. See, we ask this question to our customers. Having listened to our customers as hundreds of them, one thing is clear, when we ask this question to our customers, ever wonder why and how large scale companies like AWS are able to deliver massively scalable services and operate massive data centers with fewer people? Because it's automation. And it's important to think about, as you scale, automate a way things that must be automated, eliminate undifferentiated heavy lifting and help your developers move fast. All of this is vital in the day and age we live in, John. >> Yeah, I want to double down on that because I think this idea of integrating into operations is a critical key point for where success and failure kind of happen. We've seen with cloud, certainly IT departments and enterprise is going okay, cost optimization, check. Get cloud native, getting the cloud, lift and shift, I thought it through, I put some stuff in the cloud and then they go great, now I need resilience. I need resiliency, and I want to make sure things are now working okay, water flowing through the pipes, cloud's working. Then they say, "Well this is good, I got to need to integrate in with my own premises or edge or other things that are happening." Then they try to integrate into their core operations. McKinsey calls this the value driver three, integrating into core operators. We heard from them earlier in the program here at this event. This is key, it's not trivial to integrate cloud into your operations. This is what DAY2 and beyond is all about. Talk more about that. >> Yeah, that's a great point. And that's something that we've been working with customers to hands-on help learn and build it for them, right? So the acceleration of cloud adoption during the pandemic and ongoing adoption, it's going to shift the software security compliance and operational landscape dramatically. There's no escaping it. Cloud operations will no longer be an afterthought. DevOps will integrate with CloudOps. It'll provide a seamless feedback loop so that a box can be found sooner, fixed sooner, and uptime can be guaranteed. I'll give an example. One of our customers is a university. During the pandemic, their core examination application went down and they couldn't fix it on time because of lack of resources. For them, it's vital to have adopted cloud operations sooner but the runway they had was very little. Fortunately, we had the solution for them there. Within a week, they were able to take their entire on-prem application online, not just take the application but provide an autonomous cloud operations layer to their existing IT team with our platform, upscale them, and then about 14,000 students took their exams without any disruption. Now this customer and customers such as themselves have come to expect that level of integrated cloud operations into their application portfolio. It's important to address that with a platform that simplifies it. >> Venkat, real quick. Define, what is autonomous CloudOps platform? What does that mean? >> So let's take an example here, right? Customers who are trying to move an existing workload to cloud bring a traditional set of application. Then customers who are born in the cloud build microservices or server less based applications. Then there is containers. Now, all three the person surface areas that customers, particularly the IT teams have to manage. With the growing surface area, with the adoption of infrastructure as core, it becomes more nuanced to think about, how do we simplify? And in simplification comes automation. When a developer provision certain resource, previously, they used to be filing a ticket. Central IT team has to respond. Developers don't want that anymore. They want to innovate faster but at the same time Central IT team wants to have some governance in play. The best way to get out of the way of developers is automating it. And providing autonomous cloud operations means developers can deploy newer workloads faster, but with a level of guaranteed guardrail on security compliance and costs that sets them free. This is what we mean by autonomous cloud operations, closing the gap in skills, closing the gap in tooling, empowering your developers without thinking about the traditional model but enabling them to do things that's more in a rapid pace. That's what we mean by autonomous cloud operations. >> You had a great market opportunity. I think this is obviously a no brainer. As people say in the industry "cloud is scale is proven". Even post COVID if people don't have a cloud growth strategy they're pretty much going to be toast. McKinsey calls this a trillion dollar at a minimum not including potential new use cases, new pioneering applications coming. So pretty much, well the verdict is there, this is cloud. I got to ask you about MontyCloud as you guys have a business. Give or take a quick minute to explain the business of MontyCloud, some vitals or how people buy the product, the business model. Take a quick minute to explain MontyCloud business. >> Sure thing. John, see, our entire goal is to simplify cloud operations. Because what we learned is what seems to be complex about cloud adoption is that everybody is expected to be an expert on everything in the new era, but most teams are not ready to run efficient cloud operations at scale, as the cloud footprint is growing. This means we have to redefine certain conversations here. We talk directly to infrastructure architects, cloud architects, application owners. And in general, we talk to people who are leading their IT digital transformation for their companies. What we are enabling our customers is, they must demand that the traditional operation model must change to enable newer application patterns. For this, we are expecting customers want to standardize things, right? IT leaders are beginning to say, "All right, I got to standardize my provisioning, standardize my operations, reduce the heavy lifting that comes with infrastructure's code, and enable the business team and the application team to work closely together." The best way to do that is to go solve this problem with automation. So our platform is able to go help such customers, particularly leaders who demand digital transformation. With clear KPIs, our platform can help them ask the why question easily. And then our platform can also go perform, the how part of automation. That's what we solve. Those are the kinds of customers we really have been working with, John. >> So if I'm a customer, how do I know when I need to call MontyCloud? Is it because my cloud footprint is growing which is a natural sign of growth, or is it because I have more events happening, more things to manage? When do I know I have the need to call you guys? What's the signal? What's the sign? >> So we call it the day one mindset, and also the DAY2 mindset. Customers deciding to go to cloud on day one, should think about DAY2. Because without thinking about DAY2, it can become very expensive, right? When a customer's thinking about digital transformation, could be a lift and shift or it could be starting a new application pattern in the cloud, we can certainly help starting right that day because there are a couple of things they have to do, right? They have to standardize the cloud operations which means setting up the cloud accounts, setting up guardrails, enabling teams to go provision with self service. You want to start the right way. So we are happy to help on the day one journey itself and we can automate DAY2 along with it. So standardizing infrastructure operations, standardizing provisioning, security, visibility, compliance, cost. If any of this is an important milestone that customers have to achieve in their cloud journey, we can help. >> By the way, I would just point out that we were just talking on another session around lift and shift is not a no-brainer either if not thought through and remediated correctly that cost could go through the roof. I mean, we've seen evidence of lift and shift fails just because they didn't think it through. Just to your point. I mean, that's not a no brainer. Quickly explain why lift and shift is not as easy as it looks. >> Sure thing. So lift and shift is great to get started, but why sometimes it fails is that the connotations about wanting to keep your Opex down while giving up CapEx is at odds with each other, right? Cloud is great for reducing your Capex. But ongoing operations, of the DAY2 operations, can add a lot of burden to the operational expenses. What customers find out is after moving to the cloud, the cost overruns are happening because of resources that are not provisioned correctly, resources that should not be running. Wild Wild West kind of