>> Narrator: "theCUBE's" live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (intro music) >> Good afternoon, everyone. Welcome back to "theCUBE's" day three coverage of MWC23. Lisa Martin here in Spain, Barcelona, Spain with Dave Nicholson. We're going to have a really interesting conversation next. We're going to really dig into MWC, it's history, where it's going, some of the controversy here. Please welcome our guests. We have Jim Harris, International Best Selling Author of "Blindsided." And Carolina Milanese is here, President and Principle Analyst of creative strategies. Welcome to "theCUBE" guys. Thank you. >> Thanks. So great to be here. >> So this is day three. 80,000 people or so. You guys have a a lot of history up at this event. Caroline, I want to start with you. Talk a little bit about that. This obviously the biggest one in, in quite a few years. People are ready to be back, but there's been some, a lot of news here, but some controversy going on. Give us the history, and your perspective on some of the news that's coming out from this week's event. >> It feels like a very different show. I don't know if I would say growing up show, because we are still talking about networks and mobility, but there's so much more now around what the networks actually empower, versus the network themselves. And a little bit of maybe that's where some of the controversy is coming from, carriers still trying to find their identity, right, of, of what their role is in all there is to do with a connected world. I go back a long way. I go back to when Mobile World Congress was called, was actually called GSM, and it was in Khan. So, you know, we went from France to Spain. But just looking at the last full Mobile World Congress here in Barcelona, in pre-pandemic to now, very different show. We went from a show that was very much focused on mobility and smartphones, to a show that was all about cars. You know, we had cars everywhere, 'cause we were talking about smart cities and connected cars, to now a show this year that is very much focused on B2B. And so a lot of companies that are here to either work with the carriers, or also talk about sustainability for instance, or enable what is the next future evolution of computing with XR and VR. >> So Jim, talk to us a little bit about your background. You, I was doing a little sleuthing on you. You're really focusing on disruptive innovation. We talk about disruption a lot in different industries. We're seeing a lot of disruption in telco. We're seeing a lot of frenemies going on. Give us your thoughts about what you're seeing at this year's event. >> Well, there's some really exciting things. I listened to the keynote from Orange's CEO, and she was complaining that 55% of the traffic on her network is from five companies. And then the CEO of Deutsche Telecom got up, and he was complaining that 60% of the traffic on his network is from six entities. So do you think they coordinated pre, pre-show? But really what they're saying is, these OTT, you know, Netflix and YouTube, they should be paying us for access. Now, this is killer funny. The front page today of the show, "Daily," the CO-CEO of Netflix says, "Hey, we make less profit than the telcos, "so you should be paying us, "not the other way around." You know, we spend half of the money we make just on developing content. So, this is really interesting. The orange CEO said, "We're not challenging net neutrality. "We don't want more taxes." But boom. So this is disruptive. Huge pressure. 67% of all mobile traffic is video, right? So it's a big hog bandwidth wise. So how are they going to do this? Now, I look at it, and the business model for the, the telcos, is really selling sim cards and smartphones. But for every dollar of revenue there, there's five plus dollars in apps, and consulting and everything else. So really, but look at how they're structured. They can't, you know, take somebody who talks to the public and sells sim cards, and turn 'em in, turn 'em in to an app developer. So how are they going to square this circle? So I see some, they're being disrupted because they're sticking to what they've historically done. >> But it's interesting because at the end of the day, the conversation that we are having right now is the conversation that we had 10 years ago, where carriers don't want to just be a dumb pipe, right? And that's what they are now returning to. They tried to be media as well, but that didn't work out for most carriers, right? It is a little bit better in the US. We've seen, you know, some success there. But, but here has been more difficult. And I think that's the, the concern, that even for the next, you know, evolution, that's the, their role. >> So how do they, how do they balance this dumb pipe idea, with the fact that if you make the toll high enough, being a dumb pipe is actually a pretty good job. You know, sit back, collect check, go to the beach, right? So where, where, where, where does this end up? >> Well, I think what's going to happen is, if you see five to 15 X the revenue on top of a pipe, you know, the hyperscalers are going to start going after the business. The consulting companies like PWC, McKinsey, the app developers, they're... So how do you engage those communities as a telco to get more revenue? I think this is a question that they really need to look at. But we tend to stick within our existing business model. I'll just give you one stat that blows me away. Uber is worth more than every taxi cab company in North America added together. And so the taxi industry owns billions in assets in cars and limousines. Uber doesn't own a single vehicle. So having a widely distributed app, is a huge multiplier on valuation. And I look to a company like Safari in Kenya, which developed M-Pesa, which Pesa means mo, it's mobile money in Swahili. And 25% of the country's GDP is facilitated by M-Pesa. And that's not even on smartphones. They're feature phones, Nokia phones. I call them dumb phones, but Nokia would call them "feature phones." >> Yeah. >> So think about that. Like 25, now transactions are very small, and the cut is tiny. But when you're facilitating 25% of a country's GDP, >> Yeah. >> Tiny, over billions of transactions is huge. But that's not the way telcos have historically thought or worked. And so M-Pesa and Safari shows the way forward. What do you think on that? >> I, I think that the experience, and what they can layer on top from a services perspective, especially in the private sector, is also important. I don't, I never believe that a carrier, given how they operate, is the best media company in the world, right? It is a very different world. But I do think that there's opportunity, first of all, to, to actually tell their story in a different way. If you're thinking about everything that a network actually empowers, there's a, there's a lot there. There's a lot that is good for us as, as society. There's a lot that is good for business. What can they do to start talking about differently about their services, and then layer on top of what they offer? A better way to actually bring together private and public network. It's not all about cellular, wifi and cellular coming together. We're talking a lot about satellite here as well. So, there's definitely more there about quality of service. Is, is there though, almost a biological inevitability that prevents companies from being able to navigate that divide? >> Hmm. >> Look at, look at when, when, when we went from high definition 720P, very exciting, 1080P, 4K. Everybody ran out and got a 4K TV. Well where was the, where was the best 4K content coming from? It wasn't, it wasn't the networks, it wasn't your cable operator, it was YouTube. It was YouTube. If you had suggested that 10 years before, that that would happen, people would think that you were crazy. Is it possible for folks who are now leading their companies, getting up on stage, and daring to say, "This content's coming over, "and I want to charge you more "for using my pipes." It's like, "Really? Is that your vision? "That's the vision that you want to share with us here?" I hear the sound of dead people walking- (laughing) when I hear comments like that. And so, you know, my students at Wharton in the CTO program, who are constantly looking at this concept of disruption, would hear that and go, "Ooh, gee, did the board hear what that person said?" I, you know, am I being too critical of people who could crush me like a bug? (laughing) >> I mean, it's better that they ask the people with money than not consumers to pay, right? 'Cause we've been through a phase where the carriers were actually asking for more money depending on critical things. Like for instance, if you're doing business email, then were going to charge you more than if you were a consumer. Or if you were watching video, they would charge you more for that. Then they understood that a consumer would walk away and go somewhere else. So they stopped doing that. But to your point, I think, and, and very much to what you focus from a disruption perspective, look at what Chat GTP and what Microsoft has been doing. Not much talk about this here at the show, which is interesting, but the idea that now as a consumer, I can ask new Bing to get me the 10 best restaurants in Barcelona, and I no longer go to Yelp, or all the other businesses where I was going to before, to get their recommendation, what happens to them? You're, you're moving away, and you're taking eyeballs away from those websites. And, and I think that, that you know, your point is exactly right. That it's, it's about how, from a revenue perspective, you are spending a lot of money to facilitate somebody else, and what's in it for you? >> Yeah. And to be clear, consumers pay for everything. >> Always. Always. (laughs) >> Taxpayers and consumers always pay for everything. So there is no, "Well, we're going to make them pay, so you don't have to pay." >> And if you are not paying, you are the product. Exactly. >> Yes. (laughing) >> Carolina, talk a little bit about what you're seeing at the event from some of the infrastructure players, the hyperscalers, obviously a lot of enterprise focus here at this event. What are some of the things that you're seeing? Are you impressed with, with their focus in telco, their focus to partner, build an ecosystem? What are you seeing? >> I'm seeing also talk about sustainability, and enabling telco to be more sustainable. You know, there, there's a couple of things that are a little bit different from the US where I live, which is that telcos in Europe, have put money into sustainability through bonds. And so they use the money that they then get from the bonds that they create, to, to supply or to fuel their innovation in sustainability. And so there's a dollar amount on sustainability. There's also an opportunity obviously from a growth perspective. And there's a risk mitigation, right? Especially in Europe, more and more you're going to be evaluated based on how sustainable you are. So there are a lot of companies here, if you're thinking about the Ciscos of the world. Dell, IBM all talking about sustainability and how to help carriers measure, and then obviously be more sustainable with their consumption and, and power. >> Going to be interesting to see where that goes over the years, as we talk to, every company we talk to at whatever show, has an ESG sustainability initiative, and only, well, many of them only want to work with other companies who have the same types of initiative. So a lot of, great that there's focus on sustainability, but hopefully we'll see more action down the road. Wanted to ask you about your book, "Blind," the name is interesting, "Blindsided." >> Well, I just want to tag on to this. >> Sure. >> One of the most exciting things for me is fast charging technology. And Shalmie, cell phone, or a smartphone maker from China, just announced yesterday, a smartphone that charges from 0 to 100% in five minutes. Now this is using GAN FEST technology. And the leader in the market is a company called Navitas. And this has profound implications. You know, it starts with the smartphone, right? But then it moves to the laptops. And then it'll move to EV's. So, as we electrify the $10 trillion a year transportation industry, there's a huge opportunity. People want charging faster. There's also a sustainability story that, to Carolina's point, that it uses less electricity. So, if we electrify the grid in order to support transportation, like the Tesla Semi's coming out, there are huge demands over a period. We need energy efficiency technologies, like this GAN FEST technology. So to me, this is humongous. And it, we only see it here in the show, in Shalmie, saying, "Five minutes." And everybody, the consumers go, "Oh, that's cool." But let's look at the bigger story, which is electrifying transportation globally. And this is going to be big. >> Yeah. And, and to, and to double click on that a little bit, to be clear, when we talk about fast charging today, typically it's taking the battery from a, not a zero state of charge, but a relatively low state of charge to 80%. >> Yep. >> Then it tapers off dramatically. And that translates into less range in an EV, less usable time on any other device, and there's that whole linkage between the power in, and the battery's ability to be charged, and how much is usable. And from a sustainability perspective, we are going to have an avalanche of batteries going into secondary use cases over time. >> They don't get tossed into landfills contrary to what people might think. >> Yep. >> In fact, they are used in a variety of ways after their primary lifespan. But that, that is, that in and of itself is a revolutionary thing. I'm interested in each of your thoughts on the China factor. Glaringly absent here, from my perspective, as sort of an Apple fanboy, where are they? Why aren't they talking about their... They must, they must feel like, "Well we just don't need to." >> We don't need to. We just don't need to. >> Absolutely. >> And then you walk around and you see these, these company names that are often anglicized, and you don't necessarily immediately associate them with China, but it's like, "Wait a minute, "that looks better than what I have, "and I'm not allowed to have access to that thing." What happens in the future there geopolitically? >> It's a pretty big question for- >> Its is. >> For a short little tech show. (Caroline laughs) But what happens as we move forward? When is the entire world going to be able to leverage in a secure way, some of the stuff that's coming out of, if they're not the largest economy in the world yet, they shortly will be. >> What's the story there? >> Well, it's interesting that you mentioned First Apple that has never had a presence at Mobile World Congress. And fun enough, I'm part of the GSMA judges for the GLOMO Awards, and last night I gave out Best Mobile Phone for last year, and it was to the iPhone4 Team Pro. and best disruptive technology, which was for the satellite function feature on, on the new iPhone. So, Apple might not be here, but they are. >> Okay. >> And, and so that's the first thing. And they are as far as being top of mind to every competitor in the smartphone market still. So a lot of the things that, even from a design perspective that you see on some of the Chinese brands, really remind you of, of Apple. What is interesting for me, is how there wouldn't be, with the exception of Samsung and Motorola, there's no one else here that is non-Chinese from a smartphone point of view. So that's in itself, is something that changed dramatically over the years, especially for somebody like me that still remember Nokia being the number one in the market. >> Huh. >> So. >> Guys, we could continue this conversation. We are unfortunately out of time. But thank you so much for joining Dave and me, talking about your perspectives on the event, the industry, the disruptive forces. It's going to be really interesting to see where it goes. 'Cause at the end of the day, it's the consumers that just want to make sure I can connect wherever I am 24 by seven, and it just needs to work. Thank you so much for your insights. >> Thank you. >> Lisa, it's been great. Dave, great. It's a pleasure. >> Our pleasure. For our guests, and for Dave Nicholson, I'm Lisa Martin. You're watching, "theCUBE," the leader in live and emerging tech coverage coming to you day three of our coverage of MWC 23. Stick around. Our next guest joins us momentarily. (outro music)

>> TheCUBE presents Ignite 22 brought to you by Palo Alto Networks. >> Welcome back to Las Vegas, everyone. We're glad you're with us. This is theCUBE live at Palo Alto Ignite 22 at the MGM Grant in Las Vegas. Lisa Martin here with Dave Vellante, day one of our coverage. We've had great conversations. The cybersecurity landscape is so interesting Dave, it's such a challenging problem to solve but it's so diverse and dynamic at the same time. >> You know, Lisa theCUBE started in May of 2010 in Boston. We called it the chowder event, chowder and Lobster. It was a EMC world, 2010. BJ Jenkins, who's here, of course, was a longtime friend of theCUBE and made the, made the transition into from, well, it's still data, data to, to cyber. So >> True. And BJ is back with us. BJ Jenkins, president Palo Alto Networks great to have you back on theCUBE. >> It is great to be here in person on theCube >> Isn't it great? >> In Vegas. It's awesome. >> And we can tell by your voice will be, will be gentle. You, you've been in Vegas typical Vegas occupational hazard of losing the voice. >> Yeah. It was one of the benefits of Covid. I didn't lose my voice at home sitting talking to a TV. You lose it when you come to Vegas. >> Exactly. >> But it's a small price to pay. >> So things kick off yesterday with the partner summit. You had a keynote then, you had a customer, a CISO on stage. You had a keynote today, which we didn't get to see. But talk to us a little bit about the lay of the land. What are you hearing from CISOs, from CIOs as we know security is a board level conversation. >> Yeah, I, you know it's been an interesting three or four months here. Let me start with that. I think, cybersecurity in general is still front and center on CIOs and CISO's minds. It has to be, if you saw Wendy's presentation today and the threats out there companies have to have it front and center. I do think it's been interesting though with the macro uncertainty. We've taken to calling this year the revenge of the CFO and you know these deals in cybersecurity are still a top priority but they're getting finance and procurements, scrutiny which I think in this environment is a necessity but it's still a, you know, number one number two imperative no matter who you talked to, in my mind >> It was interesting what Nikesh was saying in the last conference call that, hey we just have to get more approvals. We know this. We're, we're bringing more go-to-market people on board. We, we have, we're filling the pipeline 'cause we know they're going to split up deals big deals go into smaller chunks. So the question I have for you is is how are you able to successfully integrate those people so that you can get ahead of that sort of macro transition? >> Yeah I, you know, I think there's two things I'd say about uncertain macro situations and Dave, you know how old I am. I'm pretty old. I've been through a lot of cycles. And in those cycles I've always found stronger companies with stronger value proposition separate themselves actually in uncertain, economic times. And so I think there's actually an opportunity here. The message tilts a little bit though where it's been about innovation and new threat vectors to one of you have 20, 30, 40 vendors you can consolidate become more effective in your security posture and save money on your TCOs. So one of the things as we bring people on board it's training them on that business value proposition. How do you take a customer who's got 20 or 30 tools take 'em down to 5 or 10 where Palo is more central and strategic and be able to demonstrate that value. So we do that through, we're making a huge investment in our people but macroeconomic times also puts some stronger people back on the market and we're able to incorporate them into the business. >> What are the conditions that are necessary for that consolidation? Like I would imagine if you're, if you're a big customer of a big, you know, competitor of yours that that migration is going to be harder than if you're dealing with lots of little point tools. Do those, do those point tools, are they sort of is it the end of the subscription? Is it just stuff that's off the books now? What's, the condition that is ripe for that kind of consolidation? >> Look, I think the challenge coming into this year was skills. And so customers had all of these point products. It required a lot more human intervention as Nikesh was talking about to integrate them or make them work. And as all of us know finding people with cybersecurity skills over the last 12 months has been incredibly hard. That drove, if you know, if you think about that a CIO and a CISO sitting there going, I have all all this investment in tools. I don't have the people to operate 'em. What do I need to do? What we tried to do is elevate that conversation because in a customer, everybody who's bought one of those, they they bought it to solve a problem. And there's people with affinity for that tool. They're not just going to say I want to get consolidated and give up my tool. They're going to wrap their arms around it. And so what we needed to do and this changed our ecosystem strategy too how we leverage partners. We needed to get into the CIO and CISO and say look at this chaos you have here and the challenges around people that it's, it's presenting you. We can help solve that by, by standardizing, consolidating taking that integration away from you as Nikesh talked about, and making it easier for your your high skill people to work on high skill, you know high challenges in there. >> Let chaos reign, and then reign in the chaos. >> Yes. >> Andy Grove. >> I was looking at some stats that there's 26 million developers but less than 3 million cybersecurity professionals. >> Talked about that skills gap and what CISOs and CIOs are facing is do you consider from a value prop perspective Palo Alto Networks to be a, a facilitator of helping organizations deal with that skills gap? >> I think there's a short term and a long term. I think Nikesh today talked about the long term that we'll never win this battle with human beings. We're going to have to win it with automation. That, that's the long term the short term right here and now is that people need people with cybersecurity skills. Now what we're trying to do, you know, is multifaceted. We work with universities to standardize programs to develop skills that people can come into the marketplace with. We run our own programs inside the company. We have a cloud academy program now where we take people high aptitude for sales and technical aptitude and we will put them through a six month boot camp on cloud and they'll come out of that ready to really work with the leading experts in cloud security. The third angle is partners, right, there are partners in the marketplace who want to drive their business into high services areas. They have people, they know how to train. We give them, we partner with them to give them training. Hopefully that helps solve some of the short-term gaps that are out there today. >> So you made the jump from data storage to security and >> Yeah. >> You know, network security, all kinds of security. What was that like? What you must have learned a lot in the last better part of a decade? >> Yeah. >> Take us through that. >> You know, so the first jump was from EMC. I was 15 years there to be CEO of Barracuda. And you know, it was interesting because EMC was, you know large enterprise for the most part. At Barracuda we had, you know 250,000 small and mid-size enterprises. And it was, it's interesting to get into security in small and mid-size businesses because, you know Wendy today was talking about nation states. For small and mid-size business, it's common thievery right? It's ransomware, it's, and, those customers don't have, you know, the human and financial resources to keep up with the threat factor. So, you know, Nikesh talked about how it's taken 'em four and a half years to get into cybersecurity. I remember my first week at Barracuda, I was talking with a customer who had, you know, breached data shut down. There wasn't much bitcoin back then so it was just a pure ransom. And I'm like, wow, this is, you know, incredible industry. So it's been a good, you know, transition for me. I still think data is at the heart of all of this. Right? And I have always believed there's a strong connection between the things I learned growing up at EMC and what I put into practice today at Palo Alto Networks. >> And how about a culture because I, you know I know have observed the EMC culture >> Yeah. >> And you were there in really the heyday. >> Yeah. >> Right? Which was an awesome place. And it seems like Palo Alto obviously, different times but you know, similar like laser focus on solving problems, you know, obviously great, you know value sellers, you know, you guys aren't the commodity >> Yeah. For Product. But there seemed to be some similarities from afar. I don't know Palo Alto as well as I know EMC. >> I think there's a lot. When I joined EMC, it was about, it was 2 billion in in revenue and I think when I left it was over 20, 20, 21. And, you know, we're at, you know hopefully 5, 5 5 in revenue. I feel like it's this very similar, there's a sense of urgency, there's an incredible focus on the customer. you know, Near and Moche are definitely different individuals but the both same kind of disruptive, Israeli force out there driving the business. There are a lot of similarities. I, you know, the passion, I feel privileged as a, you know go to market person that I have this incredible portfolio to go, you know, work with customers on. It's a lucky position to be in, but very I feel like it is a movie I've seen before. >> Yeah. And but, and the course, the challenges from the, the target that you're disrupting is different. It was, you know, EMC had a lot of big, you know IBM obviously was, you know, bigger target whereas you got thousands of, you know, smaller companies. >> Yes. >> And, and so that's a different dynamic but that's why the consolidation play is so important. >> Look at, that's why I joined Palo Alto Networks when I was at Barracuda for nine years. It just fascinated me, that there was 3000 plus players in security and why didn't security evolve like the storage market did or the server market or network where working >> Yeah, right. >> You know, two or three big gorillas came to, to dominate those markets. And it's, I think it's what Nikesh talked about today. There was a new problem in best of breed. It was always best of breed. You can never in security go in and, you know, say, Hey it's good I saved us some money but I got the third best product in the marketplace. And there was that kind of gap between products. I, believe in why I joined here I think this is my last gig is we have a chance to change that. And this is the first company as I look from the outside in that had best of breed as, you know Nikesh said 13 categories. >> Yeah. >> And you know, we're in the leaders quadrant and it's a conversation I have with customers. You don't have to sacrifice best of breed but get the benefits of a platform. And I, think that resonates today. I think we have a chance to change the industry from that viewpoint. >> Give us a little view of the voice of the customer. You had, was it Sabre? >> Yeah. >> That was on >> Scott Moser, The CISO from Sabre. >> Give us a view, what are you hearing from the voice of the customer? Obviously they're quite a successful customer but challenges, concerns, the partnership. >> Yeah. Look, I think security is similar to industries where we come up with magic marketing phrases and, you know, things to you know, make you want to procure our solutions. You know, zero trust is one. And you know, you'll talk to customers and they're like, okay, yes. And you know, the government, right? Joe, Joe Biden's putting out zero trust executive orders. And the, the problem is if you talk to customers, it's a journey. They have legacy infrastructure they have business drivers that you know they just don't deal with us. They've got to deal with the business side who's trying to make the money that keeps the, the company going. it's really helped them draw a map from where they're at today to zero trust or to a better security architecture. Or, you know, they're moving their apps into the cloud. How am I going to migrate? Right? Again, that discussion three years ago was around lift and shift, right? Today it's about, well, no I need cloud native developed apps to service the business the way I want to, I want to service it. How do I, so I, I think there's this element of a trusted partner and relationship. And again, I think this is why you can't have 40 or 50 of those. You got to start narrowing it down if you want to be able to meet and beat the threats that are out there for you. So I, you know, the customers, I see a lot of 'em. It's, here's where I'm at help me get here to a better position. And they know it's, you know Scott said in our keynote today, you don't just, you know have layer three firewall policies and decide, okay tomorrow I'm going to go to layer seven. That, that's not how it works. Right? There's, and, and by the way these things are a mission critical type areas. So there's got to be a game plan that you help customers go through to get there. >> Definitely. Last question, my last question for you is, is security being a board level conversation I was reading some stats from a survey I think it was the what's new in Cypress survey that that Palo Alto released today that showed that while significant numbers of organizations think they've got a cyber resiliency playbook, there's a lot of disconnect or lack of alignment at the boardroom. Are you in those conversations? How can you help facilitate that alignment between the executive team and the board when it comes to security being so foundational to any business? >> Yeah, it's, I've been on three, four public company boards. I'm on, I'm on two today. I would say four years ago, this was a almost a taboo topic. It was a, put your head in the sand and pray to God nothing happened. And you know, the world has changed significantly. And because of the number of breaches the impact it's had on brand, boards have to think about this in duty of care and their fiduciary duty. Okay. So then you start with a board that may not have the technical skills. The first problem the security industry had is how do I explain your risk profile in a way you can understand it. I'm, I'm on the board of Generac that makes home generators. It's a manufacturing, you know, company but they put Wifi modules in their boxes so that the dealers could help do the maintenance on 'em. And all of a sudden these things were getting attacked. Right? And they're being used for bot attacks. >> Yeah. >> Everybody on their board had a manufacturing background. >> Ah. >> So how do you help that board understand the risk they have that's what's changed over the last four years. It's a constant discussion. It's one I have with CISOs where they're like help us put it in layman's terms so they understand they know what we're doing and they feel confident but at the same time understand the marketplace better. And that's a journey for us. >> That Generac example is a great one because, you know, think about IOT Technologies. They've historically been air gaped >> Yes. >> By design. And all of a sudden the business comes in and says, "Hey we can put wifi in there", you know >> Connect it to a home Wifi system that >> Make our lives so much easier. Next thing you know, it's being used to attack. >> Yeah. >> So that's why, as you go around the world are you discerning, I know you were just in Japan are you discerning significant differences in sort of attitudes toward, towards cyber? Whether it's public policy, you know things like regulation where you, they don't want you sharing data, but as as a cyber company, you want to share that data with you know, public and private? >> Look it, I, I think around the world we see incredible government activity first of all. And I think given the position we're in we get to have some unique conversations there. I would say worldwide security is an imperative. I, no matter where I go, you know it's in front of everybody's mind. The, on the, the governance side, it's really what do we need to adapt to make sure we meet local regulations. And I, and I would just tell you Dave there's ways when you do that, and we talk with governments that because of how they want to do it reduce our ability to give them full insight into all the threats and how we can help them. And I do think over time governments understand that we can anonymize the data. There's, but that, that's a work in process. Definitely there is a balance. We need to have privacy, we need to have, you know personal security for people. But there's ways to collect that data in an anonymous way and give better security insight back into the architectures that are out there. >> All right. A little shift the gears here. A little sports question. We've had some great Boston's sports guests on theCUBE right? I mean, Randy Seidel, we were talking about him. Peter McKay, Snyk, I guess he's a competitor now but you know, there's no question got >> He got a little funding today. I saw that. >> Down round. But they still got a lot of money. Not of a down round, but they were, but yeah, but actually, you know, he was on several years ago and it was around the time they were talking about trading Brady. He said Never trade Brady. And he got that right. We, I think we can agree Brady's the goat. >> Yes. >> The big question I have for you is, Belichick. Do you ever question Has your belief in him as the greatest coach of all time wavered, you know, now that- No. Okay. >> Never. >> Weigh in on that. >> Never, he says >> Still the Goat. >> I'll give you my best. You know, never In Bill we trust. >> Okay. Still. >> All right >> I, you know, the NFL is a unique property that's designed for parody and is designed, I mean actively designed to not let Mr. Craft and Bill Belichick do what they do every year. I feel privileged as a Boston sports fan that in our worst years we're in the seventh playoff spot. And I have a lot of family in Chicago who would kill for that position, by the way. And you know, they're in perpetual rebuilding. And so look, and I think he, you know the way he's been able to manage the cap and the skill levels, I think we have a top five defense. There's different ways to win titles. And if I, you know, remember in Brady's last title with Boston, the defense won us that Super Bowl. >> Well thanks for weighing in on that because there's a lot of crazy talk going on. Like, 'Hey, if he doesn't beat Arizona, he's got to go.' I'm like, what? So, okay, I'm sometimes it takes a good good loyal fan who's maybe, you know, has >> The good news in Boston is we're emotional fans too so I understand you got to keep the long term long term in mind. And we're, we're in a privileged position in Boston. We've got Celtics, we've got Bruins we've got the Patriots right on the edge of the playoffs and we need the Red Sox to get to work. >> Yeah, no, you know they were last, last year so maybe they're going to win it all like they usually do. So >> Fingers crossed. >> Crazy worst to first. >> Exactly. Well you said, in Bill we trust it sounds like from our conversation in BJ we trust from the customers, the partners. >> I hope so. >> Thank you so much BJ, for coming back on theCUBE giving us the lay of the land, what's new, the voice of the customer and how Palo Alto was really differentiated in the market. We always appreciate your, coming on the show you >> Honor and privilege seeing you here. Thanks. >> You may be thinking that you were watching ESPN just now but you know, we call ourselves the ESPN at Tech News. This is Lisa Martin for Dave Vellante and our guest. You're watching theCUBE, the Leader and live emerging in enterprise tech coverage. (upbeat music)

foreign continued coverage of AWS re invent my name is Savannah Peterson and I am very excited to be joined by two brilliant blokes in the space of efficiency and performance whether you're on Prem or in the cloud today's discussion is going to be fascinating please welcome Brad and Simon to the show how are you Simon coming in from the UK how you feeling well thank you excellent and Brad we have you coming in from Seattle how are you this morning doing fine thank you excellent and feeling bookish given your background love that I know that you both really care about efficiency and performance it's a very hot topic both of the show and in the industry right now I'm curious I'm going to open it up with you Simon what challenges and I think you've actually continued to tackle these throughout the course of your career what challenges were you facing and wanting to solve when you started yellow dog um really we're just looking at cloud and coming from an on-premise environment really wanted to be able to make accessing Cloud particularly a volume to be simple and straightforward um if you look at today at the number of instance types available from the major Cloud providers there's more than seven thousand different instance types whereas on-prem you go along you select your processes you select your systems it's already be really easy when you hit the cloud you've just got this amazing amount of choice so really it was all about how can you make Intelligent Decisions for you know are you going to run your workload how to match it with what you've got on premise and that was really the inspiration for Rafael so staying there for just a second what does yellow dog provide customers is a SAS system so um you get to it by accessing through the yellow platform and what it allows people to do is to be able to make Intelligent Decisions about where to run their workload would that be on premise or in the cloud it has a wealth of information it understands the costs the performance the latency and the availability of every different instance type in all different clouds it really allows people to uh to be able to make use of that information provision exactly what they need and to be able to run their workloads yeah it also includes a provisioner and it also includes a scheduler as well which is a cloud native scheduler so it's designed to be able to cope with um with cloud in terms of things like spots and interruptions and be able to uh to reschedule and fail over between clouds if there's ever need to do so yeah that sounds incredible and I know this means a lot for partners like AMD Brad talk to me about the partnership and what this means for AMD for your customers yeah absolutely it you know we're excited to be aligned with the uh with a company like yellow dog it's it's um you know the the importance of compute is becoming more and more prevalent every day and it's it's always been top of mind but especially now when you think about what the uh what the economy and the rest of the world is kind of facing over the next you know probably a year or longer it's so important that um that you're able to maximize your dollars and your spend and doing away with uh with uh with absolute certainty that you've got the right type of people behind you uh ensuring that you're your dollars are being spent very wisely and the great thing about yell dogs that they have tremendous insight into uh into cost optimization computer optimization across the entire Globe their their indexes is quite remarkable and what it does is it allows uh customers to actually see just how performant and cost efficient AMD is so it allows us to really put our best foot forward and and gives customers a chance to understand something that they probably weren't uh more familiar with the fact that uh that AMD uh is a tremendous a tremendous value in the marketplace yeah and and uh Simon can you tell us a little bit more about the yellow dog index I'm glad you brought that up Brad yes the yellow index is uh is essentially it's live it's available for anyone to access you can just go to index.yam.tech and you'll be able to see pretty much every single instance type that's available from all the major Cloud providers and be able to make your selection are you looking for GPU type nodes are you looking for AMD processors are you looking just for performance essentially what you're able to do is create a live view of effectively what's available in different data centers around the world and the price at this moment in time also just uh as Brad mentioned in terms of you know cost efficiency and uh and being taking green values seriously as we should we should do the yellow index also has the ability to be able to see at that point in time where the best place to be at a runner job is based upon the lowest carbon impact of running at this moment in time and that for many organizations gives an amazing Insight in not just about being able to find the the understand fishing processes but being able to ensure the greenest energy possible is powering that process when you want to be able to run your workload it's so powerful what you just said and I think when we exactly it's not just about it's not just about power but it's about place when we are are looking at Global Computing at scale what I know that there's ESG advantages in and ESG being a very hot topic when we're talking about AMW on AWS and and and leveraging tools like yellow dog what other sorts of advantages Beyond being least carbon impactful can your Mutual customers benefit from so it's not like I say there's many other features you know a very important thing when you're running a high performance Computing workload is being able to match the instruction set that you're running on premise and then being able to use that in the cloud as well and also to be able to make Intelligent Decisions of where should something run should would something be more efficient um to build on premise should we always try and maximize our on-premise resources before going into the cloud there's a lot about being able to just be able to make decisions and yellow itself it makes thousands of decisions per second to be in a workout where the the best and most optimized places to to run your workload yeah so Brad you work with a lot of companies at scale what type of scale is possible when leveraging Technologies like AMD and yellow dog combined well you know I love the fact that you mentioned uh you know HPC and it's one of the areas that actually is most exciting for for me personally and for and for AMD with the combination of yellow dog and AWS and AWS launched the very first HPC uh instance type last year and you know we're we're we haven't even begun to answer a question we haven't gotten to see um the full-scale capability in the cloud when it comes to these uh these very coordinated and very refined workloads that are running at massive scale and and uh you know we've got some some products we'll be launched in the near future as well that are incredibly performant and you know to be honest I don't think I don't think we have even come close to seeing the scale relative to somebody's very optimized workloads in HPC uh that that we're capable of so um we're excited we're excited for the next few years to see how how we can wrap in um some of the tremendous success that AMD has had on-prem in these these these massive compute centers and replicating that same success inside AWS with companies like yellow dog it's uh it we're excited to see what uh what's what's going to come forward can you give us a preview of anything on the record that gets you really excited about the future I was going to ask you what what had you looking forward to 2023 and Beyond but nothing well not nothing official of course uh but um I will say this you know AMD has recently successful had the launch for Genoa uh it's our next next-gen release and it is um it is proving to be it absolutely is the dominant compute engine it at this point that exists and you know when you start to couple that with the the prowess of AWS you know you could see that over time becoming something potentially that um you know um can really start to change the compute landscape quite a bit so we're hopeful that you know in the future we'll have something along those lines uh with AWS and others and um we're very uh we're very bullish in that area love it uh Simon what about you you've been passionate about low carbon I.T for a long time is carbon neutral Tech in our future what I realize is a bold and lofty claim for you but feel free to give us any of your future predictions um yeah so well I started here trying to build solutions for you know many years ago so 2006 um I was part of a team that launched the the world's lowest powered Windows PC that was actually based on the AMD technology back then so uh you can tell that AMD have been working on a low power for us for a long time in terms of carbon neutral yes I think um certainly there's a there's a few data centers around the world now that are getting very close to uh to carbon neutral some of which may have already achieved it so that's really interesting but so you know the the second part of that is really the the manufacturer of everything that goes into those Services systems and being able to to get to uh you know a net zero on those over a period of time and when we do that which is yeah not without challenges but but certainly possible then we really will have carbon neutral I.T which will be uh a benefit to everyone you know mankind itself yeah casual statement and I have to say that I wholeheartedly agree I think that it's one of the greater challenges of Our Generation especially as what we're able to do in HPC in particular since we're talking about it is only going to grow and scale and magnitude and the amount of data that we have to organize certain process is is wild even today so I love that I'm curious is there anything that you can share with us that's in the pipeline for Yellow Dog anything coming up in the future that's very exciting um so we're coming up very soon um we're going to release something called um version 4 again log which contains um what we call a resource framework which is all about making sure you've got everything you need before you run a job either on-prem or in the cloud so that might be anything from making sure you've got the right licenses making sure that your data is all in the right location making sure you've got all aspects of your workflow ready before you start launching compute and start really but you know burning through dollars with computer could potentially sat there uh not not doing anything until other tasks keep catch up so we're really excited about this new V4 release which will uh which will come out very soon awesome we can't wait to learn more about that hopefully here again on the cube Brad what do Partnerships with companies like yellowdog meme for you and for the customers that you're able to serve yeah it's it's incredibly important I it's you know there's one of the difficulties in in compute that we have today especially in Cloud compute there's there's so much available at this point I mean there was a point in time it was very simple and straightforward it's not even close to being that anymore green so you know one of the things I love about yellow dog itself is actually it does a great they do a great job of making very complex situations and environments fairly simple to understand especially from a business perspective and so one of the things that we love about it is it actually helps our customers you know the AMD direct customers better understand how to properly use our technology and to get the most out of it and so it's difficult for us to articulate that message because you know we are a Semiconductor Company so sometimes it's a little tough to be able to articulate workloads and applications in the way that our customer base will be able to understand but you know it's it's so critical to have companies like yellow dog in the middle that can actually you know make that translation for us directly to the customer um you know and and especially too when you start thinking about ESG and environmental relationships and I'd like to make a comment and one of the things that is fantastic about AMD AWS and yellow we all share the same Mission and we're very public about those missions about just being better to the to the planet and um you know AMD has taken some very aggressive uh targets through 2025 much beyond anything that the industry has expected and you know because of that we are you know we are the most um we are the most power efficient xa6 product on the marketplace and it's not even close and you know I look forward to the day when uh you know you start looking at instance types inside these public Cloud providers in conjunction with the old dog and you can actually even start to see maybe potentially what that carbon footprint is based on those decisions you make on compute and um you know considering that more than half to spend for everybody is generally compute in these environments it's critical to really know what your true impact in the world is and um it's just one of the best parts about a partnership like this oh what a wonderful note to close on and I love both the Synergy between all the partners on a technology level but most importantly on a mission level because none of it matters if we don't have a planet that we can continue to innovate on so I'm I'm really grateful that you're both here fighting a good fight working together and also making a lot of information available for companies of all different sizes as they're navigating very complex decision trees in and operating their stack so thank you both Simon and Brad I really appreciate your time it's been incredibly insightful and thank you to our audience for tuning in to our continuing coverage of AWS re invent here on thecube my name is Savannah Peterson and I look forward to learning more with you soon foreign [Music]

