(gentle music) >> Hello, and welcome to theCUBE's coverage of International Women's Day. I'm John Furrier, host of theCUBE. I've got a great guest here, CUBE alumni and very impressive, inspiring, Rachel Mushahwar Skaff, who's a managing director and general manager at AWS. Rachel, great to see you. Thanks for coming on. >> Thank you so much. It's always a pleasure to be here. You all make such a tremendous impact with reporting out what's happening in the tech space, and frankly, investing in topics like this, so thank you. >> It's our pleasure. Your career has been really impressive. You worked at Intel for almost a decade, and that company is very tech, very focused on Moore's law, cadence of technology power in the industry. Now at AWS, powering next-generation cloud. What inspired you to get into tech? How did you get here and how have you approached your career journey, because it's quite a track record? >> Wow, how long do we have? (Rachel and John laugh) >> John: We can go as long as you want. (laughs) It's great. >> You know, all joking aside, I think at the end of the day, it's about this simple statement. If you don't get goosebumps every single morning that you're waking up to do your job, it's not good enough. And that's a bit about how I've made all of the different career transitions that I have. You know, everything from building out data centers around the world, to leading network and engineering teams, to leading applications teams, to going and working for, you know, the largest semiconductor in the world, and now at AWS, every single one of those opportunities gave me goosebumps. And I was really focused on how do I surround myself with humans that are better than I am, smarter than I am, companies that plan in decades, but live in moments, companies that invest in their employees and create like artists? And frankly, for me, being part of a company where people know that life is finite, but they want to make an infinite impact, that's a bit about my career journey in a nutshell. >> Yeah. What's interesting is that, you know, over the years, a lot's changed, and a theme that we're hearing from leaders now that are heading up large teams and running companies, they have, you know, they have 20-plus years of experience under their belt and they look back and they say, "Wow, "things have changed and it's changing faster now, "hopefully faster to get change." But they all talk about confidence and they talk about curiosity and building. When did you know that this was going to be something that you got the goosebumps? And were there blockers in your way and how did you handle that? (Rachel laughs) >> There's always blockers in our way, and I think a lot of people don't actually talk about the blockers. I think they make it sound like, hey, I had this plan from day one, and every decision I've made has been perfect. And for me, I'll tell you, right, there are moments in your life that mark a differentiation and those moments that you realize nothing will be the same. And time is kind of divided into two parts, right, before this moment and after this moment. And that's everything from, before I had kids, that's a pretty big moment in people's lives, to after I had kids, and how do you work through some of those opportunities? Before I got married, before I got divorced. Before I went to this company, after I left this company. And I think the key for all of those is just having an insatiable curiosity around how do you continue to do better, create better and make better? And I'll tell you, those blockers, they exist. Coming back from maternity leave, hard. Coming back from a medical leave, hard. Coming back from caring for a sick parent or a sick friend, hard. But all of those things start to help craft who you are as a human being, not as a leader, but as a human being, and allows you to have some empathy with the people that you surround yourself with, right? And for me, it's, (sighs) you can think about these blockers in one of two ways. You can think about it as, you know, every single time that you're tempted to react in the same way to a blocker, you can be a prisoner of your past, or you can change how you react and be a pioneer of the future. It's not a blocker when you think about it in those terms. >> Mindset matters, and that's really a great point. You brought up something that's interesting, I want to bring this up. Some of the challenges in different stages of our lives. You know, one thing that's come out of this set of interviews, this, of day and in conversations is, that I haven't heard before, is the result of COVID, working at home brought empathy about people's personal lives to the table. That came up in a couple interviews. What's your reaction to that? Because that highlights that we're human, to your point of view. >> It does. It does. And I'm so thankful that you don't ask about balance because that is a pet peeve of mine, because there is no such thing as balance. If you're in perfect balance, you are not moving and you're not changing. But when you think about, you know, the impact of COVID and how the world has changed since that, it has allowed all of us to really think about, you know, what do we want to do versus what do we have to do? And I think so many times, in both our professional lives and our personal lives, we get caught up in doing what we think we have to do to get ahead versus taking a step back and saying, "Hey, what do I want to do? "And how do I become a, you know, "a better human?" And many times, John, I'm asked, "Hey, "how do you define success or achievement?" And, you know, my answer is really, for me, the greatest results that I've achieved, both personally and professionally, is when I eliminate the word success and balance from my vocabulary, and replace them with two words: What's my contribution and what's my impact? Those things make a difference, regardless of gender. And I'll tell you, none of it is easy, ever. I think all of us have been broken, we've been stretched, we've been burnt out. But I also think what we have to talk about as leaders in the industry is how we've also found endurance and resilience. And when we felt unsteady, we've continued to go forward, right? When we can't decide, the best answer is do what's uncomfortable. And all of those things really stemmed from a part of what happened with COVID. >> Yeah, yeah, I love the uncomfortable and the balance highlight. You mentioned being off balance. That means you're growing, you're not standing still. I want to get your thoughts on this because one thing that has come out again this year, and last year as well, is having a team with you when you do it. So if you're off balance and you're going to stretch, if you have a good team with you, that's where people help each other. Not just pick them up, but like maybe get 'em back on track again. So, but if you're solo, you fall, (laughs) you fall harder. So what's your reaction to that? 'Cause this has come up, and this comes up in team building, workforce formation, goal setting, contribution. What's your reaction to that? >> So my reaction to that that is pretty simple. Nobody gets there on their own at all, right? Passion and ambition can only take you so far. You've got to have people and teams that are supporting you. And here's the funny thing about people, and frankly, about being a leader that I think is really important: People don't follow for you. People follow for who you help them become. Think about that for a second. And when you think about all the amazing things that companies and teams are able to do, it's because of those people. And it's because you have leaders that are out there, inspiring them to take what they believe is impossible and turn it into the possible. That's the power of teams. >> Can you give an example of your approach on how you do that? How do you build your teams? How do you grow them? How do you lead them effectively and also make 'em inclusive, diverse and equitable? >> Whew. I'll give you a great example of some work that we're doing at AWS. This year at re:Invent, for the first time in its history, we've launched an initiative with theCUBE called Women of the Cloud. And part of Women of the Cloud is highlighting the business impact that so many of our partners, our customers and our employees have had on the social, on the economic and on the financials of many companies. They just haven't had the opportunity to tell their story. And at Amazon, right, it is absolutely integral to us to highlight those examples and continue to extend that ethos to our partners and our customers. And I think one of the things that I shared with you at re:Invent was, you know, as U2's Bono put it, (John laughs) "We'll build it better than we did before "and we are the people "that we've been waiting for." So if we're not out there, advocating and highlighting all the amazing things that other women are doing in the ecosystem, who will? >> Well, I've got to say, I want to give you props for that program. Not only was it groundbreaking, it's still running strong. And I saw some things on LinkedIn that were really impressive in its network effect. And I met at least half a dozen new people I never would have met before through some of that content interaction and engagement. And this is like the power of the current world. I mean, getting the voices out there creates momentum. And it's good for Amazon. It's not just personal brand building for my next job or whatever, you know, reason. It's sharing and it's attracting others, and it's causing people to connect and meet each other in that world. So it's still going strong. (laughs) And this program we did last year was part of Rachel Thornton, who's now at MessageBird, and Mary Camarata. They were the sponsors for this International Women's Day. They're not there anymore, so we decided we're going to do it again because the impact is so significant. We had the Amazon Education group on. It's amazing and it's free, and we've got to get the word out. I mean, talk about leveling up fast. You get in and you get trained and get certified, and there's a zillion jobs out (laughs) there in cloud, right, and partners. So this kind of leadership is really important. What was the key learnings that you've taken away and how do you extend this opportunity to nurture the talent out there in the field? Because when you throw the content out there from great leaders and practitioners and developers, it attracts other people. >> It does. It does. So look, I think there's two types of people, people that are focused on being and people who are focused on doing. And let me give you an example, right? When we think about labels of, hey, Rachel's a female executive who launched Women of the Cloud, that label really limits me. I'd rather just be a great executive. Or, hey, there's a great entrepreneur. Let's not be a great entrepreneur. Just go build something and sell it. And that's part of this whole Women of the cloud, is I don't want people focused on what their label is. I want people sharing their stories about what they're doing, and that's where the lasting impact happens, right? I think about something that my grandmother used to tell me, and she used to tell me, "Rachel, how successful "you are, doesn't matter. "The lasting impact that you have "is your legacy in this very finite time "that you have on Earth. "Leave a legacy." And that's what Women of the Cloud is about. So that people can start to say, "Oh, geez, "I didn't know that that was possible. "I didn't think about my career in that way." And, you know, all of those different types of stories that you're hearing out there. >> And I want to highlight something you said. We had another Amazonian on the program for this day earlier and she coined a term, 'cause inside Amazon, you have common language. One of them is bar raising. Raise the bar, that's an Amazonian (Rachel laughs) term. It means contribute and improve and raise the bar of capability. She said, "Bar raising is gender neutral. "The bar is a bar." And I'm like, wow, that was amazing. Now, that means your contribution angle there highlights that. What's the biggest challenge to get that mindset set in culture, in these- >> Oh. >> 'Cause it's that simple, contribution is neutral. >> It absolutely is neutral, but it's like I said earlier, I think so many times, people are focused on success and being a great leader versus what's the contribution I'm making and how am I doing as a leader, you know? And when it comes to a lot of the leadership principles that Amazon has, including bar raising, which means insisting on the highest standards, and then those standards continue to raise every single time. And what that is all about is having all of our employees figure out, how do I get better every single day, right? That's what it's about. It's not about being better than the peer next to you. It's about how do I become a better leader, a better human being than I was yesterday? >> Awesome. >> You know, I read this really cute quote and I think it really resonates. "You meditate to upgrade your software "and you work out to upgrade your hardware." And while it's important that we're all ourselves at work, we can't deny that a lot of times, ourselves still need that meditation or that workout. >> Well, I hope I don't have any zero days in my software out there, so, but I'm going to definitely work on that. I love that quote. I'm going to use that. Thank you very much. That was awesome. I got to ask you, I know you're really passionate about, and we've talked about this, around, so you're a great leader but you're also focused on what's behind you in the generation, pipelining women leaders, okay? Seats at the table, mentoring and sponsorship. What can we do to build a strong pipeline of leaders in technology and business? And where do you see the biggest opportunity to nurture the talent in these fields? >> Hmm, you know, that's great, great question. And, you know, I just read a "Forbes" article by another Amazonian, Tanuja Randery, who talked about, you know, some really interesting stats. And one of the stats that she shared was, you know, by 2030, less than 25% of tech specialists will be female, less than 25%. That's only a 6% growth from where we are in 2023, so in seven years. That's alarming. So we've really got to figure out what are the kinds of things that we're going to go do from an Amazon perspective to impact that? And one of the obvious starting points is showcasing tech careers to girls and young women, and talking openly about what a technology career looks like. So specifically at Amazon, we've got an AWS Git IT program that helps schools and educators bring in tech role models to show them what potential careers look like in tech. I think that's one great way that we can help build the pipeline, but once we get the pipeline, we also have to figure out how we don't let that pipeline leak. Meaning how do we keep women and, you know, young women on their tech career? And I think big part of that, John, is really talking about how hard it is, but it's also greater than you can ever imagine. And letting them see executives that are very authentic and will talk about, geez, you know, the challenges of COVID were a time of crisis and accelerated change, and here's what it meant to me personally and here's what we were able to solve professionally. These younger generations are all about social impact, they're about economic impact and they're about financial impact. And if we're not talking about all three of those, both from how AWS is leading from the front, but how its executives are also taking that into their personal lives, they're not going to want to go into tech. >> Yeah, and I think one of the things you mentioned there about getting people that get IT, good call out there, but also, Amazon's going to train 30 million people, put hundreds of millions of dollars into education. And not only are they making it easier to get in to get trained, but once you're in, even savvy folks that are in there still have to accelerate. And there's more ways to level up, more things are happening, but there's a big trend around people changing careers either in their late 20s, early 30s, or even those moments you talk about, where it's before and after, even later in the careers, 40s, 50s. Leaders like, well, good experience, good training, who were in another discipline who re-skilled. So you have, you know, more certifications coming in. So there's still other pivot points in the pipeline. It's not just down here. And that, I find that interesting. Are you seeing that same leadership opportunities coming in where someone can come into tech older? >> Absolutely. You know, we've got some amazing programs, like Amazon Returnity, that really focuses on how do we get other, you know, how do we get women that have taken some time off of work to get back into the workforce? And here's the other thing about switching careers. If I look back on my career, I started out as a civil engineer, heavy highway construction. And now I lead a sales team at the largest cloud company in the world. And there were, you know, twists and turns around there. I've always focused on how do we change and how do we continue to evolve? So it's not just focused on, you know, young women in the pipeline. It's focused on all gender and all diverse types throughout their career, and making sure that we're providing an inclusive environment for them to bring in their unique skillsets. >> Yeah, a building has good steel. It's well structured. Roads have great foundations. You know, you got the builder in you there. >> Yes. >> So I have to ask you, what's on your mind as a tech athlete, as an executive at AWS? You know, you got your huge team, big goals, the economy's got a little bit of a headwind, but still, cloud's transforming, edge is exploding. What's your outlook as you look out in the tech landscape these days and how are you thinking about it? What your plans? Can you share a little bit about what's on your mind? >> Sure. So, geez, there's so many trends that are top of mind right now. Everything from zero trust to artificial intelligence to security. We have more access to data now than ever before. So the opportunities are limitless when we think about how we can apply technology to solve some really difficult customer problems, right? Innovation sometimes feels like it's happening at a rapid pace. And I also say, you know, there are years when nothing happens, and then there's years when centuries happen. And I feel like we're kind of in those years where centuries are happening. Cloud technologies are refining sports as we know them now. There's a surge of innovation in smart energy. Everyone's supply chain is looking to transform. Custom silicon is going mainstream. And frankly, AWS's customers and partners are expecting us to come to them with a point of view on trends and on opportunities. And that's what differentiates us. (John laughs) That's what gives me goosebumps- >> I was just going to ask you that. Does that give you goosebumps? How could you not love technology with that excitement? I mean, AI, throw in AI, too. I just talked to Swami, who heads up the AI and database, and we just talked about the past 24 months, the change. And that is a century moment happening. The large language models, computer vision, more compute. Compute's booming than ever before. Who thought that was going to happen, is still happening? Massive change. So, I mean, if you're in tech, how can you not love tech? >> I know, even if you're not in tech, I think you've got to start to love tech because it gives you access to things you've never had before. And frankly, right, change is the only constant. And if you don't like change, you're going to like being irrelevant even less than you like change. So we've got to be nimble, we've got to adapt. And here's the great thing, once we figure it out, it changes all over again. And it's not something that's easy for any of us to operate. It's hard, right? It's hard learning new technology, it's hard figuring out what do I do next? But here's the secret. I think it's hard because we're doing it right. It's not hard because we're doing it wrong. It's just hard to be human and it's hard to figure out how we apply all this different technology in a way that positively impacts us, you know, economically, financially, environmentally and socially. >> And everyone's different, too. So you got to live those (mumbles). I want to get one more question in before we, my last question, which is about you and your impact. When you talk to your team, your sales, you got a large sales team, North America. And Tanuja, who you mentioned, is in EMEA, we're going to speak with her as well. You guys lead the front lines, helping customers, but also delivering the revenue to the company, which has been fantastic, by the way. So what's your message to the troops and the team out there? When you say, "Take that hill," like what is the motivational pitch, in a few sentences? What's the main North Star message in today's marketplace when you're doing that big team meeting? >> I don't know if it's just limited to a team meeting. I think this is a universal message, and the universal message for me is find your edge, whatever that may be. Whether it is the edge of what you know about artificial intelligence and neural networks or it's the edge of how do we migrate our applications to the cloud more quickly. Or it's the edge of, oh, my gosh, how do I be a better parent and still be great at work, right? Find your edge, and then sharpen it. Go to the brink of what you think is possible, and then force yourself to jump. Get involved. The world is run by the people that show up, professionally and personally. (John laughs) So show up and get started. >> Yeah as Steve Jobs once said, "The future "that everyone looks at was created "by people no smarter than you." And I love that quote. That's really there. Final question for you. I know we're tight on time, but I want to get this in. When you think about your impact on your company, AWS, and the industry, what's something you want people to remember? >> Oh, geez. I think what I want people to remember the most is it's not about what you've said, and this is a Maya Angelou quote. "It's not about what you've said to people "or what you've done, "it's about how you've made them feel." And we can all think back on leaders or we can all think back on personal moments in our lives where we felt like we belonged, where we felt like we did something amazing, where we felt loved. And those are the moments that sit with us for the rest of our lives. I want people to remember how they felt when they were part of something bigger. I want people to belong. It shouldn't be uncommon to talk about feelings at work. So I want people to feel. >> Rachel, thank you for your time. I know you're really busy and we stretched you a little bit there. Thank you so much for contributing to this wonderful day of great leaders sharing their stories. And you're an inspiration. Thanks for everything you do. We appreciate you. >> Thank you. And let's go do some more Women of the Cloud videos. >> We (laughs) got more coming. Bring those stories on. Back up the story truck. We're ready to go. Thanks so much. >> That's good. >> Thank you. >> Okay, this is theCUBE's coverage of International Women's Day. It's not just going to be March 8th. That's the big celebration day. It's going to be every quarter, more stories coming. Stay tuned at siliconangle.com and thecube.net here, with bringing all the stories. I'm John Furrier, your host. Thanks for watching. (gentle music)

>>Okay, welcome back everyone to the Cube's live coverage here in Monaco for the MoCo crypto summit. I'm John fur. You're host of the cube. We got a great guest Dustin plant Boltz who is a crypto advisor, but also the crypto editor for Forbes Monaco here. Seeing the official event, the AAL event of the Monaco crypto summit in Monaco, your coverage area for Forbes, your MCing. Welcome to the >>Cube. Thank you for having me. And it's, it's always fun when I get to have an event in our backyard, cuz I get to hear what others know. And to me I'm very curious. Yeah. Always >>Learning. So you're on the MC on the stage here, you know, queue in the program online great program. So it's innovative event, inaugural event, great name by the way. Crypto summit and mono crypto >>Summit. Yeah, the MoCo crypto summit. >>That sounds like I want to attend every year. >>You're you're more than welcome to attend next year. >>Well, I hope so. Either way. I'm at the Al event with you. So gimme the take on what's on stage. What's been the program, like what's your observations going on here at the event today? >>So what we're starting to see globally is this digitization of things and the people that are part of the innovation side. And so that's what we've been able to see this morning. We're we're now at the break is what sort of companies are out there, the good ones and what are they building? Is this innovation? Is it even innovative and figuring out how they're gonna do it and the roadmaps to getting there from the metaverses to NFTs and even to decentralized finance. >>Yeah, it's the number one question I get is what's legit. What's not legit. And then you're starting to see the, the, the wheat and the shaft separating here and you know, something called crypto winter. But I don't see it. I mean, I see correction for some of the bad things going on in terms of not having the right underpinning infrastructure, the creative ideas are amazing. We're also seeing like digital bits and other platforms kind of coming together to enable the creators and, and the NFT side for instance has been huge. What has been your observation on that enablement? Because you have two schools of thoughts. You have the total nerds we're up and down building everything. Then you have artists and creators, whether it's music, tech apps building, they don't necessarily want to get 'em to the covers. They don't want to deal with all that. Yeah. Have you seen, what's your, what's your take on that? >>So I I'm seeing that a lot of these major brands, you know, they they're striving for excellence. You know, they're being more careful of who they partner with and the types of companies and you know, they, they look at it from reality and a little tough love to figure out should they align their brand. So what we're seeing here is is that there is so much inertia moving forward. That we're just at the beginning of this thing. Yeah. McKinsey recently said that the ecosystem will be over $30 trillion. So when you recognize that we are so early and it's those right now, or some might say are the risk takers. But to me there, aren't taking risk. They're being a part of making history. >>Yeah. You get the pioneers and you get the financial. So as they come together, how do you see the market? Cause what I've noticed with crypto and here in, in this, this market is international. One lot of international finance us is kind of lag behind. You got all kinds of rules, but you got the, the combination of the, the future billionaires. Sure. Okay. The pioneers and then the financeers yeah. Coming the money, the money and the power coming together. What's your reporting show you that's going on right now? What should people know about on how this is evolving? What they shouldn't >>Expect? Well, so you have a group that wants to become cryers they're seeing these individuals globally. They're making lots and lots of money, but what they don't realize is that not everybody is gonna have that outcome, but looking at the technology aspect of it and how it's going to improve a system that many can agree is collectively broken legacy just can't move beyond. It was never designed to you'll see people take shots at certain card companies and I go, but you recognize they developed the assembly line. And so I'm seeing that the smart money they got in long ago, believe it or not. And those now they're looking out for their errors are the ones that saying, I will not have an excuse when my, my grandkids or my, my nieces or my nephews, when they come and ask, where were you when the greatest transformational shift in human history, from both education to jobs, to careers and even wealth was being shifted to a digital world, why were you on the sideline waiting? And so I think what we're gonna see is this tsunami coming, and it's gonna start with one big player and then two and five, you go, go alone. You go far, go together. You go further. And that's what we're seeing is that this collective is moving forward >>And the community, we just had Beth Kaiser on, I've known Beth for many, many years. And she's what she's her journey has done. She's had a great mission and then gets she's a data scientist and came to Analytica. Now she's doing work with Ukraine and the rallying support around it has been impressive. And it's a community vibe, but the community's not just like sympathetic they're hands on together to your point. >>Yeah. It, but it also takes courage. I mean, you look at Britney Kaiser and what she had, and to me, courage is not, not having fear. Courage is not allowing the fear to stop. You, you know, recently asked my executive coach, who's 85 and I'm turning 39. This question of, do you let fear stop you? How do you decide? And he said, you know, you can either let, you can either ride the dragon. And I said, or let the dragon chase you. And Brittany has been one of these that made a decision to do what was right. And it came down to integrity. Yeah. >>So what are you have to these days what's going on in your world? >>What is going on in my world? So I moderate events all over and I connect and I like to ask people questions. So I'm gonna ask you, I'm gonna turn at the interviewer on the >>Interview. It's good. Natural. >>What are you learning? >>I mean, I'm learning, I mean today or this week or this month or this year. Well, I was just talking with Brittany about this. The security world is converging cloud technology, cloud computing. That revolution has just been amazing. Amazon posted their earnings yesterday. They blew it away as far as I'm concerned. So they kind of show there's no tech recession. I've learned that this recession, that we're so called in is the first downturn in tech where there's been cloud players as hyperscalers as an economic engine. Okay. So from a, from a business perspective, Amazon web services, Microsoft Azure now Google cloud, Alibaba's now in, in international version. This is the first time at downturns ever happened with cloud computing as an economic engine. And so therefore what I'm seeing is the digital transformation that's happening across the world for enterprises and entrepreneurs is not stopping. >>It's actually accelerating. So although the GDPs down in inflation is down, you're seeing a massive shift continuing to accelerate, spending and transformation with cloud technologies and decentralized. So you can almost see it kind of in the, this event and other events, even some of the bigger events, the best smartest people are working on it. The applications in all the categories are transforming. If cloud is step one, decentralized gonna be step two. So I see that kind of bridge going from cloud computing, cloud native to decentralized native. And I think a D DAPP market's gonna just explode. I think NFTs are just scratched on the surface. I think that's kind of, I won't say gimmicky, but I think no, but you're right, much more of a much more of a, an illustration that there's more coming. >>There is a lot more coming because people are seeing that there's more to an NFT than an ugly luck and J you know, ugly and JP image that there's, that there's data in there. And that your avatar will be stored as just that as an NFT. And I learned today from go of sing, that decentralization is, is the key to innovation. And I agree with that statement. Holy. >>Yeah. I mean, I think access to stuff is gonna be multidimensional. Like you think about the NFT as, as an ID, whether it's him or UN unstoppable domains is that company just got financing another round where the billion dollars, their concept is like, Hey, one NFT is your access for all of your potential identities in context. >>And isn't that exciting that we're now gonna be at this stage where you travel with you. Yeah. Instead of someone else traveling with you, you get to decide who you will be. And to me, everything you're doing in this world, this reality is now becoming part of your digital asset as a whole. >>I remember when I started my podcasting company in 20 2004, early pioneers, Evan Williams was there with Odo and you had, you know, the blogging revolution going on that whole democratization wave actually didn't happen right then. But all the people that were involved in that web two oh, kind of CRAs was all about democratization. It's kind of happening now. I mean, 15, 20 years later at web services is transformed cloud the democratization for own your own data, putting users in control. And I think in the middle of that, the Facebook's the world, the world garden data, you know, manipulation kind of took it off track a little bit. So I think now I'm, I psych to see that it's back on track to where it was. I mean, Facebook made billions of dollars. Now you got LinkedIn. I mean, LinkedIn's great for your resume, but it's also become a wall's garden with no data export. >>Yeah. And then >>No APIs keep >>Changing. Think about this. That if you wanna apply for a job, just change something quickly. Yeah. Ah, now you're the senior VP. Yeah. Before you were, you're an office manager >>Like to see the immutable block change, >>You don't get to see when did the record change. Yeah. >>Reputation data. You're a digital exhaust people gonna wanna reign that in. And I think the user in charge message that Brit Kaiser was talks about is hugely a mess under, under, under amplified concept. Digital assets are key, but the data ownership is something that I think is, is >>Powerful. So I'm gonna be launching a brand new company in and around September called cryptos. And it's a crypto career center. Think of it like the, the crypto for LinkedIn, that it's an aggregator becoming the industry standard for education, becoming the industry standard for crypto ships, with partners like ledger and moon pay and Casper labs. >>Look at this, we got an exclusive scoop on the cube. This >>Is the first time I will tell you this the first time in, in an environment like this. Yeah. That I'm excited to, I'm excited to talk about, right. Because it's time to be part of the change. Yeah, exactly. You know, as a father, I look at, I know where it's headed in the world of business. I know in the world of this, that we're gonna call the internet of connected things. Yeah. That it's gonna require you to have a certain talent skill or a certain certification. And to me, it's important to have an industry that supports one >>Staff and also, and also history on misinformation, smear campaigns can happen and ruin a career >>Overnight. Can you imagine that one little thing and because the internet never forgets. Yeah. It stays around indefinitely. >>The truth has to come out. Dustin. Great to have you on the queue. Thank you so much. Final question. What have you learned in there is MC what's your takeaway real quick? >>What I've learned is I never tire of learning. Thank you again, to learn more. Dustin plan.com. >>All right. Thanks for coming. Thank you. Cube coverage here at Monaco. I'm Shawn furry. We'll back with more coverage after this short break.