scenarios, where everybody has access to everything and they over provision. All of this together end up impacting customers' ability to go control the Opex. Then digital transformation projects are looked at from three different angles at least, right? Cost is definitely one, security is another, and then the ongoing operational tax with respect to monitoring, governance, remediation. All three when it simultaneously hits our customers, they look at lift and shift and saying, "Hey, this was cheaper on prem." But actually in the long run, this will be not just cheaper on the cloud, it can also be more efficient if they do it right. We can talk about some examples on how we help some customers with that helpful, John. >> Well, I want to get into the cloud operations, the whole dashboard in cloud operation administration. Is there anything that you could share because people are wanting more and more analytics. I mean, they're buying everything in sight. I mean, cyber security, you name it. There's more and more dashboards. No one wants another dashboard. So this is something that you guys have a strong opinion on how to think this through. Because again, at the end of the day, if you're instrumenting your network properly and your applications, your intelligence, things are changing, where's the data? Take us through your thinking around that. >> Sure thing. You are spot on. Nobody wants another dashboard that is just spewing data at them because data, without context is irrelevant in our mind, right? We want to be able to provide context, we want to be able to provide data within the context. And the dashboard to us means a customer that's looking at it, an IT leader looking at it should be able to ask the why question without working too hard at it, right? Let's bring up our dashboard. I would love to show and tell, although it's a dashboard, it is a tool that can enable IT leaders do things differently. >> John: Right, here it is. This is it right here. Okay, so this is the dashboard. Take me through it, what does it mean? >> Venkat: Yeah, let's (indistinct) right? The chart in the middle is the most important piece there. What we help our leaders, IT leaders do is, all the fullness of time of cloud adoption, we know the cloud's footprint is going to grow. The gray chart in the back, the stock chart represents the cloud footprint. As the cloud footprint continues to grow, we would like our leaders to demand that their security issues go down, their compliance issues go down and their costs to become more and more optimum. When leaders demand this, they can make things happen and our platform can help reduce all three and leaders can have this kind of dashboard to ask the why question. For example, they can compare one department with another department, ask that why question. They can compare an application that is similar in one department in another department and ask the why question, why is it more expensive? Why is it having more compliance issues? This is the kind of why questions our dashboard helps our customers perform and ask those questions, and they don't have to lift a finger, right? This entire dashboard comes to life within few minutes of them connecting their cloud accounts, where we provide visibility into operational issues, trend lines of data on how much consumption happens. And over a couple of months, they can see for themselves, make overall operation cost going down. Is my IT infrastructure now in cloud more resilient? And doesn't take more people to do it or am I able to turn on MontyClouds DAY2 bonds to go start reducing that burden or the period of time. This is what we mean by putting the power of autonomous CloudOps in our hands for customers. >> And this is what you mean by the IT powerhouse for the cloud. Is this on Amazon? So if I want to consume the product, what do I need to do to engage with you guys? What does it mean to me? Am I buying a service? Is it native? Is there agents involved? Take me through, what do I need to do? >> It's a great question. We are born in the cloud startup, which means we are super thankful for amazing technologies like Amazon infrastructure as core and the venting platform that's out there. So our platform is fully hosted, managed SaaS platform. A customer does not need to do anything but log onto montycloud.com, click a bunch of buttons, and connect their database account. They get started in under five minutes, self-service. And as they go through the platform, the guided experience where they can get to that dashboard I showed you in just a few clicks. They can get visibility, security posture assessment, compliance posture assessment, all in those few clicks. And when they decide to start using the platform more to automate and leverage the bots, they can always buy into additional services in the platform. So it's a easy to use get started in 10 minutes tops, if you will, that kind of platform >> Okay, great stuff. I want you to take me through the intelligent application flywheel that's going on here. So I can imagine that as the flywheel of success happens. Okay, got some intelligent apps, I see the dashboard, I'm getting some more visibility on the value creation, unlocking more value, new use case, all the things that happen in cloud, all good. And then I start growing, but I got builders trying to build more applications, more demand for more applications, more pressure on the infrastructure. The next question's, how do you guys simplify the cloud operation equation? Because I got to add more VPCs, I got to do more infrastructure, is it more EC Two? It can get complicated. How do you guys solve that problem? Because if the cloud footprint starts to grow because of more intelligent applications, how do you guys make it easier and simpler to scale up the intelligent infrastructure? >> Oh, that's a great question again, John. I'm going to go into a little bit of a detailed slide here. But before I do that, let's talk about two customers that we helped, right? This slide on the left, talks to those, both the customers. So what we have learned working with customers is, they have to build cloud accounts, manage cloud regions, user onboarding. Then they have to build networking infrastructure. Then they have to enable application infrastructure on top of the networking infrastructure. Application infrastructure could mean they want high-performance computing workloads or elastic services, such as queuing services, storage, or traditional VMS databases. That's a lot to build in the application infrastructure with infrastructure scope. On top of that, our customers have to deal with visibility, security, compliance costs. You get it, right? The path to intelligent applications is not easy because cloud is powerful, but it's broad, and the talent required is deep. We are able to say, how can we help our customers automate everything below the intelligent application layer. If we can do that, which we do, we can now propel our developers to go build intelligent applications without having the of also managing the underlying infrastructure. And we can help the IT operations team become cloud powerhouses because they get out of the way and enabled. Give you two examples here, right? One of our customers is a fortune 200 large ISP. They have about 10,000 servers in a particular department. And previously, when the servers were on premises, they had about a four member team managing compliance for it. When they lifted and shifted these servers into the cloud, the same model they wanted to... There are leaders that asked "Why should we continue with the same model?" They wanted MontyCloud. Now there is a DAY2 compliance board that's running, managing the 10,000 servers automatically watching on for compliance drifts, notifying them in a Slack channel, gets approval, remediates and fixes it. They were able to take those four folks and put them on the intelligent application side, I suppose to continuous infrastructure management site. Another example, a fortune 200 global networking company. It's an interesting situation, John. So on cyber Monday, they wanted to go big of obviously the cyber Monday was very important for them. The Thursday before cyber Monday, their on-premises data center and application went down and their teams wanted to move the application to cloud. And the partner that we work with, that brought this challenge to us saying hey, this fortune customer wants to go to cloud and we have this weekend. Well, we were able to go guide the partner and with our platform they were able to not only take their application from on-prem to cloud, they set up the cloud infrastructure, the networking, the application layer, the monitoring layer, the operations