welcome back to thecube's live coverage coming to you from the big apple in new york city we're talking all things aws summit but right now i've got two powerhouses you know them you love them john furrier dave vellante going to be talking about super cloud guys we've been talking a lot about this there's a big event coming up on the cube august 9th and i gotta start dave with you because we talk about it pretty much in every interview where it's relevant why super cloud yeah so john furrier years ago started a tradition lisa prior to aws which was to lay down the expectation for our audiences what they should be looking for at aws reinvent okay john when did that start 2012 2013. actually 2013 was our first but 2015 was the first time when we get access to andy jassy who wasn't doing any briefings and we realized that the whole industry started looking at amazon web services as a structural forcing function of massive change uh some say inflection point we were saying complete redefinition so you wrote the trillion dollar baby yeah right which actually turns into probably multi-trillion dollars we got it right on that one surprisingly it was pretty obvious so every year since then john has published the seminal article prior to reinvent so this year we were talking we're coming out of the isolation economy and john hedwig also also adam silevski was the new ceo so we had a one-on-one with adam that's right and then that's where the convergence between andy jassy and adam celebski kicked in which is essentially those guys work together even though they he went off and boomerang back in as they say in aws but what's interesting was is that adam zluski's point of view piggyback jassy but he had a different twist yeah some so you know low you know people who didn't have really a lot of thought into it said oh he's copying microsoft moving up the stack we're like no no no no no something structural is happening again and so john wrote the piece and he started sharing it we're collaborating he said hey dave take a take a look add your perspectives and then jerry chen had just written castles in the cloud and he talked about sub-markets and we were sort of noodling and one of the other things was in 2018 2019 around that time at aws re invent there was this friction between like snowflake and aws because redshift separated compute from storage which was snowflake's whole thing now fast forward to 2021 after we're leaving you know the covert economy by the way everyone was complaining they are asking jassy are you competing with your ecosystem the classic right trope and then in in remember jason used to use cloudera as the example i would like to maybe pick a better example snowflake became that example and what the transition was it went from hey we're kind of competitive for sure there's a lot of examples but it went from we're competitive they're stealing our stuff to you know what we're making so much money building on top of aws specifically but also the clouds and cross clouds so we said there's something new happening in the ecosystem and then just it popped up this term super cloud came up to connote a layer that floats above the hyperscale capex not is it's not pass it's not sas it's the combination of the of those things on top of a new digital infrastructure and we chose the term super cloud we liked it better than multi-cloud because multiplayer at least one other point too i think four or five years earlier dave and i across not just aws reinvent all of our other events we were speculating that there might be a tier two cloud service provider models and we've talked with intel about this and others just kind of like evaluating it staring at it and we met by tier two like maybe competing against amazon but what happened was it wasn't a tier two cloud it was a super cloud built on the capex of aws which means initially was a company didn't have to build aws to be like aws and everybody wanted to be like aws so we saw the emergence of the smart companies saying hey let's refactor our business model in the category or industry scope and to dominate with cloud scale and they did it that then continued that was the premise of chen's post which was kind of rift on the cube initially which is you can have a moat in a castle in the cloud and have a competitive advantage and a sustainable differentiation model and that's exactly what's happening and then you introduce the edge and hybrid you now have a cloud operating model that that super cloud extends as a substrate across all environments so it's not multi-cloud which sounds broken and like put it distance jointed joint barriers hybrid cloud which is the hybrid operating model at scale and you don't have to be amazon to take advantage of all the value creation since they took care of the capex now they win too on the other side because because they're selling ec2 and storage and ml and ai and this is new and this is information that people don't might not know about internally at aws there was a debate dave okay i heard this from sources do we go all in and compete and just own the whole category or open the ecosystem and coexist with [ __ ] why do we have these other companies or snowflake and guess what the decision was let's make it open ecosystem and let's have our own offerings as well and let the winner take off smart because they can't hire enough people and we just had aws and snowflake on the cube a few weeks ago talking about the partnership the co-op petition the value in it but what's been driving it is the voice of the customer but i want to ask you paint the picture for the audience of the critical key components of super cloud what are those yeah so i think first and foremost super cloud as john was saying it's not multi-cloud chuck whitten had a great phrase at dell tech world he said multi-cloud by default right versus multi-cloud by design and multi-cloud has been by default it's been this sort of i run in aws and i run my stack in azure or i run my stack in gcp and it works or i wrap my stack in a container and host it in the cloud that's what multi-cloud has been so the first sort of concept is it's a layer that that abstracts the underlying complexity of all the clouds all the primitives uh it takes advantage of maybe graviton or microsoft tooling hides all that and builds new value on top of that the other piece of of super cloud is it's ecosystem driven really interesting story you just told because literally amazon can't hire everybody right so they have to rely on the ecosystem for feature acceleration so it's it also includes a path layer a super pass layer we call it because you need to develop applications that are specific to the problem that the super cloud is solving so it's not a generic path like openshift it's specific to whether it's snowflake or [ __ ] or aviatrix so that developers can actually build on top of and not have to worry about that underlying and also there's some people that are criticizing um what we're doing in a good way because we want to have an open concept sure but here's the thing that a lot of people don't understand they're criticizing or trying to kind of shoot holes in our new structural change that we're identifying to comparing it to old that's like saying mainframe and mini computers it's like saying well the mainframe does it this way therefore there's no way that's going to be legitimate so the old thinking dave is from people that have no real foresight in the new model right and so they don't really get it right so what i'm saying is that we look at structural change structural change is structural change it either happens or it doesn't so what we're observing is the fact that a snowflake didn't design their solution to be multi-cloud they did it all on aws and then said hey why would we why are we going to stop there let's go to azure because microsoft's got a boatload of customers because they have a vertically stacking integration for their install base so if i'm snowflake why wouldn't i be on azure and the same for gcp and the same for other things so this idea that you can get the value of an amp what amazon did leverage and all that value without paying for it up front is a huge dynamic and that's not just saying oh that's cloud that's saying i have a cloud-like scale cloud-like value proposition which which will look like an ecosystem so to me the acid test is if i build on top of say [ __ ] or say snowflake or super cloud by default i'm either a category leader i own the data at scale or i'm sharing data at scale and i have an ecosystem people are building on top of me so that's a platform so that's really difficult so what's happening is these ecosystem partners are taking advantage as john said of all the hyperscale capex and they're building out their version of a distributed global system and then the other attribute of super cloud is it's got metadata management capability in other words it knows if i'm optimizing for latency where in the super cloud to get the data or how to protect privacy or sovereignty or how many copies to make to have the proper data protection or where the air gap should be for ransomware so these are examples of very specific purpose-built super clouds that are filling gaps that the hyperscalers aren't going after what's a good example of a specific super cloud that you think really articulates what you guys are talking about i think there are a lot of them i think snowflake is a really good example i think vmware is building a multi-cloud management system i think aviatrix and virtual you know private cloud networking and for high performance networking i think to a certain extent what oracle is doing with azure is is is definitely looks like a super cloud i think what capital one is doing by building on to taking their own tools and and and moving that to snowflake now that they're not cross-cloud yet but i predict that they will be of i think uh what veeam is doing in data protection uh dell what they showed at dell tech world with project alpine these are all early examples of super well here's an indicator here's how you look at the example so to me if you're just lifting and shifting that was the first gen cloud that's not changing the business model so i think the number one thing to look at is is the company whether they're in a vertical like insurance or fintech or financial are they refactoring their spend not as an i.t cost but as a refactoring of their business model yes like what snowflake did dave or they say okay i'm gonna change how i operate not change my business model per se or not my business identity if i'm gonna provide financial services i don't have to spend capex it's operating expenses i get the capex leverage i redefine i get the data at scale and now i become a service provider to everybody else because scale will determine the power law of who wins in the verticals and in the industry so we believe that snowflake is a data warehouse in the cloud they call it a data cloud now i don't think snowflake would like that dave i call them a data warehouse no a super data cloud but but so the other key here is you know the old saying that andreessen came up with i guess with every company's a software company well what does that mean it means every company software company every company is going digital well how are they going to do that they're going to do that by taking their business their data their tooling their proprietary you know moat and moving that to the cloud so they can compete at scale every company should be if they're not thinking about doing a super cloud well walmart i think i think andreessen's wrong i think i would revise and say that andreessen and the brain trust at andreas and horowitz is that that's no longer irrelevant every company isn't a software company the software industry is called open source everybody is an open source company and every company will be at super cloud that survives yeah to me to me if you're not looking at super cloud as a strategy to get value and refactor your business model take advantage of what you're paying it for but you're paying now in a new way you're building out value so that's you're either going to be a super cloud or get services from a super cloud so if you're not it's like the old joke dave if you're at the table and you don't know who the sucker is it's probably you right so if you're looking at the marketplace you're saying if i'm not a super cloud i'm probably gonna have to work with one because they're gonna have the data they're gonna have the insights they're gonna have the scale they're going to have the castle in the cloud and they will be called a super cloud so in customer conversations helping customers identify workloads to move to the cloud what are the ideal workloads and services to run in super cloud so i honestly think virtually any workload could be a candidate and i think that it's really the business that they're in that's going to define the workload i'll say what i mean so there's certain businesses where low latency high performance transactions are going to matter that's you know kind of the oracle's business there's certain businesses like snowflake where data sharing is the objective how do i share data in a governed way in a secure way in any location across the world that i can monetize so that's their objective you take a data protection company like veeam their objective is to protect data so they have very specific objectives that ultimately dictate what the workload looks like couchbase is another one they they in my opinion are doing some of the most interesting things at the edge because this is where when you when you really push companies in the cloud including the hyperscalers when they get out to the far edge it starts to get a little squishy couchbase actually is developing capabilities to do that and that's to me that's the big wild card john i think you described it accurately the cloud is expanding you've got public clouds no longer just remote services you're including on-prem and now expanding out to the near edge and the deep what do you call it deep edge or far edge lower sousa called the tiny edge right deep edge well i mean look at look at amazon's outpost announcement to me hp e is opportunity dell has opportunities the hardware box guys companies they have an opportunity to be that gear to be an outpost to be their own output they get better stacks they have better gear they just got to run cloud on it yeah right that's an edge node right so so that's that would be part of the super cloud so this is where i think people that are looking at the old models like operating systems or systems mindsets from the 80s they look they're not understanding the new architecture what i would say to them is yeah i hear what you're saying but the structural change is the nodes on the network distributed computing if you will is going to run hybrid cloud all the way across the fact that it's multiple clouds is just coincidence on who's got the best capex value that people build on for their super cloud capability so why wouldn't i be on azure if microsoft's going to give me all their customers that are running office 365 and teams great if i want to be on amazon's kind of sweet which is their ecosystem why wouldn't i want to tap into that so again you can patch it all together in the super cloud so i think the future will be distributed computing cloud architecture end to end and and we felt that was different from multi-cloud you know if you want to call it multi-cloud 2.0 that's fine but you know frankly you know sometimes we get criticized for not defining it tightly enough but we continue to evolve that definition i've never really seen a great definition from multi-cloud i think multi-cloud by default was the definition i run in multiple clouds you know it works in azure it's not a strategy it's a broken name it's a symptom right it's a symptom of multi-vendor is really what multi-cloud has been and so we felt like it was a new term of examples look what we're talking about snowflake data bricks databricks another good one these are these are examples goldman sachs and we felt like the term immediately connotes something bigger something that sits above the clouds and is part of a digital platform you know the people poo poo the metaverse because it's really you know not well defined but every 15 or 20 years this industry goes through dave let me ask you a question so uh lisa you too if i'm in the insurance vertical uh and i'm a i'm an insurance company i have competitors my customers can go there and and do business with that company and you know and they all know that they go to the same conferences but in that sector now you have new dynamics your i.t spend isn't going to keep the lights on and make your apps work your back-end systems and your mobile app to get your whatever now it's like i have cloud scale so what if i refactored my business model become a super cloud and become the major primary service provider to all the competitors and the people that are the the the channel partners of the of the ecosystem that means that company could change the category totally okay and become the dominant category leader literally in two three years if i'm geico okay i i got business in the cloud because i got the app and i'm doing transactions on geico but with all the data that they're collecting there's adjacent businesses that they can get into maybe they're in the safety business maybe they can sell data to governments maybe they can inform logistics and highway you know patterns roll up all the people that don't have the same scale they have and service them with that data and they get subscription revenue and they can build on top of the geico super insurance cloud right yes it's it's unlimited opportunity that's why it's but the multi-trillion dollar baby so talk to us you've done an amazing job of talking which i know you would of why super cloud what it is the critical components the key workloads great examples talk to us in our last few minutes about the event the cube on super cloud august 9th what's the audience going to who are they going to hear from what are they going to learn yeah so august 9th live out of our palo alto studio we're going to have a program that's going to run from 9 a.m to 1 p.m and we're going to have a number of industry luminaries in there uh kit colbert from from vmware is going to talk about you know their strategy uh benoit de javille uh from snowflake is going to is going to be there of g written house of sky-high security um i i i don't want to give it away but i think steve mullaney is going to come on adrian uh cockroft is coming on the panel keith townsend sanjeev mohan will be on so we'll be running that live and also we'll be bringing in pre-recorded interviews that we'll have prior to the show that will run post the live event it's really a pilot virtual event we want to do a physical event we're thinking but the pilot is to bring our trusted friends together they're credible that have industry experience to try to understand the scope of what we're talking about and open it up and help flesh out the definition make it an open model where we can it's not just our opinion we're observing identifying the structural changes but bringing in smart people our smart friends and companies are saying yeah we get behind this because it has it has legs for a reason so we're gonna zoom out and let people participate and let the conversation and the community drive the content and that is super important to the cube as you know dave but i think that's what's going on lisa is that it's a pilot if it has legs we'll do a physical event certainly we're getting phones to bring it off the hook for sponsors so we don't want to go and go all in on sponsorships right now because it's not about money making it's about getting that super cloud clarity around to help companies yeah we want to evolve the concept and and bring in outside perspectives well the community is one of the best places to do that absolutely organic it's an organic community where i mean people want to find out what's going on with the best practices of how to transform a business and right now digital transformation is not just getting digitized it's taking advantage of the technology to leapfrog the competition so all the successful people we talked to at least have the same common theme i'm changing my game but not changing my game to the customer i'm just going to do it differently better faster cheaper more efficient and have higher margins and beat the competition that's the company doesn't want to beat the competition go to thecube.net if you're not all they're all ready to register for the cube on supercloud august 9th 9am pacific you won't want to miss it for john furrier and dave vellante i'm lisa martin we're all coming at you from new york city at aws summit 22. i'll be right back with our next guest [Music] you

>> Instructor: "theCUBE" presents KubeCon and CloudNativeCon Europe 2022, brought to you by Red Hat, the Cloud Native Computing Foundation and its ecosystem partners. >> Welcome to Valencia, Spain and we're at KubeCon, CloudNativeCon Europe 2022. I'm Keith Townsend, and my co-host, Enrico Signoretti. Enrico's really proud of me. I've called him Enrico instead of Enrique every session. >> Every day. >> Senior IT analyst at GigaOm. We're talking to fantastic builders at KubeCon, CloudNativeCon Europe 2022 about the projects and their efforts. Enrico, up to this point, it's been all about provisioning, insecurity, what conversation have we been missing? >> Well, I mean, I think that we passed the point of having the conversation of deployment, of provisioning. Everybody's very skilled, actually everything is done at day two. They are discovering that, well, there is a security problem. There is an observability problem a and in fact, we are meeting with a lot of people and there are a lot of conversation with people really needing to understand what is happening. I mean, in their cluster work, why it is happening and all the questions that come with it. And the more I talk with people in the show floor here or even in the various sessions is about, we are growing so that our clusters are becoming bigger and bigger, applications are becoming bigger as well. So we need to now understand better what is happening. As it's not only about cost, it's about everything at the end. >> So I think that's a great set up for our guests, Matt Provo, founder and CEO of StormForge and Patrick Brixton? >> Bergstrom. >> Bergstrom. >> Yeah. >> I spelled it right, I didn't say it right, Bergstrom, CTO. We're at KubeCon, CloudNativeCon where projects are discussed, built and StormForge, I've heard the pitch before, so forgive me. And I'm kind of torn. I have service mesh. What do I need more, like what problem is StormForge solving? >> You want to take it? >> Sure, absolutely. So it's interesting because, my background is in the enterprise, right? I was an executive at UnitedHealth Group before that I worked at Best Buy and one of the issues that we always had was, especially as you migrate to the cloud, it seems like the CPU dial or the memory dial is your reliability dial. So it's like, oh, I just turned that all the way to the right and everything's hunky-dory, right? But then we run into the issue like you and I were just talking about, where it gets very very expensive very quickly. And so my first conversations with Matt and the StormForge group, and they were telling me about the product and what we're dealing with. I said, that is the problem statement that I have always struggled with and I wish this existed 10 years ago when I was dealing with EC2 costs, right? And now with Kubernetes, it's the same thing. It's so easy to provision. So realistically what it is, is we take your raw telemetry data and we essentially monitor the performance of your application, and then we can tell you using our machine learning algorithms, the exact configuration that you should be using for your application to achieve the results that you're looking for without over-provisioning. So we reduce your consumption of CPU, of memory and production which ultimately nine times out of 10, actually I would say 10 out of 10, reduces your cost significantly without sacrificing reliability. >> So can your solution also help to optimize the application in the long run? Because, yes, of course-- >> Yep. >> The lowering fluid as you know optimize the deployment. >> Yeah. >> But actually the long-term is optimizing the application. >> Yes. >> Which is the real problem. >> Yep. >> So, we're fine with the former of what you just said, but we exist to do the latter. And so, we're squarely and completely focused at the application layer. As long as you can track or understand the metrics you care about for your application, we can optimize against it. We love that we don't know your application, we don't know what the SLA and SLO requirements are for your app, you do, and so, in our world it's about empowering the developer into the process, not automating them out of it and I think sometimes AI and machine learning sort of gets a bad rap from that standpoint. And so, at this point the company's been around since 2016, kind of from the very early days of Kubernetes, we've always been, squarely focused on Kubernetes, using our core machine learning engine to optimize metrics at the application layer that people care about and need to go after. And the truth of the matter is today and over time, setting a cluster up on Kubernetes has largely been solved. And yet the promise of Kubernetes around portability and flexibility, downstream when you operationalize, the complexity smacks you in the face and that's where StormForge comes in. And so we're a vertical, kind of vertically oriented solution, that's absolutely focused on solving that problem. >> Well, I don't want to play, actually. I want to play the devils advocate here and-- >> You wouldn't be a good analyst if you didn't. >> So the problem is when you talk with clients, users, there are many of them still working with Java, something that is really tough. I mean, all of us loved Java. >> Yeah, absolutely. >> Maybe 20 years ago. Yeah, but not anymore, but still they have developers, they have porting applications, microservices. Yes, but not very optimized, et cetera, cetera, et cetera. So it's becoming tough. So how you can interact with this kind of old hybrid or anyway, not well engineered applications. >> Yeah. >> We do that today. We actually, part of our platform is we offer performance testing in a lower environment and stage and we, like Matt was saying, we can use any metric that you care about and we can work with any configuration for that application. So perfect example is Java, you have to worry about your heap size, your garbage collection tuning and one of the things that really struck me very early on about the StormForge product is because it is true machine learning. You remove the human bias from that. So like a lot of what I did in the past, especially around SRE and performance tuning, we were only as good as our humans were because of what they knew. And so, we kind of got stuck in these paths of making the same configuration adjustments, making the same changes to the application, hoping for different results. But then when you apply machine learning capability to that the machine will recommend things you never would've dreamed of. And you get amazing results out of that. >> So both me and Enrico have been doing this for a long time. Like, I have battled to my last breath the argument when it's a bare metal or a VM, look, I cannot give you any more memory. >> Yeah. >> And the argument going all the way up to the CIO and the CIO basically saying, you know what, Keith you're cheap, my developer resources are expensive, buy bigger box. >> Yeah. >> Yap. >> Buying a bigger box in the cloud to your point is no longer a option because it's just expensive. >> Yeah. >> Talk to me about the carrot or the stick as developers are realizing that they have to be more responsible. Where's the culture change coming from? Is it the shift in responsibility? >> I think the center of the bullseye for us is within those sets of decisions, not in a static way, but in an ongoing way, especially as the development of applications becomes more and more rapid and the management of them. Our charge and our belief wholeheartedly is that you shouldn't have to choose. You should not have to choose between costs or performance. You should not have to choose where your applications live, in a public private or hybrid cloud environment. And so, we want to empower people to be able to sit in the middle of all of that chaos and for those trade offs and those difficult interactions to no longer be a thing. We're at a place now where we've done hundreds of deployments and never once have we met a developer who said, "I'm really excited to get out of bed and come to work every day and manually tune my application." One side, secondly, we've never met, a manager or someone with budget that said, please don't increase the value of my investment that I've made to lift and shift us over to the cloud or to Kubernetes or some combination of both. And so what we're seeing is the converging of these groups, their happy place is the lack of needing to be able to make those trade offs, and that's been exciting for us. >> So, I'm listening and looks like that your solution is right in the middle in application performance, management, observability. >> Yeah. >> And, monitoring. >> Yeah. >> So it's a little bit of all of this. >> Yeah, so we want to be, the intel inside of all of that, we often get lumped into one of those categories, it used to be APM a lot, we sometimes get, are you observability or and we're really not any of those things, in and of themselves, but we instead we've invested in deep integrations and partnerships with a lot of that tooling 'cause in a lot of ways, the tool chain is hardening in a cloud native and in Kubernetes world. And so, integrating in intelligently, staying focused and great at what we solve for, but then seamlessly partnering and not requiring switching for our users who have already invested likely, in a APM or observability. >> So to go a little bit deeper. What does it mean integration? I mean, do you provide data to this, other applications in the environment or are they supporting you in the work that you do. >> Yeah, we're a data consumer for the most part. In fact, one of our big taglines is take your observability and turn it into action ability, right? Like how do you take that, it's one thing to collect all of the data, but then how do you know what to do with it, right? So to Matt's point, we integrate with folks like Datadog, we integrate with Prometheus today. So we want to collect that telemetry data and then do something useful with it for you. >> But also we want Datadog customers, for example, we have a very close partnership with Datadog so that in your existing Datadog dashboard, now you have-- >> Yeah. >> The StormForge capability showing up in the same location. >> Yep. >> And so you don't have to switch out. >> So I was just going to ask, is it a push pull? What is the developer experience when you say you provide developer this resolve ML learnings about performance, how do they receive it? Like, what's the developer experience. >> They can receive it, for a while we were CLI only, like any good developer tool. >> Right. >> And, we have our own UI. And so it is a push in a lot of cases where I can come to one spot, I've got my applications and every time I'm going to release or plan for a release or I have released and I want to pull in observability data from a production standpoint, I can visualize all of that within the StormForge UI and platform, make decisions, we allow you to set your, kind of comfort level of automation that you're okay with. You can be completely set and forget or you can be somewhere along that spectrum and you can say, as long as it's within, these thresholds, go ahead and release the application or go ahead and apply the configuration. But we also allow you to experience the same, a lot of the same functionality right now, in Grafana, in Datadog and a bunch of others that are coming. >> So I've talked to Tim Crawford who talks to a lot of CIOs and he's saying one of the biggest challenges or if not, one of the biggest challenges CIOs are facing are resource constraints. >> Yeah. >> They cannot find the developers to begin with to get this feedback. How are you hoping to address this biggest pain point for CIOs-- >> Yeah.6 >> And developers? >> You should take that one. >> Yeah, absolutely. So like my background, like I said at UnitedHealth Group, right. It's not always just about cost savings. In fact, the way that I look about at some of these tech challenges, especially when we talk about scalability there's kind of three pillars that I consider, right? There's the tech scalability, how am I solving those challenges? There's the financial piece 'cause you can only throw money at a problem for so long and it's the same thing with the human piece. I can only find so many bodies and right now that pool is very small, and so, we are absolutely squarely in that footprint of we enable your team to focus on the things that they matter, not manual tuning like Matt said. And then there are other resource constraints that I think that a lot of folks don't talk about too. Like, you were talking about private cloud for instance and so having a physical data center, I've worked with physical data centers that companies I've worked for have owned where it is literally full, wall to wall. You can't rack any more servers in it, and so their biggest option is, well, I could spend $1.2 billion to build a new one if I wanted to, or if you had a capability to truly optimize your compute to what you needed and free up 30% of your capacity of that data center. So you can deploy additional name spaces into your cluster, like that's a huge opportunity. >> So I have another question. I mean, maybe it doesn't sound very intelligent at this point, but, so is it an ongoing process or is it something that you do at the very beginning, I mean you start deploying this. >> Yeah. >> And maybe as a service. >> Yep. >> Once in a year I say, okay, let's do it again and see if something change it. >> Sure. >> So one spot, one single.. >> Yeah, would you recommend somebody performance test just once a year? Like, so that's my thing is, at previous roles, my role was to do performance test every single release, and that was at a minimum once a week and if your thing did not get faster, you had to have an executive exception to get it into production and that's the space that we want to live in as well as part of your CICD process, like this should be continuous verification, every time you deploy, we want to make sure that we're recommending the perfect configuration for your application in the name space that you're deploying into. >> And I would be as bold as to say that we believe that we can be a part of adding, actually adding a step in the CICD process that's connected to optimization and that no application should be released, monitored, and sort of analyzed on an ongoing basis without optimization being a part of that. And again, not just from a cost perspective, but for cost and performance. >> Almost a couple of hundred vendors on this floor. You mentioned some of the big ones Datadog, et cetera, but what happens when one of the up and comings out of nowhere, completely new data structure, some imaginative way to click to telemetry data. >> Yeah. >> How do, how do you react to that? >> Yeah, to us it's zeros and ones. >> Yeah. >> And, we really are data agnostic from the standpoint of, we're fortunate enough from the design of our algorithm standpoint, it doesn't get caught up on data structure issues, as long as you can capture it and make it available through one of a series of inputs, one would be load or performance tests, could be telemetry, could be observability, if we have access to it. Honestly, the messier the better from time to time from a machine learning standpoint, it's pretty powerful to see. We've never had a deployment where we saved less than 30%, while also improving performance by at least 10%. But the typical results for us are 40 to 60% savings and 30 to 40% improvement in performance. >> And what happens if the application is, I mean, yes Kubernetes is the best thing of the world but sometimes we have to, external data sources or, we have to connect with external services anyway. >> Yeah. >> So, can you provide an indication also on this particular application, like, where the problem could be? >> Yeah. >> Yeah, and that's absolutely one of the things that we look at too, 'cause it's, especially when you talk about resource consumption it's never a flat line, right? Like depending on your application, depending on the workloads that you're running it varies from sometimes minute to minute, day to day, or it could be week to week even. And so, especially with some of the products that we have coming out with what we want to do, integrating heavily with the HPA and being able to handle some of those bumps and not necessarily bumps, but bursts and being able to do it in a way that's intelligent so that we can make sure that, like I said, it's the perfect configuration for the application regardless of the time of day that you're operating in or what your traffic patterns look like, or, what your disc looks like, right. Like 'cause with our low environment testing, any metric you throw at us, we can optimize for. >> So Matt and Patrick, thank you for stopping by. >> Yeah. >> Yes. >> We can go all day because day two is I think the biggest challenge right now, not just in Kubernetes but application re-platforming and transformation, very, very difficult. Most CTOs and EASs that I talked to, this is the challenge space. From Valencia, Spain, I'm Keith Townsend, along with my host Enrico Signoretti and you're watching "theCube" the leader in high-tech coverage. (whimsical music)