>>Hello, everyone. Welcome to the cubes coverage here. New York city for AWS Amazon web services summit 2022. I'm John furrier, host of the cube with Dave ante. My co-host. We are breaking it down, getting an update on the ecosystem. As the GDP drops, inflations up gas prices up the enterprise continues to grow. We're seeing exceptional growth. We're here on the ground floor. Live at the Summit's packed house, 10,000 people. Eric Bradley's here. Chief STR at ETR, one of the premier enterprise research firms out there, partners with the cube and powers are breaking analysis that Dave does check that out as the hottest podcast in enterprise. Eric. Great to have you on the cube. Thanks for coming on. >>Thank you so much, John. I really appreciate the collaboration always. >>Yeah. Great stuff. Your data's amazing ETR folks watching check out ETR. They have a unique formula, very accurate. We love it. It's been moving the market. Congratulations. Let's talk about the market right now. This market is booming. Enterprise is the hottest thing, consumers kind of in the toilet. Okay. I said that all right, back out devices and, and, and consumer enterprise is still growing. And by the way, this first downturn, the history of the world where hyperscalers are on full pumping on all cylinders, which means they're still powering the revolution. >>Yeah, it's true. The hyperscalers were basically at this two sun system when Microsoft and an AWS first came around and everything was orbiting around it. And we're starting to see that sun cool off a little bit, but we're talking about a gradient here, right? When we say cool off, we're not talking to shutdown, it's still burning hot. That's for sure. And I can get it to some of the macro data in a minute, if that's all right. Or do you want me to go right? No, go go. Right. Yeah. So right now we just closed our most recent survey and that's macro and vendor specific. We had 1200 people talk to us on the macro side. And what we're seeing here is a cool down in spending. We originally had about 8.5% increase in budgets. That's cool down is 6.5 now, but I'll say with the doom and gloom and the headlines that we're seeing every day, 6.5% growth coming off of what we just did the last couple of years is still pretty fantastic as a backdrop. >>Okay. So you, you started to see John mentioned consumer. We saw that in Snowflake's earnings. For example, we, we certainly saw, you know, Walmart, other retailers, the FA Facebooks of the world where consumption was being dialed down, certain snowflake customers. Not necessarily, they didn't have mentioned any customers, but they were able to say, all right, we're gonna dial down, consumption this quarter, hold on until we saw some of that in snowflake results and other results. But at the same time, the rest of the industry is booming. But your data is showing softness within the fortune 500 for AWS, >>Not only AWS, but fortune 500 across the board. Okay. So going back to that larger macro data, the biggest drop in spending that we captured is fortune 500, which is surprising. But at the same time, these companies have a better purview into the economy. In general, they tend to see things further in advance. And we often remember they spend a lot of money, so they don't need to play catch up. They'll easily more easily be able to pump the brakes a little bit in the fortune 500. But to your point, when we get into the AWS data, the fortune 500 decrease seems to be hitting them a little bit more than it is Azure and GCP. I >>Mean, we're still talking about a huge business, right? >>I mean, they're catching up. I mean, Amazon has been transforming from owning the developer cloud startup cloud decade ago to really putting a dent on the enterprise as being number one cloud. And I still contest that they're number one by a long ways, but Azure kicking ass and catching up. Okay. You seeing people move to Azure, you got Charlie bell over there, Sean, by former Amazonians, Theresa Carlson, people are going over there, there there's lift over at Azure. >>There certainly is. >>Is there kinks in the arm or for AWS? There's >>A couple of kinks, but I think your point is really good. We need to take a second there. If you're talking about true pass or infrastructure is a service true cloud compute. I think AWS still is the powerhouse. And a lot of times the, the data gets a little muddied because Azure is really a hosted platform for applications. And you're not really sure where that line is drawn. And I think that's an important caveat to make, but based on the data, yes, we are seeing some kinks in the armor for AWS. Yes. Explain. So right now, a first of all caveat, 40% net score, which is our proprietary spending metric across the board. So we're not like raising any alarms here. It's still strong that said there are declines and there are declines pretty much across the board. The only spot we're not seeing a decline at all is in container, spend everything else is coming down specifically. We're seeing it come down in data analytics, data warehousing, and M I, which is a little bit of a concern because that, that rate of decline is not the same with Azure. >>Okay. So I gotta ask macro, I see the headwinds on the macro side, you pointed that out. Is there any insight into any underlying conditions that might be there on AWS or just a chronic kind of situational thing >>Right now? It seems situational. Other than that correlation between their big fortune 500, you know, audience and that being our biggest decline. The other aspect of the macro survey is we ask people, if you are planning to decline spend, how do you plan on doing it? And the number two answer is taking a look at our cloud spend and auditing it. So they're kind say, all right, you know, for the last 10 years it's been drunken, sail or spend, I >>Was gonna use that same line, you know, >>Cloud spend, just spend and we'll figure it out later, who cares? And then right now it's time to tighten the belts a little bit, >>But this is part of the allure of cloud at some point. Yeah. You, you could say, I'm gonna, I'm gonna dial it down. I'm gonna rein it in. So that's part of the reason why people go to the cloud. I want to, I wanna focus in on the data side of things and specifically the database. Let, just to give some context if, and correct me if I'm, I'm a little off here, but snowflake, which hot company, you know, on the planet, their net score was up around 80% consistently. It it's dropped down the last, you know, quarter, last survey to 60%. Yeah. So still highly, highly elevated, but that's relative to where Amazon is much larger, but you're saying they're coming down to the 40% level. Is that right? >>Yeah, they are. And I remember, you know, when I first started doing this 10 years ago, AWS at a 70%, you know, net score as well. So what's gonna happen over time is those adoptions are gonna get less and you're gonna see more flattening of spend, which ultimately is going to lower the score because we're looking for expansion rates. We wanna see adoption and increase. And when you see flattening a spend, it starts to contract a little bit. And you're right. Snowflake also was in the stratosphere that cooled off a little bit, but still, you know, very strong and AWS is coming down. I think the reason why it's so concerning is because a it's within the fortune 500 and their rate of decline is more than Azure right >>Now. Well, and, and one of the big trends you're seeing in database is this idea of converging function. In other words, bringing transaction and analytics right together at snowflake summit, they added the capability to handle transaction data, Mongo DB, which is largely mostly transactions added the capability in June to bring in analytic data. You see data bricks going from data engineering and data science now getting into snowflake space and analytics. So you're seeing that convergence Oracle is converging with my SQL heat wave and their core databases, couch base couch base is doing the same. Maria do virtually all these database companies are, are converging their platforms with the exception of AWS. AWS is still the right tool for the right job. So they've got Aurora, they've got RDS, they've got, you know, a dynamo DV, they've got red, they've got, you know, going on and on and on. And so the question everybody's asking is will that change? Will they start to sort of cross those swim lanes? We haven't seen it thus far. How is that affecting the data >>Performance? I mean, that's fantastic analysis. I think that's why we're seeing it because you have to be in the AWS ecosystem and they're really not playing nicely with others in the sandbox right now that now I will say, oh, Amazon's not playing nicely. Well, no, no. Simply to your point though, that there, the other ones are actually bringing in others at consolidating other different vendor types. And they're really not. You know, if you're in AWS, you need to stay within AWS. Now I will say their tools are fantastic. So if you do stay within AWS, they have a tool for every job they're advanced. And they're incredible. I think sometimes the complexity of their tools hurts them a little bit. Cause to your point earlier, AWS started as a developer-centric type of cloud. They have moved on to enterprise cloud and it's a little bit more business oriented, but their still roots are still DevOps friendly. And unless you're truly trained, AWS can be a little scary. >>So a common use case is I'm gonna be using Aurora for my transaction system and then I'm gonna ETL it into Redshift. Right. And, and I, now I have two data stores and I have two different sets of APIs and primitives two different teams of skills. And so that is probably causing some friction and complexity in the customer base that again, the question is, will they begin to expand some of those platforms to minimize some of that friction? >>Well, yeah, this is the question I wanted to ask on that point. So I've heard from people inside Amazon don't count out Redshift, we're making, we're catching up. I think that's my word, but they were kind of saying that right. Cuz Redshift is good, good database, but they're adding a lot more. So you got snowflake success. I think it's a little bit of a jealousy factor going on there within Redshift team, but then you got Azure synapse with the Synap product synapse. Yep. And then you got big query from Google big >>Query. Yep. >>What's the differentiation. What are you seeing for the data for the data warehouse or the data clouds that are out there for the customers? What's the data say, say to us? >>Yeah, unfortunately the data's showing that they're dropping a little bit whose day AWS is dropping a little bit now of their data products, Redshift and RDS are still the two highest of them, but they are starting to decline. Now I think one of the great data points that we have, we just closed the survey is we took a comparison of the legacy data. Now please forgive me for the word legacy. We're gonna anger a few people, but we Gotter data Oracle on-prem, we've got IBM. Some of those more legacy data warehouse type of names. When we look at our art survey takers that have them where their spend is going, that spends going to snowflake first, and then it's going to Google and then it's going to Microsoft Azure and, and AWS is actually declining in there. So when you talk about who's taking that legacy market share, it's not AWS right now. >>So legacy goes to legacy. So Microsoft, >>So, so let's work through in a little context because Redshift really was the first to take, you know, take the database to the cloud. And they did that by doing a one time license deal with par XL, which was an on-prem database. And then they re-engineered it, they did a fantastic job, but it was still engineered for on-prem. Then you along comes snowflake a couple years later and true cloud native, same thing with big query. Yep. True cloud native architecture. So they get a lot of props. Now what, what Amazon did, they took a page outta of the snowflake, for example, separating compute from storage. Now of course what's what, what Amazon did is actually not really completely separating like snowflake did they couldn't because of the architecture, they created a tearing system that you could dial down the compute. So little nuances like that. I understand. But at the end of the day, what we're seeing from snowflake is the gathering of an ecosystem in this true data cloud, bringing in different data types, they got to the public markets, data bricks was not able to get to the public markets. Yeah. And think is, is struggling >>And a 25 billion evaluation. >>Right. And so that's, that's gonna be dialed down, struggling somewhat from a go to market standpoint where snowflake has no troubles from a go to market. They are the masters at go to market. And so now they've got momentum. We talked to Frank sluman at the snowflake. He basically said, I'm not taking the foot off the gas, no way. Yeah. We, few of our large, you know, consumer customers dialed things down, but we're going balls to the >>Wall. Well, if you look at their show before you get in the numbers, you look at the two shows. Snowflake had their summit in person in Vegas. Data bricks has had their show in San Francisco. And if you compare the two shows, it's clear, who's winning snowflake is blew away from a, from a market standpoint. And we were at snowflake, but we weren't at data bricks, but there was really nothing online. I heard from sources that it was like less than 3000 people. So >>Snowflake was 1900 people in 2019, nearly 10,000. Yeah. In 2020, >>It's gonna be fun to sort of track that as a, as an odd caveat to say, okay, let's see what that growth is. Because in fairness, data, bricks, you know, a little bit younger, Snowflake's had a couple more years. So I'd be curious to see where they are. Their, their Lakehouse paradigm is interesting. >>Yeah. And I think it's >>And their product first company, yes. Their go to market might be a little bit weak from our analysis, but that, but they'll figure it out. >>CEO's pretty smart. But I think it's worth pointing out. It's like two different philosophies, right? It is. Snowflake is come into our data cloud. That's their proprietary environment. They're the, they think of the iPhone, right? End to end. We, we guarantee it's all gonna work. And we're in control. Snowflake is like, Hey, open source, no, bring in data bricks. I mean data bricks, open source, bring in this tool that too, now you are seeing snowflake capitulate a little bit. They announce, for instance, Apache iceberg support at their, at the snowflake summit. So they're tipping their cap to open source. But at the end of the day, they're gonna market and sell the fact that it's gonna run better in native snowflake. Whereas data bricks, they're coming at it from much more of an open source, a mantra. So that's gonna, you know, we'll see who look at, you had windows and you had apple, >>You got, they both want, you got Cal and you got Stanford. >>They both >>Consider, I don't think it's actually there yet. I, I find the more interesting dynamic right now is between AWS and snowflake. It's really a fun tit for tat, right? I mean, AWS has the S three and then, you know, snowflake comes right on top of it and announces R two, we're gonna do one letter, one number better than you. They just seem to have this really interesting dynamic. And I, and it is SLT and no one's betting against him. I mean, this guy's fantastic. So, and he hasn't used his war chest yet. He's still sitting on all that money that he raised to your point, that data bricks five, their timing just was a little off >>5 billion in >>Capital when Slootman hasn't used that money yet. So what's he gonna do? What can he do when he turns that on? He finds the right. >>They're making some acquisitions. They did the stream lit acquisitions stream. >>Fantastic >>Problem. With data bricks, their valuation is underwater. Yes. So they're recruiting and their MNAs. Yes. In the toilet, they cannot make the moves because they don't have the currency until they refactor the multiple, let the, this market settle. I I'm, I'm really nervous that they have to over factor the >>Valuation. Having said that to your point, Eric, the lake house architecture is definitely gaining traction. When you talk to practitioners, they're all saying, yeah, we're building data lakes, we're building lake houses. You know, it's a much, much smaller market than the enterprise data warehouse. But nonetheless, when you talk to practitioners that are actually doing things like self serve data, they're building data lakes and you know, snow. I mean, data bricks is right there. And as a clear leader in, in ML and AI and they're ahead of snowflake, right. >>And I was gonna say, that's the thing with data bricks. You know, you're getting that analytics at M I built into it. >>You know, what's ironic is I remember talking to Matt Carroll, who's CEO of auDA like four or five years ago. He came into the office in ma bro. And we were in temporary space and we were talking about how there's this new workload emerging, which combines AWS for cloud infrastructure, snowflake for the simple data warehouse and data bricks for the ML AI, and then all now all of a sudden you see data bricks yeah. And snowflake going at it. I think, you know, to your point about the competition between AWS and snowflake, here's what I think, I think the Redshift team is, you know, doesn't like snowflake, right. But I think the EC two team loves it. Loves it. Exactly. So, so I think snowflake is driving a lot of, >>Yeah. To John's point, there is plenty to go around. And I think I saw just the other day, I saw somebody say less than 40% of true global 2000 organizations believe that they're at real time data analytics right now. They're not really there yet. Yeah. Think about how much runway is left and how many tools you need to get to real time streaming use cases. It's complex. It's not easy. >>It's gonna be a product value market to me, snowflake in data bricks. They're not going away. Right. They're winning architectures. Yeah. In the cloud, what data bricks did would spark and took over the Haddo market. Yeah. To your point. Now that big data, market's got two players, in my opinion, snow flicking data, bricks converging. Well, Redshift is sitting there behind the curtain, their wild card. Yeah. They're wild card, Dave. >>Okay. I'm gonna give one more wild card, which is the edge. Sure. Okay. And that's something that when you talk about real time analytics and AI referencing at the edge, there aren't a lot of database companies in a position to do that. You know, Amazon trying to put outposts out there. I think it runs RDS. I don't think it runs any other database. Right. Snowflake really doesn't have a strong edge strategy when I'm talking the far edge, the tiny edge. >>I think, I think that's gonna be HPE or Dell's gonna own the outpost market. >>I think you're right. I'll come back to that. Couch base is an interesting company to watch with Capella Mongo. DB really doesn't have a far edge strategy at this point, but couch base does. And that's one to watch. They're doing some really interesting things there. And I think >>That, but they have to leapfrog bongo in my >>Opinion. Yeah. But there's a new architecture emerging at the edge and it's gonna take a number of years to develop, but it could eventually from an economic standpoint, seep back into the enterprise arm base, low end, take a look at what couch base is >>Doing. They hired an Amazon guard system. They have to leapfrog though. They need to, they can't incrementally who's they who >>Couch >>Base needs to needs to make a big move in >>Leap frog. Well, think they're trying to, that's what Capella is all about was not only, you know, their version of Atlas bringing to the cloud couch base, but it's also stretching it out to the edge and bringing converged database analytics >>Real quick on the numbers. Any data on CloudFlare, >>I was, I've been sitting here trying to get the word CloudFlare out my mouth the whole time you guys were talking, >>Is this another that's innovated in the ecosystem. So >>Platform, it was really simple for them early on, right? They're gonna get that edge network out there and they're gonna steal share from Akamai. Then they started doing exactly what Akamai did. We're gonna start rolling out some security. Their security is fantastic. Maybe some practitioners are saying a little bit too much, cuz they're not focused on one thing or another, but they are doing extremely well. And now they're out there in the cloud as well. You >>Got S3 compare. They got two, they got an S3 competitor. >>Exactly. So when I'm listening to you guys talk about, you know, a, a couch base I'm like, wow, those two would just be an absolute fantastic, you know, combination between the two of them. You mean >>CloudFlare >>Couch base. Yeah. >>I mean you got S3 alternative, right? You got a Mongo alternative basically in my >>Opinion. And you're going and you got the edge and you got the edge >>Network with security security, interesting dynamic. This brings up the super cloud date. I wanna talk about Supercloud because we're seeing a trend on we're reporting this since last year that basically people don't have to spend the CapEx to be cloud scale. And you're seeing Amazon enable that, but snowflake has become a super cloud. They're on AWS. Now they're on Azure. Why not tan expansion expand the market? Why not get that? And then it'll be on Google next, all these marketplaces. So the emergence of this super cloud, and then the ability to make that across a substrate across multiple clouds is a strategy we're seeing. What do you, what do you think? >>Well, honestly, I'm gonna be really Frank here. The, everything I know about the super cloud I know from this guy. So I've been following his lead on this and I'm looking forward to you guys doing that conference and that summit coming up from a data perspective. I think what you're saying is spot on though, cuz those are the areas we're seeing expansion in without a doubt. >>I think, you know, when you talk about things like super cloud and you talk about things like metaverse, there's, there's a, there, there look every 15 or 20 years or so this industry reinvents itself and a new disruption comes out and you've got the internet, you've got the cloud, you've got an AI and VR layer. You've got, you've got machine intelligence. You've got now gaming. There's a new matrix, emerging, super cloud. Metaverse there's something happening out there here. That's not just your, your father's SAS or is or pass. Well, >>No, it's also the spend too. Right? So if I'm a company like say capital one or Goldman Sachs, my it spend has traditionally been massive every year. Yes. It's basically like tons of CapEx comes the cloud. It's an operating expense. Wait a minute, Amazon has all the CapEx. So I'm not gonna dial down my budget. I want a competitive advantage. So next thing they know they have a super cloud by default because they just pivoted their, it spend into new capabilities that they then can sell to the market in FinTech makes total sense. >>Right? They're building out a digital platform >>That would, that was not possible. Pre-cloud >>No, it wasn't cause you weren't gonna go put all that money into CapEx expenditure to build that out. Not knowing whether or not the market was there, but the scalability, the ability to spend, reduce and be flexible with it really changes that paradigm entire. >>So we're looking at this market now thinking about, okay, it might be Greenfield in every vertical. It might have a power law where you have a head of the long tail. That's a player like a capital one, an insurance. It could be Liberty mutual or mass mutual that has so much it and capital that they're now gonna scale it into a super cloud >>And they have data >>And they have the data tools >>And the tools. And they're gonna bring that to their constituents. Yes, yes. And scale it using >>Cloud. So that means they can then service the entire vertical as a service provider. >>And the industry cloud is becoming bigger and bigger and bigger. I mean, that's really a way that people are delivering to market. So >>Remember in the early days of cloud, all the banks thought they could build their own cloud. Yeah. Yep. Well actually it's come full circle. They're like, we can actually build a cloud on top of the cloud. >>Right. And by the way, they can have a private cloud in their super cloud. Exactly. >>And you know, it's interesting cause we're talking about financial services insurance, all the people we know spend money in our macro survey. Do you know the, the sector that's spending the most right now? It's gonna shock you energy utilities. Oh yeah. I was gonna, the energy utilities industry right now is the one spending the most money I saw largely cuz they're playing ketchup. But also because they don't have these type of things for their consumers, they need the consumer app. They need to be able to do that delivery. They need to be able to do metrics. And they're the they're, they're the one spending right >>Now it's an arms race, but the, the vector shifts to value creation. So >>It's it just goes back to your post when it was a 2012, the trillion dollar baby. Yeah. It's a multi-trillion dollar baby that they, >>The world was going my chassis post on Forbes, headline trillion dollar baby 2012. You know, I should add it's happening. That's >>On the end. Yeah, exactly. >>Trillions of babies, Eric. Great to have you on the key. >>Thank you so much guys. >>Great to bring the data. Thanks for sharing. Check out ETR. If you're into the enterprise, want to know what's going on. They have a unique approach, very accurate in their survey data. They got a great market basket of, of, of, of, of data questions and people and community. Check it out. Thanks for coming on and sharing with. >>Thank you guys. Always enjoy. >>We'll be back with more coverage here in the cube in New York city live at summit 22. I'm John fur with Dave ante. We'll be right back.