layer, all of that within a day. And on Monday that application delivered three X sales for this customer, without that partner or the customer being a cloud expert. That's what we mean by putting that kind of power in the hands of customers. >> Yeah, and I want to go back to that slide 'cause I think there's a second section I want to look at because what you just referred to is, I think this builds into the next comment on the right-hand side, this DAY2 kind of console vision here. The idea of getting in the weeds and getting into the troubleshooting of say, that cyber Monday example is exactly the non agility scenario, right? Because, if anyone's ever worked in tech knows when you have to get to root cause on something, it can take a while, right? So you need to have the system architecture built out. So here, classic cloud architecture on the left moves to a simple kind of console model. That's kind of what you guys are offering. Am I getting that right, Venkat? Is that kind of how this works? >> Yeah, that's kind of how it works, but the path to that maybe, a quick explanation though. We look at what's on the right--- >> Put that slide back up, let's get that slide back. Okay, there it is. >> Venkat: So what's on the right side here is, every layer on the left requires specialized talent and specialized tooling. That's all customers are currently experienced in the cloud. They either have to buy into a expensive monitoring tool or buy into an expensive security posture management tool. They have to hire, you know... It's hard to find cloud talent, right? And then they have to use infrastructure as code solutions. Sometimes that is, that can get more complex to maintain. What we have in MontyCloud is that, every layer there, they can provision by clicking away. For example, when they provision their cloud accounts setting up AWS best practices, budget guardrails, security, logging and monitoring, they can click away and do it. Setting up network infrastructure like VPC is setting up AWS transit gateway, VPNs, there's templates they can click and do it. The application infrastructure, which is a growing set of application infrastructure. Imagine this John, if a developer can come in and request the IT team they would like to set up an RDS database, right? The IT team can now with DAY2, can provide the developer options of, do you want it in dev stage prod? And do you want snapshots, backup, high availability? These are all check boxes and the developer can pick and choose and they can provision what they want without additional help from the IT team. And the IT team does not have to automate any one of those because it's pre automated in our platform. >> Yeah, this is the promise of infrastructure as code. You don't got to get in to the architecture and start throwing switches and all kinds of weird stuff can happen. Someone doesn't turn off, they don't enable auto-scale and they tested for this they forgot to revert back. I mean, there's a zillion things that could go wrong, human error, as well as automation. So once you set it up, then you provide a consumable developer friendly approach. That seems to be what's happening. Okay, cool. All right, well Venkat, this is fantastic. Final minutes we have left. I want to get your thoughts on the momentum and the vision. Talk about the momentum that you guys have now in the marketplace and what's the vision for the next five years. >> Great, it's a great question. From a momentum perspective John, we take an approach of, let's work with customers and understand that we can solve some problems for them. We've been working backups with customers. We have customers that are startups, that are born in the cloud, we have customers that are enterprise customers who are having a large footprint on-prem. Then we have everybody in between like university customers who are transitioning off. So what we did is from a momentum perspective, we worried more about, do we understand the talent gap and the tooling gap that exists across the board of all customers? Because every customer, once they go to cloud, they look to achieving the same level of efficiency and simplicity like modern cloud companies. A traditional company that moves to cloud wants to act and behave like the one in the cloud customer. For us it was very important to understand a variety of customers, a variety of use cases, and then automated away. So momentum is that we are able to go help a customer that is a Greenfield customer to go to cloud easily. And we're also able to go help brownfield customers, ensure they can reduce the total cost of cloud operations on an ongoing basis. So we've been seeing customers of all sizes, even helping customers of all sizes move fast. And there's a bunch of case studies out there in our website. We are a startup, so we've been able to help those customers and earn their trust by delivering results for them. So the momentum is that, we are able to go scale up now, and scale up fast for our customers without us being in the way, technically. Or customers can go to our platform help themselves and accelerate the platform. That's the momentum we have. From a future perspective, you asked, where things are headed, right? There are a couple of things. First things first, it's important to not just predict the future, we got to create it, right? About two years back when we founded MontyCloud, the question my team asked me, my CTO asked me is, what really matters in cloud ranking, right? So we said, all right, this is provisioning automation management. Yeah, they all matter. But what seemed to really matter is there are three things that matter. That's how we came to... One is events. The cloud itself is an eventing machine, right? More than ever, the cloud infrastructure emits events at every turn, every resource, every activity is expressed as an event. So we made an early bet on building an event driven platform from the ground up. We are the only platform that is even driven. Every other platform is seen to try and solve problems which is awesome to have, but they take an approach of an API based model or an inference into log based model. So the future, we believe, belongs to eventing model because it's lightweight on the customer's infrastructure, it goes easy on the cloud providers. More importantly, it gets the customer as close as possible to when the event happens, right? That's very important, to be able to be even event-driven. If you noticed Cloud Native Foundation came up and announced recently cloud events is the right way to deal with modern SaaS platforms. We've been in cloud events from day one for us, right? So the future is in eventing model. >> And that's where the data angle, I think, connects here for this event and why you guys are a hot startup is, observability, all these things. It's all about a event driven infrastructure. It's all events. It's monitoring, it's management, it's data. At the end of the day, the data is the instrumentation, is what it is. Developers are coding. Media's data. Everything's data. Everything has to do with data. You guys have a unique approach. Venkat Krishnamachari, thank you for coming on. Appreciate it, and thanks for sharing your story here at the AWS Showcase. First inaugural Cube On CloudStartups, part of the 10 hot startups categories. Thanks for sharing. >> Thanks for the opportunity. And we hope to help a lot more customers, simply for the cloud operations and innovate with some intelligent applications that's going to change the world. >> Check out Venkat and his company all on Twitter, on Facebook, they're on every channel, all the channels are open, of course. theCUBE we're bringing you all the hot startups, extracting the signal from the noise. I'm John furrier. Thanks for watching. (Upbeat music)

>> What does it take for an entrepreneur to develop a disruptive idea, prove that it works and bring it to market. I can think of a lot of things, but one of the most important is speed. (jet engine roars) This is Dave Vellante from theCUBE inviting you to join me and John Furrier for a special CUBE on cloud startup showcase made possible by AWS. Joining theCUBE will be Michael Lebow of McKinsey. We'll also be joined by Greylock's Jerry Chen. He's going to bring the VC perspective. CIO Ben Haynes is also going to be there to lay down his practical knowledge. We'll also have Jeff Barr of AWS and together we'll feature 10 innovative companies from the AWS Global Startup Program. So if you're a technology practitioner, you'll see some of the innovations that might help transform your business. If you're an investor, you'll get a glimpse of the future and if you're an entrepreneur, you'll see how 10 companies are rocketing toward escape velocity. So join us March, 24th at 9:00 AM Pacific for theCUBE on cloud startup showcase, Innovations with Cloud Data and Cloud Ops. We'll see you there. (upbeat music)

