>> 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)

(intro music) >> Good evening. Welcome back to theCUBE's coverage of day one of Snowflake Summit 22 live from Caesar's Forum in Las Vegas. Lisa Martin, here with Dave Vellante. Dave, we have had an action-packed day one. A lot of news coming out this morning. We've talked to Snowflake folks. We've talked to partners, we've talked to customers. A lot going on today. >> It's our light day. Tomorrow it even gets more intense. >> I know. I'm a little scared. (Dave Vellante laughing) We've got another partner of Snowflakes onboard with us here. Please welcome, let me get this, Sudhir Chaturvedi, President and Executive Board Member at LTI. How did I do? >> Yeah, very well, actually. (laughing) >> Dave Vellante: Outstanding. >> Welcome to the program. Tell us a little bit about you and then talk to the audience about LTI and what you're doing with Snowflake. >> Sure. So, LTI is a global technology consulting and services firm. We had (indistinct) out of India. We're part of a large conglomerate, which is over 80 years old. Our founders were two Danish engineers who came to India and were essentially stuck when World War II broke out, and they created a company that's lasted 80 years. So we are very proud of our heritage. We come from an engineering background and frankly what we do with Snowflake is really bring that engineering DNA to Snowflake. So we are, we've been a partner of Snowflake. We are an elite partner of Snowflake, and we work with them across all regions in the world, actually. 50 plus customers today. So, we have great partnership for today. >> And I have a note here. It says you're the GSI Delivery Platform Partner of the Year. Congratulations. What does that entail? What are the requirements to get that award? >> Yeah, I know we are very proud that we are the Delivery Platform Partner of the Year this year. We were the Innovation Partner of the Year, last year. So it shows the journey from innovation to execution in showing delivery. I think what it entails is that we've been recognized for leadership and excellence in executing Snowflake programs at scale, the migration programs and the implementation programs that we've done for customers across the globe. >> Take us back, how did you first find Snowflake? When did you decide to lean in as a company? >> Yeah, it's a great question actually. You know, in fact, so we went public as a company in 2016 and at that time, how do I put it politely? People weren't expecting that much of us. They thought we'll be one amongst many other companies. And we decided that we will vector the company on data, digital, and cloud, and we'll make bets on partners that are perhaps unknown at that time. So in late 2017, early 2018, we started partnering with Snowflake. And since then I must, you know, hand it to Snowflake. We have an phenomenal partnership with them. I just met Frank this morning. Chris Degnan is their Chief Revenue Officer, Colleen Kapase. All of these people have been tremendous in terms of how they work together with us across the world to bring what essentially is phenomenal technology to our clients. >> What was the allure back then? It was, you know, cloud data warehouse, simplified data warehouse, the technically splitting storage from compute, you know, infinite, blah, blah, blah. Was that the allure and saying or did you have a broader vision? >> No, I think what happened was clients were struggling with data because data and applications in our world were sort of very tightly intertwined and they weren't really leveraging data for making realtime decisions. So the moment we saw the promise of Snowflake that you can create true data on cloud, which on sort of all data on cloud, you know what Frank was talking about this morning, and it's available in real time and you can do a lot of things on it. We said, this is technology of the future. It truly is because it separated storage and compute. It did many things that were not possible before. So I think the thing is when you see promising technology as a GSI, you always wonder, should we wait for it to be proven before we jump in? >> Dave Vellante: Right. >> Or should we jump in right up front and help them prove the model? And we decided to take the first approach where we jumped in right up front. >> Dave Vellante: You bet. >> And I think that's helped us earlier. >> Jumped in head first, pandemic hits, they go public. >> Yes. >> Lots of stuff going on. Talk to us about how you're leveraging the power this flywheel that Snowflake has created that I think is just getting bigger and faster. >> Sudhir: Absolutely. >> How are you leveraging the power of the technology to really deliver business outcomes for clients? >> No, that's a great question. And the thing with our initial focus was to get people onto data on cloud and with Snowflake, but now it's really around driving business outcomes from there. So we have a suite called Fosfor which is a data to decisions product suite, which is Snowflake ready. We've also launched PolarSled too which is based on business outcomes. So what we've done is we've done is we've actually created about 155 NorthStars. So various industry sectors, what business outcome do you want to achieve? We call that a NorthStar. And then we say, how do you achieve it with Snowflake? You know, so what we are doing is we're saying let's achieve the business outcome that's going to drive more consumption, but essentially, you know, we live in a difficult world, a increasingly difficult world. So we want to help people take better database decisions. >> Well, what are some of the more interesting ways in which your clients are using Snowflake? >> Yeah, I think when I look at, for example, we have a client in the financial services sector who was struggling with, you know, they're one of the largest asset management and fund management companies in the world. They're a household name, everybody knows them. And they probably have an EFT or some sort of 401k with them. And what they were struggling with was to say, how do I actually get various sources of data together in a way that I can make better asset, you know, better fund management decisions because otherwise it was left to a lot of very traditional equity research reporting and fund managers taking their expertise. Here, the data from multiple sources being available, running some AIML routines on it, we're able to show them patterns in various asset classes, on options, on investments that they hadn't seen before. And now that they've jumped headlong into it, 15 of their units across the world are using it now. So I think the power of once you see data in action that it's sort of, it's almost like the superpower that smart people get. It's like, yeah, like you suddenly arm them with so much more than they had previously. And then they get so much better at what they're doing. And ultimately consumers like us benefit from that. So, you know, that's really where we want to go. >> What's LTIs like best sweet spot where, you go into a client and you know, wow, this is a perfect fit for what we do? >> Yeah. So I think I would say banking and insurance is 47% of our business. We really understand that business extremely well. The other aspect of that is because we come from a manufacturing heritage. We've had that as well. And media is something we've done more recently. So, you know we've got a media cloud along with Snowflake. So I would say these are the sectors that we are, so we've been very domain focused as a client, as a company. You know, domain first, technology, we'll work with whatever technology the domain needs but that's really been helpful to us all. And this is where that whole point of NorthStar and Fosfor comes back in, which is, today, I think without the data on cloud you would've never achieved the kind of outcomes that we are able to achieve with our clients today. >> How did you feel about the recent sales pivot that Snowflake has made in terms of retail, but also healthcare and life sciences? Talk to me about that and is that enabling your joint customers to really leverage? >> Yeah, no, I think it's very exciting. We are working with clients on that. They like the new model. They're looking forward to, I think what clients are now doing is they're putting data perhaps ahead of even in these times where people are looking at, you know, we are seeing seven or eight very difficult macroeconomic trends. People are wondering, clients are wondering, what's this going to mean for their business in the future? So they're looking at spends and saying, what do I prioritize? But what I find is that that data spend only goes up, you know? So, our own data practice has sort of grown fourfold in the last six years, you know? So it's been just an exponential growth for us. And essentially Snowflake is our largest bet in that space even over every other technology that's out there. So I think clients, when they see that combination of how Snowflake is changing and what we can bring to them, I think the model works well for them. >> You know, ecosystem is one of the areas that we always pay attention to. You can see, just look around,. I mean, you compare 2019 to where we are today. What's the importance of ecosystem to LTI and how do you see it evolving? >> That's a great question. So, you know, it's like, I think in About a Boy, you know, Hugh Grant says that no man is an island. You know, and I think the same thing applies for companies. Any company, no matter what size they are, if they think that they can do everything themselves and I think they're not going to be successful in the long run. We believe that the ecosystem of partnerships is what drives all the best outcomes for our clients and our clients expect that today. They want (indistinct) partners to work together. And the thing with an ecosystem is, you know no one person can dominate an ecosystem, you know? The customer has to be at the center of the ecosystem and then everybody in the ecosystem is actually saying how best do I service the customer? So I think if you have that kind of customer centricity and you understand that ecosystems, you know, on your own you'll never be as good as an ecosystem. I think you nailed it, but it requires, a partnering ethos and that's what we really like about Snowflake. Such a strong partnering ethos. I still, I keep telling people if I text or message Chris or Colleen, I'll get a response in within 15, 20 minutes. You know, that's invaluable when you're trying to do great things for your joint clients, you know, so. >> Sounds like there's a lot of synergies there around the customer obsession, customer centricity. >> Absolutely. I think responsiveness in today's world is key. You know, I think the first people to respond, even if it's to say, you know what, I hear you I'm going to get back to you. I think, you know, people love that about you. It's easy to say customer centric. It's difficult to actually practice it in real life. And we believe that, for us, responsiveness is the key. We'll respond no matter what time of day or night. And the other thing is we'll respond even with our partners, right? We are not going to respond on our own and then bring everybody else along. Even things like, I don't know this but I can refer you to a partner who can help you do this. That's also a response. >> That responsiveness is so critical, especially in this day and age where I think one of the things that was in short supply during COVID and one of the many things is patience and tolerance. >> Correct. >> Right? On us as consumers and our business lives. So being able to respond even just to say we're checking, don't know yet, that builds trust between organizations with customers. >> Well, yeah, absolutely. In fact, you know, even the first year of the pandemic we grew nine and a half percent, year and year. >> In India, we were the fastest growing company that year. And if anybody asked me why did you grow nine and half percent when the industry grew at -1%, you know, in that financial. I think it was the speed at which we responded between February and June to client requests. We responded even before, I know I was in calls till 12:30 in the night working with clients to say, okay how do we fix this? How do we change this? How do we stop doing something? How do we cut costs, whatever they needed. And what we did in the first three months actually helped us our first four months when the first wave of the pandemic really hit. Actually clients were like these guys were on our side when times are tough. Let's sort of bet on them. And the data business actually grew. And I keep saying this, you know, whenever a big macro trend hits when there's more uncertainty, people look to the data because your judgment and experience is no longer applicable. Nobody in the world had any experience or judgment that could be applied in COVID times, right? So you need to now look at the data and say, okay, is the data telling me something that I would never come to know based on my own experience? And I think, you know, this is what I call the real database decisions is no company in the world will say we don't do it. But I think today's world, we are seeing real time data decisions being taken. We see it in the supply chain all the time. We see it in how banks are processing interest rate rises, et cetera. It's the speed at which they're acting would not be possible without a data first kind of approach they've taken. >> Right. And it has to be real time these days. >> It has to be. >> Every organization. That's no longer a nice to have. >> No, you know, and data is getting out of date also so quickly. I mean, in today's world, with the war in Ukraine I think the first thing we realized was that almost every parameter on commodity, whether it was oil or steel or shipping or whatever, it changed so rapidly that the only way to predict, many of our clients were not able to to tell their customers when they would be able to deliver products and service or products, especially manufacturing clients because they just didn't know when they would get their materials and go get their parts, et cetera. And we used data to say, okay, let's at least establish a base on which, because clients get disappointed, more customers get disappointed when you don't meet a delivery date. So we wanted to say, let's make it more predictable, even in unpredictable times. So we were able to manage expectations. We were able to do that better. Without the data there was no way it would've happened. There was just no way. And frankly, for us, Snowflake is the reason. For us it's our biggest bet in the data space. And that's how most of the work that we are doing in supply chain, in fact, I'm just headed to a manufacturing event that our team has organized, which is with Snowflake on data on cloud for manufacturing clients. So we've been slightly behind the curve compared to some of the others, but now seeing the promise and saying, hey let's go for this. >> There's a tremendous amount of potential. We're only scratching the surface. We thank you so much >> Sudhir: Thank you. >> For joining David me on the program, talking about LTI, the power of what you're doing together with Snowflake. We'll let you get to that manufacturing event. I'm sure that they are looking forward to talking to you. >> Yeah, no. Thank you so much. It was lovely to speak to you. Thank you so much. >> Likewise. My pleasure. For our guest and Dave Vellante, this is Lisa Martin signing off from the show floor of Snowflake Summit 22. Day one coverage is complete. Dave and I look forward to seeing you bright and early tomorrow for a jam packed day two. Thanks so much for watching. Take good care. (outro music)

>> From theCUBE studios in Palo Alto, in Boston, bringing you data driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vollante. >> The rise of Kubernetes came about through a combination of forces that were, in hindsight, quite a long shot. Amazon's dominance created momentum for Cloud native application development, and the need for newer and simpler experiences, beyond just easily spinning up computer as a service. This wave crashed into innovations from a startup named Docker, and a reluctant competitor in Google, that needed a way to change the game on Amazon and the Cloud. Now, add in the effort of Red Hat, which needed a new path beyond Enterprise Linux, and oh, by the way, it was just about to commit to a path of a Kubernetes alternative for OpenShift and figure out a governance structure to hurt all the cats and the ecosystem and you get the remarkable ascendancy of Kubernetes. Hello and welcome to this week's Wikibon CUBE Insights powered by ETR. In this breaking analysis, we tapped the back stories of a new documentary that explains the improbable events that led to the creation of Kubernetes. We'll share some new survey data from ETR and commentary from the many early the innovators who came on theCUBE during the exciting period since the founding of Docker in 2013, which marked a new era in computing, because we're talking about Kubernetes and developers today, the hoodie is on. And there's a new two part documentary that I just referenced, it's out and it was produced by Honeypot on Kubernetes, part one and part two, tells a story of how Kubernetes came to prominence and many of the players that made it happen. Now, a lot of these players, including Tim Hawkin Kelsey Hightower, Craig McLuckie, Joe Beda, Brian Grant Solomon Hykes, Jerry Chen and others came on theCUBE during formative years of containers going mainstream and the rise of Kubernetes. John Furrier and Stu Miniman were at the many shows we covered back then and they unpacked what was happening at the time. We'll share the commentary from the guests that they interviewed and try to add some context. Now let's start with the concept of developer defined structure, DDI. Jerry Chen was at VMware and he could see the trends that were evolving. He left VMware to become a venture capitalist at Greylock. Docker was his first investment. And he saw the future this way. >> What happens is when you define infrastructure software you can program it. You make it portable. And that the beauty of this cloud wave what I call DDI's. Now, to your point is every piece of infrastructure from storage, networking, to compute has an API, right? And, and AWS there was an early trend where S3, EBS, EC2 had API. >> As building blocks too. >> As building blocks, exactly. >> Not monolithic. >> Monolithic building blocks every little building bone block has it own API and just like Docker really is the API for this unit of the cloud enables developers to define how they want to build their applications, how to network them know as Wills talked about, and how you want to secure them and how you want to store them. And so the beauty of this generation is now developers are determining how apps are built, not just at the, you know, end user, you know, iPhone app layer the data layer, the storage layer, the networking layer. So every single level is being disrupted by this concept of a DDI and where, how you build use and actually purchase IT has changed. And you're seeing the incumbent vendors like Oracle, VMware Microsoft try to react but you're seeing a whole new generation startup. >> Now what Jerry was explaining is that this new abstraction layer that was being built here's some ETR data that quantifies that and shows where we are today. The chart shows net score or spending momentum on the vertical axis and market share which represents the pervasiveness in the survey set. So as Jerry and the innovators who created Docker saw the cloud was becoming prominent and you can see it still has spending velocity that's elevated above that 40% red line which is kind of a magic mark of momentum. And of course, it's very prominent on the X axis as well. And you see the low level infrastructure virtualization and that even floats above servers and storage and networking right. Back in 2013 the conversation with VMware. And by the way, I remember having this conversation deeply at the time with Chad Sakac was we're going to make this low level infrastructure invisible, and we intend to make virtualization invisible, IE simplified. And so, you see above the two arrows there related to containers, container orchestration and container platforms, which are abstraction layers and services above the underlying VMs and hardware. And you can see the momentum that they have right there with the cloud and AI and RPA. So you had these forces that Jerry described that were taking shape, and this picture kind of summarizes how they came together to form Kubernetes. And the upper left, Of course you see AWS and we inserted a picture from a post we did, right after the first reinvent in 2012, it was obvious to us at the time that the cloud gorilla was AWS and had all this momentum. Now, Solomon Hykes, the founder of Docker, you see there in the upper right. He saw the need to simplify the packaging of applications for cloud developers. Here's how he described it. Back in 2014 in theCUBE with John Furrier >> Container is a unit of deployment, right? It's the format in which you package your application all the files, all the executables libraries all the dependencies in one thing that you can move to any server and deploy in a repeatable way. So it's similar to how you would run an iOS app on an iPhone, for example. >> A Docker at the time was a 30% company and it just changed its name from .cloud. And back to the diagram you have Google with a red question mark. So why would you need more than what Docker had created. Craig McLuckie, who was a product manager at Google back then explains the need for yet another abstraction. >> We created the strong separation between infrastructure operations and application operations. And so, Docker has created a portable framework to take it, basically a binary and run it anywhere which is an amazing capability, but that's not enough. You also need to be able to manage that with a framework that can run anywhere. And so, the union of Docker and Kubernetes provides this framework where you're completely abstracted from the underlying infrastructure. You could use VMware, you could use Red Hat open stack deployment. You could run on another major cloud provider like rec. >> Now Google had this huge cloud infrastructure but no commercial cloud business compete with AWS. At least not one that was taken seriously at the time. So it needed a way to change the game. And it had this thing called Google Borg, which is a container management system and scheduler and Google looked at what was happening with virtualization and said, you know, we obviously could do better Joe Beda, who was with Google at the time explains their mindset going back to the beginning. >> Craig and I started up Google compute engine VM as a service. And the odd thing to recognize is that, nobody who had been in Google for a long time thought that there was anything to this VM stuff, right? Cause Google had been on containers for so long. That was their mindset board was the way that stuff was actually deployed. So, you know, my boss at the time, who's now at Cloudera booted up a VM for the first time, and anybody in the outside world be like, Hey, that's really cool. And his response was like, well now what? Right. You're sitting at a prompt. Like that's not super interesting. How do I run my app? Right. Which is, that's what everybody's been struggling with, with cloud is not how do I get a VM up? How do I actually run my code? >> Okay. So Google never really did virtualization. They were looking at the market and said, okay what can we do to make Google relevant in cloud. Here's Eric Brewer from Google. Talking on theCUBE about Google's thought process at the time. >> One interest things about Google is it essentially makes no use of virtual machines internally. And that's because Google started in 1998 which is the same year that VMware started was kind of brought the modern virtual machine to bear. And so Google infrastructure tends to be built really on kind of classic Unix processes and communication. And so scaling that up, you get a system that works a lot with just processes and containers. So kind of when I saw containers come along with Docker, we said, well, that's a good model for us. And we can take what we know internally which was called Borg a big scheduler. And we can turn that into Kubernetes and we'll open source it. And suddenly we have kind of a cloud version of Google that works the way we would like it to work. >> Now, Eric Brewer gave us the bumper sticker version of the story there. What he reveals in the documentary that I referenced earlier is that initially Google was like, why would we open source our secret sauce to help competitors? So folks like Tim Hockin and Brian Grant who were on the original Kubernetes team, went to management and pressed hard to convince them to bless open sourcing Kubernetes. Here's Hockin's explanation. >> When Docker landed, we saw the community building and building and building. I mean, that was a snowball of its own, right? And as it caught on we realized we know what this is going to we know once you embrace the Docker mindset that you very quickly need something to manage all of your Docker nodes, once you get beyond two or three of them, and we know how to build that, right? We got a ton of experience here. Like we went to our leadership and said, you know, please this is going to happen with us or without us. And I think it, the world would be better if we helped. >> So the open source strategy became more compelling as they studied the problem because it gave Google a way to neutralize AWS's advantage because with containers you could develop on AWS for example, and then run the application anywhere like Google's cloud. So it not only gave developers a path off of AWS. If Google could develop a strong service on GCP they could monetize that play. Now, focus your attention back to the diagram which shows this smiling, Alex Polvi from Core OS which was acquired by Red Hat in 2018. And he saw the need to bring Linux into the cloud. I mean, after all Linux was powering the internet it was the OS for enterprise apps. And he saw the need to extend its path into the cloud. Now here's how he described it at an OpenStack event in 2015. >> Similar to what happened with Linux. Like yes, there is still need for Linux and Windows and other OSs out there. But by and large on production, web infrastructure it's all Linux now. And you were able to get onto one stack. And how were you able to do that? It was, it was by having a truly open consistent API and a commitment into not breaking APIs and, so on. That allowed Linux to really become ubiquitous in the data center. Yes, there are other OSs, but Linux buy in large for production infrastructure, what is being used. And I think you'll see a similar phenomenon happen for this next level up cause we're treating the whole data center as a computer instead of trading one in visual instance is just the computer. And that's the stuff that Kubernetes to me and someone is doing. And I think there will be one that shakes out over time and we believe that'll be Kubernetes. >> So Alex saw the need for a dominant container orchestration platform. And you heard him, they made the right bet. It would be Kubernetes. Now Red Hat, Red Hat is been around since 1993. So it has a lot of on-prem. So it needed a future path to the cloud. So they rang up Google and said, hey. What do you guys have going on in this space? So Google, was kind of non-committal, but it did expose that they were thinking about doing something that was you know, pre Kubernetes. It was before it was called Kubernetes. But hey, we have this thing and we're thinking about open sourcing it, but Google's internal debates, and you know, some of the arm twisting from the engine engineers, it was taking too long. So Red Hat said, well, screw it. We got to move forward with OpenShift. So we'll do what Apple and Airbnb and Heroku are doing and we'll build on an alternative. And so they were ready to go with Mesos which was very much more sophisticated than Kubernetes at the time and much more mature, but then Google the last minute said, hey, let's do this. So Clayton Coleman with Red Hat, he was an architect. And he leaned in right away. He was one of the first outside committers outside of Google. But you still led these competing forces in the market. And internally there were debates. Do we go with simplicity or do we go with system scale? And Hen Goldberg from Google explains why they focus first on simplicity in getting that right. >> We had to defend of why we are only supporting 100 nodes in the first release of Kubernetes. And they explained that they know how to build for scale. They've done that. They know how to do it, but realistically most of users don't need large clusters. So why create this complexity? >> So Goldberg explains that rather than competing right away with say Mesos or Docker swarm, which were far more baked they made the bet to keep it simple and go for adoption and ubiquity, which obviously turned out to be the right choice. But the last piece of the puzzle was governance. Now Google promised to open source Kubernetes but when it started to open up to contributors outside of Google, the code was still controlled by Google and developers had to sign Google paper that said Google could still do whatever it wanted. It could sub license, et cetera. So Google had to pass the Baton to an independent entity and that's how CNCF was started. Kubernetes was its first project. And let's listen to Chris Aniszczyk of the CNCF explain >> CNCF is all about providing a neutral home for cloud native technology. And, you know, it's been about almost two years since our first board meeting. And the idea was, you know there's a certain set of technology out there, you know that are essentially microservice based that like live in containers that are essentially orchestrated by some process, right? That's essentially what we mean when we say cloud native right. And CNCF was seated with Kubernetes as its first project. And you know, as, as we've seen over the last couple years Kubernetes has grown, you know, quite well they have a large community a diverse con you know, contributor base and have done, you know, kind of extremely well. They're one of actually the fastest, you know highest velocity, open source projects out there, maybe. >> Okay. So this is how we got to where we are today. This ETR data shows container orchestration offerings. It's the same X Y graph that we showed earlier. And you can see where Kubernetes lands not we're standing that Kubernetes not a company but respondents, you know, they doing Kubernetes. They maybe don't know, you know, whose platform and it's hard with the ETR taxon economy as a fuzzy and survey data because Kubernetes is increasingly becoming embedded into cloud platforms. And IT pros, they may not even know which one specifically. And so the reason we've linked these two platforms Kubernetes and Red Hat OpenShift is because OpenShift right now is a dominant revenue player in the space and is increasingly popular PaaS layer. Yeah. You could download Kubernetes and do what you want with it. But if you're really building enterprise apps you're going to need support. And that's where OpenShift comes in. And there's not much data on this but we did find this chart from AMDA which show was the container software market, whatever that really is. And Red Hat has got 50% of it. This is revenue. And, you know, we know the muscle of IBM is behind OpenShift. So there's really not hard to believe. Now we've got some other data points that show how Kubernetes is becoming less visible and more embedded under of the hood. If you will, as this chart shows this is data from CNCF's annual survey they had 1800 respondents here, and the data showed that 79% of respondents use certified Kubernetes hosted platforms. Amazon elastic container service for Kubernetes was the most prominent 39% followed by Azure Kubernetes service at 23% in Azure AKS engine at 17%. With Google's GKE, Google Kubernetes engine behind those three. Now. You have to ask, okay, Google. Google's management Initially they had concerns. You know, why are we open sourcing such a key technology? And the premise was, it would level the playing field. And for sure it has, but you have to ask has it driven the monetization Google was after? And I would've to say no, it probably didn't. But think about where Google would've been. If it hadn't open source Kubernetes how relevant would it be in the cloud discussion. Despite its distant third position behind AWS and Microsoft or even fourth, if you include Alibaba without Kubernetes Google probably would be much less prominent or possibly even irrelevant in cloud, enterprise cloud. Okay. Let's wrap up with some comments on the state of Kubernetes and maybe a thought or two about, you know, where we're headed. So look, no shocker Kubernetes for all its improbable beginning has gone mainstream in the past year or so. We're seeing much more maturity and support for state full workloads and big ecosystem support with respect to better security and continued simplification. But you know, it's still pretty complex. It's getting better, but it's not VMware level of maturity. For example, of course. Now adoption has always been strong for Kubernetes, for cloud native companies who start with containers on day one, but we're seeing many more. IT organizations adopting Kubernetes as it matures. It's interesting, you know, Docker set out to be the system of the cloud and Kubernetes has really kind of become that. Docker desktop is where Docker's action really is. That's where Docker is thriving. It sold off Docker swarm to Mirantis has made some tweaks. Docker has made some tweaks to its licensing model to be able to continue to evolve its its business. To hear more about that at DockerCon. And as we said, years ago we expected Kubernetes to become less visible Stu Miniman and I talked about this in one of our predictions post and really become more embedded into other platforms. And that's exactly what's happening here but it's still complicated. Remember, remember the... Go back to the early and mid cycle of VMware understanding things like application performance you needed folks in lab coats to really remediate problems and dig in and peel the onion and scale the system you know, and in some ways you're seeing that dynamic repeated with Kubernetes, security performance scale recovery, when something goes wrong all are made more difficult by the rapid pace at which the ecosystem is evolving Kubernetes. But it's definitely headed in the right direction. So what's next for Kubernetes we would expect further simplification and you're going to see more abstractions. We live in this world of almost perpetual abstractions. Now, as Kubernetes improves support from multi cluster it will be begin to treat those clusters as a unified group. So kind of abstracting multiple clusters and treating them as, as one to be managed together. And this is going to create a lot of ecosystem focus on scaling globally. Okay, once you do that, you're going to have to worry about latency and then you're going to have to keep pace with security as you expand the, the threat area. And then of course recovery what happens when something goes wrong, more complexity, the harder it is to recover and that's going to require new services to share resources across clusters. So look for that. You also should expect more automation. It's going to be driven by the host cloud providers as Kubernetes supports more state full applications and begins to extend its cluster management. Cloud providers will inject as much automation as possible into the system. Now and finally, as these capabilities mature we would expect to see better support for data intensive workloads like, AI and Machine learning and inference. Schedule with these workloads becomes harder because they're so resource intensive and performance management becomes more complex. So that's going to have to evolve. I mean, frankly, many of the things that Kubernetes team way back when, you know they back burn it early on, for example, you saw in Docker swarm or Mesos they're going to start to enter the scene now with Kubernetes as they start to sort of prioritize some of those more complex functions. Now, the last thing I'll ask you to think about is what's next beyond Kubernetes, you know this isn't it right with serverless and IOT in the edge and new data, heavy workloads there's something that's going to disrupt Kubernetes. So in that, by the way, in that CNCF survey nearly 40% of respondents were using serverless and that's going to keep growing. So how is that going to change the development model? You know, Andy Jassy once famously said that if they had to start over with Amazon retail, they'd start with serverless. So let's keep an eye on the horizon to see what's coming next. All right, that's it for now. I want to thank my colleagues, Stephanie Chan who helped research this week's topics and Alex Myerson on the production team, who also manages the breaking analysis podcast, Kristin Martin and Cheryl Knight help get the word out on socials, so thanks to all of you. Remember these episodes, they're all available as podcasts wherever you listen, just search breaking analysis podcast. Don't forget to check out ETR website @etr.ai. We'll also publish. We publish a full report every week on wikibon.com and Silicon angle.com. You can get in touch with me, email me directly david.villane@Siliconangle.com or DM me at D Vollante. You can comment on our LinkedIn post. This is Dave Vollante for theCUBE insights powered by ETR. Have a great week, everybody. Thanks for watching. Stay safe, be well. And we'll see you next time. (upbeat music)