(upbeat music) >> Welcome back everyone, theCUBE's three days of wall to wall coverage of Snowflake Summit '22 is coming to an end, but Dave Vellante and I, Lisa Martin are so pleased to have our final guest as none other than the co-founder and president of products at Snowflake, Benoit Dageville. Benoit, thank you so much for joining us on the program. Welcome. >> Thank you. Thank you, thank you. >> So this is day four, 'cause you guys started on Monday. This is Thursday. The amount of people that are still here speaks volumes. We've had close to 10,000 people here. >> Yeah. >> Could you ever have imagined back in the day, 10 years ago that it would come to something like this in such a short period of time? >> Absolutely not. And I always say if I had imagined that I might not have started Snowflake, right. This is somehow scary. I mean and yeah, it's huge. And you can feel the excitement of everyone. It is like mind boggling and the fact that so many people are still there after four days is great. >> Your keynote on Tuesday was fantastic. Your energy was off the charts. It was standing room only. There were overflow rooms. Like we just mentioned, a lot of people are still here. Talk about the evolution of Snowflake, this week's announcements and what it means for the future of the data cloud. >> Yeah, so evolution, I mean, I will start with the evolution. It's true that that's what we have announced. This week is not where we started necessarily. So we started really very quickly with big data combined with data warehouse as one thing. We saw that the world was moving into fragmented siloing data and we thought with Thierry, we are going to combine big data and data warehouse in one system for the cloud with this elasticity and this service simplicity. So simplicity, amazing elasticity, which is this multi workload architecture that I was explaining during the keynotes and really extreme simplicity with the service. Then we realized that there is one other attribute in the cloud, which is unique, which doesn't exist on-premise, which is collaboration. How you can connect different tenets of the platform together. And Google showed that with Google Docs. I always say to me, it was amazing that you could share document and have direct access to document that you didn't produce and you can collaborate on this document. So we wanted to do the same thing for data and this is where we created the data cloud and the marketplace where you can have all these data sets available and really the next evolution I would say is really about applications that are (indistinct) by that data, but are way simpler to use for all the tenets of the data cloud. And this is the way you can share expertise also, including, ML model, everyone talks about ML and the democratization of ML. How are you going to democratize ML? It's not by making necessary training super easy. Such that everyone can train their ML for themselves. It's by having very specialized application where data and ML is at the core, which are shared, through the marketplace and we shall leverage by many tenets of this marketplace that have no necessary knowledge about building this ML models. So that's where, yeah. >> When you and Thierry started the company, I go back to the improbable rise of Kubernetes and there were other more sophisticated container management systems back then, but they chose to focus on simplicity. And you've told me before, that was our main tenet. We are not going to worry about all the complex database stuff. You knew how to do that, but you chose not to. So my question is, did you envision solving those complex problems over time yourselves or through an ecosystem? Was this by design or did you... As you started to get into it, say let's not even try to go there let's partner to go there. >> Yeah, I mean, it's both. It's a combination of both. Snowflake, the simplicity of the platform is really important because if our partners are struggling to put their solution and build solution on top of Snowflake they will not build it. So it's very important that number one, our platform is really easy to use from day one. And that really has to be built inside the platform. You cannot build simplicity on top. You cannot have a complex solution and all of a sudden realize that, oh, this is complex. I need to build another layer on top of it to make it simpler, that will not work. So it had to be built from day one, but you're right. What is going to be Snowflake? I always say in 10 years from now, we just turn 10 years old or we are going to turn 10 years old in few months. Actually a few months, yes. >> Right. >> So for the next 10 years I really believe that most of Snowflake will not be built by Snowflake. And that's the power of the partners and these applications. When you are going to say I'm using Snowflake, actually, probably you are not going to use directly code developed by Snowflake. That code will leverage our platform, but you will use a solution that has been built on top of Snowflake. And this is the way we are going to decouple, the effort of Snowflake and multiply it. >> It's an interesting balance, isn't it? When I think of what you did with Apache Iceberg, if I use Iceberg and I'm not going to get as much functionality, but I may want that openness, but I'm going to get more functionality inside of the data cloud. And I don't know, but if you know the answer to what's going to happen. >> No, that's a super good question. So to explain what we did with Apache Iceberg, and the fact that now it's a native format for us. So everything that you can do with our internal formats, you can do it with Apache Iceberg, including security, defining masking, data masking all the governors that we have, fine grain security aspects, the replications you can define you can use (indistinct) on top of... >> But there's a but, right? But if I do that with native Snowflake tools, I'm going to get an even greater advantage, am I not? >> Yes. So that's what I'm saying. So that's why we embraced Iceberg, because I think we can bring all the benefit of Snowflake to people who have decided to use Iceberg, I mean open formats. Iceberg is a table format. So and why it was important because people had massive investments in open source in Hadoop. And we had a lot of companies saying, we love Snowflake. We want to be a Snowflake customer, but we cannot really migrate all our data. I mean, it will be really costly. And we have a lot of tools that need access, direct access. So this is why we created Iceberg because we can really... I mean, we really think that we can bring the benefit of Snowflake to this data. >> Gives customers optionality. Okay. I use this term super cloud. You don't use the term, but that's okay. And I get a lot of heat for it. But to me, what you're doing is quite a bit different than multicloud because you're creating that abstraction layer. You're bringing value above it. My question to you is, the most of the heat I get is, oh, that's just SaaS. Are you just SaaS? >> No. I mean, no, absolutely not. I mean, you're right we are a super cloud. I mean it's a much better word than saying we are multicloud. Multicloud is often viewed as oh, I have my system and now I can run this system in the different cloud providers. Snowflake is different. We have one single platform for the world, which happens to have some regions are AWS region, some regions are Azure, some regions are GCP, Google and we merge them together. We have this Snowgrid technology that connects all our regions together so that we have really one platform for the world. And that's very important because when you talk about connections of data and expertise applications you want to have global reach, right. It doesn't exist. We are not siloed by region of the world, right? You have a lot of companies which are multinational that have presence everywhere. And you want to have this global reach. The world is not a independent set of regions and countries, right. And that's the realization. So we had to create this global platform for our customers. >> And now you have people building clouds on top of your data cloud, well that to me is the next signal. In your keynote, you talked about seven pillars, all data, all workloads, global architecture, self-managed, programmable, marketplace, governance, which ones are the most important? >> All of them. It's like when you have kids, you don't want to pick and say, this one is my preferred one, so they are really important. All of them, as I said without data, there is no Snowflake, right? So all data is so important that we can reach every data, wherever it is. And Iceberg is a part of that, but all workload is really important because you don't want to put your data in one platform, if you cannot run all your workloads and workloads are much broader than just data warehousing, there is data engineering, data science, ML engineering, (indistinct) all these workloads applications. So that's critical. Programmable is where we are moving, right. We want to be the place where data applications are built. And we think we have a lot of advantages because data application needs to use many workloads at once, right? It's not that that application will do only data warehousing, they need to store their states, they need to use this new workload that we define, which is Unistore. They need to do data engineering because they need to get data, right. They have to save this data. So they need to combine many workload and if they have to stitch this workload, because the platform was not designed as one single product where everything is consistent and works together, that you have to stitch, it's complicated for this application to make it work. So Snowflake is we believe an ideal platform to run these data applications. So all workloads, programmable, obviously, so that you can program. And programmable has two aspects, which is big part of our announcement. Is both data programmability, which is running Python against petabyte, terabytes of data at scale and doing it scale out. So that's what we call data programmability. So both Java, Python and (indistinct), but also running applications like UI. And we had this acquisition of Streamlit. Streamlit now has been fully integrated in Snowflake. We announced that such that not only you can have this data programmability, but you can expose your data through this nice UIs, interactive UI to business users potentially. So it goes all the way there. Global is super important. As we say, we want to be one platform for the world. And of course, as I said, the last pillar, which is somehow critical for us, because we are cloud, we need to have governance. We need to have security of our data. And why it took us so long to do Python is not because it's out to run Python, right? Everyone can run Python it's because we had to secure it. And I talk about it creating this amazing sandboxing technology, such that when you include third party libraries and third party codes, you are guaranteed that this third party code will not reach to infiltrate your data, right. We control the environment that Snowflake provides. >> Can you share us some of the feedback from the customer? You probably had many customer conversations over the last four days. >> Look at that smile. (interviewer laughing) (Lisa laughing) >> Actually not because I was so busy everywhere. Unfortunately, I didn't speak to many customers. Saying that, I had everyone stopping me and talking about what they heard and yeah, there is a huge excitement about all of this. >> What's been the feedback around the theme of the event? The world of data collaboration. Data collaboration is so critical as every company these days must be a data company to compete, to win. What's been from just some of the feedback that you've had customers really embracing data collaboration, what Snowflake is enabling. >> Yeah. I mean, almost every company which is using Snowflake, is collaborating with data. You have heard, the number of stable edges that we have, and there is a real need for that because your data alone... You cannot make sense of your data if it is just alone. It needs to be connected with other data. You haven't not generated. So all data, when you say the first pillar of Snowflake is all data is not only about your data, but is about all the data that's created around you. That puts perspective on your own data. And that's critical and it's so painful to get. I mean, even your data is difficult to have access to your data, but imagine data that you didn't produce. And so yes, so the data collaboration is critical, and then now we expanded it to application and expertise, sharing models, for example, That's going to have a huge impact. >> All data includes now transaction data, right? >> Yes. >> That's a big part of the announcements that you guys made. >> Yeah. So and that's the motivation for that was really, if we want to run application, full application, we announced native applications, which are fully executed and run inside the (indistinct) data cloud, right. They need all the services that application need and in particular managing their states. And so we created Unistore, which is a new workload, which allows you to combine transactional data, which are generated by this application. And at the same time being able to do analytics directly on this data. So we call it Hybrid Table because it has this hybrid aspect. You can do both transactional access to this data and at the same time analytic here without having data pipeline and moving data and transforming it from the transactional system to the analytical system, right. Snowflake is one system. Again, in the spirit of simplifying everything, this is the Snowflake (indistinct). >> I can ask the same question I ask at first, (indistinct) when was the aha moment that you and Thierry had that said, this is not just a better data warehouse, it's actually more than that. You probably didn't call it a data cloud until later on, but did you know that from the beginning or was that something you kind of stumbled into? >> No. So as I said, we founded Snowflake in 2012 and Thierry and I, we locked in my apartment and we were doing the blueprint of Snowflake and trying to find what is the revolution with the cloud for this data warehouse system and analytical system, both big data and data warehouse. And the aha moment was but of course cloud, okay. What is cloud? It's elasticity, it's service and later collaboration. So in the elasticity aspect, when you ask database people, what is elasticity, they will tell you, oh, you have a cluster of nodes. Like if it is Oracle, it would be a (indistinct) cluster. And the elasticities that you can add one node, two node to this cluster without having too much impact on the existing workload, because you need to shuffle data, right. It's hard and doing it online, right, that's elasticity. If you can do that, you are elastic. We thought that that was not very interesting to do that. What is interesting with elasticity is to plug new workloads. You can plug a workload like that and that workload is running without having any impact on other workloads, which are running on the platform. So elasticity for us was having dedicated computer resources to workloads. And these computer resources could start and be part as soon as the workload starts and will shut down when the workload finishes and they will be sized exactly for the demand of that workload. And we thought the aha moment was, okay if we can do that, now we can run a workload with, let's say 10X more computer resources than what you would have used or 100X more. Okay, let's say 100X more because we paralyzed things. Now this workload can run 100X faster, right? That's assuming we do a good job in the scale, which is our IP. And if we can do that, now the computer resources that you have used, you have used them for 100 times less. So you have used 100 times more resources because you have more nodes, but because you go fast, you use them for less time, right? So if you multiply the two it's constant. So you can run and accelerate workload dramatically 10X, 100X for the same price. Even if we are not better in efficiency than competition, just having that was the magic, right? >> You know how Google founders originally had trouble raising money because who needs another search engine? Did you get from original, like when you started going to raise money, Amazon's got a database, so who needs another cloud database? Did you get that early on or was it just obvious Speiser and companies as well. >> Speiser is a little bit on the crazy side and ambitious and so Speiser is Speiser. And of course he had no doubt, but even him was saying Benoit, Thierry, Hadoop, right. Everyone is saying Hadoop is going to be the revolution. And you guys are betting actually against Hadoop because we told Speiser, Hadoop is a bad system, it's going to fail, but at the time everyone was so bullish about Hadoop, everyone was implementing Hadoop that it didn't look like it was going to fail and we were probably wrong. So there was a lot of skepticism about not leveraging Hadoop and not being an Hadoop. Okay, something being on top of Hadoop. That was number one. There was no cloud warehouse at the time we started. Redshift was not started. It was the pioneer somewhere when Snowflake was founded. So creating a data warehouse in the cloud sounded crazy to people. How am I going to move my data over there? And security and what about security, the cloud is not secure. So that was another... >> So you guys predated that Parexel move by... >> Yes. >> Okay, so that's interesting. And I thought when Redshift... I mean, Amazon announced Redshift, I was sure that Mike Speiser will come and say, guys it's too sad, but they beat you guys and they build something and actually it was the reverse. Mike Speiser was super excited and so it was interesting to me. >> Wow, that's amazing. 'Cause John Furrier and I, we were early with theCUBE. when theCUBE started it was like the beginning of Hadoop. And so we brought theCUBE to, I think it was the second Hadoop World and we was rubbing nickels together at the time. And I was so excited bring compute to storage and it made so much sense. But I remember and I won't say who it was, but an early Hadoop committer told me this is going to fail. And I'm like, what? And he started going age basis crap and all this stuff. And I was sad because I was so excited, but it turned out that you had the same (indistinct). >> Because of complexity. Okay, Hadoop failed for two reasons. One is because they decided that, oh, a lot of this database thing, you don't need transaction, you don't need SQL, you don't necessarily, you don't need to go fast. It'll be batch, normal real time interaction with data, no one needs that. >> Cheap storage. >> So a lot of compromise on the very important technology. And at the same time, extreme complexity and complexity for me was, where I was I knew that it was going to fail big time and we bet Snowflake on the failure of Hadoop indeed. >> And there was no cloud early on in Hadoop. >> And there was no cloud too. >> And that was what killed it. That was like... >> You're right. And the model that Hadoop had for data didn't work on block storage. Block storage is not as efficient as HGFS. So that was also another figure. >> Do you ever sit back and think about... So you think about how much money has poured in to separating compute from storage and cloud databases and you started it all. (interviewer laughing) >> Yeah. No, this is... >> Pretty amazing. >> Yeah. >> Right, so that's good. That means that you're onto a good idea, but a lot of people get confused that again, they think that you're a cloud data warehouse and you're not, I mean, you're much more than that. >> Yeah, I hate that. I have to say, because from day one we were not a cloud data warehouse. As I said, it was all about combining the big data, massive amount of unstructured data, petabytes stored as files. Okay, that's very important, store as files where it's very easy to drop data in the system without... Very low cost to combine with data warehouse, full multi statement transaction when people will tell you today, oh, now we are a data warehouse. They don't have multi statement transaction, right. So we had from day one multi statement transaction really efficient SQL. You could run your dashboard. So combining these two worlds was I think the crazy thing, that's the crazy innovation that Snowflake did initially. >> Yeah. >> And I know it's really easy to build data warehouse somewhere, because if you don't think about big data, petabytes, extremely structured data, you remove a lot of complexity. >> This is why Lisa, when you get excited about technology, but you always have to have a, somebody who really deeply understands technology to stink test it, all right so awesome. Thank you for sharing that story. >> Yeah. >> Fantastic. So over 5,900 customers now. I saw over 500 in the Forbes G2K, over almost 10,000 people here this year. If we think back to 2019, there was about what? Less than 2000 people. >> Yeah. >> What do you think is going to happen next year? >> I don't know. I don't like to think about next year. I mean, I always say, Snowflake is so exciting to me because it is like a TV show, right. Where you wait the next season and we have one season every year. So I'm really excited to know what is going to happen next year. And I don't want to project what I think will happen, but all these movements to the Snowflake being the platform for data application. I want to see what people are going to build on our platform. I mean, that's the excitement. >> Season 11 coming up. >> Yes. Season 11. Yes. >> No binge watching here. Benoit, it's been a pleasure to have you on the program. >> Thank you. >> Congratulations on incredible success, the momentum, the energy is contagious. We love it. (Benoit laughing) >> Thank you so much. >> Thank you. >> Bye bye. >> For Benoit Dageville and Dave Vellante, I'm Lisa Martin. You're watching theCUBE's coverage of Snowflake Summit '22. Dave and I will be right back with a wrap. (upbeat music)

>>Hello from the show floor in Las Vegas. This is the snowflake summit 22 at Caesar's forum. We've been live the last day and a half. Lisa Martin here with Dave ante covering a lot of ground. We're so excited to have the chief marketing officer at snowflake. Join us next, Denise Pearson. And welcome back to the cube. >>Thank you so much. So great to be here with you. So great to have you here at SEL meets as well. Thank >>You. That's unreal. Isn't it? Yeah. I mean, everybody's so excited to be face to face and you know, Lisa and I have been doing a few of these shows, but we, we hear the same thing over and over. It's like, oh, so good to be back, right? Yeah. >>Well, even in the keynote yesterday, when we got in, we saw a standing room only there were overflows. People are ready to hear from snowflake in person. And as we were, you were just talking with Frank, I think the 2019 show had less than 2000 people. And now here we are at close to 10,000, this step leap factor in terms of the audience and also the momentum of the company, the capabilities, lot of growth in that timeframe. Yeah, >>No. Yeah. Two, three years ago we were about 1800 people out to Hilton and San Francisco. We had about 40 partners attending this week were close to 10,000 at this year, almost 10,000 people online as well. And over over 200 partners here on the show floor, >>Right? 250 plus sessions, breakouts, keynotes, technical certifications, developer zone, a lot going on here. The buzz has been enormous from yesterday morning. It still is today. Talk about the theme of the event, the world of data collaboration. We've been talking a lot about data collaboration. Yeah. But from Snowflake's perspective, as Dave you've pointed out, that really seems like quite a differentiation of where snowflake is versus the guys in the root view mirror. Yeah. Number >>One of the very unique capabilities with snowflake is that the ability to share data with each other within your ecosystem. So you can both collaborate now on, on with your data, but also collaborate on building in a new business opportunities set together. So I think it's really a message that we, we, we fully fully own. It' really unique differentiator as well. So >>You used to talk about, you still talk about data sharing, but just kind of evolve the messaging to collaboration, explain why and, and how is that a wider scope and more appealing to the ecosystem in your >>Customers? I mean, data sharing is a terminology used for, for years and years, sounds from any data sharing it's about using, you know, FTP or, you know, APIs, those things. And we of course do it in a very, very different way where, where you do it without, you know, APIs so that you can share data with anyone in your ecosystem, without the data actually ever leaving, ever leaving your, your instance. So it's in a very different way. And also the fact that you can, again, you know, build applications together with other companies, you know, in your ecosystem. And it's a, it's a true collaboration around, you know, data in a way we've never seen before >>The other subtle change was data marketplace to marketplace. Why that change explain kind of what's behind that. >>Yeah. One of our big announcements here this week is around building native, you know, data apps and all snowflakes. Now you can both, you know, build the apps and you can distribute them and monetizing them in our marketplace. So in the past, you know, we only really had data sets within our marketplace within the data marketplace at that time. So you could now, you know, we can publish your data, you could monetize your data, but again, now moving forward, you will also be able to again, build apps and distribute them in the marketplace and also monetize them. And for Mon many startups, right? The, the big challenge is just a monetization piece as well. You build your product. You also need to find a way to, to both distribute and, and monetize it, an invoice for that product. And we solve all that for, for our customers. Now, >>A lot of customer growth, I saw Frank's slide yesterday over 5,900. I think you have 500 plus in the Forbes global 2000, a tremendous amount of growth in customers with a million plus ARR. Yes. >>Where >>Are the customers and the ecosystem in terms of that, that what you just described in the, going from the data marketplace to the marketplace are customers and, and the ecosystem influential in saying, Hey, snowflake, we need to go in this direction. >>Yeah. And also one key thing also with larger companies, they have their own marketplaces built, you know, snowflake as well. So you don't have to publish your, your, your data or app on our marketplace. The many of our larger companies, they're building those own marketplaces around themselves, you know, to distribute their data, you know, to their partners. So there are many ways you can, again, distribute and monetize their data. >>What are the marketing challenges? You, you started out kind of better data where simpler data warehouse, cloud data, warehouse, zero to snowflake was kind of the, the messaging and then the rise of the data cloud. And now it's all about applications. You're obviously building on top of that, but how, how have you, how do you think about that sort of messaging architecture going, you know, where you've come from and going forward? >>Yeah. Obviously the capabilities of the data cloud is kind of building and building it every day. And it's also a positioning that we can, you know, grow with as well. The big difference, you know, for us over the past two years is really that we are more and more really talking to the, to the business side, you know, of our, our customers that that's really where the demand is coming from. And we're truly, you know, with the data cloud, we're truly, you know, build bringing the business side and the it side together to solve these, you know, problems. And also, also together with all our partners as well. >>And I was just gonna ask you what, what's the partners role in the data cloud narrative? How do they help accomplish that? >>I would say, I mean, the data cloud is all about the partners it's, and also this event here, this event is not about, you know, snowflake it's about really our partners, you know, and our customers, you know, coming together, the data cloud is really it's. The foundation is of course, you know, the core capabilities, our platform, but then it's also all, all the data that is in there that other companies can access from our customers, but then all the applications and capabilities that are built, you know, by our partners and also our partners like the, you know, or the SI partners that are here, they are the ones, you know, doing the work, you know, with our customers, they are the ones that are, you know, migrating the data to, to snowflake and the data cloud and helping these companies build this new, you know, business model. So snowflake is a very, very partner first company. And the only thing I really care about this week here is that all this, you know, 200 partners here that they're gonna be tremendous successful if they're successful. That means that, you know, all our customers are successful as well. >>So how is your digital strategy evolving and how do you include the partners in that? >>Yeah, I mean, we learned so much over the past, you know, three, three years in regards to that. So, I mean, we all had to just accelerate our, the, the digital growth, you know, of our marketing capabilities and how to do that in a, with our, our partners. So with many of them, you know, we started developing this joint account based digital, you know, marketing program. Some, we just all had to adapt and innovate really fast, and we're gonna continue, of course, a lot of those motions as well. But at the same time, there's nothing like being out and meeting, you know, customers, you know, face to face. And what's also so important is the alignment we have with our local sales organization and our partners as well. So all these marketing programs that we develop in the fields, those are us again, opportunities cannot build those relationships as well. >>Can you talk about the sales marketing alignment at snowflake? I think it seems to be pretty strong, but we've talked a lot in the last day and a half about the retail data cloud healthcare life sciences, media finance. Talk to us about the marketing sales element, how marketing is facilitating, maybe from a campaign perspective, some of those big sales plays in the S yeah, >>Maybe both unique here. Our C Chris Dham, I think has been here early on the show. I mean, we work together for over six years now and we truly work as one, one team. We, we don't really even see the lines between sort of sales and marketings. We truly share exactly, you know, the same objectives every day. We share the same focus on, on putting our customers and our partners, you know, first, every day, his priorities, you know, are my priorities, you know, vice versa. And I think the biggest challenges we see often in some companies between sales and marketing, is that they're just shifting or it's different, you know, priorities. It's so important just to align the priorities and for us to making sure that our teams are all around the world now, or as aligned, you know, as Chris and I