>> Okay. We're now going to go into the customer panel, and we'd like to welcome Angelo Fausti, who's a software engineer at the Vera C. Rubin Observatory, and Caleb Maclachlan who's senior spacecraft operations software engineer at Loft Orbital. Guys, thanks for joining us. You don't want to miss folks this interview. Caleb, let's start with you. You work for an extremely cool company, you're launching satellites into space. Of course doing that is highly complex and not a cheap endeavor. Tell us about Loft Orbital and what you guys do to attack that problem. >> Yeah, absolutely. And thanks for having me here by the way. So Loft Orbital is a company that's a series B startup now, who, and our mission basically is to provide rapid access to space for all kinds of customers. Historically, if you want to fly something in space, do something in space, it's extremely expensive. You need to book a launch, build a bus, hire a team to operate it, have a big software teams, and then eventually worry about, a bunch like, just a lot of very specialized engineering. And what we're trying to do is change that from a super specialized problem that has an extremely high barrier of access, to a infrastructure problem. So that it's almost as simple as deploying a VM in AWS or GCP is getting your programs, your mission deployed on orbit with access to different sensors, cameras, radios, stuff like that. So, that's kind of our mission and just to give a really brief example of the kind of customer that we can serve. There's a really cool company called Totum Labs, who is working on building IoT cons, an IoT constellation for, internet of things, basically being able to get telemetry from all over the world. They're the first company to demonstrate indoor IoT which means you have this little modem inside a container that container that you track from anywhere in the world as it's going across the ocean. So, and it's really little, and they've been able to stay a small startup that's focused on their product, which is the, that super crazy, complicated, cool radio, while we handle the whole space segment for them, which just, you know, before Loft was really impossible. So that's our mission is providing space infrastructure as a service. We are kind of groundbreaking in this area and we're serving a huge variety of customers with all kinds of different missions, and obviously generating a ton of data in space that we've got to handle. >> Yeah. So amazing Caleb, what you guys do. Now, I know you were lured to the skies very early in your career, but how did you kind of land in this business? >> Yeah, so, I guess just a little bit about me. For some people, they don't necessarily know what they want to do like earlier in their life. For me I was five years old and I knew I want to be in the space industry. So, I started in the Air Force, but have stayed in the space industry my whole career and been a part of, this is the fifth space startup that I've been a part of actually. So, I've kind of started out in satellites, spent some time in working in the launch industry on rockets, then, now I'm here back in satellites and honestly, this is the most exciting of the different space startups that I've been a part of. >> Super interesting. Okay. Angelo, let's talk about the Rubin Observatory. Vera C. Rubin, famous woman scientist, galaxy guru. Now you guys, the Observatory, you're up way up high, you get a good look at the Southern sky. And I know COVID slowed you guys down a bit, but no doubt you continued to code away on the software. I know you're getting close, you got to be super excited, give us the update on the Observatory and your role. >> All right. So, yeah. Rubin is a state of the art observatory that is in construction on a remote mountain in Chile. And, with Rubin we'll conduct the large survey of space and time. We're going to observe the sky with eight meter optical telescope and take 1000 pictures every night with 2.2 Gigapixel camera. And we are going to do that for 10 years, which is the duration of the survey. >> Yeah, amazing project. Now, you earned a doctor of philosophy so you probably spent some time thinking about what's out there, and then you went out to earn a PhD in astronomy and astrophysics. So, this is something that you've been working on for the better part of your career, isn't it? >> Yeah, that's right, about 15 years. I studied physics in college. Then I got a PhD in astronomy. And, I worked for about five years in another project, the Dark Energy Survey before joining Rubin in 2015. >> Yeah, impressive. So it seems like both your organizations are looking at space from two different angles. One thing you guys both have in common of course is software, and you both use InfluxDB as part of your data infrastructure. How did you discover InfluxDB, get into it? How do you use the platform? Maybe Caleb you could start. >> Yeah, absolutely. So, the first company that I extensively used InfluxDB in, was a launch startup called Astra. And we were in the process of designing our first generation rocket there, and testing the engines, pumps, everything that goes into a rocket. And, when I joined the company our data story was not very mature. We were collecting a bunch of data in LabVIEW and engineers were taking that over to MATLAB to process it. And at first, there, you know, that's the way that a lot of engineers and scientists are used to working. And at first that was, like people weren't entirely sure that that was, that needed to change. But, it's, something, the nice thing about InfluxDB is that, it's so easy to deploy. So as, our software engineering team was able to get it deployed and, up and running very quickly and then quickly also backport all of the data that we collected this far into Influx. And, what was amazing to see and is kind of the super cool moment with Influx is, when we hooked that up to Grafana, Grafana as the visualization platform we used with Influx, 'cause it works really well with it. There was like this aha moment of our engineers who are used to this post process kind of method for dealing with their data, where they could just almost instantly easily discover data that they hadn't been able to see before, and take the manual processes that they would run after a test and just throw those all in Influx and have live data as tests were coming, and, I saw them implementing like crazy rocket equation type stuff in Influx, and it just was totally game changing for how we tested. >> So Angelo, I was explaining in my open, that you could add a column in a traditional RDBMS and do time series, but with the volume of data that you're talking about in the example that Caleb just gave, you have to have a purpose built time series database. Where did you first learn about InfluxDB? >> Yeah, correct. So, I work with the data management team, and my first project was the record metrics that measured the performance of our software, the software that we used to process the data. So I started implementing that in our relational database. But then I realized that in fact I was dealing with time series data and I should really use a solution built for that. And then I started looking at time series databases and I found InfluxDB, and that was back in 2018. The, another use for InfluxDB that I'm also interested is the visits database. If you think about the observations, we are moving the telescope all the time and pointing to specific directions in the sky and taking pictures every 30 seconds. So that itself is a time series. And every point in that time series, we call a visit. So we want to record the metadata about those visits in InfluxDB. That time series is going to be 10 years long, with about 1000 points every night. It's actually not too much data compared to other problems. It's really just a different time scale. >> The telescope at the Rubin Observatory is like, pun intended, I guess the star of the show. And I believe I read that it's going to be the first of the next gen telescopes to come online. It's got this massive field of view, like three orders of magnitude times the Hubble's widest camera view, which is amazing. Like, that's like 40 moons in an image, amazingly fast as well. What else can you tell us about the telescope? >> This telescope it has to move really fast. And, it also has to carry the primary mirror which is an eight meter piece of glass. It's very heavy. And it has to carry a camera which has about the size of a small car. And this whole structure weighs about 300 tons. For that to work, the telescope needs to be very compact and stiff. And one thing that's amazing about it's design is that, the telescope, this 300 tons structure, it sits on a tiny film of oil, which has the diameter of human hair. And that makes an, almost zero friction interface. In fact, a few people can move this enormous structure with only their hands. As you said, another aspect that makes this telescope unique is the optical design. It's a wide field telescope. So, each image has, in diameter the size of about seven full moons. And, with that, we can map the entire sky in only three days. And of course, during operations everything's controlled by software and it is automatic. There's a very complex piece of software called the Scheduler, which is responsible for moving the telescope, and the camera, which is recording 15 terabytes of data every night. >> And Angelo, all this data lands in InfluxDB, correct? And what are you doing with all that data? >> Yeah, actually not. So we use InfluxDB to record engineering data and metadata about the observations. Like telemetry, events, and commands from the telescope. That's a much smaller data set compared to the images. But it is still challenging because you have some high frequency data that the system needs to keep up, and, we need to store this data and have it around for the lifetime of the project. >> Got it. Thank you. Okay, Caleb, let's bring you back in. Tell us more about the, you got these dishwasher size satellites, kind of using a multi-tenant model, I think it's genius. But tell us about the satellites themselves. >> Yeah, absolutely. So, we have in space some satellites already that as you said, are like dishwasher, mini fridge kind of size. And we're working on a bunch more that are a variety of sizes from shoebox to, I guess, a few times larger than what we have today. And it is, we do shoot to have effectively something like a multi-tenant model where we will buy a bus off the shelf. The bus is what you can kind of think of as the core piece of the satellite, almost like a motherboard or something where it's providing the power, it has the solar panels, it has some radios attached to it. It handles the attitude control, basically steers the spacecraft in orbit, and then we build also in-house, what we call our payload hub which is, has all, any customer payloads attached and our own kind of Edge processing sort of capabilities built into it. And, so we integrate that, we launch it, and those things because they're in lower Earth orbit, they're orbiting the earth every 90 minutes. That's, seven kilometers per second which is several times faster than a speeding bullet. So we have one of the unique challenges of operating spacecraft in lower Earth orbit is that generally you can't talk to them all the time. So, we're managing these things through very brief windows of time, where we get to talk to them through our ground sites, either in Antarctica or in the North pole region. >> Talk more about how you use InfluxDB to make sense of this data through all this tech that you're launching into space. >> We basically, previously we started off when I joined the company, storing all of that as Angelo did in a regular relational database. And we found that it was so slow and the size of our data would balloon over the course of a couple days to the point where we weren't able to even store all of the data that we were getting. So we migrated to InfluxDB to store our time series telemetry from the spacecraft. So, that's things like power level, voltage, currents, counts, whatever metadata we need to monitor about the spacecraft, we now store that in InfluxDB. And that has, now we can actually easily store the entire volume of data for the mission life so far without having to worry about the size bloating to an unmanageable amount, and we can also seamlessly query large chunks of data. Like if I need to see, you know, for example, as an operator, I might want to see how my battery state of charge is evolving over the course of the year, I can have, plot in an Influx that loads that in a fraction of a second for a year's worth of data because it does, intelligent, it can intelligently group the data by assigning time interval. So, it's been extremely powerful for us to access the data. And, as time has gone on, we've gradually migrated more and more of our operating data into Influx. >> Yeah. Let's talk a little bit about, we throw this term around a lot of, you know, data driven, a lot of companies say, "Oh yes, we're data driven." But you guys really are, I mean, you got data at the core. Caleb, what does that mean to you? >> Yeah, so, you know, I think the, and the clearest example of when I saw this be like totally game changing is what I mentioned before at Astra where our engineer's feedback loop went from a lot of kind of slow researching, digging into the data to like an instant, instantaneous almost, seeing the data, making decisions based on it immediately rather than having to wait for some processing. And that's something that I've also seen echoed in my current role. But to give another practical example, as I said, we have a huge amount of data that comes down every orbit and we need to be able to ingest all of that data almost instantaneously and provide it to the operator in near real time, about a second worth of latency is all that's acceptable for us to react to see what is coming down from the spacecraft. And building that pipeline is challenging from a software engineering standpoint. My primary language is Python which isn't necessarily that fast. So what we've done is started, and the goal of being data-driven is publish metrics on individual, how individual pieces of our data processing pipeline are performing into Influx as well. And we do that in production as well as in dev. So we have kind of a production monitoring flow. And what that has done is allow us to make intelligent decisions on our software development roadmap where it makes the most sense for us to focus our development efforts in terms of improving our software efficiency, just because we have that visibility into where the real problems are. And sometimes we've found ourselves before we started doing this, kind of chasing rabbits that weren't necessarily the real root cause of issues that we were seeing. But now that we're being a bit more data driven there, we are being much more effective in where we're spending our resources and our time, which is especially critical to us as we scale from supporting a couple of satellites to supporting many, many satellites at once. >> Yeah, of course is how you reduced those dead ends. Maybe Angelo you could talk about what sort of data-driven means to you and your teams. >> I would say that, having real time visibility to the telemetry data and metrics is crucial for us. We need to make sure that the images that we collect with the telescope have good quality, and, that they are within the specifications to meet our science goals. And so if they are not, we want to know that as soon as possible and then start fixing problems. >> Caleb, what are your sort of event, you know, intervals like? >> So I would say that, as of today on the spacecraft, the event, the level of timing that we deal with probably tops out at about 20 Hertz, 20 measurements per second on things like our gyroscopes. But, the, I think the core point here of the ability to have high precision data is extremely important for these kinds of scientific applications and I'll give an example from when I worked at, on the rockets at Astra. There, our baseline data rate that we would ingest data during a test is 500 Hertz. So 500 samples per second, and in some cases we would actually need to ingest much higher rate data, even up to like 1.5 kilohertz, so extremely, extremely high precision data there where timing really matters a lot. And, you know, I can, one of the really powerful things about Influx is the fact that it can handle this. That's one of the reasons we chose it, because, there's, times when we're looking at the results of a firing where you're zooming in, you know, I talked earlier about how on my current job we often zoom out to look at a year's worth of data. You're zooming in to where your screen is preoccupied by a tiny fraction of a second, and you need to see same thing as Angelo just said, not just the actual telemetry, which is coming in at a high rate, but the events that are coming out of our controllers, so that can be something like, "Hey, I opened this valve at exactly this time," and that goes, we want to have that at, micro, or even nanosecond precision so that we know, okay, we saw a spike in chamber pressure at this exact moment, was that before or after this valve opened? That kind of visibility is critical in these kind of scientific applications, and absolutely game changing to be able to see that in near real time, and with, a really easy way for engineers to be able to visualize this data themselves without having to wait for us software engineers to go build it for them. >> Can the scientists do self-serve or do you have to design and build all the analytics and queries for your scientists? >> Well, I think that's absolutely, from my perspective that's absolutely one of the best things about Influx and what I've seen be game changing is that, generally I'd say anyone can learn to use Influx. And honestly, most of our users might not even know they're using Influx, because, the interface that we expose to them is Grafana, which is a generic graphing, open source graphing library that is very similar to Influx zone Chronograf. >> Sure. >> And what it does is, it provides this almost, it's a very intuitive UI for building your queries. So, you choose a measurement and it shows a dropdown of available measurements. And then you choose the particular fields you want to look at, and again, that's a dropdown. So, it's really easy for our users to discover and there's kind of point and click options for doing math, aggregations. You can even do like perfect kind of predictions all within Grafana, the Grafana user interface, which is really just a wrapper around the APIs and functionality that Influx provides. >> Putting data in the hands of those who have the context, the domain experts is key. Angelo, is it the same situation for you, is it self-serve? >> Yeah, correct. As I mentioned before, we have the astronomers making their own dashboards because they know what exactly what they need to visualize. >> Yeah, I mean, it's all about using the right tool for the job. I think for us, when I joined the company we weren't using InfluxDB and we were dealing with serious issues of the database growing to an incredible size extremely quickly, and being unable to like even querying short periods of data was taking on the order of seconds, which is just not possible for operations. >> Guys, this has been really formative, it's pretty exciting to see how the edge, is mountaintops, lower Earth orbits, I mean space is the ultimate edge, isn't it? I wonder if you could answer two questions to wrap here. You know, what comes next for you guys? And is there something that you're really excited about that you're working on? Caleb maybe you could go first and then Angelo you can bring us home. >> Basically what's next for Loft Orbital is more satellites, a greater push towards infrastructure, and really making, our mission is to make space simple for our customers and for everyone. And we're scaling the company like crazy now, making that happen. It's extremely exciting, an extremely exciting time to be in this company and to be in this industry as a whole. Because there are so many interesting applications out there, so many cool ways of leveraging space that people are taking advantage of, and with companies like SpaceX and the, now rapidly lowering cost of launch it's just a really exciting place to be in. We're launching more satellites, we are scaling up for some constellations, and our ground system has to be improved to match. So, there's a lot of improvements that we're working on to really scale up our control software to be best in class and make it capable of handling such a large workload, so. >> Are you guys hiring? >> We are absolutely hiring, so I would, we have positions all over the company, so, we need software engineers, we need people who do more aerospace specific stuff. So absolutely, I'd encourage anyone to check out the Loft Orbital website, if this is at all interesting. >> All right, Angelo, bring us home. >> Yeah. So what's next for us is really getting this telescope working and collecting data. And when that's happened is going to be just a deluge of data coming out of this camera and handling all that data is going to be really challenging. Yeah, I want to be here for that, I'm looking forward. Like for next year we have like an important milestone, which is our commissioning camera, which is a simplified version of the full camera, it's going to be on sky, and so yeah, most of the system has to be working by then. >> Nice. All right guys, with that we're going to end it. Thank you so much, really fascinating, and thanks to InfluxDB for making this possible, really groundbreaking stuff, enabling value creation at the Edge, in the cloud, and of course, beyond at the space. So, really transformational work that you guys are doing, so congratulations and really appreciate the broader community. I can't wait to see what comes next from having this entire ecosystem. Now, in a moment, I'll be back to wrap up. This is Dave Vellante, and you're watching theCUBE, the leader in high tech enterprise coverage. >> Welcome. Telegraf is a popular open source data collection agent. Telegraf collects data from hundreds of systems like IoT sensors, cloud deployments, and enterprise applications. It's used by everyone from individual developers and hobbyists, to large corporate teams. The Telegraf project has a very welcoming and active Open Source community. Learn how to get involved by visiting the Telegraf GitHub page. Whether you want to contribute code, improve documentation, participate in testing, or just show what you're doing with Telegraf. We'd love to hear what you're building. >> Thanks for watching Moving the World with InfluxDB, made possible by Influx Data. I hope you learned some things and are inspired to look deeper into where time series databases might fit into your environment. If you're dealing with large and or fast data volumes, and you want to scale cost effectively with the highest performance, and you're analyzing metrics and data over time, times series databases just might be a great fit for you. Try InfluxDB out. You can start with a free cloud account by clicking on the link in the resources below. Remember, all these recordings are going to be available on demand of thecube.net and influxdata.com, so check those out. And poke around Influx Data. They are the folks behind InfluxDB, and one of the leaders in the space. We hope you enjoyed the program, this is Dave Vellante for theCUBE, we'll see you soon. (upbeat music)

>>Time series data is any data that's stamped in time in some way that could be every second, every minute, every five minutes, every hour, every nanosecond, whatever it might be. And typically that data comes from sources in the physical world like devices or sensors, temperature, gauges, batteries, any device really, or things in the virtual world could be software, maybe it's software in the cloud or data and containers or microservices or virtual machines. So all of these items, whether in the physical or virtual world, they're generating a lot of time series data. Now time series data has been around for a long time, and there are many examples in our everyday lives. All you gotta do is punch up any stock, ticker and look at its price over time and graphical form. And that's a simple use case that anyone can relate to and you can build timestamps into a traditional relational database. >>You just add a column to capture time and as well, there are examples of log data being dumped into a data store that can be searched and captured and ingested and visualized. Now, the problem with the latter example that I just gave you is that you gotta hunt and Peck and search and extract what you're looking for. And the problem with the former is that traditional general purpose databases they're designed as sort of a Swiss army knife for any workload. And there are a lot of functions that get in the way and make them inefficient for time series analysis, especially at scale. Like when you think about O T and edge scale, where things are happening super fast, ingestion is coming from many different sources and analysis often needs to be done in real time or near real time. And that's where time series databases come in. >>They're purpose built and can much more efficiently support ingesting metrics at scale, and then comparing data points over time, time series databases can write and read at significantly higher speeds and deal with far more data than traditional database methods. And they're more cost effective instead of throwing processing power at the problem. For example, the underlying architecture and algorithms of time series databases can optimize queries and they can reclaim wasted storage space and reuse it. At scale time, series databases are simply a better fit for the job. Welcome to moving the world with influx DB made possible by influx data. My name is Dave Valante and I'll be your host today. Influx data is the company behind InfluxDB. The open source time series database InfluxDB is designed specifically to handle time series data. As I just explained, we have an exciting program for you today, and we're gonna showcase some really interesting use cases. >>First, we'll kick it off in our Palo Alto studios where my colleague, John furrier will interview Evan Kaplan. Who's the CEO of influx data after John and Evan set the table. John's gonna sit down with Brian Gilmore. He's the director of IOT and emerging tech at influx data. And they're gonna dig into where influx data is gaining traction and why adoption is occurring and, and why it's so robust. And they're gonna have tons of examples and double click into the technology. And then we bring it back here to our east coast studios, where I get to talk to two practitioners, doing amazing things in space with satellites and modern telescopes. These use cases will blow your mind. You don't want to miss it. So thanks for being here today. And with that, let's get started. Take it away. Palo Alto. >>Okay. Today we welcome Evan Kaplan, CEO of influx data, the company behind influx DB. Welcome Evan. Thanks for coming on. >>Hey John, thanks for having me >>Great segment here on the influx DB story. What is the story? Take us through the history. Why time series? What's the story >><laugh> so the history history is actually actually pretty interesting. Um, Paul dicks, my partner in this and our founder, um, super passionate about developers and developer experience. And, um, he had worked on wall street building a number of time series kind of platform trading platforms for trading stocks. And from his point of view, it was always what he would call a yak shave, which means you had to do a ton of work just to start doing work, which means you had to write a bunch of extrinsic routines. You had to write a bunch of application handling on existing relational databases in order to come up with something that was optimized for a trading platform or a time series platform. And he sort of, he just developed this real clear point of view is this is not how developers should work. And so in 2013, he went through why Combinator and he built something for, he made his first commit to open source in flu DB at the end of 2013. And, and he basically, you know, from my point of view, he invented modern time series, which is you start with a purpose-built time series platform to do these kind of workloads. And you get all the benefits of having something right outta the box. So a developer can be totally productive right away. >>And how many people in the company what's the history of employees and stuff? >>Yeah, I think we're, I, you know, I always forget the number, but it's something like 230 or 240 people now. Um, the company, I joined the company in 2016 and I love Paul's vision. And I just had a strong conviction about the relationship between time series and IOT. Cuz if you think about it, what sensors do is they speak time, series, pressure, temperature, volume, humidity, light, they're measuring they're instrumenting something over time. And so I thought that would be super relevant over long term and I've not regretted it. >>Oh no. And it's interesting at that time, go back in the history, you know, the role of databases, well, relational database is the one database to rule the world. And then as clouds started coming in, you starting to see more databases, proliferate types of databases and time series in particular is interesting. Cuz real time has become super valuable from an application standpoint, O T which speaks time series means something it's like time matters >>Time. >>Yeah. And sometimes data's not worth it after the time, sometimes it worth it. And then you get the data lake. So you have this whole new evolution. Is this the momentum? What's the momentum, I guess the question is what's the momentum behind >>You mean what's causing us to grow. So >>Yeah, the time series, why is time series >>And the >>Category momentum? What's the bottom line? >>Well, think about it. You think about it from a broad, broad sort of frame, which is where, what everybody's trying to do is build increasingly intelligent systems, whether it's a self-driving car or a robotic system that does what you want to do or a self-healing software system, everybody wants to build increasing intelligent systems. And so in order to build these increasing intelligent systems, you have to instrument the system well, and you have to instrument it over time, better and better. And so you need a tool, a fundamental tool to drive that instrumentation. And that's become clear to everybody that that instrumentation is all based on time. And so what happened, what happened, what happened what's gonna happen? And so you get to these applications like predictive maintenance or smarter systems. And increasingly you want to do that stuff, not just intelligently, but fast in real time. So millisecond response so that when you're driving a self-driving car and the system realizes that you're about to do something, essentially you wanna be able to act in something that looks like real time, all systems want to do that, want to be more intelligent and they want to be more real time. And so we just happen to, you know, we happen to show up at the right time in the evolution of a >>Market. It's interesting near real time. Isn't good enough when you need real time. >><laugh> yeah, it's not, it's not. And it's like, and it's like, everybody wants, even when you don't need it, ironically, you want it. It's like having the feature for, you know, you buy a new television, you want that one feature, even though you're not gonna use it, you decide that your buying criteria real time is a buying criteria >>For, so you, I mean, what you're saying then is near real time is getting closer to real time as possible, as fast as possible. Right. Okay. So talk about the aspect of data, cuz we're hearing a lot of conversations on the cube in particular around how people are implementing and actually getting better. So iterating on data, but you have to know when it happened to get, know how to fix it. So this is a big part of how we're seeing with people saying, Hey, you know, I wanna make my machine learning algorithms better after the fact I wanna learn from the data. Um, how does that, how do you see that evolving? Is that one of the use cases of sensors as people bring data in off the network, getting better with the data knowing when it happened? >>Well, for sure. So, so for sure, what you're saying is, is, is none of this is non-linear, it's all incremental. And so if you take something, you know, just as an easy example, if you take a self-driving car, what you're doing is you're instrumenting that car to understand where it can perform in the real world in real time. And if you do that, if you run the loop, which is I instrumented, I watch what happens, oh, that's wrong? Oh, I have to correct for that. I correct for that in the software. If you do that for a billion times, you get a self-driving car, but every system moves along that evolution. And so you get the dynamic of, you know, of constantly instrumenting watching the system behave and do it. And this and sets up driving car is one thing. But even in the human genome, if you look at some of our customers, you know, people like, you know, people doing solar arrays, people doing power walls, like all of these systems are getting smarter. >>Well, let's get into that. What are the top applications? What are you seeing for your, with in, with influx DB, the time series, what's the sweet spot for the application use case and some customers give some >>Examples. Yeah. So it's, it's pretty easy to understand on one side of the equation that's the physical side is sensors are sensors are getting cheap. Obviously we know that and they're getting the whole physical world is getting instrumented, your home, your car, the factory floor, your wrist, watch your healthcare, you name it. It's getting instrumented in the physical world. We're watching the physical world in real time. And so there are three or four sweet spots for us, but, but they're all on that side. They're all about IOT. So they're think about consumer IOT projects like Google's nest todo, um, particle sensors, um, even delivery engines like rapid who deliver the Instacart of south America, like anywhere there's a physical location do and that's on the consumer side. And then another exciting space is the industrial side factories are changing dramatically over time. Increasingly moving away from proprietary equipment to develop or driven systems that run operational because what, what has to get smarter when you're building, when you're building a factory is systems all have to get smarter. And then, um, lastly, a lot in the renewables sustainability. So a lot, you know, Tesla, lucid, motors, Cola, motors, um, you know, lots to do with electric cars, solar arrays, windmills, arrays, just anything that's gonna get instrumented that where that instrumentation becomes part of what the purpose >>Is. It's interesting. The convergence of physical and digital is happening with the data IOT. You mentioned, you know, you think of IOT, look at the use cases there, it was proprietary OT systems. Now becoming more IP enabled internet protocol and now edge compute, getting smaller, faster, cheaper AI going to the edge. Now you have all kinds of new capabilities that bring that real time and time series opportunity. Are you seeing IOT going to a new level? What was the, what's the IOT where's the IOT dots connecting to because you know, as these two cultures merge yeah. Operations, basically industrial factory car, they gotta get smarter, intelligent edge is a buzzword, but I mean, it has to be more intelligent. Where's the, where's the action in all this. So the >>Action, really, it really at the core, it's at the developer, right? Because you're looking at these things, it's very hard to get an off the shelf system to do the kinds of physical and software interaction. So the actions really happen at the developer. And so what you're seeing is a movement in the world that, that maybe you and I grew up in with it or OT moving increasingly that developer driven capability. And so all of these IOT systems they're bespoke, they don't come out of the box. And so the developer, the architect, the CTO, they define what's my business. What am I trying to do? Am I trying to sequence a human genome and figure out when these genes express theself or am I trying to figure out when the next heart rate monitor's gonna show up on my apple watch, right? What am I trying to do? What's the system I need to build. And so starting with the developers where all of the good stuff happens here, which is different than it used to be, right. Used to be you'd buy an application or a service or a SA thing for, but with this dynamic, with this integration of systems, it's all about bespoke. It's all about building >>Something. So let's get to the developer real quick, real highlight point here is the data. I mean, I could see a developer saying, okay, I need to have an application for the edge IOT edge or car. I mean, we're gonna have, I mean, Tesla's got applications of the car it's right there. I mean, yes, there's the modern application life cycle now. So take us through how this impacts the developer. Does it impact their C I C D pipeline? Is it cloud native? I mean, where does this all, where does this go to? >>Well, so first of all, you're talking about, there was an internal journey that we had to go through as a company, which, which I think is fascinating for anybody who's interested is we went from primarily a monolithic software that was open sourced to building a cloud native platform, which means we had to move from an agile development environment to a C I C D environment. So to a degree that you are moving your service, whether it's, you know, Tesla monitoring your car and updating your power walls, right. Or whether it's a solar company updating the arrays, right. To degree that that service is cloud. Then increasingly remove from an agile development to a C I C D environment, which you're shipping code to production every day. And so it's not just the developers, all the infrastructure to support the developers to run that service and that sort of stuff. I think that's also gonna happen in a big way >>When your customer base that you have now, and as you see, evolving with infl DB, is it that they're gonna be writing more of the application or relying more on others? I mean, obviously there's an open source component here. So when you bring in kind of old way, new way old way was I got a proprietary, a platform running all this O T stuff and I gotta write, here's an application. That's general purpose. Yeah. I have some flexibility, somewhat brittle, maybe not a lot of robustness to it, but it does its job >>A good way to think about this is versus a new way >>Is >>What so yeah, good way to think about this is what, what's the role of the developer slash architect CTO that chain within a large, within an enterprise or a company. And so, um, the way to think about it is I started my career in the aerospace industry <laugh> and so when you look at what Boeing does to assemble a plane, they build very, very few of the parts. Instead, what they do is they assemble, they buy the wings, they buy the engines, they assemble, actually, they don't buy the wings. It's the one thing they buy the, the material for the w they build the wings, cuz there's a lot of tech in the wings and they end up being assemblers smart assemblers of what ends up being a flying airplane, which is pretty big deal even now. And so what, what happens with software people is they have the ability to pull from, you know, the best of the open source world. So they would pull a time series capability from us. Then they would assemble that with, with potentially some ETL logic from somebody else, or they'd assemble it with, um, a Kafka interface to be able to stream the data in. And so they become very good integrators and assemblers, but they become masters of that bespoke application. And I think that's where it goes, cuz you're not writing native code for everything. >>So they're more flexible. They have faster time to market cuz they're assembling way faster and they get to still maintain their core competency. Okay. Their wings in this case, >>They become increasingly not just coders, but designers and developers. They become broadly builders is what we like to think of it. People who start and build stuff by the way, this is not different than the people just up the road Google have been doing for years or the tier one, Amazon building all their own. >>Well, I think one of the things that's interesting is is that this idea of a systems developing a system architecture, I mean systems, uh, uh, systems have consequences when you make changes. So when you have now cloud data center on premise and edge working together, how does that work across the system? You can't have a wing that doesn't work with the other wing kind of thing. >>That's exactly. But that's where the that's where the, you know, that that Boeing or that airplane building analogy comes in for us. We've really been thoughtful about that because IOT it's critical. So our open source edge has the same API as our cloud native stuff that has enterprise on pre edge. So our multiple products have the same API and they have a relationship with each other. They can talk with each other. So the builder builds it once. And so this is where, when you start thinking about the components that people have to use to build these services is that you wanna make sure, at least that base layer, that database layer, that those components talk to each other. >>So I'll have to ask you if I'm the customer. I put my customer hat on. Okay. Hey, I'm dealing with a lot. >>That mean you have a PO for <laugh> >>A big check. I blank check. If you can answer this question only if the tech, if, if you get the question right, I got all this important operation stuff. I got my factory, I got my self-driving cars. This isn't like trivial stuff. This is my business. How should I be thinking about time series? Because now I have to make these architectural decisions, as you mentioned, and it's gonna impact my application development. So huge decision point for your customers. What should I care about the most? So what's in it for me. Why is time series >>Important? Yeah, that's a great question. So chances are, if you've got a business that was, you know, 20 years old or 25 years old, you were already thinking about time series. You probably didn't call it that you built something on a Oracle or you built something on IBM's DB two, right. And you made it work within your system. Right? And so that's what you started building. So it's already out there. There are, you know, there are probably hundreds of millions of time series applications out there today. But as you start to think about this increasing need for real time, and you start to think about increasing intelligence, you think about optimizing those systems over time. I hate the word, but digital transformation. Then you start with time series. It's a foundational base layer for any system that you're gonna build. There's no system I can think of where time series, shouldn't be the foundational base layer. If you just wanna store your data and just leave it there and then maybe look it up every five years. That's fine. That's not time. Series time series is when you're building a smarter, more intelligent, more real time system. And the developers now know that. And so the more they play a role in building these systems, the more obvious it becomes. >>And since I have a PO for you and a big check, yeah. What is, what's the value to me as I, when I implement this, what's the end state, what's it look like when it's up and running? What's the value proposition for me. What's an >>So, so when it's up and running, you're able to handle the queries, the writing of the data, the down sampling of the data, they're transforming it in near real time. So that the other dependencies that a system that gets for adjusting a solar array or trading energy off of a power wall or some sort of human genome, those systems work better. So time series is foundational. It's not like it's, you know, it's not like it's doing every action that's above, but it's foundational to build a really compelling, intelligent system. I think that's what developers and archs are seeing now. >>Bottom line, final word. What's in it for the customer. What's what, what's your, um, what's your statement to the customer? What would you say to someone looking to do something in time series on edge? >>Yeah. So, so it's pretty clear to clear to us that if you're building, if you view yourself as being in the build business of building systems that you want 'em to be increasingly intelligent, self-healing autonomous. You want 'em to operate in real time that you start from time series. But I also wanna say what's in it for us influx what's in it for us is people are doing some amazing stuff. You know, I highlighted some of the energy stuff, some of the human genome, some of the healthcare it's hard not to be proud or feel like, wow. Yeah. Somehow I've been lucky. I've arrived at the right time, in the right place with the right people to be able to deliver on that. That's that's also exciting on our side of the equation. >>Yeah. It's critical infrastructure, critical, critical operations. >>Yeah. >>Yeah. Great stuff, Evan. Thanks for coming on. Appreciate this segment. All right. In a moment, Brian Gilmore director of IOT and emerging technology that influx day will join me. You're watching the cube leader in tech coverage. Thanks for watching >>Time series data from sensors systems and applications is a key source in driving automation and prediction in technologies around the world. But managing the massive amount of timestamp data generated these days is overwhelming, especially at scale. That's why influx data developed influx DB, a time series data platform that collects stores and analyzes data influx DB empowers developers to extract valuable insights and turn them into action by building transformative IOT analytics and cloud native applications, purpose built and optimized to handle the scale and velocity of timestamped data. InfluxDB puts the power in your hands with developer tools that make it easy to get started quickly with less code InfluxDB is more than a database. It's a robust developer platform with integrated tooling. That's written in the languages you love. So you can innovate faster, run in flex DB anywhere you want by choosing the provider and region that best fits your needs across AWS, Microsoft Azure and Google cloud flex DB is fast and automatically scalable. So you can spend time delivering value to customers, not managing clusters, take control of your time series data. So you can focus on the features and functionalities that give your applications a competitive edge. Get started for free with influx DB, visit influx data.com/cloud to learn more. >>Okay. Now we're joined by Brian Gilmore director of IOT and emerging technologies at influx data. Welcome to the show. >>Thank you, John. Great to be here. >>We just spent some time with Evan going through the company and the value proposition, um, with influx DV, what's the momentum, where do you see this coming from? What's the value coming out of this? >>Well, I think it, we're sort of hitting a point where the technology is, is like the adoption of it is becoming mainstream. We're seeing it in all sorts of organizations, everybody from like the most well funded sort of advanced big technology companies to the smaller academics, the startups and the managing of that sort of data that emits from that technology is time series and us being able to give them a, a platform, a tool that's super easy to use, easy to start. And then of course will grow with them is, is been key to us. Sort of, you know, riding along