(upbeat music) >> Welcome to the CUBE Conversation. I'm Lisa Martin. I'm talking next with Sanzio Bassini, the Head of High Performance Computing at Cineca, at DELL technologies customer. Sanzio, welcome to the CUBE. >> Thank you, it's a pleasure, it's a pleasure. >> Likewise, nice to see you. So tell us a little bit about Cineca. This is a large computing center, but a very large Italian nonprofit consortium. Tell us about it. >> Yes, Cineca been founded 50 years ago, from the university systems in Italy. For a statutory mission, which is to support, the scientific discovery, and the industry innovations, using the High Performance Computing and the correlated methodologies like a, Artificial Intelligence, which is one of the, you see the more, in a, in a adopted in those days, but together with the big data processing and and simulation. Yes, we are a consortium, which means that this is a private not-for-profit organizations. Currently, our member of the consortium, almost all the universities in Italy and also all the national agencies for those selected structures. Uh. The main quarter of Cineca is in Bologna, which is in the heart Nation, with the bunch of presence in Milan, in Rome and in Naples, so we are a consultation organization. >> And I also read that you were, are the top 10 out of the top 500 of the world's fastest super computers. That's a pretty big accomplishment. >> Yes. That is a part of our institutional missions, the last 10 to 15 years we have been to say, frequent flyers in the top 10. There been at least two, three systems that have been ranked at the top 10. Apart, the.., whatever would be the meaning of such an advance market, there's a lot of its criticalities. We are well aware. The idea is that we're enabling the scientific discovery, by means of providing the most advanced systems and the co-designing, the most advanced HPC systems to promote and support the, what is the, excellence in science. And that being part of European high-performance computing IT system. That is the case. >> Excellent. Now, talk to me about some of the challenges that Cineca is trying to solve in particular, the Human Brain Project. Talk to us a little bit about that and how you're leveraging high-performance computing to accelerate scientific discovery. >> Um, The Human Brain Project is one of the flagship project that has been co-founded by the European commission and that the participating member states. Is not as another situations that are undertaking, it's definitely a joint collaboration between members states and the European commission. There are two different right now, flagships together with another, that is in progress, which is that the quantum of flagship, the first two flagship abroad that that has been lost. The commission for operation with the participating states has been one on the digraph vein on which also we are participating in directly together with the CNR, is the national business counselor. And the second for which we are core partners of the HPC that is, the Human Brain Project. That, that is a big flagship, one million offer, of newer investment, co-founded by the participating states and that the European commission. The project it's going to set up, in what to do be the, third strategic grant agreement that they will go over the next three years, the good, the complete that the, the whole process. Then we see what is going to happen at Africa. We thought that their would be some others progress offer these big projects. It's project that would combine both the technology issues, like the designing the off high-performance computing systems that meet the requirements of the community and the big challenge, scientific challenges correlated to the physiological functions of the human brain center, including the different farm show survey to do with the behavior of the human brain. A from the pathological point of view, from the physiological point of view, that better understand the could be the way for, for a facing that. Let's say the pathology, some of those are very much correlated with respect to aging, and that it would impact the, the health, the public health systems. Some other that are correlating with what would be the support for the physiological knowledge of the, of the human brains. And finally that they, let me say, technological transfer stuff that represented the knowing off at the physiological, behavior of the human brain. Just to use a sort of metaphor to have happen from the point of view of there computational performance, the human brain is a, a, a, more than Exoscale systems, but with a energy consumption, which is very low, we are talking about some hundreds of Watts. So some hundreds of watts of energy, would provide a an extreme and computational performance. So if would could organized the technology of the high-performance computing in terms of interconnections now we're morphing the computing systems and exploitations of that kind of technologies, in I build a system that it might provide the computational power that would represent a tremendous and tremendous step ahead, in order to facing the big challenges of our base, like energies, personalized medicine, try not to change food for all those kinds of big socioeconomic challenges that we are facing. >> Yes I was reading that besides, sorry Sanzio I was reading that besides the Human Brain Project, there are other projects going on, such as that you mentioned, I'd like to understand how Cineca is working with Dell technologies. You have to translate, as you've mentioned a minute ago, the scientific requirements for discovery into high-performance computing requirements. Talk to me about how you've been doing that with partners like Dell technologies. >> Yes, in particularly in our computing architectures, we had the need to address the capability to facing the data processing involved with backed off the Human Brain Project and general speaking that is backed off the science vendor, that would combine the capability also to provide the cloud access to the system. So by main soft containers technologies and the capability also, to address what would be the creation of a Federation. So Piper problems with people proceeded in a new world. So at the end that the requirements and the terms of reference of the would matter will decline and the terms of a system that would be capable to manage, let's say, in a holistic approach, the data processing, the cloud computing services and the opportunity before for being integrated that in a Federation of HSBC system in Europe's, and with this backed off, that kind of thing, we manage a competitive dialogue procurement processor, I think I the sentence would share together with the different potential technology providers, what would be the visuals and those are the constraints (inaudible) and those other kinds of constraints like, I don't want to say, I mean, environmental kind of constraints and uh, sharing with this back of the technology provider what would it be the vision for this solution, in a very, let's say hard, the competitive dialogue, and at the end, results in a sort of, I don't want to say Darwinian processes, okay. So I mean, the survivors in terms of the different technology providers being Dell that shown the characteristics of the solution that it will be more, let's say compliant. And at the same time are flexible with respect of the combinations of very different constraints and requirements that has been the, the process that has been the outcomes of such a process. >> I like that you mentioned that Darwinian survival of the fittest and that Dell technologies has been, it sounds like a pretty flexible partner because you've got so many different needs and scientific needs to meet for different researchers. Talk to me about how you mentioned that this is a multi-national effort. How does Cineca serve and work with teams not only in Italy, but in other countries and from other institutes? >> Definitely the volume commitment that together with the, European member states is that by means of scientific merits and the peer review process, roughly speaking the arc of the production capacity, would be shared at the European level. That it's a commitment that, that there's been, that there's been a shared of that together with France, Germany, Spain, and, and with the London. So, I mean, our, half of our production capacity, it's a share of that at the European level, where also of course the Italian scientist can apply in the participates, but in a sort of offer emulations and the advanced competition for addressing what it would be the excellence in science. The remaining 50% of our production capacity is for, for the national community and, somehow to prepare and support the Italian community to be competitive on the worldwide scenario on the European and international scenario, uh that setting up would lead also to the agreement at the international level, with respect of some of the options that, that are promoted the progress in a US and in Japan also. So from this point of view, that mean that in some cases also the, access that it would come from researchers the best collaborations and the sharing options with the US researchers their or Japanese researchers in an open space. >> Open space for, it sounds like the Human Brain Project, which the HPC is powering, which has been around since 2013 is really facilitating global collaboration. Talk to me about some of the results that the high-performance computing environment has helped the Human Brain Project to achieve so far. >> The main outcomes that it will be consolidated in the next phase that will be need the by rural SPC that is the Jared undertaking um entities, that has been created for consolidating and for progressing the high-performance computing ecosystem in Europe. It represented by the Federations of high-performance computing systems at European level, there is a, a, an option that, that has been encapsulated and the elaborated inside the human brain flagship project which is called the FEHIPCSE that stand for Federation of a High-Performance Computing System in Europe. That uh provide the open service based on the two concepts on one, one is the sharing of the Heidi at a European level, so it means that the, the high demand of the users or researchers more properly. It's unique and Universal at the European level. That didn't mean better the same, we see identity management, education management with the open, and the access to the Cineca system, to the SARS system in France, to the unique system in, uh Germany to the, Diocese system in a Switzerland, to the Morocco System in a Spain. That is the part related to what will be the federated, the ID management, the others, et cetera, related to what will be the Federation off the data access. So from the point of view, again, the scientific community, mostly the community of Human Brain Project, but that will be open at other domains and other community, make sure that data in a seamless mode after European language, from the technological point of view, or let's say from the infrastructure point of view, very strong up, from the scientific point of view, uh what they think they may not, will be the most of the options is being supported by Cineca has to do with the two specific target. One is the elaboration of the data that are provided by the lands. The laws are a laboratory facility in that Florence. That is one of the four parts, and from the bottom view of the provisions of the data that is for the scattering, the, the data that would come from the mouse brains, that are use for, for (inaudible) And then the second part is for the Mayor scale studies of the cortex of the of the human brain, and that got add-on by a couple of groups that are believing that action from a European level their group of the National Researcher Counsel the CNR, that are the two main outcome on which we are in some out reference high-performance computing facilities for supporting that kind of research. Then their is in some situations they combinations of the performance a, capability of the Federation systems for addressing what will be the simulations of the overall human brain would take a lot of performance challenge simulation with bacteria that they would happen combining that they SPC facility as at European level. >> Right! So I was reading there's a case study by the way, on Cynic that Dell technologies has published. And some of the results you talked about, those that the HPC is facilitating research and results on epilepsy, spinal cord injury, brain prostheses for the blind, as well as new insights into autism. So incredibly important work that you're doing here for the Human Brain Project. One last question Sanzio, for you, what advice would you give to your peers who might be in similar situations that need to, to build and deploy and maintain high-performance computing environments? Where should they start? >> (coughs laughs) I think that at, at a certain point, that specific know how would became sort of a know how that is been, I mean, accumulated and then by some facilities and institutions around the world. There are the, the federal labs in US, the main nation model centers in Europe, the big facilities in Japan. And of course the, the big university facilities in China that are becoming, how do you say, evident and our progressively occupied increasing the space, that to say that that is somehow it, that, that, that the, those institutions would continues collaborate and sharing that there are periods I would expect off what to do, be the top level systems. Then there is a continuous sharing of uh knowledge, the experience best practices with respect off, let's say the technologies transfers towards productions and services and boosterism. Where the situation is big parenta, in the sense that, their are focused what it would be, uh the integration of the high-performance computing technology into their production workflow. And from the point of view, there is the sharing of the experience in order to provide the, a sort of, let's say, spreads and amplifications of the opportunity for supporting innovation. That is part of are solution means, in a Italy but it also, eh, er sort of um, see objective, that is addressed by the European options er supported by the European commission. I think that that sort of (inaudible) supply that in US, the, that will be coming there, sort of you see the max practice for the technology transfer to support the innovation. >> Excellent, that sharing and that knowledge transfer and collaboration. It seems to be absolutely fundamental and the environment that you've built, facilitates that. Sanzio thank you so much for sharing with us, what Cineca is doing and the great research that's going on there, and across a lot of disciplines, we appreciate you joining the program today. Thank you. >> Thank you, it's been a pleasure, thank you very much for the opportunity. >> Likewise, for Sanzio Bassini. I'm Lisa Martin. You're watching this cube conversation. (calming music)

>>Mhm Hello and welcome to the cubes coverage of AWS amazon web services, Global public Sector partner awards program. I'm john for your host of the cube here we're gonna talk about the best covid solution to great guests. Benham or with healthcare and life sciences lead at palantir Ben welcome to the cube SAm Michaels, Director of automation and compound management and Cats. National Center for advancing translational sciences and Cats. Part of the NIH National sort of health Gentlemen, thank you for coming on and and congratulations on the best covid solution. >>Thank you so much john >>so I gotta, I gotta ask you the best solution is when can I get the vaccine? How fast how long it's gonna last but I really appreciate you guys coming on. I >>hope you're vaccinated. I would say john that's outside of our hands. I would say if you've not got vaccinated, go get vaccinated right now, have someone stab you in the arm, you know, do not wait and and go for it. That's not on us. But you got that >>opportunity that we have that done. I got to get on a plane and all kinds of hoops to jump through. We need a better solution anyway. You guys have a great technical so I wanna I wanna dig in all seriousness aside getting inside. Um you guys have put together a killer solution that really requires a lot of data can let's step back and and talk about first. What was the solution that won the award? You guys have a quick second set the table for what we're talking about. Then we'll start with you. >>So the national covered cohort collaborative is a secure data enclave putting together the HR records from more than 60 different academic medical centers across the country and they're making it available to researchers to, you know, ask many and varied questions to try and understand this disease better. >>See and take us through the challenges here. What was going on? What was the hard problem? I'll see everyone had a situation with Covid where people broke through and cloud as he drove it amazon is part of the awards, but you guys are solving something. What was the problem statement that you guys are going after? What happened? >>I I think the problem statement is essentially that, you know, the nation has the electronic health records, but it's very fragmented, right. You know, it's been is highlighted is there's there's multiple systems around the country, you know, thousands of folks that have E H. R. S. But there is no way from a research perspective to actually have access in any unified location. And so really what we were looking for is how can we essentially provide a centralized location to study electronic health records. But in a Federated sense because we recognize that the data exist in other locations and so we had to figure out for a vast quantity of data, how can we get data from those 60 sites, 60 plus that Ben is referencing from their respective locations and then into one central repository, but also in a common format. Because that's another huge aspect of the technical challenge was there's multiple formats for electronic health records, there's different standards, there's different versions. And how do you actually have all of this data harmonised into something which is usable again for research? >>Just so many things that are jumping in my head right now, I want to unpack one at the time Covid hit the scramble and the imperative for getting answers quickly was huge. So it's a data problem at a massive scale public health impact. Again, we were talking before we came on camera, public health records are dirty, they're not clean. A lot of things are weird. I mean, just just massive amount of weird problems. How did you guys pull together take me through how this gets done? What what happened? Take us through the the steps He just got together and said, let's do this. How does it all happen? >>Yeah, it's a great and so john, I would say so. Part of this started actually several years ago. I explain this when people talk about in three C is that and Cats has actually established what we like to call, We support a program which is called the Clinical translation Science Award program is the largest single grant program in all of NIH. And it constitutes the bulk of the Cats budget. So this is extra metal grants which goes all over the country. And we wanted this group to essentially have a common research environment. So we try to create what we call the secure scientific collaborative platforms. Another example of this is when we call the rare disease clinical research network, which again is a consortium of 20 different sites around the nation. And so really we started working this several years ago that if we want to Build an environment that's collaborative for researchers around the country around the world, the natural place to do that is really with a cloud first strategy and we recognize this as and cats were about 600 people now. But if you look at the size of our actual research community with our grantees were in the thousands. And so from the perspective that we took several years ago was we have to really take a step back. And if we want to have a comprehensive and cohesive package or solution to treat this is really a mid sized business, you know, and so that means we have to treat this as a cloud based enterprise. And so in cats several years ago had really gone on this strategy to bring in different commercial partners, of which one of them is Palin tear. It actually started with our intramural research program and obviously very heavy cloud use with AWS. We use your we use google workspace, essentially use different cloud tools to enable our collaborative researchers. The next step is we also had a project. If we want to have an environment, we have to have access. And this is something that we took early steps on years prior that there is no good building environment if people can't get in the front door. So we invested heavily and create an application which we call our Federated authentication system. We call it unified and cats off. So we call it, you know, for short and and this is the open source in house project that we built it and cats. And we wanted to actually use this for all sorts of implementation, acting as the front door to this collaborative environment being one of them. And then also by by really this this this interest in electronic health records that had existed prior to the Covid pandemic. And so we've done some prior work via mixture of internal investments in grants with collaborative partners to really look at what it would take to harmonize this data at scale. And so like you mentioned, Covid hit it. Hit really hard. Everyone was scrambling for answers. And I think we had a bit of these pieces um, in play. And then that's I think when we turned to ban and the team at volunteer and we said we have these components, we have these pieces what we really need. Something independent that we can stand up quickly to really address some of these problems. One of the biggest one being that data ingestion and the harmonization step. And so I can let Ben really speak to that one. >>Yeah. Ben Library because you're solving a lot of collaboration problems, not just the technical problem but ingestion and harmonization ingestion. Most people can understand is that the data warehousing or in the database know that what that means? Take us through harmonization because not to put a little bit of shade on this, but most people think about, you know, these kinds of research or non profits as a slow moving, you know, standing stuff up sandwich saying it takes time you break it down. By the time you you didn't think things are over. This was agile. So take us through what made it an agile because that's not normal. I mean that's not what you see normally. It's like, hey we'll see you next year. We stand that up. Yeah. At the data center. >>Yeah, I mean so as as Sam described this sort of the question of data on interoperability is a really essential problem for working with this kind of data. And I think, you know, we have data coming from more than 60 different sites and one of the reasons were able to move quickly was because rather than saying oh well you have to provide the data in a certain format, a certain standard. Um and three C. was able to say actually just give us the data how you have it in whatever format is easiest for you and we will take care of that process of actually transforming it into a single standard data model, converting all of the medical vocabularies, doing all of the data quality assessment that's needed to ensure that data is actually ready for research and that was very much a collaborative endeavor. It was run out of a team based at johns Hopkins University, but in collaboration with a broad range of researchers who are all adding their expertise and what we were able to do was to provide the sort of the technical infrastructure for taking the transformation pipelines that are being developed, that the actual logic and the code and developing these very robust kind of centralist templates for that. Um, that could be deployed just like software is deployed, have changed management, have upgrades and downgrades and version control and change logs so that we can roll that out across a large number of sites in a very robust way very quickly. So that's sort of that, that that's one aspect of it. And then there was a bunch of really interesting challenges along the way that again, a very broad collaborative team of researchers worked on and an example of that would be unit harmonization and inference. So really simple things like when a lab result arrives, we talked about data quality, um, you were expected to have a unit right? Like if you're reporting somebody's weight, you probably want to know if it's in kilograms or pounds, but we found that a very significant proportion of the time the unit was actually missing in the HR record. And so unless you can actually get that back, that becomes useless. And so an approach was developed because we had data across 60 or more different sites, you have a large number of lab tests that do have the correct units and you can look at the data distributions and decide how likely is it that this missing unit is actually kilograms or pounds and save a huge portion of these labs. So that's just an example of something that has enabled research to happen that would not otherwise have been able >>just not to dig in and rat hole on that one point. But what time saving do you think that saves? I mean, I can imagine it's on the data cleaning side. That's just a massive time savings just in for Okay. Based on the data sampling, this is kilograms or pounds. >>Exactly. So we're talking there's more than 3.5 billion lab records in this data base now. So if you were trying to do this manually, I mean, it would take, it would take to thousands of years, you know, it just wouldn't be a black, it would >>be a black hole in the dataset, essentially because there's no way it would get done. Ok. Ok. Sam take me through like from a research standpoint, this normalization, harmonization the process. What does that enable for the, for the research and who decides what's the standard format? So, because again, I'm just in my mind thinking how hard this is. And then what was the, what was decided? Was it just on the base records what standards were happening? What's the impact of researchers >>now? It's a great quite well, a couple things I'll say. And Ben has touched on this is the other real core piece of N three C is the community, right? You know, And so I think there's a couple of things you mentioned with this, johN is the way we execute this is, it was very nimble, it was very agile and there's something to be said on that piece from a procurement perspective, the government had many covid authorities that were granted to make very fast decisions to get things procured quickly. And we were able to turn this around with our acquisition shop, which we would otherwise, you know, be dead in the water like you said, wait a year ago through a normal acquisition process, which can take time, but that's only one half the other half. And really, you're touching on this and Ben is touching on this is when he mentions the research as we have this entire courts entire, you know, research community numbering in the thousands from a volunteer perspective. I think it's really fascinating. This is a really a great example to me of this public private partnership between the companies we use, but also the academic participants that are actually make up the community. Um again, who the amount of time they have dedicated on this is just incredible. So, so really, what's also been established with this is core governance. And so, you know, you think from assistance perspective is, you know, the Palin tear this environment, the N three C environment belongs to the government, but the N 33 the entire actually, you know, program, I would say, belongs to the community. We have co governance on this. So who decides really is just a mixture between the folks on End Cats, but not just end cast as folks at End Cats, folks that, you know, and I proper, but also folks and other government agencies, but also the, the academic communities and entire these mixed governance teams that actually set the stage for all of this. And again, you know, who's gonna decide the standard, We decide we're gonna do this in Oman 5.3 point one um is the standard we're going to utilize. And then once the data is there, this is what gets exciting is then they have the different domain teams where they can ask different research questions depending upon what has interest scientifically to them. Um and so really, you know, we viewed this from the government's perspective is how do we build again the secure platform where we can enable the research, but we don't really want to dictate the research. I mean, the one criteria we did put your research has to be covid focused because very clearly in response to covid, so you have to have a Covid focus and then we have data use agreements, data use request. You know, we have entire governance committees that decide is this research in scope, but we don't want to dictate the research types that the domain teams are bringing to the table. >>And I think the National Institutes of Health, you think about just that their mission is to serve the public health. And I think this is a great example of when you enable data to be surfaced and available that you can really allow people to be empowered and not to use the cliche citizen analysts. But in a way this is what the community is doing. You're doing research and allowing people from volunteers to academics to students to just be part of it. That is citizen analysis that you got citizen journalism. You've got citizen and uh, research, you've got a lot of democratization happening here. Is that part of it was a result of >>this? Uh, it's both. It's a great question. I think it's both. And it's it's really by design because again, we want to enable and there's a couple of things that I really, you know, we we clamor with at end cats. I think NIH is going with this direction to is we believe firmly in open science, we believe firmly in open standards and how we can actually enable these standards to promote this open science because it's actually nontrivial. We've had, you know, the citizen scientists actually on the tricky problem from a governance perspective or we have the case where we actually had to have students that wanted access to the environment. Well, we actually had to have someone because, you know, they have to have an institution that they come in with, but we've actually across some of those bridges to actually get students and researchers into this environment very much by design, but also the spirit which was held enabled by the community, which, again, so I think they go they go hand in hand. I planned for >>open science as a huge wave, I'm a big fan, I think that's got a lot of headroom because open source, what that's done to software, the software industry, it's amazing. And I think your Federated idea comes in here and Ben if you guys can just talk through the Federated, because I think that might enable and remove some of the structural blockers that might be out there in terms of, oh, you gotta be affiliate with this or that our friends got to invite you, but then you got privacy access and this Federated ID not an easy thing, it's easy to say. But how do you tie that together? Because you want to enable frictionless ability to come in and contribute same time you want to have some policies around who's in and who's not. >>Yes, totally, I mean so Sam sort of already described the the UNa system which is the authentication system that encounters has developed. And obviously you know from our perspective, you know we integrate with that is using all of the standard kind of authentication protocols and it's very easy to integrate that into the family platform um and make it so that we can authenticate people correctly. But then if you go beyond authentication you also then to actually you need to have the access controls in place to say yes I know who this person is, but now what should they actually be able to see? Um And I think one of the really great things in Free C has done is to be very rigorous about that. They have their governance rules that says you should be using the data for a certain purpose. You must go through a procedure so that the access committee approves that purpose. And then we need to make sure that you're actually doing the work that you said you were going to. And so before you can get your data back out of the system where your results out, you actually have to prove that those results are in line with the original stated purpose and the infrastructure around that and having the access controls and the governance processes, all working together in a seamless way so that it doesn't, as you say, increase the friction on the researcher and they can get access to the data for that appropriate purpose. That was a big component of what we've been building out with them three C. Absolutely. >>And really in line john with what NIH is doing with the research, all service, they call this raz. And I think things that we believe in their standards that were starting to follow and work with them closely. Multifactor authentication because of the point Ben is making and you raised as well, you know, one you need to authenticate, okay. This you are who you say you are. And and we're recognizing that and you're, you know, the author and peace within the authors. E what do you authorized to see? What do you have authorization to? And they go hand in hand and again, non trivial problems. And especially, you know, when we basis typically a lot of what we're using is is we'll do direct integrations with our package. We using commons for Federated access were also even using login dot gov. Um, you know, again because we need to make sure that people had a means, you know, and login dot gov is essentially a runoff right? If they don't have, you know an organization which we have in common or a Federated access to generate a login dot gov account but they still are whole, you know beholden to the multi factor authentication step and then they still have to get the same authorizations because we really do believe access to these environment seamlessly is absolutely critical, you know, who are users are but again not make it restrictive and not make it this this friction filled process. That's very that's very >>different. I mean you think about nontrivial, totally agree with you and if you think about like if you were in a classic enterprise, I thought about an I. T. Problem like bring your own device to work and that's basically what the whole world does these days. So like you're thinking about access, you don't know who's coming in, you don't know where they're coming in from, um when the churn is so high, you don't know, I mean all this is happening, right? So you have to be prepared two Provisions and provide resource to a very lightweight access edge. >>That's right. And that's why it gets back to what we mentioned is we were taking a step back and thinking about this problem, you know, an M three C became the use case was this is an enterprise I. T. Problem. Right. You know, we have users from around the world that want to access this environment and again we try to hit a really difficult mark, which is secure but collaborative, Right? That's that's not easy, you know? But but again, the only place this environment could take place isn't a cloud based environment, right? Let's be real. You know, 10 years ago. Forget it. You know, Again, maybe it would have been difficult, but now it's just incredible how much they advanced that these real virtual research organizations can start to exist and they become the real partnerships. >>Well, I want to Well, that's a great point. I want to highlight and call out because I've done a lot of these interviews with awards programs over the years and certainly in public sector and open source over many, many years. One of the things open source allows us the code re use and also when you start getting in these situations where, okay, you have a crisis covid other things happen, nonprofits go, that's the same thing. They, they lose their funding and all the code disappears. Saying with these covid when it becomes over, you don't want to lose the momentum. So this whole idea of re use this platform is aged deplatforming of and re factoring if you will, these are two concepts with a cloud enables SAM, I'd love to get your thoughts on this because it doesn't go away when Covid's >>over, research still >>continues. So this whole idea of re platform NG and then re factoring is very much a new concept versus the old days of okay, projects over, move on to the next one. >>No, you're absolutely right. And I think what first drove us is we're taking a step back and and cats, you know, how do we ensure that sustainability? Right, Because my background is actually engineering. So I think about, you know, you want to build things to last and what you just described, johN is that, you know, that, that funding, it peaks, it goes up and then it wanes away and it goes and what you're left with essentially is nothing, you know, it's okay you did this investment in a body of work and it goes away. And really, I think what we're really building are these sustainable platforms that we will actually grow and evolve based upon the research needs over time. And I think that was really a huge investment that both, you know, again and and Cats is made. But NIH is going in a very similar direction. There's a substantial investment, um, you know, made in these, these these these really impressive environments. How do we make sure the sustainable for the long term? You know, again, we just went through this with Covid, but what's gonna come next? You know, one of the research questions that we need to answer, but also open source is an incredibly important piece of this. I think Ben can speak this in a second, all the harmonization work, all that effort, you know, essentially this massive, complex GTL process Is in the N three Seagate hub. So we believe, you know, completely and the open source model a little bit of a flavor on it too though, because, you know, again, back to the sustainability, john, I believe, you know, there's a room for this, this marriage between commercial platforms and open source software and we need both. You know, as we're strong proponents of N cats are both, but especially with sustainability, especially I think Enterprise I. T. You know, you have to have professional grade products that was part of, I would say an experiment we ran out and cast our thought was we can fund academic groups and we can have them do open source projects and you'll get some decent results. But I think the nature of it and the nature of these environments become so complex. The experiment we're taking is we're going to provide commercial grade tools For the academic community and the researchers and let them use them and see how they can be enabled and actually focus on research questions. And I think, you know, N3C, which we've been very successful with that model while still really adhering to the open source spirit and >>principles as an amazing story, congratulated, you know what? That's so awesome because that's the future. And I think you're onto something huge. Great point, Ben, you want to chime in on this whole sustainability because the public private partnership idea is the now the new model innovation formula is about open and collaborative. What's your thoughts? >>Absolutely. And I mean, we uh, volunteer have been huge proponents of reproducibility and openness, um in analyses and in science. And so everything done within the family platform is done in open source languages like python and R. And sequel, um and is exposed via open A. P. I. S and through get repository. So that as SaM says, we've we've pushed all of that E. T. L. Code that was developed within the platform out to the cats get hub. Um and the analysis code itself being written in those various different languages can also sort of easily be pulled out um and made available for other researchers in the future. And I think what we've also seen is that within the data enclave there's been an enormous amount of re use across the different research projects. And so actually having that security in place and making it secure so that people can actually start to share with each other securely as well. And and and be very clear that although I'm sharing this, it's still within the range of the government's requirements has meant that the, the research has really been accelerated because people have been able to build and stand on the shoulders of what earlier projects have done. >>Okay. Ben. Great stuff. 1000 researchers. Open source code and get a job. Where do I sign up? I want to get involved. This is amazing. Like it sounds like a great party. >>We'll send you a link if you do a search on on N three C, you know, do do a search on that and you'll actually will come up with a website hosted by the academic side and I'll show you all the information of how you can actually connect and john you're welcome to come in. Billion by all means >>billions of rows of data being solved. Great tech he's working on again. This is a great example of large scale the modern era of solving problems is here. It's out in the open, Open Science. Sam. Congratulations on your great success. Ben Award winners. You guys doing a great job. Great story. Thanks for sharing here with us in the queue. Appreciate it. >>Thank you, john. >>Thanks for having us. >>Okay. It is. Global public sector partner rewards best Covid solution palantir and and cats. Great solution. Great story. I'm john Kerry with the cube. Thanks for watching. Mm mm. >>Mhm