are as well, >>Couple other, yeah. Milestones or events come up, you're doing like, you're doing a worldwide tour and you got the dev conference in November, start with the worldwide tour. What's that all about? >>So we get little break near now, here for, for a couple of weeks. And then we're taking all the best of content here for, from, from summits and also all in our partners on a worldwide tour. We're starting in, in Asia, in August, and we're gonna target over 20 cities around the world. So, and again, I think this year, the challenge was many of our European customers and our customers in Asia. They couldn't make it. So we have smaller numbers, you know, coming from those regions. So it's more, more important than ever that we just come out to them, you know, instead, and bring this content in to them. >>Is that all face to face or at Lisa is all face to face. Is there a digital component as well? Yeah, >>It's actually gonna be all face to face and there will be some, some digital components as well. We're ending the tour in San Francisco. And that's also where we go doing all our winter announcements, you know, as well. And also our build our developer conference. That will be all virtual. The big, the global one will be all virtual at the same time, you know, from San Francisco. >>Okay. Am I confusing that with the November developer conference or >>The, that is, that is the conference, but it, that one will be virtual this year. Okay. >>So dev the build is all virtual. >>Yeah. Build be all virtual. And it's just, so we have that opportunity to reach as many people as we possibly can. >>And then is the, is this, is the intent to eventually bring them in to one place? >>Absolutely. I mean, I think the dev conference, the plan is to really take that around the world, you know, as well. We're seeing markets like Israel, for instance, there's a massive developer community that is, that is looking at snowflake right now. Markets like Indonesia, big developer segment as well. So I think it's not about, you know, having people come to us, it's about we, you know, coming out to them. So markets like Israel and Indonesia. And >>Will you also in future summits include a, a development component. You probably have something here. I just haven't seen it yet, but, but like the conference within the conference, or is it more, Hey, we want to cater to the t-shirt crowd, you know, separately, what do you, yeah, >>I think we cater to them separately. And I said again, that we it's really about taking our content, you know, out, out to them. And when we're talking about the developer audience, we're talking about hundreds and thousands of people and they can't physically, you know, come here. So our plan is really to come out and meet them where they are. How >>Did you make the decision to do this summit fourth annual in person? I'm sure the attendance figures are probably blowing your mind, but that's a, that's a big decision and that's a challenging decision to make. How did they go about doing that? >>I think, I think if there was one thing we've learned during the past three years, it's really about that. Adaptability is the new superpower, you know, of bus business. So of course we've had to adapt, you know, you know, every month. And of course, even two months ago, we were not sure, you know, how, how many people that will be able to come here today, but we're incredibly happy. Were they, were they, were they with the number of people that, you know, came here and yeah, we're already storing planning for next year. >>I mean, it definitely must have exceeded your expectations. Is that fair? Or >>We set expectations high. Yeah. Okay. But again, it's that unknown that we all had to deal with, you know, every day. And I think we're gonna continue to have to, to live with that. >>Yeah. Well, this is, yeah, this is one of the largest shows we've done. Yeah. SIM it's a reinvent, obviously different. That was last year, but this year, this is the biggest event I think we've been to, and we've been to some big brand events, so yeah. Yeah. Punching above the weight as usual. >>Yeah. And again, I wanna just give a big shout out to our whole, you know, partner ecosystem, you know, here, because again, this is very much of an ecosystem, you know, partner you in a conference and it's really all our 200 plus partners here making this conference, what it is. I mean, today >>It's remarkable to pace at which you've been able to grow the ecosystem, but why do you think that is? What's the secret there? >>I think we fully understand that we don't solve all the problems ourselves, you know, for, for our customers. It's really an ecosystem of, of products and services that solve those problems and customers. They are looking for vendors that partner well with others. They're looking for vendors that integrate well, you know, with each other. So we always have an outside in view on things and that's something we challenge ourselves every morning. We wake up, how do we put ourselves in the customer's shoes in terms of, of, of their needs and their problems and how to solve those? We don't solve them alone. We, we solve them with these 200 plus in apart. Make >>It sound so simple. >>Speaking of challenges, you have something called the startup challenge. That's in its second annual >>Yes. Tomorrow we're kicking off the, the final of the second annual startup challenge. We have three finalists here, three very different, you know, companies. And we had a couple hundred applications this year and we have everything from a company that makes AI and ML more accessible to a company, focus on, you know, retail, you know, analytics. It's gonna be very exciting tomorrow, big price for the winner. The winner is going to win a million dollar of investment from, from snowflake ventures. So >>Very exciting. It's a nice incentive. It is a nice incentive, >>Very nice incentive. And also all the exposure you will get as well. We will put a lot of our marketing support, you know, behind this companies as well. >>Excellent. >>And now the data driver awards program, we've had a couple of data drivers on the program in the last day >>And a half. Yes. We announced to know those winners as well, you know, early in the week. So a lot of recognition for both our customers, but also we're gonna see, you know, the next interesting companies here to watch tomorrow during the startup challenge, you >>Get a little bit of something for everybody here, right? I mean the, the, the, the partner awards, right? These other little side opportunities for ecosystem to get recognition, sometimes funding it's >>Yeah. Everyone wants to be recognized, you know, for the great work they're doing. So, yeah. Yeah. >>So what's next for marketing, obviously, a break and then you start the, the road show. >>So of course yesterday we made an number of very, very large in announcements. Many of those, you know, we've been working on for years here at snowflake, like Unior, you know, for instance has been probably three years, you know, in the making. So our goal now is to take all those announcements to every customer around the world, both through, you know, local events really starting this week, and then also the world tour this fall. And it's gonna be a big, big focus on the developer segments. Obviously what our most exciting announcements is, the native apps, you know, capabilities. And that finally, you know, we can bring the work, you know, to the data and not again, taking the data to the work. And as you know, our mission has really been around breaking down the data silos. Cause those have been the biggest, you know, challenges companies have faced. That's really, what's been standing in the way for customers to become you a data Rav, and now bringing the work to the data from a developer's standpoint is gonna break down even further, those silos. So, >>Yeah. And it's good physics. >>Yeah. It good physics. Yeah. >>Yeah. Tremendous opportunity. Congratulations on a great successful event. It's not even done yet, but obviously we've seen so much success. Great news coming out. We'll be excited to be hearing some of the outcomes of the road show and the developer conference coming up in the fall. We appreciate your insights, your time and for having the cube here at the summit. >>Thank you for being here. Thank you. Thanks for having >>Me, our pleasure for Denise Pearson and Dave Valante I'm Lisa Martin. You're watching the cubes coverage of snowflake summit 22 live from Las Vegas, Dave and I will be back after a short break.

>>Welcome back to the big apple, everybody. The Cube's continuous coverage here of MongoDB world 2022. We're at the new Javet center. It's it's quite nice. It was built during the pandemic. I believe on top of a former bus terminal. I'm told by our next guest Tony bear, who's the principal at DB insight of data and database expert, longtime analyst, Tony. Good to see you. Thanks for coming >>On. Thanks >>For having us. You face to face >>And welcome to New York. >>Yeah. Right. >>New York is open for business. >>So, yeah. And actually, you know, it's interesting. We've been doing a lot of these events lately and, and especially the ones in Vegas, it's the first time everybody's been out, you know, face to face, not so much here, you know, people have been out and about a lot of masks >>In, >>In New York city, but, but it's good. And, and this new venue is fantastic >>Much nicer than the old Javits. >>Yeah. And I would say maybe 3000 people here. >>Yeah. Probably, but I think like most conferences right now are kind of, they're going through like a slow ramp up. And like for instance, you know, sapphires had maybe about one third, their normal turnout. So I think that you're saying like one third to one half seems to be the norm right now are still figuring out how we're, how and where we're gonna get back together. Yeah. >>I think that's about right. And, and I, but I do think that that in most of the cases that we've seen, it's exceeded people's expectations at tenants, but anyway sure. Let's talk about Mongo, very interesting company. You know, we've been kind of been watching their progression from just sort of document database and all the features and functions they're adding, you just published a piece this morning in venture beat is time for Mongo to get into analytics. Yes. You know? Yes. One of your favorite topics. Well, can they expand analytics? They seem to be doing that. Let's dig into it. Well, >>They're taking, they've been taking slow. They've been taking baby steps and there's good reason for that because first thing is an operational database. The last thing you wanna do is slow it down with very complex analytics. On the other hand, there's huge value to be had if you would, if you could, you know, turn, let's say a smart, if you can turn, let's say an operational database or a transaction database into a smart transaction database. In other words, for instance, you know, let's say if you're, you're, you're doing, you know, an eCommerce site and a customer has made an order, that's basically been out of the norm. Whether it be like, you know, good or bad, it would be nice. Basically, if at that point you could then have a next best action, which is where analytics comes in. But it's a very lightweight form of analytics. It's not gonna, it's actually, I think probably the best metaphor for this is real time credit scoring. It's not that they're doing your scoring you in real time. It's that the model has been computed offline so that when you come on in real time, it can make a smart decision. >>Got it. Okay. So, and I think it was your article where I, I wrote down some examples. Sure. Operational, you know, use cases, patient data. There's certainly retail. We had Forbes on earlier, right? Obviously, so very wide range of, of use cases for operational will, will Mongo, essentially, in your view, is it positioned to replace traditional R D BMS? >>Well, okay. That's a long that's, that's much, it's >>Sort of a loaded question, but >>That's, that's a very loaded question. I think that for certain cases, I think it will replace R D BMS, but I still, I mean, where I, where I depart from Mongo is I do not believe that they're going to replace all R D D BMSs. I think, for instance, like when you're doing financial transactions, you know, the world has been used to table, you know, you know, columns and rows and tables. That's, it's a natural form for something that's very structured like that. On the other hand, when you take a look, let's say OT data, or you're taking a look at home listings that tends to more naturally represent itself as documents. And so there's a, so it's kind of like documents are the way that let's say you normally see the world. Relational is the way that you would structure the world. >>Okay. Well, I like that. So, but I mean, in the early days, obviously, and even to this day, it's like the target for Mongo has been Oracle. Yeah. Right, right. And so, and then, you know, you talk to a lot of Oracle customers as do I sure. And they are running the most mission, critical applications in the world, and it's like banking and financial and so many. And, and, and, you know, they've kind of carved out that space, but are we, should we be rethinking the definition of, of mission critical? Is that changing? >>Well, number one, I think what we've traditionally associated mission critical systems with is our financial transaction systems and to a less, and also let's say systems that schedule operations. But the fact is there are many forms of operations where for instance, let's say you're in a social network, do you need to have that very latest update? Or, you know, basically, can you go off, let's say like, you know, a server that's eventually consistent. In other words, the, do you absolutely have, you know, it's just like when you go on Twitter, do you naturally see all the latest tweets? It's not the system's not gonna crash for that reason. Whereas let's say if you're doing it, you know, let's say an ATM banking ATM system, that system better be current. So I think there's a delineation. The fact is, is that in a social network, arguably that operational system is mission critical, but it's mission critical in a different way from a, you know, from, let's say a banking system. >>So coming back to this idea of, of this hybrid, I think, you know, I think Gartner calls it H tab hybrid, transactional analytics >>Is changed by >>The minute, right. I mean, you mentioned that in, in your article, but basically it's bringing analytics to transactions bringing those, those roles together. Right. Right. And you're saying with Mongo, it's, it's lightweight now take, you use two other examples in your article, my SQL heat wave. Right. I think you had a Google example as well, DB, those are, you're saying much, much heavier analytics, is that correct? Or >>I we'll put it this way. I think they're because they're coming from a relational background. And because they also are coming from companies that already have, you know, analytic database or data warehouses, if you will, that their analytic, you know, capabilities are gonna be much more fully rounded than what Mongo has at this point. It's not a criticism of a Mongo MongoDB per >>Per, is that by design though? Or ne not necessarily. Is that a function of maturity? >>I think it's function of maturity. Oh, okay. I mean, look, to a certain extent, it's also a function of design in terms of that the document model is a little, it's not impossible to basically model it for analytics, but it takes more, you know, transformation to, to decide which, you know, let's say field in that document is gonna be a column. >>Now, the big thing about some of these other, these hybrid systems is, is eliminating the need for two databases, right? Eliminating the need for, you know, complex ETL. Is, is that a value proposition that will emerge with, with Mongo in your view? >>You know, I, I mean, put it this way. I think that if you take a look at how they've, how Mongo is basically has added more function to its operations, someone talking about analytics here, for instance, adding streaming, you know, adding, adding, search, adding time series, that's a matter of like where they've eliminated the need to do, you know, transformation ETL, but that's not for analytics per se for analytics. I think through, you know, I mean through replication, there's still gonna be some transformation in terms of turning, let's say data, that's, that's formed in a document into something that's represented by columns. There is a form of transformation, you know, so that said, and Mongo is already, you know, it has some NA you know, nascent capability there, but it's all, but this is still like at a rev 1.0 level, you know, I expect a lot more >>Of so refin you, how Amazon says in the fullness of time, all workloads will be in the cloud. And we could certainly debate that. What do we mean by cloud? So, but there's a sort of analog for Mongo that I'll ask you in the fullness of time, will Mongo be in a position to replace data warehouses or data lakes? No. Or, or, or, and we know the answer is no. So that's of course, yeah. But are these two worlds on a quasi collision course? I think they >>More on a convergence course or the collision course, because number one is I said, the first principle and operational database is the last thing you wanna do is slow it down. And to do all this complex modeling that let's say that you would do in a data bricks, or very complex analytics that you would do in a snowflake that is going to get, you know, you know, no matter how much you partition the load, you know, in Atlas, and yes, you can have separate nodes. The fact is you really do not wanna burden the operational database with that. And that's not what it's meant for, but what it is meant for is, you know, can I make a smart decision on the spot? In other words, kinda like close the loop on that. And so therefore there's a, a form of lightweight analytic that you can perform in there. And actually that's also the same principle, you know, on which let's say for instance, you know, my SQL heat wave and Allo DBR based on, they're not, they're predicated on, they're not meant to replace, you know, whether it be exit data or big query, the idea there is to do more of the lightweight stuff, you know, and keep the database, you know, keep the operations, you know, >>Operating. And, but from a practitioner's standpoint, I, I, I can and should isolate you're saying that node, right. That's what they'll do. Sure. How does that affect cuz my understanding is that that the Mon Mongo specifically, but I think document databases generally will have a primary node. Right? And then you can set up secondary nodes, which then you have to think about availability, but, but would that analytic node be sort of fenced off? Is that part of the >>Well, that's actually what they're, they've already, I mean, they already laid the groundwork for it last year, by saying that you can set up separate nodes and dedicate them to analytics and what they've >>As, as a primary, >>Right? Yes, yes. For analytics and what they've added, what they're a, what they are adding this year is the fact to say like that separate node does not have to be the same instance class, you know, as, as, as, as the, >>What, what does that mean? Explain >>That in other words, it's a, you know, you could have BA you know, for instance, you could have a node for operations, that's basically very eye ops intensive, whereas you could have a node let's say for analytics that might be more compute intensive or, or more he, or, or more heavily, you know, configured with, with memory per se. And so the idea here is you can tailor in a node to the workload. So that's, you know what they're saying with, you know, and I forget what they're calling it, but the idea that you can have a different type, you can specify a different type of node, a different type of instance for the analytic node, I think is, you know, is a major step forward >>And that, and that that's enabled by the cloud and architecture. >>Of course. Yes. I mean, we're separating, compute from data is, is, is the starter. And so yeah. Then at that point you can then start to, you know, you know, to go less vanilla. I think, you know, the re you know, the, you know, the, I guess the fruition of this is going to be when they say, okay, you can run your, let's say your operational nodes, you know, dedicated, but we'll let you run your analytic nodes serverless. Can't do it yet, but I've gotta believe that's on the roadmap. >>Yeah. So seq brings a lot of overhead. So you get MQL, but now square this circle for me, cuz now you got Mago talking sequel. >>They had to start doing that some time. I mean, and I it's been a court take I've had from them from the, from the get go, which I said, I understand that you're looking at this as an alternative to SQL and that's perfectly valid, but don't deny the validity of SQL or the reason why we, you know, we need it. The fact is that you have, okay, the number, you know, according to Ty index, JavaScript is the seventh, most popular language. Most SQL follows closely behind at the ninth, most popular language you don't want to cl. And the fact is those people exist in the enterprise and they're, and they're disproportionately concentrated in analytics. I mean, you know, it's getting a little less, so now we're seeing like, you know, basically, you know, Python, the programmatic, but still, you know, a lot of sequel expertise there. It does not make, it makes no sense for Mongo to, to, to ignore or to overlook that audience. I think now they're, you know, you know, they're taking baby steps to start, you know, reaching out to them. >>It's interesting. You see it going both ways. See Oracle announces a Mongo, DB, Mongo. I mean, it's just convergence. You called it not, I love collisions, you know, >>I know it's like, because you thrive on drama and I thrive on can't. We all love each other, but you know, act. But the thing is actually, I've been, I wrote about this. I forget when I think it was like 2014 or 2016. It's when we, I was noticed I was noting basically the, you know, the rise of all these specialized databases and probably Amazon, you know, AWS is probably the best exemplar of that. I've got 15 or 16 or however, number of databases and they're all dedicated purpose. Right. But I also was, you know, basically saw that inevitably there was gonna be some overlap. It's not that all databases were gonna become one and the same we're gonna be, we're gonna become back into like the, you know, into a pan G continent or something like that. But that you're gonna have a relational database that can do JSON and, and a, and a document database that can do relational. I mean, you know, it's, to me, that's a no brainer. >>So I asked Andy Ja one time, I'd love to get your take on this, about those, you know, multiple data stores at the time. They probably had a thousand. I think they're probably up to 15 now, right? Different APIs, different S et cetera. And his response. I said, why don't you make it easier for, for customers and maybe build an abstraction or converge these? And he said, well, it's by design. What if you buy this? And, and what your thoughts are, cuz I, you know, he's a pretty straight shooter. Yeah. It's by design because it allows us as the market moves, we can move with it. And if we, if we give developers access to those low level primitives and APIs, then they can move with, with at market speed. Right. And so that again, by design, now we heard certainly Mongo poo pooing that today they didn't mention, they didn't call out Amazon. Yeah. Oracle has no compunction about specifically calling out Amazon. They do it all the time. What do you make of that? Can't Amazon have its cake and eat it too. In other words, extend some of the functionality of those specific databases without going to the Swiss army. >>I I'll put it this way. You, you kind of tapped in you're, you're sort of like, you know, killing me softly with your song there, which is that, you know, I was actually kind of went on a rant about this, actually know in, you know, come, you know, you know, my year ahead sort of out predictions. And I said, look, cloud folks, it's great that you're making individual SAS, you know, products easy to use. But now that I have to mix and match SAS products, you know, the burden of integration is on my shoulders. Start making my life easier. I think a good, you know, a good example of this would be, you know, for instance, you could take something like, you know, let's say like a Google big query. There's no reason why I can't have a piece of that that might, you know, might be paired, say, you know, say with span or something like that. >>The idea being is that if we're all working off a common, you know, common storage, we, you know, it's in cloud native, we can separate the computer engines. It means that we can use the right engine for the right part of the task. And the thing is that maybe, you know, myself as a consumer, I should not have to be choosing between big query and span. But the thing is, I should be able to say, look, I want to, you know, globally distribute database, but I also wanna do some analytics and therefore behind the scenes, you know, new microservices, it could connect the two wouldn't >>Microsoft synapse be an example of doing that. >>It should be an example. I wish I, I would love to hear more from Microsoft about this. They've been radio silent for about the past two or three years in data. You hardly hear about it, but synapse is actually those actually one of the ideas I had in mind now keep in mind that with synapse, you're not talking about, let's say, you know, I mean, it's, it's obviously a sequel data warehouse. It's not pure spark. It's basically their, it was their curated version of spark, but that's fine. But again, I would love to hear Microsoft talk more about that. They've been very quiet. >>Yeah. You, you, the intent is there to >>Simplify >>It exactly. And create an abstraction. Exactly. Yeah. They have been quiet about it. Yeah. Yeah. You would expect that, that maybe they're still trying to figure it out. So what's your prognosis from Mongo? I mean, since this company IP, you know, usually I, I tell and I tell everybody this, especially my kids, like don't buy a stock at IPO. You'll always get a better chance at a cheaper price to buy it. Yeah. And even though that was true with Mongo, you didn't have a big window. No. Like you did, for instance, with, with Facebook, certainly that's been the case with snowflake and sure. Alibaba, I mean, I name a zillion style was almost universal. Yeah. But, but since that, that, that first, you know, few months, period, this, this company has been on a roll. Right. And it, it obviously has been some volatility, but the execution has been outstanding. >>No question about that. I mean, the thing is, look what I, what I, and I'm just gonna talk on the product side on the sales side. Yeah. But on the product side, from the get go, they made a product that was easy for developers. Whereas let's say someone's giving an example, for instance, Cosmo CB, where to do certain operations. They had to go through multiple services in, you know, including Azure portal with Atlas, it's all within Atlas. So they've really, it's been kinda like design thinking from the start initially with, with the core Mongo DB, you know, you, the on premise, both this predates Atlas, I mean, part of it was that they were coming with a language that developers knew was just Javas script. The construct that they knew, which was JS on. So they started with that home core advantage, but they weren't the only ones doing that. But they did it with tooling that was very intuitive to developers that met developers, where they lived and what I give them, you know, then additional credit for is that when they went to the cloud and it wasn't an immediate thing, Atlas was not an overnight success, but they employed that same design thinking to Atlas, they made Atlas a good cloud experience. They didn't just do a lift and shift the cloud. And so that's why today basically like five or six years later, Atlas's most of their business. >>Yeah. It's what, 60% of the business now. Yeah. And then Dave, on the, on the earning scholar, maybe it wasn't Dave and somebody else in response to question said, yeah, ultimately this is the future will be be 90% of the business. I'm not gonna predict when. So my, my question is, okay, so let's call that the midterm midterm ATLA is gonna be 90% of the business with some exceptions that people just won't move to the cloud. What's next is the edge. A new opportunity is Mongo architecturally suited for the, I mean, it's certainly suited for the right, the home Depot store. Sure. You know, at the edge. Yeah. If you, if you consider that edge, which I guess it is form of edge, but how about the far edge EVs cell towers, you know, far side, real time, AI inferencing, what's the requirement there, can Mongo fit there? Any thoughts >>On that? I think the AI and the inferencing stuff is interesting. It's something which really Mongo has not tackled yet. I think we take the same principle, which is the lightweight stuff. In other words, you'll say, do let's say a classification or a prediction or some sort of prescriptive action in other words, where you're not doing some convolution, neural networking and trying to do like, you know, text, text to voice or, or, or vice versa. Well, you're not trying to do all that really fancy stuff. I think that's, you know, if you're keeping it SIM you know, kinda like the kiss principle, I think that's very much within Mongo's future. I think with the realm they have, they basically have the infrastructure to go out to the edge. I think with the fact that they've embraced GraphQL has also made them a lot more extensible. So I think they certainly do have, you know, I, I do see the edge as being, you know, you know, in, in, you know, in their, in their pathway. I do see basically lightweight analytics and lightweight, let's say machine learning definitely in their >>Future. And, but, and they would, would you agree that they're in a better position to tap that opportunity than say a snowflake or an Oracle now maybe M and a can change that. R D can maybe change that, but fundamentally from an architectural standpoint yeah. Are they in a better position? >>Good question. I think that that Mongo snowflake by virtual fact, I mean that they've been all, you know, all cloud start off with, I think makes it more difficult, not impossible to move out to the edge, but it means that, and I, and know, and I, and I said, they're really starting to making some tentative moves in that direction. I'm looking forward to next week to, you know, seeing what, you know, hearing what we're gonna, what they're gonna be saying about that. But I do think, right. You know, you know, to answer your question directly, I'd say like right now, I'd say Mongo probably has a, you know, has a head start there. >>I'm losing track of time. I could go forever with you. Tony bear DB insight with tons of insights. Thanks so much for coming back with. >>It's only one insight insight, Dave. Good to see you again. All >>Right. Good to see you. Thank you. Okay. Keep it right there. Right back at the Java center, Mongo DB world 2022, you're watching the cube.