>>Hello, and welcome to this special presentation. This is the cube on cloud startups, our special event of Amazon web services, startup showcase. I'm John furrier, host of the cube, and excited to be here to talk about the hottest startups around cloud cloud computing data and the future of the enterprise. We've got Rob Harris, vice president of solutions consulting for star dog. Great company, Rob. Great to see you. Thanks for coming on. So this is a showcase presentation with AWS showcase startup showcase. You guys are a fast growing startup knowledge graph. We did a video explaining kind of what we did in the cube conversation. Um, really interesting category this, uh, eight hubs cloud startups with you guys. Talk about what you got. Take a minute to explain star dog and what you got. >>Sure. Yeah, here at startup, we are really a knowledge graph platform company. So we help build a knowledge graph for our customers tying together the data inside the organization and with data on the cloud in order for them to be able to find search and understand the context and relationship of all that data within their own organization. So that's really what we try to facilitate and make successful for our customers. >>Awesome. What market are you guys targeting? What's the market opportunity. Can you explain the market space that you're building product value in and what's your focus? >>Sure. Yeah, it's, it's pretty exciting. We do a lot from an industry perspective, we target a lot, uh, life sciences or financial the services, and it just tends to be, those are the ones that are most excited and getting started with this, but we certainly have a much broader set of customers in government or in manufacturing. What we really look for is the horizontal type solution, where you have a lot of systems that you want to tie together, or you want to have that understanding of your data all within context throughout your organization. So anybody struggling with that kind of tying of your data together, whether it's on the cloud or on prem, that's what we really go after >>Disruption. Who are you disrupting as you come into the marketplace? I love Amazon so hot startups because they got an eye clean take on something, but someone usually is being impacted. Who is, who are you guys disrupting as you come into? >>Yeah, a lot of times we find we're disrupting traditional ETL, right? So centralizing of all your data into one big platform, a lot of people have gone down this path of trying to create these large repositories data lakes, data warehouses. Yeah. We try to provide the additional value on top of them by not forcing you to continue to invest in moving and centralizing all your data together, but connecting it and providing context, um, while leaving and leveraging the mid worries. >>Awesome. Cause there's a big market opportunity as data warehouses becomes modernized and horizontal control planes and cloud computing is data is the key competitive advantage. Uh, great disruption. Great opportunity. So let's talk about the business star dog. What do you guys, uh, talk about the company, uh, where the headquarters is? The, how many employees what's the business model? How do you guys make money? Yeah, >>Well, a headquarters is always a little bit tricky nowadays is we were also distributed, but officially it is in Arlington Virginia. Uh, although we are all over the globe, uh, mostly in the United States and Europe, certainly as we look at, uh, how, how do we go to market and what do we do related to that? We have a subscription-based model where we help our customers get started usually small, um, by leveraging a package that they can run either on prem or in the cloud or directly from the AWS marketplace and letting them connect to the data and then growing out as they grow within their organization, larger, more interplay enterprise wide type of installations. So that's how we kind of go after it, uh, from, from our company perspective. >>So your go to market then for the company, is it bottoms up organic growth, kind of a freemium get in there? Or is it kind of a mid, mid tier or how do you guys look at that, that entry? >>It's a great question. That's exactly right. A lot of times we do start with a freemium type of model. We do have free trials and use usability to get started very quickly without having to talk to a salesperson or without having to pay up front in order to see the value, because we want you to be able to understand the value you're going to get out of our platform right off the bat and get started. Then after you've really tried it out and you see where it could apply within your organization, we help make it enterprise. >>I have to ask you how the business model of SAS, obviously clouds. Great. Are you guys leveraging Amazon web services marketplace at all? >>We are we're on the marketplace today, um, with the, both the free trial, as well as the ability through, you know, private offers to do whole production instances. So we're really excited about being a part of the marketplace. What we found is that sometimes customers want to run on the cloud. Sometimes they want to run on prem, wherever they want to run. We want to be sure that we're there. >>Yeah. Alex, let's pull up that slide on the hybrid, uh, architecture for these guys. So I want to bring this up since you brought up the business model and you talk about hybrid. This is interesting. This gets into the business model and this is kind of transitions into kind of the technology architecture. Could you walk me through this slide, the knowledge graph and the hybrid cloud. Why is this important for you guys and why is it important for customers? >>This is great. Thank you for, uh, for pulling this up. What this is really showing is as we look toward the future, as we really look at how people are deploying knowledge, graphs, and managing their data, we see that one of the big problems they're trying to address is what about cloud, uh, data that's on the cloud would a bit dated it's on prem. Maybe it's in multiple VPCs that you have within the Amazon environment. How do you tie all this together? And we all know that moving data around between all of these zones can be expensive and time consuming and difficult. And so we've come up with an architecture that allows you to run the knowledge, graph an agent of the knowledge graph in each of these zones. And they can all talk to each other and coordinate with each other. So they can see data that exists within that zone and pass it on to the other pieces as required or as needed to minimize your kind of in and out fees. And to leverage that all that data in one, in one place >>I asked you because this comes up a lot in our coverage, um, data mobility, uh, moving data is expensive. Um, how does that impact you guys in customers? A lot of people have been looking at, Hey, you know, the economics of the cloud are phenomenal, but at some point, if you've got a lot of data, you move compute to the data or you kind of think differently, how do you guys look at that? That trend? >>Yeah, that's, that's really our key value prop is people struggle with this. As people try to figure out how do I handle this large amount of data without having to generate all this additional costs about moving it around. We really look about how do I push that compute down to the storage layers, where the data already exists. And so if you think about our product architecture and you know, we, I know we have a slide on how our product is really built and how it's pulled together. When you look at our core core architecture, we have the graph that represents that connected data, but the exciting part of our architecture, what we do differently than everyone else is by allowing you to keep the data in its existing data silos, whether it's applications or repositories documents that you already have out there, we allow you to connect to that data where it is cross zone, whether it's on prem or on the cloud. >>And by leveraging the power of start on the virtualization engine, you can connect that data and be able to represent it from one source without having to move it around. But because we also have a persistence layer that's built into our product, you can really determine where's the best home. Is it data that you're going to use a lot and thereby should be really close to where the query engine is? Or is it something where you want to federate it out and leverage that compute at that storage layer itself? That flexibility is really why our customers come to us and are excited