(upbeat music) >> Back the cube here. I'm John Furrier with the cube. Thanks Adam in the studio. We've got two entrepreneurs here. Co-founders of LastMileXchange, Andrew Hoskin and James Grant. Guys, thanks for coming on the cube, >> John. Good to be here. >> Love to get the entrepreneur, both co-founders making it happen. I mean, the pandemic was either a tailwind or a headwind for companies and certainly the internet didn't break. Everything worked out great. So, let's just jump in, why don't we get into some of the questions. What does LMX do? Who are you guys? Take a quick minute to explain what you guys do. >> Sure. So, we're a software provider. We have a cloud-based SAS platform, which effectively it's a bit like a Skyscanner or an Expedia for networks. Carriers need to buy and sell networks from each other and we help them do that. And we have been in the cloud since day one. And so, that's what we do while we're here and it's a good place for us to tell you about it. >> I got it. I got to ask you, because one of the things being entrepreneur, you've got to read the tea leaves. One of the secrets of being a co-founder and doing anything entrepreneurial these days is you got to see the future, but then you've got to come back to the present and convince everybody, what's going on. >> Entrepreneurs: Yeah. >> What is the core value proposition? What's the day in the life of a conversation? I mean you're talking to Martians now, like, huh? What's the public cloud it's like, is it like, isn't it just the internet? It's changing. What's the value proposition? What's the conversation like? >> So, the value position for us is that we, you know, we work with our customers to accelerate the sales cycle through cloud based services. So, a lot of our customers are global tier one carriers. So, we're looking to automate their connectivity pricing, and we do that via a cloud-based solution. So it is vital to us. And particularly with having customers all across the globe, being able to sort of deploy cloud-based services makes life much easier. >> I got to ask you, one of the things that we love about cloud is the agilities. >> Yeah. >> Can you talk about the impact of what you guys are offering for the agility side. What's the impact of the consumer, the application developer, what's the impact? >> Clouds have a big play for us, big impact for our customers. So we provide our solution effectively, almost a plug and play for them. So, we do quoting really, really well. You want to know where a network is, you want to know the connectivity, we'll sort that out for you, and we can give you a solution that they can plug into their systems really quickly. In terms of, for us, when we first started, we had servers in data centers and managing software on that, but we moved to Amazon pretty early. And what we now have is, we can spin up a new customer environment in a day, which you know, from previously two, three weeks. So, cloud has been transformational for us and hopefully for our customers as well. >> And you guys target mainly carriers? >> Very much so, yeah. We're very much in the big carrier Telco space. The people that provide the fabric upon which all of this sits. >> Yeah. And then by the way, it's magic and this, it's robust. It's what we need, utilities, it's important. Last mile, obviously, as we all, some people look at it and say, go back decades, rug ban, you know, last mile is always that last nut to crack. 5G's here, the mobile sector is looking at massive growth. You're starting to see the cloud providers recognize that the edge is just another network connection. >> Yeah, absolutely. >> How do you guys see that evolving? What's going on? How do you see that affecting your business, the customers on the market? >> Well, so network, I mean, access is all about getting onto the network, whether you're talking cloud or whatever. So, if you can't get into the network, cloud is nothing. If you can't sort of back haul your 5G, you're stuck. So, what we're seeing is, even with 5G, as it rolls out, as people look to densify their networks, they still need to get all that voice stuff, all that data traffic onto fiber. So, we're seeing a lot of interest there still in knowing where connectivity options are, knowing where the network is. With James also, I mean, that other aspect of access, 10 years ago was all about fiber. But you were just telling me before about how increasingly carriers are using 5G as effectively a router in a box, ship it in by a DPD or FedEx out to a customer. >> Yeah. So typically we'll think about mobile as connecting your mobile phone, but now we're looking at sort of, mobile connecting buildings. And one of the key challenges when you're connecting a building with mobile is what the actual connectivity within the building is. So, often we will see mobile maps that show you that sort of connectivity at sort of, the outside level. But of course, you're actually going to have your infrastructure in the building. So, you need to know what the straight signal strength is there. So, we're actually working with a partner at the moment so that we can identify within a building, what the quality of the signal is. >> I mean, that's class, if you think about like, most people think of, oh, it just drops to the end point and then you've got more network behind it, wireless. You got now to work at home dynamics, IOT devices. So, you guys have the buy-sell side of things going on, you got the carriers buying and selling there. >> Entrepreneurs: Yeah, absolutely. >> And then, SD WAN is a huge market, >> Andrew: Absolutely. >> That's growing and, as well. >> And all of that relies on access. Do you know what I mean? Like, you can talk 5G, you can talk IOT. And of course, those are the exciting, sexy things in the industry. But underpinning all of that is a network. And you mentioned the word before and it's right, utility, you know, maybe it's not the sexy side of things, but you've got to have it, otherwise, nothing else works. >> You know, one of the things we do a lot of cloud cover, we cover all of Amazon shows here coming into Telco, the Telco, digital revolution that's going on here, you can see it. And some people aren't ready for it. Almost like, reminds me of the mainframe days back when I was growing up in college, it's like, oh, I'm not, I don't want to do the mainframe. I'm the new guy, I'm the young kid. I love this, a PC and mini computers. Here, it's the same thing. It's kind of like, okay, I see the cloud, but when you have infrastructure as code, >> Yeah. >> Everything gets fuzzy. >> Yeah. >> I mean, now you're talking about programmability. So, that edge at the application level, some say it's going to be a massive innovation enabler, which is going to change that infrastructure's code, which means that guys like you guys got to be able to provide programmable routes, programmable and, >> Yeah. And APR is our, and the programmability of the network, the whole interplay from whether it's quoting, whether it's ordering, whether it's delivering services, whether it's kind of somebody going into somewhere and saying, "I'd like a, a hundred gig into this building", pressing a button and 15 minutes later, everything rolls together to turn it up, is where the whole industry is going. >> Let's take that for a second. >> Sure. >> Just a mind blowing scenario right there. Sounds simple. >> Yeah. >> Compared to where we were just 15 years ago. >> Yeah. >> That scenario didn't exist. >> No. >> And it's hard. It's not trivial. >> No. >> It's not non-trivial. All right. So what's this mean for customers? Are they like buying this level now, like, are they like, where are they on the spectrum of, you know, buying and the progression of operationalizing their business to be fully robust, network end to end, visibility on workloads to network? >> I would say it often depends where the customer is. So, obviously we deal with global customers and that's one of our big selling points is that, you know, a lot of people are focusing on the US, the Western European market, you know, and the connectivity challenges that they're trying to solve there. Our customers have global customers who are looking for connectivity all throughout world. And often there'll be things like mining companies who don't have fiber going into them. And so, we need to be able to work with our customers and their suppliers to be able to automate everything, because you can only fully quote a network when you've got all the locations back. And if you're waiting for information coming back from Africa or from the former CIS, then you know, you're going to have a problem. And we're working with companies in Africa and Russia, Kazakhstan, at the moment to help them automate everything. >> You know what's interesting, I just, my mind just goes nuts here when thinking about what you guys do, because as people start rolling their own with applications, they're going to need to have this programmability, like almost on demand, they're going to need to have, I want to do a digital TV network, I want to provision something or something's hybrid or at the edge. >> You've got a football game, or you've got something like this where you need capacity, you need it quickly. You need it for an event. >> Yeah, exactly. And 5G's perfect. I mean, how many times we've all been at a soccer game or a football game. It's like, I got bars but I have no back haul. Like we all been there. >> Yeah. >> Why, oh? >> Saturated the network and everyone's doing the same thing. >> The radios working, the back haul's choking. I mean, this is real. >> Absolutely. >> How does, does 5G solve that? I mean, where does that get, how does that get solved? I mean, is it going to be ubiquitous? Truly 5G going to make us all work better? I mean, certainly for the end use of 5G is it provides speed, it provides capacity. And also for the operators it provides being able to get more people onto it. And so, and 5G is not my core strength, but it absolutely will be transformative. What I can comment on is, like you say, for an event like this or the football or anything, the Euros, it ultimately goes into a pipe. So, you've got to make sure that you've got to have the right connectivity there and the right capacity there, from the user's phone, through the towers, all the way into the network, all the way to the data center and back again. So the edge, everything, has to play together to do that, and probably, rolling it out quickly and making sure it's agile and making sure it's fast and making sure it's quick and reliable. That's what needs to all work together. I liked how you said you know, the Expedia of the networks. >> Andrew: Yeah. >> That immediately in my mind says, okay, ease of use. >> Andrew: Yeah. >> From consumption standpoint, what's the next level of growth for you guys? I'm almost imagining is programmability or cloudifying or amplifying it, make it rain. >> Yeah, certainly we are going to continue to push into, yeah, effectively digital transformation in fact, across telecoms is happening. You would think there would be a lot further ahead than it is. It's not. There are a lot of people still quoting, ordering manually. So, we're very much part of that, but certainly the ordering and the provisioning, like we've mentioned, that's a big part, but for the industry, and we're going to hopefully be part of that, or we expect to be part of that. So that's, and making sure that connectivity is there when you need it. You know, I'm here, what's there? A bit like flights, I'd like to fly to New York. Who can do it, how much will it cost? I'll buy that one please. And that's what networks should be as well. >> James, what's your vision on how the customers are progressing in their mindset? Obviously, you've got the blocking and tackling to do, you're in the market. Where are they going with the use case and the application? >> The customers are getting to the stage where they're expecting to be able to go into a portal and turn up services. So, as with many things that we're seeing throughout life today, you can go into an app, you can press a couple of buttons and you can, you can order something. So, that's what they're expecting is to be able to just go and say, I need a hundred mg here, press a few buttons. And in 10 minutes time, the circuit's not only quoted, but it's provisioned. At the moment, there's this sort of a digital divide between those that have the digitization in place and those that don't. And that's the sort of the key that we're trying to sort of help the industry with is the sort of the, the outliers and, and also the main carriers to make sure that it's not a sort of, a digital haves and a digital have-nots. >> I was just going to say that. So, if you have the digital haves and have-nots, is that a function of them just not being operationalized in their digitization? Or is it they're not set up for it or they don't have you guys? What's that have-not side of it? How do they become the haves? >> One of the biggest challenges is actually around the sort of, identifying the connectivity at a particular location. So, in some countries it's very easy to do, like the US, UK, Netherlands. We have nice sort of standard address formatting, and you can identify a building at roof level. And when it comes to turning up connectivity straight away, you want to make sure that you turn up the connectivity to the right building. And that's one of the challenges that we're seeing throughout sort of, some of the Eastern European and the LatAm, the Asian and the African markets. >> I mean, we saw what happened with Amazon instances. You've got spot instances, you get reserved instances, you're starting to see that mindset. That's a SAS mindset. >> Yeah. >> That's kind of where things are going. Is that, you guys see the same thing here or is it different? >> Yeah. Well, certainly at the enterprise space, they tend to make decisions over a longer scale. So there, maybe not so much that you sign contracts in a year's term et cetera, but yeah, certainly as a provider, a SAS provider, using all those things, the ability to to tune your expenses, tune your costs, even your resource, you know, you're turning up servers by the hour, by the minute is a big thing. And it takes a mindset change for us and our customers. >> If you don't mind me asking, how long have you guys been doing business as co-founders, when did it start? What was the guiding principle? How do you guys look back now? >> James and I met working for Verizon many years ago. You might've heard of them. And, we sort of did what we do now, in as much as James ran the commercial side of things, I ran the software side of things and we saw that connectivity was a universal problem. And so we saw our opportunity. We went out, we started LastMileXchange. We pivoted once or twice, still in the same space, but we eventually realized that where we are now was what the industry needed. And that's where we've been pushing now for quite a few years. >> I want to give you guys a lot of credit and a lot of props, congratulations. I think, you know, the digital divide has been a broadband challenge for many, many years and decades. Now, you've got that urban divide where people don't have access. And I heard stories during the pandemic that people had access in the region, but couldn't get it to the home, affordability, access, devices. These are new issues, the digital divide, they have connectivity options. >> Andrew: Yeah. >> But it's not really clear yet. So, you're starting to see a lot more of that going on. Of course, the rural areas. >> Andrew: Yeah. >> I live out in the countryside on a farm. So, I'm quite used to their challenges of connectivity. You know, when I first moved into my house, I ended up having to get to way satellite broadband and things have improved now. But when we're talking about 5G, you have people in London, they have 5G. 5G is something that I'm not going to see for three, four years probably. >> Globally, it'll democratize access because like we were saying, we're sitting in an enterprise. You can send out a rooter or a router with a SIM card in it. I mean, you can give a kid a mobile phone in the middle of, you know, Kenya, and he can have access to the world through the internet. So, you know, that increased capacity, that increased densification of networks. Okay, they're not all going to be on 5G today. James hasn't got 5G and he only lives 30 minutes out of London. But 3G, 4G, I think the gentlemen on one of the keynotes was talking there about 3G Plus. You know, effectively, that's going to roll out. The 5G's are going to be in New Yorks and London, but, >> Like, it's going to be bring your own G to your house soon. And I think this space ops is going to be great. And I think overall, just overall, the challenges and the topologies, you're going to start to see diversity in the network topology, and actually it's going to explode. >> Andrew: Yeah, absolutely. Absolutely. >> Going to be super exciting. Well, again, I think you guys are under something big. I think this idea of sasifying, making things programmable, infrastructure as code is going to be pretty big. So, thanks for coming on. And what's your take, real quick, of Cloud City. >> It's been great. We've just walked in. We both said, as we came in, we came in yesterday to set up and we were really blown away and the rest of our team arrived today and they were very impressed as well. I was going to say Telco D on the team, have done a really impressive job. >> I think you have to come here and see it to believe it because when we walked in, it was just like, this place is stunning. >> Awesome. Well that's the cube coverage, we're rocking and rolling here. We're going back to the studio to see Adam and the team. Back to you.