(upbeat music) >> Welcome back to New York City everybody. You're watching theCUBE's coverage of MongoDB World 2022. My name is Dave Vellante. Pretty good attendance here. I'd say over 3000 people, great buzz, a lot of really technical sessions. There's an executive session going on. There's a financial analyst session. So a lot of diversity in this attendee base. Vadim Supitskiy is here. He's the CTO of Forbes and Abdul Razack is the vice president of Solution Engineering at Google. Gents, thanks for coming on theCUBE. >> Thanks Dave. >> Happy to be here. >> So, Forbes, very interesting business. I'm interested in what occurred during the pandemic for you guys. Right? Everybody went digital. Obviously you guys have a tremendous brand. We all, in the business world reaped from it, but what happened during the isolation era? What happened to your business? >> Yeah, so we've been innovating and going through digital transformation for years, since we launched our website probably 25 years ago. >> But during the pandemic, because of our coverage, our foresight to create a breaking news team, our audiences and readership really skyrocketed. >> Really? >> Yeah, and at that point, we were very happy and really lucky to be in Google Cloud and MongoDB Atlas. So when the audiences went up, we didn't feel any impact, right? Our environments auto-scaled and our users didn't experience any issues at all. So we were able to focus on innovation, our users loyalty and really building cool products. So we were very lucky and happy to be in Google Cloud and MongoDB Atlas. >> So Abdul, the solution and the title you provided, obviously worked. How did you guys end up getting together? What was that like? >> Yeah, I mean, like Vadim said, maybe there's a little bit of the right place at the right time in this case, but you can see the need for digital transformation and the pandemic really accelerated that. And like Vadim said, primarily Forbes wanted to focus on innovation and customer loyalty and the way that comes to bear, is that you have a technology platform that can serve those needs. Right? Whether it is through unique applications that can be delivered, the ability for developers to build those applications quickly and seamlessly and then remove the intangibles of scalability, performance, latency, and things of that nature. So, you can see this all coming together in this scenario. >> So as consumers, we see the website, we read online, maybe sometimes in the laptop, mostly on mobile. What is it that we don't see? I mean, the apps that Abdul talked about, community. What else is there? Paint a picture of that for us. >> Yeah. There is a lot going on behind the scene. Right? So focusing on audience, building communities, but also what it allowed us to do while everything was working well, we were scaling up. Right? We were able to focus on a lot of innovation. And one of those was first-party data platform that we built. We call it Forbes One. And that's in the center of everything that we do at Forbes right now. Right? So it allows us, one, to connect our partners, advertising partners with the audiences that they're looking to engage and to connect with. And then we are growing our consumer business as well and what that allows us to do is target the right products at the right time, to the right people, on the web website and our domain. So, that's just one of the examples that we've built our full first-party data platform on these technologies and we now know our customers so well that we are able to provide them with what they want. >> So the first-party data platform is what? A self-serve for advertiser, so they can identify? >> Not just advertisers. So it's in the center of everything. So advertiser comes in, we provide the segments and users that they want to reach. Now, we are creating products as well, building cool, innovative products and offering our journalism and everything there to our readers and we are able to connect them to the right audiences at the right time, as well as personalization. Right? You come onto the website, you want to read what you want to read. So we able to create that as well, using machine learning and AI. >> So a product, it might be a data product or it might be a content product? >> It could be a data product. It could be like just personalization or something like that. It could be newsletter. Right? It could be a stand-alone product, like investing product. So, there is a lot going on there, but we want to offer the right ones at the right time, to the right audiences and building that platform has allowed us to do that. >> Okay. Now Google's got great tech. What's the tech behind all this? >> Yeah. So when Vadim talked about segmenting to personalize something that is relevant to you and providing recommendations to you. Right? And all that is based on machine learning, AI technology. The fact that Vadim has all the data curated in a in a first-party data platform gives the ability to create a seamless profile. Right? You could be interested in a couple of products. Right? And then the underlying technology can tailor that to bring what is it that you're looking for at the right place at the right time. Right? So those are recommendations, things of that nature that's all powered by AI and machine learning technology. >> So it's running on Mongo, and then you're bringing in Google AI and machine intelligence tools? Can you double click on that? >> Yeah. It's basically a combination of both, using both platform to the deploy it and we embrace Cloud. Right? So we using all the Cloud native technologies. Right? We didn't want to just lift and shift. We wanted to make sure we do it right. And we focused on automation, even if we had to take a step back, we knew that automating things was a key for us. So yes, it's been really successful, but also really informative for us to use the right tools for the job. >> And you had prior experience with Mongo, or? >> We did. >> What's your journey been like there? >> Yeah, we actually were one of the first clients of Mongo. I think we were number 11 at that time. >> 10gen. >> Yes. It was. >> We remember. >> Many years ago it was MongoDB one, right? >> Yeah. >> And at that time we we introduced contributor network for us and our audiences were scaling as well. And we used Out-of-the-Box WordPress as our publishing platform, which couldn't scale. So we had to rethink and figure out, "Alright, so what do we do?" We compared couple of no SQL databases and Mongo was a winner because they checked all the boxes and developers loved it right away. Right? They're like, "All right, this is so much faster to develop on. It's just a great tool for the job going from SQL to, to no SQL". And we scaled and we never looked back. And then obviously Atlas came, so there are kind of two inflection points here. One switch into no SQL and two going away from managing databases. Like we don't want to be in that business. Right? Updates, patches, all of that, that we had to do manually, over-provision in our environments and kind of wasteful. So being on Atlas, that was a second kind of inflection point for us, which opened it up for us to do even more innovation and move faster. >> Okay. And you're happy about this partnership, despite, I mean, you partner with Mongo obviously, Google has its own databases, that's just the nature of the world we live in, isn't it? >> No and fundamentally like that, we always believe that customer choice is the primary notion. Right? I mean, and Google Cloud platform is more of a platform and the ecosystem is critical to that. Right? It's imperative. So, like Vadim said, the combination of Google and Mongo provides a truly Cloud native platform that can serve the needs for years to come, rather than from looking at it from a legacy perspective. And that's the way we look at it. Right? I mean, there is choices all the time and sometimes it's competition. >> Yeah. Yeah. And you're still selling a lot of compute and storage and machine intelligence, so machine learning. This morning in the keynotes, we heard a lot about a lot of different capabilities. We've certainly watched Mongo evolve its platform over the last half a decade or more really. But you've mentioned the developers loved it. Right? As Mongo evolves its platform, is there trade off from a developer simplicity standpoint? Are they able to preserve that from your perspective? >> I think with Atlas, it actually makes it easier now. So when they need to create an environment, they can do it on the fly. When they need to test something, also things available to them right away. So it actually, in general, as the platform becomes more mature and more stable, which is very important, but at the same time, the flexibility remains for development and for creation of environments and things like that. So we've been pretty happy with how it transitioned, to being a more mature platform. >> Did the move to Google Cloud and Atlas change the way that you're able to deliver high availability versus what you were doing when you were self-managing? Can you talk about that a little bit? >> Yeah, absolutely. We were in a data center, so kind of one location and moving to MongoDB Atlas and Google Cloud, now we're multi region. Right? So we have a full DR strategy and we feel a lot more secure and we feel very confident that anything that happens, we can scale, we can fail over. So absolutely, this helps us a lot. And the feature that was introduced probably a few years ago to auto scale MongoDB environments as well, that has been really key for us, so we can sleep well. >> Meaning you can scale while you sleep. >> Right, exactly. Exactly. >> Yeah. Plus the other part is you don't size for peak. right? You size as you grow, and then you, you have that elasticity built in. Right? That it is the nature. And then Mongo is available on multiple Google Cloud regions. So as you expand, you don't worry about all the plumbing that you need to do and things of that nature. >> They asked us serverless this morning. >> Yeah. >> How does that affect what you guys are doing together and what are your thoughts from Google's perspective and then of course, from Forbes? >> And that's the trend that we see constantly. Right? Serverless really decouples the tie to the VMs. Right? And so it makes it much more easier to provide the elasticity and have function calls across. Right? Function as a service and things of that nature. Right? So we see a lot of promise in that. Right? We do that even within our own products and we see that giving the ability to decompose and recompose applications and would love to hear how you're leveraging that. Right? >> Yeah. We fully embrace serverless. So we use all the tools you provide, I think. If you look at our architectural diagrams, it's like all these pops-up, cloud functions, composer, app engine. So we use the full suite and we love it. >> Yeah, Yeah. Okay. And then you talked this morning about the eliminating, the trade-offs with serverless of having to either when you dial it down You have to restart, but you've solved that problem, or I guess Mongo's helped you solve that problem. Can you explain that a little bit from a technical standpoint? >> Yeah. From a technical standpoint, if you look at, like as a developer, right? If you're building an intelligent app, it has multiple components within it. Right? There is pops-up for messaging, there is cloud functions and things of that nature. So you don't worry about, when it's encompassed in a serverless architecture, you don't worry about a lot of the complexities that go on behind it and so that makes the abstraction much more easier. And it eliminates the friction that a developer goes through. I think they've talked about removing friction and that's the primary source of productivity loss, which is the friction. We used to come from a world where developers were more worried. 80% of the time they would spend on plumbing this thing and then only 20% writing code. Right? And then now this whole paradigm should flip that. Right? That's where we see the promise of it. >> Do you still do stuff on Prem or are you pretty much all in the Cloud? >> Fully in the Cloud. >> How long did that take? What was that like? >> It actually was really fast. We had a real aggressive timeline. It took us six months. >> Really? >> Yeah. Yeah. And it was aggressive, but I was happy that we did it in a short period of time. >> And what was the business impact that you saw moving to the Google Cloud? >> Yeah, so obviously after we moved to the Cloud, we wanted to measure, especially the first year, how it affected us and what were the positives out of it. And yeah, we've seen tremendous results. 58% increase in speed to market. We were releasing four times more often than when we were on Prem. We saw 73% increase in initiatives delivered and while our velocity was scaling up, we also saw 30% decreased in hot fixes and rollbacks. So it became more stable while we scaled up the velocity and obviously very happy with those results. >> Wow. Do you golf? >> I don't actually. >> Do you golf? >> No. I watch golf. >> I used to watch. Okay. Do you know what a mulligan is? >> Yeah. >> Okay. mulligan is like a do-over right. If you had a mulligan, would you do anything differently? >> You know, we learned a lot and one of the keys for me was definitely automate everything, make sure that you automate as much as possible, even if it slows you down because in the future that will help you so much and use the platform and the tools that available to you. So, serverless. Right? Use Cloud the way it's supposed to be, as much as possible and I think that's the advice I would give. >> Are there any cautions with regard to automation, either of you that you see? I mean because sometimes automation brings unintended consequences and "Oops" happens really fast. >> Yeah. It's a little bit of a process. Right? If you take a step back, right, and typically what people tend to do is, there is a standardization process and once it's standardized, the next step is you gain efficiencies by automation. Right? In this whole thing, what is underestimated is change management. And we see a lot of room for improvement around educating on change management, getting ahead of that so that you can see what is coming. So that the organization moves across that. I don't know if you saw that in your case, but we see this predominantly in other other cases. >> Yeah. I mean, for us, we wanted to make sure that all the testing was in place and things like that. So not just automation of deploying or anything like that, but make sure that there is something there to catch if something goes wrong and roll back and things like that. So you want to make sure that you protected in many areas. >> So square this circle for me, because especially with COVID, so many unknowns and one of the benefits of document database is you're not tied into a schema. You got a flexible schema. Okay. So you're changing, you can change things much more easily. So when you talk about standardization, you're talking about standardizing, what at the infrastructure layer, or where does that standardization occur? Where should it occur. >> I mean, you could have it at the business process level. >> Okay. >> You could have it at the infrastructure level. You could also have it on the administration aspect of it. So there are three areas where you could apply automation to. >> So is there an analog to flexible schema at the business process level? Is that kind of how to think about it, whereas I'm not locked into a business process schema? I have to build flexibility into that as I change my? >> No, I mean, you can apply it any which way. I mean, I don't think the schema matters so much. Right? Like, for example, if you take the Forbes US case. Right? There is content curation, for example. Right? >> Yeah, okay. >> You could take content curation. Content curation in the previous world, like in the WordPress world, was not very flexible. Right? Like that it wouldn't scale. And now you are in a world where you have a very flexible schema, but the process of curating the content can be standardized. Right? And then the next step of that is to automate that. Right? And so you could apply it in any manner if you will. >> So have you built a custom CMS? Is that what you've done there? >> Yeah. We built our own custom CMS. It's AI powered. We want to make our journalist lives easier. So we're constantly trying to figure out what can we give them to make their day-to-day job much easier. >> So the machines can curate and find the best content. >> We do recommend things. Yes, absolutely. We curate, we tell them what would be the best headline, for example, what would >> Prior to them publishing? >> Yeah. Yeah. What would be the better keywords to include and things like that, what images. Just recommendations. >> And you can automate the insertion of those WordPress to go every time they do, even though they're writing about the same topic. >> It's a recommendation process obviously, but >> There is a human intelligence to that at the end. Right? I mean, but you can create a much more informed view by curating and recommending content rather than a myopic view. >> And you're eliminating that mundane keystroke task. Wow. Amazing story guys. Thanks so much for sharing. >> Absolutely. >> All right. Keep it right there. We're live from MongoDB World 2022 in New York city. Be right back. (upbeat music)

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> The pandemic has changed the way we think about and predict the future. As we enter the third year of a global pandemic, we see the significant impact that it's had on technology strategy, spending patterns, and company fortunes Much has changed. And while many of these changes were forced reactions to a new abnormal, the trends that we've seen over the past 24 months have become more entrenched, and point to the way that's coming ahead in the technology business. Hello and welcome to this week's Wikibon CUBE Insights powered by ETR. In this Breaking Analysis, we welcome our partner and colleague and business friend, Erik Porter Bradley, as we deliver what's becoming an annual tradition for Erik and me, our predictions for Enterprise Technology in 2022 and beyond Erik, welcome. Thanks for taking some time out. >> Thank you, Dave. Luckily we did pretty well last year, so we were able to do this again. So hopefully we can keep that momentum going. >> Yeah, you know, I want to mention that, you know, we get a lot of inbound predictions from companies and PR firms that help shape our thinking. But one of the main objectives that we have is we try to make predictions that can be measured. That's why we use a lot of data. Now not all will necessarily fit that parameter, but if you've seen the grading of our 2021 predictions that Erik and I did, you'll see we do a pretty good job of trying to put forth prognostications that can be declared correct or not, you know, as black and white as possible. Now let's get right into it. Our first prediction, we're going to go run into spending, something that ETR surveys for quarterly. And we've reported extensively on this. We're calling for tech spending to increase somewhere around 8% in 2022, we can see there on the slide, Erik, we predicted spending last year would increase by 4% IDC. Last check was came in at five and a half percent. Gardner was somewhat higher, but in general, you know, not too bad, but looking ahead, we're seeing an acceleration from the ETR September surveys, as you can see in the yellow versus the blue bar in this chart, many of the SMBs that were hard hit by the pandemic are picking up spending again. And the ETR data is showing acceleration above the mean for industries like energy, utilities, retail, and services, and also, notably, in the Forbes largest 225 private companies. These are companies like Mars or Koch industries. They're predicting well above average spending for 2022. So Erik, please weigh in here. >> Yeah, a lot to bring up on this one, I'm going to be quick. So 1200 respondents on this, over a third of which were at the C-suite level. So really good data that we brought in, the usual bucket of, you know, fortune 500, global 2000 make up the meat of that median, but it's 8.3% and rising with momentum as we see. What's really interesting right now is that energy and utilities. This is usually like, you know, an orphan stock dividend type of play. You don't see them at the highest point of tech spending. And the reason why right now is really because this state of tech infrastructure in our energy infrastructure needs help. And it's obvious, remember the Florida municipality break reach last year? When they took over the water systems or they had the ability to? And this is a real issue, you know, there's bad nation state actors out there, and I'm no alarmist, but the energy and utility has to spend this money to keep up. It's really important. And then you also hit on the retail consumer. Obviously what's happened, the work from home shift created a shop from home shift, and the trends that are happening right now in retail. If you don't spend and keep up, you're not going to be around much longer. So I think the really two interesting things here to call out are energy utilities, usually a laggard in IT spend and it's leading, and also retail consumer, a lot of changes happening. >> Yeah. Great stuff. I mean, I recall when we entered the pandemic, really ETR was the first to emphasize the impact that work from home was going to have, so I really put a lot of weight on this data. Okay. Our next prediction is we're going to get into security, it's one of our favorite topics. And that is that the number one priority that needs to be addressed by organizations in 2022 is security and you can see, in this slide, the degree to which security is top of mind, relative to some other pretty important areas like cloud, productivity, data, and automation, and some others. Now people may say, "Oh, this is obvious." But I'm going to add some context here, Erik, and then bring you in. First, organizations, they don't have unlimited budgets. And there are a lot of competing priorities for dollars, especially with the digital transformation mandate. And depending on the size of the company, this data will vary. For example, while security is still number one at the largest public companies, and those are of course of the biggest spenders, it's not nearly as pronounced as it is on average, or in, for example, mid-sized companies and government agencies. And this is because midsized companies or smaller companies, they don't have the resources that larger companies do. Larger companies have done a better job of securing their infrastructure. So these mid-size firms are playing catch up and the data suggests cyber is even a bigger priority there, gaps that they have to fill, you know, going forward. And that's why we think there's going to be more demand for MSSPs, managed security service providers. And we may even see some IPO action there. And then of course, Erik, you and I have talked about events like the SolarWinds Hack, there's more ransomware attacks, other vulnerabilities. Just recently, like Log4j in December. All of this has heightened concerns. Now I want to talk a little bit more about how we measure this, you know, relatively, okay, it's an obvious prediction, but let's stick our necks out a little bit. And so in addition to the rise of managed security services, we're calling for M&A and/or IPOs, we've specified some names here on this chart, and we're also pointing to the digital supply chain as an area of emphasis. Again, Log4j really shone that under a light. And this is going to help the likes of Auth0, which is now Okta, SailPoint, which is called out on this chart, and some others. We're calling some winners in end point security. Erik, you're going to talk about sort of that lifecycle, that transformation that we're seeing, that migration to new endpoint technologies that are going to benefit from this reset refresh cycle. So Erik, weigh in here, let's talk about some of the elements of this prediction and some of the names on that chart. >> Yeah, certainly. I'm going to start right with Log4j top of mind. And the reason why is because we're seeing a real paradigm shift here where things are no longer being attacked at the network layer, they're being attacked at the application layer, and in the application stack itself. And that is a huge shift left. And that's taking in DevSecOps now as a real priority in 2022. That's a real paradigm shift over the last 20 years. That's not where attacks used to come from. And this is going to have a lot of changes. You called out a bunch of names in there that are, they're either going to work. I would add to that list Wiz. I would add Orca Security. Two names in our emerging technology study, in addition to the ones you added that are involved in cloud security and container security. These names are either going to get gobbled up. So the traditional legacy names are going to have to start writing checks and, you know, legacy is not fair, but they're in the data center, right? They're, on-prem, they're not cloud native. So these are the names that money is going to be flowing to. So they're either going to get gobbled up, or we're going to see some IPO's. And on the other thing I want to talk about too, is what you mentioned. We have CrowdStrike on that list, We have SentinalOne on the list. Everyone knows them. Our data was so strong on Tanium that we actually went positive for the first time just today, just this morning, where that was released. The trifecta of these are so important because of what you mentioned, under resourcing. We can't have security just tell us when something happens, it has to automate, and it has to respond. So in this next generation of EDR and XDR, an automated response has to happen because people are under-resourced, salaries are really high, there's a skill shortage out there. Security has to become responsive. It can't just monitor anymore. >> Yeah. Great. And we should call out too. So we named some names, Snyk, Aqua, Arctic Wolf, Lacework, Netskope, Illumio. These are all sort of IPO, or possibly even M&A candidates. All right. Our next prediction goes right to the way we work. Again, something that ETR has been on for awhile. We're calling for a major rethink in remote work for 2022. We had predicted last year that by the end of 2021, there'd be a larger return to the office with the norm being around a third of workers permanently remote. And of course the variants changed that equation and, you know, gave more time for people to think about this idea of hybrid work and that's really come in to focus. So we're predicting that is going to overtake fully remote as the dominant work model with only about a third of the workers back in the office full-time. And Erik, we expect a somewhat lower percentage to be fully remote. It's now sort of dipped under 30%, at around 29%, but it's still significantly higher than the historical average of around 15 to 16%. So still a major change, but this idea of hybrid and getting hybrid right, has really come into focus. Hasn't it? >> Yeah. It's here to stay. There's no doubt about it. We started this in March of 2020, as soon as the virus hit. This is the 10th iteration of the survey. No one, no one ever thought we'd see a number where only 34% of people were going to be in office permanently. That's a permanent number. They're expecting only a third of the workers to ever come back fully in office. And against that, there's 63% that are saying their permanent workforce is going to be either fully remote or hybrid. And this, I can't really explain how big of a paradigm shift this is. Since the start of the industrial revolution, people leave their house and go to work. Now they're saying that's not going to happen. The economic impact here is so broad, on so many different areas And, you know, the reason is like, why not? Right? The productivity increase is real. We're seeing the productivity increase. Enterprises are spending on collaboration tools, productivity tools, We're seeing an increased perception in productivity of their workforce. And the CFOs can cut down an expense item. I just don't see a reason why this would end, you know, I think it's going to continue. And I also want to point out these results, as high as they are, were before the Omicron wave hit us. I can only imagine what these results would have been if we had sent the survey out just two or three weeks later. >> Yeah. That's a great point. Okay. Next prediction, we're going to look at the supply chain, specifically in how it's affecting some of the hardware spending and cloud strategies in the future. So in this chart, ETRS buyers, have you experienced problems procuring hardware as a result of supply chain issues? And, you know, despite the fact that some companies are, you know, I would call out Dell, for example, doing really well in terms of delivering, you can see that in the numbers, it's pretty clear, there's been an impact. And that's not not an across the board, you know, thing where vendors are able to deliver, especially acute in PCs, but also pronounced in networking, also in firewall servers and storage. And what's interesting is how companies are responding and reacting. So first, you know, I'm going to call the laptop and PC demand staying well above pre-COVID norms. It had peaked in 2012. Pre-pandemic it kept dropping and dropping and dropping, in terms of, you know, unit volume, where the market was contracting. And we think can continue to grow this year in double digits in 2022. But what's interesting, Erik, is when you survey customers, is despite the difficulty they're having in procuring network hardware, there's as much of a migration away from existing networks to the cloud. You could probably comment on that. Their networks are more fossilized, but when it comes to firewalls and servers and storage, there's a much higher propensity to move to the cloud. 