to use, start off. >>That's awesome. Great, great stuff. Love, love. The slides. Love to look at some pictures that describe the architecture both as well as the product. I love how you got the enterprise high-grade applications and then you're integrating with other partners. I think that's a really key, uh, value. And I think if you're not integrating well in this modern era, you probably won't be surviving much longer. It's pretty much a game changer at this point when knows that a question on the technology and product. Now keeping it on this theme. What's your secret sauce. Every company's got a secret sauce. What is star dog's secret sauce? >>Our secret sauce is really how do we coordinate across all of those applications? So if you can imagine you have, you know, Oracle database or Redshift repository, and you're trying to be able to unify that data in real time across those applications. There's a lot of thought and needs to go about how to do that efficiently. You don't want to take all the database from both repositories, move them, all that data into one place and then figure it out. And so our query planner, how do we coordinate across the multiple applications is really what makes us different and special >>On the Symantec modeling that you're doing? Because I see there's a lot of data there. You got to kind of get an understanding context. Um, how do you guys look at reusability metadata on data? This has become a very key point on not just data warehouse, but it's becoming much more about addressability and discoverability in as fast as possible, low latency, uh, with intelligence, this has been a big discussion. How do you guys look at that aspect of the reusability of the data? >>Yeah, it's, it's one of the exciting parts about starting with a semantic graph and then extending into these capabilities around virtualization and reasoning and inference by starting with the semantic graph, we allow you to, you know, incrementally invest in building out your model and then being able to reuse that model as you, as you go through your implementations. Yeah. That's been a, a big failing as people have looked at the analytical movements recently is so many times people spin up a repository, they answer a particular question and they do an absolutely fine job, but then we have your next question. You have to spin up another repository, build more views, re ETL the data. And then the semantic technology is what allows you to create that common understanding and reuse it over and over and over again. And I think it's time for that to hit mainstream. You know, it's been around a while. It's something that has taken some time to get some adoption around, but now that we really have build up awareness around it and we've shelled, the technology can scale the large volumes. Uh, I think it's time to be able to leverage the value that reasonability brings. Yeah. >>One final question on the product and the technology and kind of the architecture is how do you guys connect the dots going forward as more and more edge nodes become available in the network as that architecture of hybrid that we talked earlier about becomes so complex and so connected. I mean, you could have more connectedness than ever before. Um, it's very complex networks graph theory, right? You're talking about a lot of edges and a lot of traversal it's billions and billions of edges. I mean, this is it's complicated. How do you guys create, how do you guys see that unfolding and how and why the star dog remained relevant in that configuration? >>Yeah. And the simple fact is that people need help, right? It can't be that you're going to define all those edges and connections by hand yourself through some systems or keys. It's a great way to get started, but it's not sufficient in order to really get the value out of that graph that you expect. And the ways we do that is twofold. The first bit is really an influencing or reasoning capability. Being able to look at this structure of the data, how it's composed and create connections between that data based on, you know, logical, logical rules. The second is machine learning, right? Machine learning is high. We use things like linear regression algorithms or other types of community detection algorithms in order to build more connections in the data so that you can get really unlock that value that you're looking for. When you're leveraging graph technology, >>A lot of secret sauce here, a lot of technology graph, super exciting. Let's get into the final segment around customer traction and what you guys have seen with customers. Um, what are some of the use cases that are popular and what happens if customers aren't going down this road? What are they missing out on? Um, I mean, it's the classic fear of missing out and fear of getting screwed over right. Are going out of business. I mean, that's, that's motivational at some level, but you know, there are the, do I wait and people who waited on cloud computing by the way were left behind and some never survived. So we're almost in this same dynamic with customers. At some point you got to put the toe in the water, so to speak or get going to take us through some customer examples and use cases where, >>Or this is working. Yeah. I think both of those areas are, are, uh, great ones to hit on. So when you think about what are we missing out on one of our largest customer bases really in pharmaceuticals. Yeah. And they're using this technology in order to find more connections in the data so that they can really decrease the amount of time for getting a drug to market on the research and development. They can look more at leveraging the data they've already connected using related items to be able to accelerate their investments and waiting costs them hundreds of millions, if not billions of dollars. So there are certainly ones where being able to adopt this technology early and get value out of early, really pays off in. And they're not the only ones. That's the only, that's the only the life sciences space. But there's also the idea to use it, as you said, really about what else am I missing out on? >>And the data fabric movement, this movement around, how do I lower the cost in my organization about moving data around creating more ETL jobs, leveraging all these data assets already have that the data fabric movement is the idea of how do we really automate that? How do we accelerate that? How do we make that an easier process so that it just doesn't cost as much to manage all this data in an organization. And I've observed that more and more. We have customers coming to us, really interested in this type of use cases that relates to our technology and they are getting ahead of their competitors by really lowering their, it costs in line to focus on these higher value activities. >>Life of the customers is what for you with, with startup? Why, how do they win? What's the reason why they buy and take the freemium. And when do they convert over? Well, take me through the progression of value. When do they see something and why do they increase their sure. >>Assumption? Yeah. That, I mean, the bottom line is you want to try to get more value out of your data at a lower cost and make it easier and faster to do. And so getting started in a single use case, trying out our free version, representing your data and taking a look at what it could look like under a common model, connecting it up with our virtualization services is a great way to try out the technology and really, you know, put your toe in the water to see is this something that would be a value to organization as you see that value unlock is you really understand that you can leverage these days assets with this lower time to value, you know, days in order to unlock a whole repository and connected to another repository. That's where we love to engage with you and help show you how you can make that successful in a more production environment. >>I like about some of the things you're talking about star dog has kind of that aspirin aspect, but also a growth, um, uh, vitamin E as well, in terms of the value proposition, a lot of companies are overwhelmed with the data, but yet you have this path towards more creation of value through the knowledge graph and reasoning and other other value. When does a customer, and this is kind of comes back to the customers who are out there potentially watching prospects or future customers. When do they know they need to call you guys up? Is it because they have too many sources? Could you take me through what it, what it looks like in a prospect's environment where they would really win with start a what's it look like? What are some of the signs that they need to engage, start