>>from the Cube Studios in Palo Alto and Boston. It's the Cube covering empowering the autonomous enterprise brought to you by Oracle Consulting. Welcome back, everybody to this special digital event coverage. The Cube is looking into the rebirth of Oracle Consulting. Janet George is here. She's group VP Autonomous for Advanced Analytics with machine learning and artificial intelligence at Oracle on she joined by Grant Gibson is VP of growth and strategy. Folks, welcome to the Cube. Thanks so much for coming on. I want to start with you because you get strategy in your title start big picture. What is the strategy with Oracle specifically as it relates to autonomous and also consulting? >>Sure. So I think you know, Oracle has a deep legacy of strength and data and over the company's successful history, it's evolved what that is from steps along the way. If you look at the modern enterprise Oracle client, I think there's no denying that we've entered the age of AI, that everyone knows that artificial intelligence and machine learning are key to their success in the business marketplace going forward. And while generally it's acknowledged that it's a transformative technology and people know that they need to take advantage of it. It's the how that's really tricky and that most enterprises, in order to really get an enterprise level, are rely on AI investment. Need to engage in projects of significant scope, and going from realizing there's an opportunity realizing there's a threat to mobilize yourself to capitalize on it is a daunting task. Certainly one that's anybody that's got any sort of legacy of success has built in processes as building systems has built in skill sets, and making that leap to be an autonomous enterprise is challenging for companies to wrap their heads around. So as part of the rebirth of Oracle Consulting, we've developed a practice around how to both manage the technology needs for that transformation as well as the human needs as well as the data science needs. >>So there's about five or six things that I want to follow up with you there, so this is a good conversation. Ever since I've been in the industry, we were talking about a sort of start stop start stopping at the ai Winter, and now it seems to be here. I almost feel like the technology never lived up to its promise you didn't have the horsepower compute power data may be so we're here today. It feels like we are entering a new era. Why is that? And how will the technology perform this time? >>So for AI to perform is very reliant on the data. We entered the age of Ai without having the right data for AI. So you can imagine that we just launched into Ai without our data being ready to be training sex for AI. So we started with big data. We started the data that was already historically transformed. Formatted had logical structures, physical structures. This data was sort of trapped in many different tools. And then suddenly Ai comes along and we see Take this data, our historical data we haven't tested to see if this has labels in it. This has learning capability in it. Just trust the data to AI. And that's why we saw the initial wave of ai sort of failing because it was not ready to fully ai ready for the generation of ai if >>you will. And part of I think the leap that clients are finding success with now is getting novel data types and you're moving from zeros and ones of structured data, too. Image language, written language, spoken language You're capturing different data sets in ways that prior tools never could. So the classifications that come out of it, the insights that come out of it, the business process transformation comes out of it is different than what we would have understood under the structure data formats. So I think it's that combination of really being able to push massive amounts of data through a cloud product processes at scale. That is what I think is the combination that takes it to the next plateau, for >>sure. The language that we use today, I feel like it's going to change. And you just started to touch on some of it, sensing our senses and visualization on the the auditory. So it's it's sort of this new experience that customers are seeing a lot of this machine intelligence behind. >>I call it the autonomous and price right, the journey to be the autonomous enterprise, and when you're on this journey to be the autonomous enterprise, you need really the platform that can help you be cloud is that platform which can help you get to the autonomous journey. But the Thomas journey does not end with the cloud. It doesn't end with the Data Lake. These are just infrastructures that are basic necessary necessities for being on that on that autonomous journey. But at the end, it's about how do you train and scale at, um, very large scale training that needs to happen on this platform for AI to be successful. And if you are an autonomous and price, then you have really figured out how to tap into AI and machine learning in a way that nobody else has to derive business value, if you will. So you've got the platform, you've got the data, and now you're actually tapping into the autonomous components ai and machine learning to derive business, intelligence and business value. >>So I want to get into a little bit of Oracle's role. But to do that, I want to talk a little bit more about the industry. So if you think about the way that the industry seems to be restructuring around data, historically, industries had their own stack value chain and if you were in in in the finance industry, you were there for life. >>So when you think about banking, for example, highly regulated industry think about our culture. These are highly regulated industries there. It was very difficult to destruct these industries. But now you look at an Amazon, right? And what does an Amazon or any other tech giants like Apple have? They have incredible amounts of data. They understand how people use for how they want to do banking. And so they've come up with a lot of cash or Amazon pay. And these things are starting to eat into the market. Right? So you would have never thought and Amazon could be a competition to a banking industry just because of regulations. But they're not hindered by the regulations because they're starting at a different level. And so they become an instant threat in an instant destructive to these highly regulated industries. That's what data does, right when you use data as your DNA for your business and you are sort of born in data or you figure out how to be autonomous. If you will capture value from that data in a very significant manner, then you can get into industries that are not traditionally your own industry. It can be like the food industry can be the cloud industry, the book industry, you know, different industries. So you know that that's what I see happening with the tech giants. >>So great, there's a really interesting point that the Gina is making that you mentioned. You started off with a couple of industries that are highly regulated, harder to disrupt, use it got disrupted. Publishing got disrupted. But you've got these regulated businesses. Defense. Automotive actually hasn't been surely disrupted yet. Tesla. Maybe a harbinger. And so you've got this spectrum of disruption. But is anybody safe from disruption? >>I don't think anyone's ever say from it. It's It's changing evolution, right? That you whether it's, you know, swapping horseshoes for cars are TV for movies or Netflix are any sort of evolution of a business. You're I wouldn't coast on any of it. And I think t earlier question around the value that we can help bring the Oracle customers is that you know, we have a rich stack of applications, and I find that the space between the applications, the data that that spans more than one of them is a ripe playground for innovations that where the data already exists inside a company, but it's trapped from both a technology and a business perspective. And that's where I think really any company can take advantage of knowing it's data better and changing itself to take advantage of what's already there. >>Yet powerful people always throw the bromide of the data is the new oil. And we've said no data is far more valuable because you can use it in a lot of different places where you can use once, and it's follow the laws of scarcity data, if you can unlock it. And so a lot of the incumbents they have built a business around whatever factory, our process and people, a lot of the trillion are starting us that become millionaires. You know, I'm talking about data is at the core data company. So So it seems like a big challenge for your incumbent customers. Clients is to put data at the core, be able to break down those silos. How do they do that? >>Grading down silos is really super critical for any business. It was okay to operate in a silo, for example. You would think that Oh, you know, I could just be payroll, inexpensive falls, and it wouldn't matter matter if I get into vendor performance management or purchasing that can operate as asylum. But anymore, we are finding that there are tremendous insights. But in vendor performance management, I expensive for these things are all connected, so you can't afford to have your data sits in silos. So grading down that silo actually gives the business very good performance right insights that they didn't have before. So that's one way to go. But but another phenomena happens When you start to great down the silos, you start to recognize what data you don't have to take your business to the next level. That awareness will not happen when you're working with existing data so that Obama's comes into form. When you great the silos and you start to figure out you need to go after a different set of data to get you to a new product creation. What would that look like? New test insights or new Catholics avoidance that that data is just you have to go through the iteration to be able to figure that out. >>Stakes is what you're saying. So this notion of the autonomous enterprise. I help me here cause I get kind of autonomous and automation coming into I t I t ops. I'm interested in how you see customers taking that beyond the technology organization into the enterprise. >>I think when is a technology problem? The company? Is it a loss? AI has to be a business problem. AI has to inform the business strategy. Ai has been companies the successful companies that have done so. 90% of my investments are going towards state. We know that most of it going towards ai this data out there about this, right? And so we look at what are these? 90 90% of the companies investments where he's going and whose doing this right who's not doing this right? One of the things we're seeing as results is that the companies that are doing it right have brought data into the business strategy. They've changed their business model, right? So it's not like making a better taxi, but coming up with global, right? So it's not like saying Okay, I'm going to have all these. I'm going to be the drug manufacturing company. I'm gonna put drugs out there in the market this is I'm going to do connected help, right? And so how does data serves the business model of being connected? Help rather than being a drug company selling drugs to my customers, right? It's a completely different way of looking at it. And so now you guys informing drug discovery is not helping you just put more drugs to the market. Rather, it's helping you come up with new drugs that would help the process of connected games. There's a >>lot of discussion in the press about, you know, the ethics of a and how far should we take a far. Can we take it from a technology standpoint, Long room there? But how far should we take it? Do you feel as though public policy will take care of that? A lot of that narrative is just kind of journalists looking for, You know, the negative story. Well, that's sort itself out. How much time do you spend with your customers talking about that >>we in Oracle, we're building our data science platform with an explicit feature called Explained Ability. Off the model on how the model came up with the features what features they picked. We can rearrange the features that the model picked. Citing Explain ability is very important for ordinary people. Trust ai because we can't trust even even they decided this contrast right to a large extent. So for us to get to that level where we can really trust what AI is picking in terms of a modern, we need to have explain ability. And I think a lot of the companies right now are starting to make that as part of their platform. >>We're definitely entering a new era the age of of AI of the autonomous enterprise folks. Thanks very much for great segment. Really appreciate it. >>Yeah. Pleasure. Thank you for having us. >>All right. And thank you and keep it right there. We'll be back with our next guest right after this short break. You're watching the Cube's coverage of the rebirth of Oracle consulting right back. Yeah, yeah, yeah, yeah, yeah, yeah

from the cube studios in Palo Alto in Boston it's the cube covering empowering the autonomous enterprise brought to you by Oracle consulting welcome back everybody to this special digital event coverage that the cube is looking into the rebirth of Oracle consulting Janet George is here she's group vp autonomous for advanced analytics with machine learning and artificial intelligence at oracle and she's joined by grant gibson is a group vp of growth and strategy at oracle folks welcome to the cube thanks so much for coming on thank you thank you great I want to start with you because you get strategy in your title like just start big picture what is the strategy with Oracle specifically as it relates to autonomous and also consulting sure so I think you know Oracle has a deep legacy of strengthened data and over the company's successful history it's evolved what that is from steps along the way if you look at the modern enterprise of Oracle client I think there's no denying that we've entered the age of AI that everyone knows that artificial intelligence and machine learning are a key to their success in the business marketplace going forward and while generally it's acknowledge that it's a transformative technology and people know that they need to take advantage of it it's the how that's really tricky and that most enterprises in order to really get an enterprise level ROI on an AI investment need to engage in projects of significant scope and going from realizing there's an opportunity to realize and there's a threat to mobilizing yourself to capitalize on it is a is a daunting task for an enemy certainly one that's you know anybody that's got any sort of legacy of success has built-in processes that's built in systems has built in skillsets and making that leap to be an autonomous enterprise is is challenging for companies to wrap their heads around so as part of the rebirth of Oracle consulting we've developed a practice around how to both manage the the technology needs for that transformation as well as the human needs as well as the data science needs to it so rather there's about five or six things that I want to followup with you there so there's gonna be good conversations Janet so ever since I've been in the industry we're talking about AI in sort of start stop start stop we had the AI winter and now it seems to be here it's almost feel like that the the technology never lived up to its promise you didn't have the horsepower a compute power you know enough data maybe so we're here today feels like we are entering a new era why is that and and how will the technology perform this time so for AI to perform it's very reliant on the data we entered the age of AI without having the right data for AI so you can imagine that we we just launched into AI without our data being ready to be training sex for AI so we started with bi data or we started the data that was already historically transformed formatted had logical structures physical structures this data was sort of trapped in many different tools and then suddenly AI comes along and we say take this data our historical data we haven't tested to see if this has labels in it this has learning capability in it we just thrust the data to AI and that's why we saw the initial wave of AI sort of failing because it was not ready to fall AI ready for the generation of AI and part of I think the leap that clients are finding success with now is getting the Apple data types and you're moving from the zeros and ones of structured data to image language written language spoken language you're capturing different data sets in ways that prior tools never could and so the classifications that come out of it the insights that come out of it the business process transformation comes out of it is different than what we would have understood under the structured data format so I think it's that combination of really being able to push massive amounts of data through a cloud product to be able to process it at scale that is what I think is the combination that takes it to the next plateau for sure the language that we use today I feel like is going to change and you just started to touch on some of them you know sensing you know they're our senses and you know the visualization and the the the the auditory so it's it's sort of this new experience that customers are saying a lot of this machine intelligence behind them I call it the autonomous enterprise right the journey to be the autonomous enterprise and when you're on this journey to be the autonomous enterprise you need really the platform that can help you be cloud is that platform which can help you get to the autonomous journey but the autonomous journey does not end with the cloud right or doesn't end with the dead lake these are just infrastructures that are basic necessary necessities for being on that on that autonomous journey but at the end it's about how do you train and scale at a very large scale training that needs to happen on this platform for AI to be successful and if you are an autonomous enterprise then you have really figured out how to tap into AI and machine learning in a way that nobody else has to derive business value if you will so you've got the platform you've got the data and now you're actually tapping into the autonomous components AI and machine learning to derive business intelligence and business value so I want to get into a little bit of Oracle's role but to do that I want to talk a little bit more about the industry so if you think about the way this the industry seems to be restructuring around data there historically Industries had their own stack or value chain and if you were in the finance industry you were there for life you know so when you think about banking for example highly regulated industry think about our geek culture these are highly regulated industries they're come it was very difficult to disrupt these industries but now you look at an Amazon right and what does an Amazon or any other tech giant like Apple have they have incredible amounts of data they understand how people use or how they want to do banking and so they've cut off the tap of cash or Amazon pay and these things are starting to eat into the market right so you would have never thought an Amazon could be a competition to your banking industry just because of regulations but they are not hindered by the regulations because they're starting at a different level and so they become an instant threat and an instant destructor to these highly regulated industries that's what data does right then you use data as you DNA for your business and you are sort of born in data or you figured out how to be autonomous if you will capture value from that data in a very significant manner then you can get into industries that are not traditionally your own industry it can be like the food industry it can be the cloud industry the book industry you know different industries so you know that that's what I see happening with the tech giants so great this is a really interesting point that Gina is making that you mentioned you started off with like a couple of industries that are highly regulated harder to disrupt you know music got disrupted publishing got disrupted but you've got these regulated businesses you know defense automotive actually hasn't been truly disrupted yet so I'm Tesla maybes a harbinger and so you've got this spectrum of disruption but is anybody safe from disruption okay I don't think anyone's ever safe from it it's it's changed in evolution right that you whether it's you know swapping horseshoes for cars or TV for movies or Netflix or any sort of evolution of a business you I wouldn't coast on any of them and I think to earlier question around the value that we can help bring to Oracle customers is that you know we have a rich stack of applications and I find that the space between the applications the data that that spans more than one of them is a ripe playground for innovations that where the data already exists inside a company but it's trapped from both a technology and a business perspective and that's where I think really any company can take advantage of knowing its data better and changing itself to take advantage of what's already there yet powerful bit people always throw the bromide out the data is the new oil and we've said no data is far more valuable because you can use it in a lot of different places or you can use once and it's has to follow laws of scarcity data if you can unlock it and so a lot of the incumbents they have built a business around whatever a factory or you know process and people a lot of the the trillion-dollar start in us that they're become trillionaires you know I'm talking about data is at the core their data company so so it seems like a big challenge for you you're incumbent customers clients is to put data hit the core be able to break down those silos how do they do that grading down silos is really super critical for any business it was okay to operate in a silo for example you would think that oh you know I could just be payroll in expense reports and it wouldn't man matter if I get into vendor performance management or purchasing that can operate as a silo but anymore we are finding that there are tremendous insights between vendor performance management I expensive all these things are all connected so you can't afford to have your data set in silos so grading down that silo actually gives the business very good performance right insights that they didn't have before so that's one way to go but but another phenomena happens when you start to great down the silos you start to recognize what data you don't have to take your business to the next level right that awareness will not happen when you're working with existing data so that awareness comes into form when you great the silos and you start to figure out you need to go after different set of data to get you to new product creation what would that look like new test insights or new capex avoidance then that data is just you have to go through the eye tration to be able to figure that out which takes is what you're saying happy so this notion of the autonomous under president help me here because I get kind of autonomous and automation coming into IT IT ops I'm interested in how you see customers taking that beyond the technology organization into the enterprise I think when AI is a technology problem the company is it at a loss ai has to be a business problem ai has to inform the business strategy ai has two main companies the successful companies that have done so 90 percent of our investments are going towards data we know that and and most of it going towards AI data out there about this right and so we looked at what are these ninety cup ninety percent of the company's investments where are these going and who is doing this right and who's not doing this right one of the things we are seeing as results is that the companies that are doing it right have brought data into their business strategy they've changed their business model right so it's not like making a better taxi but coming up with uber right so it's not like saying okay I'm going to have all these I'm going to be the drug manufacturing company I'm going to put drugs out there in the market versus I'm going to do connected health right and so how does data serve the business model of being connected health rather than being a drug company selling drugs to my customers right it's a completely different way of looking at it and so now I is informing drug discovery AI is not helping you just put more drugs to the market rather it's helping you come up with new drugs that will help the process of connected game there's a lot of discussion in the press about you know the ethics of AI and how far should we take AI and how far can we take it from a technology standpoint long roadmap there but how far should we take it do you feel as though public policy will take care of that a lot of that narrative is just kind of journalists looking for you know the negative story well that's sort itself out how much time do you spend with your customers talking about that we in Oracle we're building our data science platform with an explicit feature called explain ability off the model on how the model came up with the features what features it picked we can rearrange the features that the model picked so I think explain ability is very important for ordinary people to trust AI because we can't trust AI even even data scientists contrast AI right to a large extent so for us to get to that level where we can really trust what AI is picking in terms of a model we need to have explained ability and I think a lot of the companies right now are starting to make that as part of their platform well we're definitely entering a new era the the age of AI of the autonomous enterprise folks thanks very much for a great segment really appreciate it yeah our pleasure thank you for having us thank you alright and thank you and keep it right there we're right back with our next guest for this short break you're watching the cubes coverage of the rebirth of Oracle consulting right back you [Music]

>> Narrator: From theCUBE studios in Palo Alto and Boston, it's theCUBE, covering and powering the autonomous enterprise. Brought to you by: Oracle Consulting. >> Welcome back everybody to this special digital event coverage. TheCUBE is looking into the rebirth of Oracle Consulting. Janet George is here. She's Group VP Autonomous for Advanced Analytics with Machine Learning and Artificial Intelligence at Oracle. And she's joined by Grant Gibson as the Group VP of Growth and Strategy at Oracle. Folks, welcome to theCUBE thanks so much for coming on. >> Thank you. >> Thank you. >> Grant I want to start with you because you got strategy in your tittle, like the start big picture. What is the strategy with Oracle specifically as it relates to autonomous and also consulting. >> Sure. So I think, Oracle has a deep legacy of strength and data. And over the company's successful history, it's evolved what that is from steps along the way. And if you look at the modern enterprise at Oracle Client. I there's no denying that we've entered the age of AI. That everyone knows that artificial intelligence and machine learning are a key to their success and the business marketplace going forward. And while generally it's acknowledged that it's a transformative technology, and people know that they need to take advantage of it, it's the how that's really tricky. And that most enterprises, in order to really get an enterprise level RoI on an AI investment, need to engage in projects of significant scope. And going from realizing there's an opportunity or realizing there's a threat, to mobilizing yourself to capitalize on it is a daunting task for enterprise. Certainly one that's anybody that's got any sort of legacy of success has built in processes, has built in systems, has built in skill sets, and making that leap to be an autonomous enterprise is challenging for companies to wrap their heads around. So as part of the rebirth of Oracle Consulting, we've developed a practice around how to both manage the technology needs for that transformation, as well as the human needs, as well as the data science needs to it. >> There's about five or six things that I want to follow up with you there. So this is going to be a good conversation. Janet, ever since I've been in the industry we're talking about, AI, it's sort of start, stop, start, stop. We got the AI winter an now it seems to be here, it almost feel like the technology never lived up to its promise. We didn't have the horse power, or the compute power. Didn't have enough data maybe. So we're here today, feels like we are entering a new era. Why is that? And how will the technology perform this time. >> So for AI to perform, it's very reliant on the data. We enter the age of AI without having the right data for AI. So you can imagine that we just launched into AI without our data being ready to be training sets for AI. So we started with BI data, or we started with data that was already historically transformed, formatted, had logical structures physical structures, this data was sort of trapped in many different tools. And then suddenly AI comes along, and we say, take this data, our historical data. We haven't test it to see if this has labels in it, this has learning capability in it, we just thrust the data to AI. And that's why we saw the initial wave of AI sort of failing, because it was not ready for AI, ready for the generation of AI. >> And part of I think the leap that clients are finding success with now, is getting novel data types. And you're moving from the zeros and ones of structured data, to image, language, written language, spoken language, you're capturing different data sets in ways that prior tools never could. And so the classifications that come out of it, the insights that come out of it, the business process transformation comes out of it, is different than what we would have understood under the structured data format. So I think it's that combination of really being able to push massive amounts of data through a cloud product, to be able to process at its scale, that is what I think is the combination that takes it to the next plateau for sure. >> Beyond that, the language that we use today I feel like it's going to change, and you just started to touch on some of it. Sensing, our senses and the visualization and the auditory. So it's sort of this new experience that customers seeing. And a lot of this machine intelligence behind that, right? >> I call it the autonomous enterprise, right? The journey to be the autonomous enterprise. And when you're on this journey to be the autonomous enterprise, you need really, the platform that can help you be. Cloud is that platform which can help you get to the autonomous journey. But the autonomous journey does not end with the cloud, or doesn't end with the data lake. These are just infrastructures that are basic necessities for being on that autonomous journey. But in the end it's about how do train and scale at very large scale training that needs to happen on this platform, for AI to be successful. And if you are an autonomous enterprise, then you have really figured out how to tap into AI and machine learning in a way that nobody else has, to derive business value if you will. So you've got the platform, you've got the data and now you're actually tapping into the autonomous components, AI and machine learning, to derive business intelligence and business value. >> So I want to get into a little bit of Oracle's role, but to do that, I want to talk a little bit more about the industry. So if you think about the way this, the industry seems to be restructuring around data. You know historically, industries had their own stack or value chain. And if you were in the finance industry, you were there for life. >> So when you think about banking, for example, highly regulated industry, think about agriculture, these are highly regulated industries. It was very difficult to disrupt these industries, but now you're looking at Amazon, and what does an Amazon or any other tech giant like Apple have? They have incredible amounts of data. They understand how people use, or how they want to do banking. And so they've come up with Apple cash, or Amazon pay, and these things are starting to eat into the market. So you would have never thought an Amazon could a competition to a banking industry just because of regulations, but they are not hindered by the regulations because they are starting at a different level. And so they become an instant threat and an instant disrupter to these highly-regulated industries. That's what data does. When you use data as your DNA for your business and you are sort of born in data or you figured out how to be autonomous, if you will, capture value from that data, in a very significant manner. Then you can get into industries that are not traditionally your own industry. It can be like the food industry, it can be the cloud industry, the book industry, different industries. So that's what I see happening with the tech giants. >> So Grant, this is a really interesting point that Janet is making, that you've mentioned. You started off with like a couple of industries that are highly regulated, harder to disrupt. Music got disrupted, publishing got disrupted, but you've got these regulated businesses. Defense, Automotive actually, hasn't been truly disrupted yet, Tesla maybe is a harbinger. And so you've got this spectrum of disruption, but is anybody safe from disruption? >> I don't think anyone's ever safe from it. It's change in evolution, right? Whether it's swapping horseshoes for cars, or T.V. for movies, or Netflix or any sort of evolution of a business. I wouldn't coast on any of it. And I think to your earlier question around the value that we can help run to Oracle customers is that we have a rich sack of applications, and I find that the space between the applications, the data that spans more than one of them is a ripe playground for innovations where the data already exists inside a company but it's trapped from both a technology and a business perspective. And that's where I think really any company can take advantage of knowing its data better and changing itself to take advantage of what's already there. >> Yet powerful, but people always throw the bromide out that data is the new oil, and we've said no, data is far more valuable 'cause you can use it in a lot of different places. Oil you can use once and it has to follow the laws of scarcity, data, if you can unlock it. And so a lot of the incumbents, they have built a business around whatever, a factory or process and people. A lot of the trillion dollar start, they've become trillionaires, you know what I'm talking about. Data is at the core, they're data companies. So it seems like a big challenge for your incumbent customers, clients, is to put data at the core, be able to break down those silos, how do they do that? >> Grading down silos is really super critical for any business. If it's okay to operate in a silo for example, you would think that, oh you know I could just be payroll and expense reports and it wouldn't matter if I get into random performance management or purchasing, that can operate as a silo. But any more we are finding that there are tremendous insights between vendor performance management, eye expense reports, these things are all connected. So you can't afford to have your data sit in silos. So grading down that silo actually gives the business very good performance. Insights that they didn't have before. So that's one way to go. But another phenomena happens. Then you start to grade down the silos, you start to recognize what data you don't have to take your business to the next level. That awareness will not happen when you're working with existing data. So that event has comes into form when you grade the silos and you start to figure out you need to go after different set of data to get you to new product creation. What would that look like? New test insights or new type of avoidance. That data is just, you have to go through the iteration to be able to figure that out. >> Stakes is what you're saying. So this notion of the autonomous enterprise, help me here, 'cause I get kind of, autonomous and automation coming into IT, ITOps, I'm interested in how you see customers taking that beyond the technology organization into the enterprise. >> I think when AI is a technology problem, the company is at a loss. AI has to be a business problem. AI has to inform the business strategy. AI has to, when companies, the successful companies that have done. So 90% of our investments are going towards data, we know that. And most of it going towards AI, there's data out there about this. And so we look at, what are these 90% of the company's investments? Where are these going? And who is doing this right? And who is not doing this right? One of the things we are seeing as results is that the companies that are doing it right have brought data into their business strategy. They've changed their business model. So it's not making a better taxi, but coming up with Uber. So it's not like saying, okay I'm going to have all these, I'm going to be the drug manufacturing company, I'm going to put drugs out there in the market, versus I'm going to do connected health. And so how does data serve the business model of being connected health, rather than being a drug company selling drugs to my customers. It's a completely different way of looking at it. And so now AI is informing drug discovery. AI is not helping you just put more drugs to the market, rather, it's helping you come up with new drugs that would help the process of connected care. >> There's a lot of discussion in the press about the ethics of AI, and how far should we take AI, and how far can we take it from a technology standpoint (chuckles) long road map there, but how far should we take it. Do you feel as though public policy will take care of that? A lot of that narrative is just kind of journalists looking for the negative story. Will that sort itself out? How much time do you spend with your customers talking about that? And what's Oracle's role there? >> So we in Oracle, we're building our data science platform with an explicit feature called explainability of the model. On how the model came up with the features, what features it picked, we can rearrange the features that the model picked. So I think explainability is very important for ordinary people to trust AI, because we can't trust AI. Even data scientists can't trust AI to a large extent. So for us to get to that level where we can really trust what AI is picking in terms of a model, we need to have explainability. And I think a lot of the companies right now are starting to make that as part of their platform. >> Well we're definitely entering a new era. The age of AI, the autonomous enterprise. Folks, thanks very much for, great segment, really appreciate it. >> Yeah, a pleasure, thank you for having us. >> You're welcome. >> Thank you for having us. >> All right. And thank you. And keep it right there, we'll be right back with our next guest right after this short break. You're watching theCUBE's coverage of the rebirth of Oracle Consulting. Be right back. (gentle music)