30% of customers that ETR surveyed will replace security appliances with cloud services and 41% and 34% respectively will move to cloud compute and storage in 2022. So cloud's relentless march on traditional on-prem models continues. Erik, what do you make of this data? Please weigh in on this prediction. >> As if we needed another reason to go to the cloud. Right here, here it is yet again. So this was added to the survey by client demand. They were asking about the procurement difficulties, the supply chain issues, and how it was impacting our community. So this is the first time we ran it. And it really was interesting to see, you know, the move there. And storage particularly I found interesting because it correlated with a huge jump that we saw on one of our vendor names, which was Rubrik, had the highest net score that it's ever had. So clearly we're seeing some correlation with some of these names that are there, you know, really well positioned to take storage, to take data into the cloud. So again, you didn't need another reason to, you know, hasten this digital transformation, but here we are, we have it yet again, and I don't see it slowing down anytime soon. >> You know, that's a really good point. I mean, it's not necessarily bad news for the... I mean, obviously you wish that it had no change, would be great, but things, you know, always going to change. So we'll talk about this a little bit later when we get into the Supercloud conversation, but this is an opportunity for people who embrace the cloud. So we'll come back to that. And I want to hang on cloud a bit and share some recent projections that we've made. The next prediction is the big four cloud players are going to surpass 167 billion, an IaaS and PaaS revenue in 2022. We track this. Observers of this program know that we try to create an apples to apples comparison between AWS, Azure, GCP and Alibaba in IaaS and PaaS. So we're calling for 38% revenue growth in 2022, which is astounding for such a massive market. You know, AWS is probably not going to hit a hundred billion dollar run rate, but they're going to be close this year. And we're going to get there by 2023, you know they're going to surpass that. Azure continues to close the gap. Now they're about two thirds of the size of AWS and Google, we think is going to surpass Alibaba and take the number three spot. Erik, anything you'd like to add here? >> Yeah, first of all, just on a sector level, we saw our sector, new survey net score on cloud jumped another 10%. It was already really high at 48. Went up to 53. This train is not slowing down anytime soon. And we even added an edge compute type of player, like CloudFlare into our cloud bucket this year. And it debuted with a net score of almost 60. So this is really an area that's expanding, not just the big three, but everywhere. We even saw Oracle and IBM jump up. So even they're having success, taking some of their on-prem customers and then selling them to their cloud services. This is a massive opportunity and it's not changing anytime soon, it's going to continue. >> And I think the operative word there is opportunity. So, you know, the next prediction is something that we've been having fun with and that's this Supercloud becomes a thing. Now, the reason I say we've been having fun is we put this concept of Supercloud out and it's become a bit of a controversy. First, you know, what the heck's the Supercloud right? It's sort of a buzz-wordy term, but there really is, we believe, a thing here. We think there needs to be a rethinking or at least an evolution of the term multi-cloud. And what we mean is that in our view, you know, multicloud from a vendor perspective was really cloud compatibility. It wasn't marketed that way, but that's what it was. Either a vendor would containerize its legacy stack, shove it into the cloud, or a company, you know, they'd do the work, they'd build a cloud native service on one of the big clouds and they did do it for AWS, and then Azure, and then Google. But there really wasn't much, if any, leverage across clouds. Now from a buyer perspective, we've always said multicloud was a symptom of multi-vendor, meaning I got different workloads, running in different clouds, or I bought a company and they run on Azure, and I do a lot of work on AWS, but generally it wasn't necessarily a prescribed strategy to build value on top of hyperscale infrastructure. There certainly was somewhat of a, you know, reducing lock-in and hedging the risk. But we're talking about something more here. We're talking about building value on top of the hyperscale gift of hundreds of billions of dollars in CapEx. So in addition, we're not just talking about transforming IT, which is what the last 10 years of cloud have been like. And, you know, doing work in the cloud because it's cheaper or simpler or more agile, all of those things. So that's beginning to change. And this chart shows some of the technology vendors that are leaning toward this Supercloud vision, in our view, building on top of the hyperscalers that are highlighted in red. Now, Jerry Chan at Greylock, they wrote a piece called Castles in the Cloud. It got our thinking going, and he and the team at Greylock, they're building out a database of all the cloud services and all the sub-markets in cloud. And that got us thinking that there's a higher level of abstraction coalescing in the market, where there's tight integration of services across clouds, but the underlying complexity is hidden, and there's an identical experience across clouds, and even, in my dreams, on-prem for some platforms, so what's new or new-ish and evolving are things like location independence, you've got to include the edge on that, metadata services to optimize locality of reference and data source awareness, governance, privacy, you know, application independent and dependent, actually, recovery across clouds. So we're seeing this evolve. And in our view, the two biggest things that are new are the technology is evolving, where you're seeing services truly integrate cross-cloud. And the other big change is digital transformation, where there's this new innovation curve developing, and it's not just about making your IT better. It's about SaaS-ifying and automating your entire company workflows. So Supercloud, it's not just a vendor thing to us. It's the evolution of, you know, the, the Marc Andreessen quote, "Every company will be a SaaS company." Every company will deliver capabilities that can be consumed as cloud services. So Erik, the chart shows spending momentum on the y-axis and net score, or presence in the ETR data center, or market share on the x-axis. We've talked about snowflake as the poster child for this concept where the vision is you're in their cloud and sharing data in that safe place. Maybe you could make some comments, you know, what do you think of this Supercloud concept and this change that we're sensing in the market? >> Well, I think you did a great job describing the concept. So maybe I'll support it a little bit on the vendor level and then kind of give examples of the ones that are doing it. You stole the lead there with Snowflake, right? There is no better example than what we've seen with what Snowflake can do. Cross-portability in the cloud, the ability to be able to be, you know, completely agnostic, but then build those services on top. They're better than anything they could offer. And it's not just there. I mean, you mentioned edge compute, that's a whole nother layer where this is coming in. And CloudFlare, the momentum there is out of control. I mean, this is a company that started off just doing CDN and trying to compete with Okta Mite. And now they're giving you a full soup to nuts with security and actual edge compute layer, but it's a fantastic company. What they're doing, it's another great example of what you're seeing here. I'm going to call out HashiCorp as well. They're more of an infrastructure services, a little bit more of an open-source freemium model, but what they're doing as well is completely cloud agnostic. It's dynamic. It doesn't care if you're in a container, it doesn't matter where you are. They recently IPO'd and they're down 25%, but their data looks so good across both of our emerging technology and TISA survey. It's certainly another name that's playing on this. And another one that we mentioned as well is Rubrik. If you need storage, compute, and in the cloud layer and you need to be agnostic to it, they're another one that's really playing in this space. So I think it's a great concept you're bringing up. I think it's one that's here to stay and there's certainly a lot of vendors that fit into what you're describing. >> Excellent. Thank you. All right, let's shift to data. The next prediction, it might be a little tough to measure. Before I said we're trying to be a little black and white here, but it relates to Data Mesh, which is, the ideas behind that term were created by Zhamak Dehghani of ThoughtWorks. And we see Data Mesh is really gaining momentum in 2022, but it's largely going to be, we think, confined to a more narrow scope. Now, the impetus for change in data architecture in many companies really stems from the fact that their Hadoop infrastructure really didn't solve their data problems and they struggle to get more value out of their data investments. Data Mesh prescribes a shift to a decentralized architecture in domain ownership of data and a shift to data product thinking, beyond data for analytics, but data products and services that can be monetized. Now this a very powerful in our view, but they're difficult for organizations to get their heads around and further decentralization creates the need for a self-service platform and federated data governance that can be automated. And not a lot of standards around this. So it's going to take some time. At our power panel a couple of weeks ago on data management, Tony Baer predicted a backlash on Data Mesh. And I don't think it's going to be so much of a backlash, but rather the adoption will be more limited. Most implementations we think are going to use a starting point of AWS and they'll enable domains to access and control their own data lakes. And while that is a very small slice of the Data Mesh vision, I think it's going to be a starting point. And the last thing I'll say is, this is going to take a decade to evolve, but I think it's the right direction. And whether it's a data lake or a data warehouse or a data hub or an S3 bucket, these are really, the concept is, they'll eventually just become nodes on the data mesh that are discoverable and access is governed. And so the idea is that the stranglehold that the data pipeline and process and hyper-specialized roles that they have on data agility is going to evolve. And decentralized architectures and the democratization of data will eventually become a norm for a lot of different use cases. And Erik, I wonder if you'd add anything to this. >> Yeah. There's a lot to add there. The first thing that jumped out to me was that that mention of the word backlash you said, and you said it's not really a backlash, but what it could be is these are new words trying to solve an old problem. And I do think sometimes the industry will notice that right away and maybe that'll be a little pushback. And the problems are what you already mentioned, right? We're trying to get to an area where we can have more assets in our data site, more deliverable, and more usable and relevant to the business. And you mentioned that as self-service with governance laid on top. And that's really what we're trying to get to. Now, there's a lot of ways you can get there. Data fabric is really the technical aspect and data mesh is really more about the people, the process, and the governance, but the two of those need to meet, in order to make that happen. And as far as tools, you know, there's even cataloging names like Informatica that play in this, right? Istio plays in this, Snowflake plays in this. So there's a lot of different tools that will support it. But I think you're right in calling out AWS, right? They have AWS Lake, they have AWS Glue. They have so much that's trying to drive this. But I think the really important thing to keep here is what you said. It's going to be a decade long journey. And by the way, we're on the shoulders of giants a decade ago that have even gotten us to this point to talk about these new words because this has been an ongoing type of issue, but ultimately, no matter which vendors you use, this is going to come down to your data governance plan and the data literacy in your business. This is really about workflows and people as much as it is tools. So, you know, the new term of data mesh is wonderful, but you still have to have the people and the governance and the processes in place to get there. >> Great, thank you for that, Erik. Some great points. All right, for the next prediction, we're going to shine the spotlight on two of our favorite topics, Snowflake and Databricks, and the prediction here is that, of course, Databricks is going to IPO this year, as expected. Everybody sort of expects that. And while, but the prediction really is, well, while these two companies are facing off already in the market, they're also going to compete with each other for M&A, especially as Databricks, you know, after the IPO, you're going to have, you know, more prominence and a war chest. So first, these companies, they're both looking pretty good, the same XY graph with spending velocity and presence and market share on the horizontal axis. And both Snowflake and Databricks are well above that magic 40% red dotted line, the elevated line, to us. And for context, we've included a few other firms. So you can see kind of what a good position these two companies are really in, especially, I mean, Snowflake, wow, it just keeps moving to the right on this horizontal picture, but maintaining the next net score in the Y axis. Amazing. So, but here's the thing, Databricks is using the term Lakehouse implying that it has the best of data lakes and data warehouses. And Snowflake has the vision of the data cloud and data sharing. And Snowflake, they've nailed analytics, and now they're moving into data science in the domain of Databricks. Databricks, on the other hand, has nailed data science and is moving into the domain of Snowflake, in the data warehouse and analytics space. But to really make this seamless, there has to be a semantic layer between these two worlds and they're either going to build it or buy it or both. And there are other areas like data clean rooms and privacy and data prep and governance and machine learning tooling and AI, all that stuff. So the prediction is they'll not only compete in the market, but they'll step up and in their competition for M&A, especially after the Databricks IPO. We've listed some target names here, like Atscale, you know, Iguazio, Infosum, Habu, Immuta, and I'm sure there are many, many others. Erik, you care to comment? >> Yeah. I remember a year ago when we were talking Snowflake when they first came out and you, and I said, "I'm shocked if they don't use this war chest of money" "and start going after more" "because we know Slootman, we have so much respect for him." "We've seen his playbook." And I'm actually a little bit surprised that here we are, at 12 months later, and he hasn't spent that money yet. So I think this prediction's just spot on. To talk a little bit about the data side, Snowflake is in rarefied air. It's all by itself. It is the number one net score in our entire TISA universe. It is absolutely incredible. There's almost no negative intentions. Global 2000 organizations are increasing their spend on it. We maintain our positive outlook. It's really just, you know, stands alone. Databricks, however, also has one of the highest overall net sentiments in the entire universe, not just its area. And this is the first time we're coming up positive on this name as well. It looks like it's not slowing down. Really interesting comment you made though that we normally hear from our end-user commentary in our panels and our interviews. Databricks is really more used for the data science side. The MLAI is where it's best positioned in our survey. So it might still have some catching up to do to really have that caliber of usability that you know Snowflake is seeing right now. That's snowflake having its own marketplace. There's just a lot more to Snowflake right now than there is Databricks. But I do think you're right. These two massive vendors are sort of heading towards a collision course, and it'll be very interesting to see how they deploy their cash. I think Snowflake, with their incredible management and leadership, probably will make the first move. >> Well, I think you're right on that. And by the way, I'll just add, you know, Databricks has basically said, hey, it's going to be easier for us to come from data lakes into data warehouse. I'm not sure I buy that. I think, again, that semantic layer is a missing ingredient. So it's going to be really interesting to see how this plays out. And to your point, you know, Snowflake's got the war chest, they got the momentum, they've got the public presence now since November, 2020. And so, you know, they're probably going to start making some aggressive moves. Anyway, next prediction is something, Erik, that you and I have talked about many, many times, and that is observability. I know it's one of your favorite topics. And we see this world screaming for more consolidation it's going all in on cloud native. These legacy stacks, they're fighting to stay relevant, but the direction is pretty clear. And the same XY graph lays out the players in the field, with some of the new entrants that we've also highlighted, like Observe and Honeycomb and ChaosSearch that we've talked about. Erik, we put a big red target around Splunk because everyone wants their gold. So please give us your thoughts. >> Oh man, I feel like I've been saying negative things about Splunk for too long. I've got a bad rap on this name. The Splunk shareholders come after me all the time. Listen, it really comes down to this. They're a fantastic company that was designed to do logging and monitoring and had some great tool sets around what you could do with it. But they were designed for the data center. They were designed for prem. The world we're in now is so dynamic. Everything I hear from our end user community is that all net new workloads will be going to cloud native players. It's that simple. So Splunk has entrenched. It's going to continue doing what it's doing and it does it really, really well. But if you're doing something new, the new workloads are going to be in a dynamic environment and that's going to go to the cloud native players. And in our data, it is extremely clear that that means Datadog and Elastic. They are by far number one and two in net score, increase rates, adoption rates. It's not even close. Even New Relic actually is starting to, you know, entrench itself really well. We saw New Relic's adoption's going up, which is super important because they went to that freemium model, you know, to try to get their little bit of an entrenched customer base and that's working as well. And then you made a great list here, of all the new entrants, but it goes beyond this. There's so many more. In our emerging technology survey, we're seeing Century, Catchpoint, Securonix, Lucid Works. There are so many options in this space. And let's not forget, the biggest data that we're seeing is with Grafana. And Grafana labs as yet to turn on their enterprise. Elastic did it, why can't Grafana labs do it? They have an enterprise stack. So when you look at how crowded this space is, there has to be consolidation. I recently hosted a panel and every single guy on that panel said, "Please give me a consolidation." Because they're the end users trying to actually deploy these and it's getting a little bit confusing. >> Great. Thank you for that. Okay. Last prediction. Erik, might be a little out of your wheelhouse, but you know, you might have some thoughts on it. And that's a hybrid events become the new digital model and a new category in 2022. You got these pure play digital or virtual events. They're going to take a back seat to in-person hybrids. The virtual experience will eventually give way to metaverse experiences and that's going to take some time, but the physical hybrid is going to drive it. And metaverse is ultimately going to define the virtual experience because the virtual experience today is not great. Nobody likes virtual. And hybrid is going to become the business model. Today's pure virtual experience has to evolve, you know, theCUBE first delivered hybrid mid last decade, but nobody really wanted it. We did Mobile World Congress last summer in Barcelona in an amazing hybrid model, which we're showing in some of the pictures here. Alex, if you don't mind bringing that back up. And every physical event that we're we're doing now has a hybrid and virtual component, including the pre-records. You can see in our studios, you see that the green screen. I don't know. Erik, what do you think about, you know, the Zoom fatigue and all this. I know you host regular events with your round tables, but what are your thoughts? >> Well, first of all, I think you and your company here have just done an amazing job on this. So that's really your expertise. I spent 20 years of my career hosting intimate wall street idea dinners. So I'm better at navigating a wine list than I am navigating a conference floor. But I will say that, you know, the trend just goes along with what we saw. If 35% are going to be fully remote. If 70% are going to be hybrid, then our events are going to be as well. I used to host round table dinners on, you know, one or two nights a week. Now those have gone virtual. They're now panels. They're now one-on-one interviews. You know, we do chats. We do submitted questions. We do what we can, but there's no reason that this is going to change anytime soon. I think you're spot on here. >> Yeah. Great. All right. So there you have it, Erik and I, Listen, we always love the feedback. Love to know what you think. Thank you, Erik, for your partnership, your collaboration, and love doing these predictions with you. >> Yeah. I always enjoy them too. And I'm actually happy. Last year you made us do a baker's dozen, so thanks for keeping it to 10 this year. >> (laughs) We've got a lot to say. I know, you know, we cut out. We didn't do much on crypto. We didn't really talk about SaaS. I mean, I got some thoughts there. We didn't really do much on containers and AI. >> You want to keep going? I've got another 10 for you. >> RPA...All right, we'll have you back and then let's do that. All right. All right. Don't forget, these episodes are all available as podcasts, wherever you listen, all you can do is search Breaking Analysis podcast. Check out ETR's website at etr.plus, they've got a new website out. It's the best data in the industry, and we publish a full report every week on wikibon.com and siliconangle.com. You can always reach out on email, David.Vellante@siliconangle.com I'm @DVellante on Twitter. Comment on our LinkedIn posts. This is Dave Vellante for the Cube Insights powered by ETR. Have a great week, stay safe, be well. And we'll see you next time. (mellow music)