with them is they're successful. >>Evan was mentioning that time series has been on everyone's radar and that's in the OT business for years. Now, you go back since 20 13, 14, even like five years ago that convergence of physical and digital coming together, IP enabled edge. Yeah. Edge has always been kind of hyped up, but why now? Why, why is the edge so hot right now from an adoption standpoint? Is it because it's just evolution, the tech getting better? >>I think it's, it's, it's twofold. I think that, you know, there was, I would think for some people, everybody was so focused on cloud over the last probably 10 years. Mm-hmm <affirmative> that they forgot about the compute that was available at the edge. And I think, you know, those, especially in the OT and on the factory floor who weren't able to take Avan full advantage of cloud through their applications, you know, still needed to be able to leverage that compute at the edge. I think the big thing that we're seeing now, which is interesting is, is that there's like a hybrid nature to all of these applications where there's definitely some data that's generated on the edge. There's definitely done some data that's generated in the cloud. And it's the ability for a developer to sort of like tie those two systems together and work with that data in a very unified uniform way. Um, that's giving them the opportunity to build solutions that, you know, really deliver value to whatever it is they're trying to do, whether it's, you know, the, the out reaches of outer space or whether it's optimizing the factory floor. >>Yeah. I think, I think one of the things you also mentions genome too, dig big data is coming to the real world. And I think I, OT has been kind of like this thing for OT and, and in some use case, but now with the, with the cloud, all companies have an edge strategy now. So yeah, what's the secret sauce because now this is hot, hot product for the whole world and not just industrial, but all businesses. What's the secret sauce. >>Well, I mean, I think part of it is just that the technology is becoming more capable and that's especially on the hardware side, right? I mean, like technology compute is getting smaller and smaller and smaller. And we find that by supporting all the way down to the edge, even to the micro controller layer with our, um, you know, our client libraries and then working hard to make our applications, especially the database as small as possible so that it can be located as close to sort of the point of origin of that data in the edge as possible is, is, is fantastic. Now you can take that. You can run that locally. You can do your local decision making. You can use influx DB as sort of an input to automation control the autonomy that people are trying to drive at the edge. But when you link it up with everything that's in the cloud, that's when you get all of the sort of cloud scale capabilities of parallelized, AI and machine learning and all of that. >>So what's interesting is the open source success has been something that we've talked about a lot in the cube about how people are leveraging that you guys have users in the enterprise users that IOT market mm-hmm <affirmative>, but you got developers now. Yeah. Kind of together brought that up. How do you see that emerging? How do developers engage? What are some of the things you're seeing that developers are really getting into with InfluxDB >>What's? Yeah. Well, I mean, I think there are the developers who are building companies, right? And these are the startups and the folks that we love to work with who are building new, you know, new services, new products, things like that. And, you know, especially on the consumer side of IOT, there's a lot of that, just those developers. But I think we, you gotta pay attention to those enterprise developers as well, right? There are tons of people with the, the title of engineer in, in your regular enterprise organizations. And they're there for systems integration. They're there for, you know, looking at what they would build versus what they would buy. And a lot of them come from, you know, a strong, open source background and they, they know the communities, they know the top platforms in those spaces and, and, you know, they're excited to be able to adopt and use, you know, to optimize inside the business as compared to just building a brand new one. >>You know, it's interesting too, when Evan and I were talking about open source versus closed OT systems, mm-hmm <affirmative> so how do you support the backwards compatibility of older systems while maintaining open dozens of data formats out there? Bunch of standards, protocols, new things are emerging. Everyone wants to have a control plane. Everyone wants to leverage the value of data. How do you guys keep track of it all? What do you guys support? >>Yeah, well, I mean, I think either through direct connection, like we have a product called Telegraph, it's unbelievable. It's open source, it's an edge agent. You can run it as close to the edge as you'd like, it speaks dozens of different protocols in its own, right? A couple of which MQTT B, C U a are very, very, um, applicable to these T use cases. But then we also, because we are sort of not only open source, but open in terms of our ability to collect data, we have a lot of partners who have built really great integrations from their own middleware, into influx DB. These are companies like ke wear and high bite who are really experts in those downstream industrial protocols. I mean, that's a business, not everybody wants to be in. It requires some very specialized, very hard work and a lot of support, um, you know, and so by making those connections and building those ecosystems, we get the best of both worlds. The customers can use the platforms they need up to the point where they would be putting into our database. >>What's some of customer testimonies that they, that share with you. Can you share some anecdotal kind of like, wow, that's the best thing I've ever used. This really changed my business, or this is a great tech that's helped me in these other areas. What are some of the, um, soundbites you hear from customers when they're successful? >>Yeah. I mean, I think it ranges. You've got customers who are, you know, just finally being able to do the monitoring of assets, you know, sort of at the edge in the field, we have a customer who's who's has these tunnel boring machines that go deep into the earth to like drill tunnels for, for, you know, cars and, and, you know, trains and things like that. You know, they are just excited to be able to stick a database onto those tunnel, boring machines, send them into the depths of the earth and know that when they come out, all of that telemetry at a very high frequency has been like safely stored. And then it can just very quickly and instantly connect up to their, you know, centralized database. So like just having that visibility is brand new to them. And that's super important. On the other hand, we have customers who are way far beyond the monitoring use case, where they're actually using the historical records in the time series database to, um, like I think Evan mentioned like forecast things. So for predictive maintenance, being able to pull in the telemetry from the machines, but then also all of that external enrichment data, the metadata, the temperatures, the pressure is who is operating the machine, those types of things, and being able to easily integrate with platforms like Jupyter notebooks or, you know, all of those scientific computing and machine learning libraries to be able to build the models, train the models, and then they can send that information back down to InfluxDB to apply it and detect those anomalies, which >>Are, I think that's gonna be an, an area. I personally think that's a hot area because I think if you look at AI right now, yeah. It's all about training the machine learning albums after the fact. So time series becomes hugely important. Yeah. Cause now you're thinking, okay, the data matters post time. Yeah. First time. And then it gets updated the new time. Yeah. So it's like constant data cleansing data iteration, data programming. We're starting to see this new use case emerge in the data field. >>Yep. Yeah. I mean, I think you agree. Yeah, of course. Yeah. The, the ability to sort of handle those pipelines of data smartly, um, intelligently, and then to be able to do all of the things you need to do with that data in stream, um, before it hits your sort of central repository. And, and we make that really easy for customers like Telegraph, not only does it have sort of the inputs to connect up to all of those protocols and the ability to capture and connect up to the, to the partner data. But also it has a whole bunch of capabilities around being able to process that data, enrich it, reform at it, route it, do whatever you need. So at that point you're basically able to, you're playing your data in exactly the way you would wanna do it. You're routing it to different, you know, destinations and, and it's, it's, it's not something that really has been in the realm of possibility until this point. Yeah. Yeah. >>And when Evan was on it's great. He was a CEO. So he sees the big picture with customers. He was, he kinda put the package together that said, Hey, we got a system. We got customers, people are wanting to leverage our product. What's your PO they're sell. He's selling too as well. So you have that whole CEO perspective, but he brought up this notion that there's multiple personas involved in kind of the influx DB system architect. You got developers and users. Can you talk about that? Reality as customers start to commercialize and operationalize this from a commercial standpoint, you got a relationship to the cloud. Yep. The edge is there. Yep. The edge is getting super important, but cloud brings a lot of scale to the table. So what is the relationship to the cloud? Can you share your thoughts on edge and its relationship to the cloud? >>Yeah. I mean, I think edge, you know, edges, you can think of it really as like the local information, right? So it's, it's generally like compartmentalized to a point of like, you know, a single asset or a single factory align, whatever. Um, but what people do who wanna pro they wanna be able to make the decisions there at the edge locally, um, quickly minus the latency of sort of taking that large volume of data, shipping it to the cloud and doing something with it there. So we allow them to do exactly that. Then what they can do is they can actually downsample that data or they can, you know, detect like the really important metrics or the anomalies. And then they can ship that to a central database in the cloud where they can do all sorts of really interesting things with it. Like you can get that centralized view of all of your global assets. You can start to compare asset to asset, and then you can do those things like we talked about, whereas you can do predictive types of analytics or, you know, larger scale anomaly detections. >>So in this model you have a lot of commercial operations, industrial equipment. Yep. The physical plant, physical business with virtual data cloud all coming together. What's the future for InfluxDB from a tech standpoint. Cause you got open. Yep. There's an ecosystem there. Yep. You have customers who want operational reliability for sure. I mean, so you got organic <laugh> >>Yeah. Yeah. I mean, I think, you know, again, we got iPhones when everybody's waiting for flying cars. Right. So I don't know. We can like absolutely perfectly predict what's coming, but I think there are some givens and I think those givens are gonna be that the world is only gonna become more hybrid. Right. And then, you know, so we are going to have much more widely distributed, you know, situations where you have data being generated in the cloud, you have data gen being generated at the edge and then there's gonna be data generated sort sort of at all points in between like physical locations as well as things that are, that are very virtual. And I think, you know, we are, we're building some technology right now. That's going to allow, um, the concept of a database to be much more fluid and flexible, sort of more aligned with what a file would be like. >>And so being able to move data to the compute for analysis or move the compute to the data for analysis, those are the types of, of solutions that we'll be bringing to the customers sort of over the next little bit. Um, but I also think we have to start thinking about like what happens when the edge is actually off the planet. Right. I mean, we've got customers, you're gonna talk to two of them, uh, in the panel who are actually working with data that comes from like outside the earth, like, you know, either in low earth orbit or you know, all the way sort of on the other side of the universe. Yeah. And, and to be able to process data like that and to do so in a way it's it's we gotta, we gotta build the fundamentals for that right now on the factory floor and in the mines and in the tunnels. Um, so that we'll be ready for that one. >>I think you bring up a good point there because one of the things that's common in the industry right now, people are talking about, this is kind of new thinking is hyper scale's always been built up full stack developers, even the old OT world, Evan was pointing out that they built everything right. And the world's going to more assembly with core competency and IP and also property being the core of their apple. So faster assembly and building, but also integration. You got all this new stuff happening. Yeah. And that's to separate out the data complexity from the app. Yes. So space genome. Yep. Driving cars throws off massive data. >>It >>Does. So is Tesla, uh, is the car the same as the data layer? >>I mean the, yeah, it's, it's certainly a point of origin. I think the thing that we wanna do is we wanna let the developers work on the world, changing problems, the things that they're trying to solve, whether it's, you know, energy or, you know, any of the other health or, you know, other challenges that these teams are, are building against. And we'll worry about that time series data and the underlying data platform so that they don't have to. Right. I mean, I think you talked about it, uh, you know, for them just to be able to adopt the platform quickly, integrate it with their data sources and the other pieces of their applications. It's going to allow them to bring much faster time to market on these products. It's gonna allow them to be more iterative. They're gonna be able to do more sort of testing and things like that. And ultimately it will, it'll accelerate the adoption and the creation of >>Technology. You mentioned earlier in, in our talk about unification of data. Yeah. How about APIs? Cuz developers love APIs in the cloud unifying APIs. How do you view view that? >>Yeah, I mean, we are APIs, that's the product itself. Like everything, people like to think of it as sort of having this nice front end, but the front end is B built on our public APIs. Um, you know, and it, it allows the developer to build all of those hooks for not only data creation, but then data processing, data analytics, and then, you know, sort of data extraction to bring it to other platforms or other applications, microservices, whatever it might be. So, I mean, it is a world of APIs right now and you know, we, we bring a very sort of useful set of them for managing the time series data. These guys are all challenged with. It's >>Interesting. You and I were talking before we came on camera about how, um, data is, feels gonna have this kind of SRE role that DevOps had site reliability engineers, which manages a bunch of servers. There's so much data out there now. Yeah. >>Yeah. It's like reigning data for sure. And I think like that ability to be like one of the best jobs on the planet is gonna be to be able to like, sort of be that data Wrangler to be able to understand like what the data sources are, what the data formats are, how to be able to efficiently move that data from point a to point B and you know, to process it correctly so that the end users of that data aren't doing any of that sort of hard upfront preparation collection storage's >>Work. Yeah. That's data as code. I mean, data engineering is it is becoming a new discipline for sure. And, and the democratization is the benefit. Yeah. To everyone, data science get easier. I mean data science, but they wanna make it easy. Right. <laugh> yeah. They wanna do the analysis, >>Right? Yeah. I mean, I think, you know, it, it's a really good point. I think like we try to give our users as many ways as there could be possible to get data in and get data out. We sort of think about it as meeting them where they are. Right. So like we build, we have the sort of client libraries that allow them to just port to us, you know, directly from the applications and the languages that they're writing, but then they can also pull it out. And at that point nobody's gonna know the users, the end consumers of that data, better than those people who are building those applications. And so they're building these user interfaces, which are making all of that data accessible for, you know, their end users inside their organization. >>Well, Brian, great segment, great insight. Thanks for sharing all, all the complexities and, and IOT that you guys helped take away with the APIs and, and assembly and, and all the system architectures that are changing edge is real cloud is real. Yeah, absolutely. Mainstream enterprises. And you got developer attraction too, so congratulations. >>Yeah. It's >>Great. Well, thank any, any last word you wanna share >>Deal with? No, just, I mean, please, you know, if you're, if you're gonna, if you're gonna check out influx TV, download it, try out the open source contribute if you can. That's a, that's a huge thing. It's part of being the open source community. Um, you know, but definitely just, just use it. I think when once people use it, they try it out. They'll understand very, >>Very quickly. So open source with developers, enterprise and edge coming together all together. You're gonna hear more about that in the next segment, too. Right. Thanks for coming on. Okay. Thanks. When we return, Dave LAN will lead a panel on edge and data influx DB. You're watching the cube, the leader in high tech enterprise coverage. >>Why the startup, we move really fast. We find that in flex DB can move as fast as us. It's just a great group, very collaborative, very interested in manufacturing. And we see a bright future in working with influence. My name is Aaron Seley. I'm the CTO at HBI. Highlight's one of the first companies to focus on manufacturing data and apply the concepts of data ops, treat that as an asset to deliver to the it system, to enable applications like overall equipment effectiveness that can help the factory produce better, smarter, faster time series data. And manufacturing's really important. If you take a piece of equipment, you have the temperature pressure at the moment that you can look at to kind of see the state of what's going on. So without that context and understanding you can't do what manufacturers ultimately want to do, which is predict the future. >>Influx DB represents kind of a new way to storm time series data with some more advanced technology and more importantly, more open technologies. The other thing that influx does really well is once the data's influx, it's very easy to get out, right? They have a modern rest API and other ways to access the data. That would be much more difficult to do integrations with classic historians highlight can serve to model data, aggregate data on the shop floor from a multitude of sources, whether that be P C U a servers, manufacturing execution systems, E R P et cetera, and then push that seamlessly into influx to then be able to run calculations. Manufacturing is changing this industrial 4.0, and what we're seeing is influx being part of that equation. Being used to store data off the unified name space, we recommend InfluxDB all the time to customers that are exploring a new way to share data manufacturing called the unified name space who have open questions around how do I share this new data that's coming through my UNS or my QTT broker? How do I store this and be able to query it over time? And we often point to influx as a solution for that is a great brand. It's a great group of people and it's a great technology. >>Okay. We're now going to go into the customer panel and we'd like to welcome Angelo Fasi. Who's a software engineer at the Vera C Ruben observatory in Caleb McLaughlin whose senior spacecraft operations software engineer at loft orbital guys. Thanks for joining us. You don't wanna miss folks this interview, Caleb, let's start with you. You work for an extremely cool company. You're launching satellites into space. I mean, there, of course doing that is, is highly complex and not a cheap endeavor. Tell us about loft Orbi and what you guys do to attack that problem. >>Yeah, absolutely. And, uh, thanks for having me here by the way. Uh, so loft orbital is a, uh, company. That's a series B startup now, uh, who and our mission basically is to provide, uh, rapid access to space for all kinds of customers. Uh, historically if you want to fly something in space, do something in space, it's extremely expensive. You need to book a launch, build a bus, hire a team to operate it, you know, have a big software teams, uh, and then eventually worry about, you know, a bunch like just a lot of very specialized engineering. And what we're trying to do is change that from a super specialized problem that has an extremely high barrier of access to a infrastructure problem. So that it's almost as simple as, you know, deploying a VM in, uh, AWS or GCP is getting your, uh, programs, your mission deployed on orbit, uh, with access to, you know, different sensors, uh, cameras, radios, stuff like that. >>So that's, that's kind of our mission. And just to give a really brief example of the kind of customer that we can serve. Uh, there's a really cool company called, uh, totem labs who is working on building, uh, IOT cons, an IOT constellation for in of things, basically being able to get telemetry from all over the world. They're the first company to demonstrate indoor T, which means you have this little modem inside a container container that you, that you track from anywhere in the world as it's going across the ocean. Um, so they're, it's really little and they've been able to stay a small startup that's focused on their product, which is the, uh, that super crazy complicated, cool radio while we handle the whole space segment for them, which just, you know, before loft was really impossible. So that's, our mission is, uh, providing space infrastructure as a service. We are kind of groundbreaking in this area and we're serving, you know, a huge variety of customers with all kinds of different missions, um, and obviously generating a ton of data in space, uh, that we've gotta handle. Yeah. >>So amazing Caleb, what you guys do, I, now I know you were lured to the skies very early in your career, but how did you kinda land on this business? >>Yeah, so, you know, I've, I guess just a little bit about me for some people, you know, they don't necessarily know what they wanna do like early in their life. For me, I was five years old and I knew, you know, I want to be in the space industry. So, you know, I started in the air force, but have, uh, stayed in the space industry, my whole career and been a part of, uh, this is the fifth space startup that I've been a part of actually. So, you know, I've, I've, uh, kind of started out in satellites, did spent some time in working in, uh, the launch industry on rockets. Then, uh, now I'm here back in satellites and you know, honestly, this is the most exciting of the difference based startups. That I've been a part of >>Super interesting. Okay. Angelo, let's, let's talk about the Ruben observatory, ver C Ruben, famous woman scientist, you know, galaxy guru. Now you guys the observatory, you're up way up high. You're gonna get a good look at the Southern sky. Now I know COVID slowed you guys down a bit, but no doubt. You continued to code away on the software. I know you're getting close. You gotta be super excited. Give us the update on, on the observatory and your role. >>All right. So yeah, Rubin is a state of the art observatory that, uh, is in construction on a remote mountain in Chile. And, um, with Rubin, we conduct the, uh, large survey of space and time we are going to observe the sky with, uh, eight meter optical telescope and take, uh, a thousand pictures every night with a 3.2 gig up peaks of camera. And we are going to do that for 10 years, which is the duration of the survey. >>Yeah. Amazing project. Now you, you were a doctor of philosophy, so you probably spent some time thinking about what's out there and then you went out to earn a PhD in astronomy, in astrophysics. So this is something that you've been working on for the better part of your career, isn't it? >>Yeah, that's that's right. Uh, about 15 years, um, I studied physics in college, then I, um, got a PhD in astronomy and, uh, I worked for about five years in another project. Um, the dark energy survey before joining rubing in 2015. >>Yeah. Impressive. So it seems like you both, you know, your organizations are looking at space from two different angles. One thing you guys both have in common of course is, is, is software. And you both use InfluxDB as part of your, your data infrastructure. How did you discover influx DB get into it? How do you use the platform? Maybe Caleb, you could start. >>Uh, yeah, absolutely. So the first company that I extensively used, uh, influx DBN was a launch startup called, uh, Astra. And we were in the process of, uh, designing our, you know, our first generation rocket there and testing the engines, pumps, everything that goes into a rocket. Uh, and when I joined the company, our data story was not, uh, very mature. We were collecting a bunch of data in LabVIEW and engineers were taking that over to MATLAB to process it. Um, and at first there, you know, that's the way that a lot of engineers and scientists are used to working. Um, and at first that was, uh, like people weren't entirely sure that that was a, um, that that needed to change, but it's something the nice thing about InfluxDB is that, you know, it's so easy to deploy. So as the, our software engineering team was able to get it deployed and, you know, up and running very quickly and then quickly also backport all of the data that we collected thus far into influx and what, uh, was amazing to see. >>And as kind of the, the super cool moment with influx is, um, when we hooked that up to Grafana Grafana as the visualization platform we used with influx, cuz it works really well with it. Uh, there was like this aha moment of our engineers who are used to this post process kind of method for dealing with their data where they could just almost instantly easily discover data that they hadn't been able to see before and take the manual processes that they would run after a test and just throw those all in influx and have live data as tests were coming. And, you know, I saw them implementing like crazy rocket equation type stuff in influx, and it just was totally game changing for how we tested. >>So Angelo, I was explaining in my open, you know, you could, you could add a column in a traditional RDBMS and do time series, but with the volume of data that you're talking about, and the example of the Caleb just gave you, I mean, you have to have a purpose built time series database, where did you first learn about influx DB? >>Yeah, correct. So I work with the data management team, uh, and my first project was the record metrics that measured the performance of our software, uh, the software that we used to process the data. So I started implementing that in a relational database. Um, but then I realized that in fact, I was dealing with time series data and I should really use a solution built for that. And then I started looking at time series databases and I found influx B. And that was, uh, back in 2018. The another use for influx DB that I'm also interested is the visits database. Um, if you think about the observations we are moving the telescope all the time in pointing to specific directions, uh, in the Skype and taking pictures every 30 seconds. So that itself is a time series. And every point in that time series, uh, we call a visit. So we want to record the metadata about those visits and flex to, uh, that time here is going to be 10 years long, um, with about, uh, 1000 points every night. It's actually not too much data compared to other, other problems. It's, uh, really just a different, uh, time scale. >>The telescope at the Ruben observatory is like pun intended, I guess the star of the show. And I, I believe I read that it's gonna be the first of the next gen telescopes to come online. It's got this massive field of view, like three orders of magnitude times the Hub's widest camera view, which is amazing, right? That's like 40 moons in, in an image amazingly fast as well. What else can you tell us about the telescope? >>Um, this telescope, it has to move really fast and it also has to carry, uh, the primary mirror, which is an eight meter piece of glass. It's very heavy and it has to carry a camera, which has about the size of a small car. And this whole structure weighs about 300 tons for that to work. Uh, the telescope needs to be, uh, very compact and stiff. Uh, and one thing that's amazing about it's design is that the telescope, um, is 300 tons structure. It sits on a tiny film of oil, which has the diameter of, uh, human hair. And that makes an almost zero friction interface. In fact, a few people can move these enormous structure with only their hands. Uh, as you said, uh, another aspect that makes this telescope unique is the optical design. It's a wide field telescope. So each image has, uh, in diameter the size of about seven full moons. And, uh, with that, we can map the entire sky in only, uh, three days. And of course doing operations everything's, uh, controlled by software and it is automatic. Um there's a very complex piece of software, uh, called the scheduler, which is responsible for moving the telescope, um, and the camera, which is, uh, recording 15 terabytes of data every night. >>Hmm. And, and, and Angela, all this data lands in influx DB. Correct. And what are you doing with, with all that data? >>Yeah, actually not. Um, so we are using flex DB to record engineering data and metadata about the observations like telemetry events and commands from the telescope. That's a much smaller data set compared to the images, but it is still challenging because, uh, you, you have some high frequency data, uh, that the system needs to keep up and we need to, to start this data and have it around for the lifetime of the price. Mm, >>Got it. Thank you. Okay, Caleb, let's bring you back in and can tell us more about the, you got these dishwasher size satellites. You're kind of using a multi-tenant model. I think it's genius, but, but tell us about the satellites themselves. >>Yeah, absolutely. So, uh, we have in space, some satellites already that as you said, are like dishwasher, mini fridge kind of size. Um, and we're working on a bunch more that are, you know, a variety of sizes from shoebox to, I guess, a few times larger than what we have today. Uh, and it is, we do shoot to have effectively something like a multi-tenant model where, uh, we will buy a bus off the shelf. The bus is, uh, what you can kind of think of as the core piece of the satellite, almost like a motherboard or something where it's providing the power. It has the solar panels, it has some radios attached to it. Uh, it handles the attitude control, basically steers the spacecraft in orbit. And then we build also in house, what we call our payload hub, which is, has all, any customer payloads attached and our own kind of edge processing sort of capabilities built into it. >>And, uh, so we integrate that. We launch it, uh, and those things, because they're in lower orbit, they're orbiting the earth every 90 minutes. That's, you know, seven kilometers per second, which is several times faster than a speeding bullet. So we've got, we have, uh, one of the unique challenges of operating spacecraft and lower orbit is that generally you can't talk to them all the time. So we're managing these things through very brief windows of time, uh, where we get to talk to them through our ground sites, either in Antarctica or, you know, in the north pole region. >>Talk more about how you use influx DB to make sense of this data through all this tech that you're launching into space. >>We basically previously we started off when I joined the company, storing all of that as Angelo did in a regular relational database. And we found that it was, uh, so slow in the size of our data would balloon over the course of a couple days to the point where we weren't able to even store all of the data that we were getting. Uh, so we migrated to influx DB to store our time series telemetry from the spacecraft. So, you know, that's things like, uh, power level voltage, um, currents counts, whatever, whatever metadata we need to monitor about the spacecraft. We now store that in, uh, in influx DB. Uh, and that has, you know, now we can actually easily store the entire volume of data for the mission life so far without having to worry about, you know, the size bloating to an unmanageable amount. >>And we can also seamlessly query, uh, large chunks of data. Like if I need to see, you know, for example, as an operator, I might wanna see how my, uh, battery state of charge is evolving over the course of the year. I can have a plot and an influx that loads that in a fraction of a second for a year's worth of data, because it does, you know, intelligent, um, I can intelligently group the data by, uh, sliding time interval. Uh, so, you know, it's been extremely powerful for us to access the data and, you know, as time has gone on, we've gradually migrated more and more of our operating data into influx. >>You know, let's, let's talk a little bit, uh, uh, but we throw this term around a lot of, you know, data driven, a lot of companies say, oh, yes, we're data driven, but you guys really are. I mean, you' got data at the core, Caleb, what does that, what does that mean to you? >>Yeah, so, you know, I think the, and the clearest example of when I saw this be like totally game changing is what I mentioned before at Astro where our engineer's feedback loop went from, you know, a lot of kind of slow researching, digging into the data to like an instant instantaneous, almost seeing the data, making decisions based on it immediately, rather than having to wait for some processing. And that's something that I've also seen echoed in my current role. Um, but to give another practical example, uh, as I said, we have a huge amount of data that comes down every orbit, and we need to be able to ingest all of that data almost instantaneously and provide it to the operator. And near real time, you know, about a second worth of latency is all that's acceptable for us to react to, to see what is coming down from the spacecraft and building that pipeline is challenging from a software engineering standpoint. >>Um, our primary language is Python, which isn't necessarily that fast. So what we've done is started, you know, in the, in the goal of being data driven is publish metrics on individual, uh, how individual pieces of our data processing pipeline are performing into influx as well. And we do that in production as well as in dev. Uh, so we have kind of a production monitoring, uh, flow. And what that has done is allow us to make intelligent decisions on our software development roadmap, where it makes the most sense for us to, uh, focus our development efforts in terms of improving our software efficiency. Uh, just because we have that visibility into where the real problems are. Um, it's sometimes we've found ourselves before we started doing this kind of chasing rabbits that weren't necessarily the real root cause of issues that we were seeing. Uh, but now, now that we're being a bit more data driven, there we are being much more effective in where we're spending our resources and our time, which is especially critical to us as we scale to, from supporting a couple satellites, to supporting many, many satellites at >>Once. Yeah. Coach. So you reduced those dead ends, maybe Angela, you could talk about what, what sort of data driven means to, to you and your teams? >>I would say that, um, having, uh, real time visibility, uh, to the telemetry data and, and metrics is, is, is crucial for us. We, we need, we need to make sure that the image that we collect with the telescope, uh, have good quality and, um, that they are within the specifications, uh, to meet our science goals. And so if they are not, uh, we want to know that as soon as possible and then, uh, start fixing problems. >>Caleb, what are your sort of event, you know, intervals like? >>So I would say that, you know, as of today on the spacecraft, the event, the, the level of timing that we deal with probably tops out at about, uh, 20 Hertz, 20 measurements per second on, uh, things like our, uh, gyroscopes, but the, you know, I think the, the core point here of the ability to have high precision data is extremely important for these kinds of scientific applications. And I'll give an example, uh, from when I worked at, on the rocket at Astra there, our baseline data rate that we would ingest data during a test is, uh, 500 Hertz. So 500 samples per second. And in some cases we would actually, uh, need to ingest much higher rate data, even up to like 1.5 kilohertz. So, uh, extremely, extremely high precision, uh, data there where timing really matters a lot. And, uh, you know, I can, one of the really powerful things about influx is the fact that it can handle this. >>That's one of the reasons we chose it, uh, because there's times when we're looking at the results of a firing where you're zooming in, you know, I talked earlier about how on my current job, we often zoom out to look, look at a year's worth of data. You're zooming in to where your screen is preoccupied by a tiny fraction of a second. And you need to see same thing as Angela just said, not just the actual telemetry, which is coming in at a high rate, but the events that are coming out of our controllers. So that can be something like, Hey, I opened this valve at exactly this time and that goes, we wanna have that at, you know, micro or even nanosecond precision so that we know, okay, we saw a spike in chamber pressure at, you know, at this exact moment, was that before or after this valve open, those kind of, uh, that kind of visibility is critical in these kind of scientific, uh, applications and absolutely game changing to be able to see that in, uh, near real time and, uh, with a really easy way for engineers to be able to visualize this data themselves without having to wait for, uh, software engineers to go build it for them. >>Can the scientists do self-serve or are you, do you have to design and build all the analytics and, and queries for your >>Scientists? Well, I think that's, that's absolutely from, from my perspective, that's absolutely one of the best things about influx and what I've seen be game changing is that, uh, generally I'd say anyone can learn to use influx. Um, and honestly, most of our users might not even know they're using influx, um, because what this, the interface that we expose to them is Grafana, which is, um, a generic graphing, uh, open source graphing library that is very similar to influx own chronograph. Sure. And what it does is, uh, let it provides this, uh, almost it's a very intuitive UI for building your queries. So you choose a measurement and it shows a dropdown of available measurements. And then you choose a particular, the particular field you wanna look at. And again, that's a dropdown, so it's really easy for our users to discover. And there's kind of point and click options for doing math aggregations. You can even do like perfect kind of predictions all within Grafana, the Grafana user interface, which is really just a wrapper around the APIs and functionality of the influx provides putting >>Data in the hands of those, you know, who have the context of domain experts is, is key. Angela, is it the same situation for you? Is it self serve? >>Yeah, correct. Uh, as I mentioned before, um, we have the astronomers making their own dashboards because they know what exactly what they, they need to, to visualize. Yeah. I mean, it's all about using the right tool for the job. I think, uh, for us, when I joined the company, we weren't using influx DB and we, we were dealing with serious issues of the database growing to an incredible size extremely quickly, and being unable to like even querying short periods of data was taking on the order of seconds, which is just not possible for operations >>Guys. This has been really formative it's, it's pretty exciting to see how the edge is mountaintops, lower orbits to be space is the ultimate edge. Isn't it. I wonder if you could answer two questions to, to wrap here, you know, what comes next for you guys? Uh, and is there something that you're really excited about that, that you're working on Caleb, maybe you could go first and an Angela, you can bring us home. >>Uh, basically what's next for loft. Orbital is more, more satellites, a greater push towards infrastructure and really making, you know, our mission is to make space simple for our customers and for everyone. And we're scaling the company like crazy now, uh, making that happen, it's extremely exciting and extremely exciting time to be in this company and to be in this industry as a whole, because there are so many interesting applications out there. So many cool ways of leveraging space that, uh, people are taking advantage of. And with, uh, companies like SpaceX and the now rapidly lowering cost, cost of launch, it's just a really exciting place to be. And we're launching more satellites. We are scaling up for some constellations and our ground system has to be improved to match. So there's a lot of, uh, improvements that we're working on to really scale up our control software, to be best in class and, uh, make it capable of handling such a large workload. So >>You guys hiring >><laugh>, we are absolutely hiring. So, uh, I would in we're we need, we have PE positions all over the company. So, uh, we need software engineers. We need people who do more aerospace, specific stuff. So, uh, absolutely. I'd encourage anyone to check out the loft orbital website, if there's, if this is at all interesting. >>All right. Angela, bring us home. >>Yeah. So what's next for us is really, uh, getting this, um, telescope working and collecting data. And when that's happen is going to be just, um, the Lu of data coming out of this camera and handling all, uh, that data is going to be really challenging. Uh, yeah. I wanna wanna be here for that. <laugh> I'm looking forward, uh, like for next year we have like an important milestone, which is our, um, commissioning camera, which is a simplified version of the, of the full camera it's going to be on sky. And so yeah, most of the system has to be working by them. >>Nice. All right, guys, you know, with that, we're gonna end it. Thank you so much, really fascinating, and thanks to influx DB for making this possible, really groundbreaking stuff, enabling value creation at the edge, you know, in the cloud and of course, beyond at the space. So really transformational work that you guys are doing. So congratulations and really appreciate the broader community. I can't wait to see what comes next from having this entire ecosystem. Now, in a moment, I'll be back to wrap up. This is Dave ante, and you're watching the cube, the leader in high tech enterprise coverage. >>Welcome Telegraph is a popular open source data collection. Agent Telegraph collects data from hundreds of systems like IOT sensors, cloud deployments, and enterprise applications. It's used by everyone from individual developers and hobbyists to large corporate teams. The Telegraph project has a very welcoming and active open source community. Learn how to get involved by visiting the Telegraph GitHub page, whether you want to contribute code, improve documentation, participate in testing, or just show what you're doing with Telegraph. We'd love to hear what you're building. >>Thanks for watching. Moving the world with influx DB made possible by influx data. I hope you learn some things and are inspired to look deeper into where time series databases might fit into your environment. If you're dealing with large and or fast data volumes, and you wanna scale cost effectively with the highest performance and you're analyzing metrics and data over time times, series databases just might be a great fit for you. Try InfluxDB out. You can start with a free cloud account by clicking on the link and the resources below. Remember all these recordings are gonna be available on demand of the cube.net and influx data.com. So check those out and poke around influx data. They are the folks behind InfluxDB and one of the leaders in the space, we hope you enjoyed the program. This is Dave Valante for the cube. We'll see you soon.