>> Paige: Hello everybody and thank you for joining us today for the virtual Vertica BDC 2020. Today's breakout session is entitled End-to-End Security in Vertica. I'm Paige Roberts, Open Source Relations Manager at Vertica. I'll be your host for this session. Joining me is Vertica Software Engineers, Fenic Fawkes and Chris Morris. Before we begin, I encourage you to submit your questions or comments during the virtual session. You don't have to wait until the end. Just type your question or comment in the question box below the slide as it occurs to you and click submit. There will be a Q&A session at the end of the presentation and we'll answer as many questions as we're able to during that time. Any questions that we don't address, we'll do our best to answer offline. Also, you can visit Vertica forums to post your questions there after the session. Our team is planning to join the forums to keep the conversation going, so it'll be just like being at a conference and talking to the engineers after the presentation. Also, a reminder that you can maximize your screen by clicking the double arrow button in the lower right corner of the slide. And before you ask, yes, this whole session is being recorded and it will be available to view on-demand this week. We'll send you a notification as soon as it's ready. I think we're ready to get started. Over to you, Fen. >> Fenic: Hi, welcome everyone. My name is Fen. My pronouns are fae/faer and Chris will be presenting the second half, and his pronouns are he/him. So to get started, let's kind of go over what the goals of this presentation are. First off, no deployment is the same. So we can't give you an exact, like, here's the right way to secure Vertica because how it is to set up a deployment is a factor. But the biggest one is, what is your threat model? So, if you don't know what a threat model is, let's take an example. We're all working from home because of the coronavirus and that introduces certain new risks. Our source code is on our laptops at home, that kind of thing. But really our threat model isn't that people will read our code and copy it, like, over our shoulders. So we've encrypted our hard disks and that kind of thing to make sure that no one can get them. So basically, what we're going to give you are building blocks and you can pick and choose the pieces that you need to secure your Vertica deployment. We hope that this gives you a good foundation for how to secure Vertica. And now, what we're going to talk about. So we're going to start off by going over encryption, just how to secure your data from attackers. And then authentication, which is kind of how to log in. Identity, which is who are you? Authorization, which is now that we know who you are, what can you do? Delegation is about how Vertica talks to other systems. And then auditing and monitoring. So, how do you protect your data in transit? Vertica makes a lot of network connections. Here are the important ones basically. There are clients talk to Vertica cluster. Vertica cluster talks to itself. And it can also talk to other Vertica clusters and it can make connections to a bunch of external services. So first off, let's talk about client-server TLS. Securing data between, this is how you secure data between Vertica and clients. It prevents an attacker from sniffing network traffic and say, picking out sensitive data. Clients have a way to configure how strict the authentication is of the server cert. It's called the Client SSLMode and we'll talk about this more in a bit but authentication methods can disable non-TLS connections, which is a pretty cool feature. Okay, so Vertica also makes a lot of network connections within itself. So if Vertica is running behind a strict firewall, you have really good network, both physical and software security, then it's probably not super important that you encrypt all traffic between nodes. But if you're on a public cloud, you can set up AWS' firewall to prevent connections, but if there's a vulnerability in that, then your data's all totally vulnerable. So it's a good idea to set up inter-node encryption in less secure situations. Next, import/export is a good way to move data between clusters. So for instance, say you have an on-premises cluster and you're looking to move to AWS. Import/Export is a great way to move your data from your on-prem cluster to AWS, but that means that the data is going over the open internet. And that is another case where an attacker could try to sniff network traffic and pull out credit card numbers or whatever you have stored in Vertica that's sensitive. So it's a good idea to secure data in that case. And then we also connect to a lot of external services. Kafka, Hadoop, S3 are three of them. Voltage SecureData, which we'll talk about more in a sec, is another. And because of how each service deals with authentication, how to configure your authentication to them differs. So, see our docs. And then I'd like to talk a little bit about where we're going next. Our main goal at this point is making Vertica easier to use. Our first objective was security, was to make sure everything could be secure, so we built relatively low-level building blocks. Now that we've done that, we can identify common use cases and automate them. And that's where our attention is going. Okay, so we've talked about how to secure your data over the network, but what about when it's on disk? There are several different encryption approaches, each depends on kind of what your use case is. RAID controllers and disk encryption are mostly for on-prem clusters and they protect against media theft. They're invisible to Vertica. S3 and GCP are kind of the equivalent in the cloud. They also invisible to Vertica. And then there's field-level encryption, which we accomplish using Voltage SecureData, which is format-preserving encryption. So how does Voltage work? Well, it, the, yeah. It encrypts values to things that look like the same format. So for instance, you can see date of birth encrypted to something that looks like a date of birth but it is not in fact the same thing. You could do cool stuff like with a credit card number, you can encrypt only the first 12 digits, allowing the user to, you know, validate the last four. The benefits of format-preserving encryption are that it doesn't increase database size, you don't need to alter your schema or anything. And because of referential integrity, it means that you can do analytics without unencrypting the data. So again, a little diagram of how you could work Voltage into your use case. And you could even work with Vertica's row and column access policies, which Chris will talk about a bit later, for even more customized access control. Depending on your use case and your Voltage integration. We are enhancing our Voltage integration in several ways in 10.0 and if you're interested in Voltage, you can go see their virtual BDC talk. And then again, talking about roadmap a little, we're working on in-database encryption at rest. What this means is kind of a Vertica solution to encryption at rest that doesn't depend on the platform that you're running on. Encryption at rest is hard. (laughs) Encrypting, say, 10 petabytes of data is a lot of work. And once again, the theme of this talk is everyone has a different key management strategy, a different threat model, so we're working on designing a solution that fits everyone. If you're interested, we'd love to hear from you. Contact us on the Vertica forums. All right, next up we're going to talk a little bit about access control. So first off is how do I prove who I am? How do I log in? So, Vertica has several authentication methods. Which one is best depends on your deployment size/use case. Again, theme of this talk is what you should use depends on your use case. You could order authentication methods by priority and origin. So for instance, you can only allow connections from within your internal network or you can enforce TLS on connections from external networks but relax that for connections from your internal network. That kind of thing. So we have a bunch of built-in authentication methods. They're all password-based. User profiles allow you to set complexity requirements of passwords and you can even reject non-TLS connections, say, or reject certain kinds of connections. Should only be used by small deployments because you probably have an LDAP server, where you manage users if you're a larger deployment and rather than duplicating passwords and users all in LDAP, you should use LDAP Auth, where Vertica still has to keep track of users, but each user can then use LDAP authentication. So Vertica doesn't store the password at all. The client gives Vertica a username and password and Vertica then asks the LDAP server is this a correct username or password. And the benefits of this are, well, manyfold, but if, say, you delete a user from LDAP, you don't need to remember to also delete their Vertica credentials. You can just, they won't be able to log in anymore because they're not in LDAP anymore. If you like LDAP but you want something a little bit more secure, Kerberos is a good idea. So similar to LDAP, Vertica doesn't keep track of who's allowed to log in, it just keeps track of the Kerberos credentials and it even, Vertica never touches the user's password. Users log in to Kerberos and then they pass Vertica a ticket that says "I can log in." It is more complex to set up, so if you're just getting started with security, LDAP is probably a better option. But Kerberos is, again, a little bit more secure. If you're looking for something that, you know, works well for applications, certificate auth is probably what you want. Rather than hardcoding a password, or storing a password in a script that you use to run an application, you can instead use a certificate. So, if you ever need to change it, you can just replace the certificate on disk and the next time the application starts, it just picks that up and logs in. Yeah. And then, multi-factor auth is a feature request we've gotten in the past and it's not built-in to Vertica but you can do it using Kerberos. So, security is a whole application concern and fitting MFA into your workflow is all about fitting it in at the right layer. And we believe that that layer is above Vertica. If you're interested in more about how MFA works and how to set it up, we wrote a blog on how to do it. And now, over to Chris, for more on identity and authorization. >> Chris: Thanks, Fen. Hi everyone, I'm Chris. So, we're a Vertica user and we've connected to Vertica but once we're in the database, who are we? What are we? So in Vertica, the answer to that questions is principals. Users and roles, which are like groups in other systems. Since roles can be enabled and disabled at will and multiple roles can be active, they're a flexible way to use only the privileges you need in the moment. For example here, you've got Alice who has Dbadmin as a role and those are some elevated privileges. She probably doesn't want them active all the time, so she can set the role and add them to her identity set. All of this information is stored in the catalog, which is basically Vertica's metadata storage. How do we manage these principals? Well, depends on your use case, right? So, if you're a small organization or maybe only some people or services need Vertica access, the solution is just to manage it with Vertica. You can see some commands here that will let you do that. But what if we're a big organization and we want Vertica to reflect what's in our centralized user management system? Sort of a similar motivating use case for LDAP authentication, right? We want to avoid duplication hassles, we just want to centralize our management. In that case, we can use Vertica's LDAPLink feature. So with LDAPLink, principals are mirrored from LDAP. They're synced in a considerable fashion from the LDAP into Vertica's catalog. What this does is it manages creating and dropping users and roles for you and then mapping the users to the roles. Once that's done, you can do any Vertica-specific configuration on the Vertica side. It's important to note that principals created in Vertica this way, support multiple forms of authentication, not just LDAP. This is a separate feature from LDAP authentication and if you created a user via LDAPLink, you could have them use a different form of authentication, Kerberos, for example. Up to you. Now of course this kind of system is pretty mission-critical, right? You want to make sure you get the right roles and the right users and the right mappings in Vertica. So you probably want to test it. And for that, we've got new and improved dry run functionality, from 9.3.1. And what this feature offers you is new metafunctions that let you test various parameters without breaking your real LDAPLink configuration. So you can mess around with parameters and the configuration as much as you want and you can be sure that all of that is strictly isolated from the live system. Everything's separated. And when you use this, you get some really nice output through a Data Collector table. You can see some example output here. It runs the same logic as the real LDAPLink and provides detailed information about what would happen. You can check the documentation for specifics. All right, so we've connected to the database, we know who we are, but now, what can we do? So for any given action, you want to control who can do that, right? So what's the question you have to ask? Sometimes the question is just who are you? It's a simple yes or no question. For example, if I want to upgrade a user, the question I have to ask is, am I the superuser? If I'm the superuser, I can do it, if I'm not, I can't. But sometimes the actions are more complex and the question you have to ask is more complex. Does the principal have the required privileges? If you're familiar with SQL privileges, there are things like SELECT, INSERT, and Vertica has a few of their own, but the key thing here is that an action can require specific and maybe even multiple privileges on multiple objects. So for example, when selecting from a table, you need USAGE on the schema and SELECT on the table. And there's some other examples here. So where do these privileges come from? Well, if the action requires a privilege, these are the only places privileges can come from. The first source is implicit privileges, which could come from owning the object or from special roles, which we'll talk about in a sec. Explicit privileges, it's basically a SQL standard GRANT system. So you can grant privileges to users or roles and optionally, those users and roles could grant them downstream. Discretionary access control. So those are explicit and they come from the user and the active roles. So the whole identity set. And then we've got Vertica-specific inherited privileges and those come from the schema, and we'll talk about that in a sec as well. So these are the special roles in Vertica. First role, DBADMIN. This isn't the Dbadmin user, it's a role. And it has specific elevated privileges. You can check the documentation for those exact privileges but it's less than the superuser. The PSEUDOSUPERUSER can do anything the real superuser can do and you can grant this role to whomever. The DBDUSER is actually a role, can run Database Designer functions. SYSMONITOR gives you some elevated auditing permissions and we'll talk about that later as well. And finally, PUBLIC is a role that everyone has all the time so anything you want to be allowed for everyone, attach to PUBLIC. Imagine this scenario. I've got a really big schema with lots of relations. Those relations might be changing all the time. But for each principal that uses this schema, I want the privileges for all the tables and views there to be roughly the same. Even though the tables and views come and go, for example, an analyst might need full access to all of them no matter how many there are or what there are at any given time. So to manage this, my first approach I could use is remember to run grants every time a new table or view is created. And not just you but everyone using this schema. Not only is it a pain, it's hard to enforce. The second approach is to use schema-inherited privileges. So in Vertica, schema grants can include relational privileges. For example, SELECT or INSERT, which normally don't mean anything for a schema, but they do for a table. If a relation's marked as inheriting, then the schema grants to a principal, for example, salespeople, also apply to the relation. And you can see on the diagram here how the usage applies to the schema and the SELECT technically but in Sales.foo table, SELECT also applies. So now, instead of lots of GRANT statements for multiple object owners, we only have to run one ALTER SCHEMA statement and three GRANT statements and from then on, any time that you grant some privileges or revoke privileges to or on the schema, to or from a principal, all your new tables and views will get them automatically. So it's dynamically calculated. Now of course, setting it up securely, is that you want to know what's happened here and what's going on. So to monitor the privileges, there are three system tables which you want to look at. The first is grants, which will show you privileges that are active for you. That is your user and active roles and theirs and so on down the chain. Grants will show you the explicit privileges and inherited_privileges will show you the inherited ones. And then there's one more inheriting_objects which will show all tables and views which inherit privileges so that's useful more for not seeing privileges themselves but managing inherited privileges in general. And finally, how do you see all privileges from all these sources, right? In one go, you want to see them together? Well, there's a metafunction added in 9.3.1. Get_privileges_description which will, given an object, it will sum up all the privileges for a current user on that object. I'll refer you to the documentation for usage and supported types. Now, the problem with SELECT. SELECT let's you see everything or nothing. You can either read the table or you can't. But what if you want some principals to see subset or a transformed version of the data. So for example, I have a table with personnel data and different principals, as you can see here, need different access levels to sensitive information. Social security numbers. Well, one thing I could do is I could make a view for each principal. But I could also use access policies and access policies can do this without introducing any new objects or dependencies. It centralizes your restriction logic and makes it easier to manage. So what do access policies do? Well, we've got row and column access policies. Rows will hide and column access policies will transform data in the row or column, depending on who's doing the SELECTing. So it transforms the data, as we saw on the previous slide, to look as requested. Now, if access policies let you see the raw data, you can still modify the data. And the implication of this is that when you're crafting access policies, you should only use them to refine access for principals that need read-only access. That is, if you want a principal to be able to modify it, the access policies you craft should let through the raw data for that principal. So in our previous example, the loader service should be able to see every row and it should be able to see untransformed data in every column. And as long as that's true, then they can continue to load into this table. All of this is of course monitorable by a system table, in this case access_policy. Check the docs for more information on how to implement these. All right, that's it for access control. Now on to delegation and impersonation. So what's the question here? Well, the question is who is Vertica? And that might seem like a silly question, but here's what I mean by that. When Vertica's connecting to a downstream service, for example, cloud storage, how should Vertica identify itself? Well, most of the time, we do the permissions check ourselves and then we connect as Vertica, like in this diagram here. But sometimes we can do better. And instead of connecting as Vertica, we connect with some kind of upstream user identity. And when we do that, we let the service decide who can do what, so Vertica isn't the only line of defense. And in addition to the defense in depth benefit, there are also benefits for auditing because the external system can see who is really doing something. It's no longer just Vertica showing up in that external service's logs, it's somebody like Alice or Bob, trying to do something. One system where this comes into play is with Voltage SecureData. So, let's look at a couple use cases. The first one, I'm just encrypting for compliance or anti-theft reasons. In this case, I'll just use one global identity to encrypt or decrypt with Voltage. But imagine another use case, I want to control which users can decrypt which data. Now I'm using Voltage for access control. So in this case, we want to delegate. The solution here is on the Voltage side, give Voltage users access to appropriate identities and these identities control encryption for sets of data. A Voltage user can access multiple identities like groups. Then on the Vertica side, a Vertica user can set their Voltage username and password in a session and Vertica will talk to Voltage as that Voltage user. So in the diagram here, you can see an example of how this is leverage so that Alice could decrypt something but Bob cannot. Another place the delegation paradigm shows up is with storage. So Vertica can store and interact with data on non-local file systems. For example, HGFS or S3. Sometimes Vertica's storing Vertica-managed data there. For example, in Eon mode, you might store your projections in communal storage in S3. But sometimes, Vertica is interacting with external data. For example, this usually maps to a user storage location in the Vertica side and it might, on the external storage side, be something like Parquet files on Hadoop. And in that case, it's not really Vertica's data and we don't want to give Vertica more power than it needs, so let's request the data on behalf of who needs it. Lets say I'm an analyst and I want to copy from or export to Parquet, using my own bucket. It's not Vertica's bucket, it's my data. But I want Vertica to manipulate data in it. So the first option I have is to give Vertica as a whole access to the bucket and that's problematic because in that case, Vertica becomes kind of an AWS god. It can see any bucket, any Vertica user might want to push or pull data to or from any time Vertica wants. So it's not good for the principals of least access and zero trust. And we can do better than that. So in the second option, use an ID and secret key pair for an AWS, IAM, if you're familiar, principal that does have access to the bucket. So I might use my, the analyst, credentials, or I might use credentials for an AWS role that has even fewer privileges than I do. Sort of a restricted subset of my privileges. And then I use that. I set it in Vertica at the session level and Vertica will use those credentials for the copy export commands. And it gives more isolation. Something that's in the works is support for keyless delegation, using assumable IAM roles. So similar benefits to option two here, but also not having to manage keys at the user level. We can do basically the same thing with Hadoop and HGFS with three different methods. So first option is Kerberos delegation. I think it's the most secure. It definitely, if access control is your primary concern here, this will give you the tightest access control. The downside is it requires the most configuration outside of Vertica with Kerberos and HGFS but with this, you can really determine which Vertica users can talk to which HGFS locations. Then, you've got secure impersonation. If you've got a highly trusted Vertica userbase, or at least some subset of it is, and you're not worried about them doing things wrong but you want to know about auditing on the HGFS side, that's your primary concern, you can use this option. This diagram here gives you a visual overview of how that works. But I'll refer you to the docs for details. And then finally, option three, this is bringing your own delegation token. It's similar to what we do with AWS. We set something in the session level, so it's very flexible. The user can do it at an ad hoc basis, but it is manual, so that's the third option. Now on to auditing and monitoring. So of course, we want to know, what's happening in our database? It's important in general and important for incident response, of course. So your first stop, to answer this question, should be system tables. And they're a collection of information about events, system state, performance, et cetera. They're SELECT-only tables, but they work in queries as usual. The data is just loaded differently. So there are two types generally. There's the metadata table, which stores persistent information or rather reflects persistent information stored in the catalog, for example, users or schemata. Then there are monitoring tables, which reflect more transient information, like events, system resources. Here you can see an example of output from the resource pool's storage table which, these are actually, despite that it looks like system statistics, they're actually configurable parameters for using that. If you're interested in resource pools, a way to handle users' resource allocation and various principal's resource allocation, again, check that out on the docs. Then of course, there's the followup question, who can see all of this? Well, some system information is sensitive and we should only show it to those who need it. Principal of least privilege, right? So of course the superuser can see everything, but what about non-superusers? How do we give access to people that might need additional information about the system without giving them too much power? One option's SYSMONITOR, as I mentioned before, it's a special role. And this role can always read system tables but not change things like a superuser would be able to. Just reading. And another option is the RESTRICT and RELEASE metafunctions. Those grant and revoke access to from a certain system table set, to and from the PUBLIC role. But the downside of those approaches is that they're inflexible. So they only give you, they're all or nothing. For a specific preset of tables. And you can't really configure it per table. So if you're willing to do a little more setup, then I'd recommend using your own grants and roles. System tables support GRANT and REVOKE statements just like any regular relations. And in that case, I wouldn't even bother with SYSMONITOR or the metafunctions. So to do this, just grant whatever privileges you see fit to roles that you create. Then go ahead and grant those roles to the users that you want. And revoke access to the system tables of your choice from PUBLIC. If you need even finer-grained access than this, you can create views on top of system tables. For example, you can create a view on top of the user system table which only shows the current user's information, uses a built-in function that you can use as part of the view definition. And then, you can actually grant this to PUBLIC, so that each user in Vertica could see their own user's information and never give access to the user system table as a whole, just that view. Now if you're a superuser or if you have direct access to nodes in the cluster, filesystem/OS, et cetera, then you have more ways to see events. Vertica supports various methods of logging. You can see a few methods here which are generally outside of running Vertica, you'd interact with them in a different way, with the exception of active events which is a system table. We've also got the data collector. And that sorts events by subjects. So what the data collector does, it extends the logging and system table functionality, by the component, is what it's called in the documentation. And it logs these events and information to rotating files. For example, AnalyzeStatistics is a function that could be of use by users and as a database administrator, you might want to monitor that so you can use the data collector for AnalyzeStatistics. And the files that these create can be exported into a monitoring database. One example of that is with the Management Console Extended Monitoring. So check out their virtual BDC talk. The one on the management console. And that's it for the key points of security in Vertica. Well, many of these slides could spawn a talk on their own, so we encourage you to check out our blog, check out the documentation and the forum for further investigation and collaboration. Hopefully the information we provided today will inform your choices in securing your deployment of Vertica. Thanks for your time today. That concludes our presentation. Now, we're ready for Q&A.