(upbeat music) >> Hey everyone and welcome back to the CUBE's continuous live coverage of AWS re:Invent 2021. continuous live coverage of AWS re:Invent 2021. Lisa Martin here with David Nicholson. We have two live sets going on, we've got two remote sets, over 100 guests working with AWS and it's a massive ecosystem of partners, really digging into the next decade of cloud innovation and we're pleased to welcome back one of our CUBE alumni, Edith Harbaugh, CEO, and co-founder of LaunchDarkly. We're going to be talking about a blueprint for continuous modernization, Edith, it's great to have you, thanks for coming. >> Thanks for having me. >> So I was doing some research, you guys raised 200 million in series D in August, just a few months ago, that new funding tripled your valuation to 3 billion up more than 3x from the previous funding rounds, so rocket ship. >> Edith: Yeah. >> I also noticed you guys are on the Forbes Cloud 100 2nd year on the list, you jumped dramatically from 100 in 2020 to 47 this year, talk to us about all the innovation and acceleration that's going on at LaunchDarkly. >> Yeah, well, it's, it's great to be here, you know, I'm the CEO and co-founder and we started seven years ago in 2014 and what we were doing back then was a really new field, like I actually came up with the name feature management, just to describe what we were doing and it was this idea that you could release features to different people at different times, which sounds really simple, but it really allows you to have valves to different populations that you can then turn something on, turn something off, run a beta, do personalization and then if something's going wrong out on the field quickly and easily, turn it off. >> So as an engineer, as a long standing engineer, what were the things that really frustrated you, that you thought this, this is missing, we've got to focus on this. >> Oh my gosh, so, I was an engineering manager, I actually do a podcast too called To Be Continuous just about all the bad things I saw happen, the worst thing you could do is, is build something that nobody wants, which is really frustrating, so I think a lot of continuous delivery came out of the urge to just get stuff out quicker. The flip side of that is that if you moved too fast, a release can be catastrophic. We used to call them the push and pray release because you push stuff out and then you're just crossing your fingers that nothing breaks because if something breaks it's extremely stressful. Your mind starts flooding with endorphins and hormones, your heart rate increases and you sometimes make even worse decisions, so what LaunchDarkly and feature management allow you to do is push it out to who you want and if something is going wrong, you could turn it off without a redeploy, if things are going right, you can continue to push it out. >> And when you say feature management, you're talking about, you're talking about a level of granularity that is finer than a release version. that is finer than a release version. >> Edith: Yeah. How do you do that? >> Our customers do it, so we provide a platform where our customers and we have 2,500 worldwide, everything from IBM and Atlassian, down to like three person startups, they decide how to encapsulate a feature >> David: Okay. >> So they could push it to who they want, so, so there's a lot of really neat use cases. >> So knowing that you're providing them with the valves. >> Edith: Yes. Then they can- think differently about how they're actually developing in anticipation of delivering encapsulated features, as opposed to, here's your new release. >> Edith: Exactly. >> Okay. >> Exactly, so we have some customers who've used LaunchDarkly to actually move to the cloud. So like TrueCar was running their own data centers and they wanted a way to start moving all of that data center traffic into AWS, so they could use LaunchDarkly to manage that traffic flow and do it in a controlled way instead of just one quick switch. >> I was looking at that case study of TrueCar, they migrated 500 websites to AWS without downtime and deploying 20x per day, which is up from 1x a week, that's a massive change. >> Yeah, I think really what we give our customers is confidence that if you know that you can always have control over stuff with feature management, you actually move much quicker. You can, you can move 20 times a day if you know that if something goes wrong, you can always turn it off, you have much more confidence. >> Where are your, you having customer conversations? I know you, you coined to the term feature management, I'd love to know a bit more contextually about the evolution from feature flags to feature management and where are those customer conversations happening? are they kind of down in the technical ways? are they more higher level? given the fact that we're in such a, still a, such a state of flux with COVID? >> Yeah, so we, we didn't invent feature flagging like the smart companies like Amazon, Facebook, Netflix have been doing feature flagging for decades now, it was always a secret sauce of this is how they could manage their own functionality. What LaunchDarkly did was kind of changed it to feature management about doing it where any other customer also had the same set of tools and platforms and also on top of that things like a workflow, scheduling, integrations. So that for example, a developer could develop something and then give the keys to the product manager, say product manager, you get to, you get to run the beta now. >> So putting, putting more control back in the hands of the folks that are, that really are touching and feeling and smelling the product. >> Yeah or customer support, you know, >> Yeah or customer support, you know, if something is going wrong in the field, instead of having to wait for an engineer to fix a bug customer support could just turn it off. >> So I'm curious about, you know, when we talk about it's a, this, this sort of dovetails with something that was discussed in the keynote today, out of the gate, Adam comes out and he's talking about microprocessor technology. Now in the era of cloud, generally people would say, that stuff doesn't matter, right? It's all about the feeling of being in the cloud and the flame, you know, the, the, the field of wheat blowing in the wind and it's a feeling that you get, it's really interesting what you're doing under the covers, but who is the, who is the audience? Who, who buys this? Because I can imagine some in the engineering, on the engineering side of things, feeling like maybe they're giving up some control, but really you're giving them more tools, but is it business people who are demanding this? how, how do you go to market? >> Yeah, so it's really interesting because our core audience is developers and VP of engineering, like they love the platform. Like our Net Promoter Score is extremely high, engineers say like, this gave me my weekends back because if a bug happens I don't have to come in. >> David: Okay so they get it. >> They get it. This isn't being pushed down- from executives that don't understand the technology. >> No, I mean, a typical thing is a developer's, like, I need this to do my job and then the business people say, well, if the developers are happy, we're happy, you know, it's, it's a developers world now, you know, they're hard to hire, you have to have them and if you have anything that will make their job easier and them happier, why wouldn't you buy it? >> That's a big facilitator, so you mentioned a high, high NPS, high Net Promoter Score, we, we talk with Amazon folks about their their focus on the customer and their customer obsession if you will, that everything starts backwards, we start from the customer, 2,500 customers in such a short time period, we talked about the funding, I imagine culturally there's similarities there, if one of the things that you're able to confidently give your customers is that confidence in LaunchDarkly. >> Yeah, you know, one of the happiest parts of my job is visiting customers, you know, I, my co-founder and I personally visited, I think the first 10 or 20 customers and if they had a bug, if they wanted something, and if they had a bug, if they wanted something, we built it. And I love going on customer sites, cause it's. And I love going on customer sites, cause it's. >> When they're telling you that you gave them their weekend back. >> Edith: Yeah. >> Huge. >> That's, yeah, that.- that's not an insignificant thing when you think about what people do with their weekends, you know, so? >> Yeah, you know, it, it feels really good to have customers say, like this literally has changed the way they built software for the better. >> I can't imagine this, you know, with everything that's happened in the last 22 months with the acceleration to cloud, but all these massive pivots by businesses, in every industry just to survive in the beginning, were an advantage, something like LaunchDarkly is for those organizations, so you have to move really, really quickly and keep changing direction to kind of figure out how do we stay afloat and now how do we thrive in that, that this has probably been a real lifesaver for a lot of organizations. >> Yeah, I mean, we've seen like a ten year roadmap at our customers compressed into a month, like we had a, a retail chain in the Midwest that was thinking about doing in-store pickup and then when COVID hit, they're like, okay, this changed from a, maybe to a, we need to have this to stay afloat and now, now they can help people pick up, same with, same with restaurants having a mobile app to do delivery or pickup, it used to be when we'll get to that next year, now it's something that you have to have. >> Oh yeah. >> Because if, if you're going to go get coffee and one place has a mile long line and their place has an app, which one are you going to pick? >> So, what do people do that don't have this capability? This, I mean, this might sound like a completely naive question, I know a lot about a lot of things, so I'm okay looking dumb sometimes, it's how I learn, so I'm okay looking dumb sometimes, it's how I learn, but seriously, if you don't have these valves, then aren't you doomed then aren't you doomed to releases that are going to be panic inducing. >> It's really, it's really painful, like, I mean, that's, that's the way I used to, to release, you know, I remember it, like you're released and you would have tried to have caught all the bugs, but it would go out and if something happened, you had to fix it on the fly and even if you have a really good deployment process, that's 20 minutes, maybe two hours. >> David: Sure, and, and. >> Which, which if you're a mobile app, it could be a business killer. >> Yeah, well we're here at AWS reinvent, I mean, how does, how does this dovetail with this, the AWS mission to migrate and modernize into the cloud native world? we're talking about cloud native, you know, development and operations that you're involved with, so there's obviously a synergy there, but why specifically AWS? >> Oh, I mean, I think one of the biggest tailwinds we've had as a business is if you're releasing twice a year, we've had as a business is if you're releasing twice a year, you don't really need a tool like this, or a platform like this, your business process is completely different, but you're going to die as a company cause you can't survive on two releases a year. If you're moving to the cloud, we help you get there and once you're in the cloud, if you want to move at the speed of business, but safely, we give you that platform. Like, so, I think continuous delivery got this bad wrap because people thought that meant that you push out stuff every second and break everything. >> David: Right. >> What we do is we allow you to innovate as fast as you want, but release in a controlled way. >> I got to ask you a question, you, you talked about the customers and your love of being with customers, one of the things I can't help thinking is that what you're helping facilitate is brand reputation. If, you know, if we have an expectation and we want to go on a, on an app and order coffee, and it's down, we're going to go to the next competitor, so from a brand reputation perspective, I'm just wondering if, if any of your customer conversations kind of go in addition to the VP of engineering kind of go in addition to the VP of engineering and it focused on the folks that are leading these companies going, our reputation is on the line, people are, let's face it during COVID far less patience than we've, we've seen a lot of really impatient people, but is, is that something that you also facilitate, is the brand reputation? >> Oh, not just a brand reputation that an outage can be costly of millions of dollars, like. that an outage can be costly of millions of dollars, like. >> Lisa: $5,600 a minute, I think is what Gartner estimates. >> Yeah, but depending on what business you're in, like if you're in a bank, you absolutely need to be reliable. If you're a streaming service like streaming, one of the biggest horse races in Australia, you need to have uptime. >> Everybody needs uptime, let's, let's just be clear if I can't get door dash or whatever, it's a disaster from my perspective as a consumer and yes, we have, we have far less patience than we've ever had. >> Yeah, I mean, we have a really interesting, we have both B2C, like streaming ash, streaming apps, delivery apps, as well as B2B streaming apps, delivery apps, as well as B2B and they both have problems that we solve but honestly, the, the, the business problems with a B2B are much more challenging sometimes. >> Well Edith, thank you so much for joining David and me on the program, talking about LaunchDarkly, what you're enabling organizations to achieve in every industry, it sounds like you're riding a rocket ship. >> It's been really fun, you know, I, I love seeing a customer that's been using us for three, five years. >> David: Wow. >> And how much their life has gotten better. >> And as you said, that's, that's no small statement. Thank you so much for joining us on the program, we appreciate your insights and look forward to hearing more news from LaunchDarkly coming out. >> Thanks. >> All right, for David Nicholson, I'm Lisa Martin, you're watching theCUBE's coverage of AWS:reinvent 2021, theCUBE, the global leader in live tech coverage. (upbeat music)

(upbeat pop music) >> Well, hi everyone, John Walls here on "theCUBE" as we continue our CUBE conversations as part of the "AWS Startup Showcase". And we welcome in today Krishna Gade who is the founder and the CEO of Fiddler AI. and Krishna, good to see you today. Thanks for joining us here on the "theCUBE". >> Hey John, thanks so much for inviting us and I'm glad to be here, and looking forward to our conversation. >> Yeah me two, and first off, I want to say congratulations as I look at your company's, this tremendous roster, this list of awards that just keep coming your way. Most recently recognized by "Forbes" as one of the Top 50 AI Companies To Watch here in 2021. I know Gartner called you one of their Cool Companies not too long ago. World Economic Forum also giving you a shout out. So whatever it is you're doing, you're doing it very well, but it's got to feel good I would think, some validation to get all this kind of recognition. >> Absolutely, I know we've been very fortunate to get all the recognition. You know, part of it is also because of the space we are playing in, right? A lot of companies are, you know, operationalizing AI and therefore, you know, this whole point of, you know, explainability monitoring and governance of AI is like forefront and it's in the news for various different reasons. So there's a lot of, you know, good sort of talk that is going on in the press around how one should bear responsible AI. And we are very fortunate to be, you know, in the space and pioneering, you know, some of the technologies here. >> Right. And talking about machine learning monitoring, obviously, in the AI space, and you mentioned explainability. So let's just talk about that concept broadly first off and explain to our viewers what you mean by explainability in this particular context. >> Yeah, that's a good question. So if you think about an AI system, one of the main differences between it and a traditional software system is that it's a black box in the sense that you cannot open it up and read it's code like a traditional software system. The reason is, you know, the AI systems that are built using data and training models which are represented in this non-human readable format. And you cannot really understand how a model is actually making a prediction at any given point of time. So therefore what happens is when you are deploying these AI systems at scale for a variety of use cases, let's say credit underwriting or, you know, screening resumes, or clinical diagnosis which are extremely, you know, important for general human beings. There is a need to understand how the AI system is working. You know, why did it approve a positive person's loan or reject someone's loan? Or why did it reject someone's, you know, resume from, you know, a job screening pipeline? How is it working overall? Right? And so this is where explainability becomes important because you need to understand the AI system, you need a way to probe it, to interrogate it, to understand how the system is making predictions, how is it being influenced by various inputs you're supplying to the system. And so this gamut of technologies or the algorithms that have come across in the last, you know, few years have really matured to a point where, you know, products like Fiddler are developing them and productizing them for the general enterprise to you know, put it in their machine learning and AI workflows. >> So you're talking about context basically, right? I mean, trying to give everybody an idea. This is, you know, kind of where this inputs coming, this is where the problem is, this is where the bottleneck might be, whatever it is, and and doing that in real time. Very efficient operation here. Well, let's talk about the ML world right now and in terms of how it relates to artificial intelligence and this interaction you know, that we're seeing and the, I guess, the problem that you are trying to fix, if you will, in terms of machine learning monitoring. So let's just deal with that first off. When you look at somebody's architecture and somebody set up, what do you see? What are you looking for? And what kind of problems are you trying to solve for your clients? >> Yeah. So just following up what I said. The two main problems with operationalizing AI is one is the black box nature of AI, which I already talked about. The other problem is that the AI system is fundamentally a stochastic system or a probabilistic system. By that, I mean that its performance, you know, its predictions can change over time based on the data it is receiving. So it's not a deterministic system like traditional software systems where you expect the same output all the time, right? So when you have a system that is stochastic in nature where its performance can vary based on the data it is receiving, then you are in a situation where you have uncertainty, right? You know, you let's say you have an AI system that is deployed for serving a credit underwriting model or a fraud, you know, detection use case. And you see that, okay, sometimes accuracy is up, sometimes accuracy is down. You know, when do you want, when do you trust your predictions, when you're not. How do you know if the model is actually performing in the same manner that you trained it? All of these issues open up the need for continuous monitoring of these AI systems, because without which you may have AI systems making bad predictions for your users, hurting your business metrics, potentially making biased decisions that can put your company into a compliance or a brand reputation risk scenario. To avoid all of these things you can actually monitor these AI systems continuously so that you know exactly if they're performing the way you expect them to be. Do you to retrain them right now, right? Or do you need to shut them down because they are actually not predicting the way that you expect them to be? So this is actually very important. And so that's what Fiddler tries to solve for our customers by helping them operationalize AI with full visibility and explainability, right? So you can essentially install Fiddler in your workflow to continuously monitor your AI systems and analyze and explain them when you have questions about how they're working. >> I mean, you talked about governance earlier a little bit, you know, compliance, obviously a great critical issue, big concern, fraud detection. Security, just in general here, as we know, I mean, we keep almost every day it seems like we're hearing about some kinds of security intrusion. So, in terms of identifying vulnerabilities or in terms of identifying anomalies, whatever it might be, what kind of work are you doing in that space to give your client base the kind of comfort and the peace of mind that everybody's searching for these days? >> Right, I mean, if you step back a little bit, John, we are truly living in the age of algorithms, right? So everything that we interact with on a day-to-day basis, the movies we watch, or when we request an Uber driver, or when we go to a financial institution and request for a loan application or a mortgage, there are algorithms behind the scenes that are processing our requests and delivering the experiences that we have. Now, increasingly these algorithms are becoming AI based algorithms. And when you have these AI based algorithms, they're trained on this data that's available, that an institution may collect from their users, or they may buy from other third parties. And when you develop these AI systems based on this data, if this data is not equally distributed amongst all different ethnicity backgrounds, people coming from different cultures, different religions, different races, different genders, you may actually build systems that can make very different decisions for different individuals based on like this bias that could creep into them. And so this actually needs, this means that at the end of the day, you can actually create a dystopian world where, you know, some people get like really great decisions from your systems, where some people are left out, right? So therefore, you know, this aspect of governing your AI systems so that you're validating what you're building upfront. You're validating the data that you're using to train the systems. You're continuously monitoring the systems there so that they're actually producing the right outcomes for your users. And then you can actually explain if some customer asks you or some regulator or a third party asks you how your system is working. It's very very important. This is an emerging area in industry, certain sectors already have this, for example, financial services. It's in companies like banks, where it is mandated to have model governance, so that every model that they are deploying needs to be validated and needs to be monitored. And we are seeing the emergence of generally AI governance creeping into other sectors as well. And so this is like a broader topic that covers explainability, covers monitoring, covers detecting bias in your AI systems and ensuring that you're building safe and responsible AI for your customers and your organization. >> Yeah, I find the bias point really interesting, actually, because I hadn't really thought about these prejudices or subjectivities, you know, it might bring to our work with us in terms of what we look at, what we ignore, what we process, how we don't. But it's a really interesting point you just raised. So thank you for that. And then there's also the kind of issue with data drift too a little bit, right? It's like, where did it go (laughing)? >> Right. >> What are we doing here? What happened to it? So maybe if you could talk about that a little bit in terms of all this data that's coming in and corralling it, right? Making sure that it stays organized and stays in a way that you can analyze and process it, and then glean insight from. >> Yeah, data drift is one of the main reasons why AI systems deteriorate in performance. So for example, let's say I'm trying to build a recommendation system that predicts the items that you want to buy when you go to an E-commerce website. Now, if I have used data pre-COVID, then the user behavior was very different, right? That kind of items people were probably buying before you know, February, 2020 was like probably much different than the kind of items that people were buying after it. So what happens is when you train your AI systems on datasets that are older but then that data has changed ever since because of an event like COVID-19 has happened, or some other seasonality has kicked in, then your AI systems are seeing different distribution data. For example, you may see that suddenly, you know, people who were shopping, let's say, in March or April last year, people were shopping for all kinds of, you know, toilet paper and all kinds of things to stock up, you know, to be ready for lockdown, right? And maybe they were not buying similar amounts in there previously. So therefore, if you have an inventory management system based on AI or an E-commerce recommendation system based on AI, you know, they would see data drift, because the buying patterns are different. The amount of stuff that people are buying in terms of toilet paper has completely shifted. And so their model is actually, may not be predicting as accurately as it would, right? So therefore identifying this data drift and alerting your AI engineer so that they can be prepared for this is very important. Otherwise, what you would see is if you're an E-commerce company, this has actually happened, you know? Instacart, a grocery delivery company and another company www.etsy.com, they blogged about it where they have seen their models go down in accuracy from 90% to 65% when this data shift happened, you know, especially during COVID-19. And so you need the ability to continuously monitor for drift so that when you can catch these things earlier, and then, you know, save your business from losing, you know, in terms of business metrics like such as number of sales that you may be making, number of bad recommendations that your systems are making to your users. >> So we've talked a lot about these various components of monitoring of which, you know, all of which you do extremely well. And I was reading earlier, just a little bit about the company, and we talked about accountability. We've already talked about that. We talked about fraud detection, we talked about reliability. There was also a point about ethical considerations, you know, and so I was interested in that, hearing from you about that in terms of why that's a pillar of your service or what exactly that was pointed toward in terms of monitoring, and what you can do. >> Right. So, I guess I'll just go back to like a famous quote from Marc Andreessen. He mentioned, you know, a few years ago that software is eating the world, right? Now, what's happening is AI is eating software. All the software that we are consuming is becoming AI based software, because basically at the end of the day some intelligence is being baked into the software to make it, you know, predict more interesting things for you to make those decisions. Instead of rule-based decisions, make it more AI based decisions. And so therefore it is very important that when we are building the software, we need to use ethical practices. You know, we need to know how, where you're collecting the data from. It can be very dangerous if you don't do it and you can land into trouble. And we have seen these incidents many times, right? For example, in 2019, when Apple and Goldman Sachs came up with a credit card, a lot of customers complained about gender bias with respect to the credit card limits that the algorithm was setting. You know, in the same household, the husband and wife were getting 10 times in terms of a difference between the credit limit between a male and a female, right? Even though they probably had similar salary ranges, similar FICO scores, right? So if you do not actually make sure that, you know, you're collecting data from the right sources that your datasets are not outbalanced. If your models, if your algorithms are tested for bias you know, before hand, before you deploy them and then you're continuously monitoring them, these are all ethical practices. These are all the responsible ways of building your AI. You can actually, you know, land into trouble. Your customers will complain about it. You know, you would lose your brand reputation. And at the end of the day you'll be essentially, and instead of actually adding value to the customers, you may be actually hurting them, right? And so this is actually why it's so important, and it's become more important when the more stakes, the higher the stakes are, right? You know, for example, when it's being used for criminal justice scenarios or when it's being used for clinical diagnosis scenarios. Being able to ensure that the system is making unbiased decisions is very, very important. >> Well, before I let you go, too, I like you to touch base on your AWS relationship about, you know, what was the Genesis of that. And currently what it is that you're working on together to provide this great value to your customers. >> Absolutely. So the follow-up to this ethical AI is like Amazon as a company is interested in pursuing, you know, the responsible AI but, you know, they have a lot of AI products. So they are looking for, you know, fostering a community and ecosystem of AI technologies. And in that hypothesis they actually invested in Fiddler last year in terms of enabling us to develop this explainable AI and ethical AI technology. And so we are working with Alexa Fund and also like AWS ecosystem in terms of partnering with how effectively Fiddler can be delivered to other AWS customers through, like, through their marketplace and other sort of areas that we can distribute the software. So it's a great partnership. We are very, very excited about the opportunity to work with Alexa Fund as well as the AWS ecosystem. It increases another opportunity for us to enable a lot more customers than we than we can otherwise. So this is a great win-win situation for both Amazon and Fiddler. >> Well, it sure is. And congratulations on that and developing that partnership. I know it's working well for your clients and it's working well for Fiddler AI obviously by the number of recognitions that have been coming your way. So Krishna, we wish you continued success and thanks for the time here today on "theCUBE". >> Yep. Thank you so much, John. It was a pleasure talking to you today. >> I enjoyed it. Thank you. John Walls here wrapping up our conversation with Fiddler AI's Krishna Gade, talking today about machine learning monitoring on the "AWS Startup Showcase". (upbeat pop music)