[Music] in the 2010s organizations became keenly aware that data would become the key ingredient in driving competitive advantage differentiation and growth but to this day putting data to work remains a difficult challenge for many if not most organizations now as the cloud matures it has become a game changer for data practitioners by making cheap storage and massive processing power readily accessible we've also seen better tooling in the form of data workflows streaming machine intelligence ai developer tools security observability automation new databases and the like these innovations they accelerate data proficiency but at the same time they had complexity for practitioners data lakes data hubs data warehouses data marts data fabrics data meshes data catalogs data oceans are forming they're evolving and exploding onto the scene so in an effort to bring perspective to the sea of optionality we've brought together the brightest minds in the data analyst community to discuss how data management is morphing and what practitioners should expect in 2022 and beyond hello everyone my name is dave vellante with the cube and i'd like to welcome you to a special cube presentation analyst predictions 2022 the future of data management we've gathered six of the best analysts in data and data management who are going to present and discuss their top predictions and trends for 2022 in the first half of this decade let me introduce our six power panelists sanjeev mohan is former gartner analyst and principal at sanjamo tony bear is principal at db insight carl olufsen is well-known research vice president with idc dave meninger is senior vice president and research director at ventana research brad shimon chief analyst at ai platforms analytics and data management at omnia and doug henschen vice president and principal analyst at constellation research gentlemen welcome to the program and thanks for coming on thecube today great to be here thank you all right here's the format we're going to use i as moderator are going to call on each analyst separately who then will deliver their prediction or mega trend and then in the interest of time management and pace two analysts will have the opportunity to comment if we have more time we'll elongate it but let's get started right away sanjeev mohan please kick it off you want to talk about governance go ahead sir thank you dave i i believe that data governance which we've been talking about for many years is now not only going to be mainstream it's going to be table stakes and all the things that you mentioned you know with data oceans data lakes lake houses data fabric meshes the common glue is metadata if we don't understand what data we have and we are governing it there is no way we can manage it so we saw informatica when public last year after a hiatus of six years i've i'm predicting that this year we see some more companies go public uh my bet is on colibra most likely and maybe alation we'll see go public this year we we i'm also predicting that the scope of data governance is going to expand beyond just data it's not just data and reports we are going to see more transformations like spark jaws python even airflow we're going to see more of streaming data so from kafka schema registry for example we will see ai models become part of this whole governance suite so the governance suite is going to be very comprehensive very detailed lineage impact analysis and then even expand into data quality we already seen that happen with some of the tools where they are buying these smaller companies and bringing in data quality monitoring and integrating it with metadata management data catalogs also data access governance so these so what we are going to see is that once the data governance platforms become the key entry point into these modern architectures i'm predicting that the usage the number of users of a data catalog is going to exceed that of a bi tool that will take time and we already seen that that trajectory right now if you look at bi tools i would say there are 100 users to a bi tool to one data catalog and i i see that evening out over a period of time and at some point data catalogs will really become you know the main way for us to access data data catalog will help us visualize data but if we want to do more in-depth analysis it'll be the jumping-off point into the bi tool the data science tool and and that is that is the journey i see for the data governance products excellent thank you some comments maybe maybe doug a lot a lot of things to weigh in on there maybe you could comment yeah sanjeev i think you're spot on a lot of the trends uh the one disagreement i think it's it's really still far from mainstream as you say we've been talking about this for years it's like god motherhood apple pie everyone agrees it's important but too few organizations are really practicing good governance because it's hard and because the incentives have been lacking i think one thing that deserves uh mention in this context is uh esg mandates and guidelines these are environmental social and governance regs and guidelines we've seen the environmental rags and guidelines imposed in industries particularly the carbon intensive industries we've seen the social mandates particularly diversity imposed on suppliers by companies that are leading on this topic we've seen governance guidelines now being imposed by banks and investors so these esgs are presenting new carrots and sticks and it's going to demand more solid data it's going to demand more detailed reporting and solid reporting tighter governance but we're still far from mainstream adoption we have a lot of uh you know best of breed niche players in the space i think the signs that it's going to be more mainstream are starting with things like azure purview google dataplex the big cloud platform uh players seem to be uh upping the ante and and addressing starting to address governance excellent thank you doug brad i wonder if you could chime in as well yeah i would love to be a believer in data catalogs um but uh to doug's point i think that it's going to take some more pressure for for that to happen i recall metadata being something every enterprise thought they were going to get under control when we were working on service oriented architecture back in the 90s and that didn't happen quite the way we we anticipated and and uh to sanjeev's point it's because it is really complex and really difficult to do my hope is that you know we won't sort of uh how do we put this fade out into this nebulous nebula of uh domain catalogs that are specific to individual use cases like purview for getting data quality right or like data governance and cyber security and instead we have some tooling that can actually be adaptive to gather metadata to create something i know is important to you sanjeev and that is this idea of observability if you can get enough metadata without moving your data around but understanding the entirety of a system that's running on this data you can do a lot to help with with the governance that doug is talking about so so i just want to add that you know data governance like many other initiatives did not succeed even ai went into an ai window but that's a different topic but a lot of these things did not succeed because to your point the incentives were not there i i remember when starbucks oxley had come into the scene if if a bank did not do service obviously they were very happy to a million dollar fine that was like you know pocket change for them instead of doing the right thing but i think the stakes are much higher now with gdpr uh the floodgates open now you know california you know has ccpa but even ccpa is being outdated with cpra which is much more gdpr like so we are very rapidly entering a space where every pretty much every major country in the world is coming up with its own uh compliance regulatory requirements data residence is becoming really important and and i i think we are going to reach a stage where uh it won't be optional anymore so whether we like it or not and i think the reason data catalogs were not successful in the past is because we did not have the right focus on adoption we were focused on features and these features were disconnected very hard for business to stop these are built by it people for it departments to to take a look at technical metadata not business metadata today the tables have turned cdo's are driving this uh initiative uh regulatory compliances are beating down hard so i think the time might be right yeah so guys we have to move on here and uh but there's some some real meat on the bone here sanjeev i like the fact that you late you called out calibra and alation so we can look back a year from now and say okay he made the call he stuck it and then the ratio of bi tools the data catalogs that's another sort of measurement that we can we can take even though some skepticism there that's something that we can watch and i wonder if someday if we'll have more metadata than data but i want to move to tony baer you want to talk about data mesh and speaking you know coming off of governance i mean wow you know the whole concept of data mesh is decentralized data and then governance becomes you know a nightmare there but take it away tony we'll put it this way um data mesh you know the the idea at least is proposed by thoughtworks um you know basically was unleashed a couple years ago and the press has been almost uniformly almost uncritical um a good reason for that is for all the problems that basically that sanjeev and doug and brad were just you know we're just speaking about which is that we have all this data out there and we don't know what to do about it um now that's not a new problem that was a problem we had enterprise data warehouses it was a problem when we had our hadoop data clusters it's even more of a problem now the data's out in the cloud where the data is not only your data like is not only s3 it's all over the place and it's also including streaming which i know we'll be talking about later so the data mesh was a response to that the idea of that we need to debate you know who are the folks that really know best about governance is the domain experts so it was basically data mesh was an architectural pattern and a process my prediction for this year is that data mesh is going to hit cold hard reality because if you if you do a google search um basically the the published work the articles and databases have been largely you know pretty uncritical um so far you know that you know basically learning is basically being a very revolutionary new idea i don't think it's that revolutionary because we've talked about ideas like this brad and i you and i met years ago when we were talking about so and decentralizing all of us was at the application level now we're talking about at the data level and now we have microservices so there's this thought of oh if we manage if we're apps in cloud native through microservices why don't we think of data in the same way um my sense this year is that you know this and this has been a very active search if you look at google search trends is that now companies are going to you know enterprises are going to look at this seriously and as they look at seriously it's going to attract its first real hard scrutiny it's going to attract its first backlash that's not necessarily a bad thing it means that it's being taken seriously um the reason why i think that that uh that it will you'll start to see basically the cold hard light of day shine on data mesh is that it's still a work in progress you know this idea is basically a couple years old and there's still some pretty major gaps um the biggest gap is in is in the area of federated governance now federated governance itself is not a new issue uh federated governance position we're trying to figure out like how can we basically strike the balance between getting let's say you know between basically consistent enterprise policy consistent enterprise governance but yet the groups that understand the data know how to basically you know that you know how do we basically sort of balance the two there's a huge there's a huge gap there in practice and knowledge um also to a lesser extent there's a technology gap which is basically in the self-service technologies that will help teams essentially govern data you know basically through the full life cycle from developed from selecting the data from you know building the other pipelines from determining your access control determining looking at quality looking at basically whether data is fresh or whether or not it's trending of course so my predictions is that it will really receive the first harsh scrutiny this year you are going to see some organization enterprises declare premature victory when they've uh when they build some federated query implementations you're going to see vendors start to data mesh wash their products anybody in the data management space they're going to say that whether it's basically a pipelining tool whether it's basically elt whether it's a catalog um or confederated query tool they're all going to be like you know basically promoting the fact of how they support this hopefully nobody is going to call themselves a data mesh tool because data mesh is not a technology we're going to see one other thing come out of this and this harks back to the metadata that sanji was talking about and the catalogs that he was talking about which is that there's going to be a new focus on every renewed focus on metadata and i think that's going to spur interest in data fabrics now data fabrics are pretty vaguely defined but if we just take the most elemental definition which is a common metadata back plane i think that if anybody is going to get serious about data mesh they need to look at a data fabric because we all at the end of the day need to speak you know need to read from the same sheet of music so thank you tony dave dave meninger i mean one of the things that people like about data mesh is it pretty crisply articulates some of the flaws in today's organizational approaches to data what are your thoughts on this well i think we have to start by defining data mesh right the the term is already getting corrupted right tony said it's going to see the cold hard uh light of day and there's a problem right now that there are a number of overlapping terms that are similar but not identical so we've got data virtualization data fabric excuse me for a second sorry about that data virtualization data fabric uh uh data federation right uh so i i think that it's not really clear what each vendor means by these terms i see data mesh and data fabric becoming quite popular i've i've interpreted data mesh as referring primarily to the governance aspects as originally you know intended and specified but that's not the way i see vendors using i see vendors using it much more to mean data fabric and data virtualization so i'm going to comment on the group of those things i think the group of those things is going to happen they're going to happen they're going to become more robust our research suggests that a quarter of organizations are already using virtualized access to their data lakes and another half so a total of three quarters will eventually be accessing their data lakes using some sort of virtualized access again whether you define it as mesh or fabric or virtualization isn't really the point here but this notion that there are different elements of data metadata and governance within an organization that all need to be managed collectively the interesting thing is when you look at the satisfaction rates of those organizations using virtualization versus those that are not it's almost double 68 of organizations i'm i'm sorry um 79 of organizations that were using virtualized access express satisfaction with their access to the data lake only 39 expressed satisfaction if they weren't using virtualized access so thank you uh dave uh sanjeev we just got about a couple minutes on this topic but i know you're speaking or maybe you've spoken already on a panel with jamal dagani who sort of invented the concept governance obviously is a big sticking point but what are your thoughts on this you are mute so my message to your mark and uh and to the community is uh as opposed to what dave said let's not define it we spent the whole year defining it there are four principles domain product data infrastructure and governance let's take it to the next level i get a lot of questions on what is the difference between data fabric and data mesh and i'm like i can compare the two because data mesh is a business concept data fabric is a data integration pattern how do you define how do you compare the two you have to bring data mesh level down so to tony's point i'm on a warp path in 2022 to take it down to what does a data product look like how do we handle shared data across domains and govern it and i think we are going to see more of that in 2022 is operationalization of data mesh i think we could have a whole hour on this topic couldn't we uh maybe we should do that uh but let's go to let's move to carl said carl your database guy you've been around that that block for a while now you want to talk about graph databases bring it on oh yeah okay thanks so i regard graph database as basically the next truly revolutionary database management technology i'm looking forward to for the graph database market which of course we haven't defined yet so obviously i have a little wiggle room in what i'm about to say but that this market will grow by about 600 percent over the next 10 years now 10 years is a long time but over the next five years we expect to see gradual growth as people start to learn how to use it problem isn't that it's used the problem is not that it's not useful is that people don't know how to use it so let me explain before i go any further what a graph database is because some of the folks on the call may not may not know what it is a graph database organizes data according to a mathematical structure called a graph a graph has elements called nodes and edges so a data element drops into a node the nodes are connected by edges the edges connect one node to another node combinations of edges create structures that you can analyze to determine how things are related in some cases the nodes and edges can have properties attached to them which add additional informative material that makes it richer that's called a property graph okay there are two principal use cases for graph databases there's there's semantic proper graphs which are used to break down human language text uh into the semantic structures then you can search it organize it and and and answer complicated questions a lot of ai is aimed at semantic graphs another kind is the property graph that i just mentioned which has a dazzling number of use cases i want to just point out is as i talk about this people are probably wondering well we have relational databases isn't that good enough okay so a relational database defines it uses um it supports what i call definitional relationships that means you define the relationships in a fixed structure the database drops into that structure there's a value foreign key value that relates one table to another and that value is fixed you don't change it if you change it the database becomes unstable it's not clear what you're looking at in a graph database the system is designed to handle change so that it can reflect the true state of the things that it's being used to track so um let me just give you some examples of use cases for this um they include uh entity resolution data lineage uh um social media analysis customer 360 fraud prevention there's cyber security there's strong supply chain is a big one actually there's explainable ai and this is going to become important too because a lot of people are adopting ai but they want a system after the fact to say how did the ai system come to that conclusion how did it make that recommendation right now we don't have really good ways of tracking that okay machine machine learning in general um social network i already mentioned that and then we've got oh gosh we've got data governance data compliance risk management we've got recommendation we've got personalization anti-money money laundering that's another big one identity and access management network and i.t operations is already becoming a key one where you actually have mapped out your operation your your you know whatever it is your data center and you you can track what's going on as things happen there root cause analysis fraud detection is a huge one a number of major credit card companies use graph databases for fraud detection risk analysis tracking and tracing churn analysis next best action what-if analysis impact analysis entity resolution and i would add one other thing or just a few other things to this list metadata management so sanjay here you go this is your engine okay because i was in metadata management for quite a while in my past life and one of the things i found was that none of the data management technologies that were available to us could efficiently handle metadata because of the kinds of structures that result from it but grass can okay grafts can do things like say this term in this context means this but in that context it means that okay things like that and in fact uh logistics management supply chain it also because it handles recursive relationships by recursive relationships i mean objects that own other objects that are of the same type you can do things like bill materials you know so like parts explosion you can do an hr analysis who reports to whom how many levels up the chain and that kind of thing you can do that with relational databases but yes it takes a lot of programming in fact you can do almost any of these things with relational databases but the problem is you have to program it it's not it's not supported in the database and whenever you have to program something that means you can't trace it you can't define it you can't publish it in terms of its functionality and it's really really hard to maintain over time so carl thank you i wonder if we could bring brad in i mean brad i'm sitting there wondering okay is this incremental to the market is it disruptive and replaceable what are your thoughts on this space it's already disrupted the market i mean like carl said go to any bank and ask them are you using graph databases to do to get fraud detection under control and they'll say absolutely that's the only way to solve this problem and it is frankly um and it's the only way to solve a lot of the problems that carl mentioned and that is i think it's it's achilles heel in some ways because you know it's like finding the best way to cross the seven bridges of konigsberg you know it's always going to kind of be tied to those use cases because it's really special and it's really unique and because it's special and it's unique uh it it still unfortunately kind of stands apart from the rest of the community that's building let's say ai outcomes as the great great example here the graph databases and ai as carl mentioned are like chocolate and peanut butter but technologically they don't know how to talk to one another they're completely different um and you know it's you can't just stand up sql and query them you've got to to learn um yeah what is that carlos specter or uh special uh uh yeah thank you uh to actually get to the data in there and if you're gonna scale that data that graph database especially a property graph if you're gonna do something really complex like try to understand uh you know all of the metadata in your organization you might just end up with you know a graph database winter like we had the ai winter simply because you run out of performance to make the thing happen so i i think it's already disrupted but we we need to like treat it like a first-class citizen in in the data analytics and ai community we need to bring it into the fold we need to equip it with the tools it needs to do that the magic it does and to do it not just for specialized use cases but for everything because i i'm with carl i i think it's absolutely revolutionary so i had also identified the principal achilles heel of the technology which is scaling now when these when these things get large and complex enough that they spill over what a single server can handle you start to have difficulties because the relationships span things that have to be resolved over a network and then you get network latency and that slows the system down so that's still a problem to be solved sanjeev any quick thoughts on this i mean i think metadata on the on the on the word cloud is going to be the the largest font uh but what are your thoughts here i want to like step away so people don't you know associate me with only meta data so i want to talk about something a little bit slightly different uh dbengines.com has done an amazing job i think almost everyone knows that they chronicle all the major databases that are in use today in january of 2022 there are 381 databases on its list of ranked list of databases the largest category is rdbms the second largest category is actually divided into two property graphs and rdf graphs these two together make up the second largest number of data databases so talking about accolades here this is a problem the problem is that there's so many graph databases to choose from they come in different shapes and forms uh to bright's point there's so many query languages in rdbms is sql end of the story here we've got sci-fi we've got gremlin we've got gql and then your proprietary languages so i think there's a lot of disparity in this space but excellent all excellent points sanji i must say and that is a problem the languages need to be sorted and standardized and it needs people need to have a road map as to what they can do with it because as you say you can do so many things and so many of those things are unrelated that you sort of say well what do we use this for i'm reminded of the saying i learned a bunch of years ago when somebody said that the digital computer is the only tool man has ever devised that has no particular purpose all right guys we gotta we gotta move on to dave uh meninger uh we've heard about streaming uh your prediction is in that realm so please take it away sure so i like to say that historical databases are to become a thing of the past but i don't mean that they're going to go away that's not my point i mean we need historical databases but streaming data is going to become the default way in which we operate with data so in the next say three to five years i would expect the data platforms and and we're using the term data platforms to represent the evolution of databases and data lakes that the data platforms will incorporate these streaming capabilities we're going to process data as it streams into an organization and then it's going to roll off into historical databases so historical databases don't go away but they become a thing of the past they store the data that occurred previously and as data is occurring we're going to be processing it we're going to be analyzing we're going to be acting on it i mean we we only ever ended up with historical databases because we were limited by the technology that was available to us data doesn't occur in batches but we processed it in batches because that was the best we could do and it wasn't bad and we've continued to improve and we've improved and we've improved but streaming data today is still the exception it's not the rule right there's there are projects within organizations that deal with streaming data but it's not the default way in which we deal with data yet and so that that's my prediction is that this is going to change we're going to have um streaming data be the default way in which we deal with data and and how you label it what you call it you know maybe these databases and data platforms just evolve to be able to handle it but we're going to deal with data in a different way and our research shows that already about half of the participants in our analytics and data benchmark research are using streaming data you know another third are planning to use streaming technologies so that gets us to about eight out of ten organizations need to use this technology that doesn't mean they have to use it throughout the whole organization but but it's pretty widespread in its use today and has continued to grow if you think about the consumerization of i.t we've all been conditioned to expect immediate access to information immediate responsiveness you know we want to know if an uh item is on the shelf at our local retail store and we can go in and pick it up right now you know that's the world we live in and that's spilling over into the enterprise i.t world where we have to provide those same types of capabilities um so that's my prediction historical database has become a thing of the past streaming data becomes the default way in which we we operate with data all right thank you david well so what what say you uh carl a guy who's followed historical databases for a long time well one thing actually every database is historical because as soon as you put data in it it's now history it's no longer it no longer reflects the present state of things but even if that history is only a millisecond old it's still history but um i would say i mean i know you're trying to be a little bit provocative in saying this dave because you know as well as i do that people still need to do their taxes they still need to do accounting they still need to run general ledger programs and things like that that all involves historical data that's not going to go away unless you want to go to jail so you're going to have to deal with that but as far as the leading edge functionality i'm totally with you on that and i'm just you know i'm just kind of wondering um if this chain if this requires a change in the way that we perceive applications in order to truly be manifested and rethinking the way m applications work um saying that uh an application should respond instantly as soon as the state of things changes what do you say about that i i think that's true i think we do have to think about things differently that's you know it's not the way we design systems in the past uh we're seeing more and more systems designed that way but again it's not the default and and agree 100 with you that we do need historical databases you know that that's clear and even some of those historical databases will be used in conjunction with the streaming data right so absolutely i mean you know let's take the data warehouse example where you're using the data warehouse as context and the streaming data as the present you're saying here's a sequence of things that's happening right now have we seen that sequence before and where what what does that pattern look like in past situations and can we learn from that so tony bear i wonder if you could comment i mean if you when you think about you know real-time inferencing at the edge for instance which is something that a lot of people talk about um a lot of what we're discussing here in this segment looks like it's got great potential what are your thoughts yeah well i mean i think you nailed it right you know you hit it right on the head there which is that i think a key what i'm seeing is that essentially and basically i'm going to split this one down the middle is i don't see that basically streaming is the default what i see is streaming and basically and transaction databases um and analytics data you know data warehouses data lakes whatever are converging and what allows us technically to converge is cloud native architecture where you can basically distribute things so you could have you can have a note here that's doing the real-time processing that's also doing it and this is what your leads in we're maybe doing some of that real-time predictive analytics to take a look at well look we're looking at this customer journey what's happening with you know you know with with what the customer is doing right now and this is correlated with what other customers are doing so what i so the thing is that in the cloud you can basically partition this and because of basically you know the speed of the infrastructure um that you can basically bring these together and or and so and kind of orchestrate them sort of loosely coupled manner the other part is that the use cases are demanding and this is part that goes back to what dave is saying is that you know when you look at customer 360 when you look at let's say smart you know smart utility grids when you look at any type of operational problem it has a real-time component and it has a historical component and having predictives and so like you know you know my sense here is that there that technically we can bring this together through the cloud and i think the use case is that is that we we can apply some some real-time sort of you know predictive analytics on these streams and feed this into the transactions so that when we make a decision in terms of what to do as a result of a transaction we have this real time you know input sanjeev did you have a comment yeah i was just going to say that to this point you know we have to think of streaming very different because in the historical databases we used to bring the data and store the data and then we used to run rules on top uh aggregations and all but in case of streaming the mindset changes because the rules normally the inference all of that is fixed but the data is constantly changing so it's a completely reverse way of thinking of uh and building applications on top of that so dave menninger there seemed to be some disagreement about the default or now what kind of time frame are you are you thinking about is this end of decade it becomes the default what would you pin i i think around you know between between five to ten years i think this becomes the reality um i think you know it'll be more and more common between now and then but it becomes the default and i also want sanjeev at some point maybe in one of our subsequent conversations we need to talk about governing streaming data because that's a whole other set of challenges we've also talked about it rather in a two dimensions historical and streaming and there's lots of low latency micro batch sub second that's not quite streaming but in many cases it's fast enough and we're seeing a lot of adoption of near real time not quite real time as uh good enough for most for many applications because nobody's really taking the hardware dimension of this information like how do we that'll just happen carl so near real time maybe before you lose the customer however you define that right okay um let's move on to brad brad you want to talk about automation ai uh the the the pipeline people feel like hey we can just automate everything what's your prediction yeah uh i'm i'm an ai fiction auto so apologies in advance for that but uh you know um i i think that um we've been seeing automation at play within ai for some time now and it's helped us do do a lot of things for especially for practitioners that are building ai outcomes in the enterprise uh it's it's helped them to fill skills gaps it's helped them to speed development and it's helped them to to actually make ai better uh because it you know in some ways provides some swim lanes and and for example with technologies like ottawa milk and can auto document and create that sort of transparency that that we talked about a little bit earlier um but i i think it's there's an interesting kind of conversion happening with this idea of automation um and and that is that uh we've had the automation that started happening for practitioners it's it's trying to move outside of the traditional bounds of things like i'm just trying to get my features i'm just trying to pick the right algorithm i'm just trying to build the right model uh and it's expanding across that full life cycle of building an ai outcome to start at the very beginning of data and to then continue on to the end which is this continuous delivery and continuous uh automation of of that outcome to make sure it's right and it hasn't drifted and stuff like that and because of that because it's become kind of powerful we're starting to to actually see this weird thing happen where the practitioners are starting to converge with the users and that is to say that okay if i'm in tableau right now i can stand up salesforce einstein discovery and it will automatically create a nice predictive algorithm for me um given the data that i that i pull in um but what's starting to happen and we're seeing this from the the the companies that create business software so salesforce oracle sap and others is that they're starting to actually use these same ideals and a lot of deep learning to to basically stand up these out of the box flip a switch and you've got an ai outcome at the ready for business users and um i i'm very much you know i think that that's that's the way that it's going to go and what it means is that ai is is slowly disappearing uh and i don't think that's a bad thing i think if anything what we're going to see in 2022 and maybe into 2023 is this sort of rush to to put this idea of disappearing ai into practice and have as many of these solutions in the enterprise as possible you can see like for example sap is going to roll out this quarter this thing called adaptive recommendation services which which basically is a cold start ai outcome that can work across a whole bunch of different vertical markets and use cases it's just a recommendation engine for whatever you need it to do in the line of business so basically you're you're an sap user you look up to turn on your software one day and you're a sales professional let's say and suddenly you have a recommendation for customer churn it's going that's great well i i don't know i i think that's terrifying in some ways i think it is the future that ai is going to disappear like that but i am absolutely terrified of it because um i i think that what it what it really does is it calls attention to a lot of the issues that we already see around ai um specific to this idea of what what we like to call it omdia responsible ai which is you know how do you build an ai outcome that is free of bias that is inclusive that is fair that is safe that is secure that it's audible etc etc etc etc that takes some a lot of work to do and so if you imagine a customer that that's just a sales force customer let's say and they're turning on einstein discovery within their sales software you need some guidance to make sure that when you flip that switch that the outcome you're going to get is correct and that's that's going to take some work and so i think we're going to see this let's roll this out and suddenly there's going to be a lot of a lot of problems a lot of pushback uh that we're going to see and some of that's going to come from gdpr and others that sam jeeve was mentioning earlier a lot of it's going to come from internal csr requirements within companies that are saying hey hey whoa hold up we can't do this all at once let's take the slow route let's make ai automated in a smart way and that's going to take time yeah so a couple predictions there that i heard i mean ai essentially you disappear it becomes invisible maybe if i can restate that and then if if i understand it correctly brad you're saying there's a backlash in the near term people can say oh slow down let's automate what we can those attributes that you talked about are non trivial to achieve is that why you're a bit of a skeptic yeah i think that we don't have any sort of standards that companies can look to and understand and we certainly within these companies especially those that haven't already stood up in internal data science team they don't have the knowledge to understand what that when they flip that switch for an automated ai outcome that it's it's gonna do what they think it's gonna do and so we need some sort of standard standard methodology and practice best practices that every company that's going to consume this invisible ai can make use of and one of the things that you know is sort of started that google kicked off a few years back that's picking up some momentum and the companies i just mentioned are starting to use it is this idea of model cards where at least you have some transparency about what these things are doing you know so like for the sap example we know for example that it's convolutional neural network with a long short-term memory model that it's using we know that it only works on roman english uh and therefore me as a consumer can say oh well i know that i need to do this internationally so i should not just turn this on today great thank you carl can you add anything any context here yeah we've talked about some of the things brad mentioned here at idc in the our future of intelligence group regarding in particular the moral and legal implications of having a fully automated you know ai uh driven system uh because we already know and we've seen that ai systems are biased by the data that they get right so if if they get data that pushes them in a certain direction i think there was a story last week about an hr system that was uh that was recommending promotions for white people over black people because in the past um you know white people were promoted and and more productive than black people but not it had no context as to why which is you know because they were being historically discriminated black people being historically discriminated against but the system doesn't know that so you know you have to be aware of that and i think that at the very least there should be controls when a decision has either a moral or a legal implication when when you want when you really need a human judgment it could lay out the options for you but a person actually needs to authorize that that action and i also think that we always will have to be vigilant regarding the kind of data we use to train our systems to make sure that it doesn't introduce unintended biases and to some extent they always will so we'll always be chasing after them that's that's absolutely carl yeah i think that what you have to bear in mind as a as a consumer of ai is that it is a reflection of us and we are a very flawed species uh and so if you look at all the really fantastic magical looking supermodels we see like gpt three and four that's coming out z they're xenophobic and hateful uh because the people the data that's built upon them and the algorithms and the people that build them are us so ai is a reflection of us we need to keep that in mind yeah we're the ai's by us because humans are biased all right great okay let's move on doug henson you know a lot of people that said that data lake that term's not not going to not going to live on but it appears to be have some legs here uh you want to talk about lake house bring it on yes i do my prediction is that lake house and this idea of a combined data warehouse and data lake platform is going to emerge as the dominant data management offering i say offering that doesn't mean it's going to be the dominant thing that organizations have out there but it's going to be the predominant vendor offering in 2022. now heading into 2021 we already had cloudera data bricks microsoft snowflake as proponents in 2021 sap oracle and several of these fabric virtualization mesh vendors join the bandwagon the promise is that you have one platform that manages your structured unstructured and semi-structured information and it addresses both the beyond analytics needs and the data science needs the real promise there is simplicity and lower cost but i think end users have to answer a few questions the first is does your organization really have a center of data gravity or is it is the data highly distributed multiple data warehouses multiple data lakes on-premises cloud if it if it's very distributed and you you know you have difficulty consolidating and that's not really a goal for you then maybe that single platform is unrealistic and not likely to add value to you um you know also the fabric and virtualization vendors the the mesh idea that's where if you have this highly distributed situation that might be a better path forward the second question if you are looking at one of these lake house offerings you are looking at consolidating simplifying bringing together to a single platform you have to make sure that it meets both the warehouse need and the data lake need so you have vendors like data bricks microsoft with azure synapse new really to the data warehouse space and they're having to prove that these data warehouse capabilities on their platforms can meet the scaling requirements can meet the user and query concurrency requirements meet those tight slas and then on the other hand you have the or the oracle sap snowflake the data warehouse uh folks coming into the data science world and they have to prove that they can manage the unstructured information and meet the needs of the data scientists i'm seeing a lot of the lake house offerings from the warehouse crowd managing that unstructured information in columns and rows and some of these vendors snowflake in particular is really relying on partners for the data science needs so you really got to look at a lake house offering and make sure that it meets both the warehouse and the data lake requirement well thank you doug well tony if those two worlds are going to come together as doug was saying the analytics and the data science world does it need to be some kind of semantic layer in between i don't know weigh in on this topic if you would oh didn't we talk about data fabrics before common metadata layer um actually i'm almost tempted to say let's declare victory and go home in that this is actually been going on for a while i actually agree with uh you know much what doug is saying there which is that i mean we i remembered as far back as i think it was like 2014 i was doing a a study you know it was still at ovum predecessor omnia um looking at all these specialized databases that were coming up and seeing that you know there's overlap with the edges but yet there was still going to be a reason at the time that you would have let's say a document database for json you'd have a relational database for tran you know for transactions and for data warehouse and you had you know and you had basically something at that time that that resembles to do for what we're considering a day of life fast fo and the thing is what i was saying at the time is that you're seeing basically blur you know sort of blending at the edges that i was saying like about five or six years ago um that's all and the the lake house is essentially you know the amount of the the current manifestation of that idea there is a dichotomy in terms of you know it's the old argument do we centralize this all you know you know in in in in in a single place or do we or do we virtualize and i think it's always going to be a yin and yang there's never going to be a single single silver silver bullet i do see um that they're also going to be questions and these are things that points that doug raised they're you know what your what do you need of of of your of you know for your performance there or for your you know pre-performance characteristics do you need for instance hiking currency you need the ability to do some very sophisticated joins or is your requirement more to be able to distribute and you know distribute our processing is you know as far as possible to get you know to essentially do a kind of brute force approach all these approaches are valid based on you know based on the used case um i just see that essentially that the lake house is the culmination of it's nothing it's just it's a relatively new term introduced by databricks a couple years ago this is the culmination of basically what's been a long time trend and what we see in the cloud is that as we start seeing data warehouses as a checkbox item say hey we can basically source data in cloud and cloud storage and s3 azure blob store you know whatever um as long as it's in certain formats like you know like you know parquet or csv or something like that you know i see that as becoming kind of you know a check box item so to that extent i think that the lake house depending on how you define it is already reality um and in