out? >>Yeah. The two big things that we've seen repeated in our customer base over and over again, is if you have a large number of systems out there that aren't connected, that you don't see how all the data it can be pulled together between those systems, because the different data formats or different languages or different ways that the data is created in those systems start off, can certainly help. The second is if you have a large data warehouse or a data Lake, and you don't see the value being generated out of that, because people don't understand where the data is or what context it has with other data within those repositories, both of those situations are one where we think you'd get a lot of value out of start off. And we'd love to talk to you. >>So would, so just secondly, understand this. So if you have a lot of systems that either are not connected or connected, whatever, that's great, a lot of sources sitting around, you know, whether it's spreadsheets or Oracle or >>Red shift, whatever it is, we've loved it that's right. >>Ingest as much as possible from sources >>That's right. Ingest or connect. I mean, that's really the value that we bring is you don't have to pull it all in. You can just map and leverage the data where it lives. We have customers that have petabyte repositories that just mapped that data in to start off, and we can really facilitate pulling out the value of those systems without you having to move it around again, to another request, >>Ingest, connect, and visually see value. That's right. It sounds, it sounds like a tagline, um, great stuff. So just give some examples of who's using it. What big names? Um, obviously you guys, aren't hot startup coming out of the Amazon cloud showcase. Uh, congratulations. What are some names that have worked with you guys that can give an indicator of the company that you're keeping right now in terms of, >>Yeah, I mean our largest customer by far right now, our longest customer has been NASA. Um, so they've been a really exciting user of the platform we've been really to see them leverage the platform. Schneider electric has been a long time user, uh, Bayer FINRA in the U S which is a financial services watchdog organization. These are customers that are getting a lot of value out of our platform today, and we're excited to work with them. >>Awesome, Rob, great to see you. Congratulations. Uh, take a minute to just give the plug for the commercial. How do we engage? What's the culture like, um, you guys hiring, what's the, what's the state of that? What's the state of the company. >>Yeah, no, it's a, it's a great thank you for, uh, for bringing that up where, you know, we're an exciting growing company. Um, as we really reach out more and more to connect more people's data, we find that we're always looking at more resources on building out more conductivity between the individual data sources. So more understanding on that front, as well as more, a professional services type folks to help people through the process. We've really been trying to minimize the amount of effort that you have to have in order to get started, but we know that people like a helping hands. So we're always looking for people we're always growing and we're excited to have the chance to, you know, bring this technology out beyond just the semantic group that is historically been here. >>You know, you've got a great job. Vice-president solutions consulting, essentially you're in a product role, but more like a solution architect meets products, uh, customer facing, and also product century. You're kind of the center of all the action. So what's the coolest thing you've seen, um, from a customer standpoint or an architecture or, um, a deployment or an engagement that you've been involved with. That's been kind of like, Oh, wow, that's cool. That's game. That's something new that we've been, we wouldn't have seen a few years ago. Take us through just an example, anecdotal, you don't have to share the company name or you. >>That's a great question. Um, there is a company that is working on self-driving cars and being able to leverage the knowledge graph to pull together all of the videos and material they get from the vehicles themselves, as well as static information about the sensors. Uh, that's been pretty exciting to see. I, I, I just recently purchased the festival myself. So I'm excited about the whole self-driving car world and to be able to help them participate with these companies is, is pretty exciting. Um, we, we just help one of the large drug manufacturers come to market with one of their drugs earlier than expected. You know, that's a, that's a pretty exciting feeling to know that you can really help people, um, by just connecting the data they already have and letting them leverage those resources, uh, that that really is something that we're going to be very calm >>And the bridge to the future that the customers have to cross with you is also pretty compelling. You got industrial IOT and more and more data to take a quick minute to describe what that future looks like. >>Yeah. You know, as we see more and more automation in this process, we see a couple of different really, you know, exploding areas. The first off, you know, you hit the nail on the head is data being able to bring in more edge devices, being able to really process that data on the fly and be able to help answer questions as these changes in data are occur within these sources. Um, that's certainly part of the future. And the other thing that we're really excited about is this more automatic data discovery with an organization. How can we have an agent that goes out and kind of can infer really even what your data is about in the structure of your data without a lot of input for you. And so we've been working a lot with building up these models automatically and letting you have the foundation for integrating your data, um, and just the push of a button. So we're excited about walking, Alexa, our customers in this journey as well. >>It's, it's a fun area. You talk about reasoning. That's one of the key value propositions that you guys have. You talk about AI, you talk about bots and soon it's going to be thinking machines for us. They're going to be doing all the work. >>I hope they're not too soon, but I am excited about that idea as well. I can go. I do think that, uh, you know, if you look at organizations today, it's fascinating how it's not, that the problems are different, but we're trying to automate as much of it as possible so that we can work on that, the real value clumps of our organizations. And it's not that kind of drudgery work. I started as a DBA back in my career, um, just trying to keep the database up and running, you know, nowadays, you know, all these autonomous databases and self indexing, and self-correcting, it's just not a passive lead as much anymore. You know, we hope we can bring that to the data infrastructure automation. >>It's a double-edged sword gun, right. It's amazing, done wrong. It could cause some damage and flipped some, some pain and hurt. And so you got to figure it out, got to have the right data sets, gotta have the right software, um, and a great future. Rob Harris, congratulations for being a cannabis startup showcase here on the cube on cloud startups, uh, with AWS, uh, led partnership. Thank you for coming on and being part of this event. Thank you again. Okay. Rob Harris, vice president solutions consulting at star dog here for the coupon cloud. I'm John furrier. Thanks for watching. >>Yeah.