>>Yeah, yeah, >>yeah! >>Welcome back, everybody. To this special digital event coverage, the Cube is looking into the rebirth of Oracle Consulting. Janet George is here. She's group VP Autonomous for Advanced Analytics with machine learning and artificial intelligence at Oracle. And she's joined by Grant Gibson Group VP of growth and strategy at Oracle. Folks, welcome to the Cube. Thanks so much for coming on. Great. I want to start with you because you get strategy in your title like this. Start big picture. What is the strategy with Oracle specifically as it relates to autonomous and also consulting? >>Sure. So I think you know, Oracle has a deep legacy of strength and data and, uh uh, over the company's successful history. It's evolved what that is from steps along the way. And if you look at the modern enterprise Oracle client, I think there's no denying that we've entered the age of AI, that everyone knows that artificial intelligence and machine learning are a key to their success in the business marketplace going forward. And while generally it's acknowledged that it's a transformative technology and people know that they need to take advantage of it, it's the how that's really tricky and that most enterprises, in order to really get an enterprise level, are rely on AI investment. Need to engage in projects of significant scope, and going from realizing there's an opportunity of realizing there's a threat to mobilize yourself to capitalize on it is a daunting task or certainly one that's, you know, Anybody that's got any sort of legacy of success has built in processes as building systems has built in skill sets, and making that leap to be an autonomous enterprise is challenging for companies to wrap their heads around. So as part of the rebirth of Oracle Consulting, we've developed a practice around how to both manage the technology needs for that transformation as well as the human needs as well as the data science needs. >>So there's about five or six things that I want to follow up with you there. So this is a good conversation. Ever since I've been in the industry, we were talking about a sort of start stop start stop at the Ai Winter, and now it seems to be here is almost feel like the technology never lived up to its promise. If you didn't have the horsepower compute power data may be so we're here today. It feels like we are entering a new era. Why is that? And how will the technology perform this time? >>So for AI to perform it's very remind on the data we entered the age of Ai without having the right data for AI. So you can imagine that we just launched into Ai without our data being ready to be training sex for AI. So we started with B I data or we started the data that was already historically transformed. Formatted had logical structures, physical structures. This data was sort of trapped in many different tools. And then suddenly Ai comes along and we see Take this data, our historical data we haven't tested to see if this has labels in it. This has learning capability in it. Just trust the data to AI. And that's why we saw the initial wave of ai sort of failing because it was not ready to full ai ready for the generation of Ai, if you will. >>So, to me, this is I always say, this was the contribution that Hadoop left us, right? I mean, the dupe everybody was crazy. It turned into big data. Oracle was never that nuts about it is gonna watch, Setback and wash obviously participated, but it gathered all this data created Chief Data Lakes, which people always joke turns into data swamps. But the data is often times now within organizations least present. Now it's a matter of what? What what's The next step is >>basically about Hadoop did to the world of data. Was her dupe freed data from being stuck in tools it basically brought forth. This concept of a platform and platform is very essential because as we enter the age of AI and be entered, the better wide range of data. We can't have tools handling all of the state of the data needs to scale. The data needs to move, the data needs to grow. And so we need the concept of platforms so we can be elastic for the growth of the data, right, it can be distributed. It can grow based on the growth of the data, and it can learn from that data. So that is that's the reason why Hadoop sort of brought us into the platform board, >>right? A lot of that data ended up in the cloud. I always say, You know, for years we marched to the cadence of Moore's law. That was the innovation engine in this industry and fastest, you could get a chip in, you know, you get a little advantage, and then somebody would leapfrog. Today it's got all this data you apply machine intelligence and cloud gives you scale. It gives you agility of your customers. Are they taking advantage of the new innovation cocktail? First of all, do you buy that? How do you see them taking >>advantage of? Yeah, I think part of what James mentioned makes a lot of sense is that at the beginning, when you know you're taking the existing data in an enterprise and trying to do AI to it, you often get things that look a lot like what you already knew because you're dealing with your existing data set in your existing expertise. And part of I think the leap that clients are finding success with now is getting novel data types, and you're moving from, uh, zeros and ones of structured data, too. Image language, written language, spoken language. You're capturing different data sets in ways that prior tools never could. And so the classifications that come out of it, the insights that come out of it, the business process transformation comes out of it is different than what we would have understood under the structure data format. So I think it's that combination of really being able to push massive amounts of data through a cloud product to be able to process it at scale. That is what I think is the combination that takes it to the next plateau for sure. >>So you talked about sort of. We're entering a new era Age of a AI. You know, a lot of people, you know, kind of focus on the cloud is the current era, but it really does feel like we're moving beyond that. The language that we use today, I feel like it's going to change, and you just started to touch on some of it. Sensing, you know, there are senses and you know the visualization in the the auditory. So it's It's sort of this new experience that customers are seeing a lot of this machine intelligence behind. >>I call it the autonomous and a price right. The journey to be the autonomous enterprise. And then you're on this journey to be the autonomous enterprise you need. Really? The platform that can help you be cloud is that platform which can help you get to the autonomous journey. But the autonomous journey does not end with the cloud or doesn't end with the data lake. These are just infrastructures that are basic necessary necessities for being on that on that autonomous journey. But at the end, it's about how do you train and scale at, um, very large scale training that needs to happen on this platform for AI to be successful. And if you are an autonomous and price, then you have really figured out how to tap into AI and machine learning in a way that nobody else has to derive business value, if you will. So you've got the platform, you've got the data, and now you're actually tapping into the autonomous components ai and machine learning to derive business, intelligence and business value. >>So I want to get into a little bit of Oracle's role. But to do that I want to talk a little bit more about the industry. So if you think about the way that the industry seems to be restructuring around data. Historically, industries had their own stack value chain, and if you were in in in the finance industry, you were there for life. We had your own sales channel distribution, etcetera. But today you see companies traversing industries, which has never happened before. You know, you see apple getting into content and music, and there's so many examples are buying whole foods data is sort of the enabler. There you have a lot of organizations, your customers, that are incumbents that they don't wanna get disrupted your part big party roles to help them become that autonomous and press so they don't get disrupted. I wonder if you could maybe maybe comment on How are you doing? >>Yeah, I'll comment and then grant you China, you know. So when you think about banking, for example, highly regulated industry think about RG culture. These are highly regulated industries there. It was very difficult to destruct these industries. But now you look at an Amazon, right? And what is an Amazon or any other tech giants like Apple have? They have incredible amounts of data. They understand how people use for how they want to do banking. And so they've come up with Apple cash or Amazon pay, and these things are starting to eat into the market, right? So you would have never thought and Amazon could be a competition to a banking industry just because of regulations. But they're not hindered by the regulations because they're starting at a different level. And so they become an instant threat in an instant destructive to these highly regulated industries. That's what data does, right when you use data as your DNA for your business and you are sort of born in data or you figured out how to be autonomous. If you will capture value from that data in a very significant manner, then you can get into industries that are not traditionally your own industry. It can be like the food industry can be the cloud industry, the book industry, you know, different industries. So you know that that's what I see happening with the tech giants. >>So great, there's a really interesting point that the Gina is making that you mentioned. You started off with a couple of industries that are highly regulated, the harder to disrupt use, it got disrupted, publishing got disrupted. But you've got these regulated businesses. Defense or automotive actually hasn't been truly disrupted yet. Some Tesla, maybe a harbinger. And so you've got this spectrum of disruption. But is anybody safe from disruption? >>Kind of. I don't think anyone's ever say from it. It's It's changing evolution, right? That you whether it's, you know, swapping horseshoes for cars are TV for movies or Netflix are any sort of evolution of a business You're I wouldn't coast on any of them. And I think to the earlier question around the value that we can help bring the Oracle customers is that you know, we have a rich stack of applications, and I find that the space between the applications, the data that that spans more than one of them is a ripe playground for innovations that where the data already exists inside a company. But it's trapped from both a technology and a business perspective. Uh, and that's where I think really any company can take advantage of knowing it's data better and changing itself to take advantage of what's already there. >>Yet powerful people always throw the bromide out. The data is the new oil, and we've said. No data is far more valuable because you can use it in a lot of different places. Oil you can use once and it's follow the laws of scarcity data if you can unlock it. And so a lot of the incumbents they have built a business around, whatever a factory or a process and people, a lot of the trillion are starting us that have become billionaires. You know, I'm talking about Data's at the core. They're data companies. So So it seems like a big challenge for your incumbent customers. Clients is to put data at the core, be able to break down those silos. How do they do that? >>Grading down silos is really super critical for any business. It was okay to operate in a silo, for example. You would think that, Oh, you know, I could just be payroll and expense reports and it wouldn't matter matter if I get into vendor performance management or purchasing that can operate as a silo. But any movie of finding that there are tremendous insights between vendor performance management I expensive for these things are all connected, so you can't afford to have your data sits in silos. So grading down that silo actually gives the business very good performance, right? Insights that they didn't have before. So that's one way to go. But but another phenomena happens when you start to great down the silos, you start to recognize what data you don't have to take your business to the next level, right. That awareness will not happen when you're working with existing data so that a Venice comes into form when you great the silos and you start to figure out you need to go after a different set of data to get you to a new product creation. What would that look like? New test insights or new cap ex avoidance that that data is just you have to go through the iteration to be able to figure that out. >>It becomes it becomes a business problem, right? If you got a process now where you can identify 75% of the failures and you know the value of the other 25% of failures, that becomes a simple investment. How much money am I willing to invest to knock down some portion that 25% and it changes it from simply an I t problem or expense management problem to you know, the cash problem. >>But you still need a platform that has AP eyes that allows you to bring in those data sets that you don't have access to this enable an enabler. It's not the answer. It's not the outcome in and of itself, but it enables. And >>I always say, you can't have the best toilet if you're coming, doesn't work. You know what I mean? So you have to have your plumbing. Your plumbing has to be more modern. So you have to bring in modern infrastructure distributed computing that that you cannot. There's no compromise there, right? You have to have the right equal system for you to be able to be technologically advanced on a leader in that >>table. Stakes is what you're saying. And so this notion of the autonomous enterprise I would help me here cause I get kind of autonomous and automation coming into I t I t ops. I'm interested in how you see customers taking that beyond the technology organization into the enterprise. >>Yeah, this is this is such a great question, right? This is what I've been talking about all morning. Um, I think when AI is a technology problem, the company is that at a loss AI has to be a business problem. AI has to inform the business strategy. AI has to been companies. The successful companies that have done so. 90% of my investments are going towards state. We know that and most of it going towards AI. There's data out there about this, right? And so we look at what are these? 90 90% of the company's investments. Where are these going and whose doing this right? Who's not doing this right? One of the things we're seeing as results is that the companies that are doing it right have brought data into their business strategy. They've changed their business model, right? So it's not like making a better taxi, but coming up with a bow, right? So it's not like saying Okay, I'm going to have all these. I'm going to be the drug manufacturing company. I'm gonna put drugs out there in the market forces. I'm going to do connected help, right? And so how does data serve the business model of being connected? Help rather than being a drug company selling drugs to my customers, right? It's a completely different way of looking at it. And so now you guys informing drug discovery is not helping you just put more drugs to the market. Rather, it's helping you come up with new drugs that would help the process of connected games. There's a >>lot of discussion in the press about, you know, the ethics of AI, and how far should we take? A far. Can we take it from a technology standpoint, Long road map there? But how far should we take it? Do you feel as though of public policy will take care of that? A lot of that narrative is just kind of journalists looking for, You know, the negative story. Well, that's sort itself out. How much time do you spend with your customers talking about that and is what's Oracle's role there? I mean, Facebook says, Hey, the government should figure this out. What's your point? >>I think everybody has a role. It's a joint role, and none of us could give up our responsibilities as data scientists. We have heavy responsibility in this area on. We have heavy responsibility to advise the clients on the state area. Also, the data we come from the past has to change. That is inherently biased, right? And we tend to put data signs on biased data with the one dimensional view of the data. So we have to start looking at multiple dimensions of the data. It's got to start examining. I call it a responsible AI when you just simply take one variable or start to do machine learning with that because that's not that's not right. You have to examine the data. You got to understand how much biases in the data are you training a machine learning model with the bias? Is there diversity in the models? Is their diversity in the data? These are conversations we need to have. And we absolutely need policy around this because unless our lawmakers start to understand that we need the source of the data to change. And if we look at this, if we look at the source of the data and the source of the data is inherently biased or the source of the data has only a single representation, we're never going to change that downstream. AI is not going to help us. There so that has to change upstream. That's where the policy makers come into into play. The lawmakers come into play, but at the same time as we're building models, I think we have a responsibility to say can be triangle can be built with multiple models. Can we look at the results of these models? How are these feature's ranked? Are they ranked based on biases, sex, HP II, information? Are we taking the P I information out? Are we really looking at one variable? Somebody fell to pay their bill, but they just felt they they build because they were late, right? Voices that they don't have a bank account and be classified. Them is poor and having no bank account, you know what I mean? So all of this becomes part of response >>that humans are inherently biased, and so humans or building algorithms right there. So you say that through iteration, we can stamp out, the buyers >>can stamp out, or we can confront the bias. >>Let's make it transparent, >>make transparent. So I think that even if we can have the trust to be able to have the discussion on, is this data the right data that we're doing the analysis on On start the conversation day, we start to see the change. >>We'll wait so we could make it transparent. And I'm thinking a lot of AI is black box. Is that a problem? Is the black box you know, syndrome an issue or we actually >>is not a black box. We in Oracle, we're building our data science platform with an explicit feature called Explained Ability. Off the model on how the model came up with the features what features they picked. We can rearrange the features that the model picked, citing Explain ability is very important for ordinary people. Trust ai because we can't trust even even they designed This contrast ai right to a large extent. So for us to get to that level, where we can really trust what ai speaking in terms of a modern, we need to have explain ability. And I think a lot of the companies right now are starting to make that as part of their platform. >>So that's your promise. Toe clients is that your AI will be a that's not everybody's promised. I mean, there's a lot of black box and, you know, >>there is, if you go to open source and you start downloading, you'll get a lot of black boss. The other advantage to open source is sometimes you can just modify the black box. You know they can give you access, and you could modify the black box. But if you get companies that have released to open, source it somewhat of a black box, so you have to figure out the balance between you. Don't really worry too much about the black box. If you can see that the model has done a pretty good job as compared to other models, right if I take if I triangulate the results off the algorithm and the triangulation turns out to be reasonable, the accuracy on our values and the Matrix is show reasonable results. Then I don't really have to brief one model is to bias compared to another moderate. But I worry if if there's only one dimension to it. >>Well, ultimately much too much of the data scientists to make dismay, somebody in the business side is going to ask about cause I think this is what the model says. Why is it saying that? And you know, ethical reasons aside, you're gonna want to understand why the predictions are what they are, and certainly as you're going to examine those things as you look at the factors that are causing the predictions on the outcomes, I think there's any sort of business should be asking those responsibility questions of everything they do, ai included, for sure. >>So we're entering a new era. We kind of all agree on that. So I want to just throw a few questions out, have a little fun here, so feel free to answer in any order. So when do you think machines will be able to make better diagnoses than doctors? >>I think they already are making better diagnosis. And there's so much that I found out recently that most of the very complicated cancel surgeries are done by machines doctors to standing by and making sure that the machines are doing it well, right? And so I think the machines are taking over in some aspects. I wouldn't say all aspects. And then there's the bedside manners. You really need the human doctor and you need the comfort of talking to >>a CIO inside man. Okay, when >>do you >>think that driving and owning your own vehicle is going to be the exception rather than the rule >>that I think it's so far ahead. It's going to be very, very near future, you know, because if you've ever driven in an autonomous car, you'll find that after your initial reservations, you're going to feel a lot more safer in an autonomous car because it's it's got a vision that humans don't. It's got a communication mechanism that humans don't right. It's talking to all the fleets of cars. Richardson Sense of data. It's got a richer sense of vision. It's got a richer sense of ability to react when a kid jumps in front of the car where a human will be terrified, not able to make quick decisions, the car can right. But at the same time we're going to have we're gonna have some startup problems, right? We're going to see a I miss file in certain areas, and junk insurance companies are getting gearing themselves up for that because that's just but the data is showing us that we will have tremendously decreased death rates, right? That's a pretty good start to have AI driving up costs right >>believer. Well, as you're right, there's going to be some startup issues because this car, the vehicle has to decide. Teoh kill the person who jumped in front of me. Or do I kill the driver killing? It's overstating, but those are some of the stories >>and humans you don't. You don't question the judgment system for that. >>There's no you person >>that developed right. It's treated as a one off. But I think if you look back, you look back five years where we're way. You figure the pace of innovation and the speed and the gaps that we're closing now, where we're gonna be in five years, you have to figure it's I mean, I don't I have an eight year old son. My question. If he's ever gonna drive a car, yeah, >>How about retail? Do you think retail stores largely will disappear? >>I think retail. Will there be a customer service element to retail? But it will evolve from where it's at in a very, very high stakes, right, because now, with our if I did, you know we used to be invisible as we want. We still aren't invisible as you walk into a retail store, right, Even if you spend a lot of money in in retail. And you know now with buying patterns and knowing who the customer is and your profile is out there on the Web, you know, just getting a sense of who this person is, what their intent is walking into the store and doing doing responsible ai like bringing value to that intent right, not responsible. That will gain the trust. And as people gain the trust and then verify these, you're in the location. You're nearby. You normally by the sword suits on sale, you know, bring it all together. So I think there's a lot of connective tissue work that needs to happen. But that's all coming. It's coming together, >>not the value and what the what? The proposition of the customers. If it's simply there as a place where you go and buy, pick up something, you already know what you're going to get. That story doesn't add value. But if there's something in the human expertise and the shared felt, that experience of being in the store, that's that's where you'll see retailers differentiate themselves. I >>like, yeah, yeah, yeah, >>you mentioned Apple pay before you think traditional banks will lose control of payment systems, >>They're already losing control of payment systems, right? I mean, if you look at there was no reason for the banks to create Siri like assistance. They're all over right now, right? And we started with Alexa first. So you can see the banks are trying to be a lot more customized customer service, trying to be personalized, trying to really make it connect to them in a way that you have not connected to the bank before. The way we connected to the bank is you know, you knew the person at the bank for 20 years or since when you had your first bank account, right? That's how you connect with the banks. And then you go to a different branch, and then all of a sudden you're invisible, right? Nobody knows you. Nobody knows that you were 20 years with the bank. That's changing, right? They're keeping track of which location you're going to and trying to be a more personalized. So I think ai is is a forcing function in some ways to provide more value. If anything, >>we're definitely entering a new era. The age of of AI of the autonomous enterprise folks, thanks very much for great segment. Really appreciate it. >>Yeah. Pleasure. Thank you for having us. >>All right. And thank you and keep it right there. We'll be back with our next guest right after this short break. You're watching the Cube's coverage of the rebirth of Oracle consulting right back. Yeah, yeah, yeah, yeah.