some in some cases maybe new terminology but not a whole heck of a lot new under the sun yeah and dave menger i mean a lot of this thank you tony but a lot of this is going to come down to you know vendor marketing right some people try to co-opt the term we talked about data mesh washing what are your thoughts on this yeah so um i used the term data platform earlier and and part of the reason i use that term is that it's more vendor neutral uh we've we've tried to uh sort of stay out of the the vendor uh terminology patenting world right whether whether the term lake house is what sticks or not the concept is certainly going to stick and we have some data to back it up about a quarter of organizations that are using data lakes today already incorporate data warehouse functionality into it so they consider their data lake house and data warehouse one in the same about a quarter of organizations a little less but about a quarter of organizations feed the data lake from the data warehouse and about a quarter of organizations feed the data warehouse from the data lake so it's pretty obvious that three quarters of organizations need to bring this stuff together right the need is there the need is apparent the technology is going to continue to verge converge i i like to talk about you know you've got data lakes over here at one end and i'm not going to talk about why people thought data lakes were a bad idea because they thought you just throw stuff in a in a server and you ignore it right that's not what a data lake is so you've got data lake people over here and you've got database people over here data warehouse people over here database vendors are adding data lake capabilities and data lake vendors are adding data warehouse capabilities so it's obvious that they're going to meet in the middle i mean i think it's like tony says i think we should there declare victory and go home and so so i it's just a follow-up on that so are you saying these the specialized lake and the specialized warehouse do they go away i mean johnny tony data mesh practitioners would say or or advocates would say well they could all live as just a node on the on the mesh but based on what dave just said are we going to see those all morph together well number one as i was saying before there's always going to be this sort of you know kind of you know centrifugal force or this tug of war between do we centralize the data do we do it virtualize and the fact is i don't think that work there's ever going to be any single answer i think in terms of data mesh data mesh has nothing to do with how you physically implement the data you could have a data mesh on a basically uh on a data warehouse it's just that you know the difference being is that if we use the same you know physical data store but everybody's logically manual basically governing it differently you know um a data mission is basically it's not a technology it's a process it's a governance process um so essentially um you know you know i basically see that you know as as i was saying before that this is basically the culmination of a long time trend we're essentially seeing a lot of blurring but there are going to be cases where for instance if i need let's say like observe i need like high concurrency or something like that there are certain things that i'm not going to be able to get efficiently get out of a data lake um and you know we're basically i'm doing a system where i'm just doing really brute forcing very fast file scanning and that type of thing so i think there always will be some delineations but i would agree with dave and with doug that we are seeing basically a a confluence of requirements that we need to essentially have basically the element you know the ability of a data lake and a data laid out their warehouse we these need to come together so i think what we're likely to see is organizations look for a converged platform that can handle both sides for their center of data gravity the mesh and the fabric vendors the the fabric virtualization vendors they're all on board with the idea of this converged platform and they're saying hey we'll handle all the edge cases of the stuff that isn't in that center of data gradient that is off distributed in a cloud or at a remote location so you can have that single platform for the center of of your your data and then bring in virtualization mesh what have you for reaching out to the distributed data bingo as they basically said people are happy when they virtualize data i i think yes at this point but to this uh dave meningas point you know they have convert they are converging snowflake has introduced support for unstructured data so now we are literally splitting here now what uh databricks is saying is that aha but it's easy to go from data lake to data warehouse than it is from data warehouse to data lake so i think we're getting into semantics but we've already seen these two converge so is that so it takes something like aws who's got what 15 data stores are they're going to have 15 converged data stores that's going to be interesting to watch all right guys i'm going to go down the list and do like a one i'm going to one word each and you guys each of the analysts if you wouldn't just add a very brief sort of course correction for me so sanjeev i mean governance is going to be the maybe it's the dog that wags the tail now i mean it's coming to the fore all this ransomware stuff which really didn't talk much about security but but but what's the one word in your prediction that you would leave us with on governance it's uh it's going to be mainstream mainstream okay tony bear mesh washing is what i wrote down that's that's what we're going to see in uh in in 2022 a little reality check you you want to add to that reality check is i hope that no vendor you know jumps the shark and calls their offering a data mesh project yeah yeah let's hope that doesn't happen if they do we're going to call them out uh carl i mean graph databases thank you for sharing some some you know high growth metrics i know it's early days but magic is what i took away from that it's the magic database yeah i would actually i've said this to people too i i kind of look at it as a swiss army knife of data because you can pretty much do anything you want with it it doesn't mean you should i mean that's definitely the case that if you're you know managing things that are in a fixed schematic relationship probably a relational database is a better choice there are you know times when the document database is a better choice it can handle those things but maybe not it may not be the best choice for that use case but for a great many especially the new emerging use cases i listed it's the best choice thank you and dave meninger thank you by the way for bringing the data in i like how you supported all your comments with with some some data points but streaming data becomes the sort of default uh paradigm if you will what would you add yeah um i would say think fast right that's the world we live in you got to think fast fast love it uh and brad shimon uh i love it i mean on the one hand i was saying okay great i'm afraid i might get disrupted by one of these internet giants who are ai experts so i'm gonna be able to buy instead of build ai but then again you know i've got some real issues there's a potential backlash there so give us the there's your bumper sticker yeah i i would say um going with dave think fast and also think slow uh to to talk about the book that everyone talks about i would say really that this is all about trust trust in the idea of automation and of a transparent invisible ai across the enterprise but verify verify before you do anything and then doug henson i mean i i look i think the the trend is your friend here on this prediction with lake house is uh really becoming dominant i liked the way you set up that notion of you know the the the data warehouse folks coming at it from the analytics perspective but then you got the data science worlds coming together i still feel as though there's this piece in the middle that we're missing but your your final thoughts we'll give you the last well i think the idea of consolidation and simplification uh always prevails that's why the appeal of a single platform is going to be there um we've already seen that with uh you know hadoop platforms moving toward cloud moving toward object storage and object storage becoming really the common storage point for whether it's a lake or a warehouse uh and that second point uh i think esg mandates are uh are gonna come in alongside uh gdpr and things like that to uh up the ante for uh good governance yeah thank you for calling that out okay folks hey that's all the time that that we have here your your experience and depth of understanding on these key issues and in data and data management really on point and they were on display today i want to thank you for your your contributions really appreciate your time enjoyed it thank you now in addition to this video we're going to be making available transcripts of the discussion we're going to do clips of this as well we're going to put them out on social media i'll write this up and publish the discussion on wikibon.com and siliconangle.com no doubt several of the analysts on the panel will take the opportunity to publish written content social commentary or both i want to thank the power panelist and thanks for watching this special cube presentation this is dave vellante be well and we'll see you next time [Music] you

(upbeat music) >> Welcome back to theCUBE's continuous coverage of AWS re:invent 2021. I'm here with my co-host John Furrier and we're running one of the largest, most significant technology events in the history of 2021. Two live sets here in Las Vegas, along with our two studios. And we are absolutely delighted. We're incredibly delighted to welcome a returning alumni. It's not enough to just say that you're an alumni because you have been such a fixture of theCUBE for so many years. Annie Weinberger. And Annie is head of product marketing for applications at AWS. Annie, welcome. >> Thank you so much, it's great to be back. >> It's wonderful to have you back. Let's dive right into it. >> Okay. >> Talk to us about Connect. What does that mean when I say Connect? >> Yes, well, I think if we talk about Amazon Connect, we have to go back to the beginning of the origin story. So, over 10 years ago, when Amazon retail was looking for a solution to manage their customer service and their contact center, we went out and we looked at different solutions and nothing really met our needs. Nothing could kind of provide the scale that we needed at Amazon, or could really be as flexible as we needed to ensure that we're our customer obsession could come through in our customer service. So we built our own solution. And over the years, customers were coming to us and asking, you know, what do you use for your customer service technology? And so we launched Amazon Connect, our omni-channel cloud contact center solution just over four years ago. And it is the one of the fastest growing services at AWS. We have tens of thousands of customers using it today, like Capital One into it, Bank of Omaha, Mutual of Omaha, Best Western, you know, I can go on and on. And they're using it to have over 10 million interactions with customers every day. So it's, you know, growing phenomenally and we just couldn't be more proud to help our customers with their customer service. >> So, yeah. Talk about some of the components that go into that. What are the sort of puzzle pieces that make up AWS Connect? Because obviously connecting with a customer can take a whole bunch of different forms with email, text, voice. >> Yeah >> What's included in that? >> So it's an omni-channel cloud contact center. It provides, you know, any way you want to talk to your customers. There's traditional methods of voice. There's automated ways to connect. So IVRs or interactive voice responses where you call with voice prompts, there's chat, you know. We have Lex Bots that use the same technology that powers Alexa for natural language understanding. And I think customers really like it for a few reasons. One is that unlike kind of other contact center solutions, you can set it up in minutes. You know, American Preparatory Academy had to set up a contact center, they did it in two days. And then it's very, very easy to customize and use. So another example is, you know, when Priceline was going through COVID and they realized their call volume went up 300% overnight, and everybody was just sitting near the queue waiting to talk to an agent. So in 20 minutes, we were able to go in and very easily with a drag and drop interface, customize the ad flow so that people who had a reservation in the next 72 hours were prioritized. So very, very easily. >> You just jumped the gun on me. I was going to ask this because we never boarding that Connect during the pandemic was a huge success. >> Annie: Yes. >> It was many, many examples where people were just located, disrupted by the pandemic. And you guys had tons of traction from government public sector to commercial across the board. Adam Solecki told me in person a couple weeks ago that it was on fire, Connect was on fire. So again, a tailwind, one of those examples with the pandemic, but it highlights this idea or purpose built, ready to go. >> Pre-built the applications. >> Pre-built application. This is a phenomenon. >> It's moving up the stack for AWS. It's very exciting. I think, yeah, we had over 5,000 new contact centers stood up in March and April of 2020 alone. >> Dave: Wow. >> Give it some scale, just go back to the scale piece. Cause this is like, like amazing to stand up a call center like hours, days. Like this is like incredible to, give us some stats on some examples of how fast people were standing up Connect. >> Yeah, I mean, you could stand it up overnight. American Preparatory Academy, as I mentioned did it in two days, we had, you know, this county of Los Angeles did theirs I think at a day. You could go and right now you don't need any technical expertise, even though you have some. >> theCUBE call center, we don't need people calling. >> We had everyone from a Mexican restaurant needed to take to go orders. Cause now it's COVID and they don't have a call. They've been able to set that up, grab a phone number and start taking takeout orders all the way to like capital one, you know, with 40,000 agents that need to move remote overnight. And I think that it's because of that ease to set up, but also the scale and the way that we charge. So, you know, it's AWS consumption-based pricing. You only pay for the interactions with customers. So the barrier to entry is really, really low. You don't have to migrate everything over and buy a bunch of new licenses. You can just stand it up and you're only charged for the interactions with customers. And then if you want to scale down like into it, obviously tax season they're bringing on a lot more agents to handle calls, when those agents aren't really needed for that busy time, you're not paying for those seats. >> You're flex. Take me through the, okay, that's a win, I get that. So home run, great success. Now, the machine learning story is interesting too, because you have the purpose-built platform. There's some customizations that can happen on top of it. So it's not just, here's a general purpose piece of software. People are using some customizations. Take us through the other things. >> Well, the exciting thing is they're not even real customizations because we're AWS, we can leverage the AML services and built pre-built purpose-built features. So there it's embedded and you know, Amazon Connect has been cloud native and AI born since the very beginning. So we've taken a lot of the AI services and built them into you don't need any knowledge. You don't have to know anything about AIML. You can just go in and start leveraging it. And it has huge powerful effects for our customers. We launched three new features this year. One was Amazon Wisdom. That's part of Amazon Connect. And what that does is, you know, if you're an agent and you're on the phone and customer's asking questions, today what they have to do is go in and search across all these different knowledge repositories to find the answer or, you know, how do I issue a refund? You know, we're hearing about this feature that's broken on our product. We're listening behind the scenes to that call and then just automatically providing the knowledge articles as they're on the call saying, this is what you should do, giving them recommendations so we can help the customer much more quickly. >> I love them moving up the stack. Again, a huge fan of Connect. We've highlighting in all of our stories. It's a phenomenon that's translating to other areas, but I want to tie back in where it goes next cause on these keynotes, Adam Solecki's and today was Swami, the conversations about a horizontal data plane. And so as customers would say, use Connect, I might want, if I'm a big customer I want to integrate that into my data because it's voice data, it's call centers, customer data, but I have other databases. So how do you guys look at that integration layer snapping it together with say, a time series database, or maybe a CRM system or retail e-commerce because again, it's all data but it's connected call center. Some may think it's silo, but it's not really siloed. So, I'm a customer. How do I integrate call center? >> Yeah and it's, you know, we have a very strong partner with Salesforce. They're actually a reseller of Connect. So we work with them very, very closely. We have out of the box integrations with Salesforce, with your other, you know, analytics databases with Marketo with other services that you need. I think again, it's one of the benefits of being AWS, it's very extensible, very flexible, and really easy to bring in and share the data that we have with other systems. >> John: So it's not an issue then. >> One of the conversation points that's come up is the, this idea that a large majority of IT Spend is still on premises today. In other words, the AWS total addressable market hasn't been tapped yet. And, you imagine going through the pandemic, someone using AWS Connect to create a virtual call center, now as we hopefully come out and people some return to the office, but now they have the tools to be able to stay at home and be more flexible. Those people, maybe they weren't in the cloud that much before. But to John's point, now you start talking about connecting all of those other data sources. Well, where do those data sources belong? They belong in AWS. So, from your perspective, on the surface it looks like, well, wait, you have these products, but really those are gateways to everything else that AWS does. Is that a fair statement? >> I think it's very, yeah. Absolutely. >> Yeah. >> The big thing I want to get into is okay, we're, I mean, we don't have a lot of people calling for theCUBE but I mean, we wouldn't use the call center, but there's audio involved. Are people more going back to the old school phones for support now with the pandemic? Cause you've mentioned that earlier about the price line, having more- >> I think it's, you know, when we talk to our customers too, it's about letting, letting any customer contact you the way they want to. You know, we, you know, I was talking to Delta, spoke with us yesterday in the business application leadership session. And she said, you know, when someone has a flight issue, I'm sure you can attest to this. I did the same thing. They call, you know, if your, if your flight got canceled or it's looking like it's going to keep pushing, you don't necessarily want to go, you know, use a chat bot or send an email or a text, but there's other use cases where you just want a quick answer, you know, if you contact, I haven't received my product yet, you know, it said it was shipped, I didn't get it. I don't necessarily want to talk to someone, but so, it's just about making that available. >> On the voice side, is it other apps are integrating voice? So what's the interface to call center? Is it, can I integrate like an app voice integrated through the app or it's all phone? >> Because for the agents, there's an agent UI. So they'll see kind of calls that they have in their queue coming up, they'll see the tasks that they have to issue or refund. They'll see the kind of analytics that they have. The knowledge works. There's a supervisor view, so they could go see, you know, we with contact lens for Amazon Connect, we had a launch this, you know, this week, every event around contact lens, it lets you see the trends and sentiment of what's going on the call. It gives them like those training moments. If people aren't using the standard sign-off or the standard greeting on the call, it's a training moment and they can kind of see what's happening and get real-time alerts. If two keywords of a customer saying they cancel into the call, that can get a flag and they can go in and help the agent if necessary. So. >> All kinds of metadata extraction going on in real time. >> Yeah. >> How do you, how would AWS to go through the process of determining what should be bespoke solution hearing versus something that can be productized? And we know there are 475 different kinds of instances. However, you can come up with a package solution where people could pick features and get up and running really quickly. How is that decision making process? >> Well, I mean, you know, 90% at least of what we do build, it comes from what our customers ask for. So we don't, it's the onus is not on us. We listen to our customers, they tell us what they want us to build. Contact center solutions are their line of business applications are purchased by business decision makers and they're used to doing more buying than building. So they wanted to be more out of the box, more like pre-built, but we still are AWS. We make it very, very extensible, very easy to customize, like pull in other data sources. But when we look at how we are going to move up the stack and other areas, we just continue to listen to our customers. >> What's the biggest thing you learned in the pandemic from the team? What's the learnings coming out of the pandemic as hybrid world is upon us? >> I mean, I think a few things with, you know, starting, as you mentioned with the cloud, that the kind of idea of a contact center being a massive building, usually in the middle of America where, you know, people go and they sit and they have conversations. If that was really turned on its head and you can have very secure and accessible solutions through the cloud so that you can work from anywhere. So that was really fantastic to see. >> That's going to be interesting to see moving forward. How that paradigm shifts some centralized call centers, but a lot of this aggregated work that can be done. >> I mean, who knows the, you know, gig economy could be in the contact center, you know. >> Yeah, absolutely >> Yeah >> Maybe get some CUBE hosts, give us theCUBE Connect. We get some CUBE hosts remote. >> That's important work, yeah. >> We need, we need to talk. I got to got my phone number in that list. Annie, it's been fantastic to have you. >> Thank you guys so much. I really appreciate it. >> For John Furrier, this is Dave Nicholson telling you, thank you for joining our continuous coverage of AWS reinvent 2021. Stick with theCUBE for the best in hybrid event coverage. (upbeat music)

>>Mhm. Mhm. Mhm. >>We're back with the notion there to wrap up conv all connections. 2021 the virtual edition. Okay, let's do this. You ready? >>Absolutely. >>All right. I'll get a starting .1. Data. You ain't seen nothing yet. You just wait. We're gonna look back on the big data era of the 20 tens and joke about how trivial was compared to the next 10 years. You know, what are your thoughts on data value and data exposures? >>The data has never been more valuable. Right? The new oil your most critical asset and neither has it been more vulnerable. So you kind of get these two things value increasing. Obviously it becomes more attractive to the to the bad actors and that's the era that we're living in and really, you know what organizations are needed. You know, you kind of think about business resilience and all that. You need the ability to as someone said the anti fragile right? Keep testing it. See, you know, if if your data defenses are now ready to a point where your data is not a liability, but you can make it something that becomes a business advantage. So kind of that business resilience gap that we've been talking about. The business integrity gap uh is really you know what we had focused on. So our customers can really start just taking advantage of the value equation of the data. >>And I think you know, I'm gonna I'm gonna push my community here a little bit and I'm gonna I think we need a new new metaphor because you know why? Because data minnows it's actually more valuable than oil it's not scarce like oil we're never gonna run out of data. So I'm trying to come up with a new a new one but we'll work on that. The cloud. The 2nd point is the cloud is expanding. It's moving to the data center. We got hybrid connections were going across clouds, we got edge nodes, we got new workloads, real time streaming and ai influencing microservices and containers and Ai and all this R. P. A. And hyper automation. All the crazy buzzwords that combined your digital business stack. But the point is data is not only exploding, it's exploding everywhere. You know it's not just in one place anymore. >>Absolutely. And that's that's you know part of the challenge here that our customers space you know unless you know you're a company that was born yesterday you have applications everywhere. Your your pace is different, every customer's journey and the transformation is different. So you might take different directions, different ways. What do you do you have some sass applications you might start there or you start with some public cloud. Maybe you start using some stories in public cloud. So as you transform and as you are now creating more sprawl and that sprawl as we talked about before this is like swiss cheese you got lights that you know people can take advantage of. On the other hand because of that lack of a single unified data services platform. What you lose is the ability to take advantage of all your data, your application, mobility and people die. Let's take the example of containers and kubernetes. What is the point of having you know transportation and ability to move these containers to any cloud if your data is not available in all those clocks. What's the strategy for that? So the problems are also changing their more business centric than just that, you know, what I call the active data management era is really upon us and that's really what's gonna help take it full advantage of all of the other technologies around us. Ai microservices edge, you know, IOT you need to make sure you have a unified data services and intelligent data services platform. >>So what I see is is calm vault is essentially building that that data protection cloud or you might want to call it the data management cloud that starts to get into database and some other areas but but the concept of an abstraction layer that hides that underlying complexity of all the clouds and allows you to protect your data irrespective of location. That's to me that's how you get control of your data which is kind of point number three. And we heard Sandy this morning talking about embrace, manage and protect your data properly. It can be the defining disruptive difference for an organization, which I agree. However, I want to play devil's advocate in the sense that I think the only way you can get control of your data is do you have to embrace that sprawl in that complexity and admit you're not gonna shove it all into a single monolithic source of truth that those days are over. You have to you have to realize that the world is decentralized, It's coming it's here. So we have to implement automation and software to Federated governance and policy and security and privacy and data protection across that abstraction layer that I just just describe someone. Let's talk about that. What are your thoughts? >>Yeah. And I said that earlier too and Sonya is absolutely right. You have to embrace that. You figure out how to make it a competitive advantage. No workload left behind Commonwealth customers are able to protect everything from S 400 to dynamics 3 65 in the cloud and everything in between. Right. So microservices app multi cloud today we're top solution provider for the top clouds. We talked to Microsoft order today, AWS DCP we are driving exabytes of data and protecting exabytes of data. That is our strategic advantage. As you said, you cannot leave you know, strategies behind and say you know what that workload. Not interesting anymore with your data is in there. So that that is the approach that comprehensive platform and then I'm built on that. You start seeing protection grade, not data security, how to use tackle that intelligence from data insights from data compliance challenges my e discovery challenges. So being able to tackle those things ecosystem very key. How do I build on top of this intelligence, data services platform and ecosystem to take advantage of my data. These are all the layers that we believe. You know, it's very differentiated compared to anyone else out there. We're not forcing anyone into a single architecture and saying this is the best because you know what you have learned from 25 years, there's no such thing as one single best architecture. >>Oh, I asked 401 of the most innovative systems in the day. >>Uh but at the >>point of we talked about all the sprawl, this makes ransomware more difficult because an insidious because of the expanding supply chain, the ecosystem, the threat surface. And really the sophistication of the adversary, we've kind of talked about that and and and really new techniques are the Attackers are going mainstream. So but you know, I want to give you the last word here, I want to address two things if you would. Security. Like what's the big news there? Why the big deal and why con vault bring us home with the big picture of your differentiators? >>Absolutely. So you mentioned ransomware? Bad guys always looking to find those exit drawers and break it, Break it down security. I q we're proud to launch this today. It's kind of brings together the culmination of a lot of security that we have done allows customers to be proactive in terms of, you know, we've brought in Gamification into that security i aspect like make it easy and almost make it fun to make sure you're plugging all the holes so that your last mile of defense is secure, then you figure out how you can become more proactive. I have data intelligence that the security tools don't when the bad guys start sniffing around the data or anomaly detection and machine learning the ability to bring that intelligence a highly, you know, relevant signal into the security tools, building that bridge. And lastly, what happens when the, you know, worst case scenario happens almost like a rewind button to go back on your data. Look this is where malware came in and now you're able to just go back and delete that. So that's security, I. Q. Amazing. Or you know, customers are going to try it out and the live hands on lab that's happening and you know, there are feedback has been, this is just brilliant, they love it. So, one more, you know, innovation, we keep doing this, we go we're setting the bar a year ago, we launched them CSS, you know, air gap copy in the cloud, you know, a few weeks ago now we're saying, oh we can also do it right, well we have now innovative to the next level that's combo, you know, bringing it on why combo, 25 years of innovation, you know, it is just amazing how the company had the vision to build a distributed architecture. You talked about a distributed world are beauties, we're not forcing preference customers might have self managed applications that they want, you know, to be used software, they might have the need in some locations to have everything integrated with an appliance, you know, new workloads in the cloud. Let me see if I can start shifting to the data management as a service, which is really the next wave in the industry. And then finally, you know, what about that whole distribution that's happening again. So people will be, you know, we have that unique ability to build a platform. We have amazing ecosystem partners and the biggest companies in the world. Trust us as you heard, you know, throughout the show. So that's what's comin, you know, sustain technology differentiation to make our customers really realize their vision of, you know, leveraging their data as an asset. >>Nice job knows, I love it. Okay, that's a wrap from convoked connections 2021 this is dave vellante from an ocean air and the entire conv all team and the cube team encouraging to come back and check out the on demand videos for anything that you miss tell a friend, let us know what you think for everyone here at convoked connections. 21. Thanks for watching and we'll see you next time. Yeah. >>Mhm Yeah

(upbeat music) >> We're back. This is Dave Vellante of theCUBE, and we're here with Ray Wang, who just wrote a book reminiscent of the famous Tears for Fears song, Everybody Wants to Rule the World: Surviving and Thriving in a World of Digital Giants. Ray, great to see again, man. >> What's going on, man, how are you? >> Oh great, thanks for coming on. You know, it was crazy, been crazy, but it's good to see you face-to-face. >> Ray: This is, we're in the flesh, it's live, we're having conversations, and the information that we're getting is cut right. >> Dave: Yeah, so why did you write this book and how did you find the time? >> Hey, we're in the middle of pandemic. No, I wrote the book because what was happening was digital transformation efforts, they're starting to pop up, but companies weren't always succeeding. And something was happening with digital giants that was very different. They were winning in the marketplace. And never in the form of, if you think about extreme capitalism, if we think about capitalism in general, never in the history of capitalism have we seen growth of large companies. They get large, they fall apart, they don't have anything to build, they can't scale. Their organizations are in shambles. But what happened? If you look at 2017, the combined market cap of the FAANGs and Microsoft was 2 trillion. Today, it is almost 10.2 trillion. It's quintupled. That's never happened. And there's something behind that business model that they put into place that others have copied, from the Airbnbs to the Robloxes to what's going to happen with like a Starlink, and of course, the Robinhoods and you know, Robinhoods and Coinbases of the world. >> And the fundamental premise is all around data, right? Putting data at the core, if you don't do that, you're going to fly blind. >> It is and the secret behind that is the long-term platforms called data-driven digital networks. These platforms take the ability, large memberships, our large devices, they look at that effect. Then they look at figuring out how to actually win on data supremacy. And then of course, they monetize off that data. And that's really the secret behind that is you've got to build that capability and what they do really well is they dis-intermediate customer account control. They take the relationships, aggregate them together. So food delivery app companies are great example of that. You know, small businesses are out there that hundreds and thousands of customers. Today, what happens? Well, they've been aggregated. Millions of customers together into food delivery app. >> Well, I think, you know, this is really interesting what you're saying, because if you think about how we deal with Netflix, we don't call the Netflix sales department or the marketing department of the service, just one interface, the Netflix. So they've been able to put data at their core. Can incumbents do that? How can they do that? >> Incumbents can definitely do that. And it's really about figuring out how to automate that capture. What you really want to do is you start in the cloud, you bring the data together, and you start putting the three A's, analytics, automation, and AI are what you have to be able to put into place. And when you do do that, you now have the ability to go out and figure out how to create that flywheel effect inside those data-driven digital networks. These DDDNS are important. So in Netflix, what are they capturing? They're looking at sentiment, they're looking at context. Like why did you interact with, you know, one title versus another? Did you watch Ted Lasso? Did you switch out of Apple TV to Netflix? Well, I want to know why, right? Did you actually jump into another category? You switched into genres. After 10:00 p.m., what are you watching? Maybe something very different than what you're watching at 2:00 p.m.. How many members are in the home, right? All these questions are being answered and that's the business graph behind all this. >> How much of this is kind of related to the way organizations or companies are organized? In other words, you think about, historically, they would maybe put the process at the core or the, in a bottling plant, the manufacturing facility at the core and the data's all dispersed. Everybody talks about silos. So will AI be the answer to that? Will some new database, Snowflake? Is that the answer? What's the answer to sort of bringing that data together and how do you deal with the organizational inertia? >> Well, the trick to it is really to have a single plane to be able to access that data. I don't care where the data sits, whether it's on premise, whether it's in the cloud, whether it's in the edge, it makes no difference. That's really what you want to be able to do is bring that information together. But the glue is the context. What time was it? What's the weather outside? What location are you in? What's your heart rate? Are you smiling, right? All of those factors come into play. And what we're trying to do is take a user, right? So it could be a customer, a supplier, a partner, or an employee. And how do they interact with an order doc, an invoice, an incident, and then apply the context. And what we're doing is mining that context and information. Now, the more, back to your other point on self service and automation, the more you can actually collect those data points, the more you can capture that context, the more you're able to get to refine that information. >> Context, that's interesting, because if you think about our operational systems, we've contextualized most of them, whether it's sales, marketing, logistics, but we haven't really contextualized our data systems, our data architecture. It's generally run by a technical group. They don't necessarily have the line of business context. You see what HPE is doing today is trying to be inclusive of data on prem. I mentioned Snowflake, they're saying no way. Frank Slootman says we're not going on prem. So that's kind of interesting. So how do you see sort of context evolving with the actually the business line? Not only who has the context actually can, I hate to use the word, but I'm going to, own the data. >> You have to have a data to decisions pathway. That data decisions pathway is you start with all types of data, structured, unstructured, semi-structured, you align it to a business process as an issue, issue to resolution, order to cash, procure to pay, hire to retire. You bring that together, and then you start mining and figuring out what patterns exist. Once you have the patterns, you can then figure out the next best action. And when you get the next best action, you can compete on decisions. And that becomes a very important part. That decision piece, that's going to be automated. And when we think about that, you and I make a decision one per second, how long does it get out of management committee? Could be a week, two weeks, a quarter, a year. It takes forever to get anything out of management committee. But these new systems, if you think about machines, can make decisions a hundred times per second, a thousand times per second. And that's what we're competing against. That asymmetry is the decision velocity. How quickly you can make decisions will be a competitive weapon. >> Is there a dissonance between the fact that you just mentioned, speed, compressing, that sort of time to decision, and the flip side of that coin, quality, security, governance. How do you see squaring that circle? >> Well, that's really why we're going to have to make that, that's the automated, that's the AI piece. Just like we have all types of data, we got to spew up automated ontologies, we got to spit them up, we got to be using, we've got to put them back into play, and then we got to be able to take back into action. And so you want enterprise class capabilities. That's your data quality. That's your security. That's the data governance. That's the ability to actually take that data and understand time series, and actually make sure that the integrity of that data is there. >> What do you think about this sort of notion that increasingly, people are going to be building data products and services that can be monetized? And that's kind of goes back to context, the business lines kind of being responsible for their own data, not having to get permission to add another data source. Do you see that trend? Do you see that decentralization trend? Two-part question. And where do you see HPE fitting into that? >> I see, one, that that trend is definitely going to exist. I'll give you an example. I can actually destroy the top two television manufacturers in the world in less than five years. I could take them out of the business and I'll show you how to do it. So I'm going to make you an offer. $15 per month for the next five years. I'm going to give you a 72 inch, is it 74? 75 inch, 75 inch smart TV, 4k, big TV, right? And it comes with a warranty. And if anything breaks, I'm going to return it to you in 48 hours or less with a brand new one. I don't want your personal information. I'm only going to monitor performance data. I want to know the operations. I want to know which supplier lied to me, which components are working, what features you use. I don't need to know your personal viewing habits, okay? Would you take that deal? >> TV is a service, sure, of course I would. >> 15 bucks and I'm going to make you a better deal. For $25 a month, you get to make an upgrade anytime during that five-year period. What would happen to the two largest TV manufacturers if I did that? >> Yeah, they'd be disrupted. Now, you obviously have a pile of VC money that you're going to do that. Will you ever make money at that model? >> Well, here's why I'll get there and I'll explain. What's going to happen is I lock them out of the market for four to five years. I'm going to take 50 to 60% of the market. Yes, I got to raise $10 billion to figure out how to do that. But that's not really what happens at the end. I become a data company because I have warranty data. I'm going to buy a company that does, you know, insurance like in Asurion. I'm going to get break/fix data from like a Best Buy or a company like that. I'm going to get at safety data from an underwriter's lab. It's a competition for data. And suddenly, I know those habits better than anyone else. I'm going to go do other things more than the TV. I'm not done with the TV. I'm going to do your entire kitchen. For $100 a month, I'll do a mid range. For like $500 a month, I'm going to take your dish washer, your washer, your dryer, your refrigerator, your range. And I'll do like Miele, Gaggenau, right? If you want to go down Viking, Wolf, I'll do it for $450 a month for the next 10 years. By year five, I have better insurance information than the insurance companies from warranty. And I can even make that deal portable. You see where we're going? >> Yeah so each of those are, I see them as data products. So you've got your TV service products, you've got your kitchen products, you've got your maintenance, you know, data products. All those can be monetized. >> And I went from TV manufacturer to underwriter overnight. I'm competing on data, on insurance, and underwriting. And more importantly, here's the green initiative. Here's why someone would give me $10 billion to do it. I now control 50% of all power consumption in North America because I'm also going to do HVAC units, right? And I can actually engineer the green capabilities in there to actually do better power purchase consumption, better monitoring, and of course, smart capabilities in those, in those appliances. And that's how you actually build a model like that. And that's how you can win on a data model. Now, where does HPE fit into that? Their job is to bring that data together at the edge. They bring that together in the middle. Then they have the ability to manage that on a remote basis and actually deliver those services in the cloud so that someone else can consume it. >> All right, so if you, you're hitting on something that some people have have talked about, but it's, I don't think it's widely sort of discussed. And that is, historically, if you're in an industry, you're in that industry's vertical stack, the sales, the marketing, the manufacturing, the R&D. You become an expert in insurance or financial services or whatever, you know, automobile manufacturing or radio and television, et cetera. Obviously, you're seeing the big internet giants, those 10 trillion, you know, some of the market caps, they're using data to traverse industries. We've never seen this before. Amazon in content, you're seeing Apple in finance, others going into the healthcare. So they're technology companies that are able to traverse industries. Never seen this before, and it's because of data. >> And it's the collapsing value chains. Their data value chains are collapsing. Comms, media, entertainment, tech, same business. Whether you sell me a live stream TV, a book, a video game, or some enterprise software, it's the same data value stream on multi-sided networks. And once you understand that, you can see retail, right? Distribution, manufacturing collapsed in the same kind of way. >> So Silicon Valley broadly defined, if I can include, you know, Microsoft and Amazon in there, they seem to have a dual disruption agenda, right? One is on the technology front, disrupting, you know, the traditional enterprise business. The other is they're disrupting industries. How do you see that playing out? >> Well the problem is, they're never going to be able to get into new industries going forward because of the monopoly power that people believe they have, and that's what's going on, but they're going to invest in creating joint venture startups in other industries, as they power the tools to enable other industries to jump and leap frog from where they are. So healthcare, for example, we're going to have AI in monitoring in ways that we never seen before. You can see devices enter healthcare, but you see joint venture partnerships between a big hyperscaler and some of the healthcare providers. >> So HPE transforming into a cloud company as a service, do you see them getting into insurance as you just described in your little digital example? >> No, but I see them powering the folks that are in insurance, right? >> They're not going to compete with their customers maybe the way that Amazon did. >> No, that's actually why you would go to them as opposed to a hyperscale that might compete with you, right? So is Google going to get into the insurance business? Probably not. Would Amazon? Maybe. Is Tesla in the business? Yeah, they're definitely in insurance. >> Yeah, big time, right. So, okay. So tell me more about your book. How's it being received? What's the reaction? What's your next book? >> So the book is doing well. We're really excited. We did a 20 city book tour. We had chances to meet everybody across the board. Clients we couldn't see in a while, partners we didn't see in a while. And that was fun. The reaction is, if you read the book carefully, there are $3 trillion market cap opportunities, $1000 billion unicorns that can be built right there. >> Is, do you have a copy for me that's signed? (audience laughing) >> Ray: Sorry (coughs) I'm choking on my makeup. I can get one actually, do you want one? >> Dave: I do, I want, I want one. >> Can someone bring my book bag? I actually have one, I can sign it right here. >> Dave: Yeah, you know what? If we have a book, I'd love to hold it. >> Ray: Do you have any here as well? >> So it's obviously you know, Everybody Wants to Rule the World: Surviving and Thriving in a world of Digital Giants, available, you know, wherever you buy books. >> Yeah, so, oh, are we still going? >> Dave: Yeah, yeah, we're going. >> Okay. >> Dave: What's the next book? >> Next book? Well, it's about disrupting those digital giants and it's going to happen in the metaverse economy. If we think about where the metaverse is, not just the hardware platforms, not just the engines, not just what's going on with the platforms around defy decentralization and the content producers, we see those as four different parts today. What we're going to actually see is a whole comp, it's a confluence of events that's going to happen where we actually bring in the metaverse economy and the stuff that Neal Stephenson was writing about ages ago in Snow Crash is going to come out real. >> So, okay. So you're laying out a scenario that the big guys, the disruptors, could get disrupted. It sounds like crypto is possibly a force in that disruption. >> Ray: Decentralized currencies, crypto plays a role, but it's the value exchange mechanisms in an Algorand, in an Ether, right, in a Cardano, that actually enables that to happen because the value exchange in the smart contracts power that capability, and what we're actually seeing is the reinvention of the internet. So you think, see things like SIOM pop-up, which actually is creating the new set of the internet standards, and when those things come together, what we're actually going to move from is the seller is completely transparent, the buyer's completely anonymous and it's in a trust framework that actually allows you to do that. >> Well, you think about those protocols, the internet protocols that were invented whenever, 30 years ago, maybe more, TCP/IP, wow. I mean, okay. And they've been co-opted by the internet giants. It's the crypto guys, some of the guys you've mentioned that are actually innovating and putting, putting down new innovation really and have been well-funded to do so. >> I mean, I'll give you another example of how this could happen. About four years ago, five years ago, I wanted to buy Air Canada's mileage program, $400 million, 10 million users, 40 bucks a user. What do I want them in a mileage program? Well think about it. It's funded, a penny per mile. It's redeemed at 1.6 cents a mile. It's 2 cents if you buy magazines, 2 1/2 cents if you want, you know, electronics, jewelry, or sporting equipment. You don't lose money on these. CFOs hate them, they're just like (groans) liability on the books, but they mortgage the crap out of them in the middle of an ish problem and banks pay millions of dollars a year pour those mileage points. But I don't want it for the 10 million flyers in Canada. What I really want is the access to 762 million people in Star Alliance. What would happen if I turned that airline mileage program into cryptocurrency? One, I would be the world's largest cryptocurrency on day one. What would happen on day two? I'd be the world's largest ad network. Cookie apocalypse, go away. We don't need that anymore. And more importantly, on day three, what would I do? My ESG here? 2.2 billion people are unbanked in the world. All you need is a mobile device and a connection, now you have a currency without any government regulation around, you know, crayon banking, intermediaries, a whole bunch of people like taking cuts, loansharking, that all goes away. You suddenly have people that are now banked and you've unbanked, you've banked the unbanked. And that creates a whole very different environment. >> Not a lot of people thinking about how the big giants get disintermediated. Get the book, look into it, big ideas. Ray Wang, great to see you, man. >> Ray: Hey man, thanks a lot. >> Hey, thank you. All right and thank you for watching. Keep it right there for more great content from HPE's big GreenLake announcements. Be right back. (bright music)