(upbeat music) >> We're here theCUBE on Cloud Startup Showcase brought to you by AWS. And right now we're going to explore the next frontier for privacy, you know, security, privacy, and compliance, they're often lumped together and they're often lumped on as an afterthought bolted on to infrastructure, data and applications. But, you know, while they're certainly related they're different disciplines and they require a specific domain knowledge and expertise to really solve the challenges of today. One thing they all share is successful implementations, must be comprehensive and designed in at the start and with me to discuss going beyond compliance and designing privacy protections into products and services. Justin Antonipillai, who is the founder and CEO of WireWheel, Justin awesome having you on the AWS Startup Showcase. Thanks for being here >> Dave, thanks so much for having me. It's a real honor, and I appreciate it. Look forward to the discussion. >> So I always love to ask founders, like, take us back. Why did you start this company? Where did your inspiration come from? >> So Dave, I was very lucky. I had the honor of serving in president Obama's second term as an Acting Under Secretary for Economic Affairs. So I ran the part of the government that includes the U.S. Census Bureau and the Bureau of Economic Analysis. So core economic statistical bureaus. But I helped lead a lot of the Obama administration's, outreach and negotiations on data privacy around the world. Including on something called the EU-U.S. Privacy Shield. So at the time the two jobs I had really aligned with what our discussion is here today. The first part of it was, I could see that all around the world in the U.S. and around the world, data privacy and protecting privacy, had become a human rights issue. It was a trade issue. You could see it as a national security issue and companies all around the world were just struggling with how to get legal, how to make sure that I do it right, and how I make sure that I'm treating my customer's data, in the right way. But when I was also leading the agency, a lot of what we were trying to do was to help our U.S. citizens, our folks here around the country solve big public problems by ethically and responsibly using government data to do it. And I can talk about what that meant in a little while. So the inspiration behind why WireWheel was, we need better more technically driven ways to help companies get compliance, to show their customers that they're protecting privacy and to put customers, our customers onto a path where they can start using the customer data better, faster and stronger, but most importantly, ethically. And that's really what we try to tackle at WireWheel. >> Right, excellent. Thank you for that. I mean, yeah you know, in the early days of social media, people kind of fluffed it off and oh there is no privacy in the internet, blah, blah, blah. And then wow, it became a huge social issue and public policy really needed to step in but also technology needs this to help solve this problem. So let's try to paint a picture for people as to really dig into the problem that you solve and why it's so complicated. We actually have a graphic. It's a map of the U S that we want to pull up here. Explain this. >> Yeah, I mean, what you're saying here is that every one of your, our viewers today is going to be looking at privacy laws moving across the country Dave but there's a lot of different ones. You know, if you're a company that's launching and building your product, that you might be helping your customers your consumer facing. The law, and you're even let's assume you want to do the right thing. You want to treat that customer data responsibly and protect it. When you look at a map like this and you can see three States have already passed different privacy laws, but look at the number of different States all across the country that are considering their own privacy laws. It really could be overwhelming. And Virginia, as you can see is just about to pass it's next privacy law but there's something like 23,24 States that are moving them through. The other thing Dave, that's really important about this is, these are not just breach laws. You know, I think years ago we were all looking at these kinds of laws spreading across the country and you would be saying, okay, that's just a breach law. These laws are very comprehensive. They have a lot to them. So what we have been really helping companies with is to enable you to get compliant with a lot of these very quickly. And that's really what we've tried to take on. Because if you're trying to do the right thing there should be a way to do it. >> Got it. Yeah, I can't even imagine what the it had been so many permutations and complexities but imagine this, if this were a globe we were looking at it says it gets out of control. Okay, now you guys well you use a term called phrase beyond compliance? What do we mean by that? >> There are a couple of things. So I'd say almost every company taking a product to market right now, whether you're B2C or B2B you want to make sure you can answer the customer question and say, yes, I'm compliant. And usually that means if you're a B2C company it means that your customers can come to your site. Your site is compliant with all of the laws out there. You can take consents and preferences. You can get their data back to them. All of these are legal requirements. If you're a B2B company, you're also looking at making sure you can create some critical compliance records that's it, right? But when we think beyond compliance, we think of a couple of basic things. Number one, do you tell the story about all the trust and protection you put around your data in a way that your customers want to do business with you? I mean Dave, if you went to CES the last couple of years and you were walking into the center or looking at a virtual version of it, on every billboard, the top five, top 10 global companies advertise that they take care of your data and they're onto something, they're onto something. You can actually build a winning strategy by solving a customer's problem and also showing them that you care, and that they're trustworthy. Because there are too many products out there, that aren't. The second thing, I'm sorry, go ahead. >> No, please carry on. >> No, I mean the second thing, and then I think I'd say is going beyond compliance also means that you're thinking about how you can use that data for your customer, to solve all of their problems. And Dave, what I'd say here is imagine a world right now, in which, you know you trusted that the data that you gave to companies or to the government, was protected and that if you changed your mind and you wanted it back that they would delete it or give it back to you. Can you imagine how much more quickly we would have solved getting a COVID vaccine? Can you imagine how much data would have been available to pharmaceutical companies to actually develop a vaccine? Can you imagine how much more quickly we would have opened the economy? The thing is companies can't solve every problem that they could for a customer because customers don't trust that the data is going to be used correctly and companies don't know how to use it in that way and ethically. And that's what we're talking about when we say getting beyond compliance which is we want to enable our customers to use the data in the best way and most ethical way to solve all of their customer's problems. >> Okay, so I ask the elephant in the room question. If you asked most businesses about personal information, where it's stored, you know who has access to it, the fact is that most people can't answer it. And so when they're confronted with these uncomfortable questions. The other documents and policies that maybe check some boxes, why is that not a good idea? I mean, there's an expense to going beyond that but so why is that not just a good idea to check it off? >> Well look, a lot of companies do need to just check it off and what I mean, get it right, make sure you label and the way we've thought about this is that when you're building on a backbone like AWS, it does give you the ability to buy a lot of services quickly and scale with your company. But it also gives us an ability to comply faster by leveraging that infrastructure to get compliant faster. So if you think about it, 20 years ago whenever I wanted to buy storage or if I wanted to buy servers and look we're a company that built in the cloud, Dave it would have been very difficult for us to buy the right storage and the processing we needed, given that we were starting. But I was able to buy very small amounts of it until our customer profile grew. But that also means my data moved out of a single hard drive and out of a single set of servers, into other places that are hosted in the cloud. So the entire tech stack that all of our customers are building on means they're distributing personal data into the cloud, into SAS platforms. And there's been a really big move through integration platforms as a service to allow you to spread the personal data quickly. But that same infrastructure can be used to also get you compliant faster, and that's the differentiation. So we built a platform that enables a company to inventory their systems, to track what they're doing in those systems and to both create a compliance record faster by tracking what they're doing inside the cloud and in SAS systems. And that's the different way we've been thinking about it as we've been going to market. >> So, okay. So what actually do you sell, you sell a service? Is it a subscription? >> Yeah. >> And AWS is underneath that, maybe you could put down a picture for us. >> Sure, we're a cloud hosted software as a service. We have two core offerings. One is the WireWheel Trust Access Consent Solution. So if you go to a number of major brands, and