>> Announcer: From Chicago, it's theCUBE, covering Oracle Transformation Day 2020. Brought to you by Oracle Consulting. >> Welcome back, everybody, to this special digital event coverage that theCUBE is looking into the rebirth of Oracle Consulting. Janet George is here, she's a group VP, autonomous for advanced analytics with machine learning and artificial intelligence at Oracle, and she's joined by Grant Gibson, who's a group VP of growth and strategy at Oracle. Folks, welcome to theCUBE, thanks so much for coming on. >> Thank you. >> Thank you. >> Grant, I want to start with you because you've got strategy in your title. I'd like to start big-picture. What is the strategy with Oracle, specifically as it relates to autonomous, and also consulting? >> Sure, so, I think Oracle has a deep legacy of strength in data, and over the company's successful history, it's evolved what that is from steps along the way. And if you look at the modern enterprise, an Oracle client, I think there's no denying that we've entered the age of AI, that everyone knows that artificial intelligence and machine learning are a key to their success in the business marketplace going forward. And while generally it's acknowledged that it's a transformative technology, and people know that they need to take advantage of it, it's the how that's really tricky, and that most enterprises, in order to really get an enterprise-level ROI on an AI investment, need to engage in projects of significant scope. And going from realizing there's an opportunity or realizing there's a threat to mobilizing yourself to capitalize on it is a daunting task for enterprise. Certainly one that's, anybody that's got any sort of legacy of success has built-in processes, has built-in systems, has built-in skill sets, and making that leap to be an autonomous enterprise is challenging for companies to wrap their heads around. So as part of the rebirth of Oracle Consulting, we've developed a practice around how to both manage the technology needs for that transformation as well as the human needs, as well as the data science needs to it. So there's-- >> So, wow, there's about five or six things that I want to (Grant chuckles) follow up with you there, so this is a good conversation. Janet, ever since I've been in the industry, when you're talking about AI, it's sort of start-stop, start-stop. We had the AI winter, and now it seems to be here. It almost feels like the technology never lived up to its promise, 'cause we didn't have the horsepower, the compute power, it didn't have enough data, maybe. So we're here today, it feels like we are entering a new era. Why is that, and how will the technology perform this time? >> So for AI to perform, it's very reliant on the data. We entered the age of AI without having the right data for AI. So you can imagine that we just launched into AI without our data being ready to be training sets for AI. So we started with BI data, or we started with data that was already historically transformed, formatted, had logical structures, physical structures. This data was sort of trapped in many different tools, and then, suddenly, AI comes along, and we say, take this data, our historical data, we haven't tested it to see if this has labels in it, this has learning capability in it. We just thrust the data to AI. And that's why we saw the initial wave of AI sort of failing, because it was not ready for AI, ready for the generation of AI, if you will. >> So, to me, this is, I always say this was the contribution that Hadoop left us, right? I mean, Hadoop, everybody was crazy, it turned into big data. Oracle was never that nuts about it, they just kind of watched, sat back and watched, obviously participated. But it gathered all this data, it created cheap data lakes, (laughs) which people always joke, turns into data swamps. But the data is oftentimes now within organizations, at least present, right. >> Yes, yes, yes. >> Like now, it's a matter of what? What's the next step for really good value? >> Well, basically, what Hadoop did to the world of data was Hadoop freed data from being stuck in tools. It basically brought forth this concept of platform. And platform is very essential, because as we enter the age of AI and we enter the petabyte range of data, we can't have tools handling all of this data. The data needs to scale. The data needs to move. The data needs to grow. And so, we need the concept of platform so we can be elastic for the growth of the data. It can be distributed. It can grow based on the growth of the data. And it can learn from that data. So that's the reason why Hadoop sort of brought us into the platform world. And-- >> Right, and a lot of that data ended up in the cloud. I always say for years, we marched to the cadence of Moore's law. That was the innovation engine in this industry. As fast as you could get a chip in, you'd get a little advantage, and then somebody would leapfrog. Today, it's, you've got all this data, you apply machine intelligence, and cloud gives you scale, it gives you agility. Your customers, are they taking advantage of that new innovation cocktail? First of all, do you buy that, and how do you see them taking advantage of this? >> Yeah, I think part of what Janet mentioned makes a lot of sense, is that at the beginning, when you're taking the existing data in an enterprise and trying to do AI to it, you often get things that look a lot like what you already knew, because you're dealing with your existing data set and your existing expertise. And part of, I think, the leap that clients are finding success with now is getting novel data types. You're moving from the zeroes and ones of structured data to image, language, written language, spoken language. You're capturing different data sets in ways that prior tools never could, and so, the classifications that come out of it, the insights that come out of it, the business process transformation that comes out of it is different than what we would have understood under the structured data format. So I think it's that combination of really being able to push massive amounts of data through a cloud product to be able to process it at scale. That is what I think is the combination that takes it to the next plateau for sure. >> So you talked about sort of we're entering the new era, age of AI. A lot of people kind of focus on the cloud as sort of the current era, but it really does feel like we're moving beyond that. The language that we use today, I feel like, is going to change, and you just started to touch on some of it, sensing, our senses, and the visualization, and the auditory, so it's sort of this new experience that customers are seeing, and a lot of this machine intelligence behind that. >> I call it the autonomous enterprise, right? >> Okay. >> The journey to be the autonomous enterprise. And when you're on this journey to be the autonomous enterprise, you need, really, the platform that can help you be. Cloud is that platform which can help you get to the autonomous journey. But the autonomous journey does not end with the cloud, or doesn't end with the data lake. These are just infrastructures that are basic, necessary, necessities for being on that autonomous journey. But at the end, it's about, how do you train and scale very large-scale training that needs to happen on this platform for AI to be successful? And if you are an autonomous enterprise, then you have really figured out how to tap into AI and machine learning in a way that nobody else has to derive business value, if you will. So you've got the platform, you've got the data, and now you're actually tapping into the autonomous components, AI and machine learning, to derive business intelligence and business value. >> So I want to get into a little bit of Oracle's role, but to do that, I want to talk a little bit more about the industry. So if you think about the way the industry seems to be restructuring around data, historically, industries had their own stack or value chain, and if you were in the finance industry, you were there for life, you know? >> Yes. >> You had your own sales channel, distribution, et cetera. But today, you see companies traversing industries, which has never happened before. You see Apple getting into content, and music, and there's so many examples, Amazon buying Whole Foods. Data is sort of the enabler there. You have a lot of organizations, your customers, that are incumbents, that they don't want to get disrupted. A big part of your role is to help them become that autonomous enterprise so they don't get disrupted. I wonder if you could maybe comment on how you're doing. >> Yeah, I'll comment, and then, Grant, you can chime in. >> Great. >> So when you think about banking, for example, highly regulated industry, think about agriculture, these are highly regulated industries. It is very difficult to disrupt these industries. But now you're looking at Amazon, and what does an Amazon or any other tech giant like Apple have? They have incredible amounts of data. They understand how people use, or how they want to do, banking. And so, they've come up with Apple Cash, or Amazon Pay, and these things are starting to eat into the market. So you would have never thought an Amazon could be a competition to a banking industry, just because of regulations, but they are not hindered by the regulations because they're starting at a different level, and so, they become an instant threat and an instant disruptor to these highly regulated industries. That's what data does. When you use data as your DNA for your business, and you are sort of born in data, or you've figured out how to be autonomous, if you will, capture value from that data in a very significant manner, then you can get into industries that are not traditionally your own industry. It can be the food industry, it can be the cloud industry, the book industry, you know, different industries. So that's what I see happening with the tech giants. >> So, Grant, this is a really interesting point that Janet is making, that, you mentioned you started off with a couple of industries that are highly regulated and harder to disrupt. You know, music got disrupted, publishing got disrupted, but you've got these regulated businesses, defense. Automotive hasn't been truly disrupted yet, so Tesla maybe is a harbinger. And so, you've got this spectrum of disruption. But is anybody safe from disruption? >> (laughs) I don't think anyone's ever safe from it. It's change and evolution, right? Whether it's swapping horseshoes for cars, or TV for movies, or Netflix, or any sort of evolution of a business, I wouldn't coast on any of it. And I think, to your earlier question around the value that we can help bring to Oracle customers is that we have a rich stack of applications, and I find that the space between the applications, the data that spans more than one of them, is a ripe playground for innovations where the data already exists inside a company but it's trapped from both a technology and a business perspective, and that's where, I think, really, any company can take advantage of knowing its data better and changing itself to take advantage of what's already there. >> The powerful people always throw the bromide out that data is the new oil, and we've said, no, data's far more valuable, 'cause you can use it in a lot of different places. Oil, you can use once and it's all you can do. >> Yeah. >> It has to follow the laws of scarcity. Data, if you can unlock it, and so, a lot of the incumbents, they have built a business around whatever, a factory or process and people. A lot of the trillion-dollar startups, that become trillionaires, you know who I'm talking about, data's at the core, they're data companies. So it seems like a big challenge for your incumbent customers, clients, is to put data at the core, be able to break down those silos. How do they do that? >> Mm, grating down silos is really super critical for any business. If it's okay to operate in a silo, for example, you would think that, "Oh, I could just be payroll and expense reports, "and it wouldn't matter if I get into vendor "performance management or purchasing. "That can operate as a silo." But anymore, we are finding that there are tremendous insights between vendor performance management and expense reports, these things are all connected. So you can't afford to have your data sit in silos. So grating down that silo actually gives the business very good performance, insights that they didn't have before. So that's one way to go. But another phenomena happens. When you start to grate down the silos, you start to recognize what data you don't have to take your business to the next level. That awareness will not happen when you're working with existing data. So that awareness comes into form when you grate the silos and you start to figure out you need to go after a different set of data to get you to new product creation, what would that look like, new test insights, or new capex avoidance, that data is just, you have to go through the iteration to be able to figure that out. >> And then it becomes a business problem, right? If you've got a process now where you can identify 75% of the failures, and you know the value of the other 25% of the failures, it becomes a simple investment. "How much money am I willing to invest "to knock down some portion of that 25%?" And it changes it from simply an IT problem or an expense management problem to the universal cash problem. >> To a business problem. >> But you still need a platform that has APIs, that allows you to bring in-- >> Yes, yes. >> Those data sets that you don't have access to, so it's an enabler. It's not the answer, it's not the outcome, in and of itself, but it enables the outcome. >> Yeah, and-- >> I always say you can't have the best toilet if your plumbing doesn't work, you know what I mean? So you have to have your plumbing. Your plumbing has to be more modern. So you have to bring in modern infrastructure, distributed computing, that, there's no compromise there. You have to have the right ecosystem for you to be able to be technologically advanced and a leader in that space. >> But that's kind of table stakes, is what you're saying. >> Stakes. >> So this notion of the autonomous enterprise, help me here. 'Cause I get kind of autonomous and automation coming into IT, IT ops. I'm interested in how you see customers taking that beyond the technology organization into the enterprise. >> Yeah, this is such a great question. This is what I've been talking about all morning. I think when AI is a technology problem, the company is at a loss. AI has to be a business problem. AI has to inform the business strategy. When companies, the successful companies that have done, so, 90% of our investments are going towards data, we know that, and most of it going towards AI. There's data out there about this. And so, we look at, what are these 90% of the companies' investments, where are these going, and who is doing this right, and who is not doing this right? One of the things we are seeing as results is that the companies that are doing it right have brought data into their business strategy. They've changed their business model. So it's not making a better taxi, but coming up with Uber. So it's not like saying, "Okay, I'm going to be "the drug manufacturing company, "I'm going to put drugs out there in the market," versus, "I'm going to do connected health." And so, how does data serve the business model of being connected health, rather than being a drug company selling drugs to my customers? It's a completely different way of looking at it. And so now, AI's informing drug discovery. AI is not helping you just put more drugs to the market. Rather, it's helping you come up with new drugs that will help the process of connected care. >> There's a lot of discussion in the press about the ethics of AI, and how far should we take AI, and how far can we take it from a technology standpoint, (laughs) long road map, there. But how far should we take it? Do you feel as though public policy will take care of that, a lot of that narrative is just kind of journalists looking for the negative story? Will that sort itself out? How much time do you spend with your customers talking about that, and what's Oracle's role there? Facebook says, "Hey, the government should figure this out." What's your sort of point of view on that? >> I think everybody has a role, it's a joint role, and none of us can give up our responsibilities. As data scientists, we have heavy responsibility in this area, and we have heavy responsibility to advise the clients on this area also. The data we come from, the past, has to change. That is inherently biased. And we tend to put data science on biased data with a one-dimensional view of the data. So we have to start looking at multiple dimensions of the data. We've got to start examining, I call it irresponsible AI, when you just simply take one variable, we'll start to do machine learning with that, 'cause that's not right. You have to examine the data. You've got to understand how much bias is in the data. Are you training a machine learning model with the bias? Is there diversity in the models? Is there diversity in the data? These are conversations we need to have. And we absolutely need policy around this, because unless our lawmakers start to understand that we need the source of the data to change, and if we look at the source of the data, and the source of the data is inherently biased or the source of the data has only a single representation, we're never going to change that downstream. AI's not going to help us there. So that has to change upstream. That's where the policy makers come into play, the lawmakers come into play. But at the same time, as we're building models, I think we have a responsibility to say, "Can we triangulate? "Can we build with multiple models? "Can we look at the results of these models? "How are these features ranked? "Are they ranked based on biases, sex, age, PII information? "Are we taking the PII information out? "Are we really looking at one variable?" Somebody failed to pay their bill, but they just failed to pay their bill because they were late, versus that they don't have a bank account and we classify them as poor on having no bank account, you know what I mean? So all this becomes part of responsible AI. >> But humans are inherently biased, and so, if humans are building algorithms-- >> That's right, that's right. >> There is the bias. >> So you're saying that through iteration, we can stamp out the bias? Is that realistic? >> We can stamp out the bias, or we can confirm the bias. >> Or at least make it transparent. >> Make it transparent. So I think that even if we can have the trust to be able to have the discussion on, "Is this data "the right data that we are doing the analysis on?" and start the conversation there, we start to see the change. >> Well, wait, so we could make it transparent, then I'm thinking, a lot of AI is black box. Is that a problem? Is the black box syndrome an issue, or are we, how would we deal with it? >> Actually, AI is not a black box. We, in Oracle, we are building our data science platform with an explicit feature called explainability of the model, on how the model came up with the features, what features it picked. We can rearrange the features that the model picked. So I think explainability is very important for ordinary people to trust AI. Because we can't trust AI. Even data scientists can't trust AI, to a large extent. So for us to get to that level where we can really trust what AI's picking, in terms of a model, we need to have explainability. And I think a lot of the companies right now are starting to make that as part of their platform. >> So that's your promise to clients, is that your AI will not be a black box. >> Absolutely, absolutely. >> 'Cause that's not everybody's promise. >> Yes. >> I mean, there's a lot of black box in AI, as you well know. >> Yes, yes, there is. If you go to open source and you start downloading, you'll get a lot of black box. The other advantage to open source is sometimes you can just modify the black box. They can give you access and you can modify the black box. But if you get companies that have released to open source, it's somewhat of a black box, so you have to figure out the balance between. You don't really have to worry too much about the black box if you can see that the model has done a pretty good job as compared to other models. If I triangulate the results of the algorithm, and the triangulation turns out to be reasonable, the accuracy and the r values and the matrixes show reasonable results, then I don't really have to worry if one model is too biased compared to another model. But I worry if there's only one dimension to it. >> Mm-hm, well, ultimately, to much of the data scientists' dismay, somebody on the business side is going to ask about causality. >> That's right. >> "Well, this is what "the model says, why is it saying that?" >> Yeah, right. >> Yeah. >> And, ethical reasons aside, you're going to want to understand why the predictions are what they are, and certainly, as you go in to examine those things, as you look at the factors that are causing the predictions and the outcomes, I think any sort of business should be asking those responsibility questions of everything they do, AI included, for sure. >> So, we're entering a new era, we kind of all agree on that. So I just want to throw a few questions out and have a little fun here, so feel free to answer in any order. So when do you think machines will be able to make better diagnoses than doctors? >> I think they already are making better diagnoses. I mean, there's so much, like, I found out recently that most of the very complicated cancer surgeries are done by machines, doctors just standing by and making sure that the machines are doing it well. And so, I think the machines are taking over in some aspects, I wouldn't say all aspects. And then there's the bedside manners, where you (laughs) really need the human doctor, and you need the comfort of talking to the doctor. >> Smiley face, please! (Janet laughs) >> That's advanced AI, to give it a better bedside manner. >> Okay, when do you think that driving and owning your own vehicle is going to be the exception rather than the rule? >> That, I think, is so far ahead, it's going to be very, very near future, because if you've ever driven in an autonomous car, you'll find that after your initial reservations, you're going to feel a lot more safer in an autonomous car. Because it's got a vision that humans don't. It's got a communication mechanism that humans don't. It's talking to all the fleets of cars. >> It's got a richer sense of data. >> It's got a richer sense of data, it's got a richer sense of vision, it's got a richer sense of ability to (snaps) react when a kid jumps in front of the car. Where a human will be terrified and not able to make quick decisions, the car can. But at the same time, we're going to have some startup problems. We're going to see AI misfire in certain areas, and insurance companies are gearing themselves up for that, 'cause that's just, but the data's showing us that we will have tremendously decreased death rates. That's a pretty good start to have AI driving our cars. >> You're a believer, well, and you're right, there's going to be some startup issues, because this car, the vehicle has to decide, "Do I kill that person who jumped in front of me, "or do I kill the driver?" Not kill, I mean, that's overstating-- >> Yeah. >> But those are some of the startup things, and there will be others. >> And humans, you don't question the judgment system for that. >> Yes. >> There's no-- >> Dave: Right, they're yelling at humans. >> Person that developed, right. It's treated as a one-off. But I think if you look back five years, where were we? You figure, the pace of innovation and the speed and the gaps that we're closing now, where are we going to be in five years? >> Yeah. >> You have to figure it's, I have an eight-year-old son, and I question if he's ever going to drive a car. >> Yeah. >> Yeah. >> How about retail? Do you think retail stores largely will disappear? >> Oh, I think retail, there will be a customer service element to retail, but it will evolve from where it's at in a very, very high-stakes rate, because now, with RFID, you know who's, we used to be invisible as we walked, we still are invisible as you walk into a retail store, even if you spend a lot of money in retail. And now, with buying patterns and knowing who the customer is, and your profile is out there on the Web, just getting a sense of who this person is, what their intent is walking into the store, and doing responsible AI, bringing value to that intent, not irresponsibly, that will gain the trust, and as people gain the trust. And then RFIDs, you're in the location, you're nearby, you'd normally buy the suit, the suit's on sale, bring it all together. So I think there's a lot of connective tissue work that needs to happen, but that's all coming together. >> Yeah, it's about the value-add and what the proposition to the customer is. If it's simply there as a place where you go and pick out something you already know what you're going to get, that store doesn't add value, but if there's something in the human expertise, or in the shared, felt sudden experience of being in the store, that's where you'll see retailers differentiate themselves. >> I like to shop still. (laughs) >> Yeah, yeah. >> You mentioned Apple Pay before. Well, you think traditional banks will lose control of the payment systems? >> They're already losing control of payment systems. If you look at, there was no reason for the banks to create Siri-like assistants. They're all over right now. And we started with Alexa first. So you can see the banks are trying to be a lot more customized, customer service, trying to be personalized, trying to really make you connect to them in a way that you have not connected to the bank before. The way that you connected to the bank is you knew the person at the bank for 20 years, or since when you had your first bank account. That's how you connected with the banks. And then you go to a different branch, and then, all of a sudden, you're invisible. Nobody knows you, nobody knows that you were 20 years with the bank. That's changing. They're keeping track of which location you're going to, and trying to be a more personalized. So I think AI is a forcing function, in some ways, to provide more value, if anything. >> Well, we're definitely entering a new era, the age of AI, the autonomous enterprise. Folks, thanks very much for a great segment, really appreciate it. >> Yeah, our pleasure, thank you for having us. >> Thank you for having us. >> You're welcome, all right, and thank you. And keep it right there, we'll be right back with our next guest right after this short break. You're watching theCUBE's coverage of the rebirth of Oracle Consulting. We'll be right back. (upbeat electronic music)

>>LA from Las Vegas. It's the cube covering AWS reinvent 2019 brought to you by Amazon web services and along with its ecosystem partners. >>Welcome back to Vegas, Lisa Martin with John farrier. We are live at AWS reinvent in the expo hall at the sands convention center. There's tons of people in here. You could probably hear some of the background AWS expecting 65,000 or so folks. John, how many of those 65,000 and have you talked to in the last two days? >>Well, I can hear all the conversations happening at once. It's about hybrid cloud, IOT edge data, machine learning. my head's going to come. >>I was going to say lots of cool stuff. John and I are pleased to be joined by Greg Coralville, the VP of products and strategy for Blackberry Q. Next group. Welcome to the program >>to be here with 65,000 of our closest friends. >>His friends. Exactly. So Blackberry, cute X. What's it all about? >>What's it all about? Well, we do software. We do embedded software for mission critical systems at this event, at the AWS reinvent over showing a software and a really cool car, a karma, and we're connecting it to the AWS IOT backend services and showing some really, really cool use cases. Some of which are near term summer, which are a bit longer term are pretty exciting. Take a quick minute to describe Kunis. Is background acquired by Blackberry system history legacy? Exactly. Just take a quick minute to explain that. So we were founded in 1980 and then developing software for mission critical devices and medical, industrial. And then we started developing software for automotive in 1998 so we've been in automotive for about 20 years and developing originally an infotainment and then digital instrument clusters, telematic systems, gateways, safety systems, acoustics systems, pretty much becoming the software platform in the car because in the car, the car, the software is to be reliable, safe, secure. >>So we're trusted to deliver that. In automotive, we were acquired by Blackberry in 2010 and we're bringing the best of Blackberry and automotive and all of our other markets. So Lisa and I always talk about IOT is RPA automation. All this stuff's going on. But one of the things that comes up is we're trying to grok what's the software development environment in the cloud, in the car, and a Amazon one by having great API APIs. Yep. That was one of their core design principles. Is there a similar design principle from a car standpoint? Because if I'm an app developer, I just love, I have my mobile app sit on the car, right? But I don't want to have to become an expert on all the nuances of is there a connector? So is there going to be multiple platforms? What's the, what's the principle? Can you explain that a great question and great observation. >>So cars traditionally have been proprietary, pretty much closed systems and started open up with CarPlay and Android auto or all of a sudden you saw your mobile device being able to communicate with the car and now I could run Android apps, I could run iOS apps and started to open it up a bit. And now what you've seen is cars are becoming more connected, they're becoming more automated, eventually autonomous. Um, they're definitely, and what you're seeing in the car is in order for that car to really evolve and to offer connected services and shared mobility and the electrification that's occurring, the automotive industry is going through a disruption. We've all heard that and it really is true. So to the point where the electronics in the car, the networks in the car, the software in the car, it's getting completely redesigned and you're seeing a lot more high end processors. >>You're seeing safety critical systems, which have always been in cars, but now you're seeing a lot more complexity. And that speaks to exactly what we do. So where that car's going, if you think about it, is moving to more of a software platform. You have applications and mobile devices. Why? Because you've got Android and you've got iOS. That car is moving to that sort of a common platform where with the help of AWS connected services, the cubix Blackberry Punic software platform in the car, all of a sudden that'll open the door to that kind of environment to applications, to connected services. And that's exactly where it's going. So connectivities, it's here and it's going to be predominant through a pretty much all the vehicles coming off the line in the coming years. So you're going to see the connectivity and now we can bring the services and the apps to that vehicle. But at the same time you got to keep it safe, got to keep it secure. Gotta keep it reliable. You know, it's the classic mobile device, bingo literal device on wheels, right of two ton mobile device on wheels. >>Doc disruption sounds really cool and it's consumers. We just had this expectation that we can have whatever I want, the whole experience I want. And obviously as everything evolves, we want it to be safer and safer. And as there's laws and regulations that govern, Hey, you're going to get hefty fines if you're seeing with this device and you're driving. But disruption is really challenging, right? We talked, we got some great examples yesterday on stage with Andy Jassy of Goldman Sachs, right? How many years old are they and how they have leveraged disruption to revolutionize their consumer business or healthcare revolutionizing. I'd love to get your perspective on what are some of the automakers that are bleeding edge going, we get it. We want to work with you guys so that they understand that this the, you know, the, the mobile devices, the connected device on wheels is going to be transformative for their business. >>Good point. So first of all, every automaker we work with and we work, we work with almost 50 auto makers and we're over a hundred. We're in over 150 million vehicles and multiple systems in the cars. They're all putting safety first. That's never really changed. But that remains primary, primary objective. And to your point is how do you maintain that safety net reliability while at the same time opening the door to connectivity, making sure that vehicle is secure and resilient to attacks and whatnot. And you've seen some of those attacks in the past. And the industry is learning. Um, but that's, that's exactly what, that's what speaks to us and what we do. Same thing with AWS. If you think about what we do, we're plumbers. We, we build plumbing in the car, AWL splits, plumbing in the cloud. And I've had that call, those conversations with AWS and they're like, yeah, we're plumbers. >>And I said, so are we, we're going to get along great. But to your point, we have to keep our eye on security. Our definitely our eye on privacy and safety. And that's exactly what we do. As much as we all want the consumer apps and the connected experience at the same time, we can't compromise on that. So the good thing in automotive is there's a automotive safety standards, ISO two, six, two, six, two and whatnot, which we've certified our products to and we're going to keep doing that and keep delivering that software in the car. But that's awesome for 0.2 ton mobile device on wheels. So we got to always be aware of that. Great opportunity. People want more conduct and safety too. And that's a huge thing. Security and safety. I want to get to that in a second, but I got to ask you, um, what is the relationship that you guys have with Amazon? >>Could you explain that? And what are you guys doing at reinvent this year? Is your leg a presentation demo? Take a minute to explain the relationship between queen Nixon and Amazon web services and what you're showing here. Well, we're in the connected home exhibit. In fact, we're in the quote unquote garage where we've got a vehicle, a beautiful karma Rivero GT. And I was told it's the first time there's actually a car at reinvent. So that was pretty cool. And it's a cool car if you get a chance, come on over. And what we've done is we've taken the karma vehicle and we've actually connected it to AWS IOT. So if you think about what we do, we do software in the car, as I was saying earlier. And then we worked with the Amazon team, with the AWS team to say, okay, what can we do? So one of the things we're doing is we're doing battery monitoring and prediction in terms of the life of the battery. >>That's one of the things that we're doing. The other thing we're doing is personalized cockpit, which is, which is pretty exciting. And, and the last thing we're doing is kind of a business to business demonstration, um, where it's data orchestrations. If you think about the vehicle, there's a lot of sensors on the vehicle, a lot of information available on the vehicle. And what we're doing with AWS is pulling information from the vehicle, putting it in the cloud. And then we've got a few examples that we're using. So one of them is an application for an auto detailing company where they might want, you might want to have your vehicle detailed where we can make the position of your vehicle available, GPS, the VIN number. So the identify the identification of the vehicle. Um, and then you could actually contract with that expert detailings what we called them to come to your vehicle, clean the vehicle, detail your vehicle within a finite period of time securely. >>And then you'll get notified when it's done and whatnot. We're doing facial recognition in the vehicle and we also put some ML in machine learning in the car. We're actually showing gesture recognition where I can fold the mirrors with a, with a peace sign or victory signs. I could have the mirrors fold in. Uh, I can, I can interact with the infotainment system. I can personalize the music and whatnot. So really personalizing the cockpit. But all through the power of AWS. Sorry, what are we going to have to the car flying cars? Come on Jetsons flyers. I love this coming. Maybe not the flying carpet. Wow. Okay. Flying cars. Fine. I mean, I always say anything else that's in star Trek or star Wars will be invented. So I'm respecting some flying vehicles. All fun aside. Yeah. Now the serious conversation is safety and security. >>Worst case scenario, my car is hacked. Take over. This is a fear. Again, it's the worst. It's a doom season here. Those stories are straight. All IOT device. It's a car. How do you guys view the security posture? Um, good question. This is concerned. It might be on people's mind. Yeah. And that's what really speaks to where our company has been for almost four decades now. You know, when people would ask me, Hey, where would I find Punic software? Blackberry Punic software, I'd say almost everywhere, but the desktop. So where things have to be reliable, safe, secure work all the time. That's where you'll find our software. So factory floor, we're in laser eye surgery. Machines are in patient monitoring devices, MRI machines. And so essentially those areas which are safety critical, where safety, security and reliability, you know, our top real really industrial IOT thing, big time, big time. >>And that's the cool thing about walking around reinvent. There's all kinds of industrial devices and control. So if you go to the car now, if you think about the vehicle, same fundamental needs, reliability, safety, security, and we're trusted to deliver an automotive. So security is one of those things. It's not static. So when you, when you, when you make something that's secure, you're really building something that's resilient to attacks. So you'd be as resilient as possible to prevent attacks. And then you do whatever you can to prevent any malicious act or actions on that. So we will monitor what's going on in the system. We'll monitor any communications going to the car, for instance. So the minute we detect something a bit of normal, we can take action based on that. So that, that's absolutely key, especially given the cars connected and more and more becoming connected. >>What's the opportunity is in a trucking industry, when I think of the number of sensors on trucks, the regulations that you know for drivers safety in terms of how many hours they actually have to be able to can drive. What's the opportunity there for Q next? >>Good question. So everything we're doing in the car, which I should generalize and say a vehicle applies to trucks. So if you think about trucking or vehicles or drones or anything like that, you have multiple sensors that you have to interact with. You have to interpret that information, you have to take action based on that information. So if we look at trucking specifically, everybody knows a major shortage of truck truck, truck drivers. So when people ask me about autonomous cars and Hey, when are we going to see autonomy's vehicles? I always look at trucking and we're working with companies, trucking companies that are using our technology. And one of the first use cases that they're putting forward is something called platooning, where you'll actually have the first truck on the road with a driver and any other trucks on the road. We'll be operating autonomously essentially following like a train if you want on a highway, and then they'll have a starting location and a drop off location and that all of a sudden becomes a real world scenario, which makes use of the same sensors, LIDAR, radar cameras, et cetera. >>So from a trucking perspective, we look at it very similar to a car and automotive perspective because they need the same fundamental technologies. So pretty exciting. Like I said, what we do applies all over the place and again, all going to be connected. But grant, thanks for coming on. I really appreciate, I want to get your final thoughts, at least from my perspective on developers. When you see deep racer, you see that trend. It's kind of, they've got LIDAR, it's kind of a toy, but people geeking out on this. And so I would imagine that we're going to see an emergence of a software development environment where as a controlled sandboxes, cause yeah, they've got the concern with the industrial equipment. Exactly. Yeah. How do you balance that old school industrial mindset of, you know, IOT with the new rapid agile product development? Yeah. And to your point, we're going through that transition now. >>So this is where things like Sage maker come into play where I can develop out and develop and refine machine learning models in the cloud. You still have those tight control loops that you need and there's tools for that. So that's the deeply embedded stuff that's controlling actuators and whatnot. You still need that. But to your point, you need to be more iterative. You need to be more agile, need to develop according to the safety standards and the various industries that they might be in. So it's that is evolving and it's evolving at exactly the right pace. Really glad to see that evolution. But to your point, all of these devices are going to become interconnected. There's going to be new opportunities. And from a developer perspective, you know, we can't hire enough developers. No one can. It's really exciting whether it's IOT cloud developers or embedded developers. >>There's such an exciting future ahead. And I got to ask, this is just popped in my head. So I want to ask, cause I'm curious, um, spectrum and RF power is great, but you need connectivity to make an IOT device work, right? How do you guys, how does the car folks look at conductivity? Just when they get to a spot they can connect. So is it managing the spectrum? How are cars thinking about the connectivity? So we work very closely with the modem vendors. For instance, in today in cars you'll see Bluetooth, you'll see wifi, you'll see 4g. Obviously there's the emergence of 5g. Um, vehicle to vehicle communications is through something called DSRC. Essentially wifi 5g is going to come along, so now you're going to be able to have throughput and also what's called low latency. So quick turn around on your messages and the information being exchanged. >>So that too is evolving from a, from a QA software perspective, we'll make use of whatever modems there. But to your point, we also have to deal with the cases where I've lost connectivity. I still need that V vehicle to operate safely. And especially if you consider that the systems might be, um, uh, the systems might be connected or we don't want to make, make it such that they're dependent on that connectivity. So you have to have fail over scenarios and whatnot, but cars will become connected, devices will become connected. We're going to take advantage of that connectivity, but not be dependent on that connectivity. >>Well, Greg, please let me know when that, uh, personalized service is available so that my car can be found and detailed. They'd find it right in my driveway going lady, please. It's been a pleasure, a really cool stuff. Blackberry Kunis thank you for joining John. We'll be, we'll have to go check out that car for John furrier. I'm Lisa Martin. You're watching the cube live in Vegas at AWS. Reinvent 19. Thanks for watching.