(upbeat music) >> Okay, we're back here in theCUBE, this is day three of our coverage right here in the middle of all the action of Cloud City at Mobile World Congress. This is the hit of the entire show in Barcelona, not only in person, but out on the interwebs virtually, this is a hybrid event. This is back to real life, and theCUBE is here. I'm John Furrier and Dave Vellante and DR is here, Danielle Royston. >> Totally. >> Welcome back to theCUBE for the fourth time now at the anchor desk, coming back, we love you. >> Well, it's been a busy day, it's been a busy week. It's been an awesome week. >> John: Feeling good? >> Oh my God. >> You made the call. >> I've made the call. >> You did on your podcast what, months ago. >> Yeah, right? >> You made the call. >> Made the call. >> You're on the right side of history. >> Right, and people were like, it's going to be canceled. COVID won't be handled, blahbity blah. >> She's crazy. >> Nope, I was just crazy, I'm okay with that, right? >> Crazy good. >> Right, I'm like I'm forward looking in a lot of ways. And we were looking towards June and we're like, I think this is going to be the first event back. >> You know, the crazy ones commercial that Apple ran is one of the best commercials of all time. You can't ignore the crazy ones in a good way. You can't ignore what you're doing. And I think to me, what I'm so excited about is cause we've been covering cloud we're cloud bigots, we love the cloud, public cloud. We've been on that train from day one. But when you hear the interviews we did here in theCUBE and interviews that we talked about with the top people, Google, Amazon Web Services. We're talking about the top people, both technology leaders like Bill Vass and the people who run the telecom verticals like Alfonzo, Adolfo, I mean, Hernandez. We had Google's top networking executive, we had their industry leader and the telecom, Microsoft and the Silicon all are validating, and it's like, surround sound to what you're saying here, and it cannot be ignored. >> I mean, we are coming to a big moment in Telco, right? And I mean, I've been saying it's coming. I called 2021, the year of Public Cloud and Telco. It helped that Erickson bailed. So thank you, Erickson people. >> It was a gift. >> It was a gift. >> It was. >> It really was a gift. And it was not just for me, but I think also for the vendors in the booth, I mean, we have a Cloud City army, right? Here we go, let's start marching, and it's awesome. >> He reminds me of that baseball player that took a break, cause he had a hangover and, Cal Ripkin. >> Cal Ripkin? >> Yeah, what was that guy's name? >> Did that really happen? >> Yeah, he took a break and uh- >> New guy stepped in. >> Yeah, and so well, not Cal Ripkin. >> No, no, so before, you want to know, who was it, Lou Gehrig? >> Lou Gehrig, yeah, Lou Gehrig. >> Right, so, Lou Gehrig was nobody, and we can't remember the guy's name, nobody knows the guy's name, what was that guy's name? Nobody knows, oh, there's Lou Gehrig, he got hurt. He sat out and Lou Gehrig replaced him and never hear of him again. >> Danielle: Love it, I'll take that. >> Never, never missed a game for his entire career. So again, this is what Erickson did, they just okay, take a break. >> Yeah, but I mean, it's been great again. I had a great day yesterday, my keynote was delivered. Things are going well with the booth, we had Jon Bon Jovi. I mean, that was just epic and it was acoustic and it was right after lockdown. I think everyone was really excited to be there. But I was talking to a vendor that said we'd been able to accomplish in three days, what normally it would take three years from a sales funnel perspective. I mean, that's big and that's not me. That's not my organization. That's other organizations that are benefiting from this energy. Oh, it's awesome. >> The post isolation economy has become a living metaphor for transformation, and I've been trying to sort of grok and put the pieces together as to how this thing progresses in my interview with Portal One in particular really brought it into focus for me, anyway, I'd love to get your thoughts. One of the things we haven't talked much about is public policy, and I think about all the time, all the discussion in the United States about infrastructure, this is critical infrastructure, right? And the spectrum is a country like South Africa saying, come on in, we want to open up. We want to innovate, to me, that's the model for these tier two and tier three Telcos that are just going to disrupt the big guys, whereas, maybe China's maybe on the other end of the spectrum, very controlling, but it's the former that is going to adopt the cloud sooner, and it's going to completely transform the next decade. >> Yeah, I think this is a great technology for a smaller challenge or CSP that still is a large successful company to challenge the incumbents that are, they are dinosaurs too, they move a little bit slow, and maybe if you're a little bit faster, quicker dinosaur you'll survive longer, maybe you'll be able to transform and, and a public cloud enables that. And I think, you know I'm playing the long game here, right? Is public cloud already for every Telco in every corner of the world, no. And there's a couple of things that are barriers to that. We don't really talk about the downsides, and so maybe we sort of wrap up with- there are challenges and acknowledge there are challenges, you know, in some cases their data regulations and issues, right? And you can't right? There's not a hyperscaler in your country, right? And so you're having a little bit of challenges, but you trend this out over 10 years and then pace it with the hyperscalers that are building new data centers. They're each at 25 plus each, you know, plus or minus a few, right? They're marching along, and you trend this out over 10 years, I think one of two things happened, your data regulations are eased or a hyperscaler appears in a place you can use it, and those points converge and hopefully the software's there, and that's my effort and (claps) yeah. >> Dave: You know what's an interesting trend, DR and John, that is maybe a harbinger to this, is you just mentioned something. If the hyperscalers might not have a presence in, in a country, you know what they're doing? And our data shows this, I do that weekly series breaking analysis and the data Openstack was popping up. Like where does OpenStack come from, well, guess what, when you cut the data, it was Telcos using open source to build clouds in regions where there was no hyperscalers. >> It's a gap filler. >> Yeah, it's a gap filler, it's a bandaid. >> But I think this is where, like. outpost is such a great idea, right? Like getting outposts, and I think Microsoft has the ability to do this as well, Google less so, right? They're not providing the staff, they're doing Anthos. So you're still managing this, the rack, but they're giving you the ability to tap into their services. But I was talking to a CTO in Bolivia. He was like, we have data privacy issues in our country. There's no hyperscaler, not sure Bolivia is like next on the list for AWS, right? But he's like, I'm going to build my own public cloud. And I'm like why would you do that when you can just use outposts? And then when your data regulations release, where they get to Bolivia, you can switch and you're on the stack, and you're ready to go. I think that's what you should do. You should totally do that. >> John: Yeah, one of the things that's come up on here in the interviews, in theCUBE and here, the show is that there are risk takers and innovators and there's operators. And this has been the consistent theme around, yeah, the on-premises world you mentioned this regulation reasons, and or some workflows just have to be on premise for security reasons, whatever, that's the corner case. But the operating model of the technology architecture is shifted. And that reality, I don't think is debatable, so I find it, I got to ask you this because I'm really curious. I know you get a lot of people staring at ya, oh the public cloud's just a hosting, but why aren't people getting this architectural shift? I mean, you mentioned outpost and wavelength, which Amazon has, is a game changer. It's Amazon cloud at the hub. >> Yeah, at the edge. >> Okay, that's a low latency, again, low-hanging fruit applications, real buys, whatnot. I mean, that's an architectural dot that's been connected. Why are people getting it. >> In our industry, I think it is a lot of not invented here syndrome, right? And that's a very sort of nineties thought and I have been advocating stand on the shoulders of the greatest technologists in the world, right, and you know, there's, there is a geopolitical US thing, I think we lived through a presidency that had a sort of nationalistic approach and a lot of those conversations pop up, but I've also looked to these guys and I'm like, you're still, you still have your Huawei kit installed. And there's concerns with that too. So, and you picked it because of cost, and it's really hard to switch off of, so give me a break with your public cloud USA stuff, right? You can use it, you're just making excuses, you're just afraid. What are you afraid of, the HR implications? Let's talk about that, right? And the minute I take it there, conversation changes. >> Yeah, I talked to Teresa Carlson when she was running the public sector at AWS, she's now president of Splunk. I call her a Renaissance woman. She's been a great leader and public sector for this weird little pocket of AWS where it's a guess a sales division, but it's still its own company. >> Danielle: Yeah. >> And she's, did the CIA deal, the DOD, and the public sector partnerships are now private, a lot more private relationships, So it's not like just governments, you mentioned government and national security, and these things, you started to see the ecosystem not, not just be about companies, >> Danielle: Yeah. >> Government and private sector. So this whole vibe of the telecom being regulated, unregulated, unbundled is an interesting kind of theory. What's your thoughts and reactions to this, kind of this, melting pot of ecosystem change and evolution? >> Danielle: Yeah, I mean. I think there's a very nationalistic approach by the Telcos, right? They sort of think about the countries that they operate in. There's a couple of groups that go across multiple countries, but can there be a global Telco? Can that happen, right? Just like we say, you were saying it earlier, Netflix, right? You can say Netflix, UK. Right, and so can we have a global Telco, right. That is challenging on a lot of different levels. But think about that in a public cloud start to enable that idea, right? Elon Musk is going to get to Mars. You need a planetary level Telco. And I can, I think that day is, I mean, I don't think it's tomorrow, but I think that's like 10, 20 years away. >> Dave: You're done, we're going to see it start this decade, it's already starting. We're going to see the fruits of that dividend. >> Danielle: Yeah, it's crazy. >> I've got to ask you, you're a student of the industry and you get so much experience, it's great to have you on theCUBE and chat about, riff about these things, but, the classic who's ready for disruption question comes up, and I think there's no doubt that the Telcos as an industry has been slow moving and the role and the importance has changed. People need the need to have the internet access they need to access. >> Yeah. >> So, and you've got the edge, now applications are now running on it, since the iPhone 14 years ago, as you pointed out, people now are interested in how packets move. That's fast whether it's a doctor or an emergency worker or someone. >> Danielle: What we have done in 2020 without the internet and broadband and our mobile phones, I mean? >> You know, I think about 1920 when the Spanish flu pandemic hit a hundred years ago, those guys did not have mobile phones and they must have been bored, right? I mean, what are you going to do, right? And so, yeah I think last year really moved a lot of thinking forward in this respect, so. >> Yeah, it's always like that, that animal out in the Serengeti that gets taken down, you know, by the cheetah or the lion. How do know when someone is going to be disrupted What's the, what's the tell sign in your mind, you look at the Telco landscape. What is someone waiting to be disrupted or replaced like? >> You know what they're ostriches, how do you say that word, right? They stick their head in the sand. Like I don't want to talk about it, la la la, I don't want to, I don't want to think about it. You know, they bring up all these like roadblocks, and I'm like, okay, I'm going to come visit you in another six months to a year, and let's see what happens when the guys that are moving fast that are open-minded to this, and it's, I mean, when you start to use the public cloud, you don't, like, turn it on overnight. You start experimenting, right? You start, you take an application that is non-threatening. You have, I mean, these guys are running thousands of apps inside their data centers. Pick some boring ones, pick some old ones that no one likes, and move that to the public cloud, play with it. Right, I'm not talking about moving a whole network overnight tomorrow. You got to learn, you have no, I mean, very little talent in the Telco that know how to program against the AWS stack. Start hiring, start doing it, and you're going to start to learn about the compensation, and I used to do compensation, right? I spent a lot of time in HR, right? The compensation points and structures, they compare AWS and Google, versus a Telco. Do you want Telco stock? Do you want Google stock? >> Dave: Right, where do you want to go? >> Right, right? like that's going to challenge the HR organization in terms of compensate. How do we compensate our people when they're learning these new valuable skills? >> When you think about disruption, you know, the master or the professor of disruption, Clay Christensen, one of the best lectures he ever gave was who at Cambridge, and he gave a lecture on the steel industry, and he was describing it, it was like four layers of value in the steel industry, the value chain, it started with rebar, like the lowest end, right? >> Danielle: Yeah yeah. >> And the Telco's actually the opposite, so that, you know, when, when the international companies came in, they went after rebar, and the higher end steel companies said, nah, let them have it, that's the low margin stuff. And then eventually, uh, when they got up to the high end. >> Danielle: It was over, yeah. >> The Telcos are the opposite. They're like, the, you know, in the, in the conductivity and they're hanging on to that because it's so big, but all the high value stuff, it's already gone to the, over the top players, right. >> It's being eaten away, and I'm like, what is going to wake you guys up to realize those are your competitors, that's where the battle is, right? >> John: That's really where the value is. >> The battle of the bastards, you're there by yourself, like "Game of Thrones" and they're coming at you. >> John: You need a dragon. >> What are you doing about it? >> John: I need a dragon to compete in this market. Riding a dragon would be a good strategy. >> I know, I was just watching. Cause I have a podcast, I have a podcast called "Telco In 20" and we always put like little nuggets in the show notes, I personally reviewed them, I was just reviewing the one for the keynote that we're putting out, and I had a dragon in my keynote, right? It was a really great moment, it was really fun to do, but there's, I don't know if you guys are "Game of Thrones" fans. >> Yeah. >> Sure. >> Right, but there's a great moment when Daenerys gets her dragons, the baby dragons, and she takes over the Unsullied Army, right? And it's just this, right? Like all of a sudden the tables turn in an instant where she has nothing, and she's like on her quest, right. I'm on a quest. >> Dave: Comes out of the fire. >> Right, comes out of the fire, the unburnt, right? She has her dragons, right? She has them hatch. She takes over the Unsullied Army, right? Slaves, it starts her march, right? And I'm like, we're putting that clip into the show notes because I think that's where we are. I think I've hatched some dragons, right? The Cloud City army, let's go, let's go take on Telco. >> John: Well, I mean, this to me. >> Easy. >> It definitely have made, made it happen because I heard many people talking about cloud, this is turning into a cloud show. The question is, when does this going to be a cloud show? That's just Cloud City, it's a big section of the show. I mean, all the big players are behind it. >> Danielle: Yeah, yeah. >> Amazon Web Services, Google Azure, Ecosystem, startups, thinking differently, but everyone's agreeing why aren't we doing this? >> I think, like I said, I mean, people are like, you're such a visionary, and how did, why do you think this will work, I'm like, it's worked in every other industry. Am I really that visionary, and like, these are the three best tech companies in the world, like, are, are you kidding me? And so I think we've shown the momentum here. I think we're looking forward to 2022, you know? And that we see 2022, you got to start planning this the minute we get back, right? Like I wouldn't recommend doing this in a hundred days again, that was a very painful, but you know, February, I was, there's a sign inside NWC, February 28th. Right, we're talking seven months. You got to get going now. >> John: Let's get on the phone. >> With Telco, I mean, I think you're right on. I mean, you know, remember Skype, in the early days, right? >> Danielle: Yeah, yeah. >> It wasn't regional. It was just, plug into the internet. >> Danielle: It was just Skype, it was just WhatsApp. >> Well this is a great location, if you can get a shot guys of the people behind us, I don't know if you can, if you're watching check out the scene here, It's winding down, a lot of people having happy hour. Now this is a social construct here at Cloud City, not only is it chock full of information, reporting that we're doing and getting all the data and with the presentations on the main stage, with Adam and the studio and the team, this is a place where people are meeting and there's deals being done face to face, intimate relationships, the best of the best are here, they make the trek. So there's been a successful formula. Of course theCUBE is in the middle of all the action, which we love, we're psyched to be back. I want to thank you personally, while we have you on stage here. >> I want to thank you guys, and the crew, the crew has been amazing, turning out videos on short order. We have all these crews in different cities, it's, our own show has been virtual. You know, Adam's in Bristol, right? We're here, this was an experiment, we talked about this a hundred days ago, 90 days ago. Could we get theCUBE there, do the show but also theCUBE. >> You are a visionary, you said made for TV hybrid event with your team, produce television shows, theCUBE, we're digital, we love you guys, great alignment, but it's magical because the content doesn't end here, the show might end, they might break down the beautiful plants and the exhibits, but the community is going to continue, the content and the conversations. >> Yeah. >> So, we were looking forward to it and- >> I'm super glad, super glad we did this. >> Awesome, well, any final moments that you would like to share in the last two minutes we have, favorite moments, observations, funny things that have happened to you, weird things that have happened to you, share something that people might not know, or a favorite moment? >> I think, I don't know that people know, we have a 3D printer in the coffee shops, and so you can upload any picture and they're 3d printing, coffee art, right? So I've been seeing lots of social posts around people uploading their, their logos and things like that. I think Jon Bon Jovi, he was super thankful to be back. He thanked me personally two different times of like, I'm just glad to be out in front of people. And I think just even just the people walking around, thank you for being brave, thank you for coming back. You've helped Barcelona and we're happy to be together. Even if it is with masks, it's hard to do business with masks on, everyone's happy and psyched. >> John: Well the one thing that people cannot do relative to you is they cannot ignore you. You are making a great big wave. >> Danielle: I shout pretty loud, It's kind of hard to ignore me. >> You're making a great big wave, you're on the right side, we believe, of history, public cloud is driving the bus down main street of Cloud City, and if people don't get out of the way, they will be under the bus. >> I'm, like I said, in my keynote, it's go time let's do it. >> Okay. Thank you so much for all your attention and mission behind the cloud and the success. >> Danielle: We'll do it again. We're going to do it again soon. >> After Togi's a hundred million dollar investment, you're the CEO of Togi that, let's follow that progress, and of course, Telco DR, Danielle Royston, the digital revolution. Thanks for coming on with you. >> Thank you guys, it was super fun. >> This is theCUBE I'm John Furrier with Dave Vallante, we're going to send it back to Adam in the studio. Thanks, the team here. >> Woo! (audience applauding) >> I want to thank the team, everyone here, Adam is great, Chloe. >> Great working with you guys. >> Awesome, and what a great crew. >> So great. >> Thank you everybody. That's it for theCUBE, here on the last day, Wednesday of theCUBE, stay tuned for tomorrow more action on the main stage, here in Cloud City. Thanks for watching.

(upbeat music) >> Hey, we're back here in theCube. This is day three of our coverage right here in the middle of all the action of Cloud City at Mobile World Congress. This is the hit of the entire show in Barcelona, not only in person, but out on the interwebs virtually. This is a hybrid event. This is back to real life, and theCube is here. I'm John Furrier with Dave Vellante and D. R. is here, Danielle Royston. >> Totally. >> Welcome back to theCube for fourth time. now at the anchor desk, coming back. >> I don't know. It's been a busy day. It's been a busy week. It's been an awesome week. >> Dave: Feeling good? >> Oh, my god. >> You made the call. >> I made the call. You finished your podcast, what months ago? >> Yeah. >> Made the call. >> Made the call. You're on the right side of history. >> Right? And people were like, "It's going to be canceled. COVID won't be handled." Blahbity blah. >> She's crazy. >> And I'm like, nope. She's crazy. I'm okay with that. Right? But I'm like... >> Crazy good. >> Right, I'm like, I'm forward-looking in a lot of ways. And we were looking towards June, and we're like, "I think this is going to be the first event back. We're going to be able to do it." >> You know, the crazy one's commercial that Apple ran, probably one of the best commercials of all time. You can't ignore the crazy ones in a good way. You can't ignore what you're doing. And I think to me, what I'm so excited about is, 'cause we've been covering cloud. We're cloud bigots. We love the cloud, public cloud. We've been on that train from day one. But when you hear the interviews we did here on theCube and interviews that we talked about with the top people, Google, Amazon Web Services. We're talking about the top people, both technology leaders like Bill Vass and the people who run the Telecom Verticals like Alf, Alfonzo. >> Danielle: Yeah. >> Adolfo, I mean, Hernandez. >> Danielle: Yeah. >> We had Google's top networking executive. We had their industry leader in the telecom, Microsoft, and the Silicon. All are validating, and it's like surround sound to what you're saying here. And it cannot be ignored. >> I mean, we are coming to a big moment in Telco, right? And I mean, I've been saying that it's coming. I called 2021, the year of public cloud and Telco. It helped that Ericcson bailed. So thank you, Ericcson people. >> Dave: It was a gift. >> It was a gift. >> John: It really was. >> It really was a gift. And it was not just for me, but I think also for the vendors in the booth. I mean, we have a Cloud City army, right? Here we go. Let's start marching. And it's awesome. >> He reminds me of that baseball player that took a break 'cause he had a hangover and Cal Ripken. >> Cal Ripken, right, yeah, yeah. What was that guy's name? >> Did it really happen? >> Yeah, he took a break and... >> The new guy stepped in? >> Yeah, and so we'll go to Cal Ripken. >> No, no, so before it was it? Lou Gehrig. >> Lou Gehrig, yeah. >> Right, so Lou Gehrig was nobody. And we can't remember the guy's name. Nobody knows the guy's name. >> Danielle: Yeah, yeah. >> What was that guy's name? Nobody knows. Oh, 'cause Lou Garrett, he got hurt. >> Danielle: And Lou Gehrig stepped in. >> He sat out, and Lou Gehrig replaced him. >> Danielle: Love it. >> And never heard of him again. >> Danielle: I'll take that. >> Never missed a game. Never missed a game for his entire career. So again, this is what Ericcson did. They just okay, take a break and... >> But I mean, it's been great. Again, I had a great day yesterday. My keynote was delivered. Things are going well with the booth. We had Jon Bon Jovi. I mean, that was just epic, and it was acoustic, and it was right after lockdown. I think everyone was really excited to be there. But I was talking to a vendor that said we'd been able to accomplish in three days what normally it would take three years from a sales funnel perspective. I mean, that is, that's big, and that's not me. That's not my organization. That's other organizations that are benefiting from this energy. Oh, that's awesome. >> The post-isolation economy has become a living metaphor for transformation. And I've been trying to sort of grok and put the pieces together as to how this thing progresses. And my interview with Portaone, in particular, >> Danielle: Yeah. >> really brought it into focus for me, anyway. I'd love to get your thoughts. One of the things we haven't talked much about is public policy. And I think about all the time, all the discussion in the United States about infrastructure, this is critical infrastructure, right? >> Danielle: Yeah. >> And the spectrum is a country like South Africa saying, "Come on in. We want to open up." >> Danielle: Yeah. >> "We want to innovate." And to me that's to me, that's the model for these tier two and tier three telcos that are just going to disrupt the big guys. Whereas, you know, China, may be using the other end of the spectrum, very controlling, but it's the former that is going to adopt the cloud sooner. It's going to completely transform the next decade. >> Yeah, I think this is a great technology for a smaller challenger CSP that still is a large successful company to challenge the incumbents that are, they are dinosaurs too. They move a little bit slow. And maybe if you're a little bit faster, quicker dinosaur you'll survive longer. Maybe it will be able to transform and a public cloud enables that. And I think, you know, I'm playing the long game here, right? >> Dave: Yeah. >> Is public cloud ready for every telco in every corner of the world? No. And there's a couple of things that are barriers to that. We don't really talk about the downsides, and so maybe we sort of wrap up with, there are challenges, and I acknowledge there are challenges. You know, in some cases there are data regulations and issues, right? And you can't, right? There's not a hyperscaler in your country, right? And so you're having a little bit of challenges, but you trend this out over 10 years and then pace it with the hyperscalers are building new data centers. They're each at 25 plus each, plus or minus a few, right? They're marching along, and you trend this out over 10 years, I think one of two things happens. Your data regulations are eased or you a hyperscaler appears in a place you can use it. And those points converge, and hopefully the software's there, and that's my effort. And, yeah. >> You know what's an interesting trend, D. R., John? That is maybe a harbinger to this. You just mentioned something. If the hyperscalers might not have a presence in a country, you know what they're doing? And our data shows this, I do that weekly series "Breaking Analysis," and the data, OpenStack was popping up. >> Danielle: Yeah. >> Like where does OpenStack come from? Well, guess what. When you cut the data, it was telcos using open source to build clouds in regions where there was no hyperscaler. >> Where it didn't exist, yeah. >> So it's a-- >> Gap-filler. >> Yeah, it's a gap-filler. It's a Band-aid. >> But I think this is where like Outpost is such a great idea, right? Like getting Outposts, and I think Microsoft has the ability to do this as well, Google less so, right. They're not providing the staff. They're doing Anthos, so you're still managing this, the rack, but they're giving you the ability to tap into those services. But I was talking to a CE, a CTO in Bolivia. He was like, "We have data privacy issues in our country. There's no hyperscaler." Not sure Bolivia is like next on the list for AWS, right? But he's like, "I'm going to build my own public cloud." And I'm like, "Why would you do that when you can just use Outposts?" And then when your data regulations release or there's a, they get to Bolivia, you can switch and you're on the stack and you're ready to go. I think that's what you should do. You should totally do that. >> Yeah, and one of the things that's come up here on the interviews and theCube and here, the show, is that there are risk takers and innovators and there's operators. And this has been the consistent theme around, yeah, the on-premises world. You mentioned this regulation reasons and/or some workflows just have to be on premise for security reasons, whatever. That's the corner case. >> Danielle: Yeah. >> But the operating model of the technology architecture is shifted. >> Danielle: Yep. >> And that reality, I don't think, is debatable. So I find it. I've got to ask you this because I'm really curious. I know you get a lot of people steering 'ya, oh the public cloud's just a hosting, but why aren't people getting this architectural shift? I mean, you mentioned Outpost, and Wavelength, which Amazon has, is a game changer. It's Amazon Cloud at the hub. >> Yeah, at the edge, yeah. >> Okay, that's a low latency again, low-hanging fruit applications, robotics, whatnot. I mean, that's an architectural dot that's been connected. >> Yeah. >> Why aren't people getting it? >> In our industry, I think it is a lot of not invented here syndrome, right? And that's a very sort of nineties thought, and I have been advocating stand on the shoulders of the greatest technologists in the world. Right? And you know, there is a geopolitical US thing. I think we lived through a presidency that had a sort of nationalistic approach and a lot of those conversations pop up, but I've also looked to these guys and I'm like, you still have your Huawei kit installed, and there's concerns with that, too. So, and you picked it because of cost. And it's really hard to switch off of. >> John: Yeah. >> So give me a break with your public cloud USA stuff, right? You can use it. You're just making excuses. You're just afraid. What are you afraid of? The HR implications? Let's talk about that, right? And the minute I take it there, conversation changes. >> I talked to Teresa Carlson when she was running the public sector at AWS. She's now president of Splunk. I call her a Renaissance woman. She's been a great leader. In public sector there's been this weird little pocket of AWS where it's, I guess, a sales division, but it's still its own company. >> Danielle: Yeah. >> And she just did the CIA deal. The DOD and the public sector partnerships are now private, a lot more private relationships. So it's not like just governments. You mentioned government and national security and these things. You start to see the ecosystem, not, not just be about companies, government and private sector. So this whole vibe of the telecomm being regulated, unregulated, unbundled is an interesting kind of theory. What's your thoughts and reactions to this kind melting pot of ecosystem change and evolution? >> Yeah, I mean, I think there's a very nationalistic approach by the telcos, right? They sort of think about the countries that they operate in. There's a couple of groups that go across multiple countries, but can there be a global telco? Can that happen, right? Just like we say, you were saying it earlier, Netflix. Right? You didn't say Netflix, UK, right? And so can we have a global telco, right? That is challenging on a lot of different levels. But think about that in a public cloud starts to enable that idea. Right? Elon Musk is going to get Mars. >> Dave: Yep. >> John: Yeah. >> You need a planetary level telco, and I think that day is, I mean, I don't think it's tomorrow, but I think that's like 10, 20 years away. >> You're done. We're going to see it start this decade. It's already starting. >> Danielle: Yeah. >> But we're going to see the fruits of that dividend. >> Danielle: Right, yeah. >> I got to ask you. You're a student of the industry and you got so much experience. It's great to have you on theCube and chat about, riff about, these things, but the the classic "Who's ready for disruption?" question comes up. And I think there's no doubt that the telcos, as an industry, has been slow moving, and the role and the importance has changed. People need the need to have the internet access. They need to access. >> Danielle: Yeah. >> So and you've got the Edge. Now applications are now running on a, since the iPhone 14 years ago, as you pointed out, people now are interested in how packets move. >> Danielle: Yeah. >> That's fast, whether it's a doctor or an emergency worker or someone. >> What would we have done in 2020 without the internet and broadband and our mobile phones? I mean. >> Dave: We would have been miserable. >> You know, I think about 1920 when the Spanish flu pandemic hit a hundred years ago. Those guys did not have mobile phones, and they must have been bored, right? I mean, what are you going to do? Right? And so, yeah, I think, I think last year really moved a lot of thinking forward in this respect, so. >> Yeah, it's always like that animal out in the Serengeti that gets taken down, you know, by the cheetah or the lion. How do you know when someone is going to be disrupted? What's the, what's the tell sign in your mind? You look at the telco landscape, what is someone waiting to be disrupted or replaced look like? >> Know what? They're ostriches. Ostriches, how do you say that word right? They stick their head in the sand. Like they don't want to talk about it. La, la, la, I don't want to. I don't want to think about it. You know, they bring up all these like roadblocks, and I'm like, okay, I'm going to come visit you in another six months to a year, and let's see what happens when the guys that are moving fast that are open-minded to this. And it's, I mean, when you start to use the public cloud, you don't like turn it on overnight. You start experimenting, right? You start. You take an application that is non-threatening. You have, I mean, these guys are running thousands of apps inside their data centers. Pick some boring ones. Pick some old ones that no one likes. Move that to the public cloud. Play with it, right? I'm not talking about moving your whole network overnight tomorrow. You got to learn. You have no, I mean, very little talent in the telco that know how to program against the AWS stack. Start hiring. Start doing it. And you're going to start to learn about the compensation. And I used to do compensation, right? I spent a lot of time in HR, right? The compensation points and structures, and they can bear AWS and Google versus a telco. You want Telco stock? Do you want Google stock? >> John: Right, where do you want to go? >> Right? Right? And so you need to start. Like that's going to challenge the HR organization in terms of compensate. How do we compensate our people when they're learning these new, valuable skills? >> When you think about disruption, you know, the master or the professor of disruption, Clay Christensen, one of the best lectures he ever gave is we were at Cambridge, and he gave a lecture on the steel industry and he was describing it. It was like four layers of value in the steel industry, the value chain. It started with rebar, like the lowest end. Right? >> Danielle: Yeah, yeah. >> And the telco's actually the opposite. So, you know, when the international companies came in, they went after rebar, and the higher end steel companies said, "Nah, let them have it." >> Danielle: Let it go. >> "That's the low margin stuff." And then eventually when they got up to the high end, they all got killed. >> Danielle: It was over, yeah. >> The telcos are the opposite. They're like, you know, in the connectivity, and they're hanging on to that because it's so big, but all the high value stuff, it's already gone to the over-the-top players, right? >> It's being eaten away. And I'm like, "What is going to wake you guys up to realize those are your competitors?" That's where the battle is, right? >> Dave: That's really where the value is. >> The battle of the bastards. You're there by yourself, the Game of Thrones, and they're coming at you. >> John: You need a dragon. >> What are you doing about it? >> I need a dragon. I need a dragon to compete in this market. Riding on the dragon would be a good strategy. >> I know. I was just watching. 'Cause I have a podcast. I have a podcast called "Telco in 20," and we always put like little nuggets in the show notes. I personally review them. I was just reviewing the one for the keynote that we're putting out. And I had a dragon in my keynote, right? It was a really great moment. It was really fun to do. But there's, I don't know if you guys are Game of Thrones fans. >> Dave: Oh, yeah. >> John: For sure. >> Right? But there's a great moment when Daenerys guts her dragons, the baby dragons, and she takes over the Unsullied Army. Right? And it's just this, right? Like all of a sudden, the tables turn in an instant where she has nothing, and she's like on her quest, right? I'm on a quest. >> John: Comes out of the fire. >> Right, comes out of the fire. The unburnt, right? She has her dragons, right? She has them hatch. She takes over the Unsullied Army, right? Slays and starts her march, right? And I'm like, we're putting that clip into the show notes because I think that's where we are. I think I've hatched some dragons, right? The Cloud City Army, let's go, let's go take on Telco. >> John: Well, I mean to me... >> Easy. >> I definitely have made it happen because I heard many people talking about cloud. This is turning into a cloud show. The question is, when does this be, going to be a cloud show? You know it's just Cloud City is a big section of the show. I mean, all the big players are behind it. >> Danielle: Yeah, yeah. >> Amazon Web Services, Google, Azure, Ecosystem, startups thinking differently, but everyone's agreeing, "Why aren't we doing this?" >> I think, like I said, I mean, people are like, you're such a visionary. And how did, why do you think this will work? I'm like, it's worked in every other industry. Am I really that visionary? And like, these are the three best tech companies in the world. Like, are you kidding me? And so I think we've shown the momentum here. I think we're looking forward to 2022, you know? And do we see 2022, you get to start planning this the minute we get back. Right? >> John: Yeah. >> Like I wouldn't recommend doing this in a hundred days again. That was a very painful, but you know, February, I was, there's a sign inside NWC, February 28th, right? We're talking seven months. You got to get going now. >> John: Let's get on the phone. (John and Dave talking at the same time) >> I mean, I think you're right on. I mean, you know, remember Skype in the early days? >> Danielle: Yeah, yeah, yeah, yeah. >> It wasn't regional. >> Danielle: Yeah. >> It was just plug into the internet, right? >> Danielle: It was just Skype. It was just WhatsApp. >> Well, this great location, and if you can get a shot, guys, of the people behind us. I don't know if you can. If you're watching, check out the scene here. It's winding down. A lot of people having happy hour now. This is a social construct here at Cloud City. Not only is it chock full of information, reporting that we're doing and getting all the data and with the presentations on the main stage with Adam and the studio and the team. This is a place where people are meeting and there's deals being done face to face, intimate relationships. The best of the best are here. They make the trek, so there's been a successful formula. Of course theCube is in the middle of all the action, which we love. We're excited to be back. I want to thank you personally while we have you on stage here. >> I want to thank you guys and the crew. The crew has been amazing turning out videos on short order. We have all these crews in different cities. It's our own show has been virtual. You know, Adam's at Bristol, right? We're here. This was an experiment. We talked about this a hundred days ago, 90 days ago. Could we get theCube there and do the show, but also theCube. >> You are a visionary. And you said, made for TV hybrid event with your team, reduced television shows, theCube. We're digital. We love you guys. Great alignment, but it's magical because the content doesn't end here. The show might end. They might break down the beautiful plants and the exhibits, but the community is going to continue. The content and the conversations. >> Yeah. >> So. >> We are looking forward to it and. >> Yeah, super-glad, super-glad we did this. >> Awesome. Well, any final moments that you would like to share? And the last two minutes we have, favorite moments, observations, funny things that have happened to you, weird things that have happened to you. Share something that people might not know or a favorite moment. >> I think, I mean I don't know that people know we have a 3D printer in the coffee shops, and so you can upload any picture, and there are three 3D printing coffee art, right? So I've been seeing lots of social posts around people uploading their, their logos and things like that. I think Jon Bon Jovi, he was super-thankful to be back. He thanked me personally two different times of like, I'm just glad to be out in front of people. And I think just even just the people walking around, thank you for being brave, thank you for coming back. You've helped Barcelona, and we're happy to be together even if it is with masks. It's hard to do business with masks on. Everyone's happy and psyched. >> The one thing that people cannot do relative to you is they cannot ignore you. You are making a great big waves. >> Danielle: I shout pretty loud. It's kind of hard to ignore me. >> Okay, you're making a great big wave. You're on the right side, we believe, of history. Public cloud is driving the bus down main street of Cloud City, and if people don't get out of the way, they will be under the bus. >> And like I said, in my keynote, it's go time. Let's do it. >> Okay, thank you so much for all your tension and mission behind the cloud and the success of... >> Danielle: We'll do it again. We're going to do it again soon. >> Ketogi's hundred million dollar investment. Be the CEO of Togi as we follow that progress. And of course, Telco D. R. Danielle Royston, the digital revolution. Thanks for coming on theCube. >> Thank you, guys. It was super-fun. Thank you so much. >> This is theCube. I'm John Furrier with Dave Vellante. We're going to send it back to Adam in the studio. Thanks the team here. (Danielle clapping and cheering) I want to thank the team, everyone here. Adam is great. Chloe, great working with you guys. Awesome. And what a great crew. >> So great. >> Thank you everybody. That's it for theCube here on the last day, Wednesday, of theCube. Stay tuned for tomorrow, more action on the main stage here in Cloud City. Thanks for watching.