you go to their website, when they tell you here's the data we're collecting about you, when they collect your consents and preferences, when they collect a request for data correction or deletion of the data, all the way from the request to delivery back to the consumer, we have an end to end system that our customers use with their customers, a completely cloud hostable in a subscription. So enables even very small startups, to build that experience into their website and into their products, from the very beginning, at a cost efficient point. So if you want to stand up a compliant website or you want to build into your product that Trust Access Consent Solution, we have a SAS platform, and we have developer tools and our developer portal to let you do it quickly. The second thing we do is we have a privacy operations manager. So this is the most security center but for privacy operations. It helps you inventory your systems, actually create data flow maps and most critically create compliance records that you need to comply with, you know the European law, the Brazilian law, and that whole spectrum of U.S. privacy laws that you showed a few minutes ago. And those are the two core offerings we have. >> I love it. I mean, it's the cloud story, right? One is you don't have to spend a millions of dollars on hardware and software. And the second is, when you launch you enable small companies, not just the biggest companies you give them the same, essentially the same services. And that's a great story. Who do you sell to Justin? What does a typical customer engagement look? >> Yeah, we, in many of our customers and in the AWS say startup environment, you often don't have companies that have like a privacy officer. They often don't even have a general counsel. So we sell a package that will often go to whoever is responsible at the company for privacy compliance. And, you know, interestingly Dave in some startups that might be a marketing officer, it might be a CLO, it might be the CTO. So in startups and sort of growing companies, we've put out a lot of guidance, and our core WireWheel developer portal is meant to give even a startup all they need to stand up that experience and get it going, so that when you get that procurement imagine you're about to go sell your product, and they ask you, are you compliant, then you have that document ready to provide. We also do provide this core infrastructure for enormous enterprises. So think telecoms, think top three global technology companies. So Dave, we get excited about is we've built a core software platform privacy infrastructure that is permanently being used by some of the largest companies in the world. And our goal is to get that infrastructure at the right price point into every company in the world, right? We want to enable any company to spend and stand up the right system, that's leveraging that same privacy infrastructure that the big folks have, so that as they scale, they can continue to do the right thing. >> That's awesome. I mean, you mentioned a number of roles of marketing folks. I can even see a sales, let's say sales lead saying, okay we got this deal on the table. How do we get through the procurement because we didn't check the box, all right. So, let me ask you this. We talked a little bit about designing privacy in a and it's clear you help do that. How do you make it, you know fundamental to customer's workloads? Do they have to be like an AWS customer to take advantage of that concept? Or how did they make it part of their workflow? >> Yeah, so there's a couple of critical things. How do you make it part of the workflow? The first thing is, you go to any company's website right now, they have to be compliant with the California law. So a very straightforward thing we do is we can for both B2B and B2C companies stand up an entire customer experience that matches the scale of the company that enables it to be compliant. That means you have a trust center that shows the right information to your customers, it collects the consents, preferences, and it stands up with a portal to request data. These are basics. And for a company that's standing up the internal operations, we can get them app collecting that core record and create a compliance record very fast. With larger companies, Dave you're right. I mean, when you're talking about understanding your entire infrastructure and understanding where you're storing and processing data it could seem overwhelming, but the truth is, the way we onboard our customers is we get you compliance on your product and website first, right? We focus on your product to get that compliance record done. We focus on your website so that you can sell your product. And then we go through the rest of the major systems where you're handling personal information, your sales, your marketing, you know, it's like a natural process. So larger enterprises we have a pretty straightforward way that we get them up and running, but even small startups we can get them to a point of getting them compliant and starting to think about other things very, very quickly. >> And so Justin, you're a government so you understand big, but how I talk about the secret ingredient that allows you to do this at scale and still handle all that diversity, like what we showed in that graphic, the different locations, different local laws, data sovereignty, et cetera. >> Yeah, there's a couple things on the secret source. One is, we have to think about our customers every day. And we had to understand that companies will use whatever their infrastructure is to build. Like you've seen, even on AWS there are so many different services you can use. So number one, we always think with an engineering point of view in mind. Understand the tools, understand the infrastructure in a way that brings that kind of basic visibility to whoever it is that's handling privacy, that basic understanding. The second is, we focused on core user experience for the non-technical user. It's really easy to get started. It's really easy to stand up your privacy page and your privacy policy. It's really easy to collect that and make that first record. The third is, and you know, this is one of those key things. When I was in the government, I met with folks in the intelligence community at one point day, and this always stuck with me. They were telling me that 20 years ago, you know to do the kind of innovation that you have going on now, you would have had to have had a defense contract. You would have had to have invested an enormous amount of money to buy the processing and the services and the team. But the ability for me as a startup founder, to understand the big picture and understand that companies need to be compliant fast, get their website compliant fast, get their product compliant fast, but build on a cloud infrastructure that allowed me to scale was incredible. Because it allows us to do a lot with our customers that a company like ours would have been really challenged to do without that cloud backbone. >> Love this, the agility and the innovation. Last question, give us the company update Justin, you know where are you? What can you share with us, fundraising, head count, are you generating revenue? Where you are? >> Oh yeah, we're excited as I mentioned, we are already the privacy platform of choice of some of the larger brands in the world, which we're very excited about. And we help them solve both the trust, access consent problem for their customers, and we help with the privacy operations management. We recently announced a new $20 million infusion of capital, led by a terrific venture capital fund, ForgePoint Capital. We've been lucky to have been supported by NEA, Sands Capital, Revolution Capital, Pritzker Capital, PSP. And so we have a terrific group of investors behind us. We are scaling, we've grown the company a lot in the last year. Obviously it's been an interesting and challenging year with COVID, but we are really focused on growing our sales team, our marketing team, and we're going to be offering some pretty exciting solutions here for the rest of the year. >> The timing was unbelievable, you had the cloud at your beck and call, you had the experience in government. You've got your background as a lawyer. And it all came in, and the legal come into the forefront of public policy, just a congratulations on all your progress today. We're really looking forward to seeing you guys rocket in the future. I really appreciate you coming on. >> Dave, thanks so much for having me, really enjoyed it. And I look forward to seeing you soon. >> Great, and thank you for watching everyone is Dave Vellante for theCUBE on cloud startups. Keep it right there. (upbeat music)