(melodious music) >> Voiceover: From around the globe. It's theCUBE with digital coverage of IBM Think 2021. Brought to you by IBM. >> Hi and welcome back to theCUBE's coverage of IBM Think 2021 virtual. I'm John Furrier, host of theCUBE. We're a virtual this year. But we'll be in real life soon, right around the corner, as we come out of COVID. We got a great guest, a CUBE alumni, Jerry Cuomo, IBM Fellow VP, CTO for IBM automation. Jerry, great to see you. Been on since almost since the early days of theCUBE. Good to see you >> Yeah, John. Thrilled to be back again. Thank you. >> What I love about our conversations, one is you're super technical. You've got patents under your belt. You're on the cutting edge. You've been involved in web services and web technologies for a long, long time. You're constantly riding the wave. And also you're a creator of a great podcast called "The Art of Automation", which is the subject of this discussion. As automation becomes central in cloud operations and Hybrid Cloud, which is the main theme of this event this year and the industry. So great to see you. First give us a little background for the folks that may not know you, about your history with IBM and who you are. >> Yeah. So thanks John. So I'm Jerry Cuomo. I've been with IBM for about three decades and I started my career at IBM research in Yorktown at the dawn of the internet. And I've been incredibly fortunate as you mentioned, to be on the forefront of many technology trends over the last three decades, internet software, middleware, including being one of the founding fathers of WebSphere software. I recently helped launch the IBM blockchain initiative and now all about AI powered automation. Which actually brings me back to my roots of studying AI in graduate school. So it's kind of come full circle for me, really enjoying the topic. >> It's funny you mentioned AI in graduate school. I was really kind of into AI when I was an undergraduate and get a master's degree in Computer Science. I kind of went the MBA route. But if you think about what was going on in the eighties during those systems times, a lot of the concepts of systems programming and cloud operations kind of gel well together. So you got this confluence of computer science and engineering AKA now DevOps, DevSecOps, coming together. This is actually a really unique time to bring back the best of the best concepts. Whether it's AI and systems and computer science and engineering into automation. Could you share your view on this? Because you're in a unique position, you've been there done that, now you're on the cutting edge. What's your thoughts? >> Yeah, absolutely John. And just when you think of automation and time, automation is not new. That literally, if you go into Wikipedia and you look up automation, you see patents and references to like steam engine regulators at the dawn of the industrial era, right? So automation has been around and in its simplest form, automation whether it was back then, whether it was in the eighties or today it's about applying technology that uses technology software to perform tasks that were once exclusively done by us, humans. But now what we're seeing is AI coming into the picture and changing the landscape in an interesting way. But I think at its essence, automation is this two-step dance of both eliminating repetitive mundane tasks that help reduce errors and free up our time. So we get back the gift of time, but also helps. It's not about taking jobs away at that point, it's a sentence of two-step dance. That's step one. But if you stop there, you're not getting the full value. Step two is to augment our skills. And to use automation, to help augment our skills. And we get speed, we get quality, we get lower costs, we get improved user experience. So whether it was back in the steam engine times or today with AI, automation is evolving with technology. >> And it's interesting too, as a student of the history of the computer industry, as you are and now a creator with your podcast, which we'll get to in a second. You're starting to see the intersection of these concerts and not bespoke as much as they used to be. You got transformation, digital transformation and innovation are connected and scale. If you think about those three concepts, they don't stand alone anymore. They can stand alone, but they work better together. Transformation. And it is the innovation, innovation provides cloud scale. So if you think about automation, automation is powering this dynamic of taking all that undifferentiated heavy lifting and moving the creativity and the skillset into higher integrated areas. Can you share your thoughts? >> Yeah, no, right on there. When you talk about transformation, jeez, look around us. The pandemic has made, transformation and specifically digital transformation, the default. So everything is digital. Whether it's ordering a pizza, visiting a doctor through telemedicine, or this zoom WebEx based workplace that we live in. But picture a telemedicine environment, talking about transformation and going digital. With 10 X more users, they can't hire 10 X more support staff. And think about it. I forgot my password, does this work on my version of the Apple iPhone or all of that kind of stuff? So their support desks are lit up, right? So as they scale digitally, automation is the relief that that comes into play, which is just in time. So the digital transformation needs automation. And John, I think about it like this, businesses like cars have become computers. So they're programmable. So automation software just like in the cars, it makes the car self-driving. I think about the Tesla model three, which I recently test drove. So with this digital acceleration, digital opens the door for automation. And now we can muse about self-driving business. We can muse about maybe that's step one, right? That's the remove repetitive work, but maybe we can actually augment business to have an autopilot. So it doesn't need us there all the time to drive. And that's the scale that you talked about. That's the scale we need. So automation is really like the peanut butter and chocolate. Digital is the peanut butter automation is the chocolate. They go well together and they produce amazing tastes. >> Yeah. That's a really interesting insight. And I was just put an exclamation point on that because you mentioned self-driving business you're implying, you said the business is a computer. So if you just think about that mind blowing concept for a second. If it's a computer, what's the operating system and what's the suite of applications that are on top of it? So, okay. Let's go in the old days you had a windows machine and you had office, which was a system software, applications software construct. If you map that to the entire company, you're talking about Red Hat and IBM will kind of come working together. Kind of connects the dots a little bit on what Red Hat could, because they're not breaking system company. So if Hybrid Cloud is the system, edge, hybrid, then you got the application suite is all software for the business. >> That's right. That's right. And if you listen to anything these days about what IBM stands for, it's Hybrid Cloud and think Red Hat as kind of the core element of that with OpenShift and AI. And both of those really matter in terms of automation and maybe I'll come back to the Hybrid Cloud or Red Hat thing in a second, but let's just talk about Watson and AI, which is the application. You mentioned scale, which I'm so glad you did. AI could help scale automation. And the trick is, is that automation sometimes gets stuck. It gets stuck when it's working with data that is noisy or unstructured. So there's a lot of structured data in your organization. With that, we can breeze through automation. But if there is more ambiguous data, unstructured, noisy, you need a human in the loop. And when you get a human in the loop, it slows things down. So what AI can start to do, AI and its subordinates, machine learning, natural language processing, computer vision. We can start to make sense of both unstructured and structured data together and we can make a big deal going forward. So that's the AI part. You mentioned Red Hat and, and Hybrid Cloud Park. Well, think about it this way, when you shop, how many stores do you... You don't just shop in one store, right? You go to specialty stores to pick up that special catsup, I don't know or mustard. (audio cuts) In one store and maybe do shopping in another store. Customers using clouds John, aren't very different. They have their specialty places to go. Maybe they're going to be running workloads in Google, involving search and AI related to search. And they're going to be using other clouds for more specialty things. From that perspective, that's a view of hybrid. Customers today, take that shopping analogy. They're going to be using Salesforce or Servicenow, IBM cloud, they have a private cloud. So when you think about automating that world, it's the real world. It's how we shop, whether it's for groceries or for cloud. The Hybrid Cloud is a reality. And how do you make sense of that? Because when an average customer has five clouds, how do you deal with five things? How do you make it easy, normalize? And that's what Red hat really does. >> It's interesting. I'll just share with you though. When I interviewed Arvin, who is now the CEO of IBM when he was at Red Hat summit 2019 in San Francisco. Before he made the acquisition, I was peppering him with questions. Like, you need to get this cloud and he loves cloud, you know, he loves cloud. So he was smiling. He just wanted to say it, he wanted to just say it. And I think Red hat brings that operating kind of mindset where the clouds are just subsystems in the OS of the middleware, which is now software which is software defined business. And this kind of is the talk of your views. Now you have a podcast called "Art of Automation". We want to get that in there, for the folks watching. Search for the podcast, "Art of Automation". This is the stories that you tell. Tell us some stories, from this phenomena. What's the impact of automation for the holistic picture? >> Well, it starts with a lot of, I guess it starts with customers. The stories start with the customer. So we're hearing from customers that AI and automation is where they're investing in 2021. For all the reasons we briefly mentioned, and IBM has a lot to offer there. So we've made AI powered automation a priority. But John, in the pursuit of making it a priority, I've started talking with many of our subject matter experts and was floored by their knowledge, their energy, their passion, and their stories. And I said, we can't keep this to ourselves. We can't keep this locked away. We have to share it. We have to let it out. So basically this is what started the podcast around that. And since then, we've had many industry luminaries from IBM and outside. Starting with customers. We had Klaus Jensen who is the CIO of Memorial Clones Kettering Hospital to talk about automation in healthcare. And he shared great stories. You need to listen to them, about automation is not going to take the place of doctors. But automation will help better read x-rays and look at those shades of gray on the x-ray and interpret it much better than we can. And be able to ingest all of the up-to-date medical research to provide pointers and make connections that the human may not be able to do in that moment. So the two working together are better than any individual. Carol Polson recently joined me to talk, and she's the CIO for Cooperators, to talk about automation and insurance. And she had some great stories too. So John, with that, a bunch of IBM, great IBM fellows like Rama Akkiraju, who is one of Forbes top 20 women in AI research. Talking about AI ops. And also Ruchir Puri talking, and Ruchir has been working on Watson since jeopardy to tell stories about ultimately now how we're teaching AI to code and all the modern programming languages. And really automating application modernization and the like. Four keyed episodes in, we have those under our belt. About 6,000 downloads so far. So it's coming along pretty well. Thanks for asking, John. >> The key is you're a content creator now, as well as a fellow. And this is the democratization, as we say direct to audience, share those stories. Also here, I think you released an ebook. Tell us a little quickly about that. We've got one minute left, give a quick plug for the ebook. >> The book echoes the podcast. Chapters relate to the episodes of the book. We're dropping the first five chapters plus forward for free on the IBM website. Other chapters will become available and drop as they become available. The book makes the content searchable on the internet. We go into more detail with advice on how to get started. You get to hear the topics and the voice of those subject matter experts. And I really suggest you go out and check it out. >> All right, Jerry Cuomo. IBM fellow VP, CTO, IBM automation. Also a content creator, podcast "Art of Automation." Jerry, we're going to list it out on our Silicon angle and our cube sites, gets you some extra love on that. Love the podcast, love the focus on sharing from experts in the field. Thanks for coming on. >> Yeah. Thank you so much for having me again, John. >> Okay, I'm John Furrier with theCUBE. Here for IBM Think 2021. Thanks for watching.

>>from around the globe. It's the >>cube >>With digital coverage of IBM think 2021 brought to you by IBM. Hello and welcome back to the cubes coverage of IBM Think 2021 virtual. I'm john for a year host of the cube. We're virtual this year in real life. Soon, right around the corner as we come out of code, we've got a great guest cube alumni jerry, cuomo IBM fellow V P C T O for IBM automation jerry, Great to see you uh nonsense got almost since the early days of the cube. Good to see you, >>john thrilled to be back again. Thank you >>what I love about our conversations. One is your super technical, you've got patents under your belt during the cutting edge. You've been involved in web services and web technologies for a long, long time. You constantly riding the wave and also your creator of a great podcast called the art of automation, which is the subject of this discussion as automation becomes central in cloud operations and hybrid cloud, which is the main theme of this event this year and the industry so great to see you. Uh First team is a little background for the folks that may not know you about your history with IBM and who you are. >>Yeah, so thanks john, So I'm I'm jerry Carrillo, I've been with IBM for about three decades and I started my career at IBM research in Yorktown at the dawn of the internet and I've been incredibly fortunate, as you mentioned to be on the forefront of many technology trends over the last three decades. Internet software middleware, including being one of the founding fathers of web sphere software, uh I recently helped launch the IBM Blockchain initiative and now all about aI powered automation, which actually brings me back to my roots of studying AI and graduate school. So it's kind of come full circle for me, you know, really you know, enjoying the topic. >>You know, these funny, you mentioned aI in graduate school, I was really kind of into a I when I was an undergraduate and get a masters degree in computer science, I kind of went the NBA route. But if you think about what was going on in the eighties during those systems times, a lot of the concepts of systems programming and cloud operations kind of gel well together. So you've got this confluence of computer science and engineering A. K A. Now devops sec cops coming together. This is actually a really unique time to bring back the best of the best concepts, whether it's A I and systems and computer science and engineering into the automation. Could you share your view on this because you're in a unique position, you've been there, done that now. You're on the cutting edge with your thoughts. >>Yeah, absolutely, john And just when you think of automation and time, automation is not new, literally, if you go into Wikipedia and you look up automation, you see patents and references to like steam engine regulators at the dawn of the industrial era. Right? So automation has been around and and in the simplest form automation, whether it was back then, um whether it was in the 80s or today, it's about applying technology and that that performs, that uses like technology software to perform tasks that were once exclusively done by us humans. Right? So, but now what we're seeing is a I coming into the picture and and changing the landscape in an interesting way. But I think at its essence, you know, automation is this two step dance of both eliminating repetitive, mundane tasks. That helped reduce errors and free up our time. So we get back the gift of time but also helps. It's not about taking jobs away at that point, as I said, it's a two step dance, that's step one. But if you stop there, you're not getting the full value. Step two is to augment our skills right? And and to use automation to help augment our skills and we get speed, we get quality, we get lower costs, we get improved user experience. So whether it was back in the steam engine times or today with a I automation is evolving with technology >>and it's interesting to its you know, as a student of the history of the computer industry as you are and now a creator with your podcast which we'll get to in a second, you're starting to see the intersection of these concepts are not bespoke as much as they used to be. You got transformation. Digital, transformation and innovation are connected and scale. If you think about those three concepts they don't stand alone anymore. They can stand alone but they work better together transformation. And is the innovation innovation provides cloud scale. So if you think about automation, automation is powering this dynamic of taking all that undifferentiated heavy lifting and moving the creativity and the skill set into higher integrated areas. Can you share >>your john Yeah no right on there when you talk about transformation, jeez look around us, the pandemic has made transformation and specifically digital transformation the default so everything is digital. You know whether it's ordering a pizza, you know visiting a doctor through telemedicine or or this zoom webex based workplace that we live in. But picture of telemedicine environment right? Talking about transformation and going digital With 10 x more users. They can't hire 10 x more support staff and think about it I forgot my password. Um Does does this work on my version of the Apple iphone or all of that kind of stuff? So their support desks or lit up? Right. So uh as they scale digitally automation is the relief that that comes into play which is which is just in time. Right? So the digital transformation needs automation and john I think about it like this um businesses like cars are have become computers right? So they are programmable. So automation software just like in the cars it makes you know the car self driving? I think about the Tesla model three which I recently test drove. Um so with this digital acceleration digital opens the door for automation And now we can use about a self driving business. We can use about uh maybe that's step one, right? That's the um remove repetitive work, but maybe we can actually augment business to have an autopilot so it doesn't eat us there all the time to drive. And that's the scale that you talked about. That's the scale we need. So automation is really like the peanut butter and chocolate Digital is the peanut butter, automation is the chocolate. They go well together and they produce amazing tastes. >>You know, that's a really, that's a really interesting insight and I will just put an exclamation point on that because you mentioned self driving business, you're implying, you said the computer, the business is a computer. So if you just just think about that mind blowing concept for a second, if it's a computer, what's the operating system and what's the suite of applications that are on top of it? So, Okay, let's go in the old days at a Windows machine and you had office, which is a system software, applications, software construct. Okay, If you map that to the entire company, you're talking about Red hat and IBM kind of come working together. Kind of connects the dots a little bit on what Red Hackett because they're not bring system company. So if hybrid cloud is the system mm hybrid, then you got the applications suite is all software for the That's >>right. That's right. And if you, you know, if you listen to anything these days about what IBM stands words, hybrid cloud and and think red hat as as, you know, kind of the core element of that with open shift in a I right. And both of those really matter in terms of automation and maybe I'll come back to the hybrid cloud and red hat thing in a second. Let's just talk about you know Watson and Ai, you know, which is the application and you mentioned scale, which I'm so glad you did. You know a I could help scale automation. And the trick is is that ai automation sometimes gets stuck right? It gets stuck when it's working with data that is noisy or unstructured. Right? So there's a lot of structured data in your organization and it it with that we can breeze through automation. But if there is more ambiguous data unstructured noisy, you need a human in the loop. And when you get a human in the loop, it slows things down. So what a I can start to do a I. And its subordinates, machine learning, natural language processing, computer vision. We can start to make sense of both unstructured and structured data together and we can make a big deal going forward. Right? So that's that's the way I part you mentioned Red hat and and hybrid cloud part. We'll think about it this way. When you shop, how many stores do you don't just shop in one store? Right. You you go to specialty stores to pick up that special uh ketchup, I don't know or must store and maybe do shopping another store, customers using clouds john aren't very different. You know, they have their specialty places to go. Maybe they're going to be running workloads and google involving search and a I related to search, right? And they're going to be using other clouds for more specialty things. Right? So from that perspective, that's a view of hybrid, you know, customers today, you know, take that shopping analogy, they're going to be using sales force or service Now, IBM cloud, they have a private cloud, right? So, when you think about automating that world, All right. It's the real world. It's how we shop, whether it's for groceries or for cloud, right? So the hybrid cloud is a reality. Um and how do you make sense of a high of that? Right, Because when when an average customer has five clouds, How do you deal with five things? Right. How do you make it easy normalize? And that's what red hat really >>does. It's interesting. I just just share with you the and I interviewed Arvin um who is now the ceo of IBM when he was at Red Hat some in 2019 in SAn Francisco before he made the acquisition here that I was, I was peppered with questions like you know, you need to get this cloud and he loves cloud, you know, he loves clouds. So so he was smiling, he just wanted to say it, I wanted to just say it and I think Red Hat brings that operating kind of mindset where the clouds are just subsystems in the Os >>yes of the middle >>where which is now software which is software to find business. And this kind of is the talk of your, your your views. Now you have a podcast called Art of automation. Want to get that in there for the folks watching uh search for the podcast, Art of automation. This is the stories that you tell. Tell us some stories from this phenomenon. What's the impact of automation for the holistic picture? >>Well, it starts with a lot of, I guess it starts with customers. The stories start with the customers. So we're hearing from customers that Ai and automation is where they're investing in 2021. Um for all the reasons we briefly mentioned and and IBM has a lot to offer there. So we've made a I powered automation of priority but john in the pursuit of making it a priority. I've started talking with many of our subject matter experts and was floored by their knowledge, their energy, their passion and their stories. And I said we can't keep this to ourselves, we can't keep this locked away, we have to share it, we have to let it out. So, so basically this is what started the podcast around that. And since then we've had many industry luminaries from IBM and outside, starting with customers, we had claus Jensen who is the ceo of memorial clones Kettering hospital to talk about automation and health care. And he shared great stories. You need to listen to them about. Automation is not going to take the place of doctors, but automation will help better read um x rays and look at those shades of gray on the X ray and interpret it much better than we can and be able to ingest all of the up to date medical research to provide pointers and make connections that the human may not be able to do in that moment. Right. So the two working together or better than any individual carol Poulsen recently joined me to talk and she's the C. I. O. For cooperators to talk about automation and insurance. And she had some great stories to uh so john with that a bunch of IBM great IBM fellows like Rama Agra jew, who is one of Forbes top 20 women in AI research, talking about Ai ops and also Russia near pori talking and Russia has been working on Watson since jeopardy to tell stories about ultimately now how we're teaching ai to code and all the modern programming languages and really automating application modernization and the like uh, 14 episodes in we have those under our belt, About 6000 downloads so far. So it's it's coming along pretty well. Thanks. >>Thanks for being done. Yeah. The key is your your content creator now as well as a fellow and this is the democratization, as we say, direct to audience, share those stories also here. And thank you released an e book. Tell us a little quickly about that. We've got one minute left, give a quick plug. >>The book echoes the podcast chapters relate to the, to the episodes of the book. We're dropping the first five chapters plus forward for free on the IBM website. Other chapters will become available um, and drop as they become available. The book makes the content searchable on the internet. We go into more detail with advice on how to get started. You get to hear the topics and the voice of those subject matter experts and uh I really, you know, suggest you go out and check it out. >>Alright, jerry, cuomo IBM fellow VPC T IBM automation um also a content creator podcast, art of automation, jerry. We're gonna lift it listed on our silicon angle and our cube sites. Get you some extra love on that. Love the podcast. Love the focus on sharing from experts in the field. Thanks for coming on. >>Thank you so much for having me again, john >>Okay. I'm John Fryer with the Cube here for IBM think 2021. Thanks for watching.

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

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