(upbeat music) >> Hello and welcome today's session of the cube presentation of the 80 best startup showcase. The next big thing in security featuring Anitian for the security track. I'm your host John Furrier. We're here with the CEO of Anitian, Rakesh Narasimhan, and Aditya Muppavarapu global segment leader of Dev ops for 80 minutes partner network, Rakesh, Aditya, Thanks for coming on. Appreciate it. >> Thank you very much, John. Pleasure is mine. >> So this is the track session. We're going to get into the, the into the details on the leadership of digital transformation and dev sec ops automation, cloud security and compliance. So let's get started. But first Rakesh, we last talked you guys had some awards, RSA conference, 2021, virtual. You guys got some serious awards. Give us the update. >> Yeah, thank you very much, John. Yeah, we were, you know, humbled to be recognized. You know, industry recognition is always a great thing. We deliver value for customers and the industry is recognizing it. So at the RSA conference, we got seven different awards you know, very excited that we were chosen for, you know publishers choice and security company of the year editor's choice and blood security and heart company in cloud security automation. So really thrilled about the recognition thanks. >> Awesome. Seven awards. I mean, RSA is obviously a show that's in transition itself. They're transforming no longer part of Dell technologies now kind of on their own kind of speaks to the wave we're in. So congratulations on the success. They're hot startup here in security track. Give us a quick overview what you guys are enabling because this transformation is everywhere. It's in every sector, it's in every vertical dev sec ops shifting left, you know day two operations get ops. All. This is all talking to one thing, developer, productivity programmable infrastructure with security. Rakesh give us a quick overview of >> Yeah. Exactly. Right. John, I think there's a big shift happening obviously to the cloud and, you know, affects every one of our lives in productivity in enterprise applications, consumers you name it. There's a huge change happening, but central to that theme is security. And so it's one of the areas we focus on Anitian is the fastest way for both existing and new applications to be developed in the cloud. And so we make sure that you can get there fastest time to value and time to revenue pretty quickly by providing the best secure and compliance environment for you. That's really the core of what we do as a company. And we look forward to helping all of our customers and the industry >> Aditya you're a global segment lead at AWS partner network. You seeing on successful companies, you've got a winner here, obviously a success story. I want to get your take on this because this is a trend in cloud native scale, you know, heart, you know horizontally scalable, large scale, but shifting left, okay. Get ops big topics where code is being inspected in real time. People want automation. So I've got to ask you, what does shift left mean to to being out there and this in the security world? What does that mean? >> So, instead of applying your security and compliance guard rails only in production, we also need to apply them across your application development and delivery cycles. Instead of having one gate that becomes a bottleneck we should have multiple checkpoints at various stages. This provides a fast feedback for the developers while they're still in the context of developing that feature. So it's easier and less expensive fix the issues and what it is not is this doesn't mean you move all your focus to dev and ignore production. It also doesn't mean developers are now responsible for security and you can get rid of your security teams. We needed a process and a mechanism in place to leverage the expertise off the security teams and offer their services to the developers very early on in the development cycles, thereby enabling and empowering developers to write secure and compliant code >> I mean, to me not to put my old school hat on, but it's, you know I think to me, I view it as security at the point of coding right at the point of, I don't want to say point of sale but the point of writing the code and the old days it used to be like a patches and getting updates and provisioned into, into production. Same that kind of concept. But as a developer, that's kind of the focus is getting the latest knowledge either through tools and technologies to make it easier for me as a developer to inject at the point of code. Is that right? >> That's right. Yeah. >> So what makes Anitian so different and what's successful within AWS? That's, what's the why the success there? Can you share with us why they're so unique in AWS? >> So I think the biggest case for that is really you know, security, oftentimes security is thought of as an impediment sometimes actually believe it or not. So the configuration, the management, the deployment all of that, you got to be able to do and you got to be able to do that at scale. The great thing about the cloud at is scale and a big portion of that is automation. So what we at Anitian have done is taken that lifecycle of taking, you know applications on a variety of states. If you will, if you're trying to get to production you're trying to do one of two things. You're either you're trying to get into a compliance standard, like Fed Ramp you want a very predictable process, or you're just trying to get an application secure pretty quickly. So how can you do either one of those things becomes the challenge and we help you do that by having a pre-engineered environment where configuration defining deployment all that becomes very consistent and very predictable which means we've automated it in a way that it can scale. You can sort of almost have this regularly happening and not just one application with multiple applications for any company. That is, I think the biggest obstacle that has happened for a lot of folks in the enterprise for sure, to try to get to production and keep that cycle going continuously. And we help with that in a big way. That's one of the reasons why we're having a lot of adoption customers working with partners of course and getting industry recognition for it. >> Yeah. I mean, this is one of the benefits of cloud. I want to get you guys both reaction to this, where as things get going, it's kind of like that, you're you you got to take advantage. You can take advantage of all these solutions. So how many of his customer, I want to look for solutions that help me move the ball forward, not backwards right? So, or help me move the ball forward without building anything that I don't need or that's already been built. So here it sounds like if I get this right Anitian is saying, Hey if you're an Amazon customer I can accelerate you with Fed Ramp compliance. So you don't have to spend all these cycle times getting ready or hiring or operationalizing it is that right? I mean, is that the value proposition? >> They're very accurate, John. So what happens is, you know, we're working with Amazon web services, who's really innovated quite a bit in building all the building blocks, if you will. And so, you know, we're standing on the shoulders of giants if you will, to basically get the max level of automation and acceleration happen. So that just like customers have gotten used to not having to buy servers, but guide, compute and storage. If you will, now they're able to secure and also become compliant with the services that we offer. That level of acceleration I think is needed. If you believe that there's going to be a lot more cloud applications, lot more cloud. If you're going to achieve scale, you've got to automate. And if you want to automate, but secure as well you need a mechanism to doing that. That's really where Anitian comes in, if you will. >> Yeah. And I think Fedramp to me is just a great low hanging fruit example because everyone wants to get into the public sector market. They know how hard it is. Kind of like, you know, we want to do it, but stand in line we've got to get some resources. I'm not kind of get that. But the question I want to get to you Rakesh and Aditya is the bigger picture, which is, as you said more cloud applications are coming. So customers in the enterprise have, have or are building fast dev ops teams accelerate the security paradigm. How do you help those, those folks? Because that's really kind of where the action's going. The puck is going to go there too. Right? So beyond Fed Ramp there's other things >> Right? So I think, I think the way we approached it is really, there's like at least two different sets of customers, right? In the federal market itself. You just think about a commercial SAS companies who are trying to enter the, the, the, the the public sector market. Well, you need to clear a standard like Fed Ramp. So we're the fastest way to not just complete it but be able to start selling and producing revenue. That'd be market per using that functionality. If you will, to that market. Similarly, there's a lot of public sector organizations who are trying to move to the cloud because they have traditionally developed applications and architectures based on what they've done over the last 20 plus years. Well guess what, they're also trying to migrate. So how do you help both commercial companies as well as public sector companies transition, if you will to the cloud in a secure way, but also meeting a public standard. We're helping both those organizations to do that migration and that journey if you will, but it's premised on with pre-engineered it, it's the fastest way for you to get there for you to be able to provide your capability and functionality to the larger marketplace. That's one of the main reasons why I think the productivity jump is enormously high because that's how you get to larger marketplace, if you will, to serve that market >> Aditya. So they have to change your title from global segment leader, dev ops to dev sec ops 80 of his partner network here with this solution in a way it's kind of becoming standard. >> Yeah. Security is getting him embedded into all of your development and delivery life cycle. So that dev sec Ops is becoming more and more critical with customers migrating to the cloud and modernizing their applications. >> How much has automation playing into this? Because one of the things we're talking about fueling digital transformation is the automation component of the security piece here Rakesh How important is automation and what how do you set yourself up for that to be successful? >> That's big question. I think that the big key to that is automation. I think automation is there in general in the cloud space. People expect it, frankly. But I think that the key thing what we have done is pre-integrated not just our platform but a variety of the partner ecosystem are on AWS. And so when a customer is looking forward to taking an application and going to the cloud they're not just getting functionality from us and AWS but also a lot of partner functionality around it so that they don't have to build it. Remember this discussion we had earlier about how do you jumpstart that? Well, it's, it's, it's really, instead of them having the best of breed assemble we've pre done it for them, which means it's predictable, it's consistent it's configured correctly. They can rely on it. That allows us to be able to help them move faster which means they can go serve larger markets and obviously make money around it. >> Rakesh, I got to follow up on that and ask you specifically around this business model. Obviously cloud has become great service. Everyone kind of knows that and then kind of sees the edge coming next and all these other issues that are going to provide more opportunities. But I got to ask you for your company what industries and business models are you disrupting? >> Yeah, I think primarily to we're a classic example of software eating the world, right? Primarily what happens is most of the folks that certainly in the compliance arena are really trying to figure out how to do it themselves, right? And then that's primarily the group of people who are sort of trying to figure that out. And then there's a class of who do consulting who are trying to consult with you and what you should do. And we have taken a very software oriented approach built on Amazon that we will not only help you fast forward that but also, you know, get you compliant but also keep you compliant because it's a cycle much like in other industries you've seen there used to be a time when people that email and they used to run email servers and ran the email servers and backups and things of that nature that transitioned over time where people procure that service from somebody else. And it's still a secure, it's still a scalable and they can rely on that service without having to be in that business if you will. So we see us disrupting the consulting and do it yourself world to actually providing a dependable service out there that you can rely on for security and compliance. >> Awesome. Aditya, I got to ask you on the Amazon side obviously you see a lot of it there. What are some of the challenges that you see with security? >> One of the main challenges I see that is that the landscape itself is rapidly changing. As customers are migrating to the cloud and modernizing what used to be a simple monolithic application running on a server and a office or a data center is now distributed hybrid and spans across development practices like microservices managed services, packaged applications, et cetera and also in the infrastructure platform choices have dramatically increased to from on-prem to call data centers, to edge computing, IOT VMs containers, serverless a lot more options. All these leads to more complexity and it increased the number of threat vectors exponentially though this advancement was great from a usability perspective. It now created a whole slew of challenges. This, this is complex. It's very hard to keep up. It's not something you set and forget. One needs to make sure you have the right guardrails in place to make sure you're continuously compliant with with your own policies are also with regulatory compliance frameworks that are needed for your business. Like GDPR, PCI, DSS, Nast, HIPAA Sox, Fed Ramp, et cetera >> For Rakesh. We're specifically on the dev ops efficiency with Amazon. What do you guys, what's your top few value proposition points? You say >> Biggest value proposition honestly is keeping and maintaining security while you're in compliance at scale with speed. I think those are big issues for companies. Like if you, if you're a company you're trying to be in the cloud, you want to enter the federal market. For example, you got to get that quickly. So what could take a lot of money? 18 - 24 months, our prawn malleable we've just completely automated back. And so within a quarter, depending on quickly the two organizations can work. We can get you into the marketplace. That that speed is of enormous value to companies. But also to remember that as Aditya pointed out there's a lot of complexity in the kind of architecture that is evolved but we have to feel like people like in the issue of what we can help customers would is as much as you take advantage of all the cloud style architecture providing the simplicity of providing security consistently and providing compliance consistently quickly. I think there'll always be a value for that because people are always trying to get faster and cheaper quicker. And I think we're able to do that. But remember, security is not just about fast. It's got to be secure, right? We got to be effective, not just efficient but I think that's a big value prop that we're able to bring to the table on AWS. >> Well I want to go, I got you here. I'll see what showcasing you guys as the hot startup who is your customer on Amazon? I'll see, you have customers that sell in marketplace for fedramp. That's a huge, that's the people who are in business to sell software but also other enterprises as well. Right? So could you just quickly break down your customers? And then when do they know it's time to call a Anitian? >> Yeah, so we have two large groups of customers. If you will. Certainly the commercial segment, as well as in the public sector and the commercial side, you have lots of companies in the cyber security enterprise collaboration as a little robotic process automation, all those categories of companies in the commercial environment they're trying to enter the public sector federal market to go sell their services. Well, you have to get compliant. We are the fastest path to get you there time to value type of revenue we can accomplish for you. That's a group of customers we, we have in market. And then we have the other side, which is a lot of government agencies who are themselves trying to migrate to the cloud. So if you're trying to get your applications for sure once on hybrid or on-premise, and you're trying to go to the AWS cloud, well, we're a great way for you to have a pre-engineered environment into which you can move in. So not only are you secure it's, pre-built, it can scale to the cloud that you're in front of migrate to. So we have both those particular sites if you will, of the marketplace. And then in market, we have lots of agencies, big and small and the government side, but also all these categories in the commercial side that I mentioned >> For Rakesh, Anitian's helping a lot of companies sell them to the public sector market. How big is the public sector federal market >> Right? Yeah. Billions of dollars. More than $250 billion is what people say but it's a very large market, but, but remember it's any any commercial SAS company who's trying to go into that federal market is a target market. We can help that customer get in into that market. >> And just real quick, their choice alternative to not working with the Anitian is what? months the pain. And what's the heavy lift as Andy Jassy would say the heavy lifting, undifferentiated lifting a lot of paperwork, a lot of hoops to jump through. Good. Can you just paint a picture of the paths with, and without >> There's three key areas that I think customers or, you know companies have to do, A. they have to understand the standard B. They have to really figure out the technology the integration, the partners, and the platform itself. It's a lift to basically get all of that together and then actually produce the documentation produce all the configuration and in a repeatable way. And that's just to get one application up there. Well, guess what? Not only do you need to get that up there you need to keep that compliant. And then our future standards come in. You need to go upgrade to that. So the best way for me to describe that is either you you come to the Anitian and we make that age just a service that is subscribed to to keep you compliant and grow or you can try to build it yourself, or you try to go get consulting companies to tell you what to do. You still have to do the work. So those are your sort of choices, if you will, which is one of the reasons why we're enjoying the growth we are because we're making it easy and productive for for companies to get there faster. >> Aditya, I want to get to you real quick. Obviously AWS partnering, they're also known as APN. You guys see some of the best hot startups. They all kind of have the same pattern like this. They do something that's hard. They make it easier. They go faster, more. Cost-effective what's the pattern in this cloud-scale world as startups. We're going to be featuring, you know, every as much as we can hot startups coming out of your network, there's a pattern here. What would you say? They are? Well as the DevOps obviously cloud native, besides iterate, move faster. What's the pattern you're seeing for the successful companies. >> It's like, like Andy's says, it's figuring out how to continuously reinvent yourself is the key to stay successful in this market. >> Awesome. For Rakesh, real big success. Congratulations on your awards. I got to ask you, we're asking all the, all the companies this question, what is your defining contribution to the future of cloud scale? >> Great question. I think when I think about what can be accomplished in the future, not just in the past, I think cloud is a huge phenomenon that has completely up-ended the architecture for all sorts of things commercial government, you know, consumer and enterprise. If you will, I would think we would be humbly the people who will ensure that lots of B2B companies and government organizations are able to move to the cloud and are able to be secure and compliant because I believe that there'll be more and more of that happening in the cloud. And the more that is available, just like the commercial world is takes advantage of all those features. I feel like public government organizations also can accomplish the same things very quickly because of folks like us, which means you have a larger segment of population that you can support. That's only going to make the planet more successful. I'm a big optimist when it comes to tech. I know there's a lot of folks who would look down upon tech or I'll think about it as not great. I'm a very big optimist around tech improving people's lives. And I think we have our own humble role in enabling that to happen in the security and compliance >> Well, anything, in my opinion I'm really a big fan of your work and your team. Anything that could bring great innovation into the public sector faster and more effective as good win for society. So I think it's a great mission. Thanks for, for sharing and congratulations on your awards and thanks for being part of our 80 best startup showcase. Appreciate it Rakesh thank you >> Thank you. >> Okay. This is the cube coverage of 80 startup showcase. I'm John for your host of the cube. This is the next big thing in security Anitian in the security track. Thanks for watching. (Up beat music)

(upbeat music) >> Welcome to the special CUBE presentation of the AWS Global Public Sector Partner Awards Program. I'm here with the leader of the partner program, Sandy Carter, Vice President, AWS, Amazon Web Services @Sandy_Carter on Twitter, prolific on social and great leader. Sandy, great to see you again. And congratulations on this great program we're having here. In fact, thanks for coming out for this keynote. Well, thank you, John, for having me. You guys always talk about the coolest thing. So we had to be part of it. >> Well, one of the things that I've been really loving about this success of public sector we talked to us before is that as we start coming out of the pandemic, is becoming very clear that the cloud has helped a lot of people and your team has done amazing work, just want to give you props for that and say, congratulations, and what a great time to talk about the winners. Because everyone's been working really hard in public sector, because of the pandemic. The internet didn't break. And everyone stepped up with cloud scale and solve some problems. So take us through the award winners and talk about them. Give us an overview of what it is. The criteria and all the specifics. >> Yeah, you got it. So we've been doing this annually, and it's for our public sector partners overall, to really recognize the very best of the best. Now, we love all of our partners, John, as you know, but every year we'd like to really hone in on a couple who really leverage their skills and their ability to deliver a great customer solution. They demonstrate those Amazon leadership principles like working backwards from the customer, having a bias for action, they've engaged with AWS and very unique ways. And as well, they've contributed to our customer success, which is so very important to us and to our customers as well. >> That's awesome. Hey, can we put up a slide, I know we have slide on the winners, I want to look at them, with the tiles here. So here's a list of some of the winners. I see a nice little stars on there. Look at the gold star. I knows IronNet, CrowdStrike. That's General Keith Alexander's company, I mean, super relevant. Presidio, we've interviewed them before many times, got Palantir in there. And is there another one, I want to take a look at some of the other names here. >> In overall we had 21 categories. You know, we have over 1900 public sector partners today. So you'll notice that the awards we did, a big focus on mission. So things like government, education, health care, we spotlighted some of the brand new technologies like Containers, Artificial Intelligence, Amazon Connect. And we also this year added in awards for innovative use of our programs, like think big for small business and PTP as well. >> Yeah, well, great roundup, they're looking forward to hearing more about those companies. I have to ask you, because this always comes up, we're seeing more and more ecosystem discussions when we talk about the future of cloud. And obviously, we're going to, you know, be at Mobile World Congress, theCUBE, back in physical form, again, (indistinct) will continue to go on. The notion of ecosystem is becoming a key competitive advantage for companies and missions. So I have to ask you, why are partners so important to your public sector team? Talk about the importance of partners in context to your mission? >> Yeah, you know, our partners are critical. We drive most of our business and public sector through partners. They have great relationships, they've got great skills, and they have, you know, that really unique ability to meet the customer needs. If I just highlighted a couple of things, even using some of our partners who won awards, the first is, you know, migrations are so critical. Andy talked at Reinvent about still 96% of applications still sitting on premises. So anybody who can help us with the velocity of migrations is really critical. And I don't know if you knew John, but 80% of our migrations are led by partners. So for example, we gave awards to Collibra and Databricks as best lead migration for data as well as Datacom for best data lead migration as well. And that's because they increase the velocity of migrations, which increases customer satisfaction. They also bring great subject matter expertise, in particular around that mission that you're talking about. So for instance, GDIT won best Mission Solution For Federal, and they had just an amazing solution that was a secure virtual desktop that reduced a federal agencies deployment process, from months to days. And then finally, you know, our partners drive new opportunities and innovate on behalf of our customers. So we did award this year for P to P, Partnering to Partner which is a really big element of ecosystems, but it was won by four points and in quizon, and they were able to work together to implement a data, implement a data lake and an AI, ML solution, and then you just did the startup showcase, we have a best startup delivering innovation too, and that was EduTech (indistinct) Central America. And they won for implementing an amazing student registration and early warning system to alert and risks that may impact a student's educational achievement. So those are just some of the reasons why partners are important. I could go on and on. As you know, I'm so passionate about my partners, >> I know you're going to talk for an hour, we have to cut you off a little there. (indistinct) love your partners so much. You have to focus on this mission thing. It was a strong mission focus in the awards this year. Why are customers requiring much more of a mission focused? Is it because, is it a part of the criteria? I mean, we're seeing a mission being big. Why is that the case? >> Well, you know, IDC, said that IT spend for a mission or something with a purpose or line of business was five times greater than IT. We also recently did our CTO study where we surveyed thousands of CTOs. And the biggest and most changing elements today is really not around the technology. But it's around the industry, healthcare, space that we talked about earlier, or government. So those are really important. So for instance, New Reburial, they won Best Emission for Healthcare. And they did that because of their new smart diagnostic system. And then we had a partner when PA consulting for Best Amazon Connect solution around a mission for providing support for those most at risk, the elderly population, those who already had pre existing conditions, and really making sure they were doing what they called risk shielding during COVID. Really exciting and big, strong focus on mission. >> Yeah, and it's also, you know, we've been covering a lot on this, people want to work for a company that has purpose, and that has missions. I think that's going to be part of the table stakes going forward. I got to ask you on the secrets of success when this came up, I love asking this question, because, you know, we're starting to see the playbooks of what I call post COVID and cloud scale 2.0, whatever you want to call it, as you're starting to see this new modern era of success formulas, obviously, large scale value creation mission. These are points we're hearing and keep conversations across the board. What do you see as the secret of success for these parties? I mean, obviously, it's indirect for Amazon, I get that, but they're also have their customers, they're your customers, customers. That's been around for a while. But there's a new model emerging. What are the secrets from your standpoint of success? you know, it's so interesting, John, that you asked me this, because this is the number one question that I get from partners too. I would say the first secret is being able to work backwards from your customer, not just technology. So take one of our award winners Cognizant. They won for their digital tolling solution. And they work backwards from the customer and how to modernize that, or Pariveda, who is one of our best energy solution winners. And again, they looked at some of these major capital projects that oil companies were doing, working backwards from what the customer needed. I think that's number one, working backwards from the customer. Two, is having that mission expertise. So given that you have to have technology, but you also got to have that expertise in the area. We see that as a big secret of our public sector partners. So education cloud, (indistinct) one for education, effectual one for government and not for profit, Accenture won, really leveraging and showcasing their global expansion around public safety and disaster response. Very important as well. And then I would say the last secret of success is building repeatable solutions using those strong skills. So Deloitte, they have a great solution for migration, including mainframes. And then you mentioned early on, CloudStrike and IronNet, just think about the skill sets that they have there for repeatable solutions around security. So I think it's really around working backwards from the customer, having that mission expertise, and then building a repeatable solution, leveraging your skill sets. >> That's a great formula for success. I got you mentioned IronNet, and cybersecurity. One of things that's coming up is, in addition to having those best practices, there's also like real problems to solve, like, ransomware is now becoming a government and commercial problem, right. So (indistinct) seeing that happen a lot in DC, that's a front burner. That's a societal impact issue. That's like a cybersecurity kind of national security defense issue, but also, it's a technical one. And also public sector, through my interviews, I can tell you the past year and a half, there's been a lot of creativity of new solutions, new problems or new opportunities that are not yet identified as problems and I'd love to get your thoughts on my concern is with Jeff Bar yesterday from AWS, who's been blogging all the the news and he is a leader in the community. He was saying that he sees like 5G in the edge as new opportunities where it's creative. It's like he compared to the going to the home improvement store where he just goes to buy one thing. He does other things. And so there's a builder culture. And I think this is something that's coming out of your group more, because the pandemic forced these problems, and they forced new opportunities to be creative, and to build. What's your thoughts? >> Yeah, so I see that too. So if you think about builders, you know, we had a partner, Executive Council yesterday, we had 900, executives sign up from all of our partners. And we asked some survey questions like, what are you building with today? And the number one thing was artificial intelligence and machine learning. And I think that's such a new builders tool today, John, and, you know, one of our partners who won an award for the most innovative AI&ML was Kablamo And what they did was they use AI&ML to do a risk assessment on bushfires or wildfires in Australia. But I think it goes beyond that. I think it's building for that need. And this goes back to, we always talk about #techforgood. Presidio, I love this award that they won for best nonprofit, the Cherokee Nation, which is one of our, you know, Native American heritage, they were worried about their language going out, like completely out like no one being able to speak yet. And so they came to Presidio, and they asked how could we have a virtual classroom platform for the Cherokee Nation? And they created this game that's available on your phone, so innovative, so much of a builder's culture to capture that young generation, so they don't you lose their language. So I do agree. I mean, we're seeing builders everywhere, we're seeing them use artificial intelligence, Container, security. And we're even starting with quantum, so it is pretty powerful of what you can do as a public sector partner. >> I think the partner equation is just so wide open, because it's always been based on value, adding value, right? So adding value is just what they do. And by the way, you make money doing it if you do a good job of adding value. And, again, I just love riffing on this, because Dave and I talked about this on theCUBE all the time, and it comes up all the time in cloud conversations. The lock in isn't proprietary technology anymore, its value, and scale. So you starting to see builders thrive in that environment. So really good points. Great best practice. And I think I'm very bullish on the partner ecosystems in general, and people do it right, flat upside. I got to ask you, though, going forward, because this is the big post COVID kind of conversation. And last time we talked on theCUBE about this, you know, people want to have a growth strategy coming out of COVID. They want to be, they want to have a tail win, they want to be on the right side of history. No one wants to be in the losing end of all this. So last year in 2021 your goals were very clear, mission, migrations, modernization. What's the focus for the partners beyond 2021? What are you guys thinking to enable them, 21 is going to be a nice on ramp to this post COVID growth strategy? What's the focus beyond 2021 for you and your partners? >> Yeah, it's really interesting, we're going to actually continue to focus on those three M's mission, migration and modernization. But we'll bring in different elements of it. So for example, on mission, we see a couple of new areas that are really rising to the top, Smart Cities now that everybody's going back to work and (indistinct) down, operations and maintenance and global defense and using gaming and simulation. I mean, think about that digital twin strategy and how you're doing that. For migration, one of the big ones we see emerging today is data-lead migration. You know, we have been focused on applications and mainframes, but data has gravity. And so we are seeing so many partners and our customers demanding to get their data from on premises to the cloud so that now they can make real time business decisions. And then on modernization. You know, we talked a lot about artificial intelligence and machine learning. Containers are wicked hot right now, provides you portability and performance. I was with a startup last night that just moved everything they're doing to ECS our Container strategy. And then we're also seeing, you know, crippin, quantum blockchain, no code, low code. So the same big focus, mission migration, modernization, but the underpinnings are going to shift a little bit beyond 2021. >> That's great stuff. And you know, you have first of all people don't might not know that your group partners and Amazon Web Services public sector, has a big surface area. You talking about government, health care, space. So I have to ask you, you guys announced in March the space accelerator and you recently announced that you selected 10 companies to participate in the accelerated program. So, I mean, this is this is a space centric, you know, targeting, you know, low earth orbiting satellites to exploring the surface of the Moon and Mars, which people love. And because the space is cool, let's say the tech and space, they kind of go together, right? So take us through, what's this all about? How's that going? What's the selection, give us a quick update, while you're here on this space accelerated selection, because (indistinct) will have had a big blog post that went out (indistinct). >> Yeah, I would be thrilled to do that. So I don't know if you know this. But when I was young, I wanted to be an astronaut. We just helped through (indistinct), one of our partners reach Mars. So Clint, who is a retired general and myself got together, and we decided we needed to do something to help startups accelerate in their space mission. And so we decided to announce a competition for 10 startups to get extra help both from us, as well as a partner Sarafem on space. And so we announced it, everybody expected the companies to come from the US, John, they came from 44 different countries. We had hundreds of startups enter, and we took them through this six week, classroom education. So we had our General Clint, you know, helping and teaching them in space, which he's done his whole life, we provided them with AWS credits, they had mentoring by our partner, Sarafem. And we just down selected to 10 startups, that was what Vernors blog post was. If you haven't read it, you should look at some of the amazing things that they're going to do, from, you know, farming asteroids to, you know, helping with some of the, you know, using small vehicles to connect to larger vehicles, when we all get to space. It's very exciting. Very exciting, indeed, >> You have so much good content areas and partners, exploring, it's a very wide vertical or sector that you're managing. Is there any pattern? Well, I want to get your thoughts on post COVID success again, is there any patterns that you're seeing in terms of the partner ecosystem? You know, whether its business model, or team makeup, or more mindset, or just how they're organizing that that's been successful? Is there like a, do you see a trend? Is there a certain thing, then I've got the working backwards thing, I get that. But like, is there any other observations? Because I think people really want to know, am I doing it right? Am I being a good manager, when you know, people are going to be working remotely more? We're seeing more of that. And there's going to be now virtual events, hybrid events, physical events, the world's coming back to normal, but it's never going to be the same. Do you see any patterns? >> Yeah, you know, we're seeing a lot of small partners that are making an entrance and solving some really difficult problems. And because they're so focused on a niche, it's really having an impact. So I really believe that that's going to be one of the things that we see, I focus on individual creators and companies who are really tightly aligned and not trying to do everything, if you will. I think that's one of the big trends. I think the second we talked about it a little bit, John, I think you're going to see a lot of focus on mission. Because of that purpose. You know, we've talked about #techforgood, with everything going on in the world. As people have been working from home, they've been reevaluating who they are, and what do they stand for, and people want to work for a company that cares about people. I just posted my human footer on LinkedIn. And I got my first over a million hits on LinkedIn, just by posting this human footer, saying, you know what, reply to me at a time that's convenient for you, not necessarily for me. So I think we're going to see a lot of this purpose driven mission, that's going to come out as well. >> Yeah, and I also noticed that, and I was on LinkedIn, I got a similar reaction when I started trying to create more of a community model, not so much have people attend our events, and we need butts in the seats. It was much more personal, like we wanted you to join us, not attend and be like a number. You know, people want to be part of something. This seem to be the new mission. >> Yeah, I completely agree with that. I think that, you know, people do want to be part of something and they want, they want to be part of the meaning of something too, right. Not just be part of something overall, but to have an impact themselves, personally and individually, not just as a company. And I think, you know, one of the other trends that we saw coming up too, was the focus on technology. And I think low code, no code is giving a lot of people entry into doing things I never thought they could do. So I do think that technology, artificial intelligence Containers, low code, no code blockchain, those are going to enable us to even do greater mission-based solutions. >> Low code, no code reduces the friction to create more value, again, back to the value proposition. Adding value is the key to success, your partners are doing it. And of course, being part of something great, like the Global Public Sector Partner Awards list is a good one. And that's what we're talking about here. Sandy, great to see you. Thank you for coming on and sharing your insights and an update and talking more about the 2021, Global Public Sector partner Awards. Thanks for coming on. >> Thank you, John, always a pleasure. >> Okay, the Global Leaders here presented on theCUBE, again, award winners doing great work in mission, modernization, again, adding value. That's what it's all about. That's the new competitive advantage. This is theCUBE. I'm John Furrier, your host, thanks for watching. (upbeat music)