>> Narrator: From Las Vegas, it's theCUBE. Covering Qualys Security Conference 2019. Brought to you by Qualys. >> Hey, welcome back, you ready with Jeff Frick here with theCUBE. We are at the Qualys Security Conference in Las Vegas. This show's been going on, I think, 19 years. This is our first time here. We're excited to be here, and we've got, there's always these people that go between the vendor and the customer and back and forth. We've had it go one way, now we've got somebody who was at Qualys and now is out implementing the technology. We're excited to welcome Grant Johnson. He is the director of Risk and Compliance for Ancestry. Grant, great to see you. >> Thank you for having me, great to be here. >> Yeah, it is always interesting to me and there's always a lot of people at these shows that go back and forth between, and their creating the technology and delivering the technology versus implementing the technology and executing at the customer side. So, you saw an opportunity at Ancestry, what opportunity did you see and why did you make that move? >> Well it's a good question, I was really happy where I was at, I worked for here at Qualys for a long time. But, I had a good colleague of mine from way back just say, hey look, he took over as the chief information security officer at Ancestry and said, "they've got an opportunity here, do you want it?" I said, "hey sure." I mean, it was really kind of a green field. It was the ability to get in on the ground floor, designing the processes, the environment, the people and everything to, what I saw is really a really cool opportunity, they were moving to the cloud. Complete cloud infrastructure which was a few years ago, you know, a little uncommon so it was just and opportunity to learn a lot of different things and kind of be thinking through some different processes and the way to fix it. >> Right, right, so you've been there for a little while now. Over three years, what was the current state and then what was the opportunity to really make some of those changes, as kind of this new initiative with this new see, so? >> No, yeah, we were traditional. You know, a server data center kind of background and everything like that. But with the way the company was starting to go as we were growing it, really just crazy, just at a crazy clip, to where we really couldn't sustain. We wanted to go global, we wanted to move Ancertry out to Europe and to other environments and just see the growth that was going to happen there, and there just wasn't a way that we could do it with the traditional data center model. We're plugging those in all over the place, so the ideas is, we're going to go to a cloud and with going to the cloud, we could really rethink the way that we do security and vulnerability management, and as we went from a more traditional bottle which is, where you scan and tell people to patch and do things like that, to where we can try to start to bake vulnerability management into the process and do a lot of different things. And you know, we've done some pretty cool things that way, I think as a company and, always evolving, always trying to be better and better every day but it was a lot of fun and it's been really kind of a neat ride. >> So, was there a lot of app redesign and a whole bunch of your core infrastructure. Not boxes, but really kind of software infrastructure that had to be redone around a cloud focus so you can scale? >> Yeah. There absolutely was. We really couldn't lift and shift. We really had to take, because we were taking advantage of the cloud environment, if we just lifted and shifted our old infrastructure in there, it wasn't going to take advantage of that cloud expansion like we needed it to. >> Right. >> We needed it to be able to handle it tide, of high tide, low tide, versus those traffic times when we're high and low. So it really took a rewrite. And it was a lot of really neat people coming together. We basically, at the onset of this right when I started in 2016, our chief technology officer got up and said, "we're going to burn the ships." We have not signed the contract for our data center to renew at 18 months. So we have to go to the cloud. And it was really neat to see hundreds of people really come together and really make that happen. I've been involved in the corporate world for a long time in IT. And a lot of those projects fail. And it was really neat to see a big project like that actually get off the ground. >> Right, right. It's funny, the burning the ship analogy is always an interesting one. (grant laughs) Which you know, Arnold Schwarzenegger never had a plan B. (grant laughs) Because if you have plan B, you're going to fall back. So just commit and go forward. >> A lot of truth to that. Right, you're flying without a net, whatever kind of metaphor you want to use on that one. Yeah, but you have to succeed and there is a lot that'll get it done I think, if you just don't have that plan B like you said. >> Right, so talk about kind of where Ancestry now is in terms of being able to roll out apps quicker, in terms of being able to scale much larger, in terms of being able to take advantages of a lot more attack surface area, which probably in the old model was probably not good. Now those are actually new touch points for customers. >> It's a brave new world on a lot of aspects. I mean, to the first part of that, we're just a few days away from cyber Monday. Which is you know, our normal rate clip of transactions is about 10 to 12 transactions a second. >> So still a bump, is cyber Monday still a bump? >> It's still huge for us. >> We have internet at home now. We don't have to go to work to get on the internet to shop. >> You know, crazy enough, it still is. You know, over the course of the week, and kind of starting on Thanksgiving, we scale to have about 250 transactions a second. So that was one of the good parts of the cloud, do you invest and the big iron and in the big piping for your peak times of the year. Or and it sits, your 7-10% utilization during the rest of the year, but you can handle those peaks well. So I mean, we're just getting into the time of year, so that's where our cloud expansion, where a lot of the value for that has come. In terms, of attack surface, yeah, absolutely. Five years ago, I didn't even know what a container was. And we're taking advantage a lot of that technology to be able to move nimbly. You can't spin up a server fast enough to meet the demands of user online clicking things. You really have to go with containers and that also increases what you really need to be able to secure with people and the process and technology and everything like that. >> Right. >> So it's been a challenge. It's been really revitalizing and really, really neat to me to get in there and learn some new things and new stuff like that. >> That's great. So I want to ask you. It may be a little sensitive, not too sensitive but kind of sensitive right. Is with 23 and Me and Ancestry, and DNA registries, et cetera, it's opened up this whole new conversation around cold case and privacy and blah blah blah. I don't want to get into that. That's a whole different conversation, but in terms of your world and in terms of risking compliance, that's a whole different type of a data set I think that probably existed in the early days of Ancestry.com >> Yeah >> Where you're just trying to put your family tree together. So, how does that increased value, increased sensitivity, increased potential opportunity for problems impact the way that you do your job and the way that you structure your compliance systems? >> Boy. Honestly, that is part of the reason why I joined the company. Is that I really kind of saw this opportunity. Kind of be a part of really a new technology that's coming online. I'd have to say. >> Or is it no different than everyone else's personal information and those types of things? Maybe it's just higher profile in the news today. >> Not it all, no. It kind of inherent within our company. We realized that our ability to grow and stay affable or just alive as a business, we pivot on security. And security for us and privacy is at the fore front. And I think one of the key changes that's done for maybe in other companies that I get is, people from our development teams, to our operations teams, to our security department, to our executives. We don't have to sell security to em. They really get it. It's our customer privacy and their data that we're asking people to share their most personal data with us. We can give you a new credit card. Or, you can get a new credit card number issued. We can't give you a new DNA sequence. >> Right. >> So once that's out there, it's out there and it is the utmost to us. And like I said, we don't have to sell security internally, and with that we've gotten a lot of support internally to be able to implement the kind of things that we needed to implement to keep that data as secure as we can. >> Right, well that's nice to hear and probably really nice for you to be able to execute your job that you don't have to sell securities. It is important, important stuff. >> Grant: Yes, that's absolutely true. >> All right, good. So we are jamming through digital transformation. If we talk a year from now, what's on your plate for the next year? >> We just continue to evolve. We're trying to still continue the build in some of those processes that make us better, stronger, faster, as we go through, to respond to threats. And just really kind of handle the global expansion that our company's undergoing right now. Just want to keep the lights on and make sure that nobody even thinks about security when they can do this. I can't speak for them, but I think we really want to lead the world in terms of privacy and customer trust and things like that. So there are a lot of things that I think we've got coming up that we really want to kind of lead the way on. >> Good, good. I think that is a great objective and I think you guys are in a good position to be the shining light to be, kind of guiding in that direction 'cause it's important stuff, really important stuff. >> Yeah, we hope so, we really do. >> Well Grant, nothing but the best to you. Good luck and keep all that stuff locked down. >> Thank you, thank you so much! Thanks for having me. >> He's Grant, I'm Jeff. You're watching theCube. We're at the Qualys Security Conference at the Bellagio in La Vegas. Thanks for watching. We'll see you next time. (upbeat music)

>> from Seattle WASHINGTON. It's the Q covering AWS. Imagine by Amazon Web service is >> Hey, welcome back. You're ready. Geoffrey here with the Cube. We're in downtown Seattle at the AWS. Imagine Education event. It's a second year of the event. It's about 800. People were here last year, too, I think was 400 people. So it's growing quickly, like everything at a W s. It's all about education. That's that's public school, private school, university K through 12 community college, everything you can imagine. It's a really comprehensive kind of area that Amazon's focusing on. We're excited. Have our next guest really from the school district emanating from school district? He's Travis Pocky, the senior director Information Service is for Portland Public School. I'm a proud graduate of the Portland public school system. So, Travis, great to see you. Great to see you. Absolutely. So for stop impressions of the show, you said you weren't able to make it this year. I got to sit in the key notes this morning with Theresa and Andrew. A couple sessions just kind of your impressions of being at an event like this, >> you know, it's really fantastic tohave an event that ties together AWS and education. And in that education space. It's a great resource for people that are using AWS within the education community. So this has been fantastic. >> Yeah, it's good because the education is not necessarily touted as the most progressive in his street. Exactly. So the fact that they made this commitment is pretty significant. Exactly. So you had a recent a recent significant event within your kind of I d journey cloud during one of you could tell us what you guys just recently completed. >> Sure. So we recently migrated our peoples off application from our on Prem Data Center to the cloud. And, you know, one of the one of the real challenges we had was there was no extra money to do this work. So we had kind of come across the idea that the hardware was end of life. It was gonna be about a $500,000 replacement cost. In addition to that, we had several on staff positions that really weren't available. It had become such a niche skill set that we really had a lot of trouble trying to get those positions filled. So, in addition to that my boss came around the corner and said, By the way, we have a 10% budget cut. So how do we resolve all of that plus address? This really big problem with the system, not even >> time, was ticking, right? Your hardware time was >> ticking. It it was really bad. I mean, we were at a point where we were getting young 9 12th page refresh times. And, you know, the user community had kind of gotten to the point where our numbers for satisfaction looked really good because we didn't get complaints because the user community had gotten so disillusioned by making those complaints and not getting any results that they just gave up on complaining. So way were out of time, >> year out of time. So, you know, typically, a cloud migration of an old application is not necessarily the easiest place to get started on your cloud journey. Did you already have some experience with powder? Was this really kind of your first foray into this area? >> You know, I had worked at a start up a few years before, and we did our entire infrastructure on eight of us. So that was my my introduction to AWS and HBO Service's. And there was a lot of there's a lot of people that were looking away from that as a solution. It didn't seem like the viable thing to do. And yes, we were advised not to try the e r P first. But that was our use case. And if >> we were gonna do it, we were gonna do it big. So we did. He brought in some consultants. I would assume that helped out. Are you guys doing all in house? >> Actually, what we did was we looked for a managed service provider. So are our use case in that we had many positions that we couldn't get filled was that we needed the virtual infrastructure. But we also needed the people to do some of those tasks for us. So that was That was our partnership, was we work with a manage solution provider called High Street and High Street. Really helped us with that process. >> It So how long did it take? When did you get complete? >> Um, we went from idea to completion in four months. >> Idea to completion in four months. Yes. Wow. >> And that was That was unprecedented. No, nobody expected it to work. Certainly nobody expected it to work that fast. And when you do these migrations Ew, you I understand that it is going to be a high stress situation. And the one of the major things that AWS did for us was it gave us that virtual infrastructure so that we could run in tandem. We could actually continue to run completely as we were in production and run the new systems and run all the tests. So we were able to get cut over in no time with almost no stress. I think we had one problem when we went live. >> So then what did your boss say when he came around the corner? Good job, Travis. Okay, great. So you know, there's there's a whole bunch of components to cloud right that have a lot of benefits security. Like we said, it's actually a lot more secure than a lot of time on your own stuff. There's cost savings, and there's infrastructure leverage that you can get, but more importantly, and we've heard a lot of the stories here is it opens up on opportunity for innovation, opens up an opportunity to try any things to move fast. So I wonder if, if you know that kind of unintended consequence of this process or do you think you've kind of sold the house people that you know? Look, it worked. We did it fast. Assume it's close to budget or close two timing. And now, you know, sit here for two days and listen to all the crazy, cool, innovative things that people are doing with X, right, etcetera. So where do you go? Where do you go next? >> You know, one of the one of the unintended consequences of it was was granting us a D R process. So we had a We had a very basic deal, our system in place and by moving to the cloud, Not only do we make it insulated from any events that might happen in our primary building, which is also our primary data center, but it gave us that ability due to fail over and persist through through a significant event. One of the other things that's done those it's given our develop access to tools that they just didn't have access to before. So, one of the places where were expended experimenting pretty heavily. Yoon is is Lambda. So server lis functions trying to get to the point where we can enhance our existing software by making calls out to our Amazon vpc and data that exists out there without having to make hard core modifications to the internal systems right way were actually able to do a demo of that within 30 minutes. So that's that normal process would take about two weeks to >> write. So it was there, Is there new stuff on the horizon is just our use, like a kid in a candy store, like now, you know, look at the power flexibility that we have it. We just didn't have the kind of strapped payroll data center before. >> Absolutely, Right now we're trying. I think the biggest struggle is trying to figure out what we tackle next. There's a lot of things out there, you know. We have ah, data interchange platform. It would be great if we could replace that with AWS functions and lame duck calls. >> Um, it >> I think that's probably going to be our next biggest tackle. Is is that after that, we'd really like to start rewriting some of our in house written APS completely in AWS service is and I think that's gonna be a huge win for the district. >> Okay. And then do you guys purchase a lot of these other softer applications? Is a lot of companies here That blackboard is just the one that always comes into my head not to pick on them specifically, but you guys have a ton of those types of applications and installed as well. >> Way definitely tend to leverage bought first. Okay, But some of them likes women have been fantastic partners for us. And that's one of the ones that we've really leaned on because of how intricate some of our policies are school meant as the capability to implement that for us, >> right? Right. Well, sounds pretty exciting, but that's but the question is, when does the grand opening of the of Grant? That's what I wanted. I need the date. So afterwards you could wait a little. They just finished a beautiful remote. I don't even know how many millions and millions of dollars are spent, but a lot. Very yes, a lot. All right. Well, Travis, congratulations. Four months. The AARP move. I don't know. Throw that as a challenge. Maybe somebody else could beat it. That's pretty good. Absolutely. Alright. Thanks for stopping by. Thank you. Alright, He's Travis. I'm Jeff. You're watching the Cube. Where? Aws. Imagine education in downtown Seattle. Thanks for watching. We'll see you next time.

>> Announcer: Live from Boston, Massachusetts, it's theCUBE, covering Red Hat Summit 2019. Brought to you by Red Hat. >> Welcome back here on theCUBE, joined by Stu Miniman, I'm John Walls, as we wrap up our coverage here of the Red Hat Summit here in 2019. We've been here in Boston all week, three days, Stu, of really fascinating programming on one hand, the keynotes showing quite a diverse ecosystem that Red Hat has certainly built, and we've seen that array of guests reflected as well here, on theCUBE. And you leave with a pretty distinct impression about the vast reach, you might say, of Red Hat, and how they diversified their offerings and their services. >> Yeah, so, John, as we've talked about, this is the sixth year we've had theCUBE here. It's my fifth year doing it and I'll be honest, I've worked with Red Hat for 19 years, but the first year I came, it was like, all right, you know, I know lots of Linux people, I've worked with Linux people, but, you know, I'm not in there in the terminal and doing all this stuff, so it took me a little while to get used to. Today, I know not only a lot more people in Red Hat and the ecosystem, but where the ecosystem is matured and where the portfolio is grown. There's been some acquisitions on the Red Hat side. There's a certain pending acquisition that is kind of a big deal that we talked about this week. But Red Hat's position in this IT marketplace, especially in the hybrid and multi-cloud world, has been fun to watch and really enjoyed digging in it with you this week and, John Walls, I'll turn the camera to you because- >> I don't like this. (laughing) >> It was your first time on the program. Yeah, you know- >> I like asking you the questions. >> But we have to do this, you know, three days of Walls to Miniman coverage. So let's get the Walls perspective. >> John: All right. >> On your take. You've been to many shows. >> John: Yeah, no, I think that what's interesting about what I've seen here at Red Hat is this willingness to adapt to the marketplace, at least that's the impression I got, is that there are a lot of command and control models about this is the way it's going to be, and this is what we're going to give you, and you're gonna have to take it and like it. And Red Hat's just on the other end of that spectrum, right? It's very much a company that's built on an open source philosophy. And it's been more of what has the marketplace wanted? What have you needed? And now how can we work with you to build it and make it functional? And now we're gonna just offer it to a lot of people, and we're gonna make a lot of money doing that. And so, I think to me, that's at least what I got talking to Jim Whitehurst, you know about his philosophy and where he's taken this company, and has made it obviously a very attractive entity, IBM certainly thinks so to the tune of 34 billion. But you see that. >> Yeah, it's, you know, some companies say, oh well, you know, it's the leadership from the top. Well, Jim's philosophy though, it is The Open Organization. Highly recommend the book, it was a great read. We've talked to him about the program, but very much it's 12, 13 thousand people at the company. They're very much opinionated, they go in there, they have discussions. It's not like, well okay, one person pass this down. It's we're gonna debate and argue and fight. Doesn't mean we come to a full consensus, but open source at the core is what they do, and therefore, the community drives a lot of it. They contribute it all back up-stream, but, you know, we know what Red Hat's doing. It's fascinating to talk to Jim about, yeah you know, on the days where I'm thinking half glass empty, it's, you know, wow, we're not yet quite four billion dollars of the company, and look what an impact they had. They did a study with IDC and said, ten trillion dollars of the economy that they touch through RHEL, but on the half empty, on the half full days, they're having a huge impact outside. He said 34 billion dollars that IBM's paying is actually a bargain- >> It's a great deal! (laughing) >> for where they're going. But big announcements. RHEL 8, which had been almost five years in the works there. Some good advancements there. But the highlight for me this week really was OpenShift. We've been watching OpenShift since the early days, really pre-Kubernetes. It had a good vision and gained adoption in the marketplace, and was the open source choice for what we called Paths back then. But, when Kubernetes came around, it really helped solidify where OpenShift was going. It is the delivery mechanism for containerization and that container cluster management and Red Hat has a leadership position in that space. I think that almost every customer that we talked to this week, John, OpenShift was the underpinning. >> John: Absolutely. >> You would expect that RHEL's underneath there, but OpenShift as the lever for digital transformation. And that was something that I really enjoyed talking to. DBS Bank from Singapore, and Delta, and UPS. It was, we talked about their actual transformation journeys, both the technology and the organizational standpoint, and OpenShift really was the lever to give them that push. >> You know, another thing, I know you've been looking at this and watching this for many many years. There's certainly the evolution of open source, but we talked to Chris Wright earlier, and he was talking about the pace of change and how it really is incremental. And yet, if you're on the outside looking in, and you think, gosh, technology is just changing so fast, it's so crazy, it's so disruptive, but to hear it from Chris, not so. You don't go A to Z, you go A to B to C to D to D point one. (laughing) It takes time. And there's a patience almost and a cadence that has this slow revolution that I'm a little surprised at. I sense they, or got a sense of, you know, a much more rapid change of pace and that's not how the people on the inside see it. >> Yeah. Couple of comment back at that. Number one is we know how much rapid change there is going because if you looked at the Linux kernel or what's happening with Kubernetes and the open source, there's so much change going on there. There's the data point thrown out there that, you know, I forget, that 75% or 95% of all the data in the world was created in the last two years. Yet, only 2% of that is really usable and searchable and things like that. That's a lot of change. And the code base of Linux in the last two years, a third of the code is completely overhauled. This is technology that has been around for decades. But if you look at it, if you think about a company, one of the challenges that we had is if they're making those incremental change, and slowly looking at them, a lot of people from the outside would be like, oh, Red Hat, yeah that's that little Linux company, you know, that I'm familiar with and it runs on lots of places there. When we came in six years ago, there was a big push by Red Hat to say, "We're much more than Linux." They have their three pillars that we spent a lot of time through from the infrastructure layer to the cloud native to automation and management. Lots of shows I go to, AnsiballZ all over the place. We talked about OpenShift 4 is something that seems to be resonating. Red Hat takes a leadership position, not just in the communities and the foundations, but working with their customers to be a more trusted and deeper partner in what they're doing with digital transformation. There might have been little changes, but, you know, this is not the Red Hat that people would think of two years or five years ago because a large percentage of Red Hat has changed. One last nugget from Chris Wright there, is, you know, he spent a lot of time talking about AI. And some of these companies go buzzwords in these environments, but, you know, but he hit a nice cogent message with the punchline is machines enhance human intelligence because these are really complex systems, distributed architectures, and we know that the people just can't keep up with all of the change, and the scope, and the scale that they need to handle. So software should be able to be helping me get my arms around it, as well as where it can automate and even take actions, as long as we're careful about how we do it. >> John: Sure. There's another, point at least, I want to pick your brain about, is really the power of presence. The fact that we have the Microsoft CEO on the stage. Everybody thought, well (mumbles) But we heard it from guest after guest after guest this week, saying how cool was that? How impressive was that? How monumental was that? And, you know, it's great to have that kind of opportunity, but the power of Nadella's presence here, it's unmistakable in the message that has sent to this community. >> Yeah, you know, John, you could probably do a case study talking about culture and the power of culture because, I talked about Red Hat's not the Red Hat that you know. Well, the Satya Nadella led Microsoft is a very different Microsoft than before he was on board. Not only are they making great strides in, you know, we talk about SaaS and public cloud and the like, but from a partnership standpoint, Microsoft of old, you know, Linux and Red Hat were the enemy and you know, Windows was the solution and they were gonna bake everything into it. Well, Microsoft partnered with many more companies. Partnerships and ecosystem, a key message this week. We talked about Microsoft with Red Hat, but, you know, announcement today was, surprised me a little bit, but when we think about it, not too much. OpenShift supported on VMware environments, so, you know, VMware has in that family of Dell, there's competitive solutions against OpenShift and, you know, so, and virtualization. You know, Red Hat has, you know, RHV, the Red Hat Virtualization. >> John: Right, right, right. >> The old day of the lines in the swim lanes, as one of our guests talked about, really are there. Customers are living in a heterogeneous, multi-cloud world and the customers are gonna go and say, "You need to work together, before you're not gonna be there." >> Azure. Right, also we have Azure compatibility going on here. >> Stu: Yeah, deep, not just some tested, but deep integration. I can go to Azure and buy OpenShift. I mean that, the, to say it's in the, you know, not just in the marketplace, but a deep integration. And yeah, there was a little poke, if our audience caught it, from Paul Cormier. And said, you know, Microsoft really understands enterprise. That's why they're working tightly with us. Uh, there's a certain other large cloud provider that created Kubernetes, that has their own solution, that maybe doesn't understand enterprise as much and aren't working as closely with Red Hat as they might. So we'll see what response there is from them out there. Always, you know, we always love on theCUBE to, you know, the horse is on the track and where they're racing, but, you know, more and more all of our worlds are cross-pollinating. You know, the AI and AI Ops stuff. The software ecosystems because software does have this unifying factor that the API economy, and having all these things work together, more and more. If you don't, customers will go look for solutions that do provide the full end to end solution stuff they're looking for. >> All right, so we're, I've got a couple in mind as far as guests we've had on the show. And we saw them in action on the keynotes stage too. Anybody that jumps out at you, just like, wow, that was cool, that was, not that we, we love all of our children, right? (laughing) But every once in awhile, there's a story or two that does stand out. >> Yeah, so, it is so tough, you know. I loved, you know, the stories. John, I'm sure I'm going to ask you, you know, Mr. B and what he's doing with the children. >> John: Right, Franklin Middle School. >> And the hospitals with Dr. Ellen and the end of the brains. You know, those tech for good are phenomenal. For me, you know, the CIOs that we had on our first day of program. Delta was great and going through transformation, but, you know, our first guest that we had on, was DBS Bank in Singapore and- >> John: David Gledhill. >> He was so articulate and has such a good story about, I took outsourced environments. I didn't just bring it into my environment, say okay, IT can do it a little bit better, and I'll respond to business. No, no, we're going to total restructure the company. Not we're a software company. We're a technology company, and we're gonna learn from the Googles of the world and the like. And he said, We want to be considered there, you know, what was his term there? It was like, you know, bank less, uh, live more and bank less. I mean, what- >> Joyful banking, that was another of his. >> Joyful banking. You don't think of a financial institution as, you know, we want you to think less of the bank. You know, that's just a powerful statement. Total reorganization and, as we mentioned, of course, OpenShift, one of those levers underneath helping them to do that. >> Yeah, you mentioned Dr. Ellen Grant, Boston Children's Hospital, I think about that. She's in fetal neuroimaging and a Professor of Radiology at Harvard Medical School. The work they're doing in terms of diagnostics through imaging is spectacular. I thought about Robin Goldstone at the Livermore Laboratory, about our nuclear weapon monitoring and efficacy of our monitoring. >> Lawrence Livermore. So good. And John, talk about the diversity of our guests. We had expats from four different countries, phenomenal accents. A wonderful slate of brilliant women on the program. From the customer side, some of the award winners that you interviewed. The executives on the program. You know, Stefanie Chiras, always great, and Denise who were up on the keynotes stage. Denise with her 3D printed, new Red Hat logo earrings. Yeah, it was an, um- >> And a couple of old Yanks (laughing). Well, I enjoyed it, Stu. As always, great working with you, and we thank you for being with us as well. For now, we're gonna say so long. We're gonna see you at the next Red Hat Summit, I'm sure, 2020 in San Francisco. Might be a, I guess a slightly different company, but it might be the same old Red Hat too, but they're going to have 34 billion dollars behind them at that point and probably riding pretty high. That will do it for our CUBE coverage here from Boston. Thanks for much for joining us. For Stu Miniman, and our entire crew